diff --git a/.claude/skills/_shared/actuarial-reference.md b/.claude/skills/_shared/actuarial-reference.md new file mode 100644 index 0000000..a999fc3 --- /dev/null +++ b/.claude/skills/_shared/actuarial-reference.md @@ -0,0 +1,138 @@ +# Actuarial Reference for Mortality Model Work + +Shared reference for the `model-review`, `nn-review`, and `mortality-table-build` skills. + +## Key Terminology + +### A/E Ratio (Actual to Expected) +- **A/E = 1.00**: Model perfectly predicts mortality +- **A/E < 1.00**: Model over-predicts (actual deaths less than expected) +- **A/E > 1.00**: Model under-predicts (actual deaths more than expected) + +### Select vs Ultimate Mortality +- **Select Period**: First 15-25 policy years after underwriting +- **Select Mortality**: Lower mortality due to recent underwriting (healthy lives) +- **Ultimate Mortality**: Stable mortality after select period wears off +- **Selection Wear-off**: Gradual increase in mortality as select advantage fades + +### VBT 2015 +- **VBT**: Valuation Basic Table (SOA industry mortality table) +- **qx_vbt15**: Mortality rate from VBT 2015 +- Used as benchmark for model comparisons + +## Data Dimensions + +### Insurance Plans +| Plan | Description | Mortality Characteristics | +|------|-------------|--------------------------| +| Term | Temporary coverage | Generally better mortality (more underwriting) | +| Perm | Permanent whole life | Stable, traditional product | +| UL | Universal Life | Flexible premium, moderate mortality | +| ULSG | UL with Secondary Guarantee | Often older ages, different risk profile | +| VL | Variable Life | Investment component, varied demographics | +| VLSG | VL with Secondary Guarantee | Similar to ULSG | + +### Preferred Classes +- **Number of Preferred Classes**: 1-4 tiers of underwriting +- **Preferred Class**: Specific tier (1=best, 4=standard) +- **class_enh**: Combined field (e.g., "2_1" = 2 classes, tier 1) + +### Face Amount Bands +Higher face amounts typically indicate more rigorous underwriting, higher +socioeconomic status, and better mortality experience. + +## Expected Patterns + +### By Duration +| Duration | Expected A/E Pattern | Reason | +|----------|---------------------|--------| +| 1-5 | Lower (0.60-0.80) | Strong select effect | +| 6-10 | Rising (0.80-0.95) | Select wearing off | +| 11-15 | Near 1.00 | Approaching ultimate | +| 16+ | Stable (~1.00) | Ultimate mortality | + +### By Age +| Age Band | Considerations | +|----------|---------------| +| 30-50 | Lower deaths, higher variance | +| 51-70 | Core mortality experience | +| 71-80 | Increasing mortality rates | +| 81+ | High mortality, credibility concerns | + +### By Smoker Status +| Status | Expected Pattern | +|--------|-----------------| +| NS (Non-Smoker) | Better mortality, majority of exposure | +| S (Smoker) | Higher mortality, smaller population | + +## Model Quality Benchmarks + +### A/E Tolerance by Segment Size +| Exposure Level | Acceptable A/E Range | +|----------------|---------------------| +| Very High (>$100B) | 0.95 - 1.05 | +| High ($10B-$100B) | 0.90 - 1.10 | +| Medium ($1B-$10B) | 0.85 - 1.15 | +| Low (<$1B) | 0.75 - 1.25 | + +### Overfitting Indicators +- Train A/E significantly better than Test A/E (>0.05 difference) +- Very low training loss but high validation loss +- Model performs well on seen data but poorly on holdout + +## Standard Review Dimensions + +When grouping A/E for review, use these dimensions (binning shown where relevant): + +- `sex` (M/F) +- `smoker_status` (NS/S) +- `attained_age` (binned: 30-40, 41-50, 51-60, 61-70, 71-80, 81+) +- `duration` (binned: 1-5, 6-10, 11-15, 16-20, 21+) +- `observation_year` +- `insurance_plan` (Term, Perm, UL, ULSG, VL, VLSG) +- `face_amount_band` or `binned_face` +- `class_enh` (preferred class combination) + +## Feature Engineering Best Practices + +### Splines for Age +- Use B-splines with 6-10 knots for `attained_age` +- Quantile-based knots capture data distribution +- Degree 3 (cubic) provides smooth curves + +### Encoding by Model Family +| Feature | GLM | GAM | CatBoost | Neural | +|---------|-----|-----|----------|--------| +| sex | OHE | OHE | native cat | OHE/ordinal | +| smoker_status | OHE | OHE | native cat | OHE/ordinal | +| insurance_plan | OHE | OHE | native cat | Embedding | +| face_amount_band | ordinal | spline | native cat | Embedding | +| preferred_class | OHE | OHE | native cat | Embedding | +| attained_age | binned/poly | spline | numeric | spline or numeric | + +### Interaction Terms to Consider +- `duration × preferred_class` (select effect varies by underwriting) +- `attained_age × smoker_status` (age-mortality slope differs) +- `duration × insurance_plan` (select patterns vary by product) + +## Model-Family Notes + +### CatBoost +- Handles categoricals natively; no OHE needed +- Use built-in feature importance (`PredictionValuesChange`, `LossFunctionChange`) +- SHAP via TreeExplainer (fast, exact) + +### GLM (`morai.models.core.GLM`) +- Coefficients are log-odds (logit link) or log-rates (log link) — interpret carefully +- Use `calc_likelihood_ratio` to compare nested models +- Watch for unstable coefficients (large SE) on sparse categories + +### GAM (`morai.models.r.GAMR`) +- Inspect smooth term plots — non-monotonic curves at the tails often indicate + over-flexible splines +- Effective degrees of freedom (EDF) per term signals complexity + +### Neural (`morai.models.neural.Neural`) +- Embeddings for high-cardinality categoricals +- SHAP via KernelExplainer (slow, sample 100 background / 100 explain) +- Inspect training/validation loss curves for overfitting diff --git a/.claude/skills/model-review/skill.md b/.claude/skills/model-review/skill.md new file mode 100644 index 0000000..3eb316a --- /dev/null +++ b/.claude/skills/model-review/skill.md @@ -0,0 +1,150 @@ +--- +name: model-review +description: Review a fitted mortality model (GLM, GAM, CatBoost, or Neural) focused on actual-vs-expected (A/E) analysis. Use when evaluating model fit, segment-level A/E, rate comparisons vs VBT, partial dependence, and overfitting checks. Model-family-agnostic; branches into family-specific diagnostics in the addendum section. +allowed-tools: Read, Bash, Write, Edit, Glob, Grep +--- + +# Mortality Model Review (A/E Focused) + +Generalized review framework for any fitted mortality model exposing a +scikit-learn-style `.predict(X)` interface. Works for GLM, GAM, CatBoost, and +Neural models in this codebase. + +**This skill reviews pre-trained models. It does not train them.** + +For terminology, tolerance bands, expected A/E patterns, and standard review +dimensions, read `../_shared/actuarial-reference.md` before beginning. + +## Inputs + +- A fitted model (typically loaded via `joblib.load("files/models/.joblib")`) +- Train and test datasets with at minimum: `death_claim_amount`, `amount_exposed`, + `qx_raw`, `qx_vbt15`, and the standard review dimensions +- The `mapping` and preprocessing artifacts used to encode features for the model + +## Workflow + +### 1. Sanity Checks +- Confirm model type (`type(model).__name__`) and identify the family (GLM, GAM, + CatBoost, Neural) +- Confirm predictions run end-to-end on a small slice +- Compute `expected_claims = model.predict(X) * amount_exposed` + +### 2. Overall A/E +- Train A/E and Test A/E +- Flag overfitting if `|Train A/E - Test A/E| > 0.05` OR if Train A/E is in band + but Test A/E is out of band + +### 3. A/E by Dimension +Group by each dimension in the shared reference's "Standard Review Dimensions" +list. Report on both train and test: + +``` +| Dimension Value | Actual | Expected | Exposure | A/E | +``` + +Apply tolerance bands from the shared reference (sized by exposure level). +Flag any segment outside band. + +### 4. Rate Comparison Charts +Use `morai.experience.charters.compare_rates` to plot weighted-average +(`amount_exposed`-weighted) rates for `qx_raw`, `qx_vbt15`, and `qx_model` +across age, duration, and other key dimensions. + +### 5. Partial Dependence (model-agnostic) +Use `morai.experience.charters.pdp` — it calls `model.predict` so it works for +all families. + +Priority features: +1. `duration` — line color by `insurance_plan` +2. `attained_age` — line color by `class_enh` or `binned_face` +3. `insurance_plan` — overall effect + +Settings: `weight="amount_exposed"`, `secondary="death_count"`, `center="per_x"`. + +### 6. Family-Specific Addendum + +Run only the section matching the model's family. + +#### GLM (`morai.models.core.GLM`) +- Coefficient table (estimate, SE, p-value) — flag |p| > 0.05 on retained terms +- Likelihood ratio vs a reduced model via `calc_likelihood_ratio` +- Sparse-category check: coefficients with very large SE + +#### GAM (`GAMPy` / `GAMStats`) +- Smooth term plots — call out non-monotonic tails +- EDF per term +- Compare in-sample deviance to a comparable GLM if available + +#### CatBoost +- Built-in importance: `PredictionValuesChange` and `LossFunctionChange` +- SHAP via TreeExplainer (fast, exact) — bar + summary +- Categorical handling: confirm `cat_features` were declared at fit time + +#### Neural (`morai.models.neural.Neural`) +- Defer to the `nn-review` skill for SHAP (KernelExplainer), embedding cosine / + PCA, and loss-curve inspection + +## Output Template + +```markdown +## Model Review — () + +### Overall +- Train A/E: X.XX +- Test A/E: X.XX +- Overfitting risk: Low / Medium / High + +### A/E by Dimension + + +### Rate Comparison Highlights + + +### Family-Specific Findings + + +### Key Findings +1. ... +2. ... + +### Recommendations +1. ... +2. ... +``` + +## Common Code Patterns + +### A/E Calculation +```python +ae = df.groupby(dim).agg( + actual=("death_claim_amount", "sum"), + expected=("expected_claims", "sum"), + exposure=("amount_exposed", "sum"), +) +ae["A/E"] = ae["actual"] / ae["expected"] +``` + +### Model-Agnostic PDP +```python +from morai.experience import charters +charters.pdp( + model=model, + df=md_encoded, + x_axis="duration", + line_color="insurance_plan", + weight="amount_exposed", + secondary="death_count", + mapping=mapping, +) +``` + +### Rate Comparison +```python +charters.compare_rates( + df=md, + rates=["qx_raw", "qx_vbt15", "qx_model"], + x_axis="attained_age", + weight="amount_exposed", +) +``` diff --git a/.claude/skills/mortality-table-build/skill.md b/.claude/skills/mortality-table-build/skill.md new file mode 100644 index 0000000..12484b0 --- /dev/null +++ b/.claude/skills/mortality-table-build/skill.md @@ -0,0 +1,126 @@ +--- +name: mortality-table-build +description: Build a mortality rate table from a fitted model. Use when generating a 1-D or 2-D rate table, applying graduation, adding an ultimate period, building select/ultimate structure, or producing an output table for downstream use. Distinct from model-review — this skill produces a deliverable table, not diagnostics. +allowed-tools: Read, Bash, Write, Edit, Glob, Grep +--- + +# Mortality Table Build + +Workflow for turning a fitted mortality model into a deliverable rate table +using `morai.experience.tables` and `morai.forecast.graduation`. + +For terminology (select/ultimate, VBT, class_enh) and feature conventions, read +`../_shared/actuarial-reference.md` before beginning. + +## Inputs + +- A fitted model (`files/models/.joblib`) with `.predict(X)` +- The `mapping`, `preprocess_feature_dict`, and `preprocess_params` used at fit +- A target grid (or let `generate_table` build one from the mapping) +- Optional: benchmark table for comparison (e.g., VBT 2015) + +## Workflow + +### 1. Define the Table Shape +Decide: +- **Dimensions** the table is indexed by (e.g., `attained_age`, `duration`, + `sex`, `smoker_status`) +- **Multiplier features** vs base features — multiplier features become a + separate `mult_table` (see `mult_method="glm"` vs `"mean"` in + `tables.generate_table`) +- **Select period length** (e.g., 25 years) and ultimate handling + +### 2. Generate Raw Rates +```python +from morai.experience import tables + +rate_table, mult_table = tables.generate_table( + model=model, + mapping=mapping, + preprocess_feature_dict=preprocess_feature_dict, + preprocess_params=preprocess_params, + grid=None, # auto-build from mapping + mult_features=["insurance_plan", "class_enh"], + mult_method="glm", +) +``` + +### 3. Add Attained-Age / Issue-Age / Duration Columns +Ensure all three columns exist and are consistent: +```python +rate_table = tables.add_aa_ia_dur_cols(rate_table, max_age=121) +tables.check_aa_ia_dur_cols(rate_table) +``` + +### 4. Graduate Rates (optional but usually needed) +Use `morai.forecast.graduation` (WHL methods) to smooth rates along +`attained_age`. Graduate within each (sex, smoker, duration) slice; never across +those splits. + +### 5. Add Ultimate Period +For select/ultimate structures: +```python +rate_table = tables.add_ultimate(rate_table, ...) +``` +Confirm the join point (last select duration → first ultimate age) is smooth — +visualize the transition. + +### 6. Build Select/Ultimate Table (if applicable) +```python +su = tables.get_su_table(rate_table, select_period=25) +``` + +### 7. Validate +- Compare to benchmark (e.g., VBT 2015) with `tables.compare_tables` +- Spot-check monotonicity: rates should generally rise with `attained_age` +- Spot-check select effect: low duration < high duration at same attained age +- Confirm no NaN, no zero/negative rates, no rates > 1 + +### 8. Output +```python +tables.output_table(rate_table, ...) +``` +Write to `files/dataset/.`. Document the source model, +generation date, and any graduation parameters in a sibling README or in the +mapping metadata. + +## Output Template + +```markdown +## Mortality Table Build — + +### Source +- Model: +- Generation date: +- Grid: + +### Structure +- Select period: years +- Multiplier features: +- Multiplier method: + +### Graduation +- Method: +- Parameters: <…> + +### Validation +- Comparison to : +- Monotonicity checks: +- Select wear-off pattern: + +### Deliverable +- Path: +- Rows: +``` + +## Pitfalls + +- **Encoding mismatch:** rate_table must be generated with the SAME + `preprocess_feature_dict` and `preprocess_params` used to train the model +- **Multiplier inflation at high rates:** with `mult_method="glm"`, multipliers + diverge as the base prediction approaches 1 — sanity-check the high-age tail +- **Ultimate join discontinuity:** if the model wasn't trained on long + durations, the ultimate period it produces can jump — consider blending with a + reference ultimate table +- **Graduating across select/ultimate boundary:** don't — graduate select and + ultimate separately diff --git a/.claude/skills/nn-review/skill.md b/.claude/skills/nn-review/skill.md new file mode 100644 index 0000000..cecfcea --- /dev/null +++ b/.claude/skills/nn-review/skill.md @@ -0,0 +1,97 @@ +--- +name: nn-review +description: Neural-network-specific diagnostics for a fitted mortality model. Use AFTER running the model-review skill to cover the items unique to neural nets — SHAP via KernelExplainer, categorical embedding analysis (cosine similarity, PCA), and training/validation loss inspection. +allowed-tools: Read, Bash, Write, Edit, Glob, Grep +--- + +# Neural Network Mortality Model — NN-Only Diagnostics + +This skill covers the items unique to neural network mortality models. For the +generic A/E review (overall + segmented A/E, rate comparisons, PDP, tolerance +bands), run the `model-review` skill first. + +For terminology and feature conventions, read +`../_shared/actuarial-reference.md`. + +## When to Use + +- After `model-review` on a `morai.models.neural.Neural` model +- When you need SHAP explanations, embedding analysis, or overfitting checks + that draw on the NN training history + +## Loading + +```python +import joblib +model = joblib.load("files/models/neural.joblib") +``` + +## 1. SHAP (KernelExplainer) + +Neural models don't have a fast TreeExplainer path — use KernelExplainer with +sampling. + +```python +from morai.models import neural +import shap + +Shap = neural.Shap(model=model, background_df=X_train, n_samples=100, seed=42) +shap_values = Shap.compute_values(explain_df=X_test, n_samples=100, seed=42) + +shap.plots.bar(Shap.shap_values) +shap.summary_plot(Shap.shap_values, Shap.sample_explain_df) +# Plus: waterfall (single prediction), dependence (feature interaction) +``` + +Settings: 100 background samples, 100 explain samples, `seed=42` for +reproducibility. + +## 2. Embedding Analysis + +For categorical features encoded with embeddings (typically `insurance_plan`, +`face_amount_band`, `class_enh`): + +1. **Cosine similarity heatmap** — which categories the model treats as similar +2. **PCA 2D plot** — visualize category relationships +3. **Embedding weights table** — raw dimension values + +Check that the learned similarities match actuarial intuition (e.g., UL near +ULSG, VL near VLSG). Surprising groupings often indicate sparse-category issues +or that the model is using the embedding as a proxy for something else. + +## 3. Overfitting via Training History + +- Plot train vs validation loss across epochs +- Flag if validation loss bottoms out early then rises (classic overfit) +- Cross-reference with the Train/Test A/E gap from `model-review` + +## 4. NN-Specific Improvement Suggestions + +If early-duration A/E is poor: +- Add explicit duration indicators (dur_1, dur_2, …) +- Add a select-period flag +- Consider separate select/ultimate models + +If age A/E shows patterns: +- Revisit spline configuration (n_knots, degree) +- Consider `attained_age × smoker_status` interaction + +If a categorical embedding looks noisy: +- Check minimum exposure per category; merge sparse categories +- Try OHE for low-cardinality features instead of an embedding +- Reduce embedding dimensionality + +## Output Addendum + +Append to the `model-review` output: + +```markdown +### NN-Specific Findings +- SHAP top features: … +- Embedding observations: … +- Loss curve / overfitting read: … + +### NN-Specific Recommendations +1. … +2. … +``` diff --git a/CHANGELOG.md b/CHANGELOG.md index cab0635..055bf46 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,27 @@ # Changelog +## [0.3.5](https://github.com/jkoestner/morai/tree/v0.3.5) + +[Full Changelog](https://github.com/jkoestner/morai/compare/v0.3.4...v0.3.5) + +**Enhancements** +- add claude skills (model-review, nn-review, mortality-table-build) +- cdc analytic enhancements (weekly, flu, cod refinements, new data check) +- create experience study calcs (variance, credibility, ci, summarize data) +- enhance the neural model including + - new methods (score, rebalance_ae, set_deterministic, _loss) + - provide test/train a/e during fit + - add new parameters (device, min_delta, ae_interval, loss_target) + +**Documentation** +- update tests + +**Bugs** +- fixed a few neural model bugs + - add weight decay only on non-bias non-embedding columns + - fix `init bias` for binomial path + - load the best state epoch instead of last state epoch + ## [0.3.4](https://github.com/jkoestner/morai/tree/v0.3.4) [Full Changelog](https://github.com/jkoestner/morai/compare/v0.3.3...v0.3.4) diff --git a/CLAUDE.md b/CLAUDE.md index b4380f6..69dd51a 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -93,3 +93,18 @@ morai/ - HMD integration requires authentication credentials - Neural network models require the `[neural]` optional dependency - R-based GAM models require the `[r]` optional dependency and R installation + +## Project Skills + +Project-scoped Claude skills live in `.claude/skills/`: + +| Skill | Purpose | +|-------|---------| +| `model-review` | Generalized A/E review for any fitted mortality model (GLM, GAM, CatBoost, Neural). Covers overall + segmented A/E, rate comparisons vs VBT, PDP, and overfitting checks. | +| `nn-review` | Neural-only diagnostics — SHAP (KernelExplainer), embedding analysis, training/validation loss. Run after `model-review`. | +| `mortality-table-build` | Build a deliverable rate table from a fitted model using `morai.experience.tables` (generate, graduate, add ultimate, output). | + +Shared actuarial reference (terminology, A/E tolerance bands, expected +patterns, encoding guidance) lives at +`.claude/skills/_shared/actuarial-reference.md` and is read on demand by each +skill. diff --git a/README.md b/README.md index 5a2aac8..e67e6a4 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ - [Other Tools](#other-tools) - [Jupyter Lab Usage](#jupyter-lab-usage) - [Logging](#logging) + - [Claude Skills](#claude-skills) - [Coverage](#coverage) ## Overview @@ -81,7 +82,7 @@ services: morai: image: dmbymdt/morai:latest container_name: morai - command: gunicorn -b 0.0.0.0:8001 morai.dashboard.app:server + command: gunicorn -b 0.0.0.0:8001 --timeout 180 morai.dashboard.app:server restart: unless-stopped environment: MORAI_FILES_PATH: /code/morai/files # setting the files path for morai @@ -129,6 +130,16 @@ from morai.utils import custom_logger custom_logger.set_log_level("DEBUG") ``` +### Claude Skills + +Project-scoped Claude Code skills live in `.claude/skills/`: + +- **`model-review`** — A/E review for any fitted mortality model (GLM, GAM, CatBoost, Neural) +- **`nn-review`** — neural-network-specific diagnostics (SHAP, embeddings, loss curves) +- **`mortality-table-build`** — build a deliverable rate table from a fitted model + +Shared actuarial reference material is in `.claude/skills/_shared/`. + ### Coverage To see the test coverage the following command is run in the root directory. diff --git a/docs/mortality_improvement_tools.md b/docs/mortality_improvement_tools.md new file mode 100644 index 0000000..45a8899 --- /dev/null +++ b/docs/mortality_improvement_tools.md @@ -0,0 +1,53 @@ +Mortality Improvement Tools + +- MIM tool: Provides tools to compare mortality improvement models. It uses SSA, NCHS, and Insured data + - https://www.soa.org/resources/research-reports/2023/mortality-improvement-model/ + - There are 2 tools that are provided, the "2021-mim-application-tool-v4.xlsm" and "2021-min-data-analysis-v4b.xlsm" + - Application Tool - this lets you replicate the model results such as MP-2021, O2-2021, Insured, or own + - Data Tool - this provides datasets for comparison including population (SSA and NCHS) and the insured + - Notes + - The MIM dataset (NCHS) is not available to public at the granularity used (single age and FIPs) + - The county quintile subsets, using FIPs, were created by Dr. Magali Barbieri, Ph.D. at the following location. + - https://www.soa.org/resources/research-reports/2020/us-mort-rate-socioeconomic/ + - The dataset does include FIPs mapping for each quintile for each of the different eras using different data sources + - Census Bureau (1980, 1990, 2000) + - https://www.census.gov/programs-surveys/decennial-census/data.html + - American Community Surveys - ACS (5-year increments since 2005) + - https://www.census.gov/programs-surveys/acs + - It was recommended to have the SIS stay constant, as it avoids large counties such as LA going from 1 decile to another and keeps the trends smoother. It was seen that the outcome was similar. + - Another paper that references the FIPs mapping is the annual population mortality report. This used the 2008-2012 ACS quintiles and held them constant. + - https://www.soa.org/resources/research-reports/2022/us-population-mortality/ + +- ILEC Mortality Improvement + - Starting in 2025 the AG38/VM20 scale was switched from using a general population dataset to an insured dataset. The insured dataset was normalized to avoid distribution changes. The data was also smoothed by creating qx rates using the neighboring 4 cells on each side. + - https://www.soa.org/resources/research-reports/2024/ind-life-mort-tools/ + - Personal Note: + - The scale shows that insured data had negative mortality improvement in older ages. Using a CatBoost model I did not see the same mortality disimprovement at older ages and saw some improvement where data was credible up to attained age 90 + +- RPEC Mortality Improvement Report + - The report provides an update to the mortality improvement for retirement planning pension funds. Within the report it describes how the mortality improvement tables were created + - https://www.soa.org/resources/research-reports/2025/rpec-mort-improvement-update/ + - MP-2021 (section 2.1) + - https://www.soa.org/resources/experience-studies/2021/mortality-improvement-scale-mp-2021/ + - data: SSA (1950-2016), CDC (2017-2019) mortality rates + - transform: log(qx) - this allows the rates to be smoother across ages + - order: order 3 + - dimension: 2-dimension + - initialize year: 2017 - this is a 2-year stepback + - horizontal-convergence (age): 10 years (e.g. 2027) + - diagonal-convergence (birth-cohort): 20 years (e.g. 2037) + - horizontal/diagonal blending percentage: 50% / 50% + - long-term rates + - personal research: to replicate use log(qx) and have horizontal-lamda (age) = 400 and vertical-lamda (year) = 100 + - MP-2015 + - https://www.soa.org/resources/experience-studies/2015/research-2015-mp/ + - data: SSA (1950-2011) mortality rates + - transform: log(qx) - this allows the rates to be smoother across ages + - order: order 3 + - dimension: 2-dimension + - initialize year: 2009 - this is a 2-year stepback + - horizontal-convergence (age): 20 years (e.g. 2029) + - diagonal-convergence (birth-cohort): 20 years (e.g. 2039) + - horizontal/diagonal blending percentage: 50% / 50% + - long-term rates + \ No newline at end of file diff --git a/docs/population_mortality_resources.md b/docs/population_mortality_resources.md new file mode 100644 index 0000000..88a5a18 --- /dev/null +++ b/docs/population_mortality_resources.md @@ -0,0 +1,91 @@ +# Executive Summary + +There are several public sources for general population mortality data and exposure data. Each of the sources has valuable information, however the user should understand the limitations for their intended purpose. + +The purpose of this note is to consolidate information on working with each of these sources. It should be used as a resource for members of ILEC. + +--- + +## Center for Disease Control (CDC) + +The Center for Disease control is the national public health agency of the United States. It is part of the U.S. Department of Health and Human Services (HHS) and is government-funded. + +The agency provides multiple datasets for mortality, including underlying cause and multiple cause of death, through the WONDER query tool: + +- **2018 to Current:** Multiple cause of death (provisional) + - https://wonder.cdc.gov/mcd-icd10-provisional.html +- **1999 to 2020:** Underlying cause of death + - https://wonder.cdc.gov/ucd-icd10.html +- **1979 to 1998:** Compressed mortality + - https://wonder.cdc.gov/cmf-icd9.html +- **1968 to 1978:** Compressed mortality + - https://wonder.cdc.gov/cmf-icd8.html + +### Sources + +- Mortality Data is provided by NCHS +- Based on 50 states and District of Columbia. Nonresidents (e.g. nonresident aliens, nationals living abroad, residents of Puerto Rico, Guam, the Virgin Islands, and other territories of the U.S.) and fetal deaths are excluded. +- The population estimates are U.S. Census Bureau estimates of U.S. national, state, and county resident populations. +- CDC Wonder simply carries the latest available exposure data forward in time. So in 2025, they are using data for 2024 (or maybe even for 2023). The 65+ population is, in fact, growing across time, so the CDC overestimates mortality rates. + +### Caveats + +- There is a lag in when deaths are considered final and it will take a few weeks for deaths to be recorded. It can take 6 months for deaths to be recorded with the correct underlying cause. +- There is a lag in population data and can take 2 years for it to be populated. + +### Tool Limitations + +- Manual queries are limited to 75,000 rows and results are suppressed when there are 30 deaths or less to protect privacy. It also doesn't show exposure information for ages 86+. This means that users cannot query for mortality rates across each county individually (or other similar granular splits). It also means that other sources of exposures need to be used. + +--- + +## Human Mortality Database (HMD) + +The Human Mortality Database is a collaborative project between University of California, Berkeley and Max Planck Institute for Demographic Research (Germany) to collect mortality data for different countries. There are currently over 40 countries' data available. + +- **1933 – 2023:** Deaths and Population + - https://www.mortality.org/Country/Country?cntr=USA + +### Sources + +- Mortality Data is provided by NCHS +- For the years 1933–1969, deaths in the HMD cover both residents and nonresidents (i.e., the de facto population), and for the period starting in 1970, they only cover residents. +- Based on 50 states and District of Columbia. Nonresidents (e.g. nonresident aliens, nationals living abroad, residents of Puerto Rico, Guam, the Virgin Islands, and other territories of the U.S.) and fetal deaths are excluded. +- The population estimates are U.S. Census Bureau estimates of U.S. national, state, and county resident populations. + +--- + +## Census Bureau (Census) + +The census bureau is a government agency that provides population estimates in July of each year. It should be noted that data is frequently revised for prior years as new census is surveyed and prior estimates are adjusted. + +- **Population Estimates Dataset:** + - https://www2.census.gov/programs-surveys/popest/datasets/ + +--- + +## Congressional Budget Office (CBO) + +The Congressional Budget Office has projections for demographics every year as well as a report. The demographics include population, immigrants, emigrants, mortality, and fertility. + +- **2025 to 2055:** The Demographic Outlook + - https://www.cbo.gov/about/products/major-recurring-reports#1 (Demographic Outlook section) +- **2025 to 2055:** Data + - https://www.cbo.gov/data/budget-economic-data (Demographic Projections section) + +--- + +## Social Security Administration (SSA) + +The Social Security Administration has projections for the social security program. + +- **2025:** Annual Report + - https://www.ssa.gov/oact/tr/2025/ +- **2025:** Population + - https://www.ssa.gov/oact/tr/2025/V_A_demo.html#271410 +- **2025:** Downloadables + - https://www.ssa.gov/OACT/Downloadables/CY/index.html + +### Caveats + +- The SSA data captures more than the other data sources. In addition to the 50 states and DC, the SSA data captures the following population segments: civilian residents of Puerto Rico, the Virgin Islands, Guam, American Samoa, and the Northern Mariana Islands; Federal civilian employees and persons in the U.S. Armed Forces abroad and their dependents; non-citizens living abroad who are insured for Social Security benefits; and all other U.S. citizens abroad. For instance, the Census had 335M population in 2023 while the SSA had 342M population. diff --git a/files/integrations/cdc/cdc_reference.xlsx b/files/integrations/cdc/cdc_reference.xlsx index a36c7ef..ee92864 100644 Binary files a/files/integrations/cdc/cdc_reference.xlsx and b/files/integrations/cdc/cdc_reference.xlsx differ diff --git a/files/integrations/cdc/xml/README.md b/files/integrations/cdc/xml/README.md new file mode 100644 index 0000000..7397956 --- /dev/null +++ b/files/integrations/cdc/xml/README.md @@ -0,0 +1,54 @@ +# General + +API documentation: https://wonder.cdc.gov/wonder/help/wonder-api.html +Helpful support API info: https://github.com/alipphardt/cdc-wonder-api + +Group results by: +- Injury Intent (D76.V22) for "B_1"; +- and by Injury Mechanism (D76.V23) for "B_2"; + +where data are limited to: +- single-year age groups (O_age value is D76.V52) for ages < 18; +- cause of death is set to Injury Intent (O_ucd values is D76.V22), and D76.V22 is limited to the 5 injury categories only; + +and show: +- number of deaths (D76.M1); +- population estimates (D76.M2); +- crude death rates (D76.M3). + +## Common changes to template + +- update O_show_totals from `True` to `False` +- Remove the M2 (population estimates), M3 (crude death rate), M32, and M34 (Crude 95%) columns + +## List of Queries + +Each query gets to a granularity of death. If querying too granular the deaths will be surpressed. Weekly, cause of death, and ages are granular fields. +Weekly data has to be queried separately from monthly because of the timing differences. + +- mcd18_cod: year, age_group, cod_sub_chapter +- mcd18_mi: year, age_group, gender +- mcd18_monthly: year, month +- mcd18_monthly_cod: year, month, cod_chapter +- mcd18_weekly: year, week +- mcd18_weekly_pic: year, week - filtered by flu, infection, covid cod_chapter + +## Limitations + +The API will not be able "limit or group results by any location field, such as Region, Division, State or County, or Urbanization". In this case the queries will need to be done from the wonder database itself and saved as text. + +### Saved Queries + +- 1999-2020 + - All: https://wonder.cdc.gov/controller/saved/D76/D351F671 + - Q1: https://wonder.cdc.gov/controller/saved/D76/D400F814 + - Q2: https://wonder.cdc.gov/controller/saved/D76/D400F815 + - Q3: https://wonder.cdc.gov/controller/saved/D76/D400F821 + - Q4: https://wonder.cdc.gov/controller/saved/D76/D400F820 + - Q5: https://wonder.cdc.gov/controller/saved/D76/D400F822 +- 1979-1998 + - Q1: https://wonder.cdc.gov/controller/saved/D16/D351F716 + - Q2: https://wonder.cdc.gov/controller/saved/D16/D351F717 + - Q3: https://wonder.cdc.gov/controller/saved/D16/D351F718 + - Q4: https://wonder.cdc.gov/controller/saved/D16/D351F720 + - Q5: https://wonder.cdc.gov/controller/saved/D16/D351F721 \ No newline at end of file diff --git a/files/integrations/cdc/xml/mcd18_monthly.xml b/files/integrations/cdc/xml/mcd18_monthly.xml index 9f4fac2..6fe0263 100644 --- a/files/integrations/cdc/xml/mcd18_monthly.xml +++ b/files/integrations/cdc/xml/mcd18_monthly.xml @@ -129,14 +129,6 @@ M_1 D176.M1 - - M_2 - D176.M2 - - - M_3 - D176.M3 - O_MMWR false diff --git a/files/integrations/cdc/xml/mcd18_monthly_cod.xml b/files/integrations/cdc/xml/mcd18_monthly_cod.xml new file mode 100644 index 0000000..10f2680 --- /dev/null +++ b/files/integrations/cdc/xml/mcd18_monthly_cod.xml @@ -0,0 +1,564 @@ + + + B_1 + D176.V1-level1 + + + B_2 + D176.V1-level2 + + + B_3 + D176.V2-level1 + + + B_4 + *None* + + + B_5 + *None* + + + F_D176.V1 + *All* + + + F_D176.V10 + *All* + + + F_D176.V100 + *All* + + + F_D176.V13 + *All* + + + F_D176.V2 + A00-B99 + C00-D48 + D50-D89 + E00-E88 + F01-F99 + G00-G98 + H00-H57 + H60-H93 + I00-I99 + J09-J18 + K00-K92 + L00-L98 + M00-M99 + N00-N98 + O00-O99 + P00-P96 + Q00-Q99 + R00-R99 + U00-U99 + V01-Y89 + 999--999 + + + F_D176.V25 + *All* + + + F_D176.V26 + *All* + + + F_D176.V27 + *All* + + + F_D176.V77 + *All* + + + F_D176.V79 + *All* + + + F_D176.V80 + *All* + + + F_D176.V9 + *All* + + + I_D176.V1 + *All* (All Dates) + + + + I_D176.V10 + *All* (The United States) + + + + I_D176.V100 + *All* (All Dates) + + + + I_D176.V2 + A00-B99 (Certain infectious and parasitic diseases) +C00-D48 (Neoplasms) +D50-D89 (Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism) +E00-E88 (Endocrine, nutritional and metabolic diseases) +F01-F99 (Mental and behavioural disorders) +G00-G98 (Diseases of the nervous system) +H00-H57 (Diseases of the eye and adnexa) +H60-H93 (Diseases of the ear and mastoid process) +I00-I99 (Diseases of the circulatory system) +J09-J18 (Influenza and pneumonia) +K00-K92 (Diseases of the digestive system) +L00-L98 (Diseases of the skin and subcutaneous tissue) +M00-M99 (Diseases of the musculoskeletal system and connective tissue) +N00-N98 (Diseases of the genitourinary system) +O00-O99 (Pregnancy, childbirth and the puerperium) +P00-P96 (Certain conditions originating in the perinatal period) +Q00-Q99 (Congenital malformations, deformations and chromosomal abnormalities) +R00-R99 (Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified) +U00-U99 (Codes for special purposes) +V01-Y89 (External causes of morbidity and mortality) +999--999 (Data not shown due to 6 month lag to account for delays in death certificate completion for certain causes of death.) + + + + I_D176.V25 + All Causes of Death + + + + I_D176.V27 + *All* (The United States) + + + + I_D176.V77 + *All* (The United States) + + + + I_D176.V79 + *All* (The United States) + + + + I_D176.V80 + *All* (The United States) + + + + I_D176.V9 + *All* (The United States) + + + + L_D176.V15 + *All* + + + L_D176.V16 + *All* + + + M_1 + D176.M1 + + + O_MMWR + false + + + O_V100_fmode + freg + + + O_V10_fmode + freg + + + O_V13_fmode + fadv + + + O_V15_fmode + fadv + + + O_V16_fmode + fadv + + + O_V1_fmode + freg + + + O_V25_fmode + freg + + + O_V26_fmode + fadv + + + O_V27_fmode + freg + + + O_V2_fmode + freg + + + O_V77_fmode + freg + + + O_V79_fmode + freg + + + O_V80_fmode + freg + + + O_V9_fmode + freg + + + O_aar + aar_none + + + O_aar_pop + 0000 + + + O_age + D176.V5 + + + O_dates + YEAR + + + O_death_location + D176.V79 + + + O_death_urban + D176.V89 + + + O_export-format + xls + + + O_javascript + on + + + O_location + D176.V9 + + + O_mcd + D176.V13 + + + O_oc-sect1-request + close + + + O_precision + 1 + + + O_race + D176.V42 + + + O_rate_per + 100000 + + + O_show_totals + false + + + O_timeout + 600 + + + O_title + + + + O_ucd + D176.V2 + + + O_urban + D176.V19 + + + VM_D176.M6_D176.V10 + + + + VM_D176.M6_D176.V17 + *All* + + + VM_D176.M6_D176.V1_S + *All* + + + VM_D176.M6_D176.V42 + *All* + + + VM_D176.M6_D176.V7 + *All* + + + V_D176.V1 + + + + V_D176.V10 + + + + V_D176.V100 + + + + V_D176.V11 + *All* + + + V_D176.V12 + *All* + + + V_D176.V13 + + + + V_D176.V13_AND + + + + V_D176.V15 + + + + V_D176.V15_AND + + + + V_D176.V16 + + + + V_D176.V16_AND + + + + V_D176.V17 + *All* + + + V_D176.V18 + *All* + + + V_D176.V19 + *All* + + + V_D176.V2 + + + + V_D176.V20 + *All* + + + V_D176.V21 + *All* + + + V_D176.V22 + *All* + + + V_D176.V23 + *All* + + + V_D176.V25 + + + + V_D176.V26 + + + + V_D176.V26_AND + + + + V_D176.V27 + + + + V_D176.V4 + *All* + + + V_D176.V42 + *All* + + + V_D176.V43 + *All* + + + V_D176.V44 + *All* + + + V_D176.V5 + *All* + + + V_D176.V51 + *All* + + + V_D176.V52 + *All* + + + V_D176.V6 + 00 + + + V_D176.V7 + *All* + + + V_D176.V77 + + + + V_D176.V79 + + + + V_D176.V80 + + + + V_D176.V81 + *All* + + + V_D176.V82 + *All* + + + V_D176.V89 + *All* + + + V_D176.V9 + + + + action-Send + Send + + + dataset_code + D176 + + + dataset_label + Provisional Mortality Statistics, 2018 through Last Week + + + dataset_vintage + May 9, 2026 as of May 17, 2026 + + + finder-stage-D176.V1 + codeset + + + finder-stage-D176.V10 + codeset + + + finder-stage-D176.V100 + codeset + + + finder-stage-D176.V13 + codeset + + + finder-stage-D176.V15 + + + + finder-stage-D176.V16 + + + + finder-stage-D176.V2 + codeset + + + finder-stage-D176.V25 + codeset + + + finder-stage-D176.V26 + codeset + + + finder-stage-D176.V27 + codeset + + + finder-stage-D176.V77 + codeset + + + finder-stage-D176.V79 + codeset + + + finder-stage-D176.V80 + codeset + + + finder-stage-D176.V9 + codeset + + + saved_id + + + + stage + request + + \ No newline at end of file diff --git a/files/integrations/cdc/xml/mcd18_weekly.xml b/files/integrations/cdc/xml/mcd18_weekly.xml new file mode 100644 index 0000000..a938cc3 --- /dev/null +++ b/files/integrations/cdc/xml/mcd18_weekly.xml @@ -0,0 +1,520 @@ + + + B_1 + D176.V100-level1 + + + B_2 + D176.V100-level2 + + + B_3 + *None* + + + B_4 + *None* + + + B_5 + *None* + + + F_D176.V1 + *All* + + + F_D176.V10 + *All* + + + F_D176.V100 + *All* + + + F_D176.V13 + *All* + + + F_D176.V2 + *All* + + + F_D176.V25 + *All* + + + F_D176.V26 + *All* + + + F_D176.V27 + *All* + + + F_D176.V77 + *All* + + + F_D176.V79 + *All* + + + F_D176.V80 + *All* + + + F_D176.V9 + *All* + + + I_D176.V1 + *All* (All Dates) + + + + I_D176.V10 + *All* (The United States) + + + + I_D176.V100 + *All* (All Dates) + + + + I_D176.V2 + *All* (All Causes of Death) + + + + I_D176.V25 + All Causes of Death + + + + I_D176.V27 + *All* (The United States) + + + + I_D176.V77 + *All* (The United States) + + + + I_D176.V79 + *All* (The United States) + + + + I_D176.V80 + *All* (The United States) + + + + I_D176.V9 + *All* (The United States) + + + + L_D176.V15 + *All* + + + L_D176.V16 + *All* + + + M_1 + D176.M1 + + + O_MMWR + false + + + O_V100_fmode + freg + + + O_V10_fmode + freg + + + O_V13_fmode + fadv + + + O_V15_fmode + fadv + + + O_V16_fmode + fadv + + + O_V1_fmode + freg + + + O_V25_fmode + freg + + + O_V26_fmode + fadv + + + O_V27_fmode + freg + + + O_V2_fmode + freg + + + O_V77_fmode + freg + + + O_V79_fmode + freg + + + O_V80_fmode + freg + + + O_V9_fmode + freg + + + O_aar + aar_none + + + O_aar_pop + 0000 + + + O_age + D176.V5 + + + O_dates + MMWR + + + O_death_location + D176.V79 + + + O_death_urban + D176.V89 + + + O_javascript + on + + + O_location + D176.V9 + + + O_mcd + D176.V13 + + + O_oc-sect1-request + close + + + O_precision + 1 + + + O_race + D176.V42 + + + O_rate_per + 100000 + + + O_show_totals + false + + + O_timeout + 600 + + + O_title + + + + O_ucd + D176.V2 + + + O_urban + D176.V19 + + + VM_D176.M6_D176.V10 + + + + VM_D176.M6_D176.V17 + *All* + + + VM_D176.M6_D176.V1_S + *All* + + + VM_D176.M6_D176.V42 + *All* + + + VM_D176.M6_D176.V7 + *All* + + + V_D176.V1 + + + + V_D176.V10 + + + + V_D176.V100 + + + + V_D176.V11 + *All* + + + V_D176.V12 + *All* + + + V_D176.V13 + + + + V_D176.V13_AND + + + + V_D176.V15 + + + + V_D176.V15_AND + + + + V_D176.V16 + + + + V_D176.V16_AND + + + + V_D176.V17 + *All* + + + V_D176.V18 + *All* + + + V_D176.V19 + *All* + + + V_D176.V2 + + + + V_D176.V20 + *All* + + + V_D176.V21 + *All* + + + V_D176.V22 + *All* + + + V_D176.V23 + *All* + + + V_D176.V25 + + + + V_D176.V26 + + + + V_D176.V26_AND + + + + V_D176.V27 + + + + V_D176.V4 + *All* + + + V_D176.V42 + *All* + + + V_D176.V43 + *All* + + + V_D176.V44 + *All* + + + V_D176.V5 + *All* + + + V_D176.V51 + *All* + + + V_D176.V52 + *All* + + + V_D176.V6 + 00 + + + V_D176.V7 + *All* + + + V_D176.V77 + + + + V_D176.V79 + + + + V_D176.V80 + + + + V_D176.V81 + *All* + + + V_D176.V82 + *All* + + + V_D176.V89 + *All* + + + V_D176.V9 + + + + action-Send + Send + + + dataset_code + D176 + + + dataset_label + Provisional Mortality Statistics, 2018 through Last Week + + + dataset_vintage + April 4, 2026 as of April 12, 2026 + + + finder-stage-D176.V1 + codeset + + + finder-stage-D176.V10 + codeset + + + finder-stage-D176.V100 + codeset + + + finder-stage-D176.V13 + codeset + + + finder-stage-D176.V15 + + + + finder-stage-D176.V16 + + + + finder-stage-D176.V2 + codeset + + + finder-stage-D176.V25 + codeset + + + finder-stage-D176.V26 + codeset + + + finder-stage-D176.V27 + codeset + + + finder-stage-D176.V77 + codeset + + + finder-stage-D176.V79 + codeset + + + finder-stage-D176.V80 + codeset + + + finder-stage-D176.V9 + codeset + + + saved_id + + + + stage + request + + \ No newline at end of file diff --git a/files/integrations/cdc/xml/mcd18_weekly_influenza.xml b/files/integrations/cdc/xml/mcd18_weekly_influenza.xml new file mode 100644 index 0000000..93c58e2 --- /dev/null +++ b/files/integrations/cdc/xml/mcd18_weekly_influenza.xml @@ -0,0 +1,524 @@ + + + B_1 + D176.V100-level1 + + + B_2 + D176.V100-level2 + + + B_3 + *None* + + + B_4 + *None* + + + B_5 + *None* + + + F_D176.V1 + *All* + + + F_D176.V10 + *All* + + + F_D176.V100 + *All* + + + F_D176.V13 + *All* + + + F_D176.V2 + J09-J18 + + + F_D176.V25 + *All* + + + F_D176.V26 + *All* + + + F_D176.V27 + *All* + + + F_D176.V77 + *All* + + + F_D176.V79 + *All* + + + F_D176.V80 + *All* + + + F_D176.V9 + *All* + + + I_D176.V1 + *All* (All Dates) + + + + I_D176.V10 + *All* (The United States) + + + + I_D176.V100 + *All* (All Dates) + + + + I_D176.V2 + J09-J18 (Influenza and pneumonia) + + + + I_D176.V25 + All Causes of Death + + + + I_D176.V27 + *All* (The United States) + + + + I_D176.V77 + *All* (The United States) + + + + I_D176.V79 + *All* (The United States) + + + + I_D176.V80 + *All* (The United States) + + + + I_D176.V9 + *All* (The United States) + + + + L_D176.V15 + *All* + + + L_D176.V16 + *All* + + + M_1 + D176.M1 + + + O_MMWR + false + + + O_V100_fmode + freg + + + O_V10_fmode + freg + + + O_V13_fmode + fadv + + + O_V15_fmode + fadv + + + O_V16_fmode + fadv + + + O_V1_fmode + freg + + + O_V25_fmode + freg + + + O_V26_fmode + fadv + + + O_V27_fmode + freg + + + O_V2_fmode + freg + + + O_V77_fmode + freg + + + O_V79_fmode + freg + + + O_V80_fmode + freg + + + O_V9_fmode + freg + + + O_aar + aar_none + + + O_aar_pop + 0000 + + + O_age + D176.V5 + + + O_dates + MMWR + + + O_death_location + D176.V79 + + + O_death_urban + D176.V89 + + + O_export-format + xls + + + O_javascript + on + + + O_location + D176.V9 + + + O_mcd + D176.V13 + + + O_oc-sect1-request + close + + + O_precision + 1 + + + O_race + D176.V42 + + + O_rate_per + 100000 + + + O_show_totals + false + + + O_timeout + 600 + + + O_title + + + + O_ucd + D176.V2 + + + O_urban + D176.V19 + + + VM_D176.M6_D176.V10 + + + + VM_D176.M6_D176.V17 + *All* + + + VM_D176.M6_D176.V1_S + *All* + + + VM_D176.M6_D176.V42 + *All* + + + VM_D176.M6_D176.V7 + *All* + + + V_D176.V1 + + + + V_D176.V10 + + + + V_D176.V100 + + + + V_D176.V11 + *All* + + + V_D176.V12 + *All* + + + V_D176.V13 + + + + V_D176.V13_AND + + + + V_D176.V15 + + + + V_D176.V15_AND + + + + V_D176.V16 + + + + V_D176.V16_AND + + + + V_D176.V17 + *All* + + + V_D176.V18 + *All* + + + V_D176.V19 + *All* + + + V_D176.V2 + + + + V_D176.V20 + *All* + + + V_D176.V21 + *All* + + + V_D176.V22 + *All* + + + V_D176.V23 + *All* + + + V_D176.V25 + + + + V_D176.V26 + + + + V_D176.V26_AND + + + + V_D176.V27 + + + + V_D176.V4 + *All* + + + V_D176.V42 + *All* + + + V_D176.V43 + *All* + + + V_D176.V44 + *All* + + + V_D176.V5 + *All* + + + V_D176.V51 + *All* + + + V_D176.V52 + *All* + + + V_D176.V6 + 00 + + + V_D176.V7 + *All* + + + V_D176.V77 + + + + V_D176.V79 + + + + V_D176.V80 + + + + V_D176.V81 + *All* + + + V_D176.V82 + *All* + + + V_D176.V89 + *All* + + + V_D176.V9 + + + + action-Send + Send + + + dataset_code + D176 + + + dataset_label + Provisional Mortality Statistics, 2018 through Last Week + + + dataset_vintage + April 18, 2026 as of April 26, 2026 + + + finder-stage-D176.V1 + codeset + + + finder-stage-D176.V10 + codeset + + + finder-stage-D176.V100 + codeset + + + finder-stage-D176.V13 + codeset + + + finder-stage-D176.V15 + + + + finder-stage-D176.V16 + + + + finder-stage-D176.V2 + codeset + + + finder-stage-D176.V25 + codeset + + + finder-stage-D176.V26 + codeset + + + finder-stage-D176.V27 + codeset + + + finder-stage-D176.V77 + codeset + + + finder-stage-D176.V79 + codeset + + + finder-stage-D176.V80 + codeset + + + finder-stage-D176.V9 + codeset + + + saved_id + + + + stage + request + + \ No newline at end of file diff --git a/files/integrations/cdc/xml/mcd18_weekly_pic.xml b/files/integrations/cdc/xml/mcd18_weekly_pic.xml new file mode 100644 index 0000000..0471538 --- /dev/null +++ b/files/integrations/cdc/xml/mcd18_weekly_pic.xml @@ -0,0 +1,528 @@ + + + B_1 + D176.V100-level1 + + + B_2 + D176.V100-level2 + + + B_3 + D176.V2-level1 + + + B_4 + *None* + + + B_5 + *None* + + + F_D176.V1 + *All* + + + F_D176.V10 + *All* + + + F_D176.V100 + *All* + + + F_D176.V13 + *All* + + + F_D176.V2 + A00-B99 + J09-J18 + U00-U99 + + + F_D176.V25 + *All* + + + F_D176.V26 + *All* + + + F_D176.V27 + *All* + + + F_D176.V77 + *All* + + + F_D176.V79 + *All* + + + F_D176.V80 + *All* + + + F_D176.V9 + *All* + + + I_D176.V1 + *All* (All Dates) + + + + I_D176.V10 + *All* (The United States) + + + + I_D176.V100 + *All* (All Dates) + + + + I_D176.V2 + A00-B99 (Certain infectious and parasitic diseases) +J09-J18 (Influenza and pneumonia) +U00-U99 (Codes for special purposes) + + + + I_D176.V25 + All Causes of Death + + + + I_D176.V27 + *All* (The United States) + + + + I_D176.V77 + *All* (The United States) + + + + I_D176.V79 + *All* (The United States) + + + + I_D176.V80 + *All* (The United States) + + + + I_D176.V9 + *All* (The United States) + + + + L_D176.V15 + *All* + + + L_D176.V16 + *All* + + + M_1 + D176.M1 + + + O_MMWR + false + + + O_V100_fmode + freg + + + O_V10_fmode + freg + + + O_V13_fmode + fadv + + + O_V15_fmode + fadv + + + O_V16_fmode + fadv + + + O_V1_fmode + freg + + + O_V25_fmode + freg + + + O_V26_fmode + fadv + + + O_V27_fmode + freg + + + O_V2_fmode + freg + + + O_V77_fmode + freg + + + O_V79_fmode + freg + + + O_V80_fmode + freg + + + O_V9_fmode + freg + + + O_aar + aar_none + + + O_aar_pop + 0000 + + + O_age + D176.V5 + + + O_dates + MMWR + + + O_death_location + D176.V79 + + + O_death_urban + D176.V89 + + + O_export-format + xls + + + O_javascript + on + + + O_location + D176.V9 + + + O_mcd + D176.V13 + + + O_oc-sect1-request + close + + + O_precision + 1 + + + O_race + D176.V42 + + + O_rate_per + 100000 + + + O_show_totals + false + + + O_timeout + 600 + + + O_title + + + + O_ucd + D176.V2 + + + O_urban + D176.V19 + + + VM_D176.M6_D176.V10 + + + + VM_D176.M6_D176.V17 + *All* + + + VM_D176.M6_D176.V1_S + *All* + + + VM_D176.M6_D176.V42 + *All* + + + VM_D176.M6_D176.V7 + *All* + + + V_D176.V1 + + + + V_D176.V10 + + + + V_D176.V100 + + + + V_D176.V11 + *All* + + + V_D176.V12 + *All* + + + V_D176.V13 + + + + V_D176.V13_AND + + + + V_D176.V15 + + + + V_D176.V15_AND + + + + V_D176.V16 + + + + V_D176.V16_AND + + + + V_D176.V17 + *All* + + + V_D176.V18 + *All* + + + V_D176.V19 + *All* + + + V_D176.V2 + + + + V_D176.V20 + *All* + + + V_D176.V21 + *All* + + + V_D176.V22 + *All* + + + V_D176.V23 + *All* + + + V_D176.V25 + + + + V_D176.V26 + + + + V_D176.V26_AND + + + + V_D176.V27 + + + + V_D176.V4 + *All* + + + V_D176.V42 + *All* + + + V_D176.V43 + *All* + + + V_D176.V44 + *All* + + + V_D176.V5 + *All* + + + V_D176.V51 + *All* + + + V_D176.V52 + *All* + + + V_D176.V6 + 00 + + + V_D176.V7 + *All* + + + V_D176.V77 + + + + V_D176.V79 + + + + V_D176.V80 + + + + V_D176.V81 + *All* + + + V_D176.V82 + *All* + + + V_D176.V89 + *All* + + + V_D176.V9 + + + + action-Send + Send + + + dataset_code + D176 + + + dataset_label + Provisional Mortality Statistics, 2018 through Last Week + + + dataset_vintage + May 9, 2026 as of May 17, 2026 + + + finder-stage-D176.V1 + codeset + + + finder-stage-D176.V10 + codeset + + + finder-stage-D176.V100 + codeset + + + finder-stage-D176.V13 + codeset + + + finder-stage-D176.V15 + + + + finder-stage-D176.V16 + + + + finder-stage-D176.V2 + codeset + + + finder-stage-D176.V25 + codeset + + + finder-stage-D176.V26 + codeset + + + finder-stage-D176.V27 + codeset + + + finder-stage-D176.V77 + codeset + + + finder-stage-D176.V79 + codeset + + + finder-stage-D176.V80 + codeset + + + finder-stage-D176.V9 + codeset + + + saved_id + + + + stage + request + + \ No newline at end of file diff --git a/files/integrations/cdc/xml/mcd99_monthly_cod.xml b/files/integrations/cdc/xml/mcd99_monthly_cod.xml new file mode 100644 index 0000000..6eed809 --- /dev/null +++ b/files/integrations/cdc/xml/mcd99_monthly_cod.xml @@ -0,0 +1,358 @@ + + + B_1 + D76.V1-level1 + + + B_2 + D76.V1-level2 + + + B_3 + D76.V2-level1 + + + B_4 + *None* + + + B_5 + *None* + + + F_D76.V1 + *All* + + + F_D76.V10 + *All* + + + F_D76.V2 + A00-B99 + C00-D48 + D50-D89 + E00-E88 + F01-F99 + G00-G98 + H00-H57 + H60-H93 + I00-I99 + J09-J18 + K00-K92 + L00-L98 + M00-M99 + N00-N98 + O00-O99 + P00-P96 + Q00-Q99 + R00-R99 + U00-U99 + V01-Y89 + + + F_D76.V25 + *All* + + + F_D76.V27 + *All* + + + F_D76.V9 + *All* + + + I_D76.V1 + *All* (All Dates) + + + + I_D76.V10 + *All* (The United States) + + + + I_D76.V2 + A00-B99 (Certain infectious and parasitic diseases) +C00-D48 (Neoplasms) +D50-D89 (Diseases of the blood and blood-forming organs and certain disorders involving the immune mechanism) +E00-E88 (Endocrine, nutritional and metabolic diseases) +F01-F99 (Mental and behavioural disorders) +G00-G98 (Diseases of the nervous system) +H00-H57 (Diseases of the eye and adnexa) +H60-H93 (Diseases of the ear and mastoid process) +I00-I99 (Diseases of the circulatory system) +J09-J18 (Influenza and pneumonia) +K00-K92 (Diseases of the digestive system) +L00-L98 (Diseases of the skin and subcutaneous tissue) +M00-M99 (Diseases of the musculoskeletal system and connective tissue) +N00-N98 (Diseases of the genitourinary system) +O00-O99 (Pregnancy, childbirth and the puerperium) +P00-P96 (Certain conditions originating in the perinatal period) +Q00-Q99 (Congenital malformations, deformations and chromosomal abnormalities) +R00-R99 (Symptoms, signs and abnormal clinical and laboratory findings, not elsewhere classified) +U00-U99 (Codes for special purposes) +V01-Y89 (External causes of morbidity and mortality) + + + + I_D76.V25 + All Causes of Death + + + + I_D76.V27 + *All* (The United States) + + + + I_D76.V9 + *All* (The United States) + + + + M_1 + D76.M1 + + + M_2 + D76.M2 + + + M_3 + D76.M3 + + + O_V10_fmode + freg + + + O_V1_fmode + freg + + + O_V25_fmode + freg + + + O_V27_fmode + freg + + + O_V2_fmode + freg + + + O_V9_fmode + freg + + + O_aar + aar_none + + + O_aar_pop + 0000 + + + O_age + D76.V5 + + + O_export-format + xls + + + O_javascript + on + + + O_location + D76.V9 + + + O_oc-sect1-request + close + + + O_precision + 1 + + + O_rate_per + 100000 + + + O_timeout + 600 + + + O_title + + + + O_ucd + D76.V2 + + + O_urban + D76.V19 + + + VM_D76.M6_D76.V10 + + + + VM_D76.M6_D76.V17 + *All* + + + VM_D76.M6_D76.V1_S + *All* + + + VM_D76.M6_D76.V7 + *All* + + + VM_D76.M6_D76.V8 + *All* + + + V_D76.V1 + + + + V_D76.V10 + + + + V_D76.V11 + *All* + + + V_D76.V12 + *All* + + + V_D76.V17 + *All* + + + V_D76.V19 + *All* + + + V_D76.V2 + + + + V_D76.V20 + *All* + + + V_D76.V21 + *All* + + + V_D76.V22 + *All* + + + V_D76.V23 + *All* + + + V_D76.V24 + *All* + + + V_D76.V25 + + + + V_D76.V27 + + + + V_D76.V4 + *All* + + + V_D76.V5 + *All* + + + V_D76.V51 + *All* + + + V_D76.V52 + *All* + + + V_D76.V6 + 00 + + + V_D76.V7 + *All* + + + V_D76.V8 + *All* + + + V_D76.V9 + + + + action-Send + Send + + + dataset_code + D76 + + + dataset_label + Underlying Cause of Death, 1999-2020 + + + dataset_vintage + 2020 + + + finder-stage-D76.V1 + codeset + + + finder-stage-D76.V10 + codeset + + + finder-stage-D76.V2 + codeset + + + finder-stage-D76.V25 + codeset + + + finder-stage-D76.V27 + codeset + + + finder-stage-D76.V9 + codeset + + + saved_id + + + + stage + request + + \ No newline at end of file diff --git a/morai/dashboard/app.py b/morai/dashboard/app.py index e455547..d55f9fa 100644 --- a/morai/dashboard/app.py +++ b/morai/dashboard/app.py @@ -19,7 +19,7 @@ import dash_mantine_components as dmc from dash_extensions.enrich import DashProxy, ServersideOutputTransform, dcc, html -from morai.dashboard.utils import dashboard_helper as dh +from morai.dashboard.components import common_build from morai.utils import custom_logger # _ ____ ____ @@ -148,7 +148,8 @@ ) ) -dh.register_export_callback(app) +common_build.register_export_callback(app) +common_build.register_filter_callbacks(app) # Navbar toggle callback diff --git a/morai/dashboard/assets/slider.css b/morai/dashboard/assets/slider.css new file mode 100644 index 0000000..79ef456 --- /dev/null +++ b/morai/dashboard/assets/slider.css @@ -0,0 +1,37 @@ +.dash-slider-container { + display: grid; + grid-template-areas: + "slider slider" + "min max"; + grid-template-columns: minmax(50px, 1fr) minmax(50px, 1fr); + gap: 12px; + align-items: center; +} + +.dash-slider-container .dash-slider-wrapper { + grid-area: slider; + width: 100%; + min-width: 0; +} + +.dash-slider-container .dash-range-slider-input { + width: 100% !important; + min-width: 0; + box-sizing: border-box; +} + +.dash-slider-container .dash-range-slider-min-input { + grid-area: min; +} + +.dash-slider-container .dash-range-slider-max-input { + grid-area: max; +} + +@container (max-width: 300px) { + + .dash-slider-container .dash-range-slider-min-input, + .dash-slider-container .dash-range-slider-max-input { + display: block !important; + } +} \ No newline at end of file diff --git a/morai/dashboard/components/common_build.py b/morai/dashboard/components/common_build.py new file mode 100644 index 0000000..5127e1c --- /dev/null +++ b/morai/dashboard/components/common_build.py @@ -0,0 +1,386 @@ +"""Common build structures used in dashboard components.""" + +import ast +from dataclasses import dataclass, field +from typing import Any + +import dash_bootstrap_components as dbc +import dash_mantine_components as dmc +from dash import html +from dash_extensions.enrich import dcc + +from morai.utils import custom_logger + +logger = custom_logger.setup_logging(__name__) + + +@dataclass +class TabSpec: + """ + Specification for a single tab in a tabbed content area. + + Attributes + ---------- + label : str + Display name shown on the tab. + tab_id : str + Identifier suffix for the tab (e.g. "chart", "table"). The full + component id is namespaced by page in build_tabbed_content. + + """ + + label: str + tab_id: str + + def full_id(self, prefix: str) -> str: + """Get the full component id for this tab, namespaced by page.""" + return f"{prefix}-tab-{self.tab_id}" + + +@dataclass +class FilterPanel: + """ + Specification for the filter offcanvas panel. + + Attributes + ---------- + children : list + Components rendered inside the offcanvas body. The page is + responsible for the contents (bookmarks, reset button, filter + controls, etc.). + title : str + Title shown in the offcanvas header. + button_label : str + Text on the trigger button. + + """ + + children: list[Any] = field(default_factory=list) + title: str = "Filters" + button_label: str = "Show Filters" + + +def _build_tabbed_content( + tabs: list, + prefix: str, + active_tab: str | None = None, + filter_panel: FilterPanel | None = None, + include_secondary_chart: bool = False, +) -> list: + """ + Build a tabbed content area with optional filter button and exports. + + Notes + ----- + - the containing Tabs component will have id "{prefix}-tabs" + - the tab content container will have id "{prefix}-tab-content" + - each tab will have an id of the form "{prefix}-tab-{tab_id}" + - the offcanvas filter button will have id "{prefix}-open-offcanvas-button" + + Parameters + ---------- + tabs : list of TabSpec + Tab specifications in display order. + prefix : str + prefix to use for the ids of all components + (tabs, content container, filter button) + active_tab : str, optional + tab_id of the initially active tab. Defaults to the first tab. + filter_panel : FilterPanel, optional + If provided, renders a trigger button and offcanvas with the + given children. If None, no filter UI is rendered. + include_secondary_chart : bool + Whether to render a secondary chart container below the tabs. + + """ + # build each tab + tab_components = [ + dbc.Tab( + children=[], + label=t.label, + tab_id=f"{prefix}-tab-{t.tab_id}", + label_class_name="fw-bold", + active_label_class_name="text-primary", + ) + for t in tabs + ] + + # set active tab (default to first if not specified) + default_active = active_tab or tabs[0].full_id(prefix) + + # add filter button if needed, then tabs, then exports + content = [] + if filter_panel is not None: + content.append( + dbc.Button( + [html.I(className="fas fa-filter me-2"), filter_panel.button_label], + id={"type": "open-offcanvas-button", "prefix": prefix}, + className="mb-3", + color="primary", + style={"width": "auto"}, + ) + ) + content.append( + dbc.Tabs( + tab_components, + id=f"{prefix}-tabs", + active_tab=default_active, + className="mb-3", + ) + ) + + # add loaders and optional secondary chart + loaders = [ + dcc.Loading( + id=f"{prefix}-loading-tab-content", + custom_spinner=dmc.Skeleton(visible=True, h="100%", w="100%"), + children=html.Div( + id=f"{prefix}-tab-content", + className="bg-white rounded-3 shadow-sm p-4 border border-light", + ), + ), + ] + if include_secondary_chart: + loaders.append( + dcc.Loading( + id=f"{prefix}-loading-chart-secondary", + custom_spinner=dmc.Skeleton(visible=True, h="100%", w="100%"), + children=html.Div( + id=f"{prefix}-chart-secondary", + className=( + "mt-4 bg-white rounded-3 shadow-sm p-4 border border-light" + ), + ), + ) + ) + content.append(html.Div(loaders, className="h-100")) + + # filter offcanvas + if filter_panel is not None: + content.append( + dbc.Offcanvas( + filter_panel.children, + id={"type": "filters-offcanvas", "prefix": prefix}, + title=filter_panel.title, + placement="end", + scrollable=True, + is_open=False, + className="offcanvas", + ) + ) + + return content + + +def _build_export_button(tab: str, page: str) -> html.Button: + """ + Build an export button. + + Parameters + ---------- + tab : str + The tab to create the button for + page : page + The page to create the button for + + Returns + ------- + export_button : html.Button + An html export button + + """ + return html.Button( + [html.I(className="fas fa-download me-2"), "Export to CSV"], + id={"type": "export-button", "tab": tab, "page": page}, + className="btn btn-primary mt-2 mb-2", + style={"display": "inline-block"}, + ) + + +def register_export_callback(app) -> None: + """ + Register a universal callback for exporting table data to CSV. + + This function should be called once in the app initialization to register + the export functionality for all data tables across the application. + + Parameters + ---------- + app : dash.Dash + The Dash application instance. + + Notes + ----- + For this callback to work, the following components must be present: + 1. A Download component with id="download-dataframe-csv" in each page's layout + 2. Export buttons with pattern-matching ID: + {"type": "export-button", "tab": , "page": } + 3. Data tables with pattern-matching ID: + {"type": "data-table", "tab": , "page": } + + The tab and page values must match between the button and table for proper pairing. + + """ + import dash # noqa: PLC0415 + import pandas as pd # noqa: PLC0415 + from dash_extensions.enrich import ( # noqa: PLC0415 + ALL, + Input, + Output, + State, + callback, + callback_context, + dcc, + ) + + @callback( + Output("download-dataframe-csv", "data"), + Input({"type": "export-button", "tab": ALL, "page": ALL}, "n_clicks"), + State({"type": "data-table", "tab": ALL, "page": ALL}, "rowData"), + prevent_initial_call=True, + ) + def export_table( + n_clicks_list: list[int | None], table_data_list: list[list[Any]] + ) -> None: + """ + Export table data to CSV. + + This generic function handles exporting data from any + table with an export button. + + The button and table must use pattern-matching IDs + with the following structure: + - Button: + {"type": "export-button", "tab": , "page": } + - Table: + {"type": "data-table", "tab": , "page": } + + Where identifies the specific tab + and identifies the page. + """ + ctx = callback_context + if not ctx.triggered or not ctx.triggered[0]["value"]: + return dash.no_update + + triggered_id = ctx.triggered[0]["prop_id"].split(".")[0] + button_id = ast.literal_eval(triggered_id) + tab = button_id["tab"] + page = button_id["page"] + + # Find the matching table data by comparing both tab and page values + for i, table_data in enumerate(table_data_list): + if not table_data: + continue + + # Get the corresponding table ID + table_id = ctx.states_list[0][i]["id"] + + # Check if this table matches the clicked button's tab and page + if table_id["tab"] == tab and table_id["page"] == page: + df = pd.DataFrame(table_data) + filename = f"{page}_{tab}.csv" + return dcc.send_data_frame(df.to_csv, filename, index=False) + + return dash.no_update + + +def register_filter_callbacks(app) -> None: + """ + Register universal filter behavior callbacks. + + This function should be called once during app initialization to register: + - Filter offcanvas toggle (for any page with the filter button) + - Filter checklist collapse/expand + - Filter reset + + For these callbacks to work, components must use pattern-matching IDs: + + Offcanvas toggle: + - Button: id="{prefix}-open-offcanvas-button" + - Offcanvas: id="{prefix}-filters-offcanvas" + + Collapse: + - Collapse button: + {"type": "filter-collapse-button", "prefix": , "index": } + - Collapse container: + {"type": "filter-collapse", "prefix": , "index": } + + Reset: + - Reset button: {"type": "filter-reset-button", "prefix": } + - String filters: {"type": "filter-str", "prefix": , "index": } + - Numeric filters: {"type": "filter-num", "prefix": , "index": } + - Initial values store: id="{prefix}-store-initial-filters" with shape: + {"str_cols": [...], "num_cols": [...], "min_max": {"col_min": x, "col_max": y}} + """ + import dash # noqa: PLC0415 + from dash_extensions.enrich import ( # noqa: PLC0415 + ALL, + MATCH, + Input, + Output, + State, + callback, + callback_context, + ) + + # offcanvas toggle + @callback( + Output({"type": "filters-offcanvas", "prefix": MATCH}, "is_open"), + Input({"type": "open-offcanvas-button", "prefix": MATCH}, "n_clicks"), + State({"type": "filters-offcanvas", "prefix": MATCH}, "is_open"), + prevent_initial_call=True, + ) + def toggle_filters_offcanvas(n_clicks, is_open): + """Toggle the filters offcanvas.""" + if n_clicks: + return not is_open + return is_open + + # collapse checklists + @callback( + Output({"type": "filter-collapse", "prefix": MATCH, "index": ALL}, "is_open"), + Output( + {"type": "filter-collapse-button", "prefix": MATCH, "index": ALL}, + "children", + ), + Input( + {"type": "filter-collapse-button", "prefix": MATCH, "index": ALL}, + "n_clicks", + ), + State({"type": "filter-collapse", "prefix": MATCH, "index": ALL}, "is_open"), + State( + {"type": "filter-collapse-button", "prefix": MATCH, "index": ALL}, + "children", + ), + prevent_initial_call=True, + ) + def toggle_collapse(n_clicks, is_open, children): + """Toggle collapse state of filter checklists.""" + if not n_clicks or not any(n_clicks): + raise dash.exceptions.PreventUpdate + + ctx = callback_context + if not ctx.triggered: + return [False] * len(is_open), children + + button_id = ctx.triggered[0]["prop_id"].split(".")[0] + button_idx = ast.literal_eval(button_id)["index"] + + new_is_open = [] + new_children = [] + for col, is_open_state, child in zip( + [x["id"]["index"] for x in ctx.inputs_list[0]], + is_open, + children, + strict=False, + ): + new_state = not is_open_state if col == button_idx else is_open_state + new_is_open.append(new_state) + label = child[0]["props"]["children"] + new_children.append( + [ + html.Span(label, style={"flex-grow": 1}), + html.I(className=f"fas fa-chevron-{'up' if new_state else 'down'}"), + ] + ) + return new_is_open, new_children diff --git a/morai/dashboard/components/dash_formats.py b/morai/dashboard/components/dash_formats.py index 4c790bf..a575e74 100644 --- a/morai/dashboard/components/dash_formats.py +++ b/morai/dashboard/components/dash_formats.py @@ -1,11 +1,13 @@ """Components - dash formats.""" +import pandas as pd + from morai.utils import custom_logger logger = custom_logger.setup_logging(__name__) -def get_column_defs(table): +def get_column_defs(table: pd.DataFrame) -> list: """ Get the column definitions. @@ -88,7 +90,7 @@ def get_column_defs(table): return column_defs -def remove_column_defs(column_defs, col_name): +def remove_column_defs(column_defs: list, col_name: str) -> list: """ Remove a column from the column definitions. @@ -111,7 +113,7 @@ def remove_column_defs(column_defs, col_name): return column_defs -def group_column_defs(column_defs): +def group_column_defs(column_defs: list) -> list: """ Create column groups for the column definitions. @@ -129,7 +131,7 @@ def group_column_defs(column_defs): The column definitions """ - groups = {} + groups: dict = {} # parse the column definitions to group them by prefix for col_def in column_defs: diff --git a/morai/dashboard/pages/cdc.py b/morai/dashboard/pages/cdc.py index 1728be2..4a557e4 100644 --- a/morai/dashboard/pages/cdc.py +++ b/morai/dashboard/pages/cdc.py @@ -1,4 +1,9 @@ -"""CDC dashboard.""" +""" +CDC dashboard. + +If running dashboard in container, ensure timeout is long enough for CDC queries +to run (can take up to 150 seconds). Timeout is setup in compose file. +""" import threading import time @@ -7,9 +12,10 @@ import dash_ag_grid as dag import dash_bootstrap_components as dbc import dash_extensions.enrich as dash -import dash_mantine_components as dmc +import numpy as np import pandas as pd import plotly.express as px +import plotly.graph_objects as go from dash_extensions.enrich import ( ALL, Input, @@ -22,7 +28,7 @@ ) from sklearn.linear_model import LinearRegression -from morai.dashboard.components import dash_formats +from morai.dashboard.components import common_build, dash_formats from morai.dashboard.utils import dashboard_helper as dh from morai.experience import charters from morai.integrations import cdc @@ -38,19 +44,56 @@ thread_lock = threading.Lock() # initialize variables +DAYS_SINCE_LAST_UPDATE = 1 +CDC_WAIT_TIME_SECONDS = 16 +FILTER_MI_PREFIX = "cdc-mi" +FILTER_COD_PREFIX = "cdc-cod" +FILTER_COD_TRENDS_PREFIX = "cdc-cod-trends" +FILTER_MONTHLY_PREFIX = "cdc-monthly" # provides when to use data from 18 dataset as there is overlap in 99 dataset # the 99 dataset ends in 2020 and the 18 dataset starts in 2018 NEW_DATASET_START_YEAR = 2021 # training for cod trend and population trend -TRAIN_START_YEAR = 2023 -TRAIN_END_YEAR = 2025 +TRAIN_START_YEAR = 2010 +TRAIN_END_YEAR = 2019 # grouping for cod analysis CATEGORY_COL = "simple_grouping" +# ibnr adjustment factors for the lag week +IBNR_FACTORS = { + 0: 0.27, + 1: 0.64, + 2: 0.76, + 3: 0.85, + 4: 0.90, + 5: 0.94, + 6: 0.96, + 7: 0.97, + 8: 0.98, + 9: 0.99, + 10: 0.99, + 11: 0.99, + 12: 0.99, + 13: 0.99, +} +PARTIAL_WEEKS_TO_EXCLUDE = 7 +# mi +ROLLING_MI_YEARS = 10 def layout(): """CDC layout.""" - last_updated = cdc.get_last_updated() + last_updated_meta = cdc.get_last_updated() + recent_week = last_updated_meta["recent_week"] + ibnr_factors_included = { + recent_week - k: v + for k, v in IBNR_FACTORS.items() + if k >= PARTIAL_WEEKS_TO_EXCLUDE + } + ibnr_factors_excluded = { + recent_week - k: v + for k, v in IBNR_FACTORS.items() + if k < PARTIAL_WEEKS_TO_EXCLUDE + } return html.Div( [ dcc.Store(id="store-cdc-results", storage_type="session"), @@ -135,7 +178,7 @@ def layout(): html.Li( [ html.A( - "2018-present", + "2018-present (ICD-10)", href="https://wonder.cdc.gov/mcd-icd10-provisional.html", target="_blank", ), @@ -156,11 +199,15 @@ def layout(): html.Ul( [ html.Li( - "'Other' deaths → ~4 months" + "'Other' deaths → ~4 months (contributes to all causes)" ), html.Li( "'Delay' deaths → ~6 months (mainly external causes)" ), + html.Li( + f"The partial {last_updated_meta['data_through'].year} data is annualized using a factor of (365 / " + f"{last_updated_meta['days_elapsed']}). " + ), ] ), ] @@ -191,13 +238,30 @@ def layout(): html.I( className="fas fa-clock me-1 text-muted" ), + html.Label( + "Last Updated: ", + ), html.Span( - last_updated, + pd.Timestamp( + last_updated_meta["last_updated"] + ).date(), id="last-updated-text", - className="text-muted", + ), + html.Br(), + html.I( + className="fas fa-clock me-1 text-muted" + ), + html.Label( + "Data Through: ", + ), + html.Span( + last_updated_meta[ + "data_through" + ].date(), + id="data-through-text", ), ], - className="text-center small", + className="text-center small text-muted", ), ], xs=12, @@ -243,13 +307,7 @@ def layout(): dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - "Deaths by cause of death over time. Current year is annualized (365 / ", - html.Span( - "days elapsed", - id="cod-days-elapsed-text", - className="fw-semibold", - ), - ").", + "The heatmap and top causes use the most recent year of data.", ], color="info", className="py-2 mb-3 small", @@ -257,18 +315,26 @@ def layout(): dbc.Row( [ dbc.Col( - [ - dcc.Loading( - id="loading-cdc-cod", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="COD time", tab_id="cod-time" ), - children=html.Div( - id="cdc-cod", - className="bg-white rounded-3 shadow-sm p-3", + common_build.TabSpec( + label="COD Heatmap", + tab_id="cod-heatmap", ), - ), - ], + common_build.TabSpec( + label="COD Top Causes - Names", + tab_id="cod-top-causes-names", + ), + common_build.TabSpec( + label="COD Top Causes - Deaths", + tab_id="cod-top-causes-deaths", + ), + ], + prefix=FILTER_COD_PREFIX, + ), width=10, ), dbc.Col( @@ -288,7 +354,7 @@ def layout(): ), dbc.CardBody( html.Div( - id="cdc-cod-filters", + id=f"{FILTER_COD_PREFIX}-filters", ), ), ], @@ -299,41 +365,93 @@ def layout(): ], className="mb-4", ), - dbc.Alert( - [ - html.I(className="fas fa-info-circle me-2"), - "Treemap breakdown of deaths by category for year ", - html.Span( - "—", - id="cod-tree-year-text", - className="fw-semibold", - ), - ".", - ], - color="info", - className="py-2 mb-3 small", - ), + ], + title=[ + html.I(className="fas fa-chart-pie me-2"), + "Cause of Death Analysis", + ], + ), + # COD Excess Section + dbc.AccordionItem( + [ dbc.Row( - dcc.Loading( - id="loading-cdc-cod-heatmap", - custom_spinner=dmc.Skeleton(visible=True, h="100%"), - children=html.Div( - id="cdc-cod-heatmap", - className="bg-white rounded-3 shadow-sm p-3", + dbc.Col( + dbc.Button( + [ + html.I(className="fas fa-sync-alt me-2"), + "Load Trends", + ], + id="button-cod-trends", + color="primary", + className="shadow-sm", ), + width="auto", ), - className="mb-4", + className="mb-3", ), dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - "Top causes of death by age group for year ", - html.Span( - "—", - id="cod-table-year-text", - className="fw-semibold", + "Standardized mortality rate compared to a linear regression baseline trained on " + f"{TRAIN_START_YEAR}-{TRAIN_END_YEAR}. ", + html.Br(), + "The 'cause of death excess' won't match the 'population excess' as the 'cause of death' " + "uses standardized deaths while the 'population' uses crude deaths. As the population gets " + "older the 'population excess' metric will be a better indicator of excess mortality trends.", + # lightbulb observations + html.I( + className="fas fa-lightbulb ms-2 text-warning", + id="cod-trends-obs-trigger", + style={"cursor": "pointer"}, + ), + dbc.Popover( + [ + dbc.PopoverHeader( + [ + html.I( + className="fas fa-lightbulb me-2 text-warning" + ), + "Observations (through 2025)", + ] + ), + dbc.PopoverBody( + html.Ul( + [ + html.Li( + [ + html.Span( + "↓ ", + className="text-success fw-bold", + ), + html.Strong( + "Improving: " + ), + "respiratory, nervous, external", + ], + className="small", + ), + html.Li( + [ + html.Span( + "↑ ", + className="text-danger fw-bold", + ), + html.Strong( + "Deteriorating: " + ), + "neoplasms, circulatory", + ], + className="small mb-0", + ), + ], + className="mb-0 ps-3", + ), + ), + ], + target="cod-trends-obs-trigger", + trigger="hover focus click", + placement="bottom", ), - ".", ], color="info", className="py-2 mb-3 small", @@ -341,51 +459,102 @@ def layout(): dbc.Row( [ dbc.Col( - dcc.Loading( - id="loading-cdc-top-causes", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" - ), - children=html.Div( - [ - dbc.Tabs( + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="Trends", tab_id="trends" + ), + common_build.TabSpec( + label="Actuals", + tab_id="actuals", + ), + common_build.TabSpec( + label="Actual - Predicted", + tab_id="table-diff", + ), + common_build.TabSpec( + label="Actual / Predicted", + tab_id="table-pct", + ), + common_build.TabSpec( + label="Data", + tab_id="data", + ), + ], + prefix=FILTER_COD_TRENDS_PREFIX, + ), + width=10, + ), + dbc.Col( + dbc.Card( + [ + dbc.CardHeader( + html.H5( [ - dbc.Tab( - html.Div( - id="cdc-top-cause-names", - className="bg-white rounded-3 shadow-sm p-3", - ), - label="Names", - tab_id="tab-names", + html.I( + className="fas fa-filter me-2" ), - dbc.Tab( - html.Div( - id="cdc-top-cause-deaths", - className="bg-white rounded-3 shadow-sm p-3", - ), - label="Deaths", - tab_id="tab-deaths", + "Filters", + ], + className="mb-0", + ), + className="bg-light", + ), + dbc.CardBody( + html.Div( + [ + html.Label( + "Simple Grouping", + className="fw-bold", + ), + dcc.Dropdown( + id={ + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "simple_grouping", + }, + options=[ + { + "label": "total", + "value": "total", + } + ], + value="total", + clearable=False, + className="ms-2", + ), + html.Label( + "Age Group", + className="fw-bold", + ), + dcc.Dropdown( + id={ + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "age_groups", + }, + options=[], + multi=True, + clearable=True, + className="ms-2", ), ], - id="cdc-top-causes-tabs", - active_tab="tab-names", ), - ], - className="bg-white rounded-3 shadow-sm p-3", - ), + ), + ], + className="shadow-sm h-100", ), - width=12, + width=2, ), ], - className="g-3 mb-3", ), ], title=[ - html.I(className="fas fa-chart-pie me-2"), - "Cause of Death Analysis", + html.I(className="fas fa-chart-line me-2"), + "Cause of Death - Excess Trends", ], ), - # COD Excess Section + # Population Excess Section dbc.AccordionItem( [ dbc.Row( @@ -393,9 +562,9 @@ def layout(): dbc.Button( [ html.I(className="fas fa-sync-alt me-2"), - "Load Trends", + "Load Population Trends", ], - id="button-cod-trends", + id="button-pop-trends", color="primary", className="shadow-sm", ), @@ -406,64 +575,34 @@ def layout(): dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - f"Excess deaths vs. a linear regression baseline trained on {TRAIN_START_YEAR}-{TRAIN_END_YEAR}. " - "Current year annualized (365 / ", - html.Span( - "days elapsed", - id="cod-trend-days-elapsed-text", - className="fw-semibold", - ), - "). Population differences are not accounted for.", + f"Lee-Carter population model trained on {TRAIN_START_YEAR}-{TRAIN_END_YEAR}, extrapolated through the current year. ", + html.Br(), + f"Note: population stats have a ~2-year lag; {TRAIN_START_YEAR}-{TRAIN_END_YEAR} trend is extrapolated to fill the gap.", ], color="info", className="py-2 mb-3 small", ), dbc.Row( - [ - dbc.Tabs( - [ - dbc.Tab( - label="Trends", - tab_id="tab-trends-chart", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - dbc.Tab( - label="Table-Amt", - tab_id="tab-trends-table-amt", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - dbc.Tab( - label="Table-%", - tab_id="tab-trends-table-pct", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - ], - id="tabs-cod-trends", - active_tab="tab-trends-chart", - className="mb-3", - ), - dcc.Loading( - id="loading-cdc-cod-trends", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="Excess Chart", tab_id="excess-chart" ), - children=html.Div( - id="cdc-cod-trends", - className="bg-white rounded-3 shadow-sm p-3", + common_build.TabSpec( + label="Excess Table", + tab_id="excess-table", ), - ), - ], + ], + prefix="cdc-pop-trends", + ), ), ], title=[ html.I(className="fas fa-chart-line me-2"), - "Cause of Death - Excess Trends", + "Population - Excess Trends", ], ), - # Population Excess Section + # Monthly Analysis Section dbc.AccordionItem( [ dbc.Row( @@ -471,9 +610,9 @@ def layout(): dbc.Button( [ html.I(className="fas fa-sync-alt me-2"), - "Load Population Trends", + "Load Monthly", ], - id="button-pop-trends", + id="button-monthly", color="primary", className="shadow-sm", ), @@ -484,58 +623,106 @@ def layout(): dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - f"Lee-Carter population model trained on {TRAIN_START_YEAR}-{TRAIN_END_YEAR}, extrapolated through the current year. " - "Current year annualized (365 / ", - html.Span( - "days elapsed", - id="pop-trend-days-elapsed-text", - className="fw-semibold", - ), - f"). Note: population stats have a ~2-year lag; {TRAIN_START_YEAR}-{TRAIN_END_YEAR} trend is extrapolated to fill the gap.", + "Total US deaths per month.", + html.Br(), + "The COD chart defaults to using PIC (pneumonia, infections, covid) which shows the " + "seasonal pattern. PIC may have an indirect effect on other COD such " + "as circulatory", ], color="info", className="py-2 mb-3 small", ), dbc.Row( [ - dbc.Tabs( - [ - dbc.Tab( - label="Excess-Chart", - tab_id="tab-pop-trends-chart", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - dbc.Tab( - label="Excess-Table", - tab_id="tab-pop-trends-table", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - ], - id="tabs-pop-trends", - active_tab="tab-pop-trends-chart", - className="mb-3", - ), - dcc.Loading( - id="loading-cdc-pop-trends", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" + dbc.Col( + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="All-causes Chart", + tab_id="chart", + ), + common_build.TabSpec( + label="All-causes Table", + tab_id="table", + ), + common_build.TabSpec( + label="COD Chart", + tab_id="pic-chart", + ), + ], + prefix=FILTER_MONTHLY_PREFIX, ), - children=html.Div( - id="cdc-pop-trends", - className="bg-white rounded-3 shadow-sm p-3", + width=10, + ), + dbc.Col( + dbc.Card( + [ + dbc.CardHeader( + html.H5( + [ + html.I( + className="fas fa-filter me-2" + ), + "Filters", + ], + className="mb-0", + ), + className="bg-light", + ), + dbc.CardBody( + html.Div( + [ + html.Label( + "Simple Chapter", + className="fw-bold", + ), + dcc.Dropdown( + id={ + "type": "filter-str", + "prefix": FILTER_MONTHLY_PREFIX, + "index": "simple_chapter", + }, + # default options + options=[ + { + "label": "infectious", + "value": "infectious", + }, + { + "label": "respiratory", + "value": "respiratory", + }, + { + "label": "special", + "value": "special", + }, + ], + value=[ + "infectious", + "respiratory", + "special", + ], + multi=True, + clearable=True, + className="ms-2", + ), + ], + ), + ), + ], + className="shadow-sm h-100", ), + width=2, ), ], ), ], title=[ - html.I(className="fas fa-chart-line me-2"), - "Population - Excess Trends", + html.I(className="fas fa-calendar-alt me-2"), + "Monthly Analysis", ], ), - # Monthly Analysis Section + # Weekly Analysis Section dbc.AccordionItem( [ dbc.Row( @@ -543,9 +730,9 @@ def layout(): dbc.Button( [ html.I(className="fas fa-sync-alt me-2"), - "Load Monthly", + "Load Weekly", ], - id="button-monthly", + id="button-weekly", color="primary", className="shadow-sm", ), @@ -556,29 +743,42 @@ def layout(): dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - "Total US deaths per month.", + "Total US deaths per week. The deaths also have an adjustment which accounts " + "for the lag in reporting.", + html.Br(), + f"Excluding recent weeks due to incomplete factors, which are {ibnr_factors_excluded}.", + html.Br(), + f"Lag adjustments for recent weeks are {ibnr_factors_included}.", ], color="info", className="py-2 mb-3 small", ), dbc.Row( - [ - dcc.Loading( - id="loading-cdc-monthly", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="All-causes Chart", tab_id="chart" ), - children=html.Div( - id="cdc-monthly", - className="bg-white rounded-3 shadow-sm p-3", + common_build.TabSpec( + label="All-causes Table", + tab_id="table", ), - ), - ], + common_build.TabSpec( + label="Flu Chart", + tab_id="flu-chart", + ), + common_build.TabSpec( + label="Flu Table", + tab_id="flu-table", + ), + ], + prefix="cdc-weekly", + ), ), ], title=[ html.I(className="fas fa-calendar-alt me-2"), - "Monthly Analysis", + "Weekly Analysis", ], ), # Mortality Improvement Section @@ -602,26 +802,34 @@ def layout(): dbc.Alert( [ html.I(className="fas fa-info-circle me-2"), - "Age-adjusted mortality rates (2000 standard) and year-over-year improvement. " - "Crude adjusted rate = deaths / population weighted by 2000 age distribution. " - "Rolling average is a 10-year window.", + "Mortality improvement calculated using the crude adjusted rate, which accounts for changes in age distribution over time. ", + html.Br(), + "Crude adjusted rate = weighted sum (deaths / population) weighted by 2000 population age distribution. (per 100,000 people) ", + html.Br(), + "whl = whittaker-henderson-lowrie method for smoothing (order = 3, lamda=400)", ], color="info", className="py-2 mb-3 small", ), - # Chart and Filters Row dbc.Row( [ dbc.Col( - dcc.Loading( - id="loading-cdc-mi", - custom_spinner=dmc.Skeleton( - visible=True, h="100%" - ), - children=html.Div( - id="cdc-mi", - className="bg-white rounded-3 shadow-sm p-3", - ), + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec( + label="Year-Chart", + tab_id="mi-year-chart", + ), + common_build.TabSpec( + label="Year-Table", + tab_id="mi-year-table", + ), + common_build.TabSpec( + label="Age-Chart", + tab_id="mi-age-chart", + ), + ], + prefix=FILTER_MI_PREFIX, ), width=10, ), @@ -642,7 +850,7 @@ def layout(): ), dbc.CardBody( html.Div( - id="cdc-mi-filters", + id=f"{FILTER_MI_PREFIX}-filters", ), ), ], @@ -653,17 +861,6 @@ def layout(): ], className="mb-4", ), - # mi table - dbc.Row( - dcc.Loading( - id="loading-cdc-mi-table", - custom_spinner=dmc.Skeleton(visible=True, h="100%"), - children=html.Div( - id="cdc-mi-table", - className="bg-white rounded-3 shadow-sm p-3", - ), - ), - ), ], title=[ html.I(className="fas fa-chart-bar me-2"), @@ -705,12 +902,23 @@ def update_cdc_data_async(n_clicks): def background_task(): try: - last_updated = pd.to_datetime(cdc.get_last_updated()) - days_since_update = (pd.Timestamp.now() - last_updated).days - if days_since_update < 7: + last_update_meta = cdc.get_last_updated() + last_updated = pd.to_datetime(last_update_meta["last_updated"]) + days_since_update = (pd.Timestamp.now().date() - last_updated.date()).days + # check if data was updated recently + if days_since_update < DAYS_SINCE_LAST_UPDATE: return "recent", None + # check if data is newer than the last update + data_through = pd.to_datetime(last_update_meta["data_through"]) + new_data_through = cdc.get_cdc_data_xml(xml_filename="mcd18_check.xml")[ + "data_through" + ].unique() + if new_data_through <= data_through: + return "no_update", None + # get new data and update the database + time.sleep(CDC_WAIT_TIME_SECONDS) refresh_cdc_data() - new_last_updated = cdc.get_last_updated() + new_last_updated = cdc.get_last_updated()["last_updated"] with thread_lock: return "success", new_last_updated except Exception as e: @@ -722,11 +930,19 @@ def background_task(): if status == "recent": return ( True, - "Data was recently updated. Please wait 7 days before updating again.", + f"Data was recently updated. Please wait {DAYS_SINCE_LAST_UPDATE} days before updating again.", "warning", "Warning", dash.no_update, ) + elif status == "no_update": + return ( + True, + "No new data available. Please check back later.", + "warning", + "warning", + dash.no_update, + ) elif status == "success": return ( True, @@ -749,23 +965,24 @@ def background_task(): [ Output("cod-active-filters-card", "children"), Output("cdc-cod-filters", "children"), - Output("cdc-cod", "children"), - Output("cdc-cod-heatmap", "children"), - Output("cdc-top-cause-names", "children"), - Output("cdc-top-cause-deaths", "children"), + Output(f"{FILTER_COD_PREFIX}-tab-content", "children"), Output("cdc-toast", "is_open", allow_duplicate=True), Output("cdc-toast", "children", allow_duplicate=True), - Output("cod-days-elapsed-text", "children"), - Output("cod-tree-year-text", "children"), - Output("cod-table-year-text", "children"), ], - Input("button-cod", "n_clicks"), [ - State({"type": "cdc_cod-str-filter", "index": ALL}, "value"), - State({"type": "cdc_cod-num-filter", "index": ALL}, "value"), + Input(f"{FILTER_COD_PREFIX}-tabs", "active_tab"), + Input("button-cod", "n_clicks"), + ], + [ + State( + {"type": "filter-str", "prefix": FILTER_COD_PREFIX, "index": ALL}, "value" + ), + State( + {"type": "filter-num", "prefix": FILTER_COD_PREFIX, "index": ALL}, "value" + ), ], ) -def display_cdc_cod(n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): +def display_cdc_cod(active_tab, n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): """Create cdc cod.""" if n_clicks is None: raise dash.exceptions.PreventUpdate @@ -776,17 +993,19 @@ def display_cdc_cod(n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): if not db_filepath.exists(): logger.error("Database does not exist.") return dash.no_update, dash.no_update, True, "Database does not exist" - tables = sql.get_tables(db_filepath=db_filepath) states_info = dh._inputs_flatten_list(callback_context.states_list) - # check if table does not exist in database - if "mcd99_cod" not in tables or "mcd18_cod" not in tables: - logger.error("Table `mcd99_cod` or `mcd18_cod` does not exist in database.") - return dash.no_update, dash.no_update, True, "Table does not exist in database" - # get the data - mcd99_cod = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_cod") - mcd18_cod = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_cod") + try: + mcd99_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd99_cod" + ) + mcd18_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_cod" + ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, dash.no_update, True, f"Failed to load data: {e}" # filter and concat mcd18_cod = mcd18_cod[mcd18_cod["year"] >= NEW_DATASET_START_YEAR] @@ -820,36 +1039,78 @@ def display_cdc_cod(n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): most_recent_year = cod_all["year"].max() - # create the charts - cdc_cod_chart = charters.chart( - df=cod_all, - x_axis="year", - y_axis="deaths", - color=CATEGORY_COL, - type="area", - category_orders=category_orders, - ) - - cdc_cod_heatmap = px.treemap( - cod_all[ - (cod_all[CATEGORY_COL] != "total") & (cod_all["year"] == most_recent_year) - ], - path=[px.Constant("all"), CATEGORY_COL, "icd_sub_chapter"], - values="deaths", - # skip first color to match the first chart - color_discrete_sequence=px.colors.qualitative.Plotly[1:], - ) - - cdc_top_cause_deaths, cdc_top_cause_names = cdc.get_top_deaths_by_age_group( - df=cod_all, year=most_recent_year - ) + # create tab content + if active_tab == f"{FILTER_COD_PREFIX}-tab-cod-time": + cdc_cod_chart = charters.chart( + df=cod_all, + x_axis="year", + y_axis="deaths", + color=CATEGORY_COL, + type="area", + category_orders=category_orders, + ) + tab_content = html.Div([dcc.Graph(figure=cdc_cod_chart)]) + elif active_tab == f"{FILTER_COD_PREFIX}-tab-cod-heatmap": + cdc_cod_heatmap = px.treemap( + cod_all[ + (cod_all[CATEGORY_COL] != "total") + & (cod_all["year"] == most_recent_year) + ], + path=[px.Constant("all"), CATEGORY_COL, "icd_sub_chapter"], + values="deaths", + # skip first color to match the first chart + color_discrete_sequence=px.colors.qualitative.Plotly[1:], + ) + tab_content = html.Div([dcc.Graph(figure=cdc_cod_heatmap)]) + elif active_tab == f"{FILTER_COD_PREFIX}-tab-cod-top-causes-names": + cdc_top_cause_deaths, cdc_top_cause_names = cdc.get_top_deaths_by_age_group( + df=cod_all, year=most_recent_year + ) + columnDefs = dash_formats.get_column_defs(cdc_top_cause_names) + grid = dag.AgGrid( + id={ + "type": "data-table", + "tab": "cod-top-causes-names", + "page": FILTER_COD_PREFIX, + }, + rowData=cdc_top_cause_names.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "defaultColDef": { + "width": 110, + }, + }, + ) + tab_content = html.Div([grid]) + elif active_tab == f"{FILTER_COD_PREFIX}-tab-cod-top-causes-deaths": + cdc_top_cause_deaths, cdc_top_cause_names = cdc.get_top_deaths_by_age_group( + df=cod_all, year=most_recent_year + ) + columnDefs = dash_formats.get_column_defs(cdc_top_cause_deaths) + grid = dag.AgGrid( + id={ + "type": "data-table", + "tab": "cod-top-causes-deaths", + "page": FILTER_COD_PREFIX, + }, + rowData=cdc_top_cause_deaths.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "defaultColDef": { + "width": 110, + }, + }, + ) + tab_content = html.Div([grid]) + else: + tab_content = dash.no_update # create the filters cdc_cod_filters = dash.no_update if not cdc_cod_num_filters: cdc_cod_filters = dh.generate_filters( df=cod_all, - prefix="cdc_cod", + prefix=FILTER_COD_PREFIX, config=None, exclude_cols=[ "deaths", @@ -859,6 +1120,7 @@ def display_cdc_cod(n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): "icd_sub_chapter", "crude_95_confidence_interval", "m33", + "data_through", ], )["filters"] @@ -895,72 +1157,119 @@ def display_cdc_cod(n_clicks, cdc_cod_str_filters, cdc_cod_num_filters): return ( active_filters_card, cdc_cod_filters, - dcc.Graph(figure=cdc_cod_chart), - dcc.Graph(figure=cdc_cod_heatmap), - dag.AgGrid( - rowData=cdc_top_cause_names.to_dict("records"), - columnDefs=dash_formats.get_column_defs(cdc_top_cause_names), - dashGridOptions={ - "defaultColDef": { - "width": 110, - }, - }, - ), - dag.AgGrid( - rowData=cdc_top_cause_deaths.to_dict("records"), - columnDefs=dash_formats.get_column_defs(cdc_top_cause_deaths), - dashGridOptions={ - "defaultColDef": { - "width": 110, - }, - }, - ), + tab_content, False, "", - days_elapsed, - most_recent_year, - most_recent_year, ) @callback( [ - Output("cdc-cod-trends", "children"), + Output(f"{FILTER_COD_TRENDS_PREFIX}-tab-content", "children"), + Output( + { + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "simple_grouping", + }, + "options", + ), + Output( + { + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "age_groups", + }, + "options", + ), Output("cdc-toast", "is_open", allow_duplicate=True), Output("cdc-toast", "children", allow_duplicate=True), - Output("cod-trend-days-elapsed-text", "children"), ], [ Input("button-cod-trends", "n_clicks"), - Input("tabs-cod-trends", "active_tab"), + Input(f"{FILTER_COD_TRENDS_PREFIX}-tabs", "active_tab"), + Input( + { + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "simple_grouping", + }, + "value", + ), ], + State( + { + "type": "filter-str", + "prefix": FILTER_COD_TRENDS_PREFIX, + "index": "age_groups", + }, + "value", + ), ) -def display_cdc_cod_trends(n_clicks, active_tab): +def display_cdc_cod_trends(n_clicks, active_tab, option_value, age_group_value): """Create cdc cod trends.""" if n_clicks is None: raise dash.exceptions.PreventUpdate # initialize db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" - tables = sql.get_tables(db_filepath=db_filepath) - - # check if table does not exist in database - if "mcd99_cod" not in tables or "mcd18_cod" not in tables: - logger.error("Table `mcd99_cod` or `mcd18_cod` does not exist in database.") - return dash.no_update, dash.no_update, True, "Table does not exist in database" # get the data - mcd99_cod = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_cod") - mcd18_cod = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_cod") + try: + mcd99_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd99_cod" + ) + mcd18_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_cod" + ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return ( + dash.no_update, + dash.no_update, + dash.no_update, + True, + f"Failed to load data: {e}", + ) + + # get options + age_group_options = [ + {"label": str(v), "value": v} for v in mcd18_cod["age_groups"].dropna().unique() + ] # filter and concat mcd18_cod = mcd18_cod[mcd18_cod["year"] >= NEW_DATASET_START_YEAR] cod_all = pd.concat([mcd99_cod, mcd18_cod], ignore_index=True) + include_age_groups = age_group_value if age_group_value else cdc.AGE_GROUP_ORDER + cod_all = cod_all[cod_all["age_groups"].isin(include_age_groups)] + + # map category_col and add every combo of [year, age_groups, category_col] cod_all = cdc.map_reference( df=cod_all, col=CATEGORY_COL, on_dict={"icd_sub_chapter": "wonder_sub_chapter"}, ) + cod_all = ( + cod_all.groupby(["year", "age_groups", CATEGORY_COL]) + .agg({"deaths": "sum", "population": "first"}) + .reset_index() + ) + idx = pd.MultiIndex.from_product( + [ + cod_all["year"].unique(), + cod_all[CATEGORY_COL].unique(), + cod_all["age_groups"].unique(), + ], + names=["year", CATEGORY_COL, "age_groups"], + ) + cod_all = ( + cod_all.set_index(["year", CATEGORY_COL, "age_groups"]) + .reindex(idx) + .reset_index() + ) + cod_all["population"] = cod_all.groupby(["year", "age_groups"])[ + "population" + ].transform("first") # normalize the partial deaths cod_all["deaths"] = cod_all["deaths"].astype(float) @@ -972,25 +1281,47 @@ def display_cdc_cod_trends(n_clicks, active_tab): cod_all.loc[mask, "deaths"] *= factor_parial_year # create totals column - totals = cod_all.groupby("year").sum(numeric_only=True).reset_index() + totals = ( + cod_all.groupby(["year", "age_groups"]) + .agg({"deaths": "sum", "population": "first"}) + .reset_index() + ) totals[CATEGORY_COL] = "total" cod_all = pd.concat([cod_all, totals], ignore_index=True) + cod_all["age_groups"] = pd.Categorical( + cod_all["age_groups"], categories=cdc.AGE_GROUP_ORDER, ordered=True + ) category_orders = charters.get_category_orders( df=cod_all, category=CATEGORY_COL, measure="deaths" ) + # calculate crude_adj + cod_all = cdc.map_reference( + df=cod_all, + col="population_%", + on_dict={"age_groups": "age_bucket"}, + sheet_name="age_std_2000", + ) + total_weight = cod_all.groupby("age_groups")["population_%"].first().sum() + cod_all["population_%"] = cod_all["population_%"] / total_weight + cod_all["crude_adj"] = ( + cod_all["deaths"] / cod_all["population"] * cod_all["population_%"] * 100000 + ) + # train the data based on year and the category using linear regression train_df = cod_all[ (cod_all["year"] >= TRAIN_START_YEAR) & (cod_all["year"] <= TRAIN_END_YEAR) ] - train_df = train_df.groupby(["year", CATEGORY_COL])["deaths"].sum().reset_index() + train_df = ( + train_df.groupby(["year", CATEGORY_COL])[["crude_adj"]].sum().reset_index() + ) # create the models models = {} for cod in train_df[CATEGORY_COL].unique(): cod_subset = train_df[train_df[CATEGORY_COL] == cod] X = (cod_subset["year"] - TRAIN_START_YEAR).values.reshape(-1, 1) - y = cod_subset["deaths"].values + y = cod_subset["crude_adj"].values model = LinearRegression().fit(X, y) models[cod] = { "model": model, @@ -999,69 +1330,153 @@ def display_cdc_cod_trends(n_clicks, active_tab): } # make the predictions - test_df = cod_all[(cod_all["year"] >= (TRAIN_END_YEAR + 1))] - test_df = test_df.groupby(["year", CATEGORY_COL])["deaths"].sum().reset_index() + test_df = cod_all[(cod_all["year"] >= TRAIN_START_YEAR)] + test_df = ( + test_df.groupby(["year", CATEGORY_COL])[["crude_adj", "deaths", "population"]] + .sum() + .reset_index() + ) for cod, model in models.items(): mask = test_df[CATEGORY_COL] == cod - if mask.sum() > 0: - X = (test_df.loc[mask, "year"] - TRAIN_START_YEAR).values.reshape(-1, 1) - test_df.loc[mask, "pred"] = model["model"].predict(X) - - test_df["diff_abs"] = test_df["deaths"] - test_df["pred"] - test_df["diff_pct"] = (test_df["deaths"] - test_df["pred"]) / test_df["pred"] - - # create the tab content - if active_tab == "tab-trends-chart": - display = True - y_axis = "diff_abs" - elif active_tab == "tab-trends-table-amt": - display = False - y_axis = "diff_abs" - elif active_tab == "tab-trends-table-pct": - display = False - y_axis = "diff_pct" - test_df["year"] = test_df["year"].astype(str) - - cdc_cod_trends_chart = charters.chart( - df=test_df, - x_axis="year", - y_axis=y_axis, - color=CATEGORY_COL, - type="area", - category_orders=category_orders, - display=display, - ) + X = (test_df.loc[mask, "year"] - TRAIN_START_YEAR).values.reshape(-1, 1) + test_df.loc[mask, "predictions"] = model["model"].predict(X) - if active_tab == "tab-trends-chart": - tab_content = dcc.Graph(figure=cdc_cod_trends_chart) - else: + test_df["diff_pct"] = (test_df["crude_adj"] - test_df["predictions"]) / test_df[ + "predictions" + ] + test_df["diff_amt"] = ( + (test_df["population"] * test_df["crude_adj"]) + - (test_df["population"] * test_df["predictions"]) + ) / 100000 + test_df["deaths_pred"] = test_df["deaths"] - test_df["diff_amt"] + test_df["year"] = test_df["year"].astype(str) # needed for column pivot + + # chart + if active_tab == f"{FILTER_COD_TRENDS_PREFIX}-tab-trends": + cdc_cod_trends_chart = charters.compare_rates( + df=test_df[test_df[CATEGORY_COL] == option_value], + x_axis="year", + rates=["crude_adj", "predictions"], + display=True, + ) + tab_content = html.Div([dcc.Graph(figure=cdc_cod_trends_chart)]) + # table amount + elif active_tab == f"{FILTER_COD_TRENDS_PREFIX}-tab-actuals": + cdc_cod_trends_chart = charters.chart( + df=test_df, + x_axis="year", + y_axis="deaths", + color=CATEGORY_COL, + type="area", + category_orders=category_orders, + display=False, + ) + pivot = cdc_cod_trends_chart.pivot( + index=CATEGORY_COL, columns="year", values="deaths" + ) + pivot.index = pd.Categorical( + pivot.index, categories=category_orders[CATEGORY_COL], ordered=True + ) + pivot = pivot[sorted(pivot.columns, reverse=True)] + pivot = pivot.sort_index().reset_index() + columnDefs = dash_formats.get_column_defs(pivot) + grid = dag.AgGrid( + rowData=pivot.to_dict("records"), + columnDefs=columnDefs, + ) + tab_content = html.Div([grid]) + elif active_tab == f"{FILTER_COD_TRENDS_PREFIX}-tab-table-diff": + cdc_cod_trends_chart = charters.chart( + df=test_df, + x_axis="year", + y_axis="diff_amt", + color=CATEGORY_COL, + type="area", + category_orders=category_orders, + display=False, + ) pivot = cdc_cod_trends_chart.pivot( - index=CATEGORY_COL, columns="year", values=y_axis + index=CATEGORY_COL, columns="year", values="diff_amt" ) pivot.index = pd.Categorical( pivot.index, categories=category_orders[CATEGORY_COL], ordered=True ) + pivot = pivot[sorted(pivot.columns, reverse=True)] pivot = pivot.sort_index().reset_index() columnDefs = dash_formats.get_column_defs(pivot) - tab_content = dag.AgGrid( + grid = dag.AgGrid( + id={ + "type": "data-table", + "tab": "diff_amt", + "page": FILTER_COD_TRENDS_PREFIX, + }, rowData=pivot.to_dict("records"), columnDefs=columnDefs, ) + export_button = common_build._build_export_button( + "diff_amt", FILTER_COD_TRENDS_PREFIX + ) + tab_content = html.Div([export_button, grid]) + # table pct + elif active_tab == f"{FILTER_COD_TRENDS_PREFIX}-tab-table-pct": + cdc_cod_trends_chart = charters.chart( + df=test_df, + x_axis="year", + y_axis="diff_pct", + color=CATEGORY_COL, + type="area", + category_orders=category_orders, + display=False, + ) + pivot = cdc_cod_trends_chart.pivot( + index=CATEGORY_COL, columns="year", values="diff_pct" + ) + pivot.index = pd.Categorical( + pivot.index, categories=category_orders[CATEGORY_COL], ordered=True + ) + pivot = pivot[sorted(pivot.columns, reverse=True)] + pivot = pivot.sort_index().reset_index() + pivot.columns = [ + col if col == "index" else f"{col}_pct" for col in pivot.columns + ] + columnDefs = dash_formats.get_column_defs(pivot) + grid = dag.AgGrid( + rowData=pivot.to_dict("records"), + columnDefs=columnDefs, + ) + tab_content = html.Div([grid]) + elif active_tab == f"{FILTER_COD_TRENDS_PREFIX}-tab-data": + columnDefs = dash_formats.get_column_defs(test_df) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "data", "page": FILTER_COD_TRENDS_PREFIX}, + rowData=test_df.to_dict("records"), + columnDefs=columnDefs, + ) + export_button = common_build._build_export_button( + "data", FILTER_COD_TRENDS_PREFIX + ) + tab_content = html.Div([export_button, grid]) + else: + tab_content = dash.no_update - return tab_content, False, "", days_elapsed + category_options = [ + {"label": str(v), "value": v} + for v in sorted(test_df[CATEGORY_COL].dropna().unique()) + ] + + return tab_content, category_options, age_group_options, False, "" @callback( [ - Output("cdc-pop-trends", "children"), + Output("cdc-pop-trends-tab-content", "children"), Output("cdc-toast", "is_open", allow_duplicate=True), Output("cdc-toast", "children", allow_duplicate=True), - Output("pop-trend-days-elapsed-text", "children"), ], [ Input("button-pop-trends", "n_clicks"), - Input("tabs-pop-trends", "active_tab"), + Input("cdc-pop-trends-tabs", "active_tab"), ], ) def display_cdc_pop_trends(n_clicks, active_tab): @@ -1071,16 +1486,14 @@ def display_cdc_pop_trends(n_clicks, active_tab): # initialize db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" - tables = sql.get_tables(db_filepath=db_filepath) - - # check if table does not exist in database - if "mcd99_mi" not in tables or "mcd18_mi" not in tables: - logger.error("Table `mcd99_mi` or `mcd18_mi` does not exist in database.") - return dash.no_update, True, "Table does not exist in database" # get the data - mcd99_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_mi") - mcd18_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_mi") + try: + mcd99_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_mi") + mcd18_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_mi") + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, True, f"Failed to load data: {e}" # filter and concat mcd18_mi = mcd18_mi[mcd18_mi["year"] >= NEW_DATASET_START_YEAR] @@ -1186,117 +1599,411 @@ def display_cdc_pop_trends(n_clicks, active_tab): ) excess_grouped["deaths_lc"] = excess_grouped["population"] * excess_grouped["qx_lc"] - # create the chart - chart = charters.compare_rates( - excess_grouped, - x_axis="year", - rates=["qx_raw", "qx_lc"], - weights=["population"], - ) - - # create the table - table = ( + # result + result = ( excess_grouped.groupby(["year"], observed=True) .sum(numeric_only=True) .reset_index() ) - table["excess_lc_pct"] = table["deaths"] / table["deaths_lc"] - table["qx_raw"] = table["deaths"] / table["population"] - table["qx_lc"] = table["deaths_lc"] / table["population"] + result["excess_lc"] = result["deaths"] / result["deaths_lc"] + result["crude_rt"] = result["deaths"] / result["population"] * 100000 + result["crude_rt_lc"] = result["deaths_lc"] / result["population"] * 100000 - if active_tab == "tab-pop-trends-chart": - tab_content = dcc.Graph(figure=chart) - else: - columnDefs = dash_formats.get_column_defs(table) - tab_content = dag.AgGrid( - rowData=table.to_dict("records"), + # chart + if active_tab == "cdc-pop-trends-tab-excess-chart": + chart = charters.compare_rates( + result, + x_axis="year", + rates=["crude_rt", "crude_rt_lc"], + ) + tab_content = html.Div([dcc.Graph(figure=chart)]) + # table + elif active_tab == "cdc-pop-trends-tab-excess-table": + columnDefs = dash_formats.get_column_defs(result) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "excess-table", "page": "cdc-pop-trends"}, + rowData=result.to_dict("records"), columnDefs=columnDefs, ) + export_button = common_build._build_export_button( + "excess-table", "cdc-pop-trends" + ) + tab_content = html.Div([export_button, grid]) + else: + tab_content = dash.no_update - return tab_content, False, "", days_elapsed + return tab_content, False, "" @callback( [ - Output("cdc-monthly", "children"), + Output(f"{FILTER_MONTHLY_PREFIX}-tab-content", "children"), + Output( + { + "type": "filter-str", + "prefix": FILTER_MONTHLY_PREFIX, + "index": "simple_chapter", + }, + "options", + ), Output("cdc-toast", "is_open", allow_duplicate=True), Output("cdc-toast", "children", allow_duplicate=True), ], - Input("button-monthly", "n_clicks"), + [ + Input("button-monthly", "n_clicks"), + Input(f"{FILTER_MONTHLY_PREFIX}-tabs", "active_tab"), + ], + State( + { + "type": "filter-str", + "prefix": FILTER_MONTHLY_PREFIX, + "index": "simple_chapter", + }, + "value", + ), ) -def display_cdc_monthly(n_clicks): +def display_cdc_monthly(n_clicks, active_tab, chapter_values): """Create cdc monthly.""" if n_clicks is None: raise dash.exceptions.PreventUpdate # initialize db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" - tables = sql.get_tables(db_filepath=db_filepath) + simple_chapter_options = dash.no_update - # check if table does not exist in database - if "mcd18_monthly" not in tables: - logger.error("Table `mcd18_monthly` does not exist in database.") - return ( - dash.no_update, - dash.no_update, - True, - "Table `mcd18_monthly` does not exist in database.", + # get the data + try: + mcd18_monthly = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_monthly" ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, dash.no_update, True, f"Failed to load data: {e}" - # get the data - mcd18_monthly = cdc.get_cdc_data_sql( - db_filepath=db_filepath, table_name="mcd18_monthly" - ) + # filter last_updated = mcd18_monthly["added_at"].max() mcd18_monthly = mcd18_monthly[mcd18_monthly["added_at"] == last_updated] - cdc_monthly_chart = charters.chart( - df=mcd18_monthly, - x_axis="month", - y_axis="deaths", - type="area", + + # color mapping + years = sorted(mcd18_monthly["year"].unique()) + current_year = years[-1] + prior = years[:-1] + prior_color_map = px.colors.sample_colorscale( + "Blues", [0.3 + 0.7 * i / max(len(prior) - 1, 1) for i in range(len(prior))] + ) + color_discrete_map = dict(zip(prior, prior_color_map, strict=False)) + color_discrete_map[current_year] = "crimson" + + # chart + if active_tab == f"{FILTER_MONTHLY_PREFIX}-tab-chart": + cdc_monthly_chart = charters.chart( + df=mcd18_monthly, + x_axis="month", + y_axis="deaths", + color="year", + type="line", + color_discrete_map=color_discrete_map, + ) + tab_content = html.Div([dcc.Graph(figure=cdc_monthly_chart)]) + + # table + elif active_tab == f"{FILTER_MONTHLY_PREFIX}-tab-table": + columnDefs = dash_formats.get_column_defs(mcd18_monthly) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "table", "page": FILTER_MONTHLY_PREFIX}, + rowData=mcd18_monthly.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "defaultColDef": { + "sortable": True, + "filter": True, + "resizable": True, + }, + }, + className="ag-theme-alpine", + columnSize="sizeToFit", + ) + export_button = common_build._build_export_button( + "table", FILTER_MONTHLY_PREFIX + ) + tab_content = html.Div([export_button, grid]) + + # pic chart + elif active_tab == f"{FILTER_MONTHLY_PREFIX}-tab-pic-chart": + # get the data + try: + mcd18_monthly_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_monthly_cod" + ) + mcd99_monthly_cod = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd99_monthly_cod" + ) + mcd18_monthly_cod = mcd18_monthly_cod[mcd18_monthly_cod["year"] >= 2021] + mcd99_monthly_cod = mcd99_monthly_cod[mcd99_monthly_cod["year"] >= 2010] + monthly_cod = pd.concat( + [mcd18_monthly_cod, mcd99_monthly_cod], ignore_index=True + ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, dash.no_update, True, f"Failed to load data: {e}" + + # format data + monthly_cod = cdc.map_reference( + df=monthly_cod, + col="simple_chapter", + on_dict={"icd_chapter": "wonder_chapter"}, + ) + + # get options + simple_chapter_options = [ + {"label": str(v), "value": v} + for v in sorted(monthly_cod["simple_chapter"].dropna().unique()) + ] + + # filter + monthly_cod_filtered = monthly_cod[ + monthly_cod["simple_chapter"].isin(chapter_values) + ].copy() + + # zero out the pandemic (2020-2022) + mask_excluded = monthly_cod_filtered["year"].between(2020, 2022) + monthly_cod_filtered.loc[mask_excluded, "deaths"] = np.nan + + # compute monthly_avg using 2019 and prior + max_date = monthly_cod_filtered["reported_date"].max() + cutoff = max_date - pd.DateOffset(months=2) + monthly_avg = ( + monthly_cod_filtered[monthly_cod_filtered["year"] <= 2019] + .groupby(["reported_date", "month"])["deaths"] + .sum() + .groupby(level="month") + .mean() + ) + + # map the average + monthly_cod_filtered["avg"] = monthly_cod_filtered["month"].map(monthly_avg) + monthly_cod_filtered.loc[ + monthly_cod_filtered["reported_date"] > cutoff, "avg" + ] = np.nan + monthly_cod_filtered.loc[mask_excluded, "avg"] = np.nan + + # create chart + cdc_monthly_cod_chart = charters.chart( + df=monthly_cod_filtered, + x_axis="reported_date", + y_axis="deaths", + color="simple_chapter", + type="area", + ) + + # add average + cdc_monthly_cod_chart.add_trace( + go.Scatter( + x=monthly_cod_filtered["reported_date"], + y=monthly_cod_filtered["avg"], + mode="lines", + name="monthly avg", + line={"color": "black", "width": 2}, + ) + ) + + tab_content = html.Div([dcc.Graph(figure=cdc_monthly_cod_chart)]) + else: + tab_content = dash.no_update + + return tab_content, simple_chapter_options, False, "" + + +@callback( + [ + Output("cdc-weekly-tab-content", "children"), + Output("cdc-toast", "is_open", allow_duplicate=True), + Output("cdc-toast", "children", allow_duplicate=True), + ], + [ + Input("button-weekly", "n_clicks"), + Input("cdc-weekly-tabs", "active_tab"), + ], +) +def display_cdc_weekly(n_clicks, active_tab): + """Create cdc monthly.""" + if n_clicks is None: + raise dash.exceptions.PreventUpdate + + # initialize + db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" + + # get the data + try: + mcd18_weekly = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_weekly" + ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, True, f"Failed to load data: {e}" + + # filter + last_updated = mcd18_weekly["added_at"].max() + mcd18_weekly = mcd18_weekly[mcd18_weekly["added_at"] == last_updated] + mcd18_weekly.dropna(subset=["mmwr_week"], inplace=True) + mcd18_weekly = mcd18_weekly.sort_values("mmwr_week_date").reset_index(drop=True) + mcd18_weekly["recent_week"] = range(len(mcd18_weekly) - 1, -1, -1) + + # add ibnr adjusted deaths to current year + years = sorted(mcd18_weekly["mmwr_year"].unique()) + current_year = years[-1] + prior_years = years[:-1] + current_year_adj = mcd18_weekly[mcd18_weekly["mmwr_year"] == current_year].copy() + current_year_adj["deaths"] = current_year_adj["deaths"] / current_year_adj[ + "recent_week" + ].map(IBNR_FACTORS).fillna(1.0) + current_year_adj = current_year_adj[ + current_year_adj["recent_week"] >= PARTIAL_WEEKS_TO_EXCLUDE + ] + current_year_adj["mmwr_year"] = f"{current_year}_adj" + mcd18_weekly = pd.concat([mcd18_weekly, current_year_adj], ignore_index=True) + + # color mapping + prior_color_map = px.colors.sample_colorscale( + "Blues", + [0.3 + 0.7 * i / max(len(prior_years) - 1, 1) for i in range(len(prior_years))], ) + color_discrete_map = dict(zip(prior_years, prior_color_map, strict=False)) + color_discrete_map[current_year] = "crimson" + color_discrete_map[f"{current_year}_adj"] = "crimson" + + # cdc weekly + if active_tab == "cdc-weekly-tab-chart": + cdc_weekly_chart = charters.chart( + df=mcd18_weekly, + x_axis="mmwr_week", + y_axis="deaths", + color="mmwr_year", + type="line", + color_discrete_map=color_discrete_map, + ) + for trace in cdc_weekly_chart.data: + if trace.name == f"{current_year}_adj": + trace.line.dash = "dash" + tab_content = html.Div([dcc.Graph(figure=cdc_weekly_chart)]) + elif active_tab == "cdc-weekly-tab-table": + mcd18_weekly = mcd18_weekly.sort_values( + ["mmwr_week_date", "mmwr_year"], ascending=False + ) + columnDefs = dash_formats.get_column_defs(mcd18_weekly) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "table", "page": "cdc-weekly"}, + rowData=mcd18_weekly.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "defaultColDef": { + "sortable": True, + "filter": True, + "resizable": True, + }, + }, + className="ag-theme-alpine", + columnSize="sizeToFit", + ) + export_button = common_build._build_export_button("table", "cdc-weekly") + tab_content = html.Div([export_button, grid]) - return dcc.Graph(figure=cdc_monthly_chart), False, "" + # cdc weekly influenza + elif active_tab in {"cdc-weekly-tab-flu-chart", "cdc-weekly-tab-flu-table"}: + try: + mcd18_weekly_pic = cdc.get_cdc_data_sql( + db_filepath=db_filepath, table_name="mcd18_weekly_pic" + ) + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, True, f"Failed to load data: {e}" + + # calculate average (excluding recent weeks) + mcd18_weekly_pic = mcd18_weekly_pic[ + mcd18_weekly_pic["icd_chapter"] == "Diseases of the respiratory system" + ] + max_date = mcd18_weekly_pic["mmwr_week_date"].max() + cutoff = max_date - pd.Timedelta(weeks=PARTIAL_WEEKS_TO_EXCLUDE) + weekly_avg = ( + mcd18_weekly_pic[mcd18_weekly_pic["mmwr_week_date"] <= cutoff] + .groupby("mmwr_week")["deaths"] + .mean() + ) + mcd18_weekly_pic["avg"] = mcd18_weekly_pic["mmwr_week"].map(weekly_avg) + mcd18_weekly_pic.loc[mcd18_weekly_pic["mmwr_week_date"] > cutoff, "avg"] = None + if active_tab == "cdc-weekly-tab-flu-chart": + cdc_flu_chart = charters.compare_rates( + df=mcd18_weekly_pic, + x_axis="mmwr_week_date", + rates=["deaths", "avg"], + ) + tab_content = html.Div([dcc.Graph(figure=cdc_flu_chart)]) + else: + columnDefs = dash_formats.get_column_defs(mcd18_weekly_pic) + grid = dag.AgGrid( + id={ + "type": "data-table", + "tab": "table", + "page": "cdc-weekly-flu", + }, + rowData=mcd18_weekly_pic.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "defaultColDef": { + "sortable": True, + "filter": True, + "resizable": True, + }, + }, + className="ag-theme-alpine", + columnSize="sizeToFit", + ) + export_button = common_build._build_export_button("table", "cdc-weekly-flu") + tab_content = html.Div([export_button, grid]) + else: + tab_content = dash.no_update + + return tab_content, False, "" @callback( [ - Output("cdc-mi", "children"), - Output("cdc-mi-table", "children"), - Output("cdc-mi-filters", "children"), + Output(f"{FILTER_MI_PREFIX}-tab-content", "children"), + Output(f"{FILTER_MI_PREFIX}-filters", "children"), Output("cdc-toast", "is_open", allow_duplicate=True), Output("cdc-toast", "children", allow_duplicate=True), ], - Input("button-mi", "n_clicks"), [ - State({"type": "cdc_mi-str-filter", "index": ALL}, "value"), - State({"type": "cdc_mi-num-filter", "index": ALL}, "value"), + Input("button-mi", "n_clicks"), + Input(f"{FILTER_MI_PREFIX}-tabs", "active_tab"), + ], + [ + State( + {"type": "filter-str", "prefix": FILTER_MI_PREFIX, "index": ALL}, "value" + ), + State( + {"type": "filter-num", "prefix": FILTER_MI_PREFIX, "index": ALL}, "value" + ), ], ) -def display_cdc_mi(n_clicks, cdc_mi_str_filters, cdc_mi_num_filters): +def display_cdc_mi(n_clicks, active_tab, cdc_mi_str_filters, cdc_mi_num_filters): """Create cdc mi.""" if n_clicks is None: raise dash.exceptions.PreventUpdate # initialize db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" - tables = sql.get_tables(db_filepath=db_filepath) states_info = dh._inputs_flatten_list(callback_context.states_list) - # check if table does not exist in database - if "mcd18_mi" not in tables: - logger.error("Table `mcd18_mi` does not exist in database.") - return ( - dash.no_update, - dash.no_update, - True, - "Table `mcd18_mi` does not exist in database", - ) - # get the data - mcd79_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd79_mi") - mcd99_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_mi") - mcd18_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_mi") + try: + mcd79_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd79_mi") + mcd99_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd99_mi") + mcd18_mi = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name="mcd18_mi") + except Exception as e: + logger.error(f"Failed to load table: {e}") + return dash.no_update, dash.no_update, True, f"Failed to load data: {e}" + mcd18_mi = mcd18_mi[mcd18_mi["year"] >= NEW_DATASET_START_YEAR] mi = pd.concat([mcd79_mi, mcd99_mi, mcd18_mi], axis=0, ignore_index=True) @@ -1313,40 +2020,59 @@ def display_cdc_mi(n_clicks, cdc_mi_str_filters, cdc_mi_num_filters): mi["age_groups"] = pd.Categorical( mi["age_groups"], categories=cdc.AGE_GROUP_ORDER, ordered=True ) + mi["era"] = pd.cut( + mi["year"], + bins=[0, 2019, 2024, float("inf")], + labels=["1979-2019", "2020-2024", "2025+"], + ) # filter the data filtered_mi = dh.filter_data(df=mi, callback_context=states_info) + mi_df = cdc.calc_mi(df=filtered_mi, rolling=ROLLING_MI_YEARS) + + # chart + if active_tab == f"{FILTER_MI_PREFIX}-tab-mi-year-chart": + mi_df = mi_df[mi_df["age_groups"] == "All ages"] + cdc_mi_chart = charters.compare_rates( + df=mi_df, + x_axis="year", + rates=["1_year_mi_pct", f"{ROLLING_MI_YEARS}_year_mi_pct", "whl_3_pct"], + ) + tab_content = html.Div([dcc.Graph(figure=cdc_mi_chart)]) + # table + elif active_tab == f"{FILTER_MI_PREFIX}-tab-mi-year-table": + mi_df = mi_df[mi_df["age_groups"] == "All ages"] + columnDefs = dash_formats.get_column_defs(mi_df) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "table", "page": "cdc-mi"}, + rowData=mi_df.sort_values(by="year", ascending=False).to_dict("records"), + columnDefs=columnDefs, + defaultColDef={"resizable": True, "sortable": True, "filter": True}, + ) + export_button = common_build._build_export_button("table", FILTER_MI_PREFIX) + tab_content = html.Div([export_button, grid]) + elif active_tab == f"{FILTER_MI_PREFIX}-tab-mi-age-chart": + age_cats = mi_df["age_groups"].cat.remove_unused_categories() + mi_df["age_groups_order"] = age_cats.map( + { + cat: f"{i + 1:02d}: {cat}" + for i, cat in enumerate(age_cats.cat.categories) + } + ).fillna(mi_df["age_groups"].astype(str)) + cdc_mi_chart = charters.compare_rates( + df=mi_df[mi_df["year"] == mi_df["year"].max()], + x_axis="age_groups_order", + rates=["1_year_mi_pct", f"{ROLLING_MI_YEARS}_year_mi_pct", "whl_3_pct"], + ) + tab_content = html.Div([dcc.Graph(figure=cdc_mi_chart)]) + else: + tab_content = dash.no_update - # create the charts - rolling = 10 - mi_df = cdc.calc_mi(df=filtered_mi, rolling=rolling) - cdc_mi_chart = charters.compare_rates( - df=mi_df, - x_axis="year", - rates=["1_year_mi", f"{rolling}_year_mi", "whl_3"], - ) - - # mortality improvement table - columnDefs = dash_formats.get_column_defs(mi_df) - export_button = html.Button( - "Export to CSV", - id={"type": "export-button", "tab": "mi", "page": "cdc"}, - className="btn btn-primary mt-2 mb-2", - ) - grid = dag.AgGrid( - id={"type": "data-table", "tab": "mi", "page": "cdc"}, - rowData=mi_df.sort_values(by="year", ascending=False).to_dict("records"), - columnDefs=columnDefs, - defaultColDef={"resizable": True, "sortable": True, "filter": True}, - ) - mi_table = html.Div([export_button, grid]) - - # create the filters - cdc_mi_filters = dash.no_update + # create the filters (if the don't exist) if not cdc_mi_num_filters: cdc_mi_filters = dh.generate_filters( df=mi, - prefix="cdc_mi", + prefix=FILTER_MI_PREFIX, config=None, exclude_cols=[ "deaths", @@ -1355,51 +2081,13 @@ def display_cdc_mi(n_clicks, cdc_mi_str_filters, cdc_mi_num_filters): "added_at", "crude_95_confidence_interval", "m33", + "data_through", ], )["filters"] + else: + cdc_mi_filters = dash.no_update - return dcc.Graph(figure=cdc_mi_chart), mi_table, cdc_mi_filters, False, "" - - -@callback( - Output({"type": "cdc_mi-collapse", "index": ALL}, "is_open"), - Output({"type": "cdc_mi-collapse-button", "index": ALL}, "children"), - Input({"type": "cdc_mi-collapse-button", "index": ALL}, "n_clicks"), - State({"type": "cdc_mi-collapse", "index": ALL}, "is_open"), - State({"type": "cdc_mi-collapse-button", "index": ALL}, "children"), - prevent_initial_call=True, -) -def toggle_cdc_mi_collapse(n_clicks, is_open, children): - """Toggle collapse state of filter checklists.""" - if not n_clicks or not any(n_clicks): - raise dash.exceptions.PreventUpdate - - return dh.toggle_collapse( - callback_context=callback_context, - is_open=is_open, - children=children, - ) - - -@callback( - [ - Output({"type": "cdc_cod-collapse", "index": ALL}, "is_open"), - Output({"type": "cdc_cod-collapse-button", "index": ALL}, "children"), - Input({"type": "cdc_cod-collapse-button", "index": ALL}, "n_clicks"), - State({"type": "cdc_cod-collapse", "index": ALL}, "is_open"), - State({"type": "cdc_cod-collapse-button", "index": ALL}, "children"), - ], -) -def toggle_cdc_cod_collapse(n_clicks, is_open, children): - """Toggle collapse state of filter checklists.""" - if not n_clicks or not any(n_clicks): - raise dash.exceptions.PreventUpdate - - return dh.toggle_collapse( - callback_context=callback_context, - is_open=is_open, - children=children, - ) + return tab_content, cdc_mi_filters, False, "" # _____ _ _ @@ -1413,7 +2101,7 @@ def refresh_cdc_data() -> None: """ Refresh the cdc data. - Includes a 15 second sleep per call. + Includes a ~16 second sleep per call, due to CDC guidelines. """ try: db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" @@ -1424,7 +2112,7 @@ def refresh_cdc_data() -> None: table_name="mcd18_cod", if_exists="replace", ) - time.sleep(15) + time.sleep(CDC_WAIT_TIME_SECONDS) mcd18_monthly = cdc.get_cdc_data_xml( xml_filename="mcd18_monthly.xml", parse_date_col="Month" @@ -1435,7 +2123,7 @@ def refresh_cdc_data() -> None: table_name="mcd18_monthly", if_exists="replace", ) - time.sleep(15) + time.sleep(CDC_WAIT_TIME_SECONDS) mcd18_mi = cdc.get_cdc_data_xml(xml_filename="mcd18_mi.xml") sql.export_to_sql( @@ -1444,5 +2132,38 @@ def refresh_cdc_data() -> None: table_name="mcd18_mi", if_exists="replace", ) + time.sleep(CDC_WAIT_TIME_SECONDS) + + mcd18_weekly = cdc.get_cdc_data_xml( + xml_filename="mcd18_weekly.xml", parse_date_col="mmwr_week_date" + ) + sql.export_to_sql( + df=mcd18_weekly, + db_filepath=db_filepath, + table_name="mcd18_weekly", + if_exists="replace", + ) + time.sleep(CDC_WAIT_TIME_SECONDS) + + mcd18_weekly_pic = cdc.get_cdc_data_xml( + xml_filename="mcd18_weekly_pic.xml", parse_date_col="mmwr_week_date" + ) + sql.export_to_sql( + df=mcd18_weekly_pic, + db_filepath=db_filepath, + table_name="mcd18_weekly_pic", + if_exists="replace", + ) + time.sleep(CDC_WAIT_TIME_SECONDS) + + mcd18_monthly_cod = cdc.get_cdc_data_xml( + xml_filename="mcd18_monthly_cod.xml", parse_date_col="Month" + ) + sql.export_to_sql( + df=mcd18_monthly_cod, + db_filepath=db_filepath, + table_name="mcd18_monthly_cod", + if_exists="replace", + ) except Exception as e: logger.error(f"Error refreshing cdc data: {e}") diff --git a/morai/dashboard/pages/experience.py b/morai/dashboard/pages/experience.py index 6262058..e439e6e 100644 --- a/morai/dashboard/pages/experience.py +++ b/morai/dashboard/pages/experience.py @@ -23,7 +23,7 @@ html, ) -from morai.dashboard.components import dash_formats +from morai.dashboard.components import common_build, dash_formats from morai.dashboard.utils import dashboard_helper as dh from morai.experience import charters, experience from morai.forecast import metrics @@ -36,6 +36,8 @@ dash.register_page(__name__, path="/experience", title="morai - Experience", order=2) +FILTER_PREFIX = "experience" + # _ _ # | | __ _ _ _ ___ _ _| |_ # | | / _` | | | |/ _ \| | | | __| @@ -55,6 +57,15 @@ def layout(): _build_cards_row(), _build_main_content(), dcc.Download(id="download-dataframe-csv"), + dbc.Toast( + id="experience-toast", + header="Error", + is_open=False, + dismissable=True, + icon="danger", + className="toast", + style={"position": "fixed", "top": 10, "right": 10, "zIndex": 1050}, + ), ], className="container-fluid px-4 py-3", ) @@ -140,199 +151,20 @@ def _build_main_content(): [ # Selectors column (left) _build_selectors_column(), - # Chart column (middle) + # chart column (middle) dbc.Col( - [ - # Filter toggle button - dbc.Button( - [ - html.I(className="fas fa-filter me-2"), - "Show Filters", - ], - id="open-offcanvas-button", - className="mb-3", - color="primary", - ), - # Tabs and content - dbc.Tabs( - [ - dbc.Tab( - children=[], - label="Chart", - tab_id="tab-chart", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - dbc.Tab( - children=[], - label="Table", - tab_id="tab-table", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - dbc.Tab( - children=[], - label="Rank", - tab_id="tab-rank", - label_class_name="fw-bold", - active_label_class_name="text-primary", - ), - ], - id="tabs", - active_tab="tab-chart", - className="mb-3", - ), - html.Div( - [ - dcc.Loading( - id="loading-tab-content", - custom_spinner=dmc.Skeleton( - visible=True, h="100%", w="100%" - ), - children=html.Div( - id="tab-content", - className="bg-white rounded-3 shadow-sm p-4 border border-light", - ), - ), - dcc.Loading( - id="loading-chart-secondary", - custom_spinner=dmc.Skeleton( - visible=True, h="100%", w="100%" - ), - children=html.Div( - id="chart-secondary", - className="mt-4 bg-white rounded-3 shadow-sm p-4 border border-light", - ), - ), - ], - className="h-100", - ), - # Offcanvas for filters - dbc.Offcanvas( - [ - html.H4( - [ - html.I(className="fas fa-filter me-2"), - "Data Filters", - ], - className="mb-3", - ), - dbc.Card( - [ - dbc.CardHeader( - html.H6( - [ - html.I( - className="fas fa-bookmark me-2" - ), - "Bookmarks", - ], - className="mb-0", - ) - ), - dbc.CardBody( - [ - dbc.Row( - [ - dbc.Col( - dbc.Input( - id="bookmark-name-input", - placeholder="Bookmark name...", - size="sm", - ), - width=9, - ), - dbc.Col( - dbc.Button( - "Save", - id="save-bookmark-button", - color="success", - size="sm", - n_clicks=0, - className="w-100", - ), - width=3, - ), - ], - className="mb-2 g-1", - ), - dcc.Dropdown( - id="bookmark-dropdown", - placeholder="Select bookmark...", - className="mb-2", - ), - dbc.Row( - [ - dbc.Col( - dbc.Button( - [ - html.I( - className="fas fa-upload me-1" - ), - "Load", - ], - id="load-bookmark-button", - color="primary", - size="sm", - n_clicks=0, - className="w-100", - ), - width=6, - ), - dbc.Col( - dbc.Button( - [ - html.I( - className="fas fa-trash me-1" - ), - "Delete", - ], - id="delete-bookmark-button", - color="outline-danger", - size="sm", - n_clicks=0, - className="w-100", - ), - width=6, - ), - ], - className="g-1", - ), - ], - className="p-2", - ), - ], - className="mb-3 shadow-sm", - ), - html.Button( - [ - html.I(className="fas fa-undo me-2"), - "Reset Filters", - ], - id="reset-filters-button", - n_clicks=0, - className="btn btn-outline-primary w-100 shadow-sm mb-4", - ), - html.Div( - id="chart-filters", - className="overflow-auto custom-scrollbar", - style={ - "max-height": "calc(100vh - 300px)", - "backgroundColor": "#f0f7f0", - "padding": "15px", - "borderRadius": "8px", - "width": "100%", - }, - ), - ], - id="filters-offcanvas", - title="", - placement="end", - scrollable=True, - is_open=False, - className="offcanvas", + common_build._build_tabbed_content( + tabs=[ + common_build.TabSpec(label="Chart", tab_id="chart"), + common_build.TabSpec(label="Table", tab_id="table"), + common_build.TabSpec(label="Rank", tab_id="rank"), + ], + prefix=FILTER_PREFIX, + filter_panel=common_build.FilterPanel( + children=_build_filter_children() ), - ], + include_secondary_chart=True, + ), xs=12, lg=9, ), @@ -407,6 +239,110 @@ def _build_selectors_column(): ) +def _build_filter_children(): + """Build the contents of the filter offcanvas panel.""" + return [ + html.H4( + [html.I(className="fas fa-filter me-2"), "Data Filters"], + className="mb-3", + ), + _build_bookmarks_card(), + html.Button( + [html.I(className="fas fa-undo me-2"), "Reset Filters"], + id={"type": "filter-reset-button", "prefix": FILTER_PREFIX}, + n_clicks=0, + className="btn btn-outline-primary w-100 shadow-sm mb-4", + ), + html.Div( + id=f"{FILTER_PREFIX}-filters", + className="overflow-auto custom-scrollbar", + style={ + "max-height": "calc(100vh - 300px)", + "backgroundColor": "#f0f7f0", + "padding": "15px", + "borderRadius": "8px", + "width": "100%", + }, + ), + ] + + +def _build_bookmarks_card(): + """Build the bookmarks card inside the filter panel.""" + return dbc.Card( + [ + dbc.CardHeader( + html.H6( + [html.I(className="fas fa-bookmark me-2"), "Bookmarks"], + className="mb-0", + ) + ), + dbc.CardBody( + [ + dbc.Row( + [ + dbc.Col( + dbc.Input( + id="bookmark-name-input", + placeholder="Bookmark name...", + size="sm", + ), + width=9, + ), + dbc.Col( + dbc.Button( + "Save", + id="save-bookmark-button", + color="success", + size="sm", + n_clicks=0, + className="w-100", + ), + width=3, + ), + ], + className="mb-2 g-1", + ), + dcc.Dropdown( + id="bookmark-dropdown", + placeholder="Select bookmark...", + className="mb-2", + ), + dbc.Row( + [ + dbc.Col( + dbc.Button( + [html.I(className="fas fa-upload me-1"), "Load"], + id="load-bookmark-button", + color="primary", + size="sm", + n_clicks=0, + className="w-100", + ), + width=6, + ), + dbc.Col( + dbc.Button( + [html.I(className="fas fa-trash me-1"), "Delete"], + id="delete-bookmark-button", + color="outline-danger", + size="sm", + n_clicks=0, + className="w-100", + ), + width=6, + ), + ], + className="g-1", + ), + ], + className="p-2", + ), + ], + className="mb-3 shadow-sm", + ) + + # ____ _ _ _ _ # / ___|__ _| | | |__ __ _ ___| | _____ # | | / _` | | | '_ \ / _` |/ __| |/ / __| @@ -418,7 +354,7 @@ def _build_selectors_column(): [ Output("store-initial-filters", "data"), Output("chart-selectors", "children"), - Output("chart-filters", "children"), + Output(f"{FILTER_PREFIX}-filters", "children"), Output("card", "children"), ], [Input("store-dataset", "data")], @@ -432,7 +368,7 @@ def load_data(dataset, config, saved_filter_values): logger.debug("generate selectors and filters") filter_dict = dh.generate_filters( df=dataset, - prefix="chart", + prefix=FILTER_PREFIX, config=config, initial_values=saved_filter_values, ) @@ -483,21 +419,23 @@ def load_data(dataset, config, saved_filter_values): @callback( [ - Output("tab-content", "children"), - Output("chart-secondary", "children"), + Output(f"{FILTER_PREFIX}-tab-content", "children"), + Output(f"{FILTER_PREFIX}-chart-secondary", "children"), Output("filtered-card", "children"), + Output("experience-toast", "is_open"), + Output("experience-toast", "children"), ], [ # tabs - Input("tabs", "active_tab"), + Input(f"{FILTER_PREFIX}-tabs", "active_tab"), # update button Input("update-content-button", "n_clicks"), # tools State("tool-selector", "value"), # selectors State({"type": "chart-selector", "index": ALL}, "value"), - State({"type": "chart-str-filter", "index": ALL}, "value"), - State({"type": "chart-num-filter", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), # stores State("store-dataset", "data"), State("store-initial-filters", "data"), @@ -520,289 +458,292 @@ def update_tab_content( if not n_clicks and not active_tab: # Prevent update if neither trigger is present raise dash.exceptions.PreventUpdate - logger.debug("creating tab content") - config_dataset = config["datasets"][config["general"]["dataset"]] - - # callback context - states_info = dh._inputs_flatten_list(callback_context.states_list) - x_axis = dh._inputs_parse_id(states_info, "x_axis_selector") - y_axis = dh._inputs_parse_id(states_info, "y_axis_selector") - color = dh._inputs_parse_id(states_info, "color_selector") - chart_type = dh._inputs_parse_id(states_info, "chart_type_selector") - rates = dh._inputs_parse_id(states_info, "rates_selector") - weights = dh._inputs_parse_id(states_info, "weights_selector") - secondary = dh._inputs_parse_id(states_info, "secondary_selector") - x_bins = dh._inputs_parse_id(states_info, "x_bins_selector") - add_line = dh._inputs_parse_id(states_info, "add_line_selector") - y_log = dh._inputs_parse_id(states_info, "y_log_selector") - numerator = dh._inputs_parse_id(states_info, "numerator_selector") - denominator = dh._inputs_parse_id(states_info, "denominator_selector") - target = dh._inputs_parse_id(states_info, "target_selector") - normalize = dh._inputs_parse_id(states_info, "normalize_selector") - relative = dh._inputs_parse_id(states_info, "relative_selector") - relative_to = dh._inputs_parse_id(states_info, "relative_to_selector") - relative_cols = dh._inputs_parse_id(states_info, "relative_cols_selector") - subset_dict = dh._inputs_parse_id(states_info, "subset_dict_selector") - flip_x_color = dh._inputs_parse_id(states_info, "flip_x_color_selector") - rank_columns = dh._inputs_parse_id(states_info, "rank_columns_selector") - if not subset_dict or subset_dict.strip() == "": - subset_dict = None - else: - try: - subset_dict = ast.literal_eval(subset_dict) - except Exception: - logger.warning(f"`{subset_dict}` is not a dictionary.") + try: + logger.debug("creating tab content") + config_dataset = config["datasets"][config["general"]["dataset"]] + + # callback context + states_info = dh._inputs_flatten_list(callback_context.states_list) + x_axis = dh._inputs_parse_id(states_info, "x_axis_selector") + y_axis = dh._inputs_parse_id(states_info, "y_axis_selector") + color = dh._inputs_parse_id(states_info, "color_selector") + chart_type = dh._inputs_parse_id(states_info, "chart_type_selector") + rates = dh._inputs_parse_id(states_info, "rates_selector") + weights = dh._inputs_parse_id(states_info, "weights_selector") + secondary = dh._inputs_parse_id(states_info, "secondary_selector") + x_bins = dh._inputs_parse_id(states_info, "x_bins_selector") + add_line = dh._inputs_parse_id(states_info, "add_line_selector") + y_log = dh._inputs_parse_id(states_info, "y_log_selector") + numerator = dh._inputs_parse_id(states_info, "numerator_selector") + denominator = dh._inputs_parse_id(states_info, "denominator_selector") + target = dh._inputs_parse_id(states_info, "target_selector") + normalize = dh._inputs_parse_id(states_info, "normalize_selector") + relative = dh._inputs_parse_id(states_info, "relative_selector") + relative_to = dh._inputs_parse_id(states_info, "relative_to_selector") + relative_cols = dh._inputs_parse_id(states_info, "relative_cols_selector") + subset_dict = dh._inputs_parse_id(states_info, "subset_dict_selector") + flip_x_color = dh._inputs_parse_id(states_info, "flip_x_color_selector") + rank_columns = dh._inputs_parse_id(states_info, "rank_columns_selector") + if not subset_dict or subset_dict.strip() == "": subset_dict = None + else: + try: + subset_dict = ast.literal_eval(subset_dict) + except Exception: + logger.warning(f"`{subset_dict}` is not a dictionary.") + subset_dict = None + + # filter the dataset - only filter what's needed + # pre-collect to avoid multiple downstream collections + logger.debug("filtering dataset") + filtered_df = dh.filter_data( + df=dataset, + callback_context=states_info, + str_cols=filter_dict["str_cols"], + num_cols=filter_dict["num_cols"], + ) + if isinstance(filtered_df, pl.LazyFrame): + filtered_df = filtered_df.collect().lazy() + logger.debug("filtered dataset") - # filter the dataset - only filter what's needed - # pre-collect to avoid multiple downstream collections - logger.debug("filtering dataset") - filtered_df = dh.filter_data( - df=dataset, - callback_context=states_info, - str_cols=filter_dict["str_cols"], - num_cols=filter_dict["num_cols"], - ) - if isinstance(filtered_df, pl.LazyFrame): - filtered_df = filtered_df.collect().lazy() - logger.debug("filtered dataset") - - # create cards - card_list = dh.get_card_list(config) - filtered_card = dh.generate_card( - df=filtered_df, - card_list=card_list, - title="Filtered", - color="LightGreen", - ) - - chart_secondary = None - tab_content = None - - # Early validation for chart/table tabs - if (active_tab in ["tab-chart", "tab-table"]) and ( - x_axis is None or y_axis is None - ): - return "Select X-Axis and Y-Axis to view content", None, filtered_card + # create cards + card_list = dh.get_card_list(config) + filtered_card = dh.generate_card( + df=filtered_df, + card_list=card_list, + title="Filtered", + color="LightGreen", + ) - # update tab content based on tab and tool - if active_tab == "tab-chart": - if tool == "compare": - chart = charters.compare_rates( - df=filtered_df, - x_axis=x_axis, - rates=rates, - weights=weights, - secondary=secondary, - x_bins=x_bins, + chart_secondary = None + tab_content = None + + # Early validation for chart/table tabs + if ( + active_tab in [f"{FILTER_PREFIX}-tab-chart", f"{FILTER_PREFIX}-tab-table"] + ) and (x_axis is None or y_axis is None): + return ( + "Select X-Axis and Y-Axis to view content", + None, + filtered_card, + False, + "", ) - elif tool == "chart": - if normalize: - filtered_df = experience.normalize( + + # update tab content based on tab and tool + if active_tab == f"{FILTER_PREFIX}-tab-chart": + if tool == "compare": + chart = charters.compare_rates( df=filtered_df, - features=normalize, - normalize_col=numerator, - weight_col=denominator, - add_norm_col=False, - ratio=True, + x_axis=x_axis, + rates=rates, + weights=weights, + secondary=secondary, + x_bins=x_bins, ) - chart = charters.chart( - df=filtered_df, - x_axis=x_axis, - y_axis=y_axis if chart_type != "heatmap" else color, - color=color if chart_type != "heatmap" else y_axis, - type=chart_type, - numerator=numerator, - denominator=denominator, - x_bins=x_bins, - add_line=add_line, - y_log=y_log, - ) - if secondary: - if color: - type = "histogram" - barmode = "group" - else: - type = "bar" - barmode = "relative" - chart_secondary = charters.chart( + elif tool == "chart": + if normalize: + filtered_df = experience.normalize( + df=filtered_df, + features=normalize, + normalize_col=numerator, + weight_col=denominator, + add_norm_col=False, + ratio=True, + ) + chart = charters.chart( + df=filtered_df, + x_axis=x_axis, + y_axis=y_axis if chart_type != "heatmap" else color, + color=color if chart_type != "heatmap" else y_axis, + type=chart_type, + numerator=numerator, + denominator=denominator, + x_bins=x_bins, + add_line=add_line, + y_log=y_log, + ) + if secondary: + if color: + type = "histogram" + barmode = "group" + else: + type = "bar" + barmode = "relative" + chart_secondary = charters.chart( + df=filtered_df, + x_axis=x_axis, + y_axis=secondary, + color=color, + type=type, + x_bins=x_bins, + barmode=barmode, + ) + chart_secondary = dcc.Graph(figure=chart_secondary) + elif tool == "target": + chart = charters.target( + df=filtered_df, + target=[target], + features=config_dataset["columns"]["features"], + numerator=numerator, + denominator=denominator, + add_line=add_line, + ) + elif tool == "relative": + chart = charters.relative_risk( + df=filtered_df, + y_axis=y_axis, + features=relative, + numerator=numerator, + denominator=denominator, + relative_to=relative_to, + relative_cols=relative_cols, + subset_dict=subset_dict, + x_bins=x_bins, + add_line=add_line, + flip_x_color=flip_x_color, + ) + if flip_x_color: + relative, relative_cols = relative_cols, relative + if secondary: + if relative_cols: + type = "histogram" + barmode = "group" + else: + type = "bar" + barmode = "relative" + chart_secondary = charters.chart( + df=filtered_df, + x_axis=relative, + y_axis=secondary, + color=relative_cols, + type=type, + x_bins=x_bins, + barmode=barmode, + ) + chart_secondary = dcc.Graph(figure=chart_secondary) + tab_content = html.Div([dcc.Graph(figure=chart)]) + + elif active_tab == f"{FILTER_PREFIX}-tab-table": + if tool == "compare": + table = charters.compare_rates( df=filtered_df, x_axis=x_axis, - y_axis=secondary, + rates=rates, + weights=weights, + secondary=secondary, + x_bins=x_bins, + display=False, + ) + elif tool == "chart": + table = charters.chart( + df=filtered_df, + x_axis=x_axis, + y_axis=y_axis, color=color, - type=type, + type=chart_type, + numerator=numerator, + denominator=denominator, x_bins=x_bins, - barmode=barmode, + add_total=True, + display=False, ) - chart_secondary = dcc.Graph(figure=chart_secondary) - elif tool == "target": - chart = charters.target( - df=filtered_df, - target=[target], - features=config_dataset["columns"]["features"], - numerator=numerator, - denominator=denominator, - add_line=add_line, - ) - elif tool == "relative": - chart = charters.relative_risk( - df=filtered_df, - y_axis=y_axis, - features=relative, - numerator=numerator, - denominator=denominator, - relative_to=relative_to, - relative_cols=relative_cols, - subset_dict=subset_dict, - x_bins=x_bins, - add_line=add_line, - flip_x_color=flip_x_color, - ) - if flip_x_color: - relative, relative_cols = relative_cols, relative - if secondary: - if relative_cols: - type = "histogram" - barmode = "group" - else: - type = "bar" - barmode = "relative" - chart_secondary = charters.chart( + elif tool == "target": + # give blank table + table = filtered_df.head(0) + elif tool == "relative": + table = charters.relative_risk( df=filtered_df, - x_axis=relative, - y_axis=secondary, - color=relative_cols, - type=type, + y_axis=y_axis, + features=relative, + numerator=numerator, + denominator=denominator, + relative_to=relative_to, + relative_cols=relative_cols, + subset_dict=subset_dict, x_bins=x_bins, - barmode=barmode, + add_line=add_line, + flip_x_color=flip_x_color, + display=False, ) - chart_secondary = dcc.Graph(figure=chart_secondary) - tab_content = dcc.Graph(figure=chart) - elif active_tab == "tab-table": - if tool == "compare": - table = charters.compare_rates( - df=filtered_df, - x_axis=x_axis, - rates=rates, - weights=weights, - secondary=secondary, - x_bins=x_bins, - display=False, + columnDefs = dash_formats.get_column_defs(table) + grid = dag.AgGrid( + id={"type": "data-table", "tab": "table", "page": FILTER_PREFIX}, + rowData=table.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "enableRangeSelection": True, + "copyHeadersToClipboard": True, + "enableCellTextSelection": True, + "defaultColDef": { + "sortable": True, + "filter": True, + "resizable": True, + }, + }, + className="ag-theme-alpine", + columnSize="sizeToFit", ) - elif tool == "chart": - table = charters.chart( - df=filtered_df, - x_axis=x_axis, - y_axis=y_axis, - color=color, - type=chart_type, - numerator=numerator, - denominator=denominator, - x_bins=x_bins, - display=False, + + export_button = common_build._build_export_button("table", FILTER_PREFIX) + tab_content = html.Div([export_button, grid]) + + elif active_tab == f"{FILTER_PREFIX}-tab-rank": + rank_features = ( + rank_columns if rank_columns else config_dataset["columns"]["features"] ) - elif tool == "target": - # give blank table - table = filtered_df.head(0) - elif tool == "relative": - table = charters.relative_risk( + rank = metrics.ae_rank( df=filtered_df, - y_axis=y_axis, - features=relative, - numerator=numerator, - denominator=denominator, - relative_to=relative_to, - relative_cols=relative_cols, - subset_dict=subset_dict, - x_bins=x_bins, - add_line=add_line, - flip_x_color=flip_x_color, - display=False, + features=rank_features, + actuals=config_dataset["columns"]["actuals_amt"], + expecteds=config_dataset["columns"]["expecteds_amt"], + exposures=config_dataset["columns"]["exposure_amt"], + bin_threshold=20, + n_bins=10, ) - columnDefs = dash_formats.get_column_defs(table) - export_button = html.Button( - "Export to CSV", - id={"type": "export-button", "tab": "table", "page": "experience"}, - className="btn btn-primary mt-2 mb-2", - ) - - grid = dag.AgGrid( - id={"type": "data-table", "tab": "table", "page": "experience"}, - rowData=table.to_dict("records"), - columnDefs=columnDefs, - dashGridOptions={ - "enableRangeSelection": True, - "copyHeadersToClipboard": True, - "enableCellTextSelection": True, - "defaultColDef": { - "sortable": True, - "filter": True, - "resizable": True, + columnDefs = dash_formats.get_column_defs(rank) + + grid = dag.AgGrid( + id={"type": "data-table", "tab": "rank", "page": FILTER_PREFIX}, + rowData=rank.to_dict("records"), + columnDefs=columnDefs, + dashGridOptions={ + "enableRangeSelection": True, + "copyHeadersToClipboard": True, + "enableCellTextSelection": True, + "defaultColDef": { + "sortable": True, + "filter": True, + "resizable": True, + }, }, - }, - className="ag-theme-alpine", - columnSize="sizeToFit", - ) - - tab_content = html.Div([export_button, grid]) - - elif active_tab == "tab-rank": - rank_features = ( - rank_columns if rank_columns else config_dataset["columns"]["features"] - ) - rank = metrics.ae_rank( - df=filtered_df, - features=rank_features, - actuals=config_dataset["columns"]["actuals_amt"], - expecteds=config_dataset["columns"]["expecteds_amt"], - exposures=config_dataset["columns"]["exposure_amt"], - bin_threshold=20, - n_bins=10, - ) + className="ag-theme-alpine", + columnSize="sizeToFit", + ) - columnDefs = dash_formats.get_column_defs(rank) - export_button = html.Button( - "Export to CSV", - id={"type": "export-button", "tab": "rank", "page": "experience"}, - className="btn btn-primary mt-2 mb-2", - ) + rank_description = html.P( + children=[ + "Rank identifies features driving A/E deviations. " + "'Issue' ranks high-loss values with high-loss percentages. " + "'Driver' ranks high-loss values with small exposure.", + html.Br(), + "Issue = abs((actuals - expected) * (actuals/expected - 1))", + html.Br(), + "Driver = abs((actuals - expected) * (1 - exposure/total_exposure))", + ], + style={"fontSize": "0.85em", "color": "gray", "marginBottom": "6px"}, + ) - grid = dag.AgGrid( - id={"type": "data-table", "tab": "rank", "page": "experience"}, - rowData=rank.to_dict("records"), - columnDefs=columnDefs, - dashGridOptions={ - "enableRangeSelection": True, - "copyHeadersToClipboard": True, - "enableCellTextSelection": True, - "defaultColDef": { - "sortable": True, - "filter": True, - "resizable": True, - }, - }, - className="ag-theme-alpine", - columnSize="sizeToFit", - ) + export_button = common_build._build_export_button("rank", FILTER_PREFIX) + tab_content = html.Div([rank_description, export_button, grid]) - rank_description = html.P( - children=[ - "Rank identifies features driving A/E deviations. " - "'Issue' ranks high-loss values with high-loss percentages. " - "'Driver' ranks high-loss values with small exposure.", - html.Br(), - "Issue = abs((actuals - expected) * (actuals/expected - 1))", - html.Br(), - "Driver = abs((actuals - expected) * (1 - exposure/total_exposure))", - ], - style={"fontSize": "0.85em", "color": "gray", "marginBottom": "6px"}, - ) + return tab_content, chart_secondary, filtered_card, False, "" - tab_content = html.Div([rank_description, export_button, grid]) - - return tab_content, chart_secondary, filtered_card + except Exception as e: + logger.error(f"Error updating tab content: {e}") + return dash.no_update, dash.no_update, dash.no_update, True, str(e) @callback( @@ -900,101 +841,18 @@ def update_tool_description(tool): return descriptions.get(tool, "") -@callback( - [ - Output({"type": "chart-str-filter", "index": ALL}, "value"), - Output({"type": "chart-num-filter", "index": ALL}, "value"), - ], - [Input("reset-filters-button", "n_clicks")], - [State("store-dataset", "data"), State("store-initial-filters", "data")], - prevent_initial_call=True, -) -def reset_filters(n_clicks, dataset, filter_dict): - """Reset all filters to default values.""" - logger.debug("resetting filters") - str_reset_values = [[]] * len(filter_dict["str_cols"]) - num_reset_values = [ - [ - dataset.select(pl.col(col).min()).collect().item(), - dataset.select(pl.col(col).max()).collect().item(), - ] - for col in filter_dict["num_cols"] - ] - return str_reset_values, num_reset_values - - -@callback( - Output("filters-offcanvas", "is_open"), - [Input("open-offcanvas-button", "n_clicks")], - [State("filters-offcanvas", "is_open")], -) -def toggle_filters_offcanvas(n_clicks, is_open): - """Toggle the filters offcanvas.""" - if n_clicks: - return not is_open - return is_open - - -@callback( - Output({"type": "chart-collapse", "index": ALL}, "is_open"), - Output({"type": "chart-collapse-button", "index": ALL}, "children"), - Input({"type": "chart-collapse-button", "index": ALL}, "n_clicks"), - State({"type": "chart-collapse", "index": ALL}, "is_open"), - State({"type": "chart-collapse-button", "index": ALL}, "children"), - prevent_initial_call=True, -) -def toggle_collapse(n_clicks, is_open, children): - """Toggle collapse state of filter checklists.""" - if not n_clicks or not any(n_clicks): - raise dash.exceptions.PreventUpdate - - # Find which button was clicked - ctx = callback_context - if not ctx.triggered: - return [False] * len(is_open), children - - button_id = ctx.triggered[0]["prop_id"].split(".")[0] - button_idx = eval(button_id)["index"] - - # Update the collapse states and button icons - new_is_open = [] - new_children = [] - - for _, (col, is_open_state, child) in enumerate( - zip( - [x["id"]["index"] for x in ctx.inputs_list[0]], - is_open, - children, - strict=False, - ) - ): - # Update collapse state - new_state = not is_open_state if col == button_idx else is_open_state - new_is_open.append(new_state) - - # Update button content - label = child[0]["props"]["children"] # Get the column name - new_children.append( - [ - html.Span(label, style={"flex-grow": 1}), - html.I(className=f"fas fa-chevron-{'up' if new_state else 'down'}"), - ] - ) - - return new_is_open, new_children - - @callback( Output("active-filters-card", "children"), [ - Input({"type": "chart-str-filter", "index": ALL}, "value"), - Input({"type": "chart-num-filter", "index": ALL}, "value"), + Input({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + Input({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), ], [ - State({"type": "chart-str-filter", "index": ALL}, "id"), - State({"type": "chart-num-filter", "index": ALL}, "id"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "id"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "id"), State("store-initial-filters", "data"), ], + prevent_initial_call=True, ) def update_active_filters_card( str_filters, num_filters, str_filter_ids, num_filter_ids, filter_dict @@ -1038,10 +896,10 @@ def update_active_filters_card( @callback( Output("store-filter-values", "data"), - Input({"type": "chart-str-filter", "index": ALL}, "value"), - Input({"type": "chart-num-filter", "index": ALL}, "value"), - State({"type": "chart-str-filter", "index": ALL}, "id"), - State({"type": "chart-num-filter", "index": ALL}, "id"), + Input({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + Input({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "id"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "id"), prevent_initial_call=True, ) def save_filter_state(str_vals, num_vals, str_ids, num_ids): @@ -1055,9 +913,29 @@ def save_filter_state(str_vals, num_vals, str_ids, num_ids): return {"str_filters": str_filters, "num_filters": num_filters} +@callback( + Output({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + Output({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + Input({"type": "filter-reset-button", "prefix": FILTER_PREFIX}, "n_clicks"), + State("store-initial-filters", "data"), + prevent_initial_call=True, +) +def reset_filters(n_clicks, filter_dict): + """Clear the filters.""" + if not n_clicks: + raise dash.exceptions.PreventUpdate + str_reset = [[]] * len(filter_dict["str_cols"]) + num_reset = [ + [filter_dict["min_max"][f"{col}_min"], filter_dict["min_max"][f"{col}_max"]] + for col in filter_dict["num_cols"] + ] + return str_reset, num_reset + + @callback( Output("bookmark-dropdown", "options"), Input("store-bookmarks", "data"), + prevent_initial_call=True, ) def update_bookmark_dropdown(bookmarks): """Populate the bookmark dropdown from the bookmarks store.""" @@ -1071,10 +949,10 @@ def update_bookmark_dropdown(bookmarks): Output("bookmark-name-input", "value"), Input("save-bookmark-button", "n_clicks"), State("bookmark-name-input", "value"), - State({"type": "chart-str-filter", "index": ALL}, "value"), - State({"type": "chart-num-filter", "index": ALL}, "value"), - State({"type": "chart-str-filter", "index": ALL}, "id"), - State({"type": "chart-num-filter", "index": ALL}, "id"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "id"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "id"), State("store-bookmarks", "data"), prevent_initial_call=True, ) @@ -1094,14 +972,22 @@ def save_bookmark(n_clicks, name, str_vals, num_vals, str_ids, num_ids, bookmark @callback( - Output({"type": "chart-str-filter", "index": ALL}, "value", allow_duplicate=True), - Output({"type": "chart-num-filter", "index": ALL}, "value", allow_duplicate=True), + Output( + {"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, + "value", + allow_duplicate=True, + ), + Output( + {"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, + "value", + allow_duplicate=True, + ), Input("load-bookmark-button", "n_clicks"), State("bookmark-dropdown", "value"), State("store-bookmarks", "data"), - State({"type": "chart-str-filter", "index": ALL}, "id"), - State({"type": "chart-num-filter", "index": ALL}, "id"), - State({"type": "chart-num-filter", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": FILTER_PREFIX, "index": ALL}, "id"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "id"), + State({"type": "filter-num", "prefix": FILTER_PREFIX, "index": ALL}, "value"), prevent_initial_call=True, ) def load_bookmark(n_clicks, selected, bookmarks, str_ids, num_ids, current_num_vals): diff --git a/morai/dashboard/pages/input.py b/morai/dashboard/pages/input.py index 3d78c0a..851feed 100644 --- a/morai/dashboard/pages/input.py +++ b/morai/dashboard/pages/input.py @@ -41,6 +41,16 @@ def layout(): """Input layout.""" return html.Div( [ + # Toast notifications + dbc.Toast( + id="input-toast", + header="Error", + is_open=False, + dismissable=True, + icon="danger", + className="toast", + style={"position": "fixed", "top": 10, "right": 10, "zIndex": 1050}, + ), # Header section with gradient background html.Div( [ @@ -248,6 +258,8 @@ def layout(): [ Output("store-config", "data"), Output("store-dataset", "data"), + Output("input-toast", "is_open"), + Output("input-toast", "children"), ], [Input("button-load-config", "n_clicks")], [State("dataset-dropdown", "value")], @@ -256,24 +268,30 @@ def load_config(n_clicks, dataset): """Load the configuration file.""" if n_clicks is None: raise dash.exceptions.PreventUpdate - logger.debug("load config") - config = dh.load_config() - if dataset is not None: - config["general"]["dataset"] = dataset - # load dataset - # only works with parquet, csv files - # could add more - logger.debug("load data") - config_dataset = config["datasets"][config["general"]["dataset"]] - file_path = helpers.FILES_PATH / "dataset" / config_dataset["filename"] - if file_path.suffix == ".parquet": - pl.enable_string_cache() - lzdf = pl.scan_parquet(file_path) - if file_path.suffix == ".csv": - lzdf = pl.read_csv(file_path) + try: + logger.debug("load config") + config = dh.load_config() + if dataset is not None: + config["general"]["dataset"] = dataset + + # load dataset + # only works with parquet, csv files + # could add more + logger.debug("load data") + config_dataset = config["datasets"][config["general"]["dataset"]] + file_path = helpers.FILES_PATH / "dataset" / config_dataset["filename"] + if file_path.suffix == ".parquet": + pl.enable_string_cache() + lzdf = pl.scan_parquet(file_path) + if file_path.suffix == ".csv": + lzdf = pl.read_csv(file_path) + + return config, Serverside(lzdf, key=config["general"]["dataset"]), False, "" - return config, Serverside(lzdf, key=config["general"]["dataset"]) + except Exception as e: + logger.error(f"Error loading config: {e}") + return dash.no_update, dash.no_update, True, str(e) @callback( diff --git a/morai/dashboard/pages/models.py b/morai/dashboard/pages/models.py index b3c6d30..ea2b057 100644 --- a/morai/dashboard/pages/models.py +++ b/morai/dashboard/pages/models.py @@ -5,6 +5,7 @@ import dash_ag_grid as dag import dash_bootstrap_components as dbc import dash_extensions.enrich as dash +import dash_mantine_components as dmc import joblib import pandas as pd from dash_extensions.enrich import ( @@ -71,6 +72,7 @@ def layout(): dismissable=True, icon="danger", className="toast", + style={"position": "fixed", "top": 10, "right": 10, "zIndex": 1050}, ), # Main Content Accordion dbc.Accordion( @@ -192,8 +194,9 @@ def layout(): # Chart dcc.Loading( id="loading-pdp-chart", - type="default", - color="#007bff", + custom_spinner=dmc.Skeleton( + visible=True, h="100%" + ), children=html.Div( id="pdp-chart", className="bg-white rounded-3 shadow-sm p-3", @@ -442,7 +445,7 @@ def display_model_results(pathname, model_results): dbc.Col( dcc.Loading( id="loading-importance-chart", - type="dot", + custom_spinner=dmc.Skeleton(visible=True, h="100%"), children=html.Div(id="importance-chart"), ), xs=12, @@ -473,7 +476,7 @@ def display_model_results(pathname, model_results): dbc.Col( dcc.Loading( id="loading-target-chart", - type="dot", + custom_spinner=dmc.Skeleton(visible=True, h="100%"), children=html.Div(id="target-chart"), ), xs=12, @@ -574,8 +577,8 @@ def clicked_cell_target_dictionary(cell, model_data, config): State("store-model-results", "data"), State("store-dataset", "data"), State({"type": "pdp-selector", "index": ALL}, "value"), - State({"type": "pdp-str-filter", "index": ALL}, "value"), - State({"type": "pdp-num-filter", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": "pdp", "index": ALL}, "value"), + State({"type": "filter-num", "prefix": "pdp", "index": ALL}, "value"), ], ) def display_pdp( @@ -594,65 +597,70 @@ def display_pdp( if model_file is None or x_axis_col is None: return dash.no_update, True, "Model file or x-axis column is not selected." - # load model - model = joblib.load(helpers.FILES_PATH / "models" / model_file) - model_name = model_file.split(".")[0] - - # get model parameters - standardize = model_results.model[ - model_results.model["model_name"] == model_name - ].iloc[0]["preprocess_params"]["standardize"] - preset = model_results.model[model_results.model["model_name"] == model_name].iloc[ - 0 - ]["preprocess_params"]["preset"] - add_constant = model_results.model[ - model_results.model["model_name"] == model_name - ].iloc[0]["preprocess_params"]["add_constant"] - feature_dict = model_results.model[ - model_results.model["model_name"] == model_name - ].iloc[0]["feature_dict"] - - # filter_data - filtered_df = dh.filter_data(df=model_data, callback_context=states_info) - - # encode data - filtered_df = filtered_df.collect().to_pandas() - preprocess_dict = preprocessors.preprocess_data( - filtered_df, - feature_dict=feature_dict, - standardize=standardize, - preset=preset, - add_constant=add_constant, - ) - mapping = preprocess_dict["mapping"] - md_encoded = preprocess_dict["md_encoded"] - - # get parameters from config - line_color = dh._inputs_parse_id(states_info, "color_selector") - weight = dh._inputs_parse_id(states_info, "weights_selector") - secondary = dh._inputs_parse_id(states_info, "secondary_selector") - x_bins = dh._inputs_parse_id(states_info, "x_bins_selector") - - # verify parameters are in the model - if not any( - col in mapping.keys() for col in [x_axis_col, line_color, weight, secondary] - ): - return dash.no_update, True, "Column was not included in model." - - # create pdp - pdp_chart = charters.pdp( - model=model, - df=md_encoded, - x_axis=x_axis_col, - line_color=line_color, - weight=weight, - secondary=secondary, - mapping=mapping, - x_bins=x_bins, - quick=True, - ) + try: + # load model + model = joblib.load(helpers.FILES_PATH / "models" / model_file) + model_name = model_file.split(".")[0] + + # get model parameters + standardize = model_results.model[ + model_results.model["model_name"] == model_name + ].iloc[0]["preprocess_params"]["standardize"] + preset = model_results.model[ + model_results.model["model_name"] == model_name + ].iloc[0]["preprocess_params"]["preset"] + add_constant = model_results.model[ + model_results.model["model_name"] == model_name + ].iloc[0]["preprocess_params"]["add_constant"] + feature_dict = model_results.model[ + model_results.model["model_name"] == model_name + ].iloc[0]["feature_dict"] + + # filter_data + filtered_df = dh.filter_data(df=model_data, callback_context=states_info) + + # encode data + filtered_df = filtered_df.collect().to_pandas() + preprocess_dict = preprocessors.preprocess_data( + filtered_df, + feature_dict=feature_dict, + standardize=standardize, + preset=preset, + add_constant=add_constant, + ) + mapping = preprocess_dict["mapping"] + md_encoded = preprocess_dict["md_encoded"] + + # get parameters from config + line_color = dh._inputs_parse_id(states_info, "color_selector") + weight = dh._inputs_parse_id(states_info, "weights_selector") + secondary = dh._inputs_parse_id(states_info, "secondary_selector") + x_bins = dh._inputs_parse_id(states_info, "x_bins_selector") + + # verify parameters are in the model + if not any( + col in mapping.keys() for col in [x_axis_col, line_color, weight, secondary] + ): + return dash.no_update, True, "Column was not included in model." + + # create pdp + pdp_chart = charters.pdp( + model=model, + df=md_encoded, + x_axis=x_axis_col, + line_color=line_color, + weight=weight, + secondary=secondary, + mapping=mapping, + x_bins=x_bins, + quick=True, + ) + + return dcc.Graph(figure=pdp_chart), False, "" - return dcc.Graph(figure=pdp_chart), False, "" + except Exception as e: + logger.error(f"Error creating PDP: {e}") + return dash.no_update, True, str(e) # Add new callbacks for the filter offcanvas @@ -670,8 +678,8 @@ def toggle_pdp_filters_offcanvas(n_clicks, is_open): @callback( [ - Output({"type": "pdp-str-filter", "index": ALL}, "value"), - Output({"type": "pdp-num-filter", "index": ALL}, "value"), + Output({"type": "filter-str", "prefix": "pdp", "index": ALL}, "value"), + Output({"type": "filter-num", "prefix": "pdp", "index": ALL}, "value"), ], [Input("reset-pdp-filters-button", "n_clicks")], [State("store-dataset", "data"), State("store-config", "data")], diff --git a/morai/dashboard/pages/tables.py b/morai/dashboard/pages/tables.py index e904b96..be328e8 100644 --- a/morai/dashboard/pages/tables.py +++ b/morai/dashboard/pages/tables.py @@ -89,6 +89,7 @@ def layout(): dismissable=True, icon="danger", className="toast", + style={"position": "fixed", "top": 10, "right": 10, "zIndex": 1050}, ), # Description Card dbc.Card( @@ -638,73 +639,78 @@ def initialize_tables( if table1_id is None or table2_id is None: return (*no_upate_tuple, True, "No table selected.", dash.no_update) - # check if tables have changed - tables_changed = ( - prev_table1_id is None - or prev_table2_id is None - or prev_table1_id != table1_id - or prev_table2_id != table2_id - or prev_table1_mi_years != table1_mi_years - or prev_table2_mi_years != table2_mi_years - ) + try: + # check if tables have changed + tables_changed = ( + prev_table1_id is None + or prev_table2_id is None + or prev_table1_id != table1_id + or prev_table2_id != table2_id + or prev_table1_mi_years != table1_mi_years + or prev_table2_mi_years != table2_mi_years + ) - # generate filters only if tables have changed - if tables_changed: - # load tables - ( - table_1, - table_2, + # generate filters only if tables have changed + if tables_changed: + # load tables + ( + table_1, + table_2, + table_1_select_period, + table_2_select_period, + mults_1, + mults_2, + mi_table_1, + mi_table_2, + warning_tuple, + ) = load_tables(table1_id, table2_id, table1_mi_years, table2_mi_years) + + if True in warning_tuple: + return (*no_upate_tuple, *warning_tuple, dash.no_update) + + # mi + mi_1_style = {"display": "none" if mi_table_1 is None else "block"} + mi_2_style = {"display": "none" if mi_table_2 is None else "block"} + + # filters + filters_1 = dh.generate_filters( + df=table_1, + prefix="table-1", + exclude_cols=["vals", "constant"], + mult_table=mults_1, + ).get("filters") + filters_2 = dh.generate_filters( + df=table_2, + prefix="table-2", + exclude_cols=["vals", "constant"], + mult_table=mults_2, + ).get("filters") + else: + return (*no_upate_tuple, *warning_tuple, trigger_value) + + return ( + table1_id, + table2_id, + table_1.to_dict("records"), + table_2.to_dict("records"), + mults_1.to_dict("records"), + mults_2.to_dict("records"), table_1_select_period, table_2_select_period, - mults_1, - mults_2, - mi_table_1, - mi_table_2, - warning_tuple, - ) = load_tables(table1_id, table2_id, table1_mi_years, table2_mi_years) - - if True in warning_tuple: - return (*no_upate_tuple, *warning_tuple, dash.no_update) - - # mi - mi_1_style = {"display": "none" if mi_table_1 is None else "block"} - mi_2_style = {"display": "none" if mi_table_2 is None else "block"} - - # filters - filters_1 = dh.generate_filters( - df=table_1, - prefix="table-1", - exclude_cols=["vals", "constant"], - mult_table=mults_1, - ).get("filters") - filters_2 = dh.generate_filters( - df=table_2, - prefix="table-2", - exclude_cols=["vals", "constant"], - mult_table=mults_2, - ).get("filters") - else: - return (*no_upate_tuple, *warning_tuple, trigger_value) + table1_mi_years, + table2_mi_years, + filters_1, + filters_2, + mi_1_style, + mi_2_style, + False, + False, + trigger_value, + ) - return ( - table1_id, - table2_id, - table_1.to_dict("records"), - table_2.to_dict("records"), - mults_1.to_dict("records"), - mults_2.to_dict("records"), - table_1_select_period, - table_2_select_period, - table1_mi_years, - table2_mi_years, - filters_1, - filters_2, - mi_1_style, - mi_2_style, - False, - False, - trigger_value, - ) + except Exception as e: + logger.error(f"Error initializing tables: {e}") + return (*no_upate_tuple, True, str(e), dash.no_update) @callback( @@ -729,10 +735,10 @@ def initialize_tables( State("store-table-2-select", "data"), State("table-1-mi-years", "value"), State("table-2-mi-years", "value"), - State({"type": "table-1-str-filter", "index": ALL}, "value"), - State({"type": "table-1-num-filter", "index": ALL}, "value"), - State({"type": "table-2-str-filter", "index": ALL}, "value"), - State({"type": "table-2-num-filter", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": "table-1", "index": ALL}, "value"), + State({"type": "filter-num", "prefix": "table-1", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": "table-2", "index": ALL}, "value"), + State({"type": "filter-num", "prefix": "table-2", "index": ALL}, "value"), ], prevent_initial_call=True, ) @@ -763,11 +769,11 @@ def filter_tables_callback( # get filters from the callback context for description states_info = dh._inputs_flatten_list(callback_context.states_list) filters_table_1 = dh._inputs_parse_type( - states_info, "table-1-num-filter" - ) + dh._inputs_parse_type(states_info, "table-1-str-filter") + states_info, "filter-num", prefix="table-1" + ) + dh._inputs_parse_type(states_info, "filter-str", prefix="table-1") filters_table_2 = dh._inputs_parse_type( - states_info, "table-2-num-filter" - ) + dh._inputs_parse_type(states_info, "table-2-str-filter") + states_info, "filter-num", prefix="table-2" + ) + dh._inputs_parse_type(states_info, "filter-str", prefix="table-2") # filter the datasets filtered_table_1, filtered_table_2 = filter_tables( @@ -980,10 +986,10 @@ def update_graphs_from_slider(issue_age_value, compare_df): State("table-2-id", "value"), State("table-1-mi-years", "value"), State("table-2-mi-years", "value"), - State({"type": "table-1-str-filter", "index": ALL}, "value"), - State({"type": "table-1-num-filter", "index": ALL}, "value"), - State({"type": "table-2-str-filter", "index": ALL}, "value"), - State({"type": "table-2-num-filter", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": "table-1", "index": ALL}, "value"), + State({"type": "filter-num", "prefix": "table-1", "index": ALL}, "value"), + State({"type": "filter-str", "prefix": "table-2", "index": ALL}, "value"), + State({"type": "filter-num", "prefix": "table-2", "index": ALL}, "value"), ], prevent_initial_call=True, ) @@ -1180,11 +1186,11 @@ def filter_tables(table_1, table_2, mults_1, mults_2, filter_list): # callback context inputs_info = dh._inputs_flatten_list(filter_list) filters_table_1 = dh._inputs_parse_type( - inputs_info, "table-1-num-filter" - ) + dh._inputs_parse_type(inputs_info, "table-1-str-filter") + inputs_info, "filter-num", prefix="table-1" + ) + dh._inputs_parse_type(inputs_info, "filter-str", prefix="table-1") filters_table_2 = dh._inputs_parse_type( - inputs_info, "table-2-num-filter" - ) + dh._inputs_parse_type(inputs_info, "table-2-str-filter") + inputs_info, "filter-num", prefix="table-2" + ) + dh._inputs_parse_type(inputs_info, "filter-str", prefix="table-2") # filter the datasets filtered_table_1 = dh.filter_data( @@ -1367,91 +1373,3 @@ def get_su_graph(df, select_period, title): fig.update_traces(contours_coloring="heatmap", colorscale=colorscale) return dcc.Graph(figure=fig) - - -@callback( - Output({"type": "table-1-collapse", "index": ALL}, "is_open"), - Output({"type": "table-1-collapse-button", "index": ALL}, "children"), - Input({"type": "table-1-collapse-button", "index": ALL}, "n_clicks"), - State({"type": "table-1-collapse", "index": ALL}, "is_open"), - State({"type": "table-1-collapse-button", "index": ALL}, "children"), - prevent_initial_call=True, -) -def toggle_table_1_collapse(n_clicks, is_open, children): - """Toggle collapse state of filter checklists for table 1.""" - if not n_clicks or not any(n_clicks): - raise dash.exceptions.PreventUpdate - - # Find which button was clicked - ctx = callback_context - if not ctx.triggered: - return [False] * len(is_open), children - - button_id = ctx.triggered[0]["prop_id"].split(".")[0] - button_idx = eval(button_id)["index"] - - # Update the collapse states and button icons - new_is_open = [] - new_children = [] - - for _, (col, is_open_state, child) in enumerate( - zip([x["id"]["index"] for x in ctx.inputs_list[0]], is_open, children) - ): - # Update collapse state - new_state = not is_open_state if col == button_idx else is_open_state - new_is_open.append(new_state) - - # Update button content - label = child[0]["props"]["children"] # Get the column name - new_children.append( - [ - html.Span(label, style={"flex-grow": 1}), - html.I(className=f"fas fa-chevron-{'up' if new_state else 'down'}"), - ] - ) - - return new_is_open, new_children - - -@callback( - Output({"type": "table-2-collapse", "index": ALL}, "is_open"), - Output({"type": "table-2-collapse-button", "index": ALL}, "children"), - Input({"type": "table-2-collapse-button", "index": ALL}, "n_clicks"), - State({"type": "table-2-collapse", "index": ALL}, "is_open"), - State({"type": "table-2-collapse-button", "index": ALL}, "children"), - prevent_initial_call=True, -) -def toggle_table_2_collapse(n_clicks, is_open, children): - """Toggle collapse state of filter checklists for table 2.""" - if not n_clicks or not any(n_clicks): - raise dash.exceptions.PreventUpdate - - # Find which button was clicked - ctx = callback_context - if not ctx.triggered: - return [False] * len(is_open), children - - button_id = ctx.triggered[0]["prop_id"].split(".")[0] - button_idx = eval(button_id)["index"] - - # Update the collapse states and button icons - new_is_open = [] - new_children = [] - - for _, (col, is_open_state, child) in enumerate( - zip([x["id"]["index"] for x in ctx.inputs_list[0]], is_open, children) - ): - # Update collapse state - new_state = not is_open_state if col == button_idx else is_open_state - new_is_open.append(new_state) - - # Update button content - label = child[0]["props"]["children"] # Get the column name - new_children.append( - [ - html.Span(label, style={"flex-grow": 1}), - html.I(className=f"fas fa-chevron-{'up' if new_state else 'down'}"), - ] - ) - - return new_is_open, new_children diff --git a/morai/dashboard/utils/dashboard_helper.py b/morai/dashboard/utils/dashboard_helper.py index 392f365..b5ddb01 100644 --- a/morai/dashboard/utils/dashboard_helper.py +++ b/morai/dashboard/utils/dashboard_helper.py @@ -226,8 +226,6 @@ def generate_filters( filters = [] str_cols = [] num_cols = [] - prefix_str_filter = f"{prefix}-str-filter" - prefix_num_filter = f"{prefix}-num-filter" duckdb_source = None # get column types @@ -362,13 +360,21 @@ def generate_filters( html.Span(col, style={"flex-grow": 1}), html.I(className="fas fa-chevron-down"), ], - id={"type": f"{prefix}-collapse-button", "index": col}, + id={ + "type": "filter-collapse-button", + "prefix": prefix, + "index": col, + }, className="mb-2 w-100 text-start d-flex align-items-center", color="light", ), dbc.Collapse( dcc.Checklist( - id={"type": prefix_str_filter, "index": col}, + id={ + "type": "filter-str", + "prefix": prefix, + "index": col, + }, options=options, value=( initial_values.get("str_filters", {}).get(col, []) @@ -378,7 +384,11 @@ def generate_filters( className="ms-2", labelStyle={"display": "block"}, ), - id={"type": f"{prefix}-collapse", "index": col}, + id={ + "type": "filter-collapse", + "prefix": prefix, + "index": col, + }, is_open=False, ), ], @@ -397,13 +407,21 @@ def generate_filters( html.Span(col, style={"flex-grow": 1}), html.I(className="fas fa-chevron-down"), ], - id={"type": f"{prefix}-collapse-button", "index": col}, + id={ + "type": "filter-collapse-button", + "prefix": prefix, + "index": col, + }, className="mb-2 w-100 text-start d-flex align-items-center", color="light", ), dbc.Collapse( dcc.RangeSlider( - id={"type": prefix_num_filter, "index": col}, + id={ + "type": "filter-num", + "prefix": prefix, + "index": col, + }, min=min_val, max=max_val, step=1, @@ -415,9 +433,13 @@ def generate_filters( if initial_values else [min_val, max_val] ), - tooltip={"always_visible": True, "placement": "bottom"}, + tooltip={"placement": "top"}, ), - id={"type": f"{prefix}-collapse", "index": col}, + id={ + "type": "filter-collapse", + "prefix": prefix, + "index": col, + }, is_open=False, ), ], @@ -446,19 +468,31 @@ def generate_filters( html.Span(category, style={"flex-grow": 1}), html.I(className="fas fa-chevron-down"), ], - id={"type": f"{prefix}-collapse-button", "index": category}, + id={ + "type": "filter-collapse-button", + "prefix": prefix, + "index": category, + }, className="mb-2 w-100 text-start d-flex align-items-center", color="light", ), dbc.Collapse( dcc.RadioItems( - id={"type": prefix_str_filter, "index": category}, + id={ + "type": "filter-str", + "prefix": prefix, + "index": category, + }, options=options, value=default_value, className="ms-2", labelStyle={"display": "block"}, ), - id={"type": f"{prefix}-collapse", "index": category}, + id={ + "type": "filter-collapse", + "prefix": prefix, + "index": category, + }, is_open=False, ), ], @@ -539,9 +573,7 @@ def get_active_filters( return active_filters_list -def toggle_collapse( - callback_context: Any, is_open: list[bool], children: list[dict] -) -> tuple[list[Any], list[Any]]: +def toggle_collapse(callback_context: Any, is_open: list, children: list) -> tuple: """ Toggle collapse state of filter checklists. @@ -549,14 +581,14 @@ def toggle_collapse( ---------- callback_context : dash.callback_context The callback context containing states information - is_open : List[bool] + is_open : List List of current collapse states - children : List[dict] + children : List List of current button children Returns ------- - tuple[List[bool], List[List[dict]]] + tuple Updated collapse states and button children """ @@ -1322,7 +1354,7 @@ def _inputs_parse_id(input_list: list[Any], id_value: str) -> Any: return None -def _inputs_parse_type(input_list: list[Any], type_value: str) -> list[Any]: +def _inputs_parse_type(input_list: list, type_value: str, prefix: str) -> list: """ Parse inputs for type value. @@ -1334,6 +1366,8 @@ def _inputs_parse_type(input_list: list[Any], type_value: str) -> list[Any]: List of inputs. type_value : str type to parse. + prefix : str + prefix of the id to parse. Returns ------- @@ -1346,93 +1380,9 @@ def _inputs_parse_type(input_list: list[Any], type_value: str) -> list[Any]: input_id = input.get("id") # id is a dict if isinstance(input_id, dict): - if input_id.get("type") == type_value: - type_list.append(input) - return type_list - - -def register_export_callback(app) -> None: # noqa: ANN001 - """ - Register a universal callback for exporting table data to CSV. - - This function should be called once in the app initialization to register - the export functionality for all data tables across the application. - - Parameters - ---------- - app : dash.Dash - The Dash application instance. - - Notes - ----- - For this callback to work, the following components must be present: - 1. A Download component with id="download-dataframe-csv" in each page's layout - 2. Export buttons with pattern-matching ID: - {"type": "export-button", "tab": , "page": } - 3. Data tables with pattern-matching ID: - {"type": "data-table", "tab": , "page": } - - The tab and page values must match between the button and table for proper pairing. - - """ - import dash # noqa: PLC0415 - import pandas as pd # noqa: PLC0415 - from dash_extensions.enrich import ( # noqa: PLC0415 - ALL, - Input, - Output, - State, - callback, - callback_context, - dcc, - ) - - @callback( - Output("download-dataframe-csv", "data"), - Input({"type": "export-button", "tab": ALL, "page": ALL}, "n_clicks"), - State({"type": "data-table", "tab": ALL, "page": ALL}, "rowData"), - prevent_initial_call=True, - ) - def export_table( - n_clicks_list: list[int | None], table_data_list: list[list[Any]] - ) -> None: - """ - Export table data to CSV. - - This generic function handles exporting data from any - table with an export button. - - The button and table must use pattern-matching IDs - with the following structure: - - Button: - {"type": "export-button", "tab": , "page": } - - Table: - {"type": "data-table", "tab": , "page": } - - Where identifies the specific tab - and identifies the page. - """ - ctx = callback_context - if not ctx.triggered or not ctx.triggered[0]["value"]: - return dash.no_update - - triggered_id = ctx.triggered[0]["prop_id"].split(".")[0] - button_id = eval(triggered_id) - tab = button_id["tab"] - page = button_id["page"] - - # Find the matching table data by comparing both tab and page values - for i, table_data in enumerate(table_data_list): - if not table_data: + if input_id.get("type") != type_value: continue - - # Get the corresponding table ID - table_id = ctx.states_list[0][i]["id"] - - # Check if this table matches the clicked button's tab and page - if table_id["tab"] == tab and table_id["page"] == page: - df = pd.DataFrame(table_data) - filename = f"{page}_{tab}.csv" - return dcc.send_data_frame(df.to_csv, filename, index=False) - - return dash.no_update + if prefix is not None and input_id.get("prefix") != prefix: + continue + type_list.append(input) + return type_list diff --git a/morai/experience/charters.py b/morai/experience/charters.py index 4e48130..37e61be 100644 --- a/morai/experience/charters.py +++ b/morai/experience/charters.py @@ -42,6 +42,7 @@ def chart( x_bins: int | None = None, y_log: bool = False, add_line: bool = False, + add_total: bool = False, agg: str = "sum", display: bool = True, **kwargs: Any, @@ -88,6 +89,8 @@ def chart( Whether to log the y-axis. add_line : bool, optional (default=False) Whether to add a line to the chart at y-axis of 1. + add_total : bool, optional (default=False) + Whether to add a total line to the chart. agg : str, optional (default="sum") The aggregation to use for the y-axis. display : bool, optional (default=True) @@ -191,6 +194,24 @@ def chart( # return data if not display if not display: + if add_total: + # convert numeric col to string and then sum rows for total row + grouped_data[groupby_cols] = grouped_data[groupby_cols].astype(str) + total_row: dict = dict.fromkeys(groupby_cols, "Total") + total_row.update( + {agg_col: grouped_data[agg_col].sum() for agg_col in agg_cols} + ) + if use_num_and_den and y_axis in ["ratio", "risk"]: + if y_axis == "ratio": + total_row[y_axis] = total_row[numerator] / total_row[denominator] + else: + average_y_axis = (total_row[numerator] / total_row[denominator]) / ( + grouped_data[numerator].sum() / grouped_data[denominator].sum() + ) + total_row[y_axis] = average_y_axis + grouped_data = pd.concat( + [grouped_data, pd.DataFrame([total_row])], ignore_index=True + ) return grouped_data # Selecting the plot type based on the 'chart_type' parameter @@ -1086,9 +1107,9 @@ def pdp( if secondary: logger.info(f"Adding secondary to chart: [{secondary}]") - for index, line_color_value in enumerate(secondary_df[line_color].unique()): + for index, line_color_value in enumerate(pdp_df[line_color].unique()): color_index = index % num_colors - df_subset = secondary_df[secondary_df[line_color] == line_color_value] + df_subset = pdp_df[pdp_df[line_color] == line_color_value] fig.add_trace( go.Bar( x=df_subset[x_axis], diff --git a/morai/experience/credibility.py b/morai/experience/credibility.py index 2665418..ff878e0 100644 --- a/morai/experience/credibility.py +++ b/morai/experience/credibility.py @@ -1,9 +1,8 @@ """ Credibility measures. -Todo: ----- -- add in more resources including buhlmann credibility +Resources: +---------- - CREDIBILITY - HOWARD C. MAHLER AND CURTIS GARY DEAN - https://www.ressources-actuarielles.net/EXT/ISFA/1226.nsf/0/bf4517bb19eee4cec125704600554ce6/$FILE/chapter8.pdf - pymc @@ -26,6 +25,7 @@ from typing import TYPE_CHECKING +import pandas as pd from scipy import stats from morai.utils import custom_logger @@ -44,7 +44,7 @@ def limited_fluctuation( sd: float = 1, u: float = 1, groupby_cols: list[str] | None = None, -) -> pd.DataFrame: +) -> pd.Series: """ Determine the credibility of a measure based on limited fluctuation. @@ -99,8 +99,8 @@ def limited_fluctuation( Returns ------- - df : pd.DataFrame - DataFrame with additional columns for credibility measures. + credibility_lf : pd.Series + credibility of the measure based on limited fluctuation. """ # get the number of observations for full credibility @@ -109,7 +109,6 @@ def limited_fluctuation( logger.info( f"Credibility calculated using 'limited fluctuation' on '{measure}'.\n" f"Dataframe does not need to be seriatim.\n" - f"Created column 'credibility_lf'.\n" f"Full credibility threshold: {full_credibility:,.1f}\n" f"Probability measure within range: {p:,.1f}\n" f"Range +/-: {r:,.1f}\n" @@ -117,12 +116,15 @@ def limited_fluctuation( f"Mean: {u:,.1f}" ) if groupby_cols: - df = df.groupby(groupby_cols, observed=True)[[measure]].sum().reset_index() + measure_sum = df.groupby(groupby_cols, observed=True)[measure].transform("sum") + else: + measure_sum = df[measure] # calculate the credibility and cap at 1 - df["credibility_lf"] = ((df[measure] / full_credibility) ** 0.5).clip(upper=1) + credibility_lf = ((measure_sum / full_credibility) ** 0.5).clip(upper=1) + credibility_lf = credibility_lf.rename("credibility_lf") - return df + return credibility_lf def asymptotic( @@ -130,7 +132,7 @@ def asymptotic( measure: str, k: float = 270, groupby_cols: list[str] | None = None, -) -> pd.DataFrame: +) -> pd.Series: """ Determine the credibility of a measure using asymptotic credibility. @@ -172,31 +174,34 @@ def asymptotic( Returns ------- - df : pd.DataFrame - DataFrame with additional columns for credibility measures. + credibility_as : pd.Series + credibility of the measure based on asymptotic credibility. """ # calculate the credibility logger.info( f"Credibility calculated using 'asymptotic' on '{measure}'.\n" f"Dataframe does not need to be seriatim.\n" - f"Created column 'credibility_as'.\n" f"Constant k: {k}." ) if groupby_cols: - df = df.groupby(groupby_cols, observed=True)[[measure]].sum().reset_index() - df["credibility_as"] = df[measure] / (df[measure] + k) + measure_sum = df.groupby(groupby_cols, observed=True)[measure].transform("sum") + else: + measure_sum = df[measure] - return df + credibility_as = measure_sum / (measure_sum + k) + credibility_as = credibility_as.rename("credibility_as") + + return credibility_as def vm20_buhlmann( - df: pd.DataFrame, + seriatim_df: pd.DataFrame, amount_col: str, rate_col: str, exposure_col: str | None = None, groupby_cols: list[str] | None = None, -) -> pd.DataFrame: +) -> pd.Series: """ Determine the credibility of a measure using the SOA VM-20 method. @@ -235,7 +240,7 @@ def vm20_buhlmann( Parameters ---------- - df : pd.DataFrame + seriatim_df : pd.DataFrame DataFrame with the data. amount_col : str Column name of the 'amount' field. @@ -248,60 +253,58 @@ def vm20_buhlmann( Returns ------- - df : pd.DataFrame - DataFrame with additional columns for credibility measures. + credibility_vm20 : pd.Series + Credibility of the measure based on the SOA VM-20 method. """ logger.info( - "Credibility calculated using 'SOA VM-20'.\n" - "Created column 'credibility_vm20'.\n" - "Dataframe should be seriatim.\n" + "Credibility calculated using 'SOA VM-20'.\nDataframe should be seriatim.\n" ) - vm20_df = df.copy() - # check if the columns exist - if groupby_cols is None: - vm20_df["aggregate"] = "all" - groupby_cols = ["aggregate"] - - missing_cols = [ - col - for col in [amount_col, rate_col, *groupby_cols] - if col not in vm20_df.columns - ] + + # validate required columns + required = [amount_col, rate_col] + if exposure_col is not None: + required.append(exposure_col) + if groupby_cols: + required.extend(groupby_cols) + missing_cols = [col for col in required if col not in seriatim_df.columns] if missing_cols: raise ValueError( f"Missing columns: {', '.join(missing_cols)} in the DataFrame." ) # parameters + amount = seriatim_df[amount_col] + rate = seriatim_df[rate_col] if exposure_col is None: - logger.warning( - "Using approximation for credibility, due to the exposure " - "string was not provided." - ) - vm20_df["exposure"] = 1 + logger.warning("Exposure column was not provided; assuming exposure = 1.") + exposure = 1 + else: + exposure = seriatim_df[exposure_col] # calculate the vm20 parameters - vm20_df["a"] = vm20_df["amount"] * vm20_df["exposure"] * vm20_df["rate"] - vm20_df["b"] = vm20_df["amount"] ** 2 * vm20_df["exposure"] * vm20_df["rate"] - vm20_df["c"] = ( - vm20_df["amount"] ** 2 * vm20_df["exposure"] ** 2 * vm20_df["rate"] ** 2 - ) + a = amount * exposure * rate + b = amount**2 * exposure * rate + c = amount**2 * exposure**2 * rate**2 # group by and sum the vm20 parameters - vm20_df = ( - vm20_df.groupby(groupby_cols, observed=True)[["a", "b", "c"]] - .sum() - .reset_index() - ) + if groupby_cols: + group_keys = [seriatim_df[col] for col in groupby_cols] + a_sum = a.groupby(group_keys, observed=True).transform("sum") + b_sum = b.groupby(group_keys, observed=True).transform("sum") + c_sum = c.groupby(group_keys, observed=True).transform("sum") + else: + a_sum = pd.Series(a.sum(), index=seriatim_df.index) + b_sum = pd.Series(b.sum(), index=seriatim_df.index) + c_sum = pd.Series(c.sum(), index=seriatim_df.index) # calculate the credibility - vm20_df["credibility_vm20"] = vm20_df["a"] / ( - vm20_df["a"] - + ((1.090 * vm20_df["b"]) - (1.204 * vm20_df["c"])) / (0.019604 * vm20_df["a"]) + credibility_vm20 = a_sum / ( + a_sum + ((1.090 * b_sum) - (1.204 * c_sum)) / (0.019604 * a_sum) ) + credibility_vm20 = credibility_vm20.rename("credibility_vm20") - return vm20_df + return credibility_vm20 def vm20_buhlmann_approx( @@ -310,7 +313,7 @@ def vm20_buhlmann_approx( b_col: str, c_col: str, groupby_cols: list[str] | None = None, -) -> pd.DataFrame: +) -> pd.Series: """ Determine the credibility of a measure using an SOA VM-20 approximation. @@ -354,50 +357,42 @@ def vm20_buhlmann_approx( Returns ------- - df : pd.DataFrame - DataFrame with additional columns for credibility measures. + credibility_vm20_approx : pd.Series + Series with credibility values. """ logger.info( "Credibility calculated using 'SOA VM-20 approximation'.\n" - "Created column 'credibility_vm20_approx'.\n" "Dataframe does not need to be seriatim.\n" ) - vm20_df = df.copy() - # check if the columns exist - if groupby_cols is None: - vm20_df["aggregate"] = "all" - groupby_cols = ["aggregate"] - - missing_cols = [ - col - for col in [a_col, b_col, c_col, *groupby_cols] - if col not in vm20_df.columns - ] + # validate required columns + required = [a_col, b_col, c_col] + if groupby_cols: + required.extend(groupby_cols) + missing_cols = [col for col in required if col not in df.columns] if missing_cols: raise ValueError( f"Missing columns: {', '.join(missing_cols)} in the DataFrame." ) - # calculate the vm20 parameters - vm20_df["a"] = vm20_df[a_col] - vm20_df["b"] = vm20_df[b_col] - vm20_df["c"] = vm20_df[c_col] - # group by and sum the vm20 parameters - vm20_df = ( - vm20_df.groupby(groupby_cols, observed=True)[["a", "b", "c"]] - .sum() - .reset_index() - ) + if groupby_cols: + grouped = df.groupby(groupby_cols, observed=True) + a_sum = grouped[a_col].transform("sum") + b_sum = grouped[b_col].transform("sum") + c_sum = grouped[c_col].transform("sum") + else: + a_sum = pd.Series(df[a_col].sum(), index=df.index) + b_sum = pd.Series(df[b_col].sum(), index=df.index) + c_sum = pd.Series(df[c_col].sum(), index=df.index) # calculate the credibility - vm20_df["credibility_vm20_approx"] = vm20_df["a"] / ( - vm20_df["a"] - + ((1.090 * vm20_df["b"]) - (1.204 * vm20_df["c"])) / (0.019604 * vm20_df["a"]) + credibility_vm20_approx = a_sum / ( + a_sum + ((1.090 * b_sum) - (1.204 * c_sum)) / (0.019604 * a_sum) ) + credibility_vm20_approx = credibility_vm20_approx.rename("credibility_vm20_approx") - return vm20_df + return credibility_vm20_approx def buhlmann( @@ -446,19 +441,23 @@ def buhlmann( Returns ------- - df : pd.DataFrame - DataFrame with additional columns for credibility measures. + df : pd.Series + Credibility of the measure based on Bühlmann credibility. """ # calculate the credibility logger.info( f"Credibility calculated using 'bühlmann' on '{measure}'.\n" f"Dataframe does not need to be seriatim.\n" - f"Created column 'credibility_bu'.\n" f"Constant k: {k}." ) + if groupby_cols: - df = df.groupby(groupby_cols, observed=True)[[measure]].sum().reset_index() - df["credibility_bu"] = df[measure] / (df[measure] + k) + measure_sum = df.groupby(groupby_cols, observed=True)[measure].transform("sum") + else: + measure_sum = df[measure] + + credibility_bu = measure_sum / (measure_sum + k) + credibility_bu = credibility_bu.rename("credibility_bu") - return df + return credibility_bu diff --git a/morai/experience/experience.py b/morai/experience/experience.py index cf1aec0..c205b7a 100644 --- a/morai/experience/experience.py +++ b/morai/experience/experience.py @@ -12,8 +12,10 @@ import numpy as np import pandas as pd import polars as pl +from scipy import stats from morai.utils import custom_logger +from morai.utils.custom_logger import suppress_logs logger = custom_logger.setup_logging(__name__) @@ -29,6 +31,7 @@ def create_study( exposure_method: str = "annual", calendar_exposure: bool = True, get_actuals: bool = True, + amount_col: str | None = None, ) -> pd.DataFrame: """ Create an experience study including exposures and actuals. @@ -54,7 +57,8 @@ def create_study( "quarterly", "monthly", "weekly", or "daily" mapping : dict, optional default=None Mapping for the column names if they differ from the expected column names - (termination_date, termination_reason, issue_date, bos_date, eos_date). + (termination_date, termination_reason, issue_date, bos_date, eos_date) + (exposure_cnt, actuals_cnt). get_exposures : bool, optional default=True Wether to add exposures to the study exposure_method : str, optional default="annual" @@ -63,6 +67,8 @@ def create_study( Whether to use calendar year days (365/366) or policy year days as denominator. get_actuals : bool, optional default=True Wether to add actuals to the study + amount_col : str, optional default=None + Name of the amount column to use in exposure and actual calculations Returns ------- @@ -78,6 +84,24 @@ def create_study( on/after the study period (before, on_after) """ + # default column names + actuals_cnt_col = "actual_cnt" + exposures_cnt_col = "exposure_cnt" + actuals_amt_col = "actual_amt" + exposures_amt_col = "exposure_amt" + + # validate + if amount_col and amount_col not in df.columns: + raise ValueError(f"`{amount_col}` column not in the DataFrame.") + + # handle mapping + if mapping: + actuals_cnt_col = mapping.get("actual_cnt", actuals_cnt_col) + exposures_cnt_col = mapping.get("exposure_cnt", exposures_cnt_col) + actuals_amt_col = mapping.get("actual_amt", actuals_amt_col) + exposures_amt_col = mapping.get("exposure_amt", exposures_amt_col) + + # format the study df study_df = format_study_df( df=df, bos=bos, @@ -87,17 +111,23 @@ def create_study( mapping=mapping, ) if get_exposures: - study_df["exposure"] = calc_exposures( + study_df[exposures_cnt_col] = calc_exposures( study_df=study_df, study_decrement=study_decrement, exposure_method=exposure_method, calendar_exposure=calendar_exposure, mapping=mapping, ) + if amount_col: + study_df[exposures_amt_col] = ( + study_df[exposures_cnt_col] * study_df[amount_col] + ) if get_actuals: - study_df["actuals"] = calc_actuals( + study_df[actuals_cnt_col] = calc_actuals( study_df=study_df, study_decrement=study_decrement, mapping=mapping ) + if amount_col: + study_df[actuals_amt_col] = study_df[actuals_cnt_col] * study_df[amount_col] return study_df @@ -317,9 +347,11 @@ def calc_exposures( - annual: before gets proportional days, after gets a full year (Balducci) - distributed: before gets proportional days, after gets proportional days (UDD) - exact: both before and after get exact days to decrement (constant force) - - mx ≈ ux - - qx = 1 - exp(-ux) - - ux = -log(1-ux) + - A central rate (mx) can be used to approximate initial rate (qx) using + the average force of mortality (ux). + - mx ≈ ux + - qx = 1 - exp(-ux) + - ux = -log(1-qx) Expects the DataFrame to already have these columns: - termination_date, termination_reason, issue_date @@ -681,70 +713,257 @@ def calc_actuals( return actuals -def _get_study_periods( - bos_date: pd.Timestamp, - eos_date: pd.Timestamp, - study_frequency: str = "annually", -) -> list[Any]: +def calc_variance( + seriatim_df: pd.DataFrame, + rate_col: str, + exposure_col: str, + amount_col: str | None = None, +) -> pd.Series: """ - Generate study periods between bos and eos based on frequency. + Calculate the variance of a binomial distribution. - Uses pandas date_range to generate the start dates of the periods and - then calculates the end dates using an offset. + variance = amount^2 * exposure * rate * (1 - rate) + + Notes + ----- + Needs to be based on seriatim data and not aggregated data. + + Reference + --------- + https://www.soa.org/resources/tables-calcs-tools/table-development/ + page 59 Parameters ---------- - bos_date : pd.Timestamp - Beginning of study date. - eos_date : pd.Timestamp - End of study date. - study_frequency : str, optional - Frequency of study periods. Default is "annually". + seriatim_df : pd.DataFrame + DataFrame with the data. + rate_col : str + Column name of the rate. + exposure_col : str + Column name of the exposure, should be between 0 and 1. + amount_col : str, optional + Column name of the face amount. Returns ------- - periods : list - list of (bos, eos) tuples for each period. + variance : pd.Series + Series with the variance values. """ - freq_map = { - "annually": "YS", - "semi-annually": "6MS", - "quarterly": "QS", - "monthly": "MS", - "weekly": "W-MON", - "daily": "D", - } + # validations + # check the columns exist + required_cols = [rate_col, exposure_col] + if amount_col is not None: + required_cols.append(amount_col) + missing_cols = [col for col in required_cols if col not in seriatim_df.columns] + if missing_cols: + raise ValueError( + f"Missing columns: {', '.join(missing_cols)} in the DataFrame." + ) + # exposure should be between 0 and 1 + if not ((seriatim_df[exposure_col] >= 0) & (seriatim_df[exposure_col] <= 1)).all(): + logger.warning(f"Exposure column '{exposure_col}' has values outside of [0, 1]") - date_offset_kwargs = { - "annually": {"years": 1}, - "semi-annually": {"months": 6}, - "quarterly": {"months": 3}, - "monthly": {"months": 1}, - "weekly": {"weeks": 1}, - } + # calculate the variance + amount = 1 if amount_col is None else seriatim_df[amount_col] + variance = ( + amount**2 + * seriatim_df[exposure_col] + * seriatim_df[rate_col] + * (1 - seriatim_df[rate_col]) + ) - if study_frequency not in freq_map: + return variance + + +def calc_confidence_intervals( + summary_df: pd.DataFrame, + estimate_col: str, + variance_col: str, + confidence_level: float = 0.95, +) -> tuple[pd.Series, pd.Series]: + """ + Calculate the confidence intervals for a rate based on the variance. + + confidence_interval = z_score * sqrt(variance) + + Notes + ----- + Needs to be based on aggregated data and not seriatim data. + + Parameters + ---------- + summary_df : pd.DataFrame + DataFrame with the data. + estimate_col : str + Column name of the estimate, for example the rate. + variance_col : str + Column name of the variance. + confidence_level : float, optional + Confidence level for the intervals, default is 0.95. + + Returns + ------- + lower_bound, upper_bound : pd.Series + Series with the lower and upper bounds of the confidence intervals. + + """ + # check the column exists + if variance_col not in summary_df.columns: + raise ValueError(f"Column {variance_col} not found in DataFrame.") + + # calculate the confidence intervals + z_score = stats.norm.ppf(1 - (1 - confidence_level) / 2) + logger.info( + f"Confidence Intervals - z-score: {z_score:.2f}, " + f"confidence level: {confidence_level:.0%}" + ) + margin_of_error = z_score * np.sqrt(summary_df[variance_col]) + lower_bound = summary_df[estimate_col] - margin_of_error + upper_bound = summary_df[estimate_col] + margin_of_error + + return lower_bound, upper_bound + + +def summarize_study( + study_df: pd.DataFrame, + groupby_cols: list, + expecteds: list, + suffixes: list, + ratios: list, + ci: bool = False, + mapping: dict | None = None, +) -> pd.DataFrame: + """ + Summarize the study DataFrame. + + It is expected that there will be a naming convention for + actuals, exposures, expecteds, and variances. + - naming convention: + - actuals_ + - exposures_ + - expected__ + - variance__ + + Parameters + ---------- + study_df : pd.DataFrame + DataFrame with the data. + groupby_cols : list + List of columns to group by. + expecteds : list + List of expected versions to calculate rates for. + suffixes : list + List of suffixes for the actuals and exposures columns + for example ["cnt", "amt"] + ratios : list + List of ratios to calculate + for example ["ae", "ao"] + ci : bool, optional default=True + Whether to calculate confidence intervals for the rates. + mapping : dict, optional default=None + Mapping for the column names if they differ from the expected column names. + + Returns + ------- + summary_df : pd.DataFrame + DataFrame with the summarized data. + + """ + # default column names + actuals_prefix = "actual" + exposures_prefix = "exposure" + expecteds_prefix = "expected" + variance_prefix = "variance" + + # handle mapping + if mapping: + actuals_prefix = mapping.get("actuals_prefix", actuals_prefix) + exposures_prefix = mapping.get("exposures_prefix", exposures_prefix) + expecteds_prefix = mapping.get("expecteds_prefix", expecteds_prefix) + variance_prefix = mapping.get("variance_prefix", variance_prefix) + + # gather columns + actual_cols = [f"{actuals_prefix}_{suffix}" for suffix in suffixes] + exposure_cols = [f"{exposures_prefix}_{suffix}" for suffix in suffixes] + expected_cols = [ + f"{expecteds_prefix}_{version}_{suffix}" + for version in expecteds + for suffix in suffixes + ] + variance_cols = [ + f"{variance_prefix}_{version}_{suffix}" + for version in expecteds + for suffix in suffixes + ] + + # validations + # columns exist + required_cols = actual_cols + exposure_cols + expected_cols + if ci and "ae" in ratios: + required_cols += variance_cols + missing_cols = [col for col in required_cols if col not in study_df.columns] + if missing_cols: raise ValueError( - f"Unsupported frequency: {study_frequency}, " - f"supported frequencies are: {', '.join(freq_map.keys())}." + f"Missing columns: {', '.join(missing_cols)} in the DataFrame. " + f"Please check the column names and the mapping provided." ) - if study_frequency == "daily": - return [(d, d) for d in pd.date_range(bos_date, eos_date, freq="D")] - - starts = pd.date_range(bos_date, eos_date, freq=freq_map[study_frequency]) - periods = [] - for start in starts: - end = min( - start - + pd.tseries.offsets.DateOffset(**date_offset_kwargs[study_frequency]) - - pd.Timedelta(days=1), - eos_date, + # ratios are valid + invalid_ratios = [ratio for ratio in ratios if ratio not in ["ae", "ao"]] + if invalid_ratios: + raise ValueError( + f"Invalid ratios: {', '.join(invalid_ratios)}. " + f"Valid options are 'ae' and 'ao'." ) - periods.append((start, end)) - return periods + # summarize data + logger.info("creating summarized study") + logger.info(f"summing: `{', '.join(required_cols)}`") + logger.info(f"calculating ratios: `{', '.join(ratios)}`") + summary_df = ( + study_df.groupby(groupby_cols, observed=True, sort=False) + .agg(dict.fromkeys(required_cols, "sum")) + .reset_index() + ) + + # calculate ratios + for ratio in ratios: + for version in expecteds: + for suffix in suffixes: + expected_col = f"{expecteds_prefix}_{version}_{suffix}" + actual_col = f"{actuals_prefix}_{suffix}" + exposure_col = f"{exposures_prefix}_{suffix}" + if ratio == "ae": + ratio_col = f"{ratio}_{version}_{suffix}" + summary_df[ratio_col] = ( + summary_df[actual_col] / summary_df[expected_col] + ) + if ci: + variance_col = f"{variance_prefix}_{version}_{suffix}" + lower_col = f"ci_lower_ae_{version}_{suffix}" + upper_col = f"ci_upper_ae_{version}_{suffix}" + + expected_lower, expected_upper = suppress_logs( + calc_confidence_intervals + )( + summary_df=summary_df, + estimate_col=expected_col, + variance_col=variance_col, + ) + summary_df[lower_col] = ( + expected_lower / summary_df[expected_col] + ) + summary_df[upper_col] = ( + expected_upper / summary_df[expected_col] + ) + elif ratio == "ao": + ratio_col = f"{ratio}_{suffix}" + summary_df[ratio_col] = ( + summary_df[actual_col] / summary_df[exposure_col] + ) + + return summary_df def normalize( @@ -1205,59 +1424,8 @@ def calc_relative_risk( return df -def calc_variance( - df: pd.DataFrame, - rate_col: str, - exposure_col: str, - amount_col: str | None = None, -) -> pd.Series: - """ - Calculate the variance of a binomial distribution. - - variance = amount^2 * exposure * rate * (1 - rate) - - Notes - ----- - Needs to be based on seriatim data and not aggregated data. - - Reference - --------- - https://www.soa.org/resources/tables-calcs-tools/table-development/ - page 59 - - Parameters - ---------- - df : pd.DataFrame - DataFrame with the data. - rate_col : str - Column name of the rate. - exposure_col : str - Column name of the exposure. - amount_col : str, optional - Column name of the face amount. - - Returns - ------- - variance : pd.Series - Series with the variance values. - - """ - # check the columns exist - missing_cols = [col for col in [rate_col, exposure_col] if col not in df.columns] - if missing_cols: - raise ValueError( - f"Missing columns: {', '.join(missing_cols)} in the DataFrame." - ) - amount = 1 if amount_col is None else df[amount_col] - - # calculate the variance - variance = amount**2 * df[exposure_col] * df[rate_col] * (1 - df[rate_col]) - - return variance - - def calc_moments( - df: pd.DataFrame, + seriatim_df: pd.DataFrame, rate_col: str, exposure_col: str, amount_col: str | None = None, @@ -1291,7 +1459,7 @@ def calc_moments( Parameters ---------- - df : pd.DataFrame + seriatim_df : pd.DataFrame DataFrame with the data. rate_col : str Column name of the rate. @@ -1310,7 +1478,9 @@ def calc_moments( """ # check the columns exist - missing_cols = [col for col in [rate_col, exposure_col] if col not in df.columns] + missing_cols = [ + col for col in [rate_col, exposure_col] if col not in seriatim_df.columns + ] if missing_cols: raise ValueError( f"Missing columns: {', '.join(missing_cols)} in the DataFrame." @@ -1320,28 +1490,28 @@ def calc_moments( else: sffx = f"_{sffx}" logger.info(f"Adding the label: '{sffx}' to the moment columns.") - amount = 1 if amount_col is None else df[amount_col] + amount = 1 if amount_col is None else seriatim_df[amount_col] # calculate the moments logger.info( "Calculating moments for the binomial distribution, need to be seriatim data." ) - moment_1 = amount * df[exposure_col] * df[rate_col] - moment_2_p1 = amount**2 * df[exposure_col] * df[rate_col] - moment_2_p2 = amount**2 * df[exposure_col] * df[rate_col] ** 2 - moment_3_p1 = amount**3 * df[exposure_col] * df[rate_col] - moment_3_p2 = amount**3 * df[exposure_col] * df[rate_col] ** 2 - moment_3_p3 = amount**3 * df[exposure_col] * df[rate_col] ** 3 + moment_1 = amount * seriatim_df[exposure_col] * seriatim_df[rate_col] + moment_2_p1 = amount**2 * seriatim_df[exposure_col] * seriatim_df[rate_col] + moment_2_p2 = amount**2 * seriatim_df[exposure_col] * seriatim_df[rate_col] ** 2 + moment_3_p1 = amount**3 * seriatim_df[exposure_col] * seriatim_df[rate_col] + moment_3_p2 = amount**3 * seriatim_df[exposure_col] * seriatim_df[rate_col] ** 2 + moment_3_p3 = amount**3 * seriatim_df[exposure_col] * seriatim_df[rate_col] ** 3 # add the moments to the dataframe - df[f"moment{sffx}_1"] = moment_1 - df[f"moment{sffx}_2_p1"] = moment_2_p1 - df[f"moment{sffx}_2_p2"] = moment_2_p2 - df[f"moment{sffx}_3_p1"] = moment_3_p1 - df[f"moment{sffx}_3_p2"] = moment_3_p2 - df[f"moment{sffx}_3_p3"] = moment_3_p3 + seriatim_df[f"moment{sffx}_1"] = moment_1 + seriatim_df[f"moment{sffx}_2_p1"] = moment_2_p1 + seriatim_df[f"moment{sffx}_2_p2"] = moment_2_p2 + seriatim_df[f"moment{sffx}_3_p1"] = moment_3_p1 + seriatim_df[f"moment{sffx}_3_p2"] = moment_3_p2 + seriatim_df[f"moment{sffx}_3_p3"] = moment_3_p3 - return df + return seriatim_df def calc_qx_exp_ae( @@ -1387,3 +1557,69 @@ def calc_qx_exp_ae( ), ) return model_data + + +def _get_study_periods( + bos_date: pd.Timestamp, + eos_date: pd.Timestamp, + study_frequency: str = "annually", +) -> list[Any]: + """ + Generate study periods between bos and eos based on frequency. + + Uses pandas date_range to generate the start dates of the periods and + then calculates the end dates using an offset. + + Parameters + ---------- + bos_date : pd.Timestamp + Beginning of study date. + eos_date : pd.Timestamp + End of study date. + study_frequency : str, optional + Frequency of study periods. Default is "annually". + + Returns + ------- + periods : list + list of (bos, eos) tuples for each period. + + """ + freq_map = { + "annually": "YS", + "semi-annually": "6MS", + "quarterly": "QS", + "monthly": "MS", + "weekly": "W-MON", + "daily": "D", + } + + date_offset_kwargs = { + "annually": {"years": 1}, + "semi-annually": {"months": 6}, + "quarterly": {"months": 3}, + "monthly": {"months": 1}, + "weekly": {"weeks": 1}, + } + + if study_frequency not in freq_map: + raise ValueError( + f"Unsupported frequency: {study_frequency}, " + f"supported frequencies are: {', '.join(freq_map.keys())}." + ) + + if study_frequency == "daily": + return [(d, d) for d in pd.date_range(bos_date, eos_date, freq="D")] + + starts = pd.date_range(bos_date, eos_date, freq=freq_map[study_frequency]) + periods = [] + for start in starts: + end = min( + start + + pd.tseries.offsets.DateOffset(**date_offset_kwargs[study_frequency]) + - pd.Timedelta(days=1), + eos_date, + ) + periods.append((start, end)) + + return periods diff --git a/morai/experience/tables.py b/morai/experience/tables.py index da60287..84ba214 100644 --- a/morai/experience/tables.py +++ b/morai/experience/tables.py @@ -1,4 +1,15 @@ -"""Mortality Table Builder.""" +""" +Rate Table Builder. + +Provides a generic `RateTable` for any 1-d rate table (e.g. termination, +lapse, or any other actuarial rate) and a mortality-specific subclass +`MortTable` that adds SOA table support and mortality improvement (MI). + +A 1-d table has a 'vals' column with all other columns as features/keys. + +RateTable supports rate types: csv, workbook +MortTable additionally supports: soa +""" from __future__ import annotations @@ -6,7 +17,7 @@ import itertools import re from pathlib import Path -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, ClassVar import numpy as np import pandas as pd @@ -26,38 +37,46 @@ from xml.etree.ElementTree import Element -class MortTable: +class RateTable: """ - A mortality table class that can be used to build a 1-d mortality table. + A generic rate table class that can be used to build a 1-d rate table. - There are a number of functions in the class including: - - build_table: build a 1-d mortality table from a list of tables - - get_soa_xml: get the soa xml object + A 1-d table has a 'vals' column and all other columns are the features/keys + (e.g. age, duration, sex). This base class supports loading rates from + csv files and Excel workbooks. For mortality-specific functionality + (SOA tables, mortality improvement) use `MortTable`. + + Subclasses can extend the supported rate types by extending the + `_builders` class attribute with additional `{rate_type: method_name}` + entries. """ + # allowed rate_types + _builders: ClassVar = { + "csv": "_build_from_csv", + "workbook": "_build_from_workbook", + } + def __init__( self, - rate: pd.DataFrame | None = None, + rate: str | None = None, rate_filename: str | Path | None = None, ) -> None: """ - Initialize the Table class. + Initialize the RateTable. Parameters ---------- rate : str, optional (default=None) - A rate to use for the table. The rate can be "vbt15". + The rate to use for the table, looked up in the rate mapping file. rate_filename : str, optional (default=None) - The filename of the rate map. default name is rate_map.yaml. + The filename of the rate map. Default name is rate_map.yaml. """ - self.rate_table = None - self.mult_table = None - self.mi_table = None - self.rate_dict = None - self.rate_name = None - self.select_period: int | None = None - self.max_age = 121 + self.rate_table: pd.DataFrame | None = None + self.mult_table: pd.DataFrame | None = None + self.rate_dict: dict[str, Any] | None = None + self.rate_name: str | None = None if rate_filename is None: rate_filename = "rate_map.yaml" @@ -65,42 +84,54 @@ def __init__( if rate: self.rate_dict = get_rate_dict(rate, rate_filename) rate_type = next(iter(self.rate_dict["type"].keys())) - col_keys = self.rate_dict["keys"] + ["vals"] logger.info(f"building table for rate: '{rate}' with format: '{rate_type}'") self.rate_name = f"qx_{self.rate_dict['rate']}" - if rate_type == "soa": - self.rate_table = self.build_table_soa( - table_list=self.rate_dict["type"]["soa"]["table_list"], - extra_dims=self.rate_dict["type"]["soa"]["extra_dims"], - juv_list=self.rate_dict["type"]["soa"]["juv_list"], - extend=self.rate_dict["type"]["soa"]["extend"], - ) - elif rate_type == "csv": - csv_location = get_filepath(self.rate_dict["type"]["csv"]["filename"]) - # read in the csv - try: - self.rate_table = pd.read_csv(csv_location, usecols=col_keys) - except ValueError as ve: - raise ValueError(f"Error reading csv: {csv_location}. ") from ve - elif rate_type == "workbook": - workbook_location = get_filepath( - self.rate_dict["type"]["workbook"]["filename"] - ) - self.rate_table, self.mult_table = self.build_table_workbook( - file_location=workbook_location, - has_mults=self.rate_dict["type"]["workbook"]["mult_table"], + + builder_name = self._builders.get(rate_type) + if builder_name is None: + supported = sorted(self._builders.keys()) + raise ValueError( + f"rate type '{rate_type}' is not supported by " + f"{type(self).__name__}. Supported types: {supported}." ) + builder = getattr(self, builder_name) + builder() - # get mi_table - mi_filename = self.rate_dict.get("mi_table", {}).get("mi_filename") - if mi_filename: - self.mi_table = self.get_mi_table(mi_filename) + # allow subclasses to do post-build work (e.g. load mi_table) + self._post_init() + + def _post_init(self) -> None: + """Post initialization work.""" + return + + # ------------------------------------------------------------------ # + # builders # + # ------------------------------------------------------------------ # + + def _build_from_csv(self) -> None: + """Build the rate_table from a csv as specified in rate_dict.""" + assert self.rate_dict is not None + col_keys = self.rate_dict["keys"] + ["vals"] + csv_location = get_filepath(self.rate_dict["type"]["csv"]["filename"]) + try: + self.rate_table = pd.read_csv(csv_location, usecols=col_keys) + except ValueError as ve: + raise ValueError(f"Error reading csv: {csv_location}. ") from ve + + def _build_from_workbook(self) -> None: + """Build the rate_table (and mult_table) from a workbook.""" + assert self.rate_dict is not None + workbook_location = get_filepath(self.rate_dict["type"]["workbook"]["filename"]) + self.rate_table, self.mult_table = self.build_table_workbook( + file_location=workbook_location, + has_mults=self.rate_dict["type"]["workbook"]["mult_table"], + ) def build_table_workbook( self, file_location: str | Path, has_mults: bool = False - ) -> tuple[pd.DataFrame, pd.DataFrame]: + ) -> tuple[pd.DataFrame, pd.DataFrame | None]: """ - Build a 1-d mortality table from a workbook. + Build a 1-d rate table from a workbook. A 1-d table is where there is a 'vals' column and all the other columns are the features. If the workbook has a multiplier table, then the multiplier table will @@ -147,6 +178,227 @@ def build_table_workbook( return rate_table, mult_table + # ------------------------------------------------------------------ # + # derived tables # + # ------------------------------------------------------------------ # + + def calc_derived_table_from_mults( + self, + selected_dict: dict[str, list] | None = None, + keep_mult: bool = False, + rate_table: pd.DataFrame | None = None, + mult_table: pd.DataFrame | None = None, + ) -> pd.DataFrame: + """ + Calculate a derived rate table from the rate table and multiplier table. + + The derived table is calculated by multiplying the rate table by the + multiplier table given the selected multiplier columns. + + Parameters + ---------- + selected_dict : dict, optional (default=None) + The selected multiplier columns. + If None, then the first subcategory multiplier of each category + will be used. + e.g. + { + "category": ["subcategory"], + "category2": ["subcategory2"], + } + keep_mult : bool, optional (default=False) + Whether to keep the mult column in the derived table. + rate_table : pd.DataFrame, optional (default=None) + The rate table to use for calculating the derived table. + mult_table : pd.DataFrame, optional (default=None) + The multiplier table to use for calculating the derived table. + + Returns + ------- + derived_table : pd.DataFrame + The derived table. + + """ + if rate_table is None: + rate_table = self.rate_table + if mult_table is None: + mult_table = self.mult_table + if rate_table is None or mult_table is None: + raise ValueError( + "calc_derived_table_from_mults requires the rate table and " + "multiplier table to be set." + ) + + # get subcategory multipliers if not provided + if selected_dict is None: + first_mults = mult_table.groupby("category").first().reset_index() + selected_dict = ( + first_mults.set_index("category")["subcategory"] + .apply(lambda x: [x]) + .to_dict() + ) + + # select the rows in mult_table that match the selected mults + selected_mults = mult_table[ + mult_table.apply( + lambda row: ( + row["subcategory"] in selected_dict.get(row["category"], []) + ), + axis=1, + ) + ] + selected_mults_grade = [] + selected_mults_mult = [] + + # calculate the multiplier and grade if exists + derived_table = rate_table.copy() + derived_table["_mult"] = 1 + for _, row in selected_mults.iterrows(): + if "grade" in row and not pd.isna(row["grade"]): + derived_table["_mult"] *= _formula_grade( + df=derived_table, + multiple=row["multiple"], + formula=row["grade"], + ) + selected_mults_grade.append(row["subcategory"]) + else: + derived_table["_mult"] *= row["multiple"] + selected_mults_mult.append(row["subcategory"]) + + logger.info( + f"derived table average multiplier: `{derived_table['_mult'].mean():.2f}`" + ) + if selected_mults_mult: + logger.info( + f"used the following subcategories with mult: `{selected_mults_mult}`" + ) + if selected_mults_grade: + logger.info( + f"used the following subcategories with grade: `{selected_mults_grade}`" + ) + + # apply the multiplier + derived_table["vals"] = derived_table["vals"] * derived_table["_mult"] + if not keep_mult: + derived_table = derived_table.drop(columns=["_mult"]) + + return derived_table + + # ------------------------------------------------------------------ # + # internal helpers # + # ------------------------------------------------------------------ # + + def _merge_tables( + self, + merge_table: pd.DataFrame, + source_table: pd.DataFrame, + merge_keys: list[str], + column_rename: str, + extra_dims_list: list[tuple[str, Any]] | None = None, + ) -> pd.DataFrame: + """ + Merge the source table into the merge table. + + Handles: + - rename 'vals' column to a unique name during the merge + - inject extra dimension values onto the source table + - fill the merge_table 'vals' column from the merged source values + + Parameters + ---------- + merge_table : pd.DataFrame + The table to merge into. + source_table : pd.DataFrame + The table to merge from. + merge_keys : list + The keys to merge on. + column_rename : str + The column to rename. + extra_dims_list : list, optional (default=None) + A list of tuples of extra dimensions to merge. + + Returns + ------- + merge_table : pd.DataFrame + The merged table. + + """ + if extra_dims_list is None: + extra_dims_list = [] + source_table = source_table.rename(columns={"vals": column_rename}) + for dim_name, dim_value in extra_dims_list: + source_table[dim_name] = dim_value + merge_table = merge_table.merge(source_table, on=merge_keys, how="left") + merge_table["vals"] = ( + merge_table["vals"].astype(float).fillna(merge_table[column_rename]) + ) + merge_table = merge_table.drop(columns=column_rename) + return merge_table + + +class MortTable(RateTable): + """ + A mortality table class that builds a 1-d mortality table. + + Extends `RateTable` with mortality-specific functionality: + - Building tables from SOA mortality table ids (mort.soa.org) + - Mortality improvement (MI) loading and application + - Select-and-ultimate handling + - Default max_age = 121 + + Inherits csv and workbook rate loading from `RateTable`. + """ + + # extend the base registry with the soa builder + _builders: ClassVar[dict[str, str]] = { + **RateTable._builders, + "soa": "_build_from_soa", + } + + def __init__( + self, + rate: str | None = None, + rate_filename: str | Path | None = None, + ) -> None: + """ + Initialize the MortTable. + + Parameters + ---------- + rate : str, optional (default=None) + A rate to use for the table. The rate can be e.g. "vbt15". + rate_filename : str, optional (default=None) + The filename of the rate map. Default name is rate_map.yaml. + + """ + # mortality-specific state, set before super().__init__ so that + # builders called during __init__ can read them + self.mi_table: pd.DataFrame | None = None + self.select_period: int | None = None + self.max_age: int = 121 + super().__init__(rate=rate, rate_filename=rate_filename) + + def _post_init(self) -> None: + """Load the mi_table after the rate table is built, if configured.""" + assert self.rate_dict is not None + mi_filename = self.rate_dict.get("mi_table", {}).get("mi_filename") + if mi_filename: + self.mi_table = self.get_mi_table(mi_filename) + + # ------------------------------------------------------------------ # + # soa builder # + # ------------------------------------------------------------------ # + + def _build_from_soa(self) -> None: + """Build the rate_table from a list of SOA table ids.""" + assert self.rate_dict is not None + self.rate_table = self.build_table_soa( + table_list=self.rate_dict["type"]["soa"]["table_list"], + extra_dims=self.rate_dict["type"]["soa"]["extra_dims"], + juv_list=self.rate_dict["type"]["soa"]["juv_list"], + extend=self.rate_dict["type"]["soa"]["extend"], + ) + def build_table_soa( self, table_list: list[int], @@ -348,6 +600,10 @@ def get_soa_xml(self, table_id: int) -> Any: soa_xml = pymort.MortXML.from_id(table_id) return soa_xml + # ------------------------------------------------------------------ # + # mortality improvement # + # ------------------------------------------------------------------ # + def get_mi_table(self, filename: str) -> Any: """ Get the MI table from a file. @@ -460,154 +716,9 @@ def apply_mi_to_rate_table( return rate_table - def calc_derived_table_from_mults( - self, - selected_dict: dict[str, list] | None = None, - keep_mult: bool = False, - rate_table: pd.DataFrame | None = None, - mult_table: pd.DataFrame | None = None, - ) -> pd.DataFrame: - """ - Calculate a derived rate table from the rate table and multiplier table. - - The derived table is calculated by multiplying the rate table by the - multiplier table given the selected multiplier columns. - - Parameters - ---------- - selected_dict : dict, optional (default=None) - The selected multiplier columns. - If None, then the first subcategory multiplier of each category - will be used. - e.g. - { - "category": ["subcategory"], - "category2": ["subcategory2"], - } - keep_mult : bool, optional (default=False) - Whether to keep the mult column in the derived table. - rate_table : pd.DataFrame, optional (default=None) - The rate table to use for calculating the derived table. - mult_table : pd.DataFrame, optional (default=None) - The multiplier table to use for calculating the derived table. - - Returns - ------- - derived_table : pd.DataFrame - The derived table. - - """ - if rate_table is None: - rate_table = self.rate_table - if mult_table is None: - mult_table = self.mult_table - if rate_table is None or mult_table is None: - raise ValueError( - "calc_derived_table_from_mults requires the rate table and " - "multiplier table to be set." - ) - - # get subcategory multipliers if not provided - if selected_dict is None: - first_mults = mult_table.groupby("category").first().reset_index() - selected_dict = ( - first_mults.set_index("category")["subcategory"] - .apply(lambda x: [x]) - .to_dict() - ) - - # select the rows in mult_table that match the selected mults - selected_mults = mult_table[ - mult_table.apply( - lambda row: ( - row["subcategory"] in selected_dict.get(row["category"], []) - ), - axis=1, - ) - ] - selected_mults_grade = [] - selected_mults_mult = [] - - # calculate the multiplier and grade if exists - derived_table = rate_table.copy() - derived_table["_mult"] = 1 - for _, row in selected_mults.iterrows(): - if "grade" in row and not pd.isna(row["grade"]): - derived_table["_mult"] *= _formula_grade( - df=derived_table, - multiple=row["multiple"], - formula=row["grade"], - ) - selected_mults_grade.append(row["subcategory"]) - else: - derived_table["_mult"] *= row["multiple"] - selected_mults_mult.append(row["subcategory"]) - - logger.info( - f"derived table average multiplier: `{derived_table['_mult'].mean():.2f}`" - ) - if selected_mults_mult: - logger.info( - f"used the following subcategories with mult: `{selected_mults_mult}`" - ) - if selected_mults_grade: - logger.info( - f"used the following subcategories with grade: `{selected_mults_grade}`" - ) - - # apply the multiplier - derived_table["vals"] = derived_table["vals"] * derived_table["_mult"] - if not keep_mult: - derived_table = derived_table.drop(columns=["_mult"]) - - return derived_table - - def _merge_tables( - self, - merge_table: pd.DataFrame, - source_table: pd.DataFrame, - merge_keys: list[str], - column_rename: str, - extra_dims_list: list[tuple[str, Any]] | None = None, - ) -> pd.DataFrame: - """ - Merge the source table into the merge table. - - This is specifically for the MortTable class as it will handle - - rename 'vals' column - - handle extra dimensions - - check for missing values - - Parameters - ---------- - merge_table : pd.DataFrame - The table to merge into. - source_table : pd.DataFrame - The table to merge from. - merge_keys : list - The keys to merge on. - column_rename : str - The column to rename. - extra_dims_list : list, optional (default=None) - A list of tuples of extra dimensions to merge. - - Returns - ------- - merge_table : pd.DataFrame - The merged table. - - """ - if extra_dims_list is None: - extra_dims_list = [] - source_table = source_table.rename(columns={"vals": column_rename}) - for dim_name, dim_value in extra_dims_list: - source_table[dim_name] = dim_value - merge_table = merge_table.merge(source_table, on=merge_keys, how="left") - merge_table["vals"] = ( - merge_table["vals"].astype(float).fillna(merge_table[column_rename]) - ) - merge_table = merge_table.drop(columns=column_rename) - return merge_table + # ------------------------------------------------------------------ # + # internal soa helpers # + # ------------------------------------------------------------------ # def _process_soa_table( self, soa_xml: Element, table_index: int, is_select: bool @@ -665,7 +776,7 @@ def generate_table( mult_method: str = "glm", ) -> tuple[pd.DataFrame, pd.DataFrame]: """ - Generate a 1-d mortality table based on model predictions. + Generate a 1-d rate table based on model predictions. A 1-d table is where there is a 'vals' column and all the other columns are the features. @@ -708,7 +819,7 @@ def generate_table( ------- tuple rate_table : pd.DataFrame - The 1-d mortality rate_table + The 1-d rate_table mult_table : pd.DataFrame The multiplier table @@ -871,6 +982,7 @@ def map_rates( rate: str, rate_to_df_map: dict[str, str] | None = None, rate_filename: str | None = None, + table_class: type[RateTable] = MortTable, ) -> pd.DataFrame: """ Map rates to the DataFrame. @@ -879,7 +991,8 @@ def map_rates( This function also handles: - Multiples - - MI rates + - MI rates (only when `table_class` is `MortTable` or a subclass that + exposes `mi_table`) Parameters ---------- @@ -896,6 +1009,10 @@ def map_rates( rate_filename : str, optional The location of the rate map file. If none this is assumed to be in the dataset/tables folder. + table_class : type[RateTable], optional (default=MortTable) + The table class to use to load the rate. Use `MortTable` for mortality + rates (default, supports soa + mi_table) or `RateTable` for generic + rates (termination, lapse, etc - supports csv/workbook only). Returns ------- @@ -904,13 +1021,13 @@ def map_rates( """ # get the table - mt = suppress_logs(MortTable)(rate=rate, rate_filename=rate_filename) + mt = suppress_logs(table_class)(rate=rate, rate_filename=rate_filename) rate_name = mt.rate_name rate_dict = mt.rate_dict rate_type = next(iter(rate_dict["type"].keys())) rate_table = mt.rate_table mult_table = mt.mult_table - mi_table = mt.mi_table + mi_table = getattr(mt, "mi_table", None) logger.info(f"mapping rate: '{rate_name}' with format: '{rate_type}'") # create table_to_df_map if not provided diff --git a/morai/forecast/graduation.py b/morai/forecast/graduation.py index 4534f0d..6648ec5 100644 --- a/morai/forecast/graduation.py +++ b/morai/forecast/graduation.py @@ -79,21 +79,21 @@ def whl( Smoothing parameter for horizontal differences. weights : pd.Series Weights associated with the raw data (same shape as raw_data) - horizontal_expo : float + horizontal_expo : float, optional (default is 0) Exponential rate parameter for horizontal differences. - vertical_order : int + vertical_order : int, optional (default is 0) Order of differencing in the vertical direction (0 for 1D data). - vertical_lambda : float + vertical_lambda : float, optional (default is 0) Smoothing parameter for vertical differences (0 for 1D data). - vertical_expo : float + vertical_expo : float, optional (default is 0) Exponential rate parameter for vertical differences. - normalize_weights : bool + normalize_weights : bool, optional (default is True) Whether to normalize weights to sum to the number of data points. - standard_rates : pd.Series, optional + standard_rates : pd.Series, optional (default is None) Standard rates to blend with the raw rates. - standard_weights : pd.Series, optional + standard_weights : pd.Series, optional (default is None) Weights for the standard rates. - blending_factor : float + blending_factor : float (0 to 1), optional (default is 0) Blending factor for the standard rates. The higher the factor, the more the standard rates are used. @@ -103,8 +103,8 @@ def whl( Array of smoothed rates """ - from scipy.sparse import csr_matrix, diags - from scipy.sparse.linalg import spsolve + from scipy.sparse import csr_matrix, diags # noqa: PLC0415 + from scipy.sparse.linalg import spsolve # noqa: PLC0415 rates = np.array(rates, dtype=float) if weights is None: @@ -127,6 +127,20 @@ def whl( else: raise ValueError("raw_data must be a 1D or 2D array.") + # validations + if horizontal_order < 1: + raise ValueError("horizontal_order must be >= 1") + if horizontal_lambda < 0: + raise ValueError("horizontal_lambda must be non-negative") + if not 0 <= blending_factor <= 1: + raise ValueError("blending_factor must be in [0, 1]") + if rates.ndim == 1 and (vertical_order > 0 or vertical_lambda > 0): + logger.warning("vertical parameters ignored for 1D input") + if rates.ndim == 2 and vertical_order > 0 and rows <= vertical_order: + raise ValueError(f"need rows > vertical_order, got rows={rows}") + if cols <= horizontal_order and rates.ndim == 2: + raise ValueError(f"need cols > horizontal_order, got cols={cols}") + # normalize weights to number of data points if normalize_weights: total_weight = sum(weights) diff --git a/morai/forecast/metrics.py b/morai/forecast/metrics.py index 6b8f6c1..9f60c4b 100644 --- a/morai/forecast/metrics.py +++ b/morai/forecast/metrics.py @@ -331,6 +331,7 @@ def __init__( {col[1] for col in scorecard_df.columns if col[1] != ""} ) else: + self.filepath = "model_results.json" self.model = pd.DataFrame() self.scorecard = pd.DataFrame() self.importance = pd.DataFrame() diff --git a/morai/integrations/cdc.py b/morai/integrations/cdc.py index 6532dd4..8c8738b 100644 --- a/morai/integrations/cdc.py +++ b/morai/integrations/cdc.py @@ -59,7 +59,7 @@ "65-74 years", "75-84 years", "85+ years", - "total", + "All ages", ] @@ -116,11 +116,8 @@ def get_cdc_data_xml( logger.debug("creating dataframe from response") cdc_df = _xml_create_df(xml_response=xml_response) - # parse the month column + # parse the date-like column if parse_date_col: - if parse_date_col not in cdc_df.columns: - logger.warning(f"Column not found: {parse_date_col}") - return cdc_df cdc_df[parse_date_col] = _parse_date_col(df=cdc_df, col=parse_date_col) # clean the dataframe @@ -128,6 +125,12 @@ def get_cdc_data_xml( cdc_df = suppress_logs(helpers.clean_df)(cdc_df, update_cat=False) if "year" in cdc_df.columns: cdc_df["year"] = cdc_df["year"].str[:4] + if parse_date_col == "Month": + cdc_df["reported_date"] = cdc_df["month"] + cdc_df["month"] = cdc_df["month"].dt.month + if parse_date_col == "mmwr_week_date": + cdc_df["mmwr_week"] = cdc_df["mmwr_week_date"].dt.isocalendar().week + cdc_df["mmwr_year"] = cdc_df["mmwr_week_date"].dt.isocalendar().year if convert_dtypes: cdc_df = _infer_dtypes(cdc_df) @@ -218,9 +221,9 @@ def get_cdc_data_sql(db_filepath: str | Path, table_name: str) -> pd.DataFrame: return cdc_df -def get_last_updated(table_name: str | None = None) -> str: +def get_last_updated(table_name: str | None = None) -> dict: """ - Get the last updated date of a table. + Get the last updated meta of a table. Parameters ---------- @@ -229,19 +232,49 @@ def get_last_updated(table_name: str | None = None) -> str: Returns ------- - last_updated : str - The last updated date of the table. + last_update_meta : dict + A dictionary containing: + - "last_updated": The maximum "added_at" date from the table. + - "data_through": The maximum "data_through" date from the table. + - "days_elapsed": The number of days elapsed in the year + - "recent_week": The recent week data is through """ if table_name is None: table_name = "mcd18_cod" db_filepath = helpers.FILES_PATH / "integrations" / "cdc" / "cdc.sql" - tables = sql.get_tables(db_filepath=db_filepath) - if table_name not in tables: - return "" - query = f"SELECT MAX(added_at) as last_updated FROM {table_name}" - result = sql.read_sql(db_filepath, query) - return result["last_updated"].iloc[0] or "" + + # query the table + query = ( + f"SELECT MAX(added_at) as last_updated, MAX(data_through) as " + f"data_through FROM {table_name}" + ) + try: + result = sql.read_sql(db_filepath, query) + except Exception: + logger.error( + f"Error querying table `{table_name}` for last updated date: {db_filepath}" + ) + return { + "last_updated": None, + "data_through": None, + "days_elapsed": None, + } + last_updated = result["last_updated"].iloc[0] + data_through = result["data_through"].iloc[0] + start_of_year = pd.Timestamp(f"{data_through.year}-01-01") + days_elapsed = (data_through - start_of_year).days + 1 + recent_week = data_through.isocalendar().week + + # create the dict + result = { + "last_updated": last_updated, + "data_through": data_through, + "days_elapsed": days_elapsed, + "recent_week": recent_week, + } + + return result @lru_cache(maxsize=10) @@ -362,29 +395,64 @@ def calc_mi(df: pd.DataFrame, rolling: int = 10) -> pd.DataFrame: "calculating mortality improvement by using a `2000 age adjusted` crude " "mortality rate" ) - # group and calculate crude 2000 adjusted mortality rate - mi_df = ( + + # groupby year and age_groups and calculate crude 2000 adjusted mortality rate + mi_grouped = ( df.groupby(["year", "age_groups"])[["deaths", "population"]].sum().reset_index() ) - mi_df = map_reference( - df=mi_df, + mi_grouped = map_reference( + df=mi_grouped, col="population_%", on_dict={"age_groups": "age_bucket"}, sheet_name="age_std_2000", ) - mi_df["crude_adj"] = ( - mi_df["deaths"] / mi_df["population"] * 100000 * mi_df["population_%"] + total_weight = mi_grouped.groupby("age_groups")["population_%"].first().sum() + mi_grouped["population_%"] = mi_grouped["population_%"] / total_weight + mi_grouped["crude_adj"] = ( + mi_grouped["deaths"] + / mi_grouped["population"] + * 100000 + * mi_grouped["population_%"] ) - # calculate mortality improvement - mi_df = mi_df.groupby(["year"])[["crude_adj", "deaths"]].sum().reset_index() - mi_df["1_year_mi"] = 1 - (mi_df["crude_adj"] / mi_df["crude_adj"].shift(1)) - mi_df[f"{rolling}_year_mi"] = mi_df["1_year_mi"].rolling(window=rolling).mean() + # calculate mortality improvement for each age_group + mi_df_list = [] + for age_group in [*mi_grouped["age_groups"].unique(), "All ages"]: + # filter and groupby + if age_group == "All ages": + mi_df_age = mi_grouped.copy() + else: + mi_df_age = mi_grouped[mi_grouped["age_groups"] == age_group] + + mi_df_age = ( + mi_df_age.groupby(["year"])[["crude_adj", "deaths", "population"]] + .sum() + .reset_index() + ) + mi_df_age["age_groups"] = age_group + + # 1-year MI + mi_df_age["1_year_mi_pct"] = 1 - ( + mi_df_age["crude_adj"] / mi_df_age["crude_adj"].shift(1) + ) + + # rolling average MI + mi_df_age[f"{rolling}_year_mi_pct"] = ( + mi_df_age["1_year_mi_pct"].rolling(window=rolling).mean() + ) + + # whittaker-henderson-lowrie (whl) + mi_df_age = mi_df_age[mi_df_age["1_year_mi_pct"].notna()] + mi_df_age["whl_3_pct"] = graduation.whl( + rates=mi_df_age["1_year_mi_pct"], horizontal_order=3, horizontal_lambda=400 + ) + + mi_df_list.append(mi_df_age) + mi_df = pd.concat(mi_df_list, ignore_index=True) - # calculate whittaker-henderson-lowrie (whl) - mi_df = mi_df[mi_df["1_year_mi"].notna()] - mi_df["whl_3"] = graduation.whl( - rates=mi_df["1_year_mi"], horizontal_order=3, horizontal_lambda=400 + # re-apply categorical ordering + mi_df["age_groups"] = pd.Categorical( + mi_df["age_groups"], categories=AGE_GROUP_ORDER, ordered=True ) return mi_df @@ -597,7 +665,7 @@ def _xml_create_df(xml_response: str) -> pd.DataFrame: if code is not None: measure_selections.append(code.split(".")[1]) columns = byvariables + measure_selections - columns = [cdc_mapping.get(key, key) for key in columns] + columns = [cdc_mapping.get(col, col) for col in columns] num_columns = len(columns) # initialize row-span counts and values for each column @@ -653,18 +721,25 @@ def _xml_create_df(xml_response: str) -> pd.DataFrame: def _parse_date_col(df: pd.DataFrame, col: str = "Month") -> pd.Series: """ - Parse the date column to a datetime object. + Parse a cdc column that is date-like into a datetime series. + + CDC has a few columns from the wonder data that are date-like but will not + parse to a friendly format without some cleaning. - CDC has the "Month" column as a string with the format "Month., Year" - when grouping by month and year. - This function parses the month column to a datetime object. + Options + - Month + - example: "Feb., 2026 (provisional and partial)" + - clean: "2026-02-01" + - mmwr_week_date + - example: "2026 Week 13 ending April 04, 2026 (provisional)" + - clean: "2026-04-04" Parameters ---------- df : pd.DataFrame DataFrame object. col : str - Column name to parse dates. + Column name to parse dates such as "Month" or "MMWR Week". Returns ------- @@ -672,9 +747,19 @@ def _parse_date_col(df: pd.DataFrame, col: str = "Month") -> pd.Series: Series of parsed dates. """ - parsed_dates = [date.split(" (")[0].strip() for date in df[col]] - parsed_dates = [date.replace(".", "") for date in parsed_dates] - parsed_dates = pd.to_datetime(parsed_dates, format="%b, %Y") + if col not in df.columns: + logger.error(f"Column not found: {col}") + return pd.Series([None] * len(df)) + if col == "Month": + parsed_dates = [date.split(" (")[0].strip() for date in df[col]] + parsed_dates = [date.replace(".", "") for date in parsed_dates] + parsed_dates = pd.to_datetime(parsed_dates, format="%b, %Y") + elif col == "mmwr_week_date": + parsed_dates = pd.to_datetime( + df[col].str.extract(r"ending (\w+ \d{2}, \d{4})", expand=False), + format="%B %d, %Y", + errors="coerce", + ) parsed_dates = pd.Series(parsed_dates) return parsed_dates diff --git a/morai/models/neural.py b/morai/models/neural.py index ce0afee..1886a4a 100644 --- a/morai/models/neural.py +++ b/morai/models/neural.py @@ -2,6 +2,7 @@ from __future__ import annotations +import os from typing import Any import numpy as np @@ -11,6 +12,7 @@ import shap import torch import torch.nn.functional as F +from plotly.subplots import make_subplots from sklearn.decomposition import PCA from sklearn.manifold import TSNE from sklearn.metrics.pairwise import cosine_similarity @@ -21,6 +23,27 @@ logger = custom_logger.setup_logging(__name__) +# defaults +MAX_NORM = 5.0 +MAX_NEGATIVE_LOG = -30.0 + + +def set_deterministic() -> None: + """ + Enable deterministic PyTorch operations for reproducible training. + + Call this at the top of notebook prior to a CUDA call. + + Notes + ----- + Deterministic kernels 10 to 20% slower than non-deterministic ones, + so this is not enabled by default. + + """ + os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8") + torch.use_deterministic_algorithms(True, warn_only=True) + logger.info("deterministic mode enabled") + class Neural(nn.Module): """ @@ -60,6 +83,7 @@ def __init__( task: str = "poisson", embedding_cols: list | None = None, embedding_dims: dict[str, int] | None = None, + device: str | None = None, ) -> None: """ Initialize the model. @@ -74,10 +98,17 @@ def __init__( Dictionary mapping categorical feature names to their embedding dimensions e.g., {"age_group": 8, "region": 4} If None, will use min(50, (vocab_size + 1) // 2) for each feature + device : str, optional + Device to run the model on ("cuda" or "cpu"). If None, will auto-detect. """ super().__init__() - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + if device is not None: + if device == "cuda" and not torch.cuda.is_available(): + raise ValueError("device='cuda' requested but CUDA is not available") + self.device = torch.device(device) + else: + self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.task = task self.feature_names: list[str] | None = None if embedding_cols is None: @@ -227,13 +258,16 @@ def fit( y_test: pd.Series, weights_test: pd.Series, epochs: int = 100, + loss_target: float | None = None, lr: float = 0.001, batch_size: int | None = None, dropout: float = 0.0, - weight_decay: float = 0.0001, + weight_decay: float = 0.0, early_stopping: bool = True, warmup_epochs: int = 50, max_patience: int = 20, + min_delta: float = 0.0, + ae_interval: int = 10, seed: int | None = None, ) -> None: """ @@ -255,6 +289,8 @@ def fit( The weights for the testing data epochs : int, optional (default=100) The number of epochs to train the model for, by default 100 + loss_target : float, optional (default=None) + If provided, training will stop when test loss reaches this target lr : float, optional (default=0.001) The learning rate, by default 0.001. Lower values will result in batch_size: int, optional (default=None) @@ -262,7 +298,7 @@ def fit( slower learning, higher values will result in faster learning dropout : float, optional (default=0.0) Dropout rate for the model - weight_decay : float, optional (default=1e-4) + weight_decay : float, optional (default=0.0) similar to L2 regularization to prevent overfitting early_stopping : bool, optional (default=True) Whether to use early stopping based on test loss @@ -270,13 +306,17 @@ def fit( Number of epochs to wait before starting early stopping max_patience : int, optional (default=20) Number of epochs with no improvement to wait before stopping training + min_delta : float, optional (default=0.0) + Minimum change in loss to qualify as improvement for early stopping + ae_interval : int, optional (default=10) + Number of epochs between computing A/E ratios for train and test sets seed : int, optional Random seed for reproducibility + Seed will not be deterministic on it's own and would need the + set_deterministic() function to be called before creating the model. """ # defaults - MAX_NORM = 5.0 - MAX_NEGATIVE_LOG = -30.0 best_loss = float("inf") best_state = None best_epoch = 0 @@ -306,6 +346,12 @@ def fit( raise ValueError("task must be 'poisson' or 'binomial'") if not (X.index.equals(y.index) and X.index.equals(weights.index)): raise ValueError("X, y, weights must share the same index") + if not ( + X_test.index.equals(y_test.index) + and X_test.index.equals(weights_test.index) + ): + raise ValueError("X_test, y_test, weights_test must share the same index") + bad = (weights <= 0) | weights.isna() | y.isna() if bad.any(): logger.warning( @@ -315,6 +361,15 @@ def fit( y = y.loc[~bad] weights = weights.loc[~bad] + bad_test = (weights_test <= 0) | weights_test.isna() | y_test.isna() + if bad_test.any(): + logger.warning( + f"removing `{bad_test.sum()}` test rows with weights <= 0 or na" + ) + X_test = X_test.loc[~bad_test] + y_test = y_test.loc[~bad_test] + weights_test = weights_test.loc[~bad_test] + # convert y_train from rate to deaths y = y * weights y_test = y_test * weights_test @@ -348,24 +403,50 @@ def fit( # if batch_size is None or >= dataset size, use full-batch use_mini_batch = batch_size is not None and batch_size < y_torch_length + # initialize loss / ae lists train_losses, test_losses, learning_rates = [], [], [] + train_aes, test_aes, ae_epochs = [], [], [] # setup optimizer and a learning rate scheduler to reduce learning rate # when loss plateaus - opt = optim.Adam(self.parameters(), lr=lr, weight_decay=weight_decay) + # weight decay only applied to non-bias and non-embedding parameters + decay, no_decay = [], [] + for name, p in self.named_parameters(): + if "bias" in name or "embedding" in name.lower(): + no_decay.append(p) + else: + decay.append(p) + opt = optim.Adam( + [ + {"params": decay, "weight_decay": weight_decay}, + {"params": no_decay, "weight_decay": 0.0}, + ], + lr=lr, + ) + # setting min_lr to 1/128 of initial lr (7 decay steps) + min_lr = lr * (0.5**7) scheduler = optim.lr_scheduler.ReduceLROnPlateau( - opt, mode="min", factor=0.5, patience=10 + opt, + mode="min", + factor=0.5, + patience=5, + threshold=min_delta, + threshold_mode="abs", + min_lr=min_lr, ) # initialize prediction to global rate - overall_mu = float(y.sum() / weights.sum()) - logger.info(f"overall_mu: {overall_mu:.6f}") + overall_rate = float(y.sum() / weights.sum()) + if self.task == "poisson": + init_bias = float(np.log(max(overall_rate, 1e-12))) + else: # binomial + p = float(np.clip(overall_rate, 1e-12, 1 - 1e-12)) + init_bias = float(np.log(p / (1 - p))) + logger.info(f"overall_rate: {overall_rate:.6f}, init_bias: {init_bias:.6f}") with torch.no_grad(): - self.wide_linear.bias.fill_( - np.log(max(overall_mu, 1e-12)).astype(np.float32) - ) - self.output.bias.fill_(0.0) - self.output.weight.mul_(0.01) + self.wide_linear.bias.fill_(init_bias) + self.output.weight.zero_() + self.output.bias.zero_() # logging logger.info(f"epochs: {epochs:,.0f}, batch_size: {batch_size}, lr: {lr}") @@ -408,22 +489,9 @@ def fit( ] z_torch = self(batch_num, batch_embed_list) - - if self.task == "poisson": - logE = torch.log(batch_weights).clamp(min=MAX_NEGATIVE_LOG) - loglam = z_torch + logE - loss = F.poisson_nll_loss( - input=loglam, - target=batch_y, - log_input=True, - full=False, - reduction="mean", - ) - else: # binomial - loss = -( - batch_y * F.logsigmoid(z_torch) - + (batch_weights - batch_y) * F.logsigmoid(-z_torch) - ).mean() + loss = self._loss( + predictions=z_torch, target=batch_y, weights=batch_weights + ) loss.backward() torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=MAX_NORM) @@ -442,22 +510,9 @@ def fit( ] z_torch = self(X_torch_num, X_torch_embed_list) - - if self.task == "poisson": - logE = torch.log(weights_torch).clamp(min=MAX_NEGATIVE_LOG) - loglam = z_torch + logE - loss = F.poisson_nll_loss( - input=loglam, - target=y_torch, - log_input=True, - full=False, - reduction="mean", - ) - else: # binomial - loss = -( - y_torch * F.logsigmoid(z_torch) - + (weights_torch - y_torch) * F.logsigmoid(-z_torch) - ).mean() + loss = self._loss( + predictions=z_torch, target=y_torch, weights=weights_torch + ) loss.backward() torch.nn.utils.clip_grad_norm_(self.parameters(), max_norm=MAX_NORM) @@ -472,39 +527,49 @@ def fit( with torch.no_grad(): self.eval() z_torch_test = self(X_torch_test_num, X_torch_test_embed_idx) - if self.task == "poisson": - logE_test = torch.log(weights_torch_test).clamp( - min=MAX_NEGATIVE_LOG - ) - loglam_test = z_torch_test + logE_test - test_loss = F.poisson_nll_loss( - input=loglam_test, - target=y_torch_test, - log_input=True, - full=False, - reduction="mean", - ) - else: - test_loss = -( - y_torch_test * F.logsigmoid(z_torch_test) - + (weights_torch_test - y_torch_test) - * F.logsigmoid(-z_torch_test) - ).mean() + test_loss = self._loss( + predictions=z_torch_test, + target=y_torch_test, + weights=weights_torch_test, + ) test_loss_value = test_loss.item() train_losses.append(train_loss_value) test_losses.append(test_loss_value) learning_rates.append(opt.param_groups[0]["lr"]) + # track a/e ratios at intervals + if epoch % ae_interval == 0: + with torch.no_grad(): + self.eval() + eval_embed_list = [ + X_torch_embed_stacked[:, i] + for i in range(X_torch_embed_stacked.shape[1]) + ] + z_train_eval = self(X_torch_num, eval_embed_list) + if self.task == "poisson": + train_expected = torch.exp(z_train_eval) * weights_torch + test_expected = torch.exp(z_torch_test) * weights_torch_test + else: # binomial + train_expected = torch.sigmoid(z_train_eval) * weights_torch + test_expected = torch.sigmoid(z_torch_test) * weights_torch_test + train_ae = (y_torch.sum() / train_expected.sum()).item() + test_ae = (y_torch_test.sum() / test_expected.sum()).item() + ae_epochs.append(epoch + 1) + train_aes.append(train_ae) + test_aes.append(test_ae) + scheduler.step(test_loss_value) # early stopping when loss does not improve if early_stopping: - if test_loss_value < best_loss: + if test_loss_value < best_loss - min_delta: best_loss = test_loss_value best_epoch = epoch + 1 patience_counter = 0 - best_state = self.state_dict().copy() + best_state = { + k: v.detach().clone() for k, v in self.state_dict().items() + } elif epoch >= warmup_epochs: patience_counter += 1 if patience_counter >= max_patience: @@ -515,6 +580,14 @@ def fit( pbar.close() break + # stop when the test loss reaches the target + if loss_target is not None and test_loss_value <= loss_target: + logger.info( + f"loss target `{loss_target}` reached at epoch: {epoch + 1}." + ) + pbar.close() + break + pbar.set_postfix( { "train": f"{train_loss_value:,.2f}", @@ -538,42 +611,150 @@ def fit( ) self._is_fitted = True - # create loss plot - fig = go.Figure() + # a/e of final state + with torch.no_grad(): + self.eval() + final_embed_list = [ + X_torch_embed_stacked[:, i] + for i in range(X_torch_embed_stacked.shape[1]) + ] + z_train_final = self(X_torch_num, final_embed_list) + z_test_final = self(X_torch_test_num, X_torch_test_embed_idx) + if self.task == "poisson": + train_expected = torch.exp(z_train_final) * weights_torch + test_expected = torch.exp(z_test_final) * weights_torch_test + else: # binomial + train_expected = torch.sigmoid(z_train_final) * weights_torch + test_expected = torch.sigmoid(z_test_final) * weights_torch_test + final_train_ae = (y_torch.sum() / train_expected.sum()).item() + final_test_ae = (y_torch_test.sum() / test_expected.sum()).item() + final_epoch = ( + best_epoch if (early_stopping and best_state is not None) else (epoch + 1) + ) + logger.info( + f"final state A/E (epoch {final_epoch}) - " + f"train: {final_train_ae:.4f}, test: {final_test_ae:.4f}" + ) + + # creating plots + fig = make_subplots( + rows=2, + cols=1, + subplot_titles=("Loss", "A/E Ratio"), + specs=[[{"secondary_y": True}], [{"secondary_y": False}]], + vertical_spacing=0.12, + ) + + # plot - losses and learning rate fig.add_trace( - go.Scatter( - y=train_losses, - mode="lines+markers", - name="Train Loss", - ) + go.Scatter(y=train_losses, mode="lines+markers", name="Train Loss"), + row=1, + col=1, + secondary_y=False, ) fig.add_trace( - go.Scatter( - y=test_losses, - mode="lines+markers", - name="Test Loss", - ) + go.Scatter(y=test_losses, mode="lines+markers", name="Test Loss"), + row=1, + col=1, + secondary_y=False, + ) + fig.add_trace( + go.Scatter(y=learning_rates, mode="lines", name="Learning Rate"), + row=1, + col=1, + secondary_y=True, ) + + # plot - a/e ratios fig.add_trace( go.Scatter( - y=learning_rates, - mode="lines", - name="Learning Rate", - yaxis="y2", - ) + x=ae_epochs, y=train_aes, mode="lines+markers", name="Train A/E" + ), + row=2, + col=1, ) - fig.update_layout( - title="Neural Network Training", - xaxis_title="Epoch", - yaxis_title="Loss", - yaxis2={ - "title": "Learning Rate", - "overlaying": "y", - "side": "right", - }, + fig.add_trace( + go.Scatter(x=ae_epochs, y=test_aes, mode="lines+markers", name="Test A/E"), + row=2, + col=1, ) + fig.add_hline(y=1.0, line_dash="dash", line_color="gray", row=2, col=1) + + # plot - mark the state a/e of the final model + fig.add_trace( + go.Scatter( + x=[final_epoch, final_epoch], + y=[final_train_ae, final_test_ae], + mode="markers", + marker={"symbol": "star", "size": 12, "color": "black"}, + name="Final State A/E", + ), + row=2, + col=1, + ) + + # plot - format axes + fig.update_xaxes(title_text="Epoch", row=1, col=1) + fig.update_xaxes(title_text="Epoch", row=2, col=1) + fig.update_yaxes(title_text="Loss", row=1, col=1, secondary_y=False) + fig.update_yaxes(title_text="Learning Rate", row=1, col=1, secondary_y=True) + fig.update_yaxes(title_text="A/E", row=2, col=1) + fig.update_layout(title="Neural Network Training") + return fig + def score(self, X: pd.DataFrame, y: pd.Series, weights: pd.Series) -> float: + """ + Compute the loss on the given data. + + Mirrors the loss used in `fit`, in eval mode (dropout off) with no + gradients, so it's directly comparable to the train/test loss logged + during training. + + Parameters + ---------- + X : pd.DataFrame + The data to score + y : pd.Series + The labels to score + weights : pd.Series + The weights to score + + Returns + ------- + loss : float + The computed loss + + """ + if self.wide_linear is None or self.output is None: + raise RuntimeError("Model must be fitted before scoring.") + + # drop rows the loss can't use + bad = (weights <= 0) | weights.isna() | y.isna() + if bad.any(): + X = X.loc[~bad] + y = y.loc[~bad] + weights = weights.loc[~bad] + + # prepare tensors + D = y * weights + + X_num, X_embed_idx = self._prepare_input_tensor(X) + D_torch = torch.tensor( + D.to_numpy().reshape(-1), dtype=torch.float32, device=self.device + ) + w_torch = torch.tensor( + weights.to_numpy().reshape(-1), dtype=torch.float32, device=self.device + ) + + # compute loss in eval mode with no gradients + self.eval() + with torch.no_grad(): + z = self(X_num, X_embed_idx) + loss = self._loss(predictions=z, target=D_torch, weights=w_torch) + + return float(loss.item()) + def predict( self, X: pd.DataFrame | np.ndarray, @@ -638,13 +819,10 @@ def predict( # convert to rate if self.task == "poisson": - mu = np.exp(z_torch) - q = mu - predictions = np.clip(q, 1e-9, 1 - 1e-9) - + q = np.exp(z_torch) else: # binomial q = 1.0 / (1.0 + np.exp(-z_torch)) - predictions = np.clip(q, 1e-9, 1 - 1e-9) + predictions = np.clip(q, 1e-9, 1 - 1e-9) return predictions @@ -781,6 +959,97 @@ def embedding_plot_2d(self, embed_col: str, method: str = "tsne") -> go.Figure: return embedding_fig + def rebalance_ae(self, X: pd.DataFrame, y: pd.Series, weights: pd.Series) -> float: + """ + Adjust predictions so that training data A/E ratio is 1. + + Call after the `fit` method to rebalance the model's predictions + to the overall rate in the training data. + + Notes + ----- + - relativities between test and train predictions are unchanged. + + Parameters + ---------- + X : pd.DataFrame + Features + y : pd.Series + Observed rate (qx) + weights : pd.Series + Exposure + + Returns + ------- + ae : float + The aggregate A/E ratio before rebalancing. + Model is also updated in-place to rebalance predictions to training data. + + """ + if self.wide_linear is None: + raise RuntimeError("Model must be fitted before rebalancing.") + + q = self.predict(X).ravel() + w = weights.to_numpy() + actual = float((y.to_numpy() * w).sum()) + expected = float((q * w).sum()) + ae = actual / expected + logger.info(f"rebalanced intercept; A/E before = {ae:.2f}") + + with torch.no_grad(): + if self.task == "poisson": + self.wide_linear.bias.add_(float(np.log(ae))) + else: # binomial + z = np.log(q / (1.0 - q)) + delta = float(np.log(ae)) + for _ in range(25): + p = 1.0 / (1.0 + np.exp(-(z + delta))) + f = float((w * p).sum()) - actual + fp = float((w * p * (1.0 - p)).sum()) + if fp == 0: + break + delta -= f / fp + self.wide_linear.bias.add_(float(delta)) + return ae + + def _loss( + self, predictions: torch.Tensor, target: torch.Tensor, weights: torch.Tensor + ) -> torch.Tensor: + """ + Calculate the loss for the given predictions, targets, and weights. + + Parameters + ---------- + predictions : torch.Tensor + The predicted log rates (for Poisson) or logits (for Binomial). + target : torch.Tensor + The target counts (for Poisson) or successes (for Binomial). + weights : torch.Tensor + The weights (exposures for Poisson, trials for Binomial). + + Returns + ------- + loss : torch.Tensor + The computed loss value. + + """ + if self.task == "poisson": + logE = torch.log(weights).clamp(min=MAX_NEGATIVE_LOG) + loglam = predictions + logE + loss = F.poisson_nll_loss( + input=loglam, + target=target, + log_input=True, + full=False, + reduction="mean", + ) + else: # binomial + loss = -( + target * F.logsigmoid(predictions) + + (weights - target) * F.logsigmoid(-predictions) + ).mean() + return loss + def _create_embeddings(self, X: pd.DataFrame) -> int: """ Create embeddings for categorical features. @@ -887,7 +1156,9 @@ def _prepare_input_tensor( # label encode idx = mapped_values.fillna(0).astype("int64").to_numpy() - X_torch_embed_idx.append(torch.from_numpy(idx).to(self.device)) + X_torch_embed_idx.append( + torch.tensor(idx, dtype=torch.long, device=self.device) + ) return X_torch_num, X_torch_embed_idx diff --git a/morai/utils/sql.py b/morai/utils/sql.py index b59bba8..7e2acef 100644 --- a/morai/utils/sql.py +++ b/morai/utils/sql.py @@ -2,6 +2,7 @@ from __future__ import annotations +import re import sqlite3 from datetime import datetime from typing import TYPE_CHECKING @@ -59,7 +60,9 @@ def export_to_sql( conn.close() -def read_sql(db_filepath: str | Path, query: str) -> pd.DataFrame: +def read_sql( + db_filepath: str | Path, query: str, parse_dates: list | None = None +) -> pd.DataFrame: """ Read a SQLite database. @@ -69,6 +72,8 @@ def read_sql(db_filepath: str | Path, query: str) -> pd.DataFrame: Database file path. query : str Query to execute. + parse_dates : list | None, optional + List of column names to parse as dates. Returns ------- @@ -82,9 +87,24 @@ def read_sql(db_filepath: str | Path, query: str) -> pd.DataFrame: # connect to the database conn = sqlite3.connect(db_filepath) + # get table_name + match = re.compile( + r"\bFROM\s+[\"`\[]?(\w+)[\"`\]]?", + re.IGNORECASE, + ).search(query) + table_name = match.group(1) if match else None + # read the data try: - df = pd.read_sql_query(query, conn) + # infer parse_dates if not provided + if parse_dates is None and table_name is not None: + dtypes = table_dtypes(db_filepath, table_name) + parse_dates = [ + col + for col, dtype in dtypes.items() + if "DATE" in dtype.upper() or "TIME" in dtype.upper() + ] + df = pd.read_sql_query(query, conn, parse_dates=parse_dates or None) finally: conn.close() @@ -144,13 +164,13 @@ def table_remove(db_filepath: str, table_name: str) -> None: conn.close() -def table_dtypes(db_filepath: str, table_name: str) -> dict: +def table_dtypes(db_filepath: str | Path, table_name: str) -> dict: """ Get the data types of a table from a SQLite database. Parameters ---------- - db_filepath : str + db_filepath : str | Path Database file path. table_name : str Table name. diff --git a/morai/version.py b/morai/version.py index 5447810..8eb62da 100644 --- a/morai/version.py +++ b/morai/version.py @@ -1,3 +1,3 @@ """Version of app.""" -version = "0.3.4" +version = "0.3.5" diff --git a/notebooks/01.data_process.ipynb b/notebooks/01.data_process.ipynb index 9f3fa42..27e8a7a 100644 --- a/notebooks/01.data_process.ipynb +++ b/notebooks/01.data_process.ipynb @@ -35,6 +35,16 @@ "import pandas as pd" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e20df81-e53d-423b-9b8f-a665a7154609", + "metadata": {}, + "outputs": [], + "source": [ + "from morai.utils import helpers" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -190,7 +200,7 @@ "metadata": {}, "outputs": [], "source": [ - "write_path = r\"files\\partition\\0923\"" + "write_path = helpers.FILES_PATH / \"partition\" / \"0923\"" ] }, { @@ -202,27 +212,27 @@ "source": [ "files = [\n", " {\n", - " \"filepath\": r\"files\\dataset\\MIB_NAIC_20240429_2009_19.txt\",\n", + " \"filepath\": helpers.FILES_PATH / \"dataset\" / \"MIB_NAIC_20240429_2009_19.txt\",\n", " \"separator\": \"\\t\",\n", " \"schema_overrides\": pl_dtypes_19,\n", " },\n", " {\n", - " \"filepath\": r\"files\\dataset\\ILEC_2020_20260204.parquet\",\n", + " \"filepath\": helpers.FILES_PATH / \"dataset\" / \"ILEC_2020_20260204.txt\",\n", " \"schema_overrides\": pl_dtypes_20,\n", " \"rename_dict\": rename_dict_20,\n", " },\n", " {\n", - " \"filepath\": r\"files\\dataset\\ILEC_2021_20260204.parquet\",\n", + " \"filepath\": helpers.FILES_PATH / \"dataset\" / \"ILEC_2021_20260204.txt\",\n", " \"schema_overrides\": pl_dtypes_20,\n", " \"rename_dict\": rename_dict_20,\n", " },\n", " {\n", - " \"filepath\": r\"files\\dataset\\ILEC_2022_20260204.parquet\",\n", + " \"filepath\": helpers.FILES_PATH / \"dataset\" / \"ILEC_2022_20260204.txt\",\n", " \"schema_overrides\": pl_dtypes_20,\n", " \"rename_dict\": rename_dict_20,\n", " },\n", " {\n", - " \"filepath\": r\"files\\dataset\\ILEC_2023_20260204.parquet\",\n", + " \"filepath\": helpers.FILES_PATH / \"dataset\" / \"ILEC_2023_20260204.txt\",\n", " \"schema_overrides\": pl_dtypes_20,\n", " \"rename_dict\": rename_dict_20,\n", " },\n", @@ -245,7 +255,11 @@ ], "source": [ "# check schema\n", - "lzdf = pl.scan_csv(Path(files[0][\"filepath\"]), separator=files[0][\"separator\"], schema_overrides=files[0][\"schema_overrides\"])\n", + "lzdf = pl.scan_csv(\n", + " Path(files[0][\"filepath\"]),\n", + " separator=files[0][\"separator\"],\n", + " schema_overrides=files[0][\"schema_overrides\"],\n", + ")\n", "# lzdf = pl.scan_parquet(Path(files[1][\"filepath\"]))\n", "print(lzdf.collect_schema())" ] @@ -326,7 +340,9 @@ "metadata": {}, "outputs": [], "source": [ - "pl_parquet_path = r\"files\\partition\\0923\\*\\*.parquet\" # needs asterisks to load in all folders and files" + "pl_parquet_path = (\n", + " helpers.FILES_PATH / \"partition\" / \"0923\" / \"*\" / \"*.parquet\"\n", + ") # needs asterisks to load in all folders and files" ] }, { diff --git a/notebooks/02.exploratory.ipynb b/notebooks/02.exploratory.ipynb index 13c568e..1163e96 100644 --- a/notebooks/02.exploratory.ipynb +++ b/notebooks/02.exploratory.ipynb @@ -99,7 +99,9 @@ "metadata": {}, "outputs": [], "source": [ - "pl_parquet_path = r\"files/partition/0923/*/*.parquet\"" + "pl_parquet_path = (\n", + " helpers.FILES_PATH / \"partition\" / \"0923\" / \"*\" / \"*.parquet\"\n", + ") # needs asterisks to load in all folders and files" ] }, { @@ -753,7 +755,9 @@ "outputs": [], "source": [ "# write to file for predictive model\n", - "pl.from_pandas(grouped_df).write_parquet(\"files/dataset/full_mortality_grouped_1223.parquet\")" + "pl.from_pandas(grouped_df).write_parquet(\n", + " helpers.FILES_PATH / \"dataset\" / \"full_mortality_grouped_1223.parquet\"\n", + ")" ] }, { @@ -771,7 +775,7 @@ "metadata": {}, "outputs": [], "source": [ - "pl_parquet_path = r\"files/dataset/full_mortality_grouped_1223.parquet\"" + "pl_parquet_path = helpers.FILES_PATH / \"dataset\" / \"full_mortality_grouped_1223.parquet\"" ] }, { @@ -2293,7 +2297,7 @@ } } }, - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.pdp(\n", - " model=model,\n", - " df=md_encoded[\n", - " (\n", - " (md_encoded[\"insurance_plan\"] == \"UL\")\n", - " & (md_encoded[\"attained_age\"] >= 65)\n", - " & (md_encoded[\"attained_age\"] <= 95)\n", - " )\n", - " ],\n", - " x_axis=\"attained_age\",\n", - " line_color=\"smoker_status\",\n", - " weight=\"amount_exposed\",\n", - " secondary=\"death_count\",\n", - " mapping=mapping,\n", - " center=\"per_x\",\n", - " display=True,\n", - " n_jobs=-1,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "65b9375f-e50b-43f7-bdad-9eaee719fc0e", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m 2025-08-03 10:46:28 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Model: [CatBoostRegressor] for partial dependence plot. \u001b[0m\n", - "\u001b[37m 2025-08-03 10:46:28 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Weights: [amount_exposed] \u001b[0m\n", - "\u001b[37m 2025-08-03 10:46:28 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m x_axis: [duration] type: [passthrough] center: [global] \u001b[0m\n", - "\u001b[37m 2025-08-03 10:46:28 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Line feature: [insurance_plan] type: [passthrough] \u001b[0m\n", - "\u001b[37m 2025-08-03 10:46:28 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Creating 180 predictions. \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4705d49378124318a4dee2da0d0bef92", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing: 0%| | 0/180 [00:00 \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.pdp(\n", - " model=model,\n", - " df=md_encoded[((md_encoded[\"duration\"] <= 30))],\n", - " x_axis=\"duration\",\n", - " line_color=\"insurance_plan\",\n", - " weight=\"amount_exposed\",\n", - " secondary=\"death_count\",\n", - " mapping=mapping,\n", - " display=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "6cfb6ee5-1560-4347-9d31-86a1e20b20ca", - "metadata": {}, - "source": [ - "This shows that UL, ULSG, VLSG had upticks in 2018-2019, while Perm had a noticeable decrease. This may be related to company differences in 2018-2019" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "d8acb7f8-db3f-4d96-be45-2513a5d08c74", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Model: [CatBoostRegressor] for partial dependence plot. \u001b[0m\n", - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Weights: [amount_exposed] \u001b[0m\n", - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m x_axis: [observation_year] type: [passthrough] center: [global] \u001b[0m\n", - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Line feature: [insurance_plan] type: [passthrough] \u001b[0m\n", - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Creating 48 predictions. \u001b[0m\n", - "\u001b[37m 2025-08-02 16:20:05 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Running '16' cores for parallel processing. \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8b9af548689a458ba80f2c0e32f6dfe1", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing: 0%| | 0/48 [00:00 \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.pdp(\n", - " model=model,\n", - " df=md_encoded,\n", - " x_axis=\"observation_year\",\n", - " line_color=\"insurance_plan\",\n", - " weight=\"amount_exposed\",\n", - " secondary=\"death_count\",\n", - " mapping=mapping,\n", - " display=True,\n", - " n_jobs=-1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "b0ae0117-6591-4306-b38a-8288d92a7a82", - "metadata": {}, - "source": [ - "## Shap" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "66fffc1e-9dac-48e8-836b-e9fa9629257b", - "metadata": {}, - "outputs": [], - "source": [ - "import shap\n", - "\n", - "explainer = shap.TreeExplainer(model)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "5d9cad7a-b2d8-47f0-b463-dd1d2ad358db", - "metadata": {}, - "outputs": [], - "source": [ - "X_sample = X_train.sample(5000, random_state=42)\n", - "shap_values = explainer(X_sample)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "0b9797e8-f487-4886-9922-500c27f9935a", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "shap.partial_dependence_plot(\n", - " \"duration\",\n", - " model.predict,\n", - " X_sample,\n", - " ice=False,\n", - " model_expected_value=True,\n", - " feature_expected_value=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c70b6c53-b7fd-4c20-8b6a-6a9161e9fe3d", - "metadata": {}, - "outputs": [], - "source": [ - "shap.dependence_plot(\"smoker_status\", shap_values, X_sample, interaction_index=None)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "e41c2c5b-d779-48c6-bc20-6b1c8555f7b1", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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T9H/99VcXDCq4y0t6erp7xEPX93cm+KktP/30k+29997WunVry8jIsC+++MIef/xxW7Rokb300ktuGsJFF13kgt6vv/7azjrrrOxMg3bt2rm2r1y5Mupzeo0///yzffbZZ+4aJ554ouvkGD16tL3yyiuuM0Rz9xs2zLvwj7IcRowYYUcccYTrINA5X3vtNdcx8cQTT2S/VrX/+OOPd8GvgnXtO3HiRBs2bJhNmTLFPvjgA9eJIR999JGbGqEg/LTTTnP/6vmxY8dafqgTR23p3r27m25Qs2ZNN13j888/d0G4rtezZ8/s/dXOr776yrp27WoHH3ywu48333yz1atXL8e5//jjD+vTp4/rhOnXr597v1SV9v3333fZIWq3OhYAACVEWpmsivjhVOiufNmsYNufHq9R+4rlzLZH+Tth246sf/V8LN5zKo6W4/id/+4TLcW/bnWzYVeazV1h9sDHeb0ylCbpmbbHHyvN+gTdEIQjwAfiNH369Ozg1k9BnArraZ9Zs2Zlb99tt91ccJdXMC4TJkzItbBfOAXNGkXPjUaKzzvvPDfK7bn33ntdcDp8+HBXELBv377Wu3dv22effVwQv//++9v5558fcZ7cnlNQOn/+/Byv79RTT3XB/XfffeeyAPKizpC3337bBfiikXLdOwXIup+77767u78a6VdGgkbmw6c8PPLII3b11Ve7Tos77rjDjbYPHjzYZVAMGTLETZ2QW265xZ0rPxR8q2PEXzBR51d2w9ChQ13wr84X3V+1fb/99rP33nvPZRnIwIEDrX///jnOrWyFKlWq2FtvvWUHHnhg9nbtf8IJJ9iTTz5pzz77bFyfI+9+KqNCbZZNmzZZKBSyqlWrZndWKAMjfBqKpp+Ed8b4v162bJlb61bZB1yDa3CNRK+x2apk7w1YVrG8UXdG337q/pHbml9kNn+l2dYdZuWj/Ole4Z/fDXo+Fu85dSDkOL5s7OMrlTf7/AazqhXN9rsxsvAekJpi6/6YatuX9kjCn7ubisU1CgMBPhCHdevWuW9yjRhHq4ivb3yvar5GkPWNr6r6Gtn99ttvXRq3f9TfT6nkXiCYF+8HS27Ci/pt3brVvQaN4h922GFu5N0L8HeFRtg9+qG2du1al+WgayhgVzp9PAG+OhHU0eDRvezVq5fLBtiwIWu+nzoZVq1a5To2lFmgYNujlH1v9YP169e70W9lJRx66KGuY8KjjAUF/jfccEPCr1Xvsffe6z7qtarTQUG8qCNC91gp9p9++ml2gB7+nh555JHufQ5fM1Yj9H///bd7XapEG/661GGge6F9lCHinxoSi9oQTp0H4dRR4K8x4f9l4//a/9nkGlyDayRyjeQtsoqATJxndshtkdseHpRVRO/BTyK3L1v3byq9l2ofztu2ZE3s63mp+bGOX70x5+i9Mgo+vM5sz2Zmh99h9neM+f0ovTIybcUxna19Uv7crVIsrlEYCPCBOOezxxq999LhFdArbdybb6/55AoIlSavgM5fmM9P+xbkcnrqVfy///s/N4Ks9HA/Bai7SlMKNEdfo9cKqNWLGU7BdjyUcu/1fnrq1q3rqu4riJapU6e6f3UtPaJRr6k6MzSdwvshqs6AcPkdwRdNBbj77rtdpoaCfP+c++3bt7v/1/QEdQhEe8+1LTzA916XpiXoEY06OXR+AEAJsW5zzuXm1m7KCsRjLUM3YZ7Z/h3U4xxZaG/vNlkj6zNyqTGj4H/FerPuUaYM9mxjNmFu5DZd47XLs9a9H/iQ2Q9TEnp5KAX0GdmrpZXt3ibolsCHAB/Ig9K9VQBNo+YK2qMF0hq91fJ4/mJ6LVu2dAG+gt+8AnyNfOsRD2UDhFfrj0ajx1q6TynfGuFWhVP1LCptXmnlel27SgX7nn76aTf3XOn7GoHWqL4yF+65554cAX9uryevDAfvXCeffHJ2yr2f0qj8vasFRdMClI2gDI0rrrjCdfao51b3UdMh1D7/6/VSunLjHaNsA01BiHaMMg/iHb0HAJRQ7/9idtI+Zif0Mvvgn+Kumq+vbZ/9HjkC37J+1r9zlv+7TcecfaBZk9pmi/4ptntQJ7N2jc0e9XUwP3m+2Sn7mV34rNlHvxX6S0MxpL9fBvdz2atILgT4QB40x1yjwiquF20Nd40yS7Rg1gui4wl0VSyuoObgK1Vcwb2K373++usRy5foOn65BaK5Padzt2nTxhXEC89ueO6556yg6Tqi90DF88JrC/ipY8Wb5+SlzXtUjC8/Xn31VXfNZ555xhXC82d3+K//zTffuNF5b/pArP2916X7rPOqeB8AAFED/F+mmw0dbLZ7E7NVG83+c4RZmVSzW9+O3PfbrBVwrMXF/26754OszoDv7zB7/AuzKhXMrj3WbNI8s6Hf/bvfFUeZXdrPbMw0sy3bzU4/IPLcCvi1HaVb1QpmA/e1adOmUgg4yRDgA3EW14s1Aq/RVQVn8+bNsx49ekTMj/dSsZVunpeCnIMf3hER3rmggPf555/Psb83h0jFQxJ5Ttfxj1yrw+PBBx+0gqY6BprXpI4LBc/hQbaoE0ap7BrF79atm+sEGTNmjP3444/ZI/56/qmnnsrX9b17Gv5a9f+33eabQ2lmxxxzjL3wwgv27rvvumkb3vuq4nvh6flegUZ9trSSgObuKxsgPJtB0yBU/4ERfAAo5TRocORdZg+ebXZ5/6yq9+NmmQ16Mvf0fI9G7fvcbPbIILP7zsga8f/iD7OrX4kc/e+StRqN7dM+6+HnFf1D6aVOpUEHZRVhRNIhwAdyoWBV67krQI+V+q1UeVV015JpSuNWsKYAXEGZUvc1rz6v9PyCnoOv+ewHHXSQS8dXwKhCcCrSpsr2GiHWqHY4FbQTLW+n16x91OGgtPHcntPSeEr31xJ6+lrTFTTnP54igIlS0cA333zTBc+afqDraVqEAntNoVAxwyuvvNLuvPNOF4xrSTvtp6kDH3/8sXv/tNycfxpFvHQuLYV36aWXug4Dve+qlO+/l14xPa2eoDapar63TJ4Cfq0CoKwQjzqHVD1fKxJoyUIVJ1TQr+wP1RJQZ4auF6vuAACghDjwlvjm7l/wTNYjN+Ej9+GmLDQ7Ikr1/nDnPJX1AGLJyDS7+HD3vwxAJB8CfCAXGm3VKG1eAbpS4TWSr/RrVadXATYFpCroptHkeJc3K0gKFC+//HI34q1RbI1sKxBXSvhll10Wse++++5rd911lxvdVhE5tf+oo45yFehze+7xxx93wbwC1wceeMAF0VrqTkGt5soXxii+1oxXFXxV2NeIt6rMa4RcnQ3HHXdc9r5K41eHxo033mhvvPGG6/RQoK316eOp7O93yimnuCwGvU6l6eu6qnKvGgRaBSCcgvZPPvnErrrqKndvVAVfy/5pCT+N1KsmQ3hHg1Lb1EGktmq1AD3UgaCOJaX4n3HGGbt45wAAAAqAsgx7tzXbfbfsTEMkl5RQvFWwAAC7TJ0+6ihQxwtz1oBSQqtupJ0UdCsAoGAMu9Ls1KxliCdMmMDfM0km97LVAIB80dQBf6/2559/7grvdejQgaqzAACg+KlROWslByQtUvQBlFqabqB0+byWJ1RxP03BSITS8UeMGOFqGNSrV88F9qoFoKkbF154YVyFFwEAAJKquN4Fh5qV/3clI60yheRCgA+g1FIBxRYtWuS5n+bGqw5BIlSjQGn4w4YNsy1btrg5+z179rTrrrsu1yUOAQAAkra43oWHRmyaPXt2xFLJCB4BPoBSS8X5NKquADw3qmqfKFXS1wMAAKBEjN732cOsdcMcUxKRXAjwAZRaqlSvJegAAACQx+j9f47IsVlTD5FcKLIHAAAAAIitTlWzY3rk2Ny0adNAmoPYCPABAAAAALHT8y8+3KxszuRvFRFGciHABwAAAABEl5lpdv4hQbcCcWIOPgAAAAAg+uj9YZ3NmtWL+nSjRo2KvEnIHSP4AAAAAIAYxfX6Bd0KJIARfAAAgCIQSk2xlJSUoJsBAPEJhczq1zDrF3u54CVLlli9etFH9xEMAnwAAIDCVKaM2fMX29rPf7FatWoF3RoAiJ/S8/UzDMVGSiikrhkAAAAUpnnz5lnz5s2DbgYAFJjt27db+fLlg24GwjAHHwAAoAjUqVMn6CYAQIFasGBB0E2ADwE+AABAEZg1a1bQTQCAArV58+agmwAfAnwAAAAAQMIqVqwYdBPgwxx8AACAIrBu3TqrUaNG0M0AgAKTnp5uaWnUbU8mjOADAAAUAVJZAZQ0kydPDroJ8CHABwAAKAIrV64MugkAgBKOAB8AAAAAkLAGDRoE3QT4MAcfAADk341vmg35JuhWBC8lxeyl/5j17x5zF/3JlaL9AKCEWLVqFUuAJhkqIgAAgPx7d4zZivVBtyJ4qSlmTwzPNcCfOnWq7b777kXaLAAoTIsWLSLATzKk6AMAAOyqzJDZ1xPM5q2IucuOHTuKtEkAgNKHAB8AAKAgpKaavfh1zKerVatWpM0BgMLWvn37oJsAHwJ8AACAgpCRafb8V2Y7dkZ9mmJUAEqaxYsXB90E+BDgAwAAFJTVG80+GRf1qRkzZhR5cwCgMG3cuDHoJsCHAB8AAKCglEk1e3pE0K0AgCJRvnz5oJsAHwJ8AACAgkzTH/232YwlOZ5q2rRpIE0CgMLStm3boJsAHwJ8AACAgh7Ff+GrHJu3b98eSHMAoLD89ddfQTcBPgT4AAAABT2KP+Qbs22Ry+ItX748sCYBAEoHAnwAAICCtn6L2Qe/BN0KAChU9erVC7oJ8CHABwAAKGipKTmK7XXq1Cmw5gBAYahQoULQTYAPAT4AAEBBywyZ/TLDbPL87E0zZ84MtEkAUNAWLFgQdBPgQ4APAABKtuqVzJ6/2GzFULNNw8y+u92sa8vCv25aqtlz/xbb27ZtW+FfEwBQqhHgA0ha3377raWkpNjVV19tyay4tBMolVJSzL64yey0/c2eGmF23Wtm9aqbjbrDrHXDwr12eqbZq9+bbc4K7KtWrVq41wOAIsYyecknLegGAMi/devW2axZs2zRokW2YcMGy8jIsGrVqlmLFi3cXM+yZcvmevyUKVPsp59+cv9/1llnMY8qF7/99pu99NJLdv7551vPnj2Dbg4Az/d3mM1bYXbOU9GfH9DbbN/2ZgMe/Lfo3btjzGY8ZXb7yWanP1a47du0zeydn83OPdgaN25cuNcCgCKm1UH0dyeSByP4QDE2ffp0t/6ogvq99trL9t57b6tevbr9/vvv9sknn1h6enrMYzdv3uyC1rw6AZBlwoQJ9uKLL9qff/6Z47m+ffvaxo0b7b777gukbQByoQB/2VqzD3/9d9uqDVlB/rE9zcqlFX6xvaeGu/+dNm1a4V4LAIrY+vXrg24CfBjBB4qxli1bWteuXa1cuXLZ23bffXcbN26cjR8/3v0x2bFjx6jH/vzzz65joGbNmi4LoDRZvXq11a5du8DOV6ZMGatSpUqBnQ9AAerawuzPOWahUOT2sTPNLjrMrG0js8kLCrfY3vi5Zn/OZlgFQInDQFHy4VcNUIzVrVs3Irj3tGrVyv27du3aqMfNnTvX5s+fb/vvv7+lpibHj4E33njD2rVr516PshBOPfVU27JlS479rr/+ejfffdSoUTme0/ENGjTI/lqj6nXq1LFu3brZp59+al26dLFKlSpld3osXLjQzj77bGvTpo2bG6tfUg0bNrQLLrgg4tq65sUXX+z+/5JLLnHX10PnzW0O/qZNm9z+SsvVuXWNAw44wGVYhPOOv+qqq+zJJ59075/2V+fLeeedl2smBoA8NKxptjTKz0JvW6NaRVZsr0mTJoV/LQAoQnvssUfQTYAPI/hACaTAUipWrJjjuR07drjR+w4dOli9evXcPPxEaJ7/zp0749pXQWv58uXz3O/NN9+0c845xwXfCmgrV65sH3/8sZtCUBBUo0AdBoceeqgdf/zxtn37drd94sSJbirDIYccYgMGDHDtHT16tJtrP3nyZPvll6z5uieccILrEHn77bfdfpoKIbnNOdN91vV+/fVX6969u+tIUIfCBx98YPvtt5/98MMPOebyf/bZZ7ZixQo7+eST3Xvz+eef28svv+z+/9577y2QewEUa2llsirihyubZla+rFltXwG7NZuyRu0rljPbHqWTbNuOrH/1fGFTsb3XR1vmdf3M6tQp/OsBQBFOYdQACpIHAT5QwmRmZrr0fAWrrVu3zvG8guZQKJTvQnFK51cQHA+lrZ922mm57qNg+9Zbb3VtUkCtKQZyww03uMC4IChofuyxx+yKK66I2H7ggQfaypUrI9LL1I5TTjnF3n33XRec9+rVy90rzbNXgH/wwQdnj+bn5vnnn3fHH3744TZixAj3fshxxx1nAwcOtAsvvND9UgynDoC///47OwNDmQPKSnjhhRcI8AFRsbxRd0bffur+kduaX2Q2f6XZ1h1m5aP8uVPhn8BezxeFbTts80djzK5tXjTXAwCUSsmRmwugwChIVkVTBcc1atSIeG7ZsmU2depU6927d9TU/njstttuduSRR8b1OOigg/I8n0bRZ8+e7QJnL7iXWrVq2RlnnGEFQanxgwcPzrFdmQJecK/1qZcuXWqLFy92I+8SbRpAvDQaLyq85wX3ogwC9XTrdatzIZwyCbzg3usg2XPPPW3NmjXZWRnx0P5eloLoWE1XCM8uUB2CcHrtuX2tz446P7gG14h1jSIxcZ7ZIbdFPrTty/E5ty9b928qvtL0/bxtS9YUSdNDZcvYph7NS9R7zjW4BtfgGpoKWRJeR1DXKAyM4AMliIrraQS4ffv2rvieP7X+xx9/dPPBo43sx0tp9HoUFK+qtKYM+Cm4LQia96pCeH6aaqBMgXfeecel8Yf/EBf/D+lE6Ae46hv456Zpm4J4VeNXLQTVUfBEe1+8Thq1Jd5CfuocCec/Tp07/iKDqj2Q29fhtQ24BteIdo0isW6z2beTIret3ZQVxPu3eybMM9u/g+YMRRba27tN1vr0M5ZYoUtLtZSB+1j7fXuUqPeca3ANrsE1tL9/QKk4vo6grlEYCPCBEkKF25Sa37ZtW1c8z0+B/7p161zKefiSJuptlA0bNrj/V2X93Kjgm3dMXjRyHa0OQGFNTVAnRjSx2nDppZe6pe969Ojh0u7V+aEfzqpLcNddd7lzFqVonRAef+cDgDi9/4vZSfuYndDL7IOsuhpuvr62ffa72Y70opmDf8kRrlNPP6MBoKSYN28ec/CTDAE+UEKCe40I6w/HPn36RKSEh6cRKUjUfPBoVNQuLS3Nzj333FyvpXT6gpyDrznmoqkDfpMm5RyR83pGlTYVTh0UGuWOp6ifKMVq5MiRrndWc+XDVxPQfH2/aPc0N40aNXKF+tSxEv6LT50Gc+bMybNIH4ACDPB/mW42dLDZ7k3MVm00+88RZmVSzW59u/Cvrx8dWopvn/a2ZeLEwr8eAKBUI8AHirk//vjDBfda6i1WcB9tCTmPAlClk+vYeIJjbw5+PNRhkBcFvy1btnRLxWnk3JuHr3lOWjrPz3tec9xVDM/zyCOPuAyF+vXrx9U2b6k7PcJHx9VR8Nxzz+XY38ts8M+bj+Woo46yr776yk0B+OKLL7LfF7VbmRadO3eOSM8HUEiUiXPkXWYPnm12ef+sqvnjZpkNerJo0vMV4Q8+0k0RUN0PAChJdmXaJwoHAT5QjCk4V4CvkXKll6vCvT813Vt3WSPf/nlBsmDBAhfgN2vWzCpUqFDkc/DVqXDbbbe5zAEV/9OIv7dMXjQqgKd57W+99ZYbhVfAr3swZswYt5xcvJSKryr6r776qpu20L9/f5cB8Omnn0ZU1fdoaTzdn2effdaNwlevXt2aN2/uquJHc9FFF9mwYcNcxoTOrwJ63jJ5Or8q4wMoAAfeEt/c/QueyXoUNVXwP7OP+1/9nAWAkiSRGkEoGgT4QDHmjSYr/T5axXcV8vAC/GR25plnulF0zXsfMmSI60Do16+fnX766XbMMcfkCMy1XJ0CaI2Ma614jYbr9Wv5ufD6Anl5+umn3fk++eQTu/vuu12RGF3vxBNPdAF/OKXTP/DAAy5TQO1ULYK99torZoCv82oE/7rrrnOdBjpWnRk65tFHHy2wJQABJLG0MmanHWBWPWvkXllKzFUFUJKsXbuWzsskkxKichMAAMivNpeazSr8ZX+KrbH3m/Vo4/53woQJBPgASpS//vrLOnXqFHQzEIYRfAAAgIKmuhudmpp1bx1RfBMAShKC++Tzb9loAAAAFJBQdnG9/K7GAQDJLtqKRwgWAT4AAEBBq1je7NT9IjYtXrw4sOYAQGFQ4WEkFwJ8AACAgpSWanZWX7MqFYNuCQAUqlq1agXdBPgQ4AMAABSk9Eyziw/LsblDhw6BNAcACotWIEJyIcAHAAAoKKkpZj1am3VukeOphQsXBtIkACgsc+bMCboJ8CHABwAAKCiZIbNL+0V9atOmTUXeHABA6UKADwAAUFCqVjAbuE/UpypUqFDkzQGAwtSiRc5sJQSLAB8AAKCgiuude0hWBf0oWrduXeRNAoDCtGHDhqCbAB8CfAAAgEIsrueZPHlykTYHAArb6tWrg24CfNL8GwAAAJCP4nr7tDdr3yTolgBAkUlJSQm6CfAhwAcAAPl3UEcLzVlmpf5PPP2RG6O4nqdBgwZF1hwAKAqdO3cOugnwSQmFQiH/RgAAgHhNmDDBunTpEnQzAABFTFOPOnbsGHQzEIY5+AAAYJfUqFEj6CYAAAKQnp4edBPgQ4APAAB2SZ06dYJuAgAgAHTwJh8CfAAAsEtmzZoVdBMAAAGggzf5EOADAAAAABJGB2/yIcAHAAC7pHnz5kE3AQAAEOADAIBdtXnz5qCbAAAIAB28yYcAHwAA7JKVK1cG3QQAQADo4E0+aUE3AAAA5CEz0+zxL8yWrwvm+kd1N9uvQzDXBgAkdQdv48aNg24GwhDgAwCQ7Eb/bXbVULO0MmYpRXztjEyzn6ea/XhPzF06d+5cpE0CAADREeADAJDsFGRLekYw1/9pmtm0RWbtm0R9eurUqbb77rsXebMAAMGigzf5MAcfAADkrkyq2XNfxXx6x44dRdocAEByUAcvkgsBPgAAyDuD4OVvzLZuj/p0tWrVirxJAIDg0cGbfAjwAQBA3jZuM3t3TNSnGjRoUOTNAQAEjw7e5EOADwAA8paaYvb0iKhPzZgxo8ibAwAIHh28yYcAHwAA5C0zZDZultnEuUG3BACQJOjgTT4E+AAAID5p0YvtNW3aNJDmAACASAT4AAAgPumZZq+NMtu0NWLz9u3Ri+8BAEo2OniTDwE+AACInyrpv/VTxKbly5cH1hwAQHDo4E0+BPgAACABKWZPfmEWCgXdEABAwOjgTT4E+AAAIH4K7P9aYPb7rOxNnTp1CrRJAAAgCwE+AAClXfVKZs9fbLZiqNmmYWbf3W7WtWXcxfZmzpxZNO0EACQVOniTDwE+AAClWUqK2Rc3mZ22v9lTI8yue82sXnWzUXeYtW4Yu9jesB/M1m12X27btq1o2wwASAp08CYfAnwgyQwePNhSUlLst99+C7opiNNRRx3l3rONGzcG3RQgp+/vMBs6OPbzA3qb7dvebNBTZne8a/bMSLO+t5hlZJrdfnLs47bvNHtjtPvfqlWrFkLDAQDJjg7e5EOAjxLj66+/thdeeMFWrVoVc59QKGRvvfWWvfLKK5aenu62DRs2zN57770ibCkAJBEF+MvWmn3467/bVm0we3eM2bE9zcqlRT9ONfaeGu7m5Ddu3LjImgsASB508CYfAnyUGO3bt3f/zpgxI+Y+S5YscaOsrVq1srS0GH+0BuyRRx5xbezRo0fQTQFQGnRtYfbnnJxV8cfONKtcwaxto9jHTl9iNmaaTZs2rdCbCQBIPnTwJh8CfJQYTZo0scqVK7u5QBkZGVH3mT59uvu3Xbt2lqzKlStnVapUsdTU5P72VAbE+vXrg24GgF3VsKbZ0rU5t3vbGtXKvdjesyMLr20AgKRGB2/ySe4IAkiA5kArcN++fbvNnz8/x/M7duywuXPnWs2aNa1evXpWnObge9t++OEHO++886x27dpWtmxZa9asmb344os5zvHoo49a27ZtrVKlSla+fHn3eg866CCbNevfZa3q169vu+++e45jhw8f7q516623RpxP24YOHWqXXXaZNWjQwCpUqOCyDeTdd9+1Aw880J1THRS6bpcuXeztt9/OcX5tr1Onjs2bN88OOeQQ1ymjNnbt2tV++eWXHPvr/bzmmmusRYsWbj+dWxkY1113XcR+a9assUGDBrm26d4oZaxv3772+++/J3T//ffglltusUaNGrlzNmzY0K699lrLzMzM8xx//vmnnXDCCbbbbrtZxYoVXdtbtmzpzuc/PtH3F4gprYxZ7aqRj7JpZuXL5tyu4npSsZzZ9qwpSxG27fj3+VhUbO/dMbZbpRqF9IIAAEAikjNHGcgnBbUKrDRSr2Aq3OzZs93IvpfKH0RxEQW/uzIyf8EFF7jjzz33XDeC/uqrr9oll1zigmYvpf+JJ56wq666yjp06OCCcQXQCxcutFGjRrmgunXr1vm+/h133GE7d+60k046yXWU6Lry0ksv2dq1a+2YY46xpk2buqkQqmtw+umnu7oHp556ao6gvXfv3q6Naqs6ZFQb4cQTT3TvkwJir1Nm7733tokTJ7prHXfccS7A//vvv10Q/sADD7j9dG09v2LFClfwrmPHjrZ8+XJXX0EdD6NHj7a99tor4df7xhtvuI4Dvd4aNWrYxx9/bA899JCr86DOjtyMHDnSxo0bZ3369HGfxS1bttjnn39ud955py1btszVi8jP+wvkSsXyRt0Zffup+0dua36R2fyVZlt3mJWP8udAhX8Cez2fm/RMK/fWz2a3Nt+VlgMAimkGLZILAT5KlGrVqrnR1kWLFrmASsGgR0G/gqc2bdoU6DVfe+21uPdV8Kn25ZdS9zWy79UPOP74410AqZF0Bcjy4YcfutF17VfQhU/UmTF58mQ3whzunXfecQFwuJtuuskF8ArC/QH+pk2b7IwzzrBnn302e5vuy/3332+vv/66XXjhhW7bzTff7IJ7Hf/mm2+6UW5P+DQMjYAraP7ss8/s8MMPz96uzoM999zTjbp/++23Cb/exYsX29ixY905RMG5/l/v+eWXX+6yDmK59NJL7X//+19Em3Uv1NGg1/jggw9a9erVE35/46FOCS8zwrvf6mjxPg/qOFGdh/D3cenSpS5DIdbXur/K0PBeD9co2musXr3GIr/rYpg4z+yQ2yK3PTwoq4jeg59Ebl+27p+Lrc1K0/fzti1Zk/s1QyFbk5Ju5ZPkXnENrsE1uAbXKLpr6O+xkvA6grpGYSDAR4mjEXqNIKvYnjfCvG7dOje6qzRvBb8F6cgjj4x7X39gnKj//Oc/EcUBDzjgAPd6NDIf3smhHzDe6G+ZMmWsoAwcODDqawgP7nWvN2/e7H7g7bHHHvbXX3+59ih7waMflLfdFhmEaPRfAb5XJ0GUBaBOmqeeeioiUBbvdek6CuzVmaDrqXPHo0wAjeb/8ccfbkQ80cKKmtbgBfeiH+hXXnmlu68KuHML8MODd90P3Re1VedUp8Wvv/4a0RkR7/sbj1q1auXoOAin98L/Pvp/2fi/1tQHrhHcNWrXzmUefDitS//tpMhtazdlBfH+7Z4J88z275CVsh9eaG/vNmabt5nNWJL7NatWsHWH7W7NkuRecQ2uwTW4BtcoumsoYPX+3i7OryOoaxQGAnyUOM2bN3ffYAoUvR84XgGQwiiuV5SpSdGmFyig37BhQ/bX99xzjxt1Vnq+RpAVoB522GEuKN3V2gOxpjcoZV4j2gpalTkRToG5P8BXh0DdunUj9vMyG9Q7Gj6CrpR//w/UcCtXrnS9pZMmTXLz3aNRG5R94P9BHM+UD7/u3bu7f1XPITd6TzSKP2LECFu9enWO56Mt5xjP+wsUuPd/MTtpH7MTepl98E8dDM3R17bPfjfbEWV+vqdMqtk5B9vu3SL/uAMAAMEgwEeJoxFQzTOfMmWKS61RUKvK+kqpiRUA7gp/QJsbjQDvyoh6rBFojQx7NGKt4PP9999388CV8q3R8scee8x9rTntudEc+1h0D/0UXGukeevWrXbaaae5UW0F8JoOoTT08ePHR7RP9FysWgT+ffPi7d+pUyfXoRFLQWduxJPZMWbMGPev6gDoc6j374svvnDTDaKt9BDP+wsUSoD/y3SzoYPNdm9itmqj2X+OyAreb81ZKDNCRqbZxYe7nznROsQAACWbsieRXAjwUSJpJFQBvtL0VdBNwacCT3+ad0FQIbaimoMfL6Wmn3nmme4hqmaveeyaQ65Cb97IsILzXV3uRIXnNOqu+fIqwhfO/3V+siM03UJF9FTULxplAqjjQWnwp5xySoG+x/r8+HlV+TXdIxal4yu4VxV/73578lMLAChUWtXhyLvMHjzb7PL+WVXzx80yG/Rk7un5qSlm+7Q369DEtkyYUJQtBgAkCTp4kw8BPkokLcOmOTCqyK7Ar7DS84t6Dn48NAfdP21g//2zqmeHr1uvAPW7775z88E7d+7stimN/fnnn0/oerFGnT/44INdXhtV1es1L1/p/ypsFx68a6k5LxNAHScq9Pfwww+7JfX8NIddUzcSpfuj1H9vHr46i7RkoK6pzoRYwusDhFuwYIFbUhAoUgfeEt/c/QueyXrEKzNkdmm/mNk9AICSL5FMVhQNAnyUWAroNYqqJeJU0EIj1rEosNXyerHOk9sfr8m2PIjSwZWOrlR8raOu0WSl6ys41rJ1HgXCX3/9teug0HYVoVMFfq8yaLxUKE4p+Qqu1bmgayooViq65s8rqM0vZQBoOTxlSWiev16biu4pO0PTLnQdUTV+LUl33XXXuYJ7PXv2dFkMCuy1RJ6mbORn5Lxx48auir2KC+o1fvTRR+66yozIbdk9VVPVsna6tjof9F5oKUAVDVSqfrTMCaDYqVHZ7PisKT/6vgcAlD508CYfAnyUWFoOT/PPNdc5r9F7pfB7qdfRgrzi9MNLa6grkFRQr+U7VFhOAa6K74UH+Cq8p6BcDy3DphT4k08+2W0/9thj476eCuApCNcou0bRdb91PVXx13JwuxLgqzCf3sP/+7//c8H1E088YWXLlnXTHAYMGJC9n9quTITrr7/eBfi//PKLG2VXxoQKLV588cX5ur6W8lOmwJAhQ1xRPJ1PS++ptkBePvnkE7fc348//mhffvml62S64oorXFt1DqBY0/z8Cw81K1/WfalON38VZQBAyUcHb/JJCVG9CQAiqMOif//+dsstt9jtt98edHMAs28mmh2aZJ/FWc+YtcpaQmjChAkE+ABQCvHzP/kwgg8AABIbve/bMTu4l6IoHgoAAPJGgA+gVNDUAc2Dz0uDBv8GLQBiLI2nZfTCFMYKJQCA5EcHb/IhwAdQKqjYYqtWrfLcT7UDtAoDgBjqVjM7unvEpsWLF7slKwEApQsdvMmHAB9AqaCR+WHDhuW53wEHHOAKK1KeBIiRnn/x4WZl+fMBAEAHbzLiNzSAUkFLB5566qlBNwMo3jJDZucfkmNzhw4dAmkOAACIlOr7GgAAIProfb+uZk3rRp0CAwAofejgTT4E+AAAIF/F9TybNm0q8uYAAIJHB2/yIcAHAAB5a1TT7IiuMafAAABKHzp4kw8BPgAAyF1qitklR5iVKRP16datWxd5kwAAwaODN/kQ4AMAgLydd3DMpyZPnlykTQEAJAc6eJMPAT4AAMmuWqVgr39sT7OGtYJtAwAg6dDBm3xYJg8AgGTXs43Nev5Ma10toLWGu7fK9ekGDRoUWVMAAEBsBPgAABQDaYd1NWve3JIRAT4AlE78/E8+pOgDAFAM1KlTJ+gmAAAQoWzZskE3AT4E+AAAFAOzZs0KugkAAERYuHBh0E2ADwE+AAAAAAAlQEooFAoF3QgAAJC7devWWY0aNYJuBgAA2bZu3WoVK1YMuhkIwwg+AADFwObNm4NuAgAAEZYuXRp0E+BDgA8AQDGwcuXKoJsAAECEDRs2BN0E+LBMHgAABWnLdrPZyxI/LjXFbI+mhdEiAAAKRfny5YNuAnyYgw8AQEHqf5fZ8D/zd+xfj5p1bBb1Kf26TklJ2bW2AQBQgDIzMy01laTwZMK7AQBAQZq9PH/HpaWaPfdVzKenTp2a/zYBAFAIJk2aFHQT4EOADwBAMkjPNHvlO7PN26I+vWPHjiJvEgAAKF4I8AEASBabt5u9/VPUp6pVq1bkzQEAIDf16tULugnwIcAHACBZqNDeUyOiPtWgQYMibw4AALmpWLFi0E2ADwE+AADJIjNkNmGu2R+zczw1Y8aMQJoEAEAs8+fPD7oJ8CHABwAgmbhie18G3QoAAFAMEeADAJBsxfbe+MFsw5aIzU2bNg2sSQAARNOmTZugmwAfAnwAAJLN9h1mb/4QuWn79sCaAwBANCtXrgy6CfAhwAcAIBk9OdwsFMr+cvny5YE2BwAAv3Xr1gXdBPgQ4AMAkGwU109dZPYrhfUAAMmrbNmyQTcBPgT4AAAka7G9Z0dmf9mpU6dAmwMAgN8ee+wRdBPgQ4APAECyFtt752ezNRvdlzNnzgy6RQAARJg4cWLQTYAPAT4AAMlqZ4bZa6Pc/27btq3wrrNus9mFz5rVHWRW+VSzA28x+3N24V0PAFAihMJqxSA5EOADAJCs9IfTU1nF9qpWrVo418jMNOt/l9mwH80G9zN74CyzFevN+t5iNnNJ4VwTAFAi1KlTJ+gmwCfNvwEAgrRkyRL7/PPP3f/vvvvutt9+++XYZ+vWrfbmm29aZmamNWzY0I4++ugAWgoUkdnLzX6YYo33bpW/4/vebNa8ntkrl0V//v1fzMZMN3vvGrMB+2RtG7iPWdvBZre+Yzbsyvy3HQBQohVa5zPyjRF8AEmpTJkyNnv2bMvIyMjxnOYiKyUsJSUlkLYBQRTbmzZtWuGcXwF+/RpmJ/T6d1vd6llB/idjzbbvLJzrAgCKvblz5wbdBPgQ4ANISs2bN7ft27fbvHnzcjw3ffp0a9q0qesEAEpFsb0PfrG0NZsL5/zj55rt1dIs1fcnQc82Zlu2m80gTR8AgOKCAB9A0s7pqlWrls2YEbkO+IoVK2zt2rXWrl07C7rH+phjjrG6deu6NWCrVavm2nTzzTdH7KdMg3vuucdat25t5cuXtwoVKljHjh1t6NCh2fts3LjRGjdubNWrV7dJkyZFHD948GCXqXDdddcV2WtDEsoMWYtRhTRKsnStWcOaObd725asKZzrAgCKvVat8jl9DIWGOfgAkpYC5l9//dU2b95slStXzh69r1ixohvBT4SyAeKt9KqAPbfsgPT0dOvTp4/rbDj++ONdrQAF6ZMnT7ZffvklYl91AnzxxRe2zz772EknneSO/fjjj+3888+3VatW2bXXXuvmr73//vt2wAEH2Jlnnules16jjnvmmWfcsXfffXdCrxclTGbIKrz8vdltp+UcaQ+3M91s/Zac25Rmv2pD5PZaVbLOtXWHWfkofw5UKJf1r54HACAKDbowDz+5MIIPIGm1adPGjV57o/gKjjUvX9tTcwtyovjggw/stddei+sxa9asXM81ZcoUW7hwoZ122mn21ltvuVH7Bx54wIYPH27ffPNN9n5vv/22Kxh4xRVX2E8//WT33nuvPfjggzZ16lT3Gu6//35bv36927d3797uPBrBv+yyy2z58uUu2K9du7a98cYbrtMhXmvWrHEdGp5Nmza5DgjPjh07bPXq1RHHLF26NNevly1bFtFBwjVif52ekW6FIXXpOrO1m3N/HT9Py1rqLvyhAnpv/5Rj+4rfp2QdXLGc2fb0HK9jzdLl/z5fjN8PrsE1uAbX4BqFdw0dXxJeR1DXKAwpIRYvBJCEVfT33ntv69y5s3399dfuB+rJJ5/siut9//33biS8Zs2a9vLLL7sU+Xiq6OuHtDoI4qGpAZUqVYr5/Pz5812NgD333NPefffdmNMFDj30UBfYa0RegXq4Rx991B555BHXKdCvXz+3TT+ONYqvLICWLVvanDlz7NVXX7XTTz89rnYjSbS/zGz64oI9Z5lUW7d/G6vx/b2577d2k9kfvvXrr37FrEFNs2uPjdy+X4esUfo2l5q1aWg2/KbI51/6xuz8Z8wmPWrWqVkBvRAAQEny119/WadOnYJuBsKQog8gqSl4HjlypAvQlZ6vgF7BfaIaNGhQYG1q1qyZXXrppS59vkOHDu7rXr162SmnnOJS8r3q/uqQ2LZtm3Xp0iXXDg2Pjvvwww+tRYsW7tizzjrLZQkAlpFpVa49Me/9alYxO6Rzzm2aT+/f7unS3OzHqWaZmZHp/7/NNKtU3qxto11sPACgpCK4Tz4E+ACSWpMmTdz8+z/++MMFw/vtt1++zrN169a45+CXK1fO0tJy//H41FNPuSB/2LBhbpT+s88+cyn5J5xwghvV1xx+XU/z0p544glXYC8aza8P9+WXX7qaA6IOjZ07d7r2oJRrWsfmtKxibQvj3AN6Zy2V9+GvZgP++Txqvv57Y8yO7m5WPv7pIQCA0oUR/ORDgA8gqWmuvearT5gwwQXNqkafHx999JGbKxUPFdCLp0q/Ru/vvPPO7A6Efffd143AqzOiZ8+ebiRec/U13UD75kVLAl5yySWuov7BBx/s6gH873//s4cffjiudqOESk0xu7Sfbdm2rXDOrwC/V1uzc54ym7LIrE5Vs2dGuqwBu/2UwrkmAKBEyMjICLoJ8CHAB5D0VKVegb6WosvvaPZBBx2U0Bz8vCrGquhdlSpVsrep6r06BcaPH28rV65021Qpf/To0Xb11Vfbp59+miMrQMF/o0aNXMeFfkEee+yxrnjL888/b4cffrj9+eefLlPgkEMOyZ6nj1Ia4A860Cqvz/pcFTitGKH599e+avbEF1lV83u0NnvlMrN2jQvnmgCAEiGvv5lQ9AjwASQ9BdLdu3ffpXMU5Bx8Vco/77zz3Eh/+/btXceDMgy0/J2K4/Xt29ftd8YZZ7jMAY3qq5NCRffq169vixYtcsG7ivWpkJ7S+K+66ipXQV//9u/f3x3/ySefuLS3Cy+80GUF1KtXr8BeA4qJtFSzE3ub1athzWrELvyYq1FZWSa50jz9IZdmPQAAiFN+6iKhcLFMHgAkqGvXrm5UXaP1GmG/6667XKV8VbtXlX/VDAhfnk9z8JV5oKr/SulX0K8MgGuvvdaN/Gve/ZNPPukK9WkpPY86C55++mnXITBo0CDS4Eqj9EyzS47IXp4RAIBkouWLkVxYJg8AgGRcJk+LMbRqYDbjaS2x4LJEcluRAQCAosbvpuTDCD4AAEkpq7iegntRvQYAAJKJCgojuRDgAwCQjMqWMTsrq56DpPwT6AMAkCw2btwYdBPgQ4APAEAyFtc7ZT+zWlWzNy1eXABp/wAAFKBVq1YF3QT4EOADAJCMxfUuPjzoVgAAkCuyy5IPy+QBAJBM9LdShyZmvdpGbO7QoUNgTQIAIJrOnTsH3QT4MIIPAECyGXxkdnE9z8KFCwNrDgAA0fz9999BNwE+BPgAACST8uXMTj8gx+ZNmzYF0hwAAGLZuXNn0E2ADwE+AADJVFzvjAPMqlXK8VSFChUCaRIAALHUqFEj6CbAhwAfAIBiUFyvdevWRd4cAAByU7du3aCbAB8CfAAAkkFqilnXFmbdWkV9evLkyUXeJAAAcjNz5sygmwAfAnwAAJJBZiiruB4AAEA+sUweAAAF6faTbf1Tn1n1alUTOy6tjNnJ+8Z8ukGDBrveNgAAClCzZs2CbgJ8UkKhUMi/EQAA5N+8efOsefPmQTcDAIBCtWTJEmvUqFHQzUAYUvQBAChgderUCboJAAAUuhUrVgTdBPgQ4AMAUMBmzZoVdBMAAEApRIAPAAAAAEjYnnvuGXQT4EOADwBAAWP+PQCgNJg+fXrQTYAPAT4AAAVs8+bNQTcBAIBCt3379qCbAB8CfAAACtjKlSuDbgIAAIWuWrVqQTcBPgT4AAAAAICENWzYMOgmwCclFAqF/BsBACh13vzB7K738t4vJcXs0n5Zjxj0qzVF+wEAUIJNmDDBunTpEnQzECYt/AsAAEqtV74zm7Y4vn3v+9DsksPNUqMnwk2dOtV23333gm0fAABAHkjRBwAgUYtWm301IebTO3bsKNLmAAAQhN122y3oJsCHAB8AgESVSTV7ZmTMpyk6BAAoDXbu3Bl0E+BDgA8AQKIyMs2++MNs0aqoTzdo0KDImwQAQFFbtmxZ0E2ADwE+AAD5kmL20rdRn5kxY0aRtwYAAIAAHwCA/MjMNHt2pFl6RtAtAQAgEB07dgy6CfAhwAcAIL+Wr89K1fdp2rRpIM0BAKAozZo1K+gmwIcAHwCAAi62t3379kCaAwBAUdq2bVvQTYAPAT4AALtSbO/rCWZzl0dsXr488msAAEqiKlWqBN0E+BDgAwCwK1JTzV78OuhWAABQ5HbbbbegmwAfAnwAAHZ1FP+Fr812/LsWcKdOnQJtEgAARWHq1KlBNwE+BPgAAOyq1RvNPhmX/eXMmTMDbQ4AACidCPABACiIYntPjyiaokPrNptd+KxZ3UFmlU81O/AWsz9nF971AACIoXHjxkE3AT4E+EApdtRRR1lKSopt3Lgxrv2178EHH2wl+TWG+/jjj619+/ZWsWJFd45bb721UNqIEpKmP/pvsxlL3JdVq1YtnOtkZpr1v8ts2I9mg/uZPXCW2Yr1Zn1vMZuZdW0AAIpKKBQKugnwSbN8vIl//fWXm2+xadMmq1ChgrVs2dK6d+9uZcuWzfXYb775xubMmWM1a9a0k046yXbFvHnz3EOVijdv3mzlypVz591zzz2jFnsYNmyYa280Z511lnsd4XTOsWPH2sKFC23nzp3u3F26dHGvNV6JnCMjI8PGjx/v0jp1XOXKla1du3Zu/1QVcPKZMWOGex/WrVvnXrvWXO7Zs6cLRIrCb7/9ZhMnTrS0tDQ799xz4z5u8eLFNmHCBFuxYoVlZma6e9KxY0dr27Ztjn11z/744w+bO3euuyfly5d3722PHj3c/dmVc8eyYcMG+/333925tMyVKoO2bt3avQ96rf7vBX0f6KH3oUyZMlavXj3r1q2b1a9ff5fOjeCtXr3aTj/9dKtVq5Zdd911LmA78MADg24Wkn0U/4WvzB4alP8Rjb43mzWvZ/bKZdGff/8XszHTzd67xmzAPlnbBu5j1naw2a3vmA27Mv/tBwAgQUuWLHF//yJ5JBxV/PLLLzZ58mRr3ry5C6YV2Ohr/THcv39/N8oVzfz5812gpiCoIPz4448usG3WrJnVqFHDpUMq6B0xYoTrbNhrr71yHKP9unbtmmO7v2NC5/r0009t69at7jUqmJw1a5broOjTp48LvPOS6Dm0XfdI2xUcquNCwaCCwr59+0bsO2nSJPv111+tYcOGts8++7jgV9sU2B533HF5drTsqlWrVrnr6TqJ9Nrp9X/33XcuUNL7oKBWn4lRo0a51xD+3qSnp9tnn33mrqUAXT84NAI7ZcoUFyAff/zxVqlSpXydOxZ9ljViq9e0++67u3Ppnv7555/u3379+kV8vn/66ScX3Ot92HvvvV2b9bXafeSRR1qjRo3yfe5kpU6ygvoeTnajR4+2LVu22J133mlXXXVV0M1BcRnFH/KN2V2n2bRp01znXYFTgF+/htkJvf7dVrd6VpD/xg9m23ealS/c3wEAAKCEBPhr1qzJDu4PO+yw7O0KVsaMGWOzZ892I5LRRmIVDCmwURBbEA466KAcIyQarf3ggw9c0LTHHnu4Ed9wGt1u06ZNnufWKLCCycMPP9x1IIgC708++cQF1hqBzyuITuQcCxYscPdFVZd79+7ttiktWB0YGqXX/zdo0CC740CBf926dV2Hije6r6+//PJL9/7EE8zml0bG1bmikXS9rytXroz7OH1G9B6ccMIJ2e+N3qeRI0e6kfpWrVpZtWrV3HYFygruNVof/np0L9VxMm7cONdRkp9z55aVsGPHDjvmmGOy77c+s9WrV3fXUyeC9/lR29RG3YcjjjgiOzjv0KGDvfvuu+4eDRw4MHt7IudOZtEyJ0qqRYsWuX/r1KkTdFNQnKzfkhWEd6xeOOcfP9dsr5ZZS/OF69kmq5K/pgh0yvqdAwBAYdPfsyjGc/AVwEdb/kcBqEZMY1UNVgCjkUsFa7EoSNMoZ6w0er9o6Y9qg1LVvXPFuo4Crbxep4JBLzAXBdIKGJVarYA8L4mcQ8FdtPvqfe09L5qWoJFinSc8dV/XUUdLYVduVgfC2rVrbd9997VEO4fUOaHOofCOFwXACmz1voS/TqX7iD/TQcGxgmLdX92H/Jw7Fl1T5/YCcI/XhunTp+don84fPvKu6+u9WL9+vS1btixf5w7C0qVLXUeFpg3oNSjrRJ0j8czB97Z99dVX7jOr43UeZSUoOyHafHh1aJ144onunqijSz9D1Pnlp58bd9xxh+ug0Xk1lUbXePPNN3Psq2wZTbfR1Ax1jqmzcejQofm6H8qiueKKK9z/n3322a7N3jx+ZdWcf/75LrNE33Nqv/ZXOr8+i9Fewz333OPea7VfD/2cOuecc9zUHI8+w//9739dp5HOqQwVTbvRfUUxkpriiu01adKkcM6/dK1Zw5o5t3vbluT8DAIAUFgKavAWAY3ga7RWf+T651kosK5du3bU0Vz9gf/333+7EXf90R2L0qg18ql056OPPjqRZuU4j0Sbi662vPzyyy7g89L79Qd0+KikUnJ1jmiZCN68ar1OBRyxJHoO/b/aoKAonL7WH/nh99ULmKLN8dY2BbIaWS+MNH0FN8oe0PSHRAtIeYFMtLnm3jZNS4h3fwX3Cqb0WUz03LHoc5HbOfQ+KFjT94D2zeuaeq/0eU703LlJpDK3PuPR6jdEo2Bcn7fBgwe7dr/99ttuusfnn39uhxxySJ7Hq8NF2RMK4BW4K2NHHQQKuKN1FCgDSMH9pZde6jqMXn31VTvjjDNcR0f41AZNdVBmyn777Wcnn3yy+2x/+OGH7rw6Tu31qIPihx9+cN/TarOmaFxyySVRv1fycvfdd9sXX3zhplWoXZ07d3bbFZyrje+99577maapIvpe+/nnn+2tt95yP+uUMRI+jUGvQfdAnUHqGNDPSqVvf/311+5zrH31unr16uWOV9sHDRrkOhLeeecdd0/ff/99l/2BYiAzZPbrDLPJC8z65pH9sTM9a8Tfv01p9qs2RG6vVSVr1H7rDrPyUX51V/jn96ueBwCgiHixF4ppgK83UH/gRpuDqwBVQZSCLe95BTX6g1sjGbkFxAVFdQD0R71GSf3p2BrV0yih5uGrXRqx1B/ZGllVIOMF+d6HNFoqsn+fWBI9hzoE1K5otL9/31jn9uaka/9Y59sVCtp0XzW6myi1R8Gr7rc/kPVGw8Nfp94vpUjrOY3Mh79+LzvD2z/Rc8eiaypo1DXC5/d751AQpuwLfQ9oX++58Pbp+vpsSXg2SiLnzs1rr71m8VJgGB4s50ZTPPT+eh0OGl3WFIhrrrnGTTfJizJSFPQquPdouomCWL2P/tFMBbuqVeC9VypMeMEFF9gTTzxh9913n9umoF+B8fXXX2/33ntv9rH6f6WD3X777e4Yjewr6NfPGgXduqbXsaH7pRH4RCkQV4eWAvxDDz3UdSiEt13ZGf5ORHUmPPfccy4TQZ0d8uKLL7rXoDoa6qgI7+TUz0qvnXotKlr5wgsvuNfk0XZdT/dAHZ/FoU4Dskbx098YZdY3Zy2YCD9Py1rizk9F9N7+KXLb3Oeyiu9VLGe2PSt7KcK2fwJ7PQ8AQBEJ/7sWxTBFX6NNsUYEvaDeS5sW/cGqVOV40rk1InzhhRfme/Re6blKZVWAcsABB0QdodTIs+a+a2R9//33dxWxFfhpxC38NYa/nrxeYzSJnsMbxYtG2/37xjq3F5zl1b78UGaAVgPQSGq8o8LhFIQpRVmdMCp8p3/12VDwqI4Wf7sVwOn1aC67RocVbClwVpDkFfbz9k/03LGo40JBl66hwFvX1LXVBu81e+dRGrWCdhX903V0PV1XhdkUyPuvmci5c6PR4HgfGimO19VXXx2RYaDvDxWrVA0Ive950fdUeHAv+v5SZ5rukd///d//RQSrGgkPnwYkr7zyiuvw0CoN6iTwHgquVdtCdRBU20CUcSD/+9//Ij6fCswLOlVanzcvuNd0H7VH7fJ+dqmjxN8h8+STT+bIYNL3sHcPNFKvbA9lIYS/VnUSqZCmsgbCp3zkRdkt6jDy6DzhywSq3fq8hvM6pmJ9reuHF9UsqdcoEJkh29S9Wd6vo3Nzs69vtZ3Db7QNH1zp/t/2bGZ2WBdb/fZlWV//81ga2vZvKv7StTlex6ZZWTUjrFGtyGsU8/eDa3ANrsE1uEZyX6NFixYl4nUEdY3AR/AVAMRKEfanSSvgUbE7FUjLq7jZrlKbhg8f7kZH9QdyvKPXCkpUHyB8PrzX/vC5sZ7cUsHDJXoO/X+0fb39/ftG2x4eIObVPi8LwKOAKLeRY91frZ4QXuwvPxSoiIIVr1aArqsOGVXAD59WoPRtL+X622+/zd6u0XKNNitoDN8/kXPn9nnwihgqNd27N/oM6zOiNHovSNN2dRp9//33bilEPURLqilFXIUUwwO6RM6dm8Ka16tg3k+dJvr+0D2NtvRkXjUx9D6Jfx6+VxAznNcZ4XWOeMG+7lluyxx6hfA0/0vBcrQCk+rU8/YrKBpZ1/x+vXf+lSTCX4Papc+yvndyo/Pol4Lm5sei1SO8KR950ecwnH/6jz5r/g4g/7n9X/u/90vqNQpEs7rW7Jyj4nsdh3Q2/XTK/glVs4oL4mufHLkkY/ar6NLc7Mep1kBT5cI6yapMXmJWqbxZ20Yl6v3gGlyDa3ANrpHc19D0Qv+qMcXxdQR1jcADfKWFKz06PA0/Vvq+AhyNdKlXR8G+R38Ma1RP27xCUrtCAYDmyqpdmteb6NrDemPC52fnloafW+p9uETPoXvgD7rD9/fv621X4BAut/T9cG+88UbE13nVPVBHjVLIFaSEv5deh4K26X33f8j9vOwKLSmnIEgBrr4JNNdY/B0zSi/XvGu9t3qfleWha2hJQf/+iZ47FgWeqoSvHjp9zjVKr8+xfnjp3ocH4WqL7pvXe6f99I2vfaNdM5FzxxLrcxKNzl1US9rlltXh1SsIF6sTyh8s6z1X2nus1PRo2TqF7cYbb3RF8/TLTNkFCsp1r9Ujq0yIaEtH5pVar2PUefPAAw/E3Kc4rLKAf4rsXdrP5s6fn2vnVL4N6J1Vpf/DX80GZHVsuvn6740xO7o7S+QBAFDKJRTga0ROI2HhxcO8QE/pCOHbFPQoGNG83GiUkqo/jDVKWxDBvebJ5jXKGI0CwPC5tAq0FCBHG3X0OgK8kclYEj2H/l8p8Lpn4UGydw/DK/GrqJzSznUef4CvbQoq8xqtVvp2OP9ygn4KXvUeaz5yrPdSwepJJ52U63nCrxfeI+ZlUEQbvVRg5M13FwXGGslUVki0oD2Rc8eioDj8/dHouj5r/or+Hr1n4e+bUtrV7mij7YmeO6/OmYKag6/MAnXGhdPIvQL3eNtW0PSe6eeNpgvk9b2t7xFlUYwfPz5HUcA5c+YUaLv0edfSeZoeEN4po0J4fso40TQMLanoz1oIp45JdfyEL/OIYkqdXYMOtC2L5xbO+RXg92prds5TZlMWmdWpavbMSLOMTLPbTymcawIAEENRjEijEAN8FcrTH9Calxv+ZirgVAAYXjVeI6nRlqPT/FQFOSrAFT56r1E+Bdsa2ctrJFg0/0Fp+Rqt1ch9bgGcAqhoKegaOdVIuH/9Rr3OSZMmufRaL7hW+7S//qCPpyMhkXPovinA133VffHoa//Inc6l+6fz6Dhv5FTXUSAeLdV6V9O8NVIZbfRQtQv0nmmudTyjz9HoeNVqUGeFUqnzoiBO770qjhf0uaPR53rMmDHunnuV1HOjZQzVqeAtoVaQ547WOZObRObgP/zww24evDeyru9TBf1aki4/HWcFQYX+VJ1elfI/+uijHFkCKqjpdUoo00OdiSrAp0J74UX2Cjo93zt3eGaCMlzuuuuuHPuqBoACfC25p2J74Z1v3ki/OoNOOeUUV1zwqquusqeffjrHecJfK5JYWqrZgF5mdatb5XW5Z1Llm7Jyht9kdu2rZk98kVU1v0drs1cuM2uXWAYbAAC7qqiyRVFIAb7Sj7X+uoJLFbRTUK0AW2ujK+APD/BjBZFK3dcfuf6AK9Fl8jRyryJbCqQV8PnXf9fSWN7cfz2nTggFKgq6vCr6Csa0jyp4+wNajfpp7rYCHI3GKwDXaKtSgv3BrCpfq1PitNNOy9c5dB/1UECvThG1XaPxGkHVPQ0fkVa2QY8ePdx91D3Q87p36kzQiLauVdBiLTOmz4E6FaIFz9HuiebNK/jV61GHizIv9L4oYNKoq/8HhCqjawRa75HeM71fKlCnqQL+UeVEz60K7voMnHrqqdmBuEZQVSRP74XeLxVunDFjhusoUEV5f8aA9lWQptFcnV+FOPQea4TeqwngSfTcRT0HX59LdQ6pA0GfPS35ps/oQw89ZEE577zzXEX6Tz/91HXC6X1UBosCdnUuebULRAX+VMxT32/qJFNGj4LiDz74wN3z8Dobu+rYY49190WdTFq6Tt8Dame0aQqqxq8Rf00r0Qi+OiPV8aKfSaovoc+LRuzvuOMOt88zzzzjvrf1M0KfCbVbnRxepx6SXHqm2SVZWWnhmVcJGXVn3vtonv6QS7MeAAAESH+X6W9hFNMAX/THswIipZzqj08FU/rDVcFBUS7hpODeK8QVXnnbo6DJC/AVcCmt2yvaJXoNGjVVIO5PidVr0h/xSsHVH9UaadUf2wcffHCO5f68LAX/vPdEziEKXpQdoT/89dD5dE/9RSu8iuxqszoENALsdZgoayKeYnKFLdY9Uaq93gN1RmgfZXCog0KF0aLVDVAwp8wETVXw5tRrdDa8Iym/59aIq0arwzta9J5pX3UMKADXc+owUIaC2uKnz5X2VSCpDgh93vSeqZPFP8c80XMXtREjRtjll1/ulqnTvVEGwv333+8C5SApuH/sscdsyJAh9tJLL2V/H6l9WjounDodtRKHihjqe0mB/bPPPuuC/IIM8DXSrnaoE0QZA8oQ0X3SygDRivxp5QQF8K+//rrr+NLPSXWaaSUA73Oi71sVsrzttttch4ACfe9zrc6N8GX6kKT06691Q7P9d8/udIz28xsAAKAwpYSiVYRC3DSqrMAikfnOJV2y3xNlfCh1W8FYPFMaAJQSh95m9s2k/B2rDu5HzzG7Iqt6vpbPJMAHAJR0saZCIziJL2iOHGkpGilMxkA2KMl+T5TNoR9E8c57B4A8lS1jdlbf7C+T9ecfAAAF/Xc1inmKPiLtt99+QTch6ST7PdF0hvwW3UPxptoMqoeQG813z/f8aZTe4nqn7p81N/4fRTllDQCAoKgWEZILAT6AUuOmm26KWqU+nGo9eDU+gPiL6x2eY0QjryVVAQAo7kjPTz4E+ABKjUsuucRV288Nv6iQEI3U797ErGfOpUQBACjpoi2ljWAR4AMoNbTMpx5AwQmZDT4yK9AP06FDh8BaBABAUdGqXhSVTS4U2QMAIL8qlDM7/YAcmxcuXBhIcwAAQOlGgA8AQH6L653Rx6xqxRxPbdq0KZAmAQBQlOrXrx90E+BDgA8AQAEV1/NQywEAUBqUL18+6CbAhwAfAIBEac79Xi3NukZfcrN169ZF3iQAAIraggULgm4CfAjwAQBIVEjF9frFfHry5MlF2hwAAAAhwAcAIFFVKpidvF/QrQAAIFBt27YNugnwIcAHAEC6tYp/30EHmVWKPe+wQYMGBdMmAACS2LJly4JuAnxSQiHlGQIAUMrp1+H6LfHtW6mcWbmyhd0iAACS2oQJE6xLly5BNwNh0sK/AACgVBfOq1E56FYAAFBslCtXLugmwIcRfAAAAABAwhRKpqiDHEmDOfgAAAAAgIRNnDgx6CbAhwAfAAAAAIASgAAfAAAAAJCwunXrBt0E+BDgAwAAAAASVrkyxWmTDQE+AAAAACBh8+bNC7oJ8GGZPAAA4vDVvEybuCL282XLmF3cOcUqpFFNGAAABIMAHwCAPGRkhuycEZm2bLNZrNWAMkLaL9Wu7kGADwAoHVq3bh10E+BDij4AAHkYOTdkSzabZXqBfJRHWqrZ9oygWwoAQNFZtWpV0E2ADwE+AAB5eHZippVhYB4AgAjr1q0LugnwIUUfAIBcLNgQsuFzzEJBNwQAgCSTlkY4mWwYwQcAIBdDJmVaKqP3AADk0LFjx6CbAB8CfAAAYtiZEbLnJobcHHsAABBp4sSJQTcBPgT4AADE8PmckK3cGnQrAABITqEQPeDJhgAfAIAYnh5PcT0AAGKpXbt20E2AD1URAACIYva6kH27IOhWAACQvKpVqxZ0E+DDCD4AAFG8OInRewAAcjN37tygmwAfAnwAAHy2p4fsBYrrAQCAYoYAHwAAn49mhWzt9qBbAQBAcmvZsmXQTYAPAT4AAFGK66WSng8AQK7WrVsXdBPgQ4APACgx1m0L2YVfZVjdp9Ot8mPpduA7Gfbn8sTy7KetDtlPi80ySc8HACBXa9asCboJ8CHAB5BvGzdutHbt2lmjRo2CbgpgmaGQ9f8ww4ZNDdngrqn2QJ9UW7ElZH3fybCZa+OP1p+bSHE9AADikZpKOJlsWCYPJdaSJUvs888/d/+///77W4cOHXLs88ILL1jTpk3tiCOOyN6WmZlps2bNsqlTp9qGDRtsx44dVqFCBbcMSMOGDa1r165WpkyZIn0tKJzOiVtvvdVatGhhl112Wb7Ps337drvnnnvcLzidD8F5f3rIxiwxe+/oVBvQLusPjoHtUqztSxl268+ZNuyovL9vt+4M2cuTKa4HAEA89txzz6CbAB+6XFAq/PHHH5aenh7Xvt99952NGjUq+4fWvvvu60apy5YtaxMnTrSdO3cWcmtRVAH+a6+9Zq+//vounUcdQG+//bY9//zzBdY25M/7M0JWv5LZCW3/HX6vWynFBfmfzAq5yvh5eXd6yDbuKOSGAgBQQvz1119BNwE+jOCjxKtbt66tXLnS/QDS6HtutN+cOXOsefPmdthhh+V4ftu2bVauXLlCbC2iCYVCtnbtWqtVq1bQTUESG78iZHvVT7HUlMj8+p4NU+yFSSGbsdasU93cz/H0hKziesy/BwAgbxkZGUE3AT6M4KNULN9Rp04dN/quAD0369evd//GmlOuVP2g5hr9+uuvdsghh7jXomyC6tWr2+67727PPvts9j6PPfaYpaSk2Isvvmj//e9/3ZQC7as09A8++MDt88knn1iXLl2sYsWKVrVqVRs0aFCOrARNU3jppZdcBkOlSpVcp0abNm3siSeeiKutkyZNstatW1vlypVtxIgR2b8AnnvuOevcubM7p9euhx56KMfUCr3Gnj172vvvv+/213k6deoU973S1IrBgwe76Rd6z/RaGzdubAMGDHCvdfr06e7r1atX27hx49w98x7z5s1zHQpPP/207bffflavXj33+tWGHj162Ndffx3RVr2GGTNm2NKlSyPOoyyQ8Nfi571Xr7zySsT5zjzzTPe+lS9f3l1TnU0XXXRR3K+9NFu62axh5ZzbvW1LNuUetU9cEbJxywjuAQCIV82aNYNuAnwYwUepoABr+PDhNn78eOvdu3fM/TTPXubOnesCWgVZ+aHpAPFOCVCHQV5ZAQsWLLDjjjvOdUCccsop1qpVK1e1VJ0Wo0ePtksuuSRi//vvv9+2bNni9lWw+uabb9rJJ59sDz74oJsn3r9/fzv66KPtyy+/tFdffdV22203u/POO7OPv/vuu+22225zP7TPOecc175PP/3UrrjiCndvHn300Zht/fbbb+300093gbTa1r17d9dhcPnll7vOCHVK/Oc//3HnVLB87bXXunMqoA43f/58O/vss+3www+3E044Ie77qWudf/759t5777kAXR0YugezZ8+2MWPGuDnzCqD1+tS5oKwAbw6+2qSMD3VGKHVf1zzxxBPd/mqjOkdUr0GvsW/fvlajRg27+eab7b777rOtW7faTTfd5M6jGg2q+ZBIr7Y6n0499VT74Ycf3PujzgRtU2eEtpXGgnk74rx95cuY6yzZmp71/34V/vlNp+fjKa7H/HsAAOJTu3btoJsAvxBQQi1evDj0/PPPhyZMmOC+/vzzz0NDhgwJbdiwIXsfPT9ixIiI4/S1tr/00kvumLFjx4bmzZsX2rlzZ9zXHjdunDtHPI9PP/00z/O99tprCjlCd911V677Pfroo26/OnXqhNatW5fj+JSUlNA777yTvX3VqlWhpk2bhmrXrp29Ta+1Vq1aocqVK4cWLVqUvX3JkiWhVq1ahVJTU0MzZsxw23Qv27ZtG2rYsKH7etiwYaFq1aq5c86ZMyf72G+++SaUlpYWOvbYYyPau3379tBhhx3mzjl79uzs903tUXuffvrpUKL0mnbbbbdQixYtct3Pu06PHj1yPJeZmRlx/zw//vijuy99+vTJ3ua/B/Few3uvhg4d6r6eO3duqFKlShHnzq/Vq1eHtm3blv31xo0bIz73uu+6T+H0/ub29dKlS919KaprfD8/I2QP7ozrMXVV1jGVH9sZOuXDDTmu8cXsrHONnJOR6+sY/E16qMxDO+K+rv+R9vDO0N2/5H6N4vp+cA2uwTW4BtfgGtHOOX78+BLxOoK6RmFI0X9yRP1ACaqiv/fee7s071WrVtmHH37oRuYPPPDAXKvoT5kyxWbOnOmO8b5FlFLerVu3uKqFKkVcj3goS0CjxrnR69CIu0akNTLdoEGDqPsp7fvKK6+0q6++OiL1XaPPGjlXhsLy5csjjjn00EPtm2++cUXnqlSp4u6JUsIvvvjiiPR/0aj37bff7qrG/+9//3PHaIRe/1533XVum+7v999/H9GjqxF1pfwri8Kfav/OO+/YNddcY88884zLRND7pnusDABlKSS6YoHXpkWLFrmsg4MPPjjqft51NIVj7NixMc+nz4Pm/2s0XQX1jjnmGJdR4U3nCL8HOme81/Deq6FDh7osA2/ftLQ0N60hr3oRJd2yzSEbOTe+X0/Ht0mx6uVTrM2QdGtTM8WGnxj5mXnpr0w7/8tMm3R2GetUN/b6d3+vClnHV/I/lzAt1ez2fVLthl7MfgMAlA4TJkxwUz+RPEjRR6mhudCaF64l8BRIxUopUsp8x44d3UMp2gryFdD9/fffbh685o/rPLlRIO2l+xcEzb1Xqrg6KFQfQCn6WvpP6fP6109V/8N587mVih+traL56ArwVWRQ9tprrxz7ep0bmnMeTvdIQbra5d2jcEqPlyOPPDLma/QHx+p4yc9yhKoroM4GBc+6b0rBVwCuKQ7nnXde3EUStZrCLbfc4qZ1aLpDOKWDFzS9r6qboOkRuvf169e3Xr162UknneRS90vbOrMNKqfYoI6J3ecu9VLsx0Uhl94fXmjvt6Uhq5Rm1jaPaYJ71EmxfRqZ/bqUefgAAMRDtYKQXAjwUaoo0FMAq9HUfv365bm/RlM1Wq6HAjCNQGtOdF4Bvkaf411OT4GbCsHlRs+/++679ttvv7lieQqiNfKt0d9LL73UnnrqqYj9lW0Q61qx7EoyjzoJ1HmgmgBDhgxx8+2jnVuj1ipaF42yI8KpMF5+KZDX+6t7pGJ3v//+u3311Vf2wAMPuCUT86rGP3nyZNehohF7ja4r60AdBwrs77jjDvcZ2FXRPh+aw69gXu3+8ccf7eeff3bz/h955BH75ZdfWMEhDwPapril8j6cEbIB7bIC/FVbQvbe9JAd3SrFyqfl3WHwny6pNmZJZhG0FgCA4m/Tpk2uJhGSBwE+ShUFokpVVwDnHzHOixeYbt68Oc99Fej++eefcZ1XBdyUfp8XBecqEOgVCVy8eLGbaqDidBppjhU4J0qj8BKt/d5ap23bto3YrsBz5MiRrjicRqEVvGqagKdZs2buX6Xv5zaKX5DUIaNRfD2UiaFCeqrir8cNN9yQ67Gq3r9u3Tq3GoGmF3i0zSukFw91zChzQsf5adpErPvvtU/TAgYOHGifffaZffzxx+7/kXuA36uh2TkjM23KarM6Fc2emZDpiubdvm98GRAntk2xS781W7+90JsLAECxpyzOJk2aBN0MhCHAR6mj9GeNwGo03M+bV60l6Py0fJrE00upADjWPHm/eCr1a968Rp3DR+Z1fi31ploBmiNeUAH+YYcd5q71xhtvuI4DdUB4bdA2jWJruTk/pZR7Qb7S9RXkX3/99e45VdUfNmyYS51Xp4R/dF5LzOma+V21IJxG3RVQh98PZWKoDoOsXLnS/at7qetFC769qQHhWQ36f9Un8Afm3nnU4aJ9wtP3Neqv+6I0fz2v98tbIUBBezh1HKnt4cvNqINA1fi1r36BIndlUrPm3187OtOeGJ9pW3ea9Whg9kq/MtauVnzp/hXSUuyCTin26B8hqukDAIBihwAfpY6CJgV7Stv20zx0LYGmoFYPjb5q9HfFihUutd8rtJeXgp6Dr2J3WppOc8rVeaCAUh0UWj5N8+L9c+53hUbbNQqvgno6t5bX85bJ01x6PaeR+Fh1DlQgThkJKrinIF/LyKndWpf+8ccfd3O1jj32WJfSv2zZMpcVoNeiTpeCmMflFatTWr06c9QRos4Z1S9QoK+Ue1EgrakWSoVXQUHdV302tDSfOilUpFCj/0rpV2eBPi8qHqggXcG6PwjX61B6vZa30/3SfdNxOt+4ceNsn332sdNOO80V41Pavb+TQ0Vq1Pmhmgpqu+7ltGnT3Mi9OgpUQwB5q1khxYYcXsaGHJ7/c1zYOdUe+j3/xfYAACgtKLCXfAjwUSopAFSlfH/xNAX1qrqvAE4Bp9Y2FwX6CgDVMRBtdL+wHXTQQS5I1Nx71QFQZXcFj1pP/q677irw6914443uXjzxxBOu+r06ORT4K0D3z6/300j8F1984arNKwNAo9J33nmnPfzwwy7gVr2At99+2917Ba46r0b24814yIuu760n/+qrr7r3UO+ZqtKrgJ03kq9gX50muoda8177aQReUwjUTh2rTg49p/vdvn17N5Ku1xQe4Mv999/vOofUuaFaCTqPgnS9RxdeeKGr6P/KK6/Ygw8+6Io7aps6GMKnMbRo0cJlRqg+hDo8tm/f7vZR58h9993nphygaKgSf9/dzH5cZIziAwCQCxWh3mOPPYJuBsKwTB4AAD7vT8+0kz5LrNgey+QBAEoblslLPvwVAgCAz7GtU1yRPgAAEFsQma3IHSn6AIoFVZTPa+UDFbjTHHmWk8OuKlsmxS7qnGL3/UaxPQAAYlExYSQXAnwAxYLWsVdxvryoEF7fvn2LpE0o2S7olGr3/EqxPQAAYpkxYwYp+kmGAB9AsdCrVy+31F5utLydV0QP2FXNqqfYYc3NvplPsT0AAFA8EOADKBZUkV7V8YGidGnXVPtyXmLF9gAAKC2aNm0adBPgQ5E9AABi6NcixRpUDroVAAAkb40kJBcCfAAAYkhLTbFLOqdamZSgWwIAQPJZsWJF0E2ADwE+AAC5OK9TijEFHwAAFAcE+AAA5KJx1RQ7upUxig8AgE+nTp2CbgJ8CPABAMiD0vSppA8AQM5l8pBcCPABAMjDoc1TbLeqZin//OKM9kjPNKvI2jQAgFJk+/btQTcBPvwpAgBAHlJTUuz1I8vY5FWxh/HLppoN6kgePwCg9KhatWrQTYBPSigUIukQAAAAAJDwMnkVKlQIuhkIQ4o+AAAAACBh06ZNC7oJ8CHABwAAAACgBCDABwAAAAAkrEmTJkE3AT4E+AAAAACAhKWnpwfdBPgQ4AMAAAAAErZs2bKgmwAfAnwAAAAAAEoAlskDACCcfi0uX5f1r1+tqmblywbRKgAAkjJFPy0tLehmIAzvBgAA4Z4abnb5S9Gfu/0Us1sGFnWLAABISrNnz7Z27doF3QyEIUUfAIBwfy0wKxPj1+OzI83SM4q6RQAAJKWtW7cG3QT4EOADAOCXkhJ9+7J1ZiP+LOrWAACQlCpXrhx0E+BDgA8AQLw0sv/0iKBbAQBAUmjatGnQTYAPAT4AAPHKyDT7aoLZvBVBtwQAgMBNnTo16CbAhwAfAIBEpKaaDfkm6FYAAADkQIAPAECio/jPfWm2Mz3olgAAEKhGjRoF3QT4EOADAJCo1RvNPhkbdCsAAAAiEOADAJCfYnvPjAy6FQAABGrJkiVBNwE+BPgAAOQnTf/7yWYz+cMGAAAkDwJ8AADyO4r/wtdBtwIAgMB06NAh6CbAhwAfAID8juK/+LXZth1BtwQAgEAsWLAg6CbAhwAfAID8Wr/F7MNfg24FAACB2Lx5c9BNgA8BPgAA+ZWaYvbUiMK/zrrNZhc+a1Z3kFnlU80OvMXsz9mFf10AAHJRsWLFoJsAHwJ8AIHZZ599LCUlxUq7Tz/91N2H//3vf0E3BYnKDJn9Mt3s70JMUczMNOt/l9mwH80G9zN74CyzFevN+t5CkT8AQKBatWoVdBPgk+bfAJSWJT0+//xz9//7779/1AIhL7zwgjVt2tSOOOKIqOf47bffbOLEiVatWjU75ZRTSkRbAOSz2N5zX5k9eX7+ju97s1nzemavXBb9+fd/MRsz3ey9a8wG7JO1beA+Zm0Hm936jtmwK/PfdgAAdsHkyZOtS5cuQTcDYRjBR6n3xx9/WHp6ekLHZGZm2owZM1xAvWHDhgJbAzSZ2gIggWJ7r3xntmV74ZxfAX79GmYn9Pp3W93qWUH+J2PNtu8snOsCAIBihwAfpVrdunVty5Yt9tdffyVcMXTr1q1uxF1zj6ZPn16i2gIgQZu2mb3zU+Gce/xcs71amqX6fmX3bJPVqTCDTj0AQDAaNGgQdBPgQ4CPUq1ly5ZWp04dl96+bdu2uI+bNm2aVa1a1Ro1amStW7e2uXPn2o4dO0pMWwrChAkT7Nhjj7V69epZ2bJlrXr16ta9e3f74osvcj3uq6++sqOOOsqaNGliFSpUsPLly1ubNm3ssccey7Hv1KlT7fjjj7f69eu7a+g+aN9bbrklex/dy5tvvtnNEatUqZI7n/Y/8sgjbeXKlfnKstCxtWvXtrS0NKtVq5addNJJtmLFioj9TjzxRDevfty4cXbyySe7/dRGtePDDz+Meu5QKGSPPPKI+yxo35o1a9pFF12UcFYHApBSiMX2lq41a1gz53Zv25I1hXNdAADyoL+FkFx4R1Dq9ezZ04YPH27jx4+33r1757m/RtkXLlxoe+21lwvg2rZt60bdZ8+eHXX+fLK3RcFjvAFkamqqlStXLs/9vv76axs4cKCbMtC/f383N2vdunU2duxY+/LLL922WD766CM3n+uQQw6x5s2b29q1a12NgiuvvNL9/+233+720/lOOOEEmzlzpg0YMMB2331327Rpk/399982evTo7PPdcMMN9uijj1rnzp1doK32z5s3z37++WcXlCtzIl7ffvutu5amRSiAb9asmbveZ599Zr/++qtrtzoywp166qlWuXJlu/DCC239+vX21ltvuXao3Xp94T744APX6aA6CuoQ0OtW/QX1jnuvG0kqFDL7c47Z+DlmXVvG3m9netbSev5tSrNftSFye60qWaP2W3eYlY/y67rCP9+Leh4AgAAsWrTIDVAheRDgo9TTSHHjxo1typQp1rFjRzcKnBvNd9dIq0aKRSO5emgkfVcD/CDaopH2P//8M659GzZsaEcffXSu+yjIvuqqq1wArsBXo/HhMjIycj3+3nvvtWeffTZim0bkVXH/4YcftptuusmNbmv0Xq9z0KBBNnTo0KjnUibD999/70buf//9913qZVZnyjXXXOP+X0G93ivPSy+95AL4++67z7U/nIojqmPAWy1AnTHa9/HHH3cdD+HUWaPX1aJFC/f1FVdc4fZ/+umnEwrw16xZ4zoVlK3gvSf6nHifJ92XjRs3us+KZ+nSpe79jfX1smXLXOaD9zpK8jXK7dxheXdjxfDzNFvWsFLsa/w8LWuJOz8V0Xvbl+I/97ms4nsVy9mWtRusUthT7nVs3W7uChXLlej3g2twDa7BNbgG1yip1ygMKSG1CihlvMr1e++9txvZXbVqlUubVqB84IEH5lq5/p133nEBY3igq1HzX375xY3uauS1OLVFo+x6xEM/0PIa8f7xxx+tT58+riZA+Eh6NAra1dZoP4a0TT8k9dD/X3fddW70e9KkSdapUyc3Wt61a1eX8q77p+kJfupMOOCAA9zo+ptvvulGzvO7LJ/aue+++7pzPPjggxHP6Qf+YYcd5n5gK/tCNMKv91FTDg499NDsfefMmeOyDQ466CCXreEtk6fpDMcdd5zLYAinTAZ1EOg+VKlSJV9tR4K03vzQ78zSc++MyiEt1WzxELN6NWLvs3aT2R++9euvfsWsQU2za4+N3L5fh6xR+jaXmrVpaDb8psjnX/rG7PxnzCY9atapWWJtBQCgAGgqpKZUInkwgg+YudQiBYizZs2yPffcM6LnLZx63ZRmreBb/3o0z1yBowrcxZNan0xtUfV9PQqKRqAVkHfr1i1fxyt9/uqrr7ZRo0a5wNlPafqiDAWNhKvzQ/dgt912cx0Gp59+enaHR5kyZdyI/9lnn+1S5bW/RsQ1ReDiiy/OM0MinDpO9Lrefvtt94gmWoaA5tOH0y9BBeqrV6/Osa+XiRHOS/nX/gT4SUzBvarc5xbcS80qZod0zrlN8+n92z1dmpv9OFVLZkQW2vttplml8mZtGxXACwAAIHGLFy92gy1IHgT4wD9UAE6jq5on3q9fv6j7eBXqle6th5/mVWskXnPVi0tbdu7c6R7x0LkKs5dWqU4qmqdCg5pfrywAdXAoUNcIvAr0af67aJtS1xWov/vuu25O/ciRI11Wg6YFaHqA6P7pXmmfb775xt0rZQPcf//99ttvv8X9S8nLMjjmmGPcHPloonXGqJ25nS+efWPtjySSnml2SWSGTYEZ0DtrqbwPfzUbsE/WNs3Xf2+M2dHdzcqXLZzrAgCQB2UYIrkQ4AP/0Ci2UqeV+h1tLXnNq1HQrTny0ea3a7RZc9k1Au0ftU3mtiiYLsg5+Gq3MgjiPae/LXoovf29996LeE4BfjRK19dDlMmgaQxK2Vf1+h49erjtXjV6PdRBoLnyN954o91999328ssvx9U23We9Lh2vbAAgQqv6Zn32KLwAv1dbs3OeMpuyyKxOVbNnRpplZJrdHr2zCQCAouDNR0fyIMAHwih9W6O9Gtn1U2V6VZtXAOsVQQunOfKaH67jdzXAL8q2qPJ+vGuYxvNDXBXzVSBQ8+9HjBiRIwNBAXKsrAIvxd0/Wv3DDz+4lH1/J4ayCVSDILxjRPdDc+71vO6RahqEvz5dW6sVSLQ0+dzeD3Uk6DX99NNPtt9++0U8v337djd9gPVgSyHVdRh8ZNa/hUGZHZp/f+2rZk98kVU1v0drs1cuM2vXuHCuCQBAHPR3JJILAT4QRgGjCt1FS3lXsKwAVHO9o/Ge06j55s2bXZXN4tCWgp6Dr3niDz30kFsbXqP93jJ5KuSnKQeam//EE0/EHCXfY489XHE6jeLr9WuqwSeffOKqmCprwaPCdP/5z39cIUJ1dGg+vUb/NXrfqFEjl96vSv6apqDUeY3mq/K9lsbT+TUaf/755yf0ulT5Xu3q27eve12qkaBsCtVLUCeEzuevoo9SMv/+rL75P37UnXnvo3n6Qy7NegAAkCRUo0h/5yF5EOADPgratEydlkXzaDRYgaHWLc9tqTWNHs+dO9ctX6cK7yWpLYlQRXnNd9fydhrt1tx5dTKolzdWTQFRR4Mq5WvNe1WOVxCvaQh33HGH66xQgB1ep0DnUgeIlsLTaL0CeVW5Vwq+RvY1Wq90eo26qzCeOjvUEdC+fXsbMmRIrm2JRoG9AvnbbrvNrRagKvjlypVzKwvoNZO6X0qD+1P2M6sVf8FGAACAwsIyeQAA7MoyeWPuNevdrrBbBQBA0lGtKGVOInkwgg8AQH5oyn2HJlkF8AAAKIUKc3Ul5A8BPlAIMjIyXNG1eH4o7uqSetg1K1euzHOJF71P9E4jqkv7FV5xPQAAktyCBQusVq1aQTcDYQjwgUKwfPlyV+wtL5qzrTnhCM7FF1/siu7ltTxgtOUKUcpp/fkz+gTdCgAAgGwE+EAhULG3I488Ms/9KlasWCTtQWzXXXedDRgwIM/3E8hRXE/BfbV/l2kEAKC0YZm85EOADxQCrRevJdmQ/LSMnh5AQtIzzS4+POhWAAAQeNaqVm5C8iDABwAgEZpz37m5WbdWQbcEAIBArV+/PugmwIfqXgAAJEKryw7uF3QrAAAIXNmyZYNuAnwI8AEASETl8man7Bd0KwAACNwee+wRdBPgQ4APAEAixfXOPtCsMuv+AgAwYcKEoJsAHwJ8AADiRXE9AACQxCiyBwCAX0Zmzm0pZtazrVmnZkG0CACApFOnTp2gmwAfAnwAAMKd1ddswlyzzFDOAP//jguqVQAAJJ0qVaoE3QT4pIRCKgcMAAAAAEBic/C7dOkSdDMQhjn4AAAAAACUAIzgAwAAAAAStmnTJtL0kwwj+AAAAACAhK1evTroJsCHAB8AAAAAkLC1a9cG3QT4EOADAAAAABJWpkyZoJsAH+bgAwAAAABQAjCCDwAAAABI2KRJk4JuAnzSgm4AAADJbsvOkF30VaYt3JiV9NazYYo90Ie0RABA6ZaZmRl0E+BDgA8AQB4mrDB7Y+q/M9rGLQvZ3fuFrGyZlEDbBQBAkGrVqhV0E+BDij4AAAnakm7229KgWwEAQLBq1KgRdBPgQ4APAECCNHD/9XzSEgEApducOXOCbgJ8CPABAEhQRshs+BwWoQEAAMmFAB8AgHz4Y7nZ2m0E+QCA0qtFixZBNwE+BPgAAOSDQvvvFxDgAwBKrw0bNgTdBPgQ4AMAkA9pqWZfziPABwCUXqtXrw66CfAhwAcAIB/SM82GzyXABwCUXikpLBebbAjwAQDIp0UbzWavI8gHAJROnTt3DroJ8CHABwAgnzRu8RVp+gCAUmry5MlBNwE+BPgAAORTaorZyLmZQTcDAIBApKenB90E+BDgAwCQTxkhs28XaD4+o/gAgNKnRo0aQTcBPgT4AIASad22kF34VYbVfTrdKj+Wbge+k2F/Li/4QHzzTrOxSwv8tAAAJL06deoE3QT4EOADRej444931UaXLFkSdFOSwqhRo9z9uP7664NuCkqYzFDI+n+YYcOmhmxw11R7oE+qrdgSsr7vZNjMtQUb5JdJ0Tx80vQBAKXPrFmzgm4CfNL8GwC/devWuW/eRYsW2YYNGywjI8OqVatmLVq0sE6dOlnZsmWjHvPbb7/Z0qVLLTMz0/XudevWzRo3bhyx35YtW1xxjlWrVrnHtm3brG3btta3b99c5/pMnDjR5syZ49qTlpZm1atXd1U81SYkl7Fjx9qQIUPsvPPOs7333jvo5qCUeH96yMYsMXvv6FQb0C6rL3tguxRr+1KG3fpzpg07qkyBpumPmBuy2/YtsFMCAADkCwE+8jR9+nSbMmWKNWvWzFq3bm2pqaluBPr33393QfZxxx3ngmyPgu5PPvnE7aegu1y5cjZt2jQbPny49evXz5o0aRLRETBhwgSrXLmy1a1b1xYuXJhrW7Zv326ff/65u4Y6AtTBoIB/7dq1tmnTpkK9D8ifP//801588UXXweMP8A844ADbuHGjlS9fPrD2oWR6f0bI6lcyO6Htv+vz1q2U4oL8N6aEbHt6yMqnFdzavb8vz5oSUKMC6wEDAEqP5s2bB90E+BDgI08tW7a0rl27ukDds/vuu9u4ceNs/PjxLnjv2LFjxIjtjh07XDq6Ny9Hwfh7771nP//8sw0cONClZYueP/PMM61ixYpu9P61117LtS06XsG9OhVq1qxZaK+5NNm8ebOVKVPGKlSoUOTXVidQlSpVivy6xZE6sZSponuGvI1fEbK96qdY6j8/azw9G6bYC5NCNmOtWae6BXc91dj7fmHIjm9DgA8AKF1/R1JoL7nwlyLypJH18ODe06pVq+zAw7Nz506bP3++NWzYMKLohtL427dvb+vXr7eVK1dmb9d5FdzHQyO9s2fPdudRcK/Uf10vGaizQ6PRVatWda9VUxEuueQS90MvGt2D/v37u/01eq1MBGU9+D3xxBPWrl07l+Gg/erVq2cHHnig/f333xH76WtlR+i+KJtC75k6UjRFIloNAE1xOPbYY13AqDaMHDnSvRfdu3d399Xv0ksvdcepk0YWL15sZ5xxhuv8Udu813zxxRdHvOarr77a3QfRczqHHrpXuc3B1zkuvPBCa9CggTu32nnQQQe5+xwu/PjHHnvMmjZt6vbXZ0/XS/TzoXur+zB16tQcz/3666/uWqeffnrEdmUnqINLn2O9R8pyeeCBB3LcxxdeeMH23Xdf1za1UfddGQ3KbPHT69b7/ssvv1iPHj2sUqVK1qZNm5ifJ+S0dLNZw8o5t3vblmwq2Hn4aamah08lfQBA6RL+dz2SAyP4yDcvJT48QF+zZo2bo1+/fv0c+ys49X4QeP+fCKXvh0IhF8R+9913bnqAgigFmAqQ99xzz7jOozR/nSceCsQ0up0bBWEKDDWyeuKJJ9puu+1mX3/9tT333HP2119/ubb6O0hOOOEE1+7//Oc/tnr1ahs2bJidfPLJLoA++uij3T46/oorrnAdGgqwNdKtOgjff/+96+jYY489sgNPBb8KAnVeTaVQXYOPPvrIZVioFkKtWrUirq8MCAXNCr6VbaEMC53j22+/dW3W1AqP7vH777/vplYcddRRbpumVXz55ZfudSug1T56nc8//7xrmzoMdN/UybBgwQJ3/CmnnOLS9EUdA7EoKN9nn31s0qRJ1rt3bzd3f+7cue4chxxyiH311Vc5Uv0//PBD19Gk168AWl+rLerouPPOOy1el112mes0UDD+6KOPRjz3+OOPuwBfHQfh+z/11FPWpUsX9x7pfR4xYoT93//9n/u8Pvnkk9n7PvPMM64DYMCAAa4zRK9J77farGknem3h9L106KGH2n777WfXXnut+36LVu+itBTM25ER377ly5h7n7amZ/2/X4V/fuvp+YKUnmk2fA4BPgAACBYBPvJFAZ2CR/0hrQDP440wKnj187bldxRS8/W9KQBKJ99///1dUK3RVgW5ClQ1Ap2XDz74IO75+n369HEjqbnxRoq9YE3uvvtuN6KuIFgjvAr+wing/umnn7Lnnp9//vkuqL3hhhvccRqFV0Cr16mgu1GjRjGvrykOCtbV0RA+D0qdBhpt1mjyfffdF3GMgnV1FITXTrjoootce1966SWXOeBRQL1ixQq74IILsjtzFNirDoM/4DzmmGNcsKr3SMG5AvGDDz7YvRYVTtQ18qJAWMG9OiF0nNfBos4TBcfKCvjhhx8iUtXVPtWE8D6LN954o8si0T245ZZb4g6MldWgToGPP/7Y7r333uxpCyoG+emnn7qOJL0u0WtUcK82vfPOO9ntueeee1xniQoLqgNAnSeijgN/CtuVV15pe+21l3uP/AG+ik5eddVV9vDDD1tp98PCkB34bnxV6qeeU8ba1zarmGa2PUqnwLZ/Ans9X9AWbDRbtDFkTaqSpg8AKB3CB4WQHEjRR74omFy+fLkLqMODFhW8k2jzhL1AzdsnUV66tToXFEgq8FbaskaVVdVfaecanc+Lgq8jjzwyrodG43OjwFLBqGoUKCD1qONDAaJ89tlnOY5T4BdeWK5Xr14uGFYxw5kzZ7ptCtrVafHmm2/GTDXXtbXCgYJoBesa4fce6gBRgKpg2O/yyy+PCO5FmQPKjvjiiy8i7qMyCfR+qhPCo2wBL2hW7QRNBdA1dQ5lR6jzIr/UUaL7d9ttt0VkTyjA1/v9xx9/5EgH00h3eEeT2qdRdX1GNbUjXrqeOkzmzZsXkTr/+uuvuyBfGQnefVPHjdp57rnnus6O8Huvjh7dF2UyeLzvE90fjc5rP91vTStQIUu91/4Oseuuu87yQ+cPfw/VoRV+H3QtZY6E80/n8H+9bNmyiMyXorxG+9opNvSIVHu27057tu8O9/96vHhIpj2535bsr/VI2bQ8OxVfafr+a0xbst7926hKSr5fRyzqNKhXKdh7xTW4BtfgGlyDaxTlNTTQVhJeR1DXKBQhIEFjx44NPf/886HRo0fneG727Nnuub///jvHc2vWrHHP/fbbb1HPu3XrVvf8999/H/X5n376yT3/7bff5nhu3Lhx7rn58+eHitKvv/6q7/rQwIEDczy3ffv2UGpqaqhjx47Z24477ji3/8SJE3Psf+6557rnPv74Y/f11KlTQw0aNHDbqlSpEurdu3fo5ptvDi1evDj7mNdff909n9ujffv2Oa4/bdq0qK/n/PPPd88PHz7cfb1p06ZQxYoVQ3vuuWcoPT09e7+dO3eGrrzyylDDhg1DKSkpOa55/fXXZ+/77LPPum3PPfdcjuvpvdZz//d//5e9rWnTpqGaNWuG1q9fn2P/o48+2u3vfYa846+++uoc+3qvNfx+xWPBggXufevfv3/2tq5du4aqVq0aWrp0afa2fffdN897f8MNN2TvP2bMGPceli9fPsd+derUCW3cuDF73/r164eaN2/u7jPyZ8An6aH6T+8MZWRmRmy/4Mv0UKVHd4a27YzcnpefF2WG7MGdMR+pD+0M9f+A9wsAULqMHz8+6CbAhxR9JERp0ErNV9qxRogTScPPLX0/Ht5xGp3187b5R0Gj2bp1a9xz8DWn2j/SXVQ0914jyZpSoNFkzaXXfHLN69b8eqW8e4XcVDzvpJNOinoeZQL4qcBbNKoJoNTyoUOHuqkCSnHX/dLoefhoukbzX331VVc0bvDgwW4KgTISNH9fmQvx3t+CUpDvkbI29LqUUq9igvrc6jOv9H0Vv/PoNWoE/+mnn45ZPVZZBKLCk8ocUUaFagoo1V/vgTIjNC1Dvb/+exbkZ68kGNA2xS2V9+GMkA1olzVav2pLyN6bHrKjW6UU6BJ5orfv8OYkxQEAShdl0SK58NcjEgrutaa5gnvNTfeWuvPPLVcgqNToaOnsojnO+eEV5os2f97rPIhnqTcFxwU1B79FixbuXxWWU7AdPjVB6fbapoJqfipS5y8KqP11vDdnWxQ0n3baae4hCvY151tBtAJ8r9CegsNTTz3VdpWmGijVXQXztOKB5uOr82TQoEER+6kdaqeC4PAgtCDSjpSyruUQ1bnhv0dKZdd7rEKChUlTGNRhotfvfZbDpyiIpguMGTPG1TPwCiPGomkWStlX4b7wIn2iDpJo30vY9QC/V0Ozc0Zm2pTVZnUqmj0zIdMyQma371vwgbi6Zw5rzvsIAChdwgc/kBwYbkBcNO9Zwb2CmljBvWhetgI0BXrhc1A0h3zatGluNDm/Ab5+gKiSvKqyh2cI6NwzZsxwI57RqvcX5hx8dTooCNUIb/hcewXcGpmVaMGfRuHD5/BodF6PDh06uHssmqPtp0J8smHDBvevCrSpIr3meivY9lO9A41CJ+Lss892750K7amQnK6p9zScOiL0GsNHndVp4q88H55BEO88ZnVg6Ly33357xFJz6pjR+6xK/Pn9DMVLGRG1a9e2t99+2z3U6XHYYYdF7OMVDLzjjjuiZqzovnsZJV72g3+UXkv7aT4XCl6Z1BQbfmIZO7l9ij0xPtOuHZ3pgvzvBpaxdrUKPhDXnP+2NQv8tAAAJDX9bYbkwgg+8qQ11hXgK7jWaLSKuoVTZXWNYnp69uzpio4prVypyAr6FdwrCDriiCNydA6o4yC8+J4CQW+bKqHr4QWVWjJMld5V5Vyj19qmUV2dW2urx1MtvaB7GlWETlXlNcruLZP3zTffuOBYqd6qPu+noE7rm6sTQdXSlQqvDgpV3/dGxFU4T69Hxfc0Yq2gXlXldf9U7E30/wpA1Wmhe6u0et1zdR4oq0AV+BWI+qvo50brz6vAnZdq71/3XdRuXVdZBCpwp9egtukz4qd7oHZq6Tl1xijgV7X/8KKE/mkCL7/8slvqTp1Jel+V4v7uu++6NLCHHnooahHHgqSA/IwzznBL44neQ/9Sh6qmryr46tTQdArde733Ksiigo9a2UGdUZq+oKJ7t956q+v0UWFEdQzp8/Hjjz+6z2OsIorYNTUrpNiQw8vYkMML9zppqWZHtkwhEwMAAASOAB958iqWa4Q22iixAvDwAF8BnKrcK4BRKnpGRoZbm1wBUPh+4an/4RTge6O9GqH2AnzRSHL//v1dB4BGzTXCq3MffvjhhZ62HYsCvdGjR9s111zjglIF1wrglIqtYNQfGIr2+9///ufmb2uUV6P2d911l5vn7VFKuIJob1k/Bc+tWrVyI9tnnXVW9n7qKNB91trrqpivTAKlsasNulcnn3xyQq9Hxyko17n0/xpR91Pqujp2dC29zxrtVtsVtCv4D6f3TIGyloJTDQF9HlS/IVaAr04Npb7/97//dUvTacUGTRNQ587999/vXm9R0PWVxaBpEtE6aeSRRx5xHVpayk6dNHrv1QmhDgxVwNeUFdH7q04pfUZeeeUVFwgqW0OdIlrSTx0YKL7SM80Oa0ZwDwAoffxZngheiirtBd0IAEg2mmaiDillZygjA6XbmMUh2/etjKjPKbRfeWkZq12RIB8AUPr+XgofjEPwmIMPAFEoW0AZIv7ieoBfl3pGcA8AKJWiFdZGsEjRB1DiacqHVgXIjaZSaMT+mWeesTlz5rh/Vc9A8+eBWLTaXr8WBPcAACA5EOADKPE0Cq858LnRsn8q2HjppZe6OgCaIz906NCoNRQAT3pIy+ORDAcAKJ00GILkQoAPoMS76aabslceiMVbzo+yJEhEhTSz3o2CbgUAAMGYOXOmW00IyYMAH0CJ161bN/cAClJqitmBu5mVK0OKPgCgdNq2bVvQTYAPeYUAAOSDkj2OaMGvUQBA6VW1atWgmwAf/jIBACAfNJnjsGaM3gMASq/GjRsH3QT4EOADAJAP9SuZtasVdCsAAAjOtGnTgm4CfAjwAQBIUFqq2ZEtUywlhRF8AACQPAjwAQBIUHqm2eHNCe4BAKVbkyZNgm4CfAjwAQBIkEL7g5sS4AMASreMjIygmwAfAnwAAPLQsLJZ+dTM7K/3rGtWpxIBPgCgdFu6dGnQTYBPmn8DAACI1KJGig3fb7o1a9vRfV2lbNAtAgAAyCklFNJKvgAAIDc7d+60smWJ7AEA8PC7MfmQog8AQBzmzp0bdBMAAEgq/G5MPgT4AADEYcuWLUE3AQCApMLvxuRDgA8AQBwqV64cdBMAAEgq/G5MPszBBwAgDjt27LBy5coF3QwAAJIGvxuTDyP4AADEYcqUKUE3AQCApMLvxuRDgA8AAAAAQAlAgA8AQBwaNWoUdBMAAEgq/G5MPgT4AADEISUlJegmAACQVPjdmHwI8AEAiMPixYuDbgIAAEmF343JhwAfAAAAAIASgGXyAACIw/bt2618+fJBNwMAgKTB78bkwwg+AABxWLhwYdBNAAAgqfC7MfkQ4AMAEIdNmzYF3QQAAJIKvxuTDwE+AABxqFChQtBNAAAgqfC7MfkwBx8AgDikp6dbWlpa0M0AACBp8Lsx+TCCDwBAHCZPnhx0EwAASCr8bkw+dLcAQAmSkZFhM2bMCLoZJdKcOXOoFAwAQBh+N+6atm3bWpkyZawgEeADQAmi4H733XcPuhkAAADIw5QpU6xDhw5WkJiDDwAldARflW179uxpY8eOtSpVqgTdtGJt2bJldtBBB9l3331nDRo04N4WEu5r4eC+Fg7ua+HgvhYe7m3y3dfCGMEnwAeAEmrDhg1WvXp1W79+vVWrVi3o5hRrixYtst12282t99ukSRPubSHhvhYO7mvh4L4WDu5r4eHelo77SpE9AAAAAABKAAJ8AAAAAABKAAJ8ACihVNX21ltvpbptAVDKXZ8+fbJT77i3hYP7Wji4r4WD+1o4uK+Fh3tbOu4rc/ABAAAAACgBGMEHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHgIBlZmbao48+au3bt7cKFSrYbrvtZldffbVt3ry5UI4fPny47bPPPla5cmWrVauWnXTSSTZ37tyo+06fPt2OO+44q1mzptt///33t++++86Kg2S9r7fddpulpKREfTz00ENWHBTlvX333XftnHPOsc6dO1vZsmXdfZo3b17Mcy9ZssTOOussq1u3rlWsWNG6d+9u7733nhUHyXpfX3nllZif2cGDB1uyK6r7unbtWnv88cftsMMOc/vo89euXTu78MILbeHChVHPvX79ervsssuscePG7tx77LGHPfvss1YcamAn630dNWpUzM/rUUcdZcmuqO7rzp077eKLL7Zu3bpZnTp1XAX4Fi1a2Mknn2zjx4+Pem4+r48W+H0tlM+rqugDAIJz+eWX6zdj6Pjjjw+98MILoSuvvDKUlpYWOvDAA0MZGRkFevwHH3wQSklJCXXp0iX09NNPh+65555QvXr1Qg0bNgwtXrw4Yt9Zs2aFatWq5Z7Xftpfx+ncX3/9dSjZJet9vfXWW915H3300dDrr78e8ZgyZUqoOCjKe9unT59QhQoVQnvvvXeoXbt27ri5c+dGPe/q1atDLVq0CFWuXDl08803h55//nl3vI55+eWXQ8kuWe/r0KFD3fM33HBDjs/s2LFjQ8muqO7riBEjQmXKlAkddthhofvuuy80ZMiQ0H//+99QxYoVQ9WrVw/9/fffEefdvn17qEePHu5cOqfOrWvoWvo5keyS9b5+//337rwXXnhhjs+rnkt2RXVfN23aFOrWrVvosssuc7+PdF9vueWW0G677RYqW7Zs6Ntvv404L5/XywvlvhbG55UAHwACNHnyZBcYnnDCCRHbn3jiCfcD/8033yyw43fs2BFq1KhRqGnTpqGNGzdmbx8/fnwoNTU1dMEFF0Sc46STTnLb9bxHx+n4tm3bhjIzM0PJKpnvqxfgxwqmkl1R3luZP39+aOfOne7/L7300lzv3bXXXuue//TTT7O3paenuz9K1VkV/v4km2S+r16AXxyCoyDvq+6fOkb91CGqfU888cSI7eoM1HadK5yupUBg3rx5oWSVzPfVC5j0uS1uivrnQDRLlixxgWu/fv0itvN5TSmU+1oYn1cCfAAI0I033uh+sP/www8R27du3RqqVKlSjl8Eu3K898fQHXfckeM8Bx10UKhatWouWPV6oMuXL++2++l4nee3334LJatkva/+AH/9+vXZQVZxUZT31i+vQLRx48ahVq1a5dj+2muvuePeeeedULJK5vsaHuBv2LDBjeQVF0He13DqYFKmRLh9993XnUPnCqdr6Zr3339/KFkl830ND5j0u8x/f5NZMtxXdYpWqVIl1KtXr4jtfF6tUO5rYXxemYMPAAEaN26cpaamWs+ePSO2a35Xly5d3PMFdbz3/717985xnl69etmGDRtsxowZ7utJkybZ9u3bY+4bfr5klKz3Ndyee+5p1atXd+fU3P0RI0ZYcVCU9zYRS5cutcWLF2d/PsPxmc3/fQ13zDHHWLVq1dw5NXf/jTfesGSXDPdV85Y3btxo9evXj5jP++eff1rXrl3ducLpWpp/y+c18fsa7oorrrAqVaq4Oftt27Z18/iTfa54EPc1IyPDVq1aZcuWLXPPn3baabZp0yY78sgjs/fh8zquUO5rYX1eCfABIEAqCOYVYfFTERv9ctixY0eBHK99ve3R9hUFSInum4yS9b5KjRo1XHGoJ5980j755BO79957bf78+da/f39XzCzZFeW9TbRd3jminVf4zCZ+X6VSpUruj1MVmPr000/tscces23bttmZZ55pt99+uyWzZLivd999tyu8dfbZZ0cUjtu6dWvUz6uupWvyeU38voqKRqoz6oEHHnCf1+eee8793P3vf/9r5557riWzIO7r1KlTXVHShg0bugD2yy+/tP/973/u4eHzuqRQ7mthfV7T8nUUAKBAbNmyJeovDPF6ybVPuXLldvl4/SvR9g/fN/zfePZNRsl6X0W/tP30S7xjx4525ZVX2oABA1wvfrIqynubaLuEz2z+js/NwIED3SPcRRdd5FYouOuuu1yA1bx5c0tGQd/X999/362OccQRR7hVC8LPK7mdm89r4vdV9t13X9d5Gu6CCy5wI6fqRD3//PPdPskoiPuqCu9ff/21C1BnzZrlMnOUHaEsvrS0rFCRz+uWQrmvhfV5ZQQfAAKkkTH9sI9GI2TePgVxvPdvtP13Zd9klKz3NZbatWu7ZXXWrVtnY8aMsWRWlPc20XYJn9n8HZ8o/bF7zTXXWHp6un311VeWrIK8r1o68/TTT3fLZb3zzjsujTn8vJLbufm8Jn5fY1F6tTdy+sUXX1iyCuK+amnXQw45xAWUl19+uVsKV4HpiSeeGHFe4fNasPe1sD6vBPgAEKBGjRq51K5ovziU7qaUsNxG3BI5Xvt626PtK176XSL7JqNkva+58UZAdd1kVpT3NtF2eeeIdl7hM5v4fS3un9mg7uvIkSPthBNOcOuEqwNEtQvC1axZ0821jfZ51bV0TT6vid/X3PB5je/ngDLIdI+VUj579my3jc9ro0K5r4X1eSXAB4AA9ejRwxWvGTt2bI4e4QkTJrgU2II6XvvKL7/8kuM8v/76q/tDSYVdpFOnTm6ELta+klfbgpSs9zU3M2fOdP/GKhhVGu9tIjTPUX9gep/PcHxm839fi/tnNoj7qiD0uOOOs/bt29s333zjgqNoI3R77bWXjR8/PkfQoGupuBaf18Tva274vMb/c0Dz7WXNmjXuXz6vPQrlvhba53WX6/ADAPJt0qRJua6t+vrrr2dv0zrAU6dOzffxWqqtYcOGOdZrnzBhgluv/bzzzos4x4ABA9x2Pe/RcTq+TZs2oczMzFCyStb7qiXx1q1bl6O9CxYscMs91a5dO7Rly5ZQMivKe5vocm7XXHONe/7TTz+NWJqoR48eoRo1argl3pJVMt/XVatW5dimz3Hr1q1D5cqVCy1cuDCUrIr6vn755ZehChUqhPbcc8+o9y3cU089FXNdca2XHev9SAbJfF+jPb9t2za3zFuyL/FalPd1xYoVoYyMjBxtWLp0qfudpiXdNm/enL2dz2tKodzXwvi8EuADQMAGDx7sfogff/zxoRdffDF01VVXuV+Wffr0ifgl0axZM7dffo+Xd9991/2S6tKlS+jpp58O3XvvvaF69eqF6tevH1q0aFHEvjNnzgzVrFnTPa/9tL+OK1OmTGjkyJGhZJeM93Xt2rUu0Bw0aJBbM/iFF14IXX311aHq1au7+6rzFAdFeW9Hjx4duvPOO91j7733dsfpnnnbwukPJV1Tf0Ddcsstoeeffz7Ut29fd8yQIUNCyS5Z76v+KD3llFNCd999tzuv1oTWNh3z8MMPh5JdUd3XcePGuSC0fPnyoUcffdT90e9/hNu+fXuoW7du7lw6p86ta+haN910UyjZJet97d69e+iYY44J3X777e68+led0rrWZZddFkp2RXVfdS91jv/+97+hxx9/PPTss8+GrrzyStfZrN9nL730UsR5+bwOLpT7WhifVwJ8AAiYRhgfeuihUNu2bd1oWKNGjdwvg/DR4Nx+6cR7vOezzz5zf9BXrFjRBZsnnnii67GOZsqUKe4XjwJQ7a8e5a+//jpUHCTjfVWvvEb0O3bs6PbRHwcNGjRw+ybzqFKQ9/bWW29154j18FOHyhlnnOGyIRQQdO3aNfT222+HioNkva/6Q3avvfZyf6DqM6t7269fv2LR0VeU93Xo0KG53tNo51annzIo1GGic3fo0CH05JNPJnWGVLLf1/vuuy/Uq1evUJ06ddznVb+/1NE3bNiwUHFQVPf1999/D51++ukuE6dy5cqhsmXLhpo0aRIaOHBg6Oeff47aNj6vDxX4fS2Mz2uK/pN4Yj8AAAAAAEgmFNkDAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAEIMAHAAAAAKAESAu6AQAAFJVBgwbZq6++6v5/jz32sMmTJ0c8n5mZaffcc48NHTrUFixYYE2bNrXZs2fbAw88YC+//LJNmTLFUlMT7xt/7rnn3Hlnzpxp5cuXj3jusccesyuvvDL765UrV1qdOnWsqGRkZNjvv//u2rZ9+3arVauW9ejRw5o0aVKgx8e735IlS+zzzz+Peq1jjz3W6tevvwuvFgCAko0RfABAifDll19aSkpKzMdrr73m9lPw/Prrr9t9992X4xzPPPOM3XLLLXbCCSe4gP7555+3DRs22P3332//93//lyO4v/322922qVOn5jjXueeea2XKlLEvvvjCdSzs2LHDnc/viCOOcO05/vjjLQijRo2ySZMmWevWrW2fffZxr2fEiBG2bNmyAj0+0et07NjRDjzwwIhH9erVC+Q1AwBQUjGCDwAoESZOnOj+feKJJ6xmzZo5nj/88MPtu+++s8qVK9sZZ5wR9RwauT/00EPtwQcfjBhhT09Pt1NPPTXH/pdcconrKNA+4cH7k08+6c511113Wf/+/d22s88+2x555BG77LLLXIeDp3379u4xa9Ys++ijj6worVixwmUo7L333ta5c2e3rU2bNvb+++/bb7/95kbMC+L4/FynQYMG1rJly0J41QAAlFwE+ACAEkGjwxrhHTx4cEQAHa9t27a5TgKNyodToH7MMcdYhQoVchxTr149O/30090I/N133+2yA0aPHm1XXXWVnXjiiXbjjTdm7ztw4ECX6v/999/bQQcdZMlgzpw57l516NAhe1taWpq1a9fOxo0bZ5s2bbIqVars8vH5vY6yHrRffqZFAABQGvEbEwBQIig479q1a76C+/POO88qVqzo5onfdNNN7hy9e/e2uXPnuo6DQw45JOaxmj+/detWN89+4cKFLpDXiPwrr7wSsV+3bt3cvPNPPvnECoLqBahTIp5HKBSKeo7Vq1e7TpFy5crl6Ljwns9NvMfn5zrqKNE9fOmll+yzzz5ztQkAAEDuGMEHABR7GumdPn267bfffrZq1aoczyu4LFu2bMzjNQqv55Vm//jjj7tAvFmzZjZmzBj3/F577RXzWBXrO+yww+zpp5+2jz/+2Hbu3On+jTYirfP8/PPPVhA0dz1WMTo/TS+oWrVqju1btmyxSpUq5djubdu8eXOu5433+ESuo9H6Fi1a2G677eayJtatW+c6bz799FOXyl+UBQgBAChuCPABAMWeqtsrsNYouh5+Cv7btm0b83ilzH/77bdufr5S/L2U8Jtvvtn9q4AzN0rJV7E8zTUfPny4tWrVKup+mlOudP6CULt2bTvyyCPj2lfZCdGotoAKAfp525TRkJt4j0/kOpp7r0c43X/N1x87dmzcrxkAgNKIAB8AUOwpjV6U0t24ceMcz6ugWzzn0Gh8+HxvpY5rDnhu89DFq6KvAF7F/GJR8T+l88ca0U6EltuLdym7WPTaogXx3rZoQXl+jt/V6ygDo3nz5m7KhKYmMCcfAIDoCPABAMWeUrgVRCoV3T/PO5Fz5Bacx/LNN9/YNddc4zoRtMb7V1995VL2o/HmwuenTkC04FjrycdDqe7RgmJ1MkRLw1cHhCijITfxHr+r1/H2UXCvbID8vscAAJR0BPgAgGJPo+9K485v4Kd53iqQ16lTpxxp8AooN27cGHUOu6r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\n", - "" - ], - "text/plain": [ - " object size_mb\n", - "0 model_data 394.32\n", - "1 md_encoded 388.04\n", - "2 X 49.16\n", - "3 X_train 46.02\n", - "4 weights_train 13.39\n", - ".. ... ...\n", - "60 model_name 0.00\n", - "61 initial_row_count 0.00\n", - "62 model_save 0.00\n", - "63 model_load 0.00\n", - "64 model_build 0.00\n", - "\n", - "[65 rows x 2 columns]" - ] - }, - "execution_count": 245, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "jupyter_objects = helpers.memory_usage_jupyter()\n", - "jupyter_objects" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e16efa2b-1920-4ccc-9c7a-20c87a5fbf7c", - "metadata": {}, - "outputs": [], - "source": [ - "helpers.delete_jupyter_objects(objects=list(jupyter_objects[\"object\"]))" - ] - }, - { - "cell_type": "markdown", - "id": "5ee265a2-da0b-4b56-b2f1-9526847f73fe", - "metadata": {}, - "source": [ - "# Test" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4b88e99a-6f24-4212-8b3d-d59d0c47f330", - "metadata": {}, - "outputs": [], - "source": [ - "charters.compare_rates(\n", - " df=model_data,\n", - " x_axis=\"attained_age\",\n", - " rates=[\"qx_vbt15\", \"qx_glm\"],\n", - " weights=[\"amount_exposed\"],\n", - " secondary=\"death_count\",\n", - " display=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "8876aabe-ee78-4556-b022-7e50cae51dd9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "plotly_mimetype+notebook\n" - ] - } - ], - "source": [ - "import plotly.io as pio\n", - "\n", - "print(pio.renderers.default)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0dea654b-73ac-4150-a904-1f1884d9fb6f", - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.graph_objects as go\n", - "import plotly.io as pio\n", - "\n", - "# pio.renderers.default = \"jpeg\"\n", - "# notebook_connected\n", - "# jupyterlab\n", - "\n", - "fig = go.Figure(data=go.Bar(y=[2, 3, 1]))\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "id": "b50b9cdb-a240-4f0a-9ae9-01652b8f0906", - "metadata": {}, - "source": [ - "## WL" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "ef320fa1-935f-4727-838d-6b399a3d1353", - "metadata": {}, - "outputs": [], - "source": [ - "model_data = model_data[\n", - " (model_data[\"insurance_plan\"] == \"Perm\")\n", - " & (model_data[\"issue_year\"] <= 2005)\n", - " & (model_data[\"issue_year\"] >= 1990)\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "1f1fe32a-590a-444f-9cac-1b488e2993f0", - "metadata": {}, - "outputs": [], - "source": [ - "model_data[\"exp_cnt_vbt15\"] = model_data[\"qx_vbt15\"] * model_data[\"policies_exposed\"]\n", - "model_data[\"ae_vbt15_cnt\"] = np.where(\n", - " model_data[\"policies_exposed\"] == 0,\n", - " 0,\n", - " model_data[\"death_count\"] / model_data[\"exp_cnt_vbt15\"],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "id": "9f54ed3d-36aa-4194-800b-dfad92764726", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['observation_year', 'sex', 'smoker_status', 'insurance_plan',\n", - " 'issue_age', 'duration', 'face_amount_band', 'issue_year',\n", - " 'attained_age', 'soa_post_lvl_ind', 'number_of_pfd_classes',\n", - " 'preferred_class', 'amount_exposed', 'policies_exposed',\n", - " 'death_claim_amount', 'death_count', 'cen2momp1wmi_byamt',\n", - " 'cen2momp2wmi_byamt', 'class_enh', 'qx_vbt15', 'qx_raw', 'qx_log_raw',\n", - " 'exp_amt_vbt15', 'ae_vbt15', 'capped_duration', 'binned_face',\n", - " 'exp_cnt_vbt15', 'ae_vbt15_cnt'],\n", - " dtype='str')" - ] - }, - "execution_count": 86, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model_data.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "id": "ec1439ee-c100-4aa9-b0a4-e8893bc7ec7c", - "metadata": {}, - "outputs": [], - "source": [ - "feature_dict = {\n", - " \"target\": [\"ae_vbt15_cnt\"],\n", - " \"weight\": [\"exp_cnt_vbt15\"],\n", - " \"passthrough\": [\"attained_age\", \"duration\", \"observation_year\", \"issue_year\"],\n", - " \"ordinal\": [\"sex\", \"smoker_status\"],\n", - " \"ohe\": [\n", - " \"binned_face\",\n", - " \"insurance_plan\",\n", - " \"number_of_pfd_classes\",\n", - " \"preferred_class\",\n", - " ],\n", - " \"nominal\": [],\n", - "}" - ] - }, - { - "cell_type": "markdown", - "id": "2dc1d914-8d20-439f-98b3-8f3996f320a4", - "metadata": {}, - "source": [ - "**Overview**\n", - "- A CatBoost model is an ensemble of decision trees that are boosted. It is very good with non-linear data, but does not extrapolate well.\n", - "\n", - "**Feature Preprocessing:**\n", - "- CatBoost doesn't need all numeric variables and can handle categorical" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "id": "8cbdbf72-a4ba-4cb6-a28c-eab9ba2ae4b8", - "metadata": {}, - "outputs": [], - "source": [ - "model_name = \"cat\"" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "id": "e66ef39b-adb0-47d7-a085-b666bc12b311", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m 2026-02-28 22:15:53 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m using 'pass' preset which makes all features passthrough \u001b[0m\n", - "\u001b[37m 2026-02-28 22:15:53 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m model target: ['ae_vbt15_cnt'] \u001b[0m\n", - "\u001b[37m 2026-02-28 22:15:53 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m model weights: ['exp_cnt_vbt15'] \u001b[0m\n", - "\u001b[37m 2026-02-28 22:15:53 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m passthrough - (generally numeric): ['duration', 'observation_year', 'issue_year', 'attained_age', 'sex', 'smoker_status', 'number_of_pfd_classes', 'preferred_class', 'insurance_plan', 'binned_face'] \u001b[0m\n" - ] - } - ], - "source": [ - "preprocess_dict = preprocessors.preprocess_data(\n", - " model_data,\n", - " feature_dict=feature_dict,\n", - " standardize=False,\n", - " preset=\"pass\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "id": "cd8eeb42-11a0-4268-af09-29045bf65545", - "metadata": {}, - "outputs": [], - "source": [ - "X = preprocess_dict[\"X\"]\n", - "y = preprocess_dict[\"y\"]\n", - "weights = preprocess_dict[\"weights\"]\n", - "mapping = preprocess_dict[\"mapping\"]\n", - "md_encoded = preprocess_dict[\"md_encoded\"]\n", - "model_features = preprocess_dict[\"model_features\"]\n", - "\n", - "X_train, X_test, y_train, y_test, weights_train, weights_test = train_test_split(\n", - " X, y, weights, random_state=0, test_size=0.2\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "id": "05f77155-a243-4001-b787-db624b8bd18d", - "metadata": {}, - "outputs": [], - "source": [ - "cat_features = feature_dict[\"ordinal\"] + feature_dict[\"ohe\"] + feature_dict[\"nominal\"]\n", - "cat_features = list(set(cat_features) & set(model_features))" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "f98ea64a-3851-4aba-91ce-92253d9f8137", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['number_of_pfd_classes',\n", - " 'preferred_class',\n", - " 'insurance_plan',\n", - " 'binned_face',\n", - " 'sex',\n", - " 'smoker_status']" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cat_features" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "86b95b61-9db5-45d2-8fb2-c25a1efb7637", - "metadata": {}, - "outputs": [], - "source": [ - "from catboost import CatBoostRegressor\n", - "\n", - "if model_build:\n", - " model = CatBoostRegressor(\n", - " iterations=2000,\n", - " learning_rate=0.05,\n", - " depth=8,\n", - " cat_features=cat_features,\n", - " one_hot_max_size=11,\n", - " )\n", - " model.fit(X_train, y_train, sample_weight=weights_train, verbose=0)\n", - "if model_load:\n", - " model = joblib.load(f\"files/models/{model_name}.joblib\")\n", - " logger.info(f\"loaded model '{model_name}'. type: {type(model)}\")\n", - "if model_save:\n", - " joblib.dump(model, f\"files/models/{model_name}.joblib\")\n", - " logger.info(f\"saved model '{model_name}'. type: {type(model)}\")\n", - "\n", - "model_params = {\"weights\": True}\n", - "model_params.update(model.get_params())" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "id": "d378d8ea-8271-45ed-8ac1-047143e01017", - "metadata": {}, - "outputs": [], - "source": [ - "predictions = model.predict(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "id": "d2c849fb-519f-4e0a-9555-ffc56dcef7d8", - "metadata": {}, - "outputs": [], - "source": [ - "model_data[\"exp_cnt_cat\"] = predictions * model_data[\"exp_cnt_vbt15\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "id": "b9dda888-5441-49ed-b402-eb8b9ac05e07", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "feature=%{x}
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binned_face01: 0 - 24,99902: 25,000 - 99,99903: 100,000 - 249,99904: 250,000 - 4,999,99905: 5,000,000+Total
issue_year
19901.251.120.970.871.001.15
19911.221.110.920.880.721.14
19921.251.100.890.870.821.14
19931.281.100.940.831.051.17
19941.241.100.900.841.001.14
19951.281.070.920.891.061.16
19961.271.090.950.971.001.17
19971.321.100.870.930.601.18
19981.231.100.880.800.601.14
19991.351.180.970.901.661.24
20001.351.211.000.850.511.23
20011.261.180.930.761.841.17
20021.311.190.880.751.331.17
20031.321.190.850.770.601.16
20041.391.250.900.760.241.22
20051.401.280.910.730.611.22
Total1.271.120.920.860.911.16
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" - ], - "text/plain": [ - "binned_face 01: 0 - 24,999 02: 25,000 - 99,999 03: 100,000 - 249,999 \\\n", - "issue_year \n", - "1990 1.25 1.12 0.97 \n", - "1991 1.22 1.11 0.92 \n", - "1992 1.25 1.10 0.89 \n", - "1993 1.28 1.10 0.94 \n", - "1994 1.24 1.10 0.90 \n", - "1995 1.28 1.07 0.92 \n", - "1996 1.27 1.09 0.95 \n", - "1997 1.32 1.10 0.87 \n", - "1998 1.23 1.10 0.88 \n", - "1999 1.35 1.18 0.97 \n", - "2000 1.35 1.21 1.00 \n", - "2001 1.26 1.18 0.93 \n", - "2002 1.31 1.19 0.88 \n", - "2003 1.32 1.19 0.85 \n", - "2004 1.39 1.25 0.90 \n", - "2005 1.40 1.28 0.91 \n", - "Total 1.27 1.12 0.92 \n", - "\n", - "binned_face 04: 250,000 - 4,999,999 05: 5,000,000+ Total \n", - "issue_year \n", - "1990 0.87 1.00 1.15 \n", - "1991 0.88 0.72 1.14 \n", - "1992 0.87 0.82 1.14 \n", - "1993 0.83 1.05 1.17 \n", - "1994 0.84 1.00 1.14 \n", - "1995 0.89 1.06 1.16 \n", - "1996 0.97 1.00 1.17 \n", - "1997 0.93 0.60 1.18 \n", - "1998 0.80 0.60 1.14 \n", - "1999 0.90 1.66 1.24 \n", - "2000 0.85 0.51 1.23 \n", - "2001 0.76 1.84 1.17 \n", - "2002 0.75 1.33 1.17 \n", - "2003 0.77 0.60 1.16 \n", - "2004 0.76 0.24 1.22 \n", - "2005 0.73 0.61 1.22 \n", - "Total 0.86 0.91 1.16 " - ] - }, - "execution_count": 144, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ae_table = ae_crosstab(model_data, \"issue_year\", \"binned_face\", \"ae_vbt15_cnt\", weight_col=\"exp_cnt_vbt15\")\n", - "ae_table" - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "id": "e9cbce1b-4ea5-400b-8ec3-8c032cdeab98", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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binned_ia18~3536~5354~7172~90Total
issue_year
19901.801.331.191.151.25
19911.831.291.181.161.22
19921.951.401.201.091.25
19932.071.481.221.141.28
19941.991.431.181.141.24
19952.171.461.231.151.28
19962.011.501.221.101.27
19972.131.501.281.141.32
19982.011.411.191.121.23
19992.211.621.311.121.35
20001.951.591.321.161.35
20012.071.611.211.081.26
20022.351.531.271.151.31
20032.311.641.271.181.32
20042.271.811.371.111.39
20052.761.791.331.291.40
Total1.981.411.211.141.27
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" - ], - "text/plain": [ - "binned_ia 18~35 36~53 54~71 72~90 Total\n", - "issue_year \n", - "1990 1.80 1.33 1.19 1.15 1.25\n", - "1991 1.83 1.29 1.18 1.16 1.22\n", - "1992 1.95 1.40 1.20 1.09 1.25\n", - "1993 2.07 1.48 1.22 1.14 1.28\n", - "1994 1.99 1.43 1.18 1.14 1.24\n", - "1995 2.17 1.46 1.23 1.15 1.28\n", - "1996 2.01 1.50 1.22 1.10 1.27\n", - "1997 2.13 1.50 1.28 1.14 1.32\n", - "1998 2.01 1.41 1.19 1.12 1.23\n", - "1999 2.21 1.62 1.31 1.12 1.35\n", - "2000 1.95 1.59 1.32 1.16 1.35\n", - "2001 2.07 1.61 1.21 1.08 1.26\n", - "2002 2.35 1.53 1.27 1.15 1.31\n", - "2003 2.31 1.64 1.27 1.18 1.32\n", - "2004 2.27 1.81 1.37 1.11 1.39\n", - "2005 2.76 1.79 1.33 1.29 1.40\n", - "Total 1.98 1.41 1.21 1.14 1.27" - ] - }, - "execution_count": 149, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ae_table = ae_crosstab(model_data[model_data[\"binned_face\"]==\"01: 0 - 24,999\"], \"issue_year\", \"binned_ia\", \"ae_vbt15_cnt\", weight_col=\"exp_cnt_vbt15\")\n", - "ae_table" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "id": "1ebdf701-1c07-44fa-beaf-86c2d130fdf6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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binned_ia18~3536~5354~7172~90Total
issue_year
19903.6226.9467.911.53100.00
19912.9923.8970.622.50100.00
19922.6920.8472.593.89100.00
19932.3517.6274.095.94100.00
19942.4017.3773.286.95100.00
19952.2616.8072.498.45100.00
19962.2416.2571.1710.34100.00
19972.4616.0470.1011.40100.00
19982.2216.4568.9712.36100.00
19992.2514.7367.3315.69100.00
20002.6715.1964.8417.31100.00
20012.4113.6662.8221.11100.00
20022.8114.1264.1918.88100.00
20032.0612.5364.9220.49100.00
20042.0412.5963.5821.78100.00
20051.9911.8562.9623.20100.00
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", 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" + "[65 rows x 2 columns]" ] }, + "execution_count": 245, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "charters.chart(\n", - " df=model_data[model_data[\"binned_face\"]==\"01: 0 - 24,999\"],\n", - " x_axis=\"issue_year\",\n", - " y_axis=\"policies_exposed\",\n", - " color=\"binned_ia\",\n", - " type=\"bar\",\n", - ")" + "jupyter_objects = helpers.memory_usage_jupyter()\n", + "jupyter_objects" ] }, { "cell_type": "code", "execution_count": null, - "id": "b3a57e88-0813-4e49-a6ca-0a5468b5883a", + "id": "e16efa2b-1920-4ccc-9c7a-20c87a5fbf7c", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "helpers.delete_jupyter_objects(objects=list(jupyter_objects[\"object\"]))" + ] } ], "metadata": { diff --git a/notebooks/06.2.archive_models.ipynb b/notebooks/06.2.archive_models.ipynb index 2f4be60..e7553d3 100644 --- a/notebooks/06.2.archive_models.ipynb +++ b/notebooks/06.2.archive_models.ipynb @@ -117,7 +117,7 @@ "metadata": {}, "outputs": [], "source": [ - "pl_parquet_path = r\"files/dataset/mortality_grouped.parquet\"" + "pl_parquet_path = helpers.FILES_PATH / \"dataset\" / \"full_mortality_grouped_1223.parquet\"" ] }, { diff --git a/notebooks/06.3.neural.ipynb b/notebooks/06.3.neural.ipynb index d36441e..335293b 100644 --- a/notebooks/06.3.neural.ipynb +++ b/notebooks/06.3.neural.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 1, "id": "0727a01e-4528-4d09-85ee-cce15db66e2c", "metadata": { "editable": true, @@ -98,6 +98,24 @@ { "cell_type": "code", "execution_count": 4, + "id": "975fa467-f08d-4e9e-87f2-6e608664b638", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[37m 2026-06-07 03:34:47 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m deterministic mode enabled \u001b[0m\n" + ] + } + ], + "source": [ + "neural.set_deterministic()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "id": "014aad03-4ca5-42e7-861c-d48e040c9e93", "metadata": {}, "outputs": [], @@ -107,7 +125,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "a622f7a2-ebcc-4bd5-952b-b0b3cef601a8", "metadata": {}, "outputs": [], @@ -118,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "3750a6e3-588e-4c2b-9cae-f0fff6d3dae1", "metadata": {}, "outputs": [], @@ -128,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "38f10069-ab6c-40ce-9be2-487cb3ee99a0", "metadata": {}, "outputs": [], @@ -170,20 +188,29 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 61, "id": "d8d2ce16-8887-42b0-a979-a344a4fa73bb", "metadata": {}, "outputs": [], "source": [ - "pl_parquet_path = r\"files/dataset/mortality_grouped.parquet\"" + "pl_parquet_path = helpers.FILES_PATH / \"dataset\" / \"full_mortality_grouped.parquet\"" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 62, "id": "6e1cab02-378c-4431-ad61-5e75701f8cd7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_441/1085427337.py:3: DeprecationWarning: the string cache has been replaced by pl.Categories\n", + " pl.enable_string_cache()\n" + ] + } + ], "source": [ "# reading in the dataset\n", "# `enable_string_cache` helps with categorical type values\n", @@ -195,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 63, "id": "1ffdc8bc-8ba8-4342-9e5c-dde0505a22e2", "metadata": {}, "outputs": [ @@ -203,8 +230,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "row count: 1,522,971 \n", - "exposures: 9,054,258,435,620.062\n" + "row count: 9,386,630 \n", + "exposures: 40,714,555,047,710.35\n" ] } ], @@ -218,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 64, "id": "9ae6421e-c541-4b93-b778-1d6acf175d83", "metadata": {}, "outputs": [], @@ -228,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 65, "id": "d80bd3cc-0345-4ce1-92d3-d46d5db1c319", "metadata": {}, "outputs": [], @@ -246,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 66, "id": "09078663-028f-40d5-90ce-131bc243d3f0", "metadata": {}, "outputs": [], @@ -262,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 67, "id": "18eede48-4d85-4353-845e-e185acad6ef1", "metadata": {}, "outputs": [], @@ -280,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 68, "id": "c8ad4d9b-d868-451d-b8d4-2a45221d05a0", "metadata": {}, "outputs": [], @@ -314,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 69, "id": "e3128049-2046-43dc-9914-62f5fdb2e05a", "metadata": {}, "outputs": [], @@ -352,7 +379,7 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 70, "id": "bbb9e91a-7caf-410c-9f88-1ae0803b5cba", "metadata": {}, "outputs": [], @@ -362,7 +389,7 @@ }, { "cell_type": "code", - "execution_count": 158, + "execution_count": 71, "id": "32070ecb-14d4-4ad1-a10a-8abb6fd4ac5b", "metadata": {}, "outputs": [ @@ -370,13 +397,13 @@ "name": "stdout", "output_type": "stream", "text": [ - " 2026-01-25 01:56:12 | morai.forecast.preprocessors | INFO | model target: ['qx_raw'] \n", - " 2026-01-25 01:56:12 | morai.forecast.preprocessors | INFO | model weights: ['amount_exposed'] \n", - " 2026-01-25 01:56:12 | morai.forecast.preprocessors | INFO | passthrough - (generally numeric): ['face_amount_band'] \n", - " 2026-01-25 01:56:12 | morai.forecast.preprocessors | INFO | ordinal - ordinal encoded: ['observation_year', 'sex', 'smoker_status'] \n", - " 2026-01-25 01:56:13 | morai.forecast.preprocessors | INFO | ohe - one hot encoded (dropping first col): ['insurance_plan', 'class_enh'] \n", - " 2026-01-25 01:56:14 | morai.forecast.preprocessors | INFO | spline - b-spline basis expansion: ['attained_age', 'duration'] \n", - " 2026-01-25 01:56:15 | morai.forecast.preprocessors | INFO | standardizing the features with StandardScaler (that excludes OHE) \n" + "\u001b[37m 2026-06-07 03:59:14 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m model target: ['qx_raw'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:14 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m model weights: ['amount_exposed'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:14 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m passthrough - (generally numeric): ['face_amount_band'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:14 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m ordinal - ordinal encoded: ['sex', 'observation_year', 'smoker_status'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:18 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m ohe - one hot encoded (dropping first col): ['class_enh', 'insurance_plan'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:21 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m spline - b-spline basis expansion: ['attained_age', 'duration'] \u001b[0m\n", + "\u001b[37m 2026-06-07 03:59:25 \u001b[0m|\u001b[37m morai.forecast.preprocessors \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m standardizing the features with StandardScaler (that excludes OHE) \u001b[0m\n" ] } ], @@ -390,7 +417,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 88, "id": "fafc68b1-c29c-450e-83c5-c558dee99d70", "metadata": {}, "outputs": [], @@ -408,7 +435,7 @@ "# X, y, weights, random_state=0, test_size=0.2\n", "# )\n", "\n", - "test_year_mask = (model_data['observation_year'] >= 2018)\n", + "test_year_mask = (model_data['observation_year'] >= 2019)\n", "X_train = X[~test_year_mask]\n", "X_test = X[test_year_mask]\n", "y_train = y[~test_year_mask]\n", @@ -459,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 93, "id": "ca564787-57c2-47e5-b029-4b2a623d3b92", "metadata": {}, "outputs": [ @@ -467,9 +494,9 @@ "name": "stdout", "output_type": "stream", "text": [ - " 2026-01-25 14:06:44 | morai.models.neural | INFO | initialized Neural model with Torch\n", + "\u001b[37m 2026-06-07 04:08:54 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m initialized Neural model with Torch\n", "task: poisson \n", - "device: cpu \n" + "device: cuda \u001b[0m\n" ] } ], @@ -479,6 +506,7 @@ " embedding_cols=[\n", " \"face_amount_band\",\n", " ],\n", + " embedding_dims={\"face_amount_band\": 6},\n", ")" ] }, @@ -502,7 +530,34 @@ }, { "cell_type": "code", - "execution_count": 264, + "execution_count": 94, + "id": "458fc509-537d-41c9-9091-25299688dba4", + "metadata": {}, + "outputs": [], + "source": [ + "# build\n", + "model_build = True\n", + "model_save = True\n", + "model_load = False\n", + "\n", + "model_name = \"neural\"" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "0d409d90-c7c9-4e55-94fa-a637075df40f", + "metadata": {}, + "outputs": [], + "source": [ + "if model_load:\n", + " model = joblib.load(f\"files/models/{model_name}.joblib\")\n", + " logger.info(f\"loaded model '{model_name}'. type: {type(model)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 96, "id": "0c9fc68d-f4d3-41cf-83dc-2405d304824d", "metadata": {}, "outputs": [ @@ -510,25 +565,25 @@ "name": "stdout", "output_type": "stream", "text": [ - " 2026-01-25 14:06:45 | morai.models.neural | INFO | setting seed: `42` \n", - " 2026-01-25 14:06:45 | morai.models.neural | INFO | non-embedding columns: ['class_enh_2_2', 'class_enh_3_1', 'class_enh_3_2', 'class_enh_3_3', 'class_enh_4_1', 'class_enh_4_2', 'class_enh_4_3', 'class_enh_4_4', 'class_enh_NA_NA', 'class_enh_U_U', 'insurance_plan_Term', 'insurance_plan_UL', 'insurance_plan_ULSG', 'insurance_plan_VL', 'insurance_plan_VLSG', 'observation_year', 's(attained_age)_1', 's(attained_age)_2', 's(attained_age)_3', 's(attained_age)_4', 's(attained_age)_5', 's(attained_age)_6', 's(attained_age)_7', 's(attained_age)_8', 's(attained_age)_9', 's(duration)_1', 's(duration)_2', 's(duration)_3', 's(duration)_4', 's(duration)_5', 's(duration)_6', 'sex', 'smoker_status'] \n", - " 2026-01-25 14:06:45 | morai.models.neural | INFO | embedding columns: ['face_amount_band'] \n", - " 2026-01-25 14:06:45 | morai.models.neural | INFO | created embeddings for `{'face_amount_band': 6}` \n", - " 2026-01-25 14:06:46 | morai.models.neural | INFO | overall_mu: 0.004869 \n", - " 2026-01-25 14:06:46 | morai.models.neural | INFO | epochs: 500, batch_size: None, lr: 0.001 \n", - " 2026-01-25 14:06:46 | morai.models.neural | INFO | dropout: 0.0, weight_decay: 0 \n", - " 2026-01-25 14:06:46 | morai.models.neural | INFO | early stopping: enabled, warmup_epochs: 50, max_patience: 20 \n" + "\u001b[37m 2026-06-07 04:08:56 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m setting seed: `42` \u001b[0m\n", + "\u001b[37m 2026-06-07 04:08:56 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m non-embedding columns: ['class_enh_2_2', 'class_enh_3_1', 'class_enh_3_2', 'class_enh_3_3', 'class_enh_4_1', 'class_enh_4_2', 'class_enh_4_3', 'class_enh_4_4', 'class_enh_NA_NA', 'class_enh_U_U', 'insurance_plan_Term', 'insurance_plan_UL', 'insurance_plan_ULSG', 'insurance_plan_VL', 'insurance_plan_VLSG', 'observation_year', 's(attained_age)_1', 's(attained_age)_2', 's(attained_age)_3', 's(attained_age)_4', 's(attained_age)_5', 's(attained_age)_6', 's(attained_age)_7', 's(attained_age)_8', 's(attained_age)_9', 's(duration)_1', 's(duration)_2', 's(duration)_3', 's(duration)_4', 's(duration)_5', 's(duration)_6', 'sex', 'smoker_status'] \u001b[0m\n", + "\u001b[37m 2026-06-07 04:08:56 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m embedding columns: ['face_amount_band'] \u001b[0m\n", + "\u001b[37m 2026-06-07 04:08:56 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m created embeddings for `{'face_amount_band': 6}` \u001b[0m\n", + "\u001b[37m 2026-06-07 04:09:00 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m overall_rate: 0.004688, init_bias: -5.362692 \u001b[0m\n", + "\u001b[37m 2026-06-07 04:09:00 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m epochs: 1,000, batch_size: None, lr: 0.01 \u001b[0m\n", + "\u001b[37m 2026-06-07 04:09:00 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m dropout: 0.0, weight_decay: 0 \u001b[0m\n", + "\u001b[37m 2026-06-07 04:09:00 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m early stopping: enabled, warmup_epochs: 50, max_patience: 20 \u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6858bebaacbc473391c2cc35457da522", + "model_id": "b7ec8fca7c5d43b9aed98b90b68bef73", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "Training: 0%| | 0/500 [00:00 \n", - "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "charters.pdp(\n", + " model=model,\n", + " df=md_encoded[((md_encoded[\"duration\"] <= 30))],\n", + " x_axis=\"duration\",\n", + " line_color=\"insurance_plan\",\n", + " # weight=\"amount_exposed\",\n", + " secondary=\"death_count\",\n", + " mapping=mapping,\n", + " spline_dict=spline_dict,\n", + " display=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f834715c-6684-455c-99d5-713b2accc1ef", + "metadata": {}, + "source": [ + "## SHAP Analysis" + ] + }, + { + "cell_type": "markdown", + "id": "2fe1a106-1058-47a1-a4f3-61c5720ffbb3", + "metadata": {}, + "source": [ + "SHAP (SHapley Additive exPlanations) is helpful in understanding the model predictions. It should be used as an additive test, because it will not provide information on how accurate the model is." + ] + }, + { "cell_type": "code", - "execution_count": 252, + "execution_count": 120, "id": "4c9d1b1b-efb8-4bd2-8194-52e8e2c69449", "metadata": {}, "outputs": [ @@ -7587,7 +15434,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " 2026-01-25 14:02:19 | morai.models.neural | INFO | creating SHAP KernelExplainer with `100` background samples and a seed of `42` \n" + "\u001b[37m 2026-06-07 04:36:58 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m creating SHAP KernelExplainer with `100` background samples and a seed of `42` \u001b[0m\n" ] } ], @@ -7597,7 +15444,7 @@ }, { "cell_type": "code", - "execution_count": 253, + "execution_count": 121, "id": "1d9a7fe7-d24a-437a-bca2-0275c247e2ac", "metadata": {}, "outputs": [ @@ -7605,13 +15452,13 @@ "name": "stdout", "output_type": "stream", "text": [ - " 2026-01-25 14:02:20 | morai.models.neural | INFO | calculating shap_values with `300` explainer samples and a seed of `42` \n" + "\u001b[37m 2026-06-07 04:36:59 \u001b[0m|\u001b[37m morai.models.neural \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calculating shap_values with `300` explainer samples and a seed of `42` \u001b[0m\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c49e2ef5a98c404dafa5404670387de3", + "model_id": "03da38ce2ba64f23b2830fe672f82b0e", "version_major": 2, "version_minor": 0 }, @@ -7621,18 +15468,6 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "KeyboardInterrupt\n", - "\n", - "\n", - "KeyboardInterrupt\n", - "\n" - ] } ], "source": [ @@ -7641,57 +15476,84 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "id": "b8a67a75-bf60-475d-9297-84e997ca249b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "shap.summary_plot(Shap.shap_values, Shap.sample_explain_df)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "7b53258e-fe11-49e2-b64b-4816d2489193", - "metadata": {}, - "outputs": [], - "source": [ - "shap.partial_dependence_plot(\n", - " \"duration\",\n", - " model.predict,\n", - " Shap.sample_explain_df,\n", - " ice=False,\n", - " model_expected_value=True,\n", - " feature_expected_value=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 124, "id": "c75e6352-402d-4af1-9ed1-5cf2efa239a1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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", 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-0.42 0.51 0.42 0.13\n", + "08: 1,000,000 - 2,499,999 -0.04 -0.02 -0.26 0.64 0.78 -0.08\n", + "09: 2,500,000 - 4,999,999 0.21 0.35 0.16 0.46 -0.34 0.82\n", + "10: 5,000,000 - 9,999,999 -0.30 -0.16 1.13 0.26 -0.29 0.34\n", + "11: 10,000,000+ -0.84 -0.03 0.03 -1.01 -0.65 -0.06\n", + "__UNK__ 0.13 -0.57 -0.12 -0.53 -0.40 -0.02" ] }, - "execution_count": 248, + "execution_count": 98, "metadata": {}, "output_type": "execute_result" } @@ -7892,7 +15754,7 @@ }, { "cell_type": "code", - "execution_count": 249, + "execution_count": 99, "id": "f6bed133-1132-43f3-9c24-2aca4e2d4790", "metadata": {}, "outputs": [ @@ -7937,7 +15799,7 @@ ], "yaxis": "y", "z": { - "bdata": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "age_cats = mi_no_covid[\"age_groups\"].cat.remove_unused_categories()\n", + "mi_no_covid[\"age_groups_order\"] = age_cats.map(\n", + " {cat: f\"{i + 1:02d}: {cat}\" for i, cat in enumerate(age_cats.cat.categories)}\n", + ").fillna(mi_no_covid[\"age_groups\"].astype(str))\n", + "charters.compare_rates(\n", + " df=mi_no_covid[mi_no_covid[\"year\"] == 2019],\n", + " x_axis=\"age_groups_order\",\n", + " rates=[\"1_year_mi_pct\", \"10_year_mi_pct\", \"whl_3_pct\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "9abebbc8-d875-43cc-80c1-1e708a1011dc", + "metadata": {}, + "source": [ + "## SSA 2D (age/year) - Raw Mortality Rates" + ] + }, + { + "cell_type": "markdown", + "id": "2242d94d-50a6-45fc-8268-b8e8a21ae033", + "metadata": {}, + "source": [ + "The SSA provides mortality rates that can used to create a 2d mortality rate table. From this 2d table mortality improvement rates can be derived. The mortality rates are transformed for smoothing.\n", + "\n", + "- SSA downloads: https://www.ssa.gov/OACT/Downloadables/CY/index.html\n", + "- year order and lamda: 3, 100\n", + "- age order and lamda: 3, 400" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "4830d974-be37-419f-8f7c-87e04c1d3f3f", + "metadata": {}, + "outputs": [], + "source": [ + "ssa_females = r\"notebooks/files/DeathProbsE_F_Hist_TR2021.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "80456700-839a-4e35-9b7b-f069f1b1be78", + "metadata": {}, + "outputs": [], + "source": [ + "# raw mortality\n", + "df = pd.read_csv(ssa_females, skiprows=1, index_col=0)\n", + "df.columns = df.columns.astype(int)\n", + "df = df.loc[1950:2019, 15:97]\n", + "df_log = np.log(df)\n", + "\n", + "# smoothed mortality\n", + "grad = graduation.whl(\n", + " rates=df_log,\n", + " horizontal_order=3,\n", + " horizontal_lambda=400,\n", + " vertical_order=3,\n", + " vertical_lambda=100,\n", + " normalize_weights=True,\n", + ")\n", + "df_exp = np.exp(pd.DataFrame(grad, index=df.index, columns=df.columns.astype(int)))" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "ed64547a-5374-4ece-8194-afdeccb62e16", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Year
19820.0004120.0004460.0004770.0005050.0005290.0005490.0005650.0005780.0005900.000600...0.1193410.1309060.1432400.1563250.1701390.1846480.1998140.2155890.2319220.248751
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19840.0004060.0004370.0004650.0004910.0005130.0005310.0005470.0005600.0005720.000584...0.1180650.1296630.1420720.1552800.1692610.1839860.1994150.2155010.2321930.249430
19850.0004060.0004360.0004630.0004870.0005080.0005250.0005410.0005540.0005670.000580...0.1175310.1291670.1416440.1549470.1690550.1839360.1995490.2158490.2327810.250288
19860.0004080.0004360.0004620.0004850.0005040.0005210.0005360.0005500.0005630.000577...0.1169440.1286160.1411580.1545590.1687960.1838360.1996400.2161590.2333410.251126
19870.0004110.0004370.0004610.0004830.0005020.0005180.0005330.0005460.0005600.000575...0.1162540.1279490.1405430.1540250.1683740.1835570.1995330.2162540.2336650.251708
19880.0004120.0004370.0004600.0004810.0004990.0005140.0005290.0005430.0005570.000573...0.1154660.1271650.1397880.1533250.1677570.1830500.1991620.2160450.2336450.251905
19890.0004130.0004370.0004580.0004780.0004950.0005100.0005250.0005390.0005540.000571...0.1146390.1263270.1389560.1525210.1670010.1823650.1985710.2155700.2333090.251731
19900.0004120.0004350.0004550.0004740.0004900.0005050.0005200.0005340.0005490.000567...0.1138920.1255620.1381870.1517620.1662680.1816740.1979390.2150150.2328500.251388
19910.0004100.0004310.0004510.0004690.0004850.0005000.0005140.0005280.0005440.000562...0.1133530.1250180.1376460.1512340.1657640.1812060.1975190.2146560.2325660.251194
19920.0004060.0004270.0004460.0004630.0004790.0004930.0005070.0005210.0005370.000555...0.1131290.1248170.1374760.1511010.1656760.1811720.1975470.2147570.2327490.251469
19930.0004020.0004220.0004400.0004570.0004720.0004860.0004990.0005130.0005280.000546...0.1132650.1250180.1377480.1514520.1661130.1817020.1981790.2154960.2336020.252441
19940.0003960.0004160.0004340.0004500.0004650.0004780.0004910.0005040.0005190.000537...0.1137450.1256060.1384540.1522860.1670830.1828160.1994430.2169160.2351800.254179
19950.0003890.0004090.0004270.0004440.0004580.0004710.0004830.0004960.0005100.000527...0.1145100.1265220.1395330.1535400.1685240.1844540.2012840.2189650.2374390.256645
19960.0003820.0004020.0004210.0004370.0004520.0004650.0004760.0004880.0005010.000517...0.1154710.1276670.1408790.1551030.1703190.1864910.2035720.2215090.2402390.259698
19970.0003740.0003950.0004150.0004320.0004460.0004590.0004710.0004820.0004940.000508...0.1165150.1289150.1423540.1568240.1723020.1887490.2061170.2243440.2433670.263114
19980.0003660.0003880.0004090.0004270.0004420.0004550.0004670.0004780.0004890.000502...0.1175060.1301180.1437900.1585150.1742670.1910040.2086720.2272080.2465410.266596
19990.0003580.0003820.0004030.0004220.0004390.0004520.0004640.0004750.0004860.000498...0.1182980.1311080.1450010.1599700.1759840.1930010.2109630.2298000.2494400.269799
20000.0003500.0003750.0003980.0004190.0004360.0004510.0004640.0004750.0004850.000497...0.1187480.1317240.1458070.1609850.1772300.1944940.2127170.2318260.2517420.272381
20010.0003420.0003690.0003940.0004160.0004350.0004510.0004640.0004760.0004870.000498...0.1187570.1318530.1460760.1614160.1778400.1953010.2137340.2330650.2532110.274081
20020.0003330.0003620.0003880.0004120.0004330.0004500.0004650.0004770.0004890.000500...0.1182840.1314460.1457550.1611980.1777430.1953400.2139230.2334150.2537300.274774
20030.0003230.0003530.0003810.0004070.0004290.0004480.0004650.0004790.0004910.000503...0.1173560.1305310.1448670.1603540.1769580.1946290.2132990.2328890.2533100.274467
20040.0003120.0003430.0003730.0004000.0004240.0004450.0004630.0004790.0004930.000506...0.1160630.1292000.1435120.1589890.1755970.1932850.2119840.2316130.2520820.273295
20050.0002990.0003310.0003620.0003900.0004170.0004400.0004600.0004770.0004930.000508...0.1145300.1275920.1418390.1572620.1738290.1914870.2101670.2297880.2502600.271484
20060.0002840.0003170.0003480.0003790.0004070.0004320.0004540.0004740.0004920.000508...0.1128900.1258500.1400020.1553400.1718300.1894230.2080480.2276250.2480650.269269
20070.0002680.0003010.0003340.0003650.0003950.0004220.0004470.0004680.0004880.000507...0.1112710.1241130.1381540.1533850.1697780.1872810.2058290.2253400.2457260.266891
20080.0002530.0002860.0003190.0003510.0003820.0004110.0004380.0004620.0004840.000504...0.1097750.1224980.1364210.1515390.1678250.1852310.2036910.2231270.2434530.264574
20090.0002390.0002720.0003050.0003380.0003700.0004010.0004290.0004550.0004790.000502...0.1084710.1210820.1348940.1499050.1660900.1834030.2017800.2211460.2414180.262503
20100.0002270.0002590.0002930.0003260.0003590.0003910.0004210.0004490.0004750.000499...0.1074000.1199150.1336330.1485530.1646540.1818910.2002030.2195190.2397550.260822
20110.0002170.0002500.0002830.0003170.0003510.0003840.0004150.0004450.0004730.000499...0.1065580.1189980.1326420.1474940.1635340.1807200.1989920.2182790.2385020.259574
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37 rows × 83 columns

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" + ], + "text/plain": [ + " 15 16 17 18 19 20 21 \\\n", + "Year \n", + "1982 0.000412 0.000446 0.000477 0.000505 0.000529 0.000549 0.000565 \n", + "1983 0.000408 0.000440 0.000470 0.000497 0.000519 0.000539 0.000555 \n", + "1984 0.000406 0.000437 0.000465 0.000491 0.000513 0.000531 0.000547 \n", + "1985 0.000406 0.000436 0.000463 0.000487 0.000508 0.000525 0.000541 \n", + "1986 0.000408 0.000436 0.000462 0.000485 0.000504 0.000521 0.000536 \n", + "1987 0.000411 0.000437 0.000461 0.000483 0.000502 0.000518 0.000533 \n", + "1988 0.000412 0.000437 0.000460 0.000481 0.000499 0.000514 0.000529 \n", + "1989 0.000413 0.000437 0.000458 0.000478 0.000495 0.000510 0.000525 \n", + "1990 0.000412 0.000435 0.000455 0.000474 0.000490 0.000505 0.000520 \n", + "1991 0.000410 0.000431 0.000451 0.000469 0.000485 0.000500 0.000514 \n", + "1992 0.000406 0.000427 0.000446 0.000463 0.000479 0.000493 0.000507 \n", + "1993 0.000402 0.000422 0.000440 0.000457 0.000472 0.000486 0.000499 \n", + "1994 0.000396 0.000416 0.000434 0.000450 0.000465 0.000478 0.000491 \n", + "1995 0.000389 0.000409 0.000427 0.000444 0.000458 0.000471 0.000483 \n", + "1996 0.000382 0.000402 0.000421 0.000437 0.000452 0.000465 0.000476 \n", + "1997 0.000374 0.000395 0.000415 0.000432 0.000446 0.000459 0.000471 \n", + "1998 0.000366 0.000388 0.000409 0.000427 0.000442 0.000455 0.000467 \n", + "1999 0.000358 0.000382 0.000403 0.000422 0.000439 0.000452 0.000464 \n", + "2000 0.000350 0.000375 0.000398 0.000419 0.000436 0.000451 0.000464 \n", + "2001 0.000342 0.000369 0.000394 0.000416 0.000435 0.000451 0.000464 \n", + "2002 0.000333 0.000362 0.000388 0.000412 0.000433 0.000450 0.000465 \n", + "2003 0.000323 0.000353 0.000381 0.000407 0.000429 0.000448 0.000465 \n", + "2004 0.000312 0.000343 0.000373 0.000400 0.000424 0.000445 0.000463 \n", + "2005 0.000299 0.000331 0.000362 0.000390 0.000417 0.000440 0.000460 \n", + "2006 0.000284 0.000317 0.000348 0.000379 0.000407 0.000432 0.000454 \n", + "2007 0.000268 0.000301 0.000334 0.000365 0.000395 0.000422 0.000447 \n", + "2008 0.000253 0.000286 0.000319 0.000351 0.000382 0.000411 0.000438 \n", + "2009 0.000239 0.000272 0.000305 0.000338 0.000370 0.000401 0.000429 \n", + "2010 0.000227 0.000259 0.000293 0.000326 0.000359 0.000391 0.000421 \n", + "2011 0.000217 0.000250 0.000283 0.000317 0.000351 0.000384 0.000415 \n", + "2012 0.000210 0.000243 0.000277 0.000311 0.000346 0.000380 0.000412 \n", + "2013 0.000207 0.000239 0.000273 0.000308 0.000344 0.000379 0.000413 \n", + "2014 0.000206 0.000238 0.000273 0.000309 0.000345 0.000381 0.000416 \n", + "2015 0.000207 0.000241 0.000276 0.000312 0.000349 0.000387 0.000423 \n", + "2016 0.000212 0.000245 0.000281 0.000318 0.000357 0.000395 0.000433 \n", + "2017 0.000218 0.000252 0.000289 0.000327 0.000366 0.000406 0.000445 \n", + "2018 0.000227 0.000262 0.000299 0.000338 0.000378 0.000419 0.000460 \n", + "\n", + " 22 23 24 ... 88 89 90 \\\n", + "Year ... \n", + "1982 0.000578 0.000590 0.000600 ... 0.119341 0.130906 0.143240 \n", + "1983 0.000568 0.000580 0.000591 ... 0.118634 0.130204 0.142562 \n", + "1984 0.000560 0.000572 0.000584 ... 0.118065 0.129663 0.142072 \n", + "1985 0.000554 0.000567 0.000580 ... 0.117531 0.129167 0.141644 \n", + "1986 0.000550 0.000563 0.000577 ... 0.116944 0.128616 0.141158 \n", + "1987 0.000546 0.000560 0.000575 ... 0.116254 0.127949 0.140543 \n", + "1988 0.000543 0.000557 0.000573 ... 0.115466 0.127165 0.139788 \n", + "1989 0.000539 0.000554 0.000571 ... 0.114639 0.126327 0.138956 \n", + "1990 0.000534 0.000549 0.000567 ... 0.113892 0.125562 0.138187 \n", + "1991 0.000528 0.000544 0.000562 ... 0.113353 0.125018 0.137646 \n", + "1992 0.000521 0.000537 0.000555 ... 0.113129 0.124817 0.137476 \n", + "1993 0.000513 0.000528 0.000546 ... 0.113265 0.125018 0.137748 \n", + "1994 0.000504 0.000519 0.000537 ... 0.113745 0.125606 0.138454 \n", + "1995 0.000496 0.000510 0.000527 ... 0.114510 0.126522 0.139533 \n", + "1996 0.000488 0.000501 0.000517 ... 0.115471 0.127667 0.140879 \n", + "1997 0.000482 0.000494 0.000508 ... 0.116515 0.128915 0.142354 \n", + "1998 0.000478 0.000489 0.000502 ... 0.117506 0.130118 0.143790 \n", + "1999 0.000475 0.000486 0.000498 ... 0.118298 0.131108 0.145001 \n", + "2000 0.000475 0.000485 0.000497 ... 0.118748 0.131724 0.145807 \n", + "2001 0.000476 0.000487 0.000498 ... 0.118757 0.131853 0.146076 \n", + "2002 0.000477 0.000489 0.000500 ... 0.118284 0.131446 0.145755 \n", + "2003 0.000479 0.000491 0.000503 ... 0.117356 0.130531 0.144867 \n", + "2004 0.000479 0.000493 0.000506 ... 0.116063 0.129200 0.143512 \n", + "2005 0.000477 0.000493 0.000508 ... 0.114530 0.127592 0.141839 \n", + "2006 0.000474 0.000492 0.000508 ... 0.112890 0.125850 0.140002 \n", + "2007 0.000468 0.000488 0.000507 ... 0.111271 0.124113 0.138154 \n", + "2008 0.000462 0.000484 0.000504 ... 0.109775 0.122498 0.136421 \n", + "2009 0.000455 0.000479 0.000502 ... 0.108471 0.121082 0.134894 \n", + "2010 0.000449 0.000475 0.000499 ... 0.107400 0.119915 0.133633 \n", + "2011 0.000445 0.000473 0.000499 ... 0.106558 0.118998 0.132642 \n", + "2012 0.000444 0.000473 0.000501 ... 0.105919 0.118302 0.131894 \n", + "2013 0.000445 0.000476 0.000506 ... 0.105446 0.117791 0.131351 \n", + "2014 0.000450 0.000483 0.000515 ... 0.105111 0.117435 0.130979 \n", + "2015 0.000459 0.000493 0.000527 ... 0.104895 0.117213 0.130754 \n", + "2016 0.000470 0.000507 0.000543 ... 0.104782 0.117107 0.130655 \n", + "2017 0.000485 0.000523 0.000562 ... 0.104768 0.117113 0.130678 \n", + "2018 0.000502 0.000543 0.000584 ... 0.104851 0.117230 0.130819 \n", + "\n", + " 91 92 93 94 95 96 97 \n", + "Year \n", + "1982 0.156325 0.170139 0.184648 0.199814 0.215589 0.231922 0.248751 \n", + "1983 0.155692 0.169571 0.184166 0.199440 0.215347 0.231832 0.248838 \n", + "1984 0.155280 0.169261 0.183986 0.199415 0.215501 0.232193 0.249430 \n", + "1985 0.154947 0.169055 0.183936 0.199549 0.215849 0.232781 0.250288 \n", + "1986 0.154559 0.168796 0.183836 0.199640 0.216159 0.233341 0.251126 \n", + "1987 0.154025 0.168374 0.183557 0.199533 0.216254 0.233665 0.251708 \n", + "1988 0.153325 0.167757 0.183050 0.199162 0.216045 0.233645 0.251905 \n", + "1989 0.152521 0.167001 0.182365 0.198571 0.215570 0.233309 0.251731 \n", + "1990 0.151762 0.166268 0.181674 0.197939 0.215015 0.232850 0.251388 \n", + "1991 0.151234 0.165764 0.181206 0.197519 0.214656 0.232566 0.251194 \n", + "1992 0.151101 0.165676 0.181172 0.197547 0.214757 0.232749 0.251469 \n", + "1993 0.151452 0.166113 0.181702 0.198179 0.215496 0.233602 0.252441 \n", + "1994 0.152286 0.167083 0.182816 0.199443 0.216916 0.235180 0.254179 \n", + "1995 0.153540 0.168524 0.184454 0.201284 0.218965 0.237439 0.256645 \n", + "1996 0.155103 0.170319 0.186491 0.203572 0.221509 0.240239 0.259698 \n", + "1997 0.156824 0.172302 0.188749 0.206117 0.224344 0.243367 0.263114 \n", + "1998 0.158515 0.174267 0.191004 0.208672 0.227208 0.246541 0.266596 \n", + "1999 0.159970 0.175984 0.193001 0.210963 0.229800 0.249440 0.269799 \n", + "2000 0.160985 0.177230 0.194494 0.212717 0.231826 0.251742 0.272381 \n", + "2001 0.161416 0.177840 0.195301 0.213734 0.233065 0.253211 0.274081 \n", + "2002 0.161198 0.177743 0.195340 0.213923 0.233415 0.253730 0.274774 \n", + "2003 0.160354 0.176958 0.194629 0.213299 0.232889 0.253310 0.274467 \n", + "2004 0.158989 0.175597 0.193285 0.211984 0.231613 0.252082 0.273295 \n", + "2005 0.157262 0.173829 0.191487 0.210167 0.229788 0.250260 0.271484 \n", + "2006 0.155340 0.171830 0.189423 0.208048 0.227625 0.248065 0.269269 \n", + "2007 0.153385 0.169778 0.187281 0.205829 0.225340 0.245726 0.266891 \n", + "2008 0.151539 0.167825 0.185231 0.203691 0.223127 0.243453 0.264574 \n", + "2009 0.149905 0.166090 0.183403 0.201780 0.221146 0.241418 0.262503 \n", + "2010 0.148553 0.164654 0.181891 0.200203 0.219519 0.239755 0.260822 \n", + "2011 0.147494 0.163534 0.180720 0.198992 0.218279 0.238502 0.259574 \n", + "2012 0.146701 0.162703 0.179861 0.198116 0.217397 0.237626 0.258719 \n", + "2013 0.146133 0.162118 0.179267 0.197522 0.216813 0.237061 0.258183 \n", + "2014 0.145751 0.161734 0.178887 0.197153 0.216460 0.236732 0.257883 \n", + "2015 0.145527 0.161515 0.178677 0.196955 0.216278 0.236568 0.257740 \n", + "2016 0.145436 0.161432 0.178602 0.196888 0.216218 0.236514 0.257690 \n", + "2017 0.145471 0.161474 0.178648 0.196934 0.216259 0.236544 0.257704 \n", + "2018 0.145627 0.161637 0.178808 0.197081 0.216387 0.236644 0.257767 \n", + "\n", + "[37 rows x 83 columns]" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_exp.loc[1982:2019, :]" + ] + }, + { + "cell_type": "markdown", + "id": "7a230116-c8da-4913-8b1a-51bc0c59747b", + "metadata": {}, + "source": [ + "# Reload" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "id": "bddc722f-4cee-4774-aaa7-6adff11906f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 157, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import importlib\n", + "\n", + "importlib.reload(cdc)" + ] } ], "metadata": { diff --git a/notebooks/files/DeathProbsE_F_Hist_TR2021.csv b/notebooks/files/DeathProbsE_F_Hist_TR2021.csv new file mode 100644 index 0000000..040e45d --- /dev/null +++ b/notebooks/files/DeathProbsE_F_Hist_TR2021.csv @@ -0,0 +1,121 @@ +Probability of death for females by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 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diff --git a/notebooks/files/DeathProbsE_F_Hist_TR2025.csv b/notebooks/files/DeathProbsE_F_Hist_TR2025.csv new file mode 100644 index 0000000..83701f6 --- /dev/null +++ b/notebooks/files/DeathProbsE_F_Hist_TR2025.csv @@ -0,0 +1,125 @@ +Probability of death for females by calendar year and by single year of age (ages 0 to 119). +Year,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119 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diff --git a/notebooks/files/sample_experience_data.xlsx b/notebooks/files/sample_experience_data.xlsx new file mode 100644 index 0000000..f4edb7f Binary files /dev/null and b/notebooks/files/sample_experience_data.xlsx differ diff --git a/notebooks/tutorials/cdc.ipynb b/notebooks/tutorials/cdc.ipynb index 18d352d..f24a561 100644 --- a/notebooks/tutorials/cdc.ipynb +++ b/notebooks/tutorials/cdc.ipynb @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "8aeed43f-4fc4-4a6b-a2aa-78fc4e157fbf", "metadata": {}, "outputs": [], @@ -68,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "898a8feb-f241-4d9a-8d27-873cd899ae77", "metadata": {}, "outputs": [], @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "8653900d-2bbd-42e5-be13-d1b57ca33c77", "metadata": {}, "outputs": [], @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1ecef78e-98d9-4904-90a3-a63ae34afa1c", "metadata": {}, "outputs": [], @@ -105,17 +105,17 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "01ea27c3-268a-4370-b522-ecd0c89f771c", "metadata": {}, "outputs": [], "source": [ - "db_filepath = r\"files/integrations/cdc/cdc.sql\"" + "db_filepath = helpers.FILES_PATH / \"integrations\" / \"cdc\" / \"cdc.sql\"" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "1db7ddd0-45a6-4075-baf8-8f5fdf84ec44", "metadata": {}, "outputs": [ @@ -128,12 +128,19 @@ " 'mcd79_check',\n", " 'mcd99_check',\n", " 'mcd18_check',\n", - " 'mcd18_cod',\n", + " 'mcd18_weekly_influenza',\n", + " 'mcd18_monthly_pic',\n", + " 'mcd99_monthly_pic',\n", + " 'mcd99_monthly_cod',\n", + " 'mcd18_monthly_cod',\n", " 'mcd18_monthly',\n", - " 'mcd18_mi']" + " 'mcd18_mi',\n", + " 'mcd18_weekly',\n", + " 'mcd18_weekly_pic',\n", + " 'mcd18_cod']" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -162,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "id": "705e28ef-2be3-401a-afaa-04e6c98f8672", "metadata": {}, "outputs": [], @@ -172,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "047af8b0-9388-49f5-b7a2-7d1f1d9f707b", "metadata": {}, "outputs": [], @@ -191,18 +198,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "56c82b93-527d-491e-8029-f69be6feaaf0", "metadata": {}, "outputs": [], "source": [ "# variable to map\n", - "category_col = \"simple_grouping\"" + "CATEGORY_COL = \"simple_grouping\"" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "a36a90ca-2ced-4dfa-bdf8-8b1c58d48bdc", "metadata": {}, "outputs": [], @@ -214,28 +221,20 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "5b10bcc5-c1e8-4a9d-929f-f015f4c0c0fe", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m 2026-03-31 22:12:18 \u001b[0m|\u001b[37m morai.utils.helpers \u001b[0m|\u001b[33m WARNING \u001b[0m|\u001b[33m There are common columns between the DataFrames: {'simple_grouping'} \u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# map the variable from reference\n", "cod_all = cdc.map_reference(\n", - " df=cod_all, col=category_col, on_dict={\"icd_sub_chapter\": \"wonder_sub_chapter\"}\n", + " df=cod_all, col=CATEGORY_COL, on_dict={\"icd_sub_chapter\": \"wonder_sub_chapter\"}\n", ")" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 12, "id": "9a842a0c-0aba-4a52-97ce-270b4682a68a", "metadata": {}, "outputs": [], @@ -251,120 +250,40 @@ ] }, { - "cell_type": "code", - "execution_count": 59, - "id": "6abaf71d-f0a1-45c8-a757-47a48b6be34e", + "cell_type": "markdown", + "id": "75d1dd4b-4977-4d95-b338-aeb1e8653173", "metadata": {}, - "outputs": [], "source": [ - "# create totals column\n", - "totals = cod_all.groupby(\"year\").sum(numeric_only=True).reset_index()\n", - "totals[category_col] = \"total\"\n", - "totals[\"age_groups\"] = \"total\"\n", - "cod_all = pd.concat([cod_all, totals], ignore_index=True)\n", - "cod_all[\"age_groups\"] = pd.Categorical(\n", - " cod_all[\"age_groups\"],\n", - " categories=cdc.AGE_GROUP_ORDER,\n", - " ordered=True\n", - ")" + "### Chart" ] }, { "cell_type": "code", - "execution_count": 61, - "id": "afdbc575-f5b8-4339-b53d-04d12694d397", + "execution_count": 13, + "id": "9b40d75f-21ac-4f67-ad78-8c8b44b4f73a", "metadata": {}, "outputs": [], "source": [ + "# sorting by deaths\n", "category_orders = charters.get_category_orders(\n", - " df=cod_all, category=category_col, measure=\"deaths\"\n", + " df=cod_all, category=CATEGORY_COL, measure=\"deaths\"\n", ")" ] }, - { - "cell_type": "markdown", - "id": "344bb0d1-f744-4bba-bb09-8f55e8aca9b2", - "metadata": {}, - "source": [ - "### Predict" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "id": "439e6594-d968-45f5-9724-73b74d33a83f", - "metadata": {}, - "outputs": [], - "source": [ - "# train the data based on year and the category using linear regression\n", - "train_df = cod_all[(cod_all[\"year\"] >= 2015) & (cod_all[\"year\"] <= 2019)]\n", - "train_df = train_df.groupby([\"year\", category_col])[\"deaths\"].sum().reset_index()" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "id": "1647ffcd-55c7-463d-836f-46f9e643b6ff", - "metadata": {}, - "outputs": [], - "source": [ - "# create the models\n", - "models = {}\n", - "for cod in train_df[category_col].unique():\n", - " cod_subset = train_df[train_df[category_col] == cod]\n", - " X = (cod_subset[\"year\"] - 2015).values.reshape(-1, 1)\n", - " y = cod_subset[\"deaths\"].values\n", - " model = LinearRegression().fit(X, y)\n", - " models[cod] = {\n", - " \"model\": model,\n", - " \"coef\": model.coef_[0],\n", - " \"intercept\": model.intercept_,\n", - " }" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "071e05bb-a949-4e2c-91a6-d537d5021b34", - "metadata": {}, - "outputs": [], - "source": [ - "# make the predictions\n", - "test_df = cod_all[(cod_all[\"year\"] >= 2020)]\n", - "test_df = test_df.groupby([\"year\", category_col])[\"deaths\"].sum().reset_index()\n", - "\n", - "for cod, model in models.items():\n", - " mask = test_df[category_col] == cod\n", - " if mask.sum() > 0:\n", - " X = (test_df.loc[mask, \"year\"] - 2015).values.reshape(-1, 1)\n", - " test_df.loc[mask, \"pred\"] = model[\"model\"].predict(X)\n", - "\n", - "test_df[\"diff_abs\"] = test_df[\"deaths\"] - test_df[\"pred\"]\n", - "test_df[\"diff_pct\"] = (test_df[\"deaths\"] - test_df[\"pred\"]) / test_df[\"pred\"]" - ] - }, - { - "cell_type": "markdown", - "id": "061b3d48-d7b0-41cc-9222-0ead71f028d7", - "metadata": {}, - "source": [ - "### Chart" - ] - }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "543c3ba0-b04d-4580-b4c1-1630b1c7d0df", "metadata": {}, "outputs": [ { "data": { "text/html": [ - " \n", - " \n", + " \n", " " ] }, @@ -378,35 +297,6 @@ "plotlyServerURL": "https://plot.ly" }, "data": [ - { - "fillpattern": { - "shape": "" - }, - "hovertemplate": "simple_grouping=total
year=%{x}
deaths=%{y}", - "legendgroup": "total", - "line": { - "color": "#636efa" - }, - "marker": { - "symbol": "circle" - }, - "mode": "lines", - "name": "total", - "orientation": "v", - "showlegend": true, - "stackgroup": "1", - "type": "scatter", - "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", - "dtype": "i2" - }, - "xaxis": "x", - "y": { - "bdata": "AAAAAKQ7QkEAAAAA4lJCQQAAAAB2bEJBAAAAgOygQkEAAAAArKpCQQAAAACAR0JBAAAAAAOqQkEAAACAwH9CQQAAAAChekJBAAAAgO/YQkEAAACA45RCQQAAAADq0UJBAAAAAOctQ0EAAACA+GNDQQAAAAAHzUNBAAAAAL8GREEAAACAF69EQQAAAIC57ERBAAAAACl0RUEAAAAAS6ZFQQAAAIDHxEVBAAAAAJ7NSUEAAAAAympKQQAAAIDEAklBAAAAABOSR0EAAAAAUW5HQfuFWFGtKkdB", - "dtype": "f8" - }, - "yaxis": "y" - }, { "fillpattern": { "shape": "" @@ -414,7 +304,7 @@ "hovertemplate": "simple_grouping=circulatory
year=%{x}
deaths=%{y}", "legendgroup": "circulatory", "line": { - "color": "#EF553B" + "color": "#636efa" }, "marker": { "symbol": "circle" @@ -426,12 +316,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAACofLUEAAAAAALssQQAAAACYSSxBAAAAAPIsLEEAAAAAuK4rQQAAAADYaypBAAAAALJEKkEAAAAA/kEpQQAAAACGuShBAAAAAPyrKEEAAAAAMvInQQAAAADC7ydBAAAAAE7oJ0EAAAAAJAcoQQAAAACCcChBAAAAAAKmKEEAAAAA3IYpQQAAAADcpilBAAAAAIQ3KkEAAAAA0oEqQQAAAABisCpBAAAAACJXLEEAAAAAbm0sQQAAAADquyxBAAAAAEjzK0EAAAAAsiUsQWe/ECtn0ytB", + "bdata": "AAAAACofLUEAAAAAALssQQAAAACYSSxBAAAAAPIsLEEAAAAAuK4rQQAAAADYaypBAAAAALJEKkEAAAAA/kEpQQAAAACGuShBAAAAAPyrKEEAAAAAMvInQQAAAADC7ydBAAAAAE7oJ0EAAAAAJAcoQQAAAACCcChBAAAAAAKmKEEAAAAA3IYpQQAAAADcpilBAAAAAIQ3KkEAAAAA0oEqQQAAAABisCpBAAAAACJXLEEAAAAAbm0sQQAAAADquyxBAAAAAEjzK0EAAAAAoCUsQQAAAADMpyxBfAntJSTwK0E=", "dtype": "f8" }, "yaxis": "y" @@ -443,7 +333,7 @@ "hovertemplate": "simple_grouping=neoplasms
year=%{x}
deaths=%{y}", "legendgroup": "neoplasms", "line": { - "color": "#00cc96" + "color": "#EF553B" }, "marker": { "symbol": "circle" @@ -455,12 +345,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAJ4uIUEAAAAApkohQQAAAADoUCFBAAAAAEhpIUEAAAAAlmghQQAAAAAqUSFBAAAAAHx8IUEAAAAAJIQhQQAAAAAWnCFBAAAAAGqyIUEAAAAAdsQhQQAAAABs/iFBAAAAAF4OIkEAAAAAOD0iQQAAAAAuUCJBAAAAAN6LIkEAAAAAxq4iQQAAAABuuyJBAAAAANrDIkEAAAAA0sMiQQAAAAAcxiJBAAAAAGrgIkEAAAAAzvYiQQAAAAD8DSNBAAAAAKo1I0EAAAAAcmkjQeFmvxCE5SJB", + "bdata": "AAAAAJ4uIUEAAAAApkohQQAAAADoUCFBAAAAAEhpIUEAAAAAlmghQQAAAAAqUSFBAAAAAHx8IUEAAAAAJIQhQQAAAAAWnCFBAAAAAGqyIUEAAAAAdsQhQQAAAABs/iFBAAAAAF4OIkEAAAAAOD0iQQAAAAAuUCJBAAAAAN6LIkEAAAAAxq4iQQAAAABuuyJBAAAAANrDIkEAAAAA0sMiQQAAAAAcxiJBAAAAAGrgIkEAAAAAzvYiQQAAAAD8DSNBAAAAAKo1I0EAAAAAeGkjQQAAAABugSNB0V5CexHdIkE=", "dtype": "f8" }, "yaxis": "y" @@ -472,7 +362,7 @@ "hovertemplate": "simple_grouping=respiratory
year=%{x}
deaths=%{y}", "legendgroup": "respiratory", "line": { - "color": "#ab63fa" + "color": "#00cc96" }, "marker": { "symbol": "circle" @@ -484,12 +374,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAMAMDEEAAAAA+DEMQQAAAAB4EQxBAAAAAGCgDEEAAAAA8MkMQQAAAACYtQtBAAAAALA+DUEAAAAA8KcLQQAAAACIvAtBAAAAANjoDUEAAAAAAB4NQQAAAABw+AxBAAAAAIhZDkEAAAAAYAwOQQAAAADI2w9BAAAAAHiFD0EAAAAAEIwQQQAAAACAOxBBAAAAAMgAEUEAAAAAQD4RQQAAAAAAiRBBAAAAAOyAEEEAAAAAUJsOQQAAAACQERBBAAAAANjcD0EAAAAAICcQQWe/ECt2uhFB", + "bdata": "AAAAAMAMDEEAAAAA+DEMQQAAAAB4EQxBAAAAAGCgDEEAAAAA8MkMQQAAAACYtQtBAAAAALA+DUEAAAAA8KcLQQAAAACIvAtBAAAAANjoDUEAAAAAAB4NQQAAAABw+AxBAAAAAIhZDkEAAAAAYAwOQQAAAADI2w9BAAAAAHiFD0EAAAAAEIwQQQAAAACAOxBBAAAAAMgAEUEAAAAAQD4RQQAAAAAAiRBBAAAAAOyAEEEAAAAAUJsOQQAAAACQERBBAAAAANjcD0EAAAAAMCcQQQAAAAC4+hBBob2E9q2JEUE=", "dtype": "f8" }, "yaxis": "y" @@ -501,7 +391,7 @@ "hovertemplate": "simple_grouping=external
year=%{x}
deaths=%{y}", "legendgroup": "external", "line": { - "color": "#FFA15A" + "color": "#ab63fa" }, "marker": { "symbol": "circle" @@ -518,7 +408,7 @@ }, "xaxis": "x", "y": { - "bdata": "AAAAAAByAkEAAAAAkHYCQQAAAABALwNBAAAAABAIBEEAAAAAyF0EQQAAAADgwQRBAAAAABiIBUEAAAAA2CkGQQAAAADolgZBAAAAACBvBkEAAAAAkPEFQQAAAABYXwZBAAAAAGgyB0EAAAAAuI0HQQAAAABI4wdBAAAAAOiwCEEAAAAAoHIKQQAAAAAwtQxBAAAAANA1DkEAAAAAkO0NQQAAAADgrQ5BAAAAAHhQEUEAAAAAuAwTQQAAAAAgAxNBAAAAAGiLEkEAAAAAiMgQQXjqWg7+h+dA", + "bdata": "AAAAAAByAkEAAAAAkHYCQQAAAABALwNBAAAAABAIBEEAAAAAyF0EQQAAAADgwQRBAAAAABiIBUEAAAAA2CkGQQAAAADolgZBAAAAACBvBkEAAAAAkPEFQQAAAABYXwZBAAAAAGgyB0EAAAAAuI0HQQAAAABI4wdBAAAAAOiwCEEAAAAAoHIKQQAAAAAwtQxBAAAAANA1DkEAAAAAkO0NQQAAAADgrQ5BAAAAAHhQEUEAAAAAuAwTQQAAAAAgAxNBAAAAAGiLEkEAAAAAsMgQQQAAAAB4lwpB", "dtype": "f8" }, "yaxis": "y" @@ -530,7 +420,7 @@ "hovertemplate": "simple_grouping=nervous system
year=%{x}
deaths=%{y}", "legendgroup": "nervous system", "line": { - "color": "#19d3f3" + "color": "#FFA15A" }, "marker": { "symbol": "circle" @@ -542,12 +432,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAADCD9EAAAAAAADT2QAAAAABwjPdAAAAAAJAl+UAAAAAA8L/6QAAAAACgQ/tAAAAAAGB0/UAAAAAAgAH+QAAAAACg/f5AAAAAAJiqAEEAAAAAMF8AQQAAAADANwFBAAAAALCuAUEAAAAAyNYBQQAAAABghQJBAAAAAKgvBEEAAAAAiD8HQQAAAAC4sQhBAAAAAOBmCkEAAAAAOHMLQQAAAADwhQxBAAAAAEwCEEEAAAAAyLIOQQAAAAAcIxBBAAAAANgVEEEAAAAAKPEQQcSK0vhIVBFB", + "bdata": "AAAAADCD9EAAAAAAADT2QAAAAABwjPdAAAAAAJAl+UAAAAAA8L/6QAAAAACgQ/tAAAAAAGB0/UAAAAAAgAH+QAAAAACg/f5AAAAAAJiqAEEAAAAAMF8AQQAAAADANwFBAAAAALCuAUEAAAAAyNYBQQAAAABghQJBAAAAAKgvBEEAAAAAiD8HQQAAAAC4sQhBAAAAAOBmCkEAAAAAOHMLQQAAAADwhQxBAAAAAEwCEEEAAAAAyLIOQQAAAAAcIxBBAAAAANgVEEEAAAAAJPEQQQAAAABosxFBOY7jOFhCEkE=", "dtype": "f8" }, "yaxis": "y" @@ -559,7 +449,7 @@ "hovertemplate": "simple_grouping=metabolic
year=%{x}
deaths=%{y}", "legendgroup": "metabolic", "line": { - "color": "#FF6692" + "color": "#19d3f3" }, "marker": { "symbol": "circle" @@ -571,12 +461,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAOCn9kAAAAAAgAP3QAAAAAAQnvdAAAAAANBD+EAAAAAAQH/4QAAAAAAgKfhAAAAAAFAq+UAAAAAAIDT4QAAAAACAJ/hAAAAAAPA8+EAAAAAA4Of3QAAAAABQIPhAAAAAAPCl+UAAAAAAQNn5QAAAAAAQovpAAAAAABCC+0AAAAAAcOv8QAAAAAAwlv1AAAAAAIA4/0AAAAAAuD4AQQAAAAAAGAFBAAAAAGAHBEEAAAAAqPkEQQAAAADIQAVBAAAAAFiiBEEAAAAAwPoEQbYc3OxDDwVB", + "bdata": "AAAAAOCn9kAAAAAAgAP3QAAAAAAQnvdAAAAAANBD+EAAAAAAQH/4QAAAAAAgKfhAAAAAAFAq+UAAAAAAIDT4QAAAAACAJ/hAAAAAAPA8+EAAAAAA4Of3QAAAAABQIPhAAAAAAPCl+UAAAAAAQNn5QAAAAAAQovpAAAAAABCC+0AAAAAAcOv8QAAAAAAwlv1AAAAAAIA4/0AAAAAAuD4AQQAAAAAAGAFBAAAAAGAHBEEAAAAAqPkEQQAAAADIQAVBAAAAAFiiBEEAAAAA2PoEQQAAAAAYnAVBhfYS2rEmBUE=", "dtype": "f8" }, "yaxis": "y" @@ -588,7 +478,7 @@ "hovertemplate": "simple_grouping=mental
year=%{x}
deaths=%{y}", "legendgroup": "mental", "line": { - "color": "#B6E880" + "color": "#FF6692" }, "marker": { "symbol": "circle" @@ -600,12 +490,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAICB5EAAAAAAwGfmQAAAAABgxOhAAAAAAAAv60AAAAAAwEftQAAAAADg/u1AAAAAAHBr8UAAAAAAAGH2QAAAAABwA/ZAAAAAAECD+UAAAAAAgOj5QAAAAACQef1AAAAAAMiUAEEAAAAAEAsCQQAAAADYFANBAAAAAABuAkEAAAAAUKsAQQAAAADILABBAAAAAECQAEEAAAAAGIUAQQAAAAC4VgBBAAAAAFgbAkEAAAAASKEAQQAAAAAY8gBBAAAAAAA7AEEAAAAAeAUAQRsfmUCHe/9A", + "bdata": "AAAAAICB5EAAAAAAwGfmQAAAAABgxOhAAAAAAAAv60AAAAAAwEftQAAAAADg/u1AAAAAAHBr8UAAAAAAAGH2QAAAAABwA/ZAAAAAAECD+UAAAAAAgOj5QAAAAACQef1AAAAAAMiUAEEAAAAAEAsCQQAAAADYFANBAAAAAABuAkEAAAAAUKsAQQAAAADILABBAAAAAECQAEEAAAAAGIUAQQAAAAC4VgBBAAAAAFgbAkEAAAAASKEAQQAAAAAY8gBBAAAAAAA7AEEAAAAAeAUAQQAAAAAYOgBBFNpLaO+J/0A=", "dtype": "f8" }, "yaxis": "y" @@ -617,7 +507,7 @@ "hovertemplate": "simple_grouping=digestive
year=%{x}
deaths=%{y}", "legendgroup": "digestive", "line": { - "color": "#FF97FF" + "color": "#B6E880" }, "marker": { "symbol": "circle" @@ -629,12 +519,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAJA29EAAAAAAoHz0QAAAAACg7PRAAAAAABAm9UAAAAAAwGf1QAAAAADwDPVAAAAAAKBP9UAAAAAAYF71QAAAAACgvvVAAAAAANA49kAAAAAAQM71QAAAAACgQvZAAAAAAAD09kAAAAAAQDr3QAAAAACQ+/dAAAAAAECg+EAAAAAAcIb5QAAAAACQ1PlAAAAAAOBB+kAAAAAA8MT6QAAAAABwivtAAAAAAEA6/kAAAAAAYFMAQQAAAAB4MABBAAAAAABV/0AAAAAAMFb/QLuWg5stFP5A", + "bdata": "AAAAAJA29EAAAAAAoHz0QAAAAACg7PRAAAAAABAm9UAAAAAAwGf1QAAAAADwDPVAAAAAAKBP9UAAAAAAYF71QAAAAACgvvVAAAAAANA49kAAAAAAQM71QAAAAACgQvZAAAAAAAD09kAAAAAAQDr3QAAAAACQ+/dAAAAAAECg+EAAAAAAcIb5QAAAAACQ1PlAAAAAAOBB+kAAAAAA8MT6QAAAAABwivtAAAAAAEA6/kAAAAAAYFMAQQAAAAB4MABBAAAAAABV/0AAAAAAgFb/QAAAAABAQ/9AAAAAAKy9/UA=", "dtype": "f8" }, "yaxis": "y" @@ -643,27 +533,27 @@ "fillpattern": { "shape": "" }, - "hovertemplate": "simple_grouping=infectious
year=%{x}
deaths=%{y}", - "legendgroup": "infectious", + "hovertemplate": "simple_grouping=genitourinary
year=%{x}
deaths=%{y}", + "legendgroup": "genitourinary", "line": { - "color": "#FECB52" + "color": "#FF97FF" }, "marker": { "symbol": "circle" }, "mode": "lines", - "name": "infectious", + "name": "genitourinary", "orientation": "v", "showlegend": true, "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAOAt7UAAAAAAQLXsQAAAAABAWu1AAAAAAMAc70AAAAAAYHTvQAAAAADAbO9AAAAAAFAt8EAAAAAAMEzwQAAAAAAQQvBAAAAAAICl8EAAAAAA4OnwQAAAAABwcfBAAAAAAOC+8EAAAAAAoKPwQAAAAABgL/FAAAAAAGAi8UAAAAAA4InxQAAAAAAQCvFAAAAAAGDg8EAAAAAAUIrwQAAAAACAdO9AAAAAAACf8EAAAAAA4KzwQAAAAADQAvFAAAAAAJDX8EAAAAAAAIbwQBofmUB3x/BA", + "bdata": "AAAAACDi6UAAAAAAwJDqQAAAAADAxutAAAAAAABp7EAAAAAAAEbtQAAAAACgYO1AAAAAACCY7kAAAAAAIPzuQAAAAAAAi+9AAAAAACDB7kAAAAAAoPXuQAAAAACAAPBAAAAAAIAD7kAAAAAAgDjuQAAAAADgO+9AAAAAANAg8EAAAAAAYOrwQAAAAACgIvFAAAAAAPBp8UAAAAAAcK3xQAAAAACww/FAAAAAABC38kAAAAAA4EDzQAAAAABwmPNAAAAAAPC/8kAAAAAA8LnyQAAAAAAgzfJAVlVVVVGp8kA=", "dtype": "f8" }, "yaxis": "y" @@ -672,27 +562,27 @@ "fillpattern": { "shape": "" }, - "hovertemplate": "simple_grouping=genitourinary
year=%{x}
deaths=%{y}", - "legendgroup": "genitourinary", + "hovertemplate": "simple_grouping=infectious
year=%{x}
deaths=%{y}", + "legendgroup": "infectious", "line": { - "color": "#636efa" + "color": "#FECB52" }, "marker": { "symbol": "circle" }, "mode": "lines", - "name": "genitourinary", + "name": "infectious", "orientation": "v", "showlegend": true, "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAACDi6UAAAAAAwJDqQAAAAADAxutAAAAAAABp7EAAAAAAAEbtQAAAAACgYO1AAAAAACCY7kAAAAAAIPzuQAAAAAAAi+9AAAAAACDB7kAAAAAAoPXuQAAAAACAAPBAAAAAAIAD7kAAAAAAgDjuQAAAAADgO+9AAAAAANAg8EAAAAAAYOrwQAAAAACgIvFAAAAAAPBp8UAAAAAAcK3xQAAAAACww/FAAAAAABC38kAAAAAA4EDzQAAAAABwmPNAAAAAAPC/8kAAAAAA8LnyQN6pazlgmvJA", + "bdata": "AAAAAOAt7UAAAAAAQLXsQAAAAABAWu1AAAAAAMAc70AAAAAAYHTvQAAAAADAbO9AAAAAAFAt8EAAAAAAMEzwQAAAAAAQQvBAAAAAAICl8EAAAAAA4OnwQAAAAABwcfBAAAAAAOC+8EAAAAAAoKPwQAAAAABgL/FAAAAAAGAi8UAAAAAA4InxQAAAAAAQCvFAAAAAAGDg8EAAAAAAUIrwQAAAAACAdO9AAAAAAACf8EAAAAAA4KzwQAAAAADQAvFAAAAAAJDX8EAAAAAAMIXwQAAAAAAgq/BA2ktoL70e8EA=", "dtype": "f8" }, "yaxis": "y" @@ -704,7 +594,7 @@ "hovertemplate": "simple_grouping=special
year=%{x}
deaths=%{y}", "legendgroup": "special", "line": { - "color": "#EF553B" + "color": "#636efa" }, "marker": { "symbol": "circle" @@ -716,12 +606,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "0QfjB+QH5QfmB+cH6AfpBw==", + "bdata": "0QfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAAC0pkAAAAAAAAAkQAAAAAAUaxVBAAAAAHByGUEAAAAAkMYGQQAAAABAZOhAAAAAAIC33kCQTKB36o3OQA==", + "bdata": "AAAAAAC0pkAAAAAAAAAkQAAAAAAUaxVBAAAAAHByGUEAAAAAkMYGQQAAAABAZOhAAAAAAMC33kAAAAAAgAvLQPYS2ktoH8VA", "dtype": "f8" }, "yaxis": "y" @@ -733,7 +623,7 @@ "hovertemplate": "simple_grouping=other
year=%{x}
deaths=%{y}", "legendgroup": "other", "line": { - "color": "#00cc96" + "color": "#EF553B" }, "marker": { "symbol": "circle" @@ -745,12 +635,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAIDp2UAAAAAAwAPfQAAAAAAAad9AAAAAAMAh3UAAAAAAAJPeQAAAAABAR91AAAAAAMAS30AAAAAAgN3eQAAAAACAS+BAAAAAAEC84kAAAAAAIGDjQAAAAABAp+JAAAAAAICO40AAAAAAoHPkQAAAAABAXOJAAAAAAMBb30AAAAAAwCzfQAAAAAAAPOBAAAAAAIDc30AAAAAAQKnfQAAAAAAAiN9AAAAAAECU4EAAAAAA4MjgQAAAAADgluBAAAAAAMAx4EAAAAAAAPHeQMkEeqeuiPRA", + "bdata": "AAAAAIDp2UAAAAAAwAPfQAAAAAAAad9AAAAAAMAh3UAAAAAAAJPeQAAAAABAR91AAAAAAMAS30AAAAAAgN3eQAAAAACAS+BAAAAAAEC84kAAAAAAIGDjQAAAAABAp+JAAAAAAICO40AAAAAAoHPkQAAAAABAXOJAAAAAAMBb30AAAAAAwCzfQAAAAAAAPOBAAAAAAIDc30AAAAAAQKnfQAAAAAAAiN9AAAAAAECU4EAAAAAA4MjgQAAAAADgluBAAAAAAMAx4EAAAAAAQO7eQAAAAACAIeFAx3Ecx8Uh+0A=", "dtype": "f8" }, "yaxis": "y" @@ -762,7 +652,7 @@ "hovertemplate": "simple_grouping=musculoskeletal
year=%{x}
deaths=%{y}", "legendgroup": "musculoskeletal", "line": { - "color": "#ab63fa" + "color": "#00cc96" }, "marker": { "symbol": "circle" @@ -774,12 +664,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAAD5yUAAAAAAgMXKQAAAAAAAX8tAAAAAAAD/y0AAAAAAgC/MQAAAAAAAyctAAAAAAAAFzEAAAAAAABzLQAAAAAAABstAAAAAAID9ykAAAAAAgOrJQAAAAACA5MlAAAAAAACkykAAAAAAALjKQAAAAAAAWcpAAAAAAIDzyUAAAAAAgJXKQAAAAAAAzspAAAAAAIDrykAAAAAAANzKQAAAAAAAV8xAAAAAAIACzkAAAAAAgJbPQAAAAADACtBAAAAAAEBB0EAAAAAAQIjQQObgZr/Ql9BA", + "bdata": "AAAAAAD5yUAAAAAAgMXKQAAAAAAAX8tAAAAAAAD/y0AAAAAAgC/MQAAAAAAAyctAAAAAAAAFzEAAAAAAABzLQAAAAAAABstAAAAAAID9ykAAAAAAgOrJQAAAAACA5MlAAAAAAACkykAAAAAAALjKQAAAAAAAWcpAAAAAAIDzyUAAAAAAgJXKQAAAAAAAzspAAAAAAIDrykAAAAAAANzKQAAAAAAAV8xAAAAAAIACzkAAAAAAgJbPQAAAAADACtBAAAAAAEBB0EAAAAAAwIjQQAAAAADA4NBA9xLaS7g40EA=", "dtype": "f8" }, "yaxis": "y" @@ -791,7 +681,7 @@ "hovertemplate": "simple_grouping=perinatal
year=%{x}
deaths=%{y}", "legendgroup": "perinatal", "line": { - "color": "#FFA15A" + "color": "#ab63fa" }, "marker": { "symbol": "circle" @@ -803,12 +693,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAIC2y0AAAAAAAFPLQAAAAAAA88pAAAAAAACsy0AAAAAAgPbLQAAAAAAAm8tAAAAAAABIzEAAAAAAgBHMQAAAAAAAWcxAAAAAAIAJy0AAAAAAAHnJQAAAAAAAiMdAAAAAAABTx0AAAAAAAB7HQAAAAAAAY8dAAAAAAAAUx0AAAAAAALvGQAAAAAAARsZAAAAAAICNxUAAAAAAAMnEQAAAAAAAMcRAAAAAAICzwkAAAAAAgIfCQAAAAACAwcNAAAAAAABmw0AAAAAAgGfDQK/l4GY/z8BA", + "bdata": "AAAAAIC2y0AAAAAAAFPLQAAAAAAA88pAAAAAAACsy0AAAAAAgPbLQAAAAAAAm8tAAAAAAABIzEAAAAAAgBHMQAAAAAAAWcxAAAAAAIAJy0AAAAAAAHnJQAAAAAAAiMdAAAAAAABTx0AAAAAAAB7HQAAAAAAAY8dAAAAAAAAUx0AAAAAAALvGQAAAAAAARsZAAAAAAICNxUAAAAAAAMnEQAAAAAAAMcRAAAAAAICzwkAAAAAAgIfCQAAAAACAwcNAAAAAAABmw0AAAAAAAGbDQAAAAACArcJACu0ltFfZvUA=", "dtype": "f8" }, "yaxis": "y" @@ -820,7 +710,7 @@ "hovertemplate": "simple_grouping=blood
year=%{x}
deaths=%{y}", "legendgroup": "blood", "line": { - "color": "#19d3f3" + "color": "#FFA15A" }, "marker": { "symbol": "circle" @@ -832,12 +722,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAAClwUAAAAAAgB3CQAAAAACAjsJAAAAAAIDPwkAAAAAAgKLCQAAAAACAVsJAAAAAAICowkAAAAAAgDHBQAAAAACA/8JAAAAAAIA3w0AAAAAAgMDCQAAAAAAABMNAAAAAAADNw0AAAAAAgGTEQAAAAAAA3sNAAAAAAADtxEAAAAAAgNnEQAAAAAAATMVAAAAAAAD/xEAAAAAAgDXFQAAAAAAACcVAAAAAAIBTxkAAAAAAgC7HQAAAAAAAhcdAAAAAAIBJx0AAAAAAACPHQIZYURrfXMZA", + "bdata": "AAAAAAClwUAAAAAAgB3CQAAAAACAjsJAAAAAAIDPwkAAAAAAgKLCQAAAAACAVsJAAAAAAICowkAAAAAAgDHBQAAAAACA/8JAAAAAAIA3w0AAAAAAgMDCQAAAAAAABMNAAAAAAADNw0AAAAAAgGTEQAAAAAAA3sNAAAAAAADtxEAAAAAAgNnEQAAAAAAATMVAAAAAAAD/xEAAAAAAgDXFQAAAAAAACcVAAAAAAIBTxkAAAAAAgC7HQAAAAAAAhcdAAAAAAIBJx0AAAAAAgCLHQAAAAACANsdAvoT2EjrJxEA=", "dtype": "f8" }, "yaxis": "y" @@ -849,7 +739,7 @@ "hovertemplate": "simple_grouping=malformations
year=%{x}
deaths=%{y}", "legendgroup": "malformations", "line": { - "color": "#FF6692" + "color": "#19d3f3" }, "marker": { "symbol": "circle" @@ -861,12 +751,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAAAExEAAAAAAAG3EQAAAAAAAJ8RAAAAAAICXxEAAAAAAAErEQAAAAACAOsRAAAAAAIAcxEAAAAAAAEXEQAAAAAAAI8RAAAAAAIDcw0AAAAAAABjDQAAAAAAAr8JAAAAAAACwwkAAAAAAAJXCQAAAAACAeMJAAAAAAICNwkAAAAAAAFXDQAAAAAAAkMNAAAAAAAAjw0AAAAAAANbCQAAAAAAA0MJAAAAAAICDwkAAAAAAAKTCQAAAAAAAncNAAAAAAAAaw0AAAAAAgGjDQP5CrCgNSsFA", + "bdata": "AAAAAAAExEAAAAAAAG3EQAAAAAAAJ8RAAAAAAICXxEAAAAAAAErEQAAAAACAOsRAAAAAAIAcxEAAAAAAAEXEQAAAAAAAI8RAAAAAAIDcw0AAAAAAABjDQAAAAAAAr8JAAAAAAACwwkAAAAAAAJXCQAAAAACAeMJAAAAAAICNwkAAAAAAAFXDQAAAAAAAkMNAAAAAAAAjw0AAAAAAANbCQAAAAAAA0MJAAAAAAICDwkAAAAAAAKTCQAAAAAAAncNAAAAAAAAaw0AAAAAAAGrDQAAAAACAaMNATGgvof37vkA=", "dtype": "f8" }, "yaxis": "y" @@ -878,7 +768,7 @@ "hovertemplate": "simple_grouping=delay
year=%{x}
deaths=%{y}", "legendgroup": "delay", "line": { - "color": "#B6E880" + "color": "#FF6692" }, "marker": { "symbol": "circle" @@ -890,12 +780,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "6Qc=", + "bdata": "6QfqBw==", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "9E5dy3mRA0E=", + "bdata": "AAAAAIBz40BVVVVVp/wDQQ==", "dtype": "f8" }, "yaxis": "y" @@ -907,7 +797,7 @@ "hovertemplate": "simple_grouping=skin
year=%{x}
deaths=%{y}", "legendgroup": "skin", "line": { - "color": "#FF97FF" + "color": "#B6E880" }, "marker": { "symbol": "circle" @@ -919,12 +809,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAAAurEAAAAAAAKysQAAAAAAAeqxAAAAAAAAKrkAAAAAAAGSvQAAAAAAA7q9AAAAAAAAmsEAAAAAAAEitQAAAAAAAiq1AAAAAAABerkAAAAAAALKuQAAAAAAAcq5AAAAAAACsr0AAAAAAABWwQAAAAAAAjrBAAAAAAAD/sEAAAAAAADGyQAAAAAAA4bJAAAAAAAA3s0AAAAAAAOmzQAAAAAAA+bNAAAAAAADItUAAAAAAAHq4QAAAAAAABrxAAAAAAACqu0AAAAAAAMG6QFEaH5lAQLlA", + "bdata": "AAAAAAAurEAAAAAAAKysQAAAAAAAeqxAAAAAAAAKrkAAAAAAAGSvQAAAAAAA7q9AAAAAAAAmsEAAAAAAAEitQAAAAAAAiq1AAAAAAABerkAAAAAAALKuQAAAAAAAcq5AAAAAAACsr0AAAAAAABWwQAAAAAAAjrBAAAAAAAD/sEAAAAAAADGyQAAAAAAA4bJAAAAAAAA3s0AAAAAAAOmzQAAAAAAA+bNAAAAAAADItUAAAAAAAHq4QAAAAAAABrxAAAAAAACqu0AAAAAAAMG6QAAAAAAAPbpAQ3sJ7SWpuEA=", "dtype": "f8" }, "yaxis": "y" @@ -936,7 +826,7 @@ "hovertemplate": "simple_grouping=pregnancy
year=%{x}
deaths=%{y}", "legendgroup": "pregnancy", "line": { - "color": "#FECB52" + "color": "#FF97FF" }, "marker": { "symbol": "circle" @@ -948,12 +838,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH", + "bdata": "zwfQB9EH0gfTB9QH1QfWB9cH2AfZB9oH2wfcB90H3gffB+AH4QfiB+MH5AflB+YH5wfoB+kH6gc=", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAADgdkAAAAAAALB3QAAAAAAAMHhAAAAAAABgdUAAAAAAAMB+QAAAAAAAWIRAAAAAAAB4hkAAAAAAALiGQAAAAAAACIdAAAAAAAD4h0AAAAAAAECMQAAAAAAA2IhAAAAAAADgi0AAAAAAADiNQAAAAAAA2JBAAAAAAADYkEAAAAAAABSRQAAAAAAAwJJAAAAAAAA0kkAAAAAAAJiNQAAAAAAAZJBAAAAAAAD4kkAAAAAAAOyZQAAAAAAAJJNAAAAAAAAIkEAAAAAAANiNQMSK0vjImIpA", + "bdata": "AAAAAADgdkAAAAAAALB3QAAAAAAAMHhAAAAAAABgdUAAAAAAAMB+QAAAAAAAWIRAAAAAAAB4hkAAAAAAALiGQAAAAAAACIdAAAAAAAD4h0AAAAAAAECMQAAAAAAA2IhAAAAAAADgi0AAAAAAADiNQAAAAAAA2JBAAAAAAADYkEAAAAAAABSRQAAAAAAAwJJAAAAAAAA0kkAAAAAAAJiNQAAAAAAAZJBAAAAAAAD4kkAAAAAAAOyZQAAAAAAAJJNAAAAAAAAIkEAAAAAAAOCNQAAAAAAAeIlAfAntJbT3hUA=", "dtype": "f8" }, "yaxis": "y" @@ -965,7 +855,7 @@ "hovertemplate": "simple_grouping=ear
year=%{x}
deaths=%{y}", "legendgroup": "ear", "line": { - "color": "#636efa" + "color": "#FECB52" }, "marker": { "symbol": "circle" @@ -982,7 +872,7 @@ }, "xaxis": "x", "y": { - "bdata": "AAAAAAAAJEAAAAAAAAAkQAAAAAAAAEBAAAAAAAAAJkAAAAAAAABAQAAAAAAAACRAAAAAAAAAJEAAAAAAAAA1QAAAAAAAgEFAAAAAAAAALEAAAAAAAABFQAAAAAAAADdAAAAAAAAAM0AAAAAAAAAqQAAAAAAAAEFAAAAAAAAASEAAAAAAAAA8QAAAAAAAAEpAAAAAAACATUAAAAAAAIBDQAAAAAAAAENAAAAAAAAATUAAAAAAAEBRQAAAAAAAgFNA9E5dy8GNT0A=", + "bdata": "AAAAAAAAJEAAAAAAAAAkQAAAAAAAAEBAAAAAAAAAJkAAAAAAAABAQAAAAAAAACRAAAAAAAAAJEAAAAAAAAA1QAAAAAAAgEFAAAAAAAAALEAAAAAAAABFQAAAAAAAADdAAAAAAAAAM0AAAAAAAAAqQAAAAAAAAEFAAAAAAAAASEAAAAAAAAA8QAAAAAAAAEpAAAAAAACATUAAAAAAAIBDQAAAAAAAAENAAAAAAAAATUAAAAAAAEBRQAAAAAAAgFNAAAAAAACAUkA=", "dtype": "f8" }, "yaxis": "y" @@ -994,7 +884,7 @@ "hovertemplate": "simple_grouping=eye
year=%{x}
deaths=%{y}", "legendgroup": "eye", "line": { - "color": "#EF553B" + "color": "#636efa" }, "marker": { "symbol": "circle" @@ -1827,12 +1717,12 @@ } } }, - "image/png": 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", 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "charters.compare_rates(\n", + " df=test_df[test_df[CATEGORY_COL] == \"total\"],\n", + " x_axis=\"year\",\n", + " rates=[\"crude_adj\", \"predictions\"],\n", + " display=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "f5b41604-6965-4111-82f7-0b94957ba343", + "metadata": {}, + "source": [ + "## Monthly" + ] + }, + { + "cell_type": "markdown", + "id": "1ec12d14-7a68-45ad-bf82-cb04dcf6b282", + "metadata": {}, + "source": [ + "### SQL" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "ce851603-212e-4fe3-be23-38723f5163bc", + "metadata": {}, + "outputs": [], + "source": [ + "mcd18_monthly = cdc.get_cdc_data_sql(\n", + " db_filepath=db_filepath, table_name=\"mcd18_monthly\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 184, + "id": "e098cde2-9e54-439c-a7cc-5c1257bda572", + "metadata": {}, + "outputs": [], + "source": [ + "mcd18_monthly_cod = cdc.get_cdc_data_sql(\n", + " db_filepath=db_filepath, table_name=\"mcd18_monthly_cod\"\n", + ")\n", + "mcd99_monthly_cod = cdc.get_cdc_data_sql(\n", + " db_filepath=db_filepath, table_name=\"mcd99_monthly_cod\"\n", + ")\n", + "mcd18_monthly_cod = mcd18_monthly_cod[mcd18_monthly_cod[\"year\"] >= 2021]\n", + "mcd99_monthly_cod = mcd99_monthly_cod[mcd99_monthly_cod[\"year\"] >= 2010]\n", + "monthly_cod = pd.concat([mcd18_monthly_cod, mcd99_monthly_cod], ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "id": "f0c6eb4e-9157-48aa-9cf6-148f0ebe0a4a", + "metadata": {}, + "source": [ + "### Chart (all)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2cf7ba35-227e-40e5-9773-412966093d49", + "metadata": {}, + "outputs": [], + "source": [ + "# color mapping\n", + "years = sorted(mcd18_monthly[\"year\"].unique())\n", + "current_year = years[-1]\n", + "prior = years[:-1]\n", + "prior_colors = px.colors.sample_colorscale(\n", + " \"Blues\", [0.3 + 0.7 * i / max(len(prior) - 1, 1) for i in range(len(prior))]\n", + ")\n", + "color_discrete_map = dict(zip(prior, prior_colors))\n", + "color_discrete_map[current_year] = \"crimson\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "68cb0caf-923a-43c4-be8a-09d3e9b3414d", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "year=2018
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = charters.chart(\n", + " df=monthly_cod_filtered,\n", + " x_axis=\"reported_date\",\n", + " y_axis=\"deaths\",\n", + " color=\"simple_chapter\",\n", + " type=\"area\",\n", + ")\n", + "\n", + "# add average\n", + "fig.add_trace(\n", + " go.Scatter(\n", + " x=monthly_cod_filtered[\"reported_date\"],\n", + " y=monthly_cod_filtered[\"avg\"],\n", + " mode=\"lines\",\n", + " name=\"monthly avg\",\n", + " line=dict(color=\"black\", width=2),\n", + " )\n", + ")\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "d166ae8c-65b2-4df9-b50d-5e4c7410032f", + "metadata": {}, + "source": [ + "## Weekly" + ] + }, + { + "cell_type": "markdown", + "id": "d3d47668-2137-4387-980c-60ba6842c86b", + "metadata": {}, + "source": [ + "### SQL" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ff4816b0-775e-426b-b60a-718e6ac0f2c7", + "metadata": {}, + "outputs": [], + "source": [ + "mcd18_weekly = cdc.get_cdc_data_sql(db_filepath=db_filepath, table_name=\"mcd18_weekly\")" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c258ae58-0022-422c-884f-73585f3a28d6", + "metadata": {}, + "outputs": [], + "source": [ + "IBNR_FACTORS = {\n", + " 0: 0.27,\n", + " 1: 0.64,\n", + " 2: 0.76,\n", + " 3: 0.85,\n", + " 4: 0.90,\n", + " 5: 0.94,\n", + " 6: 0.96,\n", + " 7: 0.97,\n", + " 8: 0.98,\n", + " 9: 0.99,\n", + " 10: 0.99,\n", + " 11: 0.99,\n", + " 12: 0.99,\n", + " 13: 0.99,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "d7657e1b-6b46-4fa0-b6be-17f962f064c5", + "metadata": {}, + "outputs": [], + "source": [ + "# remove na\n", + "mcd18_weekly.dropna(subset=[\"mmwr_week\"], inplace=True)\n", + "mcd18_weekly = mcd18_weekly.sort_values(\"mmwr_week_date\").reset_index(drop=True)\n", + "# add week_lag\n", + "mcd18_weekly[\"recent_week\"] = range(len(mcd18_weekly) - 1, -1, -1)\n", + "# adjust for lag\n", + "years = sorted(mcd18_weekly[\"mmwr_year\"].unique())\n", + "current_year = years[-1]\n", + "prior_years = years[:-1]\n", + "current_year_adj = mcd18_weekly[mcd18_weekly[\"mmwr_year\"] == current_year].copy()\n", + "current_year_adj[\"deaths\"] = current_year_adj[\"deaths\"] / current_year_adj[\n", + " \"recent_week\"\n", + "].map(IBNR_FACTORS).fillna(1.0)\n", + "current_year_adj = current_year_adj[current_year_adj[\"recent_week\"] >= 3]\n", + "current_year_adj[\"mmwr_year\"] = f\"{current_year}_adj\"\n", + "mcd18_weekly = pd.concat([mcd18_weekly, current_year_adj], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "id": "ff9e1ac8-4984-46f7-ab75-097155445b3b", + "metadata": {}, + "outputs": [], + "source": [ + "mcd18_weekly_pic = cdc.get_cdc_data_sql(\n", + " db_filepath=db_filepath, table_name=\"mcd18_weekly_pic\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "bab94962-b3a2-470c-965b-3287a0678929", + "metadata": {}, + "source": [ + "### Chart (all)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "4b090273-c9ae-4a53-9f26-ad2466b4ee1a", + "metadata": {}, + "outputs": [], + "source": [ + "prior_colors = px.colors.sample_colorscale(\n", + " \"Blues\",\n", + " [0.3 + 0.7 * i / max(len(prior_years) - 1, 1) for i in range(len(prior_years))],\n", + ")\n", + "color_discrete_map = dict(zip(prior_years, prior_colors))\n", + "color_discrete_map[current_year] = \"crimson\"\n", + "color_discrete_map[f\"{current_year}_adj\"] = \"crimson\"" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "3daf8d11-4d84-401c-b20c-c5e4c4b06d69", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "mmwr_year=2018.0
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", + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = charters.chart(\n", + " df=mcd18_weekly,\n", + " x_axis=\"mmwr_week\",\n", + " y_axis=\"deaths\",\n", + " color=\"mmwr_year\",\n", + " type=\"line\",\n", + " color_discrete_map=color_discrete_map,\n", + ")\n", + "for trace in fig.data:\n", + " if trace.name == f\"{current_year}_adj\":\n", + " trace.line.dash = \"dash\"\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "id": "994a4c00-0590-4e9d-817d-3dfc12158e97", + "metadata": {}, + "source": [ + "### Chart (flu)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "id": "442936cf-00c2-4618-8637-4c3f10a39d1e", + "metadata": {}, + "outputs": [], + "source": [ + "# filter \n", + "mcd18_weekly_pic = mcd18_weekly_pic[\n", + " mcd18_weekly_pic[\"icd_chapter\"] == \"Diseases of the respiratory system\"\n", + "]\n", + "max_date = mcd18_weekly_pic[\"mmwr_week_date\"].max()\n", + "cutoff = max_date - pd.Timedelta(weeks=7)\n", + "# compute average\n", + "weekly_avg = (\n", + " mcd18_weekly_pic[mcd18_weekly_pic[\"mmwr_week_date\"] <= cutoff]\n", + " .groupby(\"mmwr_week\")[\"deaths\"]\n", + " .mean()\n", + ")\n", + "mcd18_weekly_pic[\"avg\"] = mcd18_weekly_pic[\"mmwr_week\"].map(weekly_avg)\n", + "mcd18_weekly_pic.loc[mcd18_weekly_pic[\"mmwr_week_date\"] > cutoff, \"avg\"] = (\n", + " None\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "id": "9943f8e3-9c3e-44f2-bf08-46ff592166c9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "mode": "lines+markers", + "name": "deaths", + "type": "scatter", + "x": [ + "2018-01-06T00:00:00.000000", + "2018-01-13T00:00:00.000000", + "2018-01-20T00:00:00.000000", + "2018-01-27T00:00:00.000000", + "2018-02-03T00:00:00.000000", + "2018-02-10T00:00:00.000000", + "2018-02-17T00:00:00.000000", + "2018-02-24T00:00:00.000000", + 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", + "image/png": 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yeardeathspopulationqx_rawqx_lcdeaths_lcexcess_lc
020152712630.0000321418820.00000.00840.00842712630.00001.0000
120162744248.0000323127513.00000.00850.00852744248.00001.0000
220172813503.0000325719178.00000.00860.00862813503.00001.0000
320182839205.0000327167434.00000.00870.00872839205.00001.0000
420192854838.0000328239523.00000.00870.00872854838.00001.0000
520203383729.0000329484123.00000.01030.00882885693.63871.1726
620213464231.0000331893745.00000.01040.00842798074.88581.2381
720223279857.0000333287557.00000.00980.00872902569.26971.1300
820233090964.0000334914895.00000.00920.00872900873.34041.0655
920243072578.0000337199501.49250.00910.00872949114.59251.0419
1020253065696.1732339575729.61510.00900.00882998726.52061.0223
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" - ], - "text/plain": [ - " year deaths population qx_raw qx_lc deaths_lc excess_lc\n", - "0 2015 2712630.0000 321418820.0000 0.0084 0.0084 2712630.0000 1.0000\n", - "1 2016 2744248.0000 323127513.0000 0.0085 0.0085 2744248.0000 1.0000\n", - "2 2017 2813503.0000 325719178.0000 0.0086 0.0086 2813503.0000 1.0000\n", - "3 2018 2839205.0000 327167434.0000 0.0087 0.0087 2839205.0000 1.0000\n", - "4 2019 2854838.0000 328239523.0000 0.0087 0.0087 2854838.0000 1.0000\n", - "5 2020 3383729.0000 329484123.0000 0.0103 0.0088 2885693.6387 1.1726\n", - "6 2021 3464231.0000 331893745.0000 0.0104 0.0084 2798074.8858 1.2381\n", - "7 2022 3279857.0000 333287557.0000 0.0098 0.0087 2902569.2697 1.1300\n", - "8 2023 3090964.0000 334914895.0000 0.0092 0.0087 2900873.3404 1.0655\n", - "9 2024 3072578.0000 337199501.4925 0.0091 0.0087 2949114.5925 1.0419\n", - "10 2025 3065696.1732 339575729.6151 0.0090 0.0088 2998726.5206 1.0223" + "title": { + "text": "Rates" + }, + "type": "-" + }, + "yaxis2": { + "anchor": "x", + "overlaying": "y", + "side": "right" + } + } + }, + "image/png": 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", + "text/html": [ + "
\n", + "
" ] }, - "execution_count": 181, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "result = (\n", - " excess_grouped.groupby([\"year\"], observed=True).sum(numeric_only=True).reset_index()\n", - ")\n", - "result[\"excess_lc\"] = result[\"deaths\"] / result[\"deaths_lc\"]\n", - "result[\"qx_raw\"] = result[\"deaths\"] / result[\"population\"]\n", - "result[\"qx_lc\"] = result[\"deaths_lc\"] / result[\"population\"]\n", - "pd.options.display.float_format = \"{:.4f}\".format\n", - "result" + "charters.compare_rates(\n", + " result,\n", + " x_axis=\"year\",\n", + " rates=[\"crude_rt\", \"crude_rt_lc\"],\n", + ")" ] }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 50, "id": "3dddfb78-fe18-4ef7-b06d-c7909142fda4", "metadata": {}, "outputs": [ @@ -6830,12 +11956,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAAJtZTkEAAACAMEpOQQAAAIDvDU5BAAAAAAhcTUEAAAAAxtxMQQAAAADxfkxBAAAAgOYxS0EAAACAlBlMQQAAAABH1ktBzczMjEtyS0GamZkZUA5LQQ==", + "bdata": "AAAAAJtZTkEAAACAMEpOQQAAAIDvDU5BAAAAAAhcTUEAAAAAxtxMQQAAAADxfkxBAAAAgOYxS0EAAACAlBlMQQAAAABH1ktBAAAAALeVS0Fy0HN8N3tLQXEPpdlRIUtB", "dtype": "f8" }, "yaxis": "y" @@ -6859,12 +11985,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAYPNhbkEAAACAc29uQQAAAKBJhG5BAAAAYPpxbkEAAADgux9uQQAAAEC5sG1BAAAAoI8cbUEAAAAAiVVsQQAAAEAVWWxBMzMzoxdMbEFmZmYGGj9sQQ==", + "bdata": "AAAAYPNhbkEAAACAc29uQQAAAKBJhG5BAAAAYPpxbkEAAADgux9uQQAAAEC5sG1BAAAAoI8cbUEAAAAAiVVsQQAAAEAVWWxBAAAAgESUbEHm2Qt9AE1sQeekI+DwQGxB", "dtype": "f8" }, "yaxis": "y" @@ -6888,12 +12014,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": 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"3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAkNcLhUEAAADYw02FQQAAAID+noVBAAAAcFfKhUEAAAAI8+eFQQAAAHC894VBAAAACJqxhUEAAACgW7KFQQAAAKBjt4VBMzMzR97vhUFmZmbuWCiGQQ==", + "bdata": "AAAAkNcLhUEAAADYw02FQQAAAID+noVBAAAAcFfKhUEAAAAI8+eFQQAAAHC894VBAAAACJqxhUEAAACgW7KFQQAAAKBjt4VBAAAAQKMmhkEtBxsYivGFQR1J9UVMLIZB", "dtype": "f8" }, "yaxis": "y" @@ -6975,12 +12101,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAuM5ag0EAAABgNEyDQQAAAFCrfYNBAAAAAM6ug0EAAABAWN2DQQAAAACUF4RBAAAAcFKyhEEAAAAo6NWEQQAAACjJKoVBMzMz08BOhUFmZmZ+uHKFQQ==", + "bdata": "AAAAuM5ag0EAAABgNEyDQQAAAFCrfYNBAAAAAM6ug0EAAABAWN2DQQAAAACUF4RBAAAAcFKyhEEAAAAo6NWEQQAAACjJKoVBAAAAwPy2hUFgZ0gRUlKFQYJw2dEkeoVB", "dtype": "f8" }, "yaxis": "y" @@ -7004,12 +12130,12 @@ "stackgroup": "1", "type": 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"stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAUDNGekEAAACg0017QQAAAGDvTnxBAAAAwGkUfUEAAACQYgZ+QQAAAGChCn9BAAAAUKINgEEAAAC4kByAQQAAACALioBBZmZmjsoAgUHNzMz8iXeBQQ==", + "bdata": "AAAAUDNGekEAAACg0017QQAAAGDvTnxBAAAAwGkUfUEAAACQYgZ+QQAAAGChCn9BAAAAUKINgEEAAAC4kByAQQAAACALioBBAAAAEMfmgEE34OJyJx+BQV5qGxeEuYFB", "dtype": "f8" }, "yaxis": "y" @@ -7091,12 +12217,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAwGyOakEAAADA9yVrQQAAAODuDGxBAAAAwMhcbUEAAAAAynVuQQAAAGD7YG9BAAAAYB/pbkEAAAAQerVwQQAAABBmhHFBmpmZCa0EckEzMzMD9IRyQQ==", + "bdata": "AAAAwGyOakEAAADA9yVrQQAAAODuDGxBAAAAwMhcbUEAAAAAynVuQQAAAGD7YG9BAAAAYB/pbkEAAAAQerVwQQAAABBmhHFBAAAAUN5nckE+wIJiNC9yQZTRKDKE4HJB", "dtype": "f8" }, "yaxis": "y" @@ -7120,12 +12246,12 @@ "stackgroup": "1", "type": "scatter", "x": { - "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpBw==", + "bdata": "3wfgB+EH4gfjB+QH5QfmB+cH6AfpB+oH", "dtype": "i2" }, "xaxis": "x", "y": { - "bdata": "AAAAQM77V0EAAADAylZYQQAAAIASrVhBAAAAwB33WEEAAACAJzJZQQAAAABdZllBAAAAALPLVkEAAAAA271YQQAAAADJoVdBZmZmJuPvV0HNzMxM/T1YQQ==", + "bdata": "AAAAQM77V0EAAADAylZYQQAAAIASrVhBAAAAwB33WEEAAACAJzJZQQAAAABdZllBAAAAALPLVkEAAAAA271YQQAAAADJoVdBAAAAwFGMWEH2ubCsnu5XQRKAayluPFhB", "dtype": "f8" }, "yaxis": "y" @@ -7941,12 +13067,12 @@ } } }, - "image/png": 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", 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k05ifOT736lzd9eATsue2Lrm9Mo9mbNZltUPpfOxE14bO2NCU0tuq+rqieVamnFUtS8n9kleebO8KmZL7bO+55WVXSyQDV9tLX53t1h/s6iAL5Mz752dbZFVeOWyYmg1Xs2BXZQMW5eW5xYFT9IKJylMES7YIIIAAAggggECBChRatQsqCGInOnZ3FDuxtSEFFqhINrg9/9P/uaN4YlQ78bcrL+yEvOfZJ0QTKtoEmbZv6TtgJPMo+WhpLK3NV2Dr7STNhtPYpfIl5/qw57bOtlkaS3vn/f+I7qphz22xslngxsqSDA4k5zBJzrtg6eyqFauXPa/IUhmPipSpIscsmcaCRFa35LqkhU2GamVLrrdHc1y+YpVssee2riqLGSdPli3Ilcwjeezka3u0djfzsu6OYultsXSVWcq6YiHZ12yYTjIvK6f1CesbNkwlud4eLb3dtcYe7XVNLDa3hV3ZUDI4ZPn3GTRCFsioyjHsKhpbbCLaZDvbo000XNU8t1WOpO3yYRStYAAAEABJREFUb77bIpld3WUT4VpAM9nmyXJYwiH9u9tDpZZknvb7Zr93JXe216XbrOR2niOAAAIIIJASAS4VTAkrmea9ABUsQIGCCoJY+9pVFS9Pv82eyuZ2sDH8ttjzHevVje6ekjz5s0QlJ0K1fW3ugbJOTG3IjOWTXGwC1tJ3Y7ETJxvaYnMXJNPZc1tn2+x4dvJrwRb7RjuZxspm31aXvMTeymKTtpY8rp3A2vAJy6eii+WzPY+Klqmix0ym+/rb73Vi9yuL59sobZFMZ48W/LGAhN0JxMps66q62HAPa0eb/yJpbHnZibI9JhcLxJh56bS2j81LYifdybQVfTRLu6uK9Q/LxxYLNFjAo/QwHesTdgVSybSW3uahsMl9La+KHnd76Syvssp12w2XRZOKbm//srabX7I/Wh+2slt7W5DLAi5l7VOddVYHC3CVlYcNXSrZjlYeSzfjvv+NApz2vLKL5Wn1s0CI1S25WLClOoG6ypaD9AgggAACCEQCXCoYMfCjMgKkRaAwBQouCGLNbCdndnJbcgy/PbeTGttuJ1PPTRktW+y5rUsudgJdMm1yvQ2ZsfXJpax9La2dwNt8Dsl09tzW2bbkYq9tfTKNPSbLlkxjj3aSbNuSi6Wxdbav5WFpbLH15ZXHtm/Pw9JYfpZv8lj2aPnatsouZmrleX7qzSqZpz2345SVn01EaovdGaWs7ZVdl2xHq4ctdtWHXZliwSbzKJlf6bSW3spv9bB0VmYru6Wz18nFtlu60k7J9ZaPLZYm2W72mNzfHkuntfS2lDyW5W95WFrbp6qL7W/5WP622PMD9m0Z/R5U5HiWxhzMI1kGsyz5u2bbrY6Wt6VPpqvMo+1n+ZQ8TnJ/22Zlt2PasZPr7TG5zbbbUlaa8ixt37KOaXWxvEouZaWz47MgUJ4AX96WJ8N6BBBAIIUCuZY1fyxyrcUob5oEqvKrUZBBkDS1B4epIQEbumBDKmxohS3Vzbas4QoWYPlk8TLxDX51ddkfAQQqK8CXt5UVI31GBLbxKXMbmzJSVA66fQFS5KAAfyxysNEocjoEqvKrQRAkHS3DMaolYAEKW2wYRelv96uasQ1pSg5fsMdRd05V6eFLVc2b/RBAAAEEEMg7gW18ytzGpmxkoEwIIIAAAgUuQBCkmh3ALsm3y9/tcvlqZsXu5QgkhxzYYzlJKrXa8ik5fMGe2/AMGw5SqYxIjAACCCCAQE4JUFgEEEAAAQQQIAhCH0AAAQQQQACB/BeghggggAACCCCAQEKAIEgCgf8IIIAAAgjkswB1QwABBBBAAAEEECgSIAhS5MBPBBBAIM0CTCWYJnAOgwACCCCAAAIIIIBAsQBBkGIKniCAAALpFEjHVILprA/HQgABBBBAAAEEEEAg+wUIgmR/G1FCBBCoigD7IIAAAggggAACCCCAAAKlBAiClALhJQL5IEAdEEAAAQQQQAABBBBAAAEEthYgCLK1CWtyW4DSI4AAAggggAACCCCAAAIIIFCmAEGQMllydSXlRgABBBBAAAEEEEAAgXwUYEr1fGxV6pQJgfwJgmRCj2MigAACCCCAAAIIIIAAAmkQYEr1NCBziNwRqEZJCYJUA49dEUAAAQQQQAABBBBAAAEEEEinAMeqngBBkOr5sTcCCCCAAAI5I8Cl1DnTVBQUAQQQQKBsAdYiUG2BLA+C8HGt2i1MBggggAACCPxHgEup/wPBAwIIIJCTAhQaAQRqQiDLgyB8XKuJRiYPBBBAAAEEEEAAAQRyWoDCI4AAAjUkkOVBkBqqJdkggAACCCCAAAIIIJCjAhQbAQQQQKDmBAiC1JwlOSGAAAIIIIAAAgjUrAC5IZBXAgz2z6vmpDI5KkAQJEcbjmIjgAACCCCAQL4LUD8EEMg3AQb751uLUp9cFCAIkoutRpkLVoBvDwq26ak4AoUnQI0RQAABBBBAAIEUCBAESQEqWSKQKgG+PUiVLPkikF0ClKYSAkSHK4FFUgQQqLAA7y3bpYJou0R5nyBX+wBBkCp0zVxt7CpUlV0QQACBdAtwPAQqJ0B0uHJepEYAgYoJ8N6yXSeItkuU9wlytQ8QBKlC18zVxq5CVdkFAQTSKsDBEEAAAQQQQAABBBBAIJUCBEFSqUveCCBQcQFSIoBAnghwvWSeNCTVQAABBBBAIC8FCILkZbNSqVwToLwIIIBA/ghwvWT+tCU1QQCB/BIgSJ1f7UltqipAEKSqcuxXUwLkgwACCCCAAAIIIIAAAikXIEidcmIOkBMCBEEy2kwcHAEEEEAAAQQQQAABBBBAAIH8Ecj2a44yFwTJnzamJggggAACCCCAAAIIIIAAAgggkBAo85qjxPps+U8QJFtagnIggAACCCCAQMoEsv1bqZRVnIwRQAABBDIuQAGyS4AgSHa1B6VBAAEEMifAWWLm7DlyygWy/VuplANwAAQQQCAzAhwVgawTqHgQhA/HWdd4FAgBBBCoUQHOEmuUk8wQQAABBApdgPojgEA2ClQ8CMKH42xsP8qEAAIIIIAAAggggED2CVAiBBBAIEsFKh4EydIKUCwEEEAAAQQQQAABBCoskIarmytcFhIigAACCKRdgCBI2sk5IAIIIIAAAgggkLcC2V8xrm7O/jaihAgggEAKBQiCpBCXrBFAAAEEEECgkASoKwIIIIAAAghkuwBBkPJaiEsly5NhPQIIIIAAAlsLpHMNf6PTqc2xEEAAAQQQyCsBgiDlNSeXSpYnw3oEEEAAgVICvEyzAH+j0wzO4RBAAAEEEMgfAYIg+dOW1AQBBBDIhADHRAABBBBAAAEEEEAgZwQIguRMU1FQBBBIjUB1rqtPTYnIFQEEEEAAAQQQQAABBFIjQBAkNa7kikD+C+RNDbmuPm+akooggAACCCCAAAIIILAdAYIg2wFiMwJlCbAOAQQQQAABBBBAAAEEEEAg9wQIguRem2W6xBwfAQQQQAABBBBAAAEEEEAAgZwUIAhSqWYjMQIIIIAAAqkQYG6aVKiSJwIIIIAAAgggUFqg4kGQ0nvyGgEE0i7AaVLayTkgAmkSYG6aNEFzGAQQQAABBBCoiEAepyEIkseNS9XyT4DTpPxrU2qEAAIIIJBqAb5CSLUw+SOQbwLUJ78FCILkd/tSOwQQQAABBBBAoMAF+AqhwDsA1a+cAKkrLECAtcJUWZaQIEiWNQjFQQABBBBAAAEEEKiiAOckVYRjtyIBfiJQGQECrJXRyqa0BEGyqTUoCwIIIIAAAgggUMMCBRUX4Jyk6r2HPRFAAIECESAIUiANTTURQAABBBBAoDAFiAtsv91JIRVUsIwGRyANAvxOpQG5iocgCFJFuORuK1ZvEAsG9AH6AH2APkAfoA/kaB/gcwyf5aI+8A0OkQO/xxl8L/8hg8dOQf9P5e9U8lyUx6oJEASpmht7IVB9Ab6aq74hOSCAAALVEmBnBBBAAIGsEeDSiZptCjzL9SQIUi4NGxBIsQBvTCkGJnsEENimABsRQAABBBDId4FC/tKxkOu+nX5NEGQ7QGxGIL8EeDfMr/asXG2Iu/3Xi2cIIIAAAgggUAACfPgpgEaufBUJglTejD0QyGEB/hLkcONVu+j/CYFVOx8yQAABBBBAAAEEEEAgVwUIguRqy2VxuTnNzuLGKfiiAYAAAggggAACCCBQcwJ8xVJzluSULoGCCILcN/UZte3cd4vlupvuKTb+bvWPOrXX0OLtr855v3ibPSm5/0VDRmn9ho22mqUcAd4Ky4HJ9GqOjwACCCCAAAIIIIBAjQrw9WeNcpJZWgQKIghikocfvL/efXaCFs56MFpGXHuJrY4CGsP+cre6n9E5Wj/t7hs0ctwULVy0NNpuAZHpT83SrMfGRtt3a7KL/jJ2UrSNH7kjkJqSEu5JjSu5IoAAAgjkvwB/Q/O/jQu1hvTtQm156p07AgUTBCmvSZZ89a3WrtugM7t0ipK02nM37bFbY709/+Po9YuvvRcFSHZttHP0+uTjDtX8jxbLrh6JVmT/D0qYMgEi3ymjJWMEEEAAgTwX4G9onjdwAVePvl3AjU/Vc0Qgz4Mg/22Fd97/RIf9dkA05KXkUJhVq9do7c/rixPWq1tHdrXHF0u/jq4S+XbVD8Xb7EmTRg0VhqFWff+jvWRBAAEEEEAAAQQQQAABBBBAAIGMC1SsAAURBLm452nRUBYbCmPDWuxKDpvnI0nUrGkj1a9XJ/lyq8e9Wuy+1brkivETYpoyNabZb8b0r2UxuWFMDeqyYEAfoA/QB+gD9IFt9YH6O3j8veTzQu71gTr8Xm/r95pt9A/6QHr6QPJcdItHXlRYwK1wyjxJaMNabP4Pu9IjWaUVK1dr3fryJzv9ctk3yaRbPa5cJX26WHr+Femeh6Q//3/SX26WHn7E0WuzHS35ylHcdySHBQP6AH2APkAfoA/QB+gDOd0HXNovp9uPz+N5e05ScP1S/KuOQMEFQUpj2fCWBjvWK15td36xITB7t9xDyaExxRsTT2z4jOM4atK4aI6Qa4bEdV73QEd3CtSqRajataTVP0jvfxjq8Zmh7vhrqOH/N9TNtweaMt3XG2/F9eVXcf173WatXc+CAX2APkAfoA8UZh9YtzHO30E+B9AH6AP0ger3AQwL0DBxWsr/agjkfRDEghq33/tYNL+HOdmEpna3F5vg1F7bRKgN6tfVky/MsZeyiVK//vZ7HdHhgOi1pbP0tp+tsIlSO7RvLbuixF43aCAdsH+gLicFuvACX9cPj2vggLjO6urr0A6BmjaVglBa8Y2jd+e5mvGEp3F3ebppdEwPTvL00iuuPl3kau1ay40FAQQQQAABBBBAAAEEKiZAKgQQQKDyAnkfBLGrOVZ+90PxpKidzxkc3e3l+E4HR1q2fdSfL5UFOtp27qsel96g4YN6qW2bltF2S2fDZ2w/225Xifx5cJ9oW1k/HEdq2kQ6pEOoM7sGGnhpPAqMXJQIkJxycqC2BwTaaadQG3+Rvlzi6PU3XU151NXNY2K6ZYynqdNcvTHb1ZKljjZtKusIrEMAAQQQQAABBBAoeAEAEEAAAQSqJJD3QRBTGXHtJcUToy6c9aBsolRbn1zsqo7npowuTmOBj+Q2e7T0tp8t948ZFg2TsfUVXWyITMsWoY46MtC5vw901RW+hl0VV69zAx17dKC9WoWqs4P077WOPvnU1Ysvu3rgYU83jorpzgkxPf6kp3fnOfrmWykIKnpU0iGAAAIIIIAAAvkpQK0QQAABBBCoqkBBBEGqipPK/erXl/ZrE+ikEwL17ePr2qFxDbrMV7ezfB12SKBmu4eyubdWrZLmf+Doqac9/XViLAqM3Pegp+decLVgoaMff3K2X8wKJNl+JqRAAAEEEEAAgSwQoAgIIFBagM+6pUV4jQAC2xAgCLINnHRusmE0uzYOddCBoc44PdCAS/xoGE2/C32d2iVQu3ahGjYMtXmztOwrR3PedjXtMU+33b1nOrsAABAASURBVO5p9G0xTX7E1WtvuPriS0e/lB5GE6azJhwLAQQQQACBVAmQLwIIIFCGAJ91y0BhFQIIlCdAEKQ8mSxYH4tJe/46VKcjAvXo5mvIIF/Dr4mr93m+Oh8baJ+9Q9WtK/38s7ToM1cvv+rqob95unFkTHeM96JJWOe+52rFCskPsqBCFAEBBBBAoOoC7IkAAggggAACCCBQbQGCINUmTG8G9RJBjzb7hjqhc6Dze/u6NhEU+dNAX93O9nV4x0DNm4XyPOn71Y4++NDRzGdcTbg3FgVGJt7v6dnnXX20wNEPa7huML0tx9EQQKA6AuyLAAIIIIAAAggggEBNCBAEqQnFDOfRuFGog34T6vRTA/XvVzSMpv/Fvk5LvD6wfahdEtvjcWn5ckdvveNq+gxPY8d5GnlLTJOmepr1mqvFnzvauDHDFeHwCCBQlgDrEEAAAQQQQAABBBBAoIYECILUEGQ2ZRPzpOZ7hDqiY6Bzfudr8EBf1w2N64I/+Drx+EBt9g20447S+vXS4sWOXkkEQSZN8TRidExjx3t67B+e3p7ravnXjuJ+NtWMshSeADVGAAEEEEAAAQQQQAABBGpOgCBIzVlmdU516kh77xXquGMC9T4v0NAr47ryCl89zvGjOUda7BmqVi3ph9WOPvzI0TPPuZp4X9H8IhPv9fR04vUH/3Rkw2yyuqL5VDjqsn0BRnVt34gUCCCAAAIIIIAAAgggUCxAEKSYovCe7LxTqHZtw+juMxf39XX9sLj+2D+uM0731eGgUE2aSEEgLV/h6J25rmY87kUTrt50c0wPT/b0yixXiz5ztH5DzdulO0cmFU+3eA0dj4arIUiyQQABBBBAAAEEEECgMAQIgmRfO2esRG6iN+y+m3TYIaHOPtPX5QPiUWDkwvN9nXxioP33C/SrBqE2JIIen3/haNbrriY/4mlkIigyZpynaTM8zXnb1Vf/cmRzkGSsIlU4MBcUVAGNXRBAAAEEEEAAAQQQQACBHBNInPZmU4kpS7YJ1K4ttWoZ6pijAvXsEejqIb6uGRLXed0DHd0pUKsWoWrXktascbRggaPnXnB17wNFw2gm3OPpqafd6C41333vKORb+2xrXsqDwFYC/JpuRcIKBBBAAAEEEEAAgZQIZCZTgiCZcc/pozZoIB2wf6AuJwW68AJf1w+Pa+CAuM7q6uvQDoGaNpWCxJnUim8cvTvP1YwnPI27y9NNo2N6cJKnl15x9ekiV+vW5TQDhUcgLwW4Kiovm5VKIYAAAggggEC2CVCejAkQBMkYff4c2EmcNTVtIh3SIdSZXQMNvDQeBUYuSgRITjk5UNsDAu20U6iNv0hfLnH0+puupjzqatStMd16u6dH/+5q9luuli5ztGlz/rhQEwQQQAABBBBAAAEEENhagDUIVFwg8e16xRNXKCVBkAoxkaiyAjZEpmWLUEcdGejc3we66gpfw66Kq9e5gY49OtBerULV2UH66SdHCz929fyLru5/qGgYzfi7Y3pypqt58x2tXCWG0VQWn/QIIIAAAghUQKDmP1ZW4KAkQaCQBRJfHEoqZAHqjkAVBGr+F4cgSBWagV2qJlC/vrRfm0AnnRCobx9f1w6Na9Blvrqd5euwQwI12z2Um+jjK1dK78139cRMT+MnxHTjyJgeSARIXnjJ1cefuFq7tmrHZy8EEEAAAQQQ+K9A4k/uf1/wDIG0CBT4QYg8FngHoPrZIkAQJFtaogDLYcNodm0c6qADQ51xeqABlxTNL9LvQl+ndgnUrl2ohg3DaIjMkmWO3pzj6pHprm4eE9MtYzxNnebqjdmulix1tGlTAQJSZQQQQAABBBDIHQFKigACCCCQFQIEQbKiGShEUiAWk/b8dahORwTq0c3XkEG+hl8TV+/zfHU+NtA+e4eqW1f691pHn3zq6sWXXT3wsKcbR8V054SYHn/S07vzHH3zrRQEyVx5RAABBBBAAIFMCnBsBBBAAAEEskWAIEi2tATlKFegXiLo0WbfUCd0DnR+b1/XJoIifxroq9vZvg7vGKh5s1BuoievWiXN/8DRU097+uvEWBQYue9BL7pt74KFjn78Kb0X/nLFY7lNygYEEECgkASoKwIIIIAAAghkkUDi1DGLSkNREKigQONGoQ76TajTTw3Uv1/RMJr+F/s6LfH6wPahdkls37xZWvaVozlvu5r2mKfbbvc08paYJk31NOs1V4s/d7RxYwUPWIVk6Q25VKGA7IIAAgikXIADIIAAAggggAAC2SVAECS72oPSVFEg5knN9wh1RMdA5/zO1+CBvq4bGlefXr5OOC5Q69ah6tWT1q+XFi929EoiCDJpiqcRo2MaO97TY//w9PZcV8u/dhT3q1gIdkMAAQRKCvAcAQQQQAABBBBAIOsECIJkXZNQoJoSqFNHar1PqM6JIEifnr6GXx3X4EG+unfzdeThgZo3D2VzkPyw2tGHHzl65jlXE+/zZHejmXivp6cTrz/4p6PvE9trqkzkg0ChCFBPBBBAAAEEEECg+gJcW119Q3IoLUAQpLQIr/NaYJeGodq3C/XbUwL1v8jX9cPjGtAvrq6nBbK71NgwG9+Xlq9w9M5cVzMe93THeE833RzTw5M9vTLL1aLPHK3fkNdMVK56AuyNAAIIIIAAAgggUCMCzLJXI4xksoUAQZAtOHhRaAJe4jegWTOp46GBup3lyyZctcDIBX/wdeLxgdrsG2jHHaUNiaDH5184mvW6q8mPeBqZCIqMGedp2gwvmnPkq385iscLTa+s+rIOAQQQQAABBBBAAAEEEMhegcQpYPYWjpIhkAmBHWpLe+8V6rhjAvU+L9DQK+O68gpfPc7xo1v3ttgzVK1a0po1jhYscKK7z9z7gKcbR8Y04R5PTz3t6oMPHX33vaOQ4HUmmpBjIrClAFfSbumRFa+yp1F4m86KDkEhEEAAAQQigez5+xgVJ09/EATJ04ZNd7Xy/Xg77xSqXdtQp3YJdHFfX9cPi+uP/eM643RfHQ4K1aSJFCQ+Sa/4xtG781zNeMLTuLs83TQ6pgcneXrpFVefLnK1bl2+S1E/BLJQIPG7mYWlKvAiZU+j8HGzwLsi1c9agex5l8haIgqWlwL0/HQ0K0GQ6iuTQwEKuInfnN13kw47JNTZZ/q6fEA8CoxcdIGvU04O1PaAQDslAicbf5G+XOLo9TddTXnU1ahbY7r1dk+P/t3V7LdcLV3maNPmAgSkyggggAACCCCAwDYECFBuA4dNCCBQLYHEqVx19mdfBBBICtSuLbVsEeqoIwOd+/tAV13ha9hVcfU6N9CxRwfaq1WoOjtIP/3kaOHHrp5/0dX9DxXdjWb83TE9OdPVvPmOVq4Sw2iSqDxKfAoU/xBAAAEEEEAAAQSyQSA/ykAQJD/akVpkqUD9+tJ+bQKddEKgvn18XTs0rkGX+dEkrIcdEqjZ7qHcxEnuypXSe/NdPTHT0/gJMd04MqYHEgGSF15y9fEnrtauzdIKUqzUC3BVZOqNOQICCCCAAAIIILA9AbbnjQBBkLxpSiqSCwJOIuCxa+NQBx0Y6ozTAw24xJfdjabfhX4030i7dqEaNgyjITJLljl6c46rR6a7unlMTLeM8TR1mqs3ZrtastTRpk25UGPKiAACCOSvADHK/G1baoYAAlsK8AqBfBIgCJJPrUldclIgFnO056/D6M4zPbr5GjLI1/Br4up9nq/OxwbaZ+9QdetK/17r6JNPXb34sqsHHvZ046iY7pwQ0+NPenp3nqNvvpWCICcJKDQCWwjk9IllItC5RWV4kdcCNHdeNy+VQyApwCMCCOSZAEGQPGtQqpOLAluf8tVLBD3a7BvqhM6Bzu/t69pEUORPA311O9vX4R0DNW8WyiZnXbVKmv+Boyef9vTXibEoMHLfg56ee8HVgoWOfvyJj+i52CMKvcw53Wu3/nUu9Oak/gggkNMCFB4BBBDIP4GCC4Jcd9M9OrXXUH23+sfi1rR1bTv3VcnlvqnPFG+358ltFw0ZpfUbNhZv4wkC6RJo3CjUQb8Jdfqpgfr3KxpG0/9iX6clXh/UPtQuie2bN0vLvnI0521X0x7zdNvtnkbfFtPkR1y99oarL7509AvDaNLVZBwHgSwVyOkwU5aaUqy8FKBSCCCAAAJ5KVBQQRALdjzx/OwyG/KsU47SwlkPFi8X9zwtSvfqnPc1/alZmvXY2Gjbbk120V/GToq28QOBTArEPKn5HqGO6BjonN/5GjzQ13VD4+rTy9cJxwVq3TpUvXrSzz9Liz5z9fKrrh76myebdPWO8Z5mPOFp7nuuVqyQ/CCTNeHYCCCQXgEuV0mvd24ejVIjgAACCCCQrwIFEwSxqzmsEe8ccYU9VHh58bX31P2Mztq10c7RPicfd6jmf7R4iytJog38QCALBOrUkVrvE6pzIgjSp6ev4VfHNXiQr+7dfB15eKDmzUPFYtL3qx198KGjmc+4mnBvLAqMTLzf07PPu/pogaMf1vBNcRY0J0VAAIHMCHBUBBBAAAEEEMgmgRr+/qYggiAWAJk99yP9eXCfcpvSrhBJDnmx9JbQhr18u+oHe1q8NGnUUGEYatX3Pxav4wkC2SywS8NQ7duF+u0pgfpf5Ed3oxnQL66upwWyu9TYMJt4XFq+3NFb77iaPsPT2HGeRt4S06Spnma95mrx5442Mgosm5uZsiFQQwJkgwACCCCAAAIIZJlADX8/m/dBEBvOYgEQuwKkXt3E1+RltOeIay+JhrrYcJhpd9+geyfPlO2XTLpXi92TT7d63G2XumLBIJf6wB6N66pDu7o6s8sOuuzC2hrxf2rrjlG1NPiPMZ19mqcD2zn6VQNp/Xpp8WJHrySCIJOmeBoxOqY7/xrT08/U1scLdtCGtXXUeCfaPpfanrJup7/yfp7Wv2e7NKid1uPVbP+vk8Nl5/egZvsCnnjSB+gDKewDjcrOe6uTUlZUSsCtVOocTPzlsm/0zvuf6LDfDogmPr38utv1rxWr1GfQiDKHtLRt01LHH3WwbD/951/J5/9ZVfywas0GsWCQ633g3+s3qHGTjTr0sF/U/ZzNGnplXFcN9nXe730ddWSgFnuGqlVLWvWd9M57gR6Z4eumMXENGrpZ/zt6k+6f8ouen7VRH3++USt/oD/kYn+gzPTbdPeBH9duyuG/nxtzuOz09XT3dY5Hn6MP0Aeq3AfK+VxdfDLKkyoJ5H0QxCY4tSs8kotdEfLrZk00adx1xfN8lCdnV47YRKglt69avUaO46hJ452j1UEoFeSSqH1B1ruA2nunX4U64IBQp5wc6OK+vq4fFtcf+8d1xum+OhwUqkmTRN8PpOUrHL0919Vjj3sae6enG0fFdP/Dnl542dXHn7pa+3MiXXa7FebvMG1Cu2e6D/B3hD6Y6T7I8emD9AH6QI72gcSfUP5XQyDvgyDbs7Fb5d5x32PFyRYuWqp5//xMR3Q4IFpnE6Ha3WGFWoxlAAAQAElEQVQsna2wiVI7tG+93QCKpc3rJfGGkdf1K5jKVbyibuLdYvfdpMMOCXX2mb4uHxCPAiMXnu/r5BMD7b9foF81CLXxF+nLJY5ef9PVlEddjbo1pltv9/To313NfsvV0mWONm2u+HFJiQACCCCAAAKFLcDHzsJuf2qPQE0LJE5rajrL3Mqvfr06+mDB59FQGZsYtcelN2j4oF6yYTFWk+M7HRzdHabzOYOjNDZR6rYmWLV9WHJEgGJWW6B2balVy1DHHBWoZ49AVw/xdc2QuM7rHujoToFatQhVu5b000+OFn7s6vkXXd3/kKcbR8Y0/u6Ynpzpat58RytXSSGfcKrdHmSAAAIIIIBAPgrU8JyI+UhEnRBAoBICBRcEsaDGc1NGF1/JYUNe7h8zrHhiVBs2Y2lKGl7c87Ti7ZbW9im5PRefU2YEUiXQoIF0wP6BupwU6MIL/OhuNAMHxHVWV1+HdgjUtGnRkVeulN6b7+qJmZ7GT4hFgZEHEgGSF15y9fEnrtauLUrHTwQQQAABBBBAAAEEEECgpgQKLggiqabsyAcBBCog4CS+vmnaRDqkQ6gzuwYaeGk8CoxclAiQnHJyoLYHBNpppzAaIrNkmaM357h6ZLqrm8fEdMsYT1OnuXpjtqslSx1t2lSBA5IEAQQQQAABBBBAAAEEECgS2OonQZCtSFiBAAKpFrAhMi1bhNGdZ879faCrrvA17Kq4ep0b6NijA+3VKlSdHaR/r3X0yaeuXnzZ1QMPe7JJV++cENPjT3p6d56jb76VgiDVpSX/dAswMird4hyvJgTotzWhSB4IIIAAAjUrQG5lCRAEKUuFdQggkHaB+vWl/doEOumEQH37+Lp2aFyDLvPV7Sxfhx0SqNnuoVxHWrVKmv+Bo6ee9vTXibEoMHLfg56ee8HVgoWOfvwpkSjtpeeANSlQ3ILFT2oyd/IqVIFUBynoroXas6g3AghkrQAFQ6AcAYIg5cCwGgEEMitgw2h2bRzqoANDnXF6oAGXFM0v0v9iX6edGujA9qF2aRRq82Zp2VeO5rztatpjnm673dPo22Ka/Iir195w9cWXjn5hGE1mG7OqR0/1WWtVy8V+OSlAkCInm41CI4BAFQXYDQEEyhcgCFK+DVsQQCDLBGIxqfkeoY7oGOic3/kaPNDXdUPj6tPL1wnHBWrdOlS9etLPP0uLPnP18quuHvqbJ7sbzR3jPc14wtPc91ytWCH5QZZVjuIggAACCCCQVQI5GzrMKkUKk9sCfB+T2+1XXukJgpQnw3oEEMgJgTp1pNb7hOqcCIL06elr+NVxDR7kq3s3X0ceHqh581AWPPl+taMPPnQ08xlXE+6NRYGRifd7evZ5Vx8tcPTDGj7s5USDU0gEEEAAgW0I1OQmTv9qUpO8clOAT4e52W7bK3WNB0F4u9weOdsRQCDVArs0DNW+XajfnhKo/0V+dDeaAf3i6npaEA2vadwoVDwuLV/u6K13XE2f4WnsOE8jb4lp0lRPs15ztfhzRxs3prqk5I8AAgggUGMCZIQAAggggEAFBGo8CEK0rALqJEEAgbQKeIl3umbNpI6HBtFEq38aWBQYueAPvk48PlCbfQPtuKO0fr20eLGjVxJBkElTPI0YHdPY8Z4e+4ent+e6Wv61o7if1qJzMAQQQKBCAiRCAAEEEEAAgYoJJE4NKpaQVAgggEA+CexQW9p7r1DHHROo93mBhl4Z15VX+Opxjq9ORwRqsWeoWrWkH1Y7+vAjR88852rifUXzi0y819PTidcf/NORDbPJJxfqgkAOClBkBBBAAAEEEECgwgIEQSpMRUIEEMh3gZ13CtWubahTuwS6uK+v64fF9cf+cZ1xuq8OB4Vq0kQKAmn5CkfvzHU143FPNuHqTTfH9PBkT6/McrXoM0frN+S7FPXLHgFKggACCCCAAAIIIFAZAYIgldEiLQIIFJSAm3iH3H036bBDQp19pq/LB8SjwMiF5/s6+cRA++8X6FcNQm1IBD0+/8LRrNddTX7E08hEUGTMOE/TZnjRrXu/+pcjm4OkoPDSUVmOgQACCCCAAAIIIIBAJQUSH/EruQfJEUAAgQIWqF1batUy1DFHBerZI9DVQ3xdMySu87oHOrpToFYtQtWuJa1Z42jBAkfPveDq3geKhtFMuMfTU0+70V1qvvveUViNmaQLuAmoOgIIIIAAAggggAACVRYgCFJlOnZEAIEMCWTdYRs0kA7YP1CXkwJdeIEf3Y1m4IC4zurq69AOgZo2lYJEwGPFN47enedqxhOext3l6abRMT04ydNLr7j6dJGrdeuyrmp5WaBEU+RlvagUAggggAACCCCAwPYFCIJs34gUCGSRAEXJBQHHkZo2kQ7pEOrMroEGXhqPAiMXJQIkp5wcqO0BgXbaKdTGX6Qvlzh6/U1XUx51NerWmG693dOjf3c1+y1XS5c52rQ5F2qcW2VMNE9uFZjSIoAAAggggAACCNSYAEGQGqMko5QLcAAEcljAhsi0bBHqqCMDnfv7QFdd4WvYVXH1OjfQsUcH2qtVqDo7SD/95Gjhx66ef9HV/Q8VDaMZf3dMT850NW++o5WrJJucNYcpKHrGBQgDZbwJKAACCCCAAAIIZEyAIEjG6Ct3YFIjgED+CdSvL+3XJtBJJwTq28fXtUPjGnSZr25n+TrskEDNdg/lJs5XV66U3pvv6omZnsZPiOnGUTHd96AXzTeyYKGjHxOBk/zToUapE2BAUOpsyRkBBPJbIPFHOb8rSO0QKAiBXAiCFERD5Eol+eicKy1FOXNRwIbR7No41EEHhjrj9EADLimaX6Tfhb5O7RKoXbtQDRuG2rxZWvaVE915Ztpjnm673dPo22Ka/Iir195w9cWXjn7ZlIsClBkBBBBAAIFsFuCTcDa3DmXLG4GUV4QgSMqJ8+sAxL/zqz2pTfYLxGLSnr8O1emIQD26+RoyyNfwa+LqfZ6vzscG2mfvUHXrSj//LC36zNXLr7p66G9Fw2juGO9Fk7DOfc/VihWSH2R/fSkhAggggAACCCBQuALUPB0CBEHSocwxsl6AuH7WNxEFLCFQLxH0aLNvqBM6Bzq/t69rE0GRPw301e1sX4d3DNS8WSjPk75f7eiDDx3NfMbVhHtjunFkTBPv9/Ts864+WuDohzWENUuw8hQBBBBAAAEEMinAsRFIkwBBkDRBc5jsFuBUMLvbh9JtX6Bxo1AH/SbU6acG6t+vaBhN/4t9nZZ4fWD7ULsktsfj0vLljt56x9X0GZ7GjvM08paYJk31NOs1V4s/d7Rx4/aPRQoEEEAAAQQQqFkBckMAgfQJZCQIct1N9+iiIaO0fsPGaLHnbTv31ZFdL9PCRUvFPwQQQACB6gnEPKn5HqGO6BjonN/5GjzQ13VD4+rTy9cJxwVq3TpUvXrS+vXS4sWOXkkEQSZN8TRidExjx3t67B+e3p7ravnXjuJ+9crC3ggggAACCGxDgE0IIIBAWgXSHgT5bvWPmv/RYvXp3kX16tbRO+9/ElX43WcnaMR1l+jWCY/KgiPRSn4ggAACCNSYQJ06Uut9QnVOBEH69PQ1/Oq4Bg/y1b2bryMPD9S8eSibg+SH1Y4+/MjRM8+5mnhf0fwiE+/19HTi9Qf/dGTDbGqsUGSEAAIIFLQAlUcAAQQQSLdA2oMgVsEGO9ZTk0YN7alefO097dZklyggYuvWrtugdeu5HjvC4QcCCCCQYoFdGoZq3y7Ub08J1P8iX9cPj+uP/eM643RfHQ4K1aSJFATS8hWO3pnracbjnmzC1ZtujunhyZ5emeVq0WdOInid4oKSPQII5J8ANUIAAQQQQCADAmkPgtSvV0cN6tfVqtVrlLwq5OTjDo2qbuvW/rw+es4PBBBAAIH0C3iJvwq77yYddkios8/0dfmAuK4fFteF5/s6+URf++8X6FcNQm3YIH3+haNZr7ua/IinkYmgyJhxnqbN8KJb9371L0c2B0n6a8AREcgNAUqJAAIIIIAAApkRSHzcTe+BbQjMVQPO1XUj7lHncwarQ/vWOr7TwVFAZNSdU6PXuzbaOb2F4mgIIIAAAuUK1K4ttWoZ6pijAvXsEejqIb6uGRLXed0DHd0pUKsWoWrXktascbRggaPnXnB17wNFw2gm3OPpqafd6C41333vKORWTOU6F9AGqooAAggggAACCGRMIO1BEKtp2zYt9dbMu7Rw1oMace0ltkoW+Hhuyuji19FKfiCAAAIIZKVAgwbSAfsH6nJSoAsv8KNhNAMHxHVWV1+HdgjUtKkUJAIeK75x9O48VzOe8DTuLk83jY7pwUmeXnrF1aeLXK1bl5XVS2GhyBoBBBBAAAEEEEAgkwIZCYJkssIcGwEEEECg5gUcR2raRDqkQ6gzuwYaeGk8CoxclAiQnHJyoLYHBNppp1Abf5G+XOLo9TddTXnU1ahbY7r1dk+P/t3V7LdcLV3maNPmmi8fOSKAAAIIIIAAAgggYAIZCYLY3V9K3xY3ue6+qc9YuVgQQACBvBIoxMrYEJmWLUIddWSgc38f6KorfA27Kq5e5wY69uhAe7UKVWcH6aefHC382NXzL7q6/6GiYTTj747pyZmu5s13tHKVGEZTiB2IOiOAAAIIIIAAAikQyEgQ5C9jJ+moju1lt8U97OD9omrZXCF229zZcz+SBUSilfxAAIF8EKAOCBQL1K8v7dcm0EknBOrbx9e1Q+MadJmvbmf5OuyQQM12D+U60sqV0nvzXT0x09P4CTHdODKmBxIBkhdecvXxJ67Wri3OkicIIIAAAggggAACCFRYIO1BELsjzOIlX+uIDgdsVch03CL3upvu0am9hkYTsSYLYGWydW0795Utr855P7kperSrU2y9LXYFC0GaiIUfFRIgEQIIbEvAhtHs2jjUQQeGOuP0QAMuKZpfpN+Fvk7tEqhdu1ANG4bREJklyxy9OcfVI9Nd3TwmplvGeJo6zdUbs10tWepo06ZtHYltCCCAAAIIIJD/AolvUvK/ktSwmgJpD4Jsq7x2i1y7fa7dRndb6aq6zQIgTzw/e4vdLaAx7C93q/sZnaOJWqfdfYNGjpuihYuWRuksIDL9qVma9djYaPtuTXaRXckSbeTHtgXYigACCFRBIBaT9vx1qE5HBOrRzdeQQb6GXxNX7/N8dT420D57h6pbV/r3WkeffOrqxZddPfCwpxtHxXTnhJgef9LTu/McffOtFARVKAC7IIAAAggggECOCoQ5Wm6KnU6BtAdB7C4wpx7fUbdOeFTr1m8srqtdjWG3yLVhMjY0pnhDDT2xqzksqztHXGEPxcuSr77V2nUbdGaXTtG6Vnvupj12a6y3538cvX7xtfeiAImV21acfNyhmv/R4i2uJLH1pRdeI4AAAgjUnEC9RNCjzb6hTugc6Pzevq5NBEX+NNBXt7N9Hd4xUPNmodzEX7RVq6T5Hzh66mlPf50YiwIj9z3o6bkXXC1YvCr9jQAAEABJREFU6OjHn/iGqOZahZwQQAABBBBAAIHcE0h8ZKzxQm83w4t7niab/6PzOYP18hvz1ePSG2TPh13eU7ZtuxlUMoEFQGyukT8P7rPVnnb1ydqf1xevtwCMXe3xxdKvo7lJvl31Q/E2e2JDdsIw1Krvf7SXLAgggECeC5QXNChvffo4GjcKddBvQp1+aqD+/YqG0fS/2NdpidcHtg+1S2L75s3Ssq8czXnb1bTHPN12u6fRt8U0+RFXr73h6osvHf2yKX1l5kgIIIAAAggggECeCeRcdTISBDGl4zsdHA0vWTjrweJHW2fbanKx4SwWALErQCzAUVbezZo20raG4OzVYveydovW1antiQUD+gB9IH/7gFvOe1x56zPXF3as62mfVq46H+2o97nS8CHS/14f6uLzQ3U5Qdpv3zDxXi/9/LO06DNXL7/q6qG/ebJJV8fd5emJp2Ka/76n77/zFPMyV4/87Utbmu5QK/v6UKHYU88t+yIeeNAH6ANV7wOZsYtORPlRZYG0B0Fs2Ev3/jcUz7lR5ZJXcMcvl32jd97/RIf9doBsYtPLr7td/1qxSn0GjSge0rJi5eothuaUztryKL0u+breDp5YMKAP0AfoA9nZB3bZ2dPB7T2d+VtXl1/iadT/uLphuKsLezs6PhEsadnCkc1B8t33jua9Lz3+lKM7/uro//yvo7vudvX0s64WLHD189rsrF8u97s6tTDN5faj7PRf+gB9oB7nQRk7D0yei/JYNYG0B0GqVsyq72XDa0pebWJXhPy6WRNNGnedbJ4PG97SYMd6xQewiVJtCMzeLfeQXTliQ2OKNyae2PAZx3HUpPHOiVfSD2s3sWBAH6AP0AdyqA+4tTep1d6bdfwJm3XRBZt1/fC4BvSLq+tpgewuNTbMxvelpf8K9drsUA8/Eup/RwW65v8GGjvB19+fiuuteZu1fBXv/9X5G/jT+s383uTQ70112pp9ea/Ixz5AnejXmewD0YkoP6oskPYgiAUeWrfaQxZMqHKpa3DHVnvuJrsjzZMvzIlytYlSv/72ex3R4YDotU2EaneHsStYbIVNlNqhfesogGKvWRBAAAEEclvAS/wlbNZM6nhooG5n+bIJVy0wcsEffJ14fKA2+wbacUdpwwbp8y8czXrd1eRHPI28OaYx4zxNm+FFc4589S9H8XhuW1B6BBBAoAICJEEAAQRyWiDx0S/95e/d7WT949k3oolH03/0LY9oV3uM+vOlskBH2859o0lahw/qpbZtWkYJbZ6S7md0jiZute12lcify5hgNUrMDwQQQACBvBDYoba0916hjjsmUO/zAg29Mq4rr/DV4xw/unVviz1D1aolrVnjaMECJ7r7zL0PFM0vMuEeT0897eqDDx3ZMJuQu/XlRZ+gEggUCfATAQQQQCDXBdIeBLErKq76n7uiu8Ik5+mw4EJyObXXUFmaVMFaUOO5KaO3uJLDrk6xdclhM5am5PFLDqm5f8ywaJhMye08RwABBBDIf4GddwrVrm2oU7sEurivr+uHxfXH/nGdcbqvDgeFatJEChIBjxXfOHp3nqsZT3iyCVdvGh3Tg5M8vfSKq08XuVq3Lv+tqGGeClAtBBBAAAEE8kAg7UGQ0gGHZOAh+WjBCEuTB7ZUAYFigcR5UfFzniCAQH4IuIm/oLvvJh12SKizz/R1+YB4FBi58HxfJ58YaP/9Av2qQaiNv0hfLnH0+puupjzqatStMd16u6dH/+5q9luuli5ztGlzfpjkcy2oGwIIIIAAAgjkh0DiI1x+VIRaIJDNAk42F46yIYBAjQnUri21ahnqmKMC9ewR6Oohvq4ZElevcwMde3SgvVqFqrOD9NNPjhZ+7Or5F13d/1DRMJrxd8f05ExX8+Y7WrlKyqJhNDXmQ0YIIIAAAggggECmBdIeBLGhLjbkJTn8pfSjbbM0mYbh+AgggAACCEjVN2jQQNqvTaCTTgjUt4+va4fGNegyP5qE9bBDAjXbPZSbiJSuXCm9N9/VEzM9jZ8Q040jY3ogESB54SVXH3/iau3a6peFHBBAAAEEEEAAgUIXSHsQxIa62JCX5PCX5OO7z07Qicd00K3/77It5uso9Aai/ghkrUDipC1ry0bBakaAXFIi4CR+d3ZtHOqgA0OdcXqgAZf4srvR9LvQj+YbadcuVMOGYTREZskyR2/OcfXIdFc3j4npljGepk5z9cZsV0uWOtq0KSVFJFMEEKiIQOJ3uSLJSIMAAgggkF0CaQ+ClFd9u0vLgQfso8kzXiwvCesRQCCbBPJ8opNsoqYsGRRI00lOLCbt+eswuvNMj26+hgzyNfyauHqf56vzsYH22TtU3brSv9c6+uRTVy++7OqBhz3dOCqmOyfE9PiTnt6d5+ibb6UgyKAXh0agkAT4O1hIrU1dEUAgjwSyJghipkd0OECLl3yd0rvD2HFYEEBgmwJsRACBpEAGT3LqJYIebfYNdULnQOf39nVtIijyp4G+up3t6/COgZo3C2WTs65aJc3/wNFTT3v668RYFBi570FPz73gasFCRz/+lKZITtKMRwQQQAABBBBAIIsFsioIksVOFK1gBKgoAgggkL0CjRuFOug3oU4/NVD/fkXDaPpf7Ou0xOsD24faJbF982Zp2VeO5rztatpjnm673dPo22Ka/Iir195w9cWXjn5hGE32NjIlQwABBBBAAIGUCmRVEMSGwrRutQdzgqS0ybeRea5t4svNXGsxyosAAjUsEPOk5nuEOqJjoHN+52vwQF/XDY2rTy9fJxwXqHXrUPXqST//LC36zNXLr7p66G+ebNLVO8Z7mvGEp7nvuVqxQvKDGi4c2SGAAAIIIIAAAlkokPYgiN35xe4AU/quMPb621U/6M+D+2SEiYPmoEAGL1PPWi0CQ1nbNBQMgXQJ1Kkjtd4nVOdEEKRPT1/Dr45r8CBf3bv5OvLwQM2bh7I5SL5f7eiDDx3NfMbVhHtjUWBk4v2enn3e1UcLHP2whjeUdLUZx0EAAQQQqJoApwNVcyv0vSwIklaD8u4OY3eJuX/MMNkEqWktEAdDIJ8E+EuQT61JXRCoMYFdGoZq3y7Ub08J1P8iP7obzYB+cXU9LZDdpcaG2cTj0vLljt56x9X0GZ7GjvM08paYJk31NOs1V4s/d7RxY40ViYwQQAABBBCotgDh+ioRFvxOaQ+CFLw4AAgggAACCGRYwEv89W/WTOp4aKBuZ/myCVftNr0X/MHXiccHarNvoB13lNavlxYvdvRKIggyaYqnEaNjGjve02P/8PT2XFfLv3YU9zNcGQ6PAAIIIIBAhQVIiICU+BiUfobrbrpHFw0ZpfUbNkaLPW/bua+O7HqZFi5aKv4hgAACCCBQIQGnQqkKN1ElfHaoLe29V6jjjgnU+7xAQ6+M68orfPU4x49u3dtiz1C1akk/rHb04UeOnnnO1cT7iuYXmXivp6cTrz/4pyMbZlO44NQcgW0LcMHmtn3YmmIBskcAgUgg7UEQmxNk/keL1ad7l2joyzvvfxIV5N1nJ2jEdZfo1gmPRoGRaCU/EEAAAQQQKC1Q8sSeM4rSOlu+rqbPzjuFatc21KldAl3c19f1w+L6Y/+4zjjdV4eDQjVpIgWBtHyFo3fmuprxuCebcPWmm2N6eLKnV2a5WvSZo3XrtiwWrxAoVIGSb1+FapCpenNcBBBAICmQ9iCIHbjBjvXUpFFDe6oXX3tPuzXZJQqI2Lq16zZo3fqN0TZ+IIBAxQSqeZ5TsYOQCoFsEaDDZ6wl3MSnht13kw47JNTZZ/q6fEA8CoxceL6vk08MtP9+gX7VINSGDdLnXzia9bqryY94GnVrTLfe7unRv7ua/ZarpcscbdqcsWpwYAQKTYD6IoAAAgiUEEh8nCnxKg1P69erowb162rV6jVKXhVy8nGHRke2dWt/Xh895wcCCFRcwKl4UlIigAACNSpQu7bUqmWoY44K1LNHoKuH+LpmSFzndQ90dKdArVqEql1L+uknRws/dvX8i67uf6hoGM34u2N6cqarefMdrVwlhXkb4OJdukY7XaUyIzECCCCAAAJbCqQ9CGJ3f7lqwLm6bsQ96nzOYHVo31rHdzo4CoiMunNq9NruILNlMXmFAAIIIIAAArki0KCBdMD+gbqcFOjCC/zobjQDB8R1Vldfh3YI1LRpUU1WrpTem+/qiZmexk8ouk3vA4kAyQsvufr4E1dr1xaly/2fGYru5D4cNUAAAQQQQKDGBdIeBLEatG3TUm/NvEt2W9wR115iq2SBj+emjFbydbSSHwgggAACCCCQ8wKOIzVtIh3SIdSZXQMNvDQeBUYuSgRITjk5UNsDAu20UxgNkVmyzNGbc1w9Mt3VzWNiumWMp6nTXL0x29WSpY42baoYB6kQQAABBBBAAIGyBDISBCmrIKxDAAEEEEAgPwUSEYD0ViwnjmZDZFq2CHXUkYHO/X2gq67wNeyquHqdG+jYowPt1SpUnR2kf6919Mmnrl582dUDD3u6cVRMd06I6fEnPb07z9E33yqanDUnKk0hEUAAAQQQQCDjAhkLgrw6533ZbXFLLrYu4yIUAIGMCnCylFF+Dp4HAhWtQjp/1xgKUdFWqV9f2q9NoJNOCNS3j69rh8Y16DJf3c7yddghgZrtHspNNN2qVdL8Dxw99bSnv06MRYGR+x709NwLrhYsdPTjT4lEFT0o6RBAAAEE8k6Av7x516Q1WqGMBEEs2GHzf8x6bGw0JMaGxUy7+4ZonpD7pj5ToxUkMwRyS4C37NxqrywrLcWphAC/a5XAylhSG0aza+NQBx0Y6ozTAw24pGh+kX4X+jq1S6B27UI1bBhq82Zp2VeO5rztatpjnm673dPo22Ka/Iir195w9cWXjn7ZlLFqcGAEEEAAgTQLEApPM3iOHS7tQZD1GzZq0vQXNOzyntE8IEkvmydkxHWXaPbcj2Rpkut5RACBVAnk15+HVCmRLwIIZJdALCbt+etQnY4I1KObryGDfA2/Jq4+vXydcFyg1q1D1asn/fyztOgzVy+/6uqhv3m6cWRMd4z3NOMJT3Pfc7ViheQH2VU3SoMAAgiUFiBkX1qE1whUXyDtQZB16zdq7boNatKo4Valt3W2zdJstZEVZQtwHlu2S2GtrWJt+bNaRTh2QwCBLBOoV1dqvU+ozokgSJ+eiaDI1XENTgRHuieCJEceHqh581AWPPl+taMPPnQ08xlXE+6NRYGRifd7evZ5Vx8tcPTDGv6oZlnTUhwECl6Ad6WC7wIApEAg7UGQ+vXqqEH9ulq1es1W1bF1ts3SbLWRFWULFPx5bNksrEUAAQQQKGyBXRqGat8u1G9PCdT/oqJhNAP6xdX1tCAaXtO4Uah4XFq+3NFb77iaPsPT2HGeRt4S06Spnma95mrx5442bixsR2qPAAIIIIBAvgmkPQhSr24dHdWxvWxOkO9W/1jsuXDR0mhOENtmaYo3pDwbFf4AABAASURBVPlJTsUU0mzD4RBAAAEEEMhVAS/xiadZM6njoUE00eqfBhYFRi74g68Tjw/UZt9AO+4orV8vLV7s6JVEEGTSFE8jRsc0drynx/7h6e25rpZ/7Sju56oC5UYAAQQQQACBxEeC9CNc3PM02Zwgnc8ZXHyHmB6X3iCbE8S2VaREqUrDJWepkiVfBBDYpgBvPtvkYSMCqRDYoba0916hjjsmUO/zAg29Mq4rr/DV4xw/mnOkxZ6hatWSfljt6MOPHD3znKuJ9xXNLzLxXk9PJ15/8E9HNswmFeUjTwQQQAABBBAoEqjJn6kLgmznkorjOx1cfGcYuzuMLbauJitHXggggIByJbiwnfdMWhKBrQRypW9vVfDsXrHzTqHatQ2ju89c3NfX9cPi+mP/uM443VeHg0I1aSIFgbR8haN35rqa8bgXTbh6080xPTzZ0yuzXC36zNH6DdldT0qHAAIIIJAzAhS0hgVSFwTZzoez+6Y+U3wVSNvOfXVk18tkQ2LEPwQQQKAmBbIkuJAlxahJWfLKtACdKi0t4CY+Ke2+m3TYIaHOPtPX5QPiUWDkwvN9nXxioP33C/SrBqE2JIIen3/haNbrriY/4mlkIigyZpynaTO86Na9X/3Lkc1BkpZCcxAEEEAggwI1++cpgxXh0HkrkPjTnv66WQBk+lOzNOuxscVXg9x761D1u2q0Xp3zfvoLxBERQKCABbYTsa0hmfQcpYYKm8Js+GCUQlyyTptA7dpSq5ahjjkqUM8ega4e4uuaIXGd1z3Q0Z0CtWoRqnYtac0aRwsWOHruBVf3PlA0jGbCPZ6eetqN7lLz3feOQn4p0tZuHAiBnBPI0QLzmSdHG66Aip32IIhNhmoBEJsTZNdGOxdTt23TMpoTZNL0F7R+w8bi9TxBAAEEUivAGUhqfbfMnQ9GW3rwKn8EGjSQDtg/UJeTAl14ga/rh8c1cEBcZ3X1dWiHQE2bSkHi7WbFN47enedqxhOext3l6abRMT04ydNLr7j6dJGrdevyx4SaIFAdAfZFAAEEUiWQ9iCIVaTBjvXUpFFDe7rFYuvWrtugdesJgmwBwwsEEMhPASc/q0WtEEBAchK/302bSId0CHVm10ADL41HgZGLEgGSU04O1PaAQDvtFGrjL9KXSxy9/qarKY+6GnVrTLfe7unRv7ua/Zarpcscbdos/hWWALVFAAEEEEihQNqDIPXr1VGD+nW1avWaFFZry6xt+I3NO5Jc7HXJFNfddM8W85NYupJp7Lmts+WiIaO4UqUkHs8RKGiBxFlOdeqf+Fa4OruzLwIZEahst69s+oxUKj0HtSEyLVuEOurIQOf+PtBVV/gadlVcvc4NdOzRgfZqFarODtJPPzla+LGr5190df9DRcNoxt8d05MzXc2b72jlKuXxMJr0tAVHQQABBBAoXIG0B0Hq1a2jPt27qKxhL2/P/1itW+2hksNkqts0NrTmi6VfF88/YvOQ2HCc0nOPnHXKUcXzkyyc9aAu7nladGhLZ+ltP1u/W5Nd9Jexk6Jt/EAAgUIXIIpR6D2gIOtf2W5f2fQFhlq/vrRfm0AnnRCobx9f1w6Na9Blvrqd5euwQwI12z2UmwgkrVwpvTff1RMzPY2fENONI2N6IBEgeeElVx9/4uqntYlEBWZHdRFAAAEEEKiKQNqDIDYnyKg7p+qd9z/RYb8dsMUVGLfdPU1PPD+7eN2pvYbK0lelYsl9LOgy4tpLigMrdiVKs6aN9OWyb5JJtvn44mvvqfsZnYv3P/m4QzX/o8XVLtc2D8pGBBBAAAEECkyA6hYJ2DCaXRuHOujAUGecHmjAJX40jKbfhb5O7RKoXbtQDRuG0RCZJcscvTnH1SPTXd06xtPo22Ka/Iir195w9cWXjn7ZVJQnPxFAAAEEEEDgvwJpD4LYVR7PTRm9xVUXdoVFWYuls/T/LW71ny356lt9sniZ9mqx+xaZlQy+2PAX22hXkXy76gd7WrzYvCVhGGrV9z8Wr+MJAggggAAC1RBgVwS2KRCLSXv+OlSnIwL16OZryCBfw6+Jq/d5vjofG2ifvUPVrSv9/LO06DNXL7/q6qG/FQ2juWO8F03COvc9VytWSH6wzUOxEQEEEEAAgbwXSHsQJFOidkWJXVnS49Ib1K93Vx3f6eDiotiVIskgzLS7b9C9k2ducave0gGT4h0TT3ZpUFssGNAH6AP0gar2AfYr1L6zU71a/P2sxmeI5k1q68hDaun3Z8Q0eICnm//X1f8d5ur88xwdd5Sjlr925HnS96sdffCho5nPuJpwbywaRnP/Q7X06iu1tOSLWgo28TtYqL+D1Ju+Tx/I3T4g/lVLoGCCIHZFiV1Z8u6zEzR77kdKXu1RWs9u1Xv8UQdvMVxmW0Nn1m2MiwUD+gB9oEp9gPcP3j8LuA9s2OTT/jXc/vV3jKttW1+nnerrj/19/e//CXT5pYHOPD3UwQdKjRuFiselpctCvfpmqAcmh7phZKBh/y/Qnff4euJZX/M/8rV6De/pvKfTB+gD9IFs7gOlz2F5XTmBtAdB7IoMuxuLDTUpXVRbZ9ssTeltNfXa5gg5qmN72WSp28vT0tpEqCXT2V1tHMdRk8Y7R6t/2RyIBQP6QOX7AGaY0QcKuw9sihd2/dPR//0gUJOmgQ49xNfvzorrTwN9XTc0rj69fJ1wXKDWrUPVqyetWy99+pmjF19xdP/Djv7fCFejxjqaMs3Ra7OV+MwUJgJWtFc62oxjpKmf8f7D+UuOn8NFJ6L8qLJA2oMgNjGpzbNhk6KWvBrDnts6m3S0yrUpY0cLqFhgxQIsttle291e9m65h72MJji9477Houf2Y+GipZr3z890RIcD7KVOPu5QWXrbz1bYRKkd2rcunijV1rEgUEkBkiOAQM4JcOeNnGsyClymQJ06Uut9QnVOBEH69PQ1/Oq4Bg/y1b2bryMPD9S8eSibg+SH1Y4+/MjRM8+5mnhf0fwiE+/19HTi9Qf/dGTDbMo8ACsRyAUB7lqVC61EGRFImUCKgiDlv7PY1RX3jxmm5LCUtp37yhYLNNhtaG3Iig1dqakaJ/OyAIsdp/M5g6O7vSRvgWtBmQ8WfB6VwbbbnCHDB/WSDYuxMtjcIXZ3GNvPtlsA58+D+9gmlioJsBMCCCCQiwLl/13LxdpQZgRKCuzSMFT7dqF+e0qg/hf50d1oBvSLq+tpQXSXGhtG4/vS8hWO3pnrasbjnmzC1ZtujunhyZ5emeVq0WeO1m8omSvPEUAAAQQQyE6BFAVBtv+NWfIuLUmWZk0byQISydc1+Vhy4tOFsx5UMgBix0gGZWx9crHAh21LLpY+uc0COLZPclulHkmMAAIIIIAAAghkuYCX+HTYrJnU8dBA3c7yo2E01w+P68LzfZ18YqD99wv0qwahNiSCHp9/4WjW664mP+JpZCIoMmacp2kzPM1529VX/3KiOUiyvLoUDwEEEECgwAQSf+bSU+PkUWxYScm7tCSDCzZPh12tcdGQUYlvEjYmk/OIQM4K8L1xzjYdBUcAAQQQKCWwQ22pVctQxxwVqGePQFcP8XXNkLjO6x7o6E6BWrUIVbuWtGaNowULHD33gqt7HygaRjPhHk9PPe1Gd6n57ntHIX8gS+nyEgEEEMg/gWyuUdqDIIZhc2rYcBi7wsJe22LPbZ1NRLpuPUEQM2HJbYHtXw+V2/Wj9AgggAAChS3QoIF0wP6BupwU6MIL/GgYzcABcZ3V1dehHQI1bSoFiYDHim8cvTvP1YwnPI27y9NNo2N6cJKnl15x9ekiV+vWFbYjtUcAgbwToEJZLpD2IIjN0WHDU8oaUmLrbJulyXI3iocAAlUWIDxUZTp2RAABBLJYwEm8vTdtIh3SIdSZXQMNvDQeBUYuSgRITjk5UNsDAu20U6iNv0hfLnH0+puupjzqatStMd16u6dH/+5q9luuli5ztGlzFleUoiGAwDYE2IRA9gukPQiS/SSUEAEEUiuQ+FowtQcgdwQQQACBLBGwITItW4Q66shA5/4+0FVX+Bp2VVy9zg107NGB9moVqs4O0k8/OVr4savnX3R1/0NFw2jG3x3TkzNdzZvvaOUqMYwmS9qUYmxDgE0IIJATAhkJgqzfsFE290fbzn11ZNfLtHDR0mgeEFt339RnxD8EEEAAAQQQQACB/BSoX1/ar02gk04I1LePr2uHxjXoMj+ahPWwQwI12z2U60grV0rvzXf1xExP4yfEdOPImB5IBEheeMnVx5+4Wrs2P31ytVaUGwEEEMgVgYwEQf4ydpJsIlSbA+Swg/eLrGwoTJ/uXTR77kdRQCRayQ8EEEAAAQQQQACBvBawYTS7Ng510IGhzjg90IBLiuYX6Xehr1O7BGrXLlTDhmE0RGbJMkdvznH1yHRXN4+J6ZYxnqZOc/XGbFdLljratCkjVBwUAQQQQCCHBNIeBLG7wyxe8rWO6HDAVkxNGjXU2nUbxMSoW9GwAgEEEEAAAQQQyEKB1BQpFpP2/HWoTkcE6tHN15BBvoZfE1fv83x1PjbQPnuHqltX+vdaR5986urFl1098LCnG0fFdOeEmB5/0tO78xx9860UBKkpI7kigAACCOSmQNqDINtiWrV6jRrUr6v69epsKxnbEEAAAQQQQACBzAtQgrQK1EsEPdrsG+qEzoHO7+3r2kRQ5E8DfXU729fhHQM1bxbKTXyyXbVKmv+Bo6ee9vTXibEoMHLfg56ee8HVgoWOfvzJSWu5ORgCCCCAQHYJJP5UpLdAdueXU4/vqFsnPLrFFR92hcioO6dGw2RsaEx6S8XREEAAAQQQQKAyAqRNrwBTSpft3bhRqIN+E+r0UwP171c0jKb/xb5OS7w+sH2oXRLbN2+Wln3laM7brqY95um22z2Nvi2myY+4eu0NV1986egXhtGUDVyRtcSUKqJEGgQQyCKBtAdBrO4X9zxNNv9H53MG6+U35qvHpTfIng+7vKdsm6VhQQABBBBAIEsF8rRYnMlkc8PSOhVrnZgnNd8j1BEdA53zO1+DB/q6bmhcfXr5OuG4QK1bh6pXT/r5Z2nRZ65eftXVQ3/zZJOu3jHe04wnPM19z9WKFZIfVOyYBZ+KCF3BdwEAEMg1gYwEQQzp+E4Ha+GsB7dYbJ1tY0EAAQQQyFYBypW/ApzJ5G/bFnbN6tSRWu8TqnMiCNKnp6/hV8c1eJCv7t18HXl4oObNQ9kcJN+vdvTBh45mPuNqwr2xKDAy8X5Pzz7v6qMFjn5YQyiqsHsStUcAgXwRyFgQJF8AqQcCCBSQAFVFAAEEEMgLgV0ahmrfLtRvTwnU/yJf1w+Pa0C/uLqeFsjuUmPDbOJxaflyR2+942r6DE9jx3m66eaYHp7s6ZVZrhZ95mj9hrzgoBIIIIBAQQkQBCmo5qayBSVQw19YFZQdlUUAAQQQKCgBL/GJuFkzqeOhgbqd5csmXLXAyAV/8HXi8YHa7BtKO7ruAAAQAElEQVRoxx2lDYmgx+dfOJr1uqvJj3gamQiKjEkER6YlgiQ258hX/3JkwZOCwqOyCBQL8OGzmIInWS2QeMvP6vJROASyQSA3y8CV7bnZbpQaAQQQQCArBHaoLe29V6jjjgnU+7xAQ6+M68orfPU4x49u3dtiz1C1aklr1jhasMCJ7j5z7wNF84tMuMfTU0+70fCa7753FPI3OSvalEKkWoCOnmph8q8ZAYIgNeOYx7lQNQQQQAABBBBAAAET2HmnUO3ahjq1S6CL+/q6flhcf+wf1xmn++pwUKgmTaQgcR644htH785zZROtjrvL002jY3pwkqeXXnH16SJX69ZZbiwIIIAAApkQIAiyLXW2IYAAAggggAACCOSsQCIekdKyu4lP0rvvJh12SKizz/R1+YB4FBi58HxfJ58YaP/9Av2qQaiNv0hfLnH0+puupjzqatStMd16u6dH/+5q9luuli5ztGlzSotK5ggggEBGBFL9PlyVSiXeusveLbvXZiNldotROgQQQAABBBBAoNAEMjFDQe3aUquWoY45KlDPHoGuHuLrmiFxndc90NGdArVqEap2Lemnnxwt/NjV8y+6uv+homE04++O6cmZrubNd7RylRhGU2gdlvoikEGBVB06E+/D26tLjgZBspFye9RsL0wB+mphtju1RgABBBBA4L8CDRpIB+wfqMtJgS68wI/uRjNwQFxndfV1aIdATZsWpV25UnpvvqsnZnoaPyEW3ab3gUSA5IWXXH38iau1a4vS8RMBBGpUgMwKTCBHgyAF1kpUN4cFuGophxuPoiOAAAIIIJASASfxHUnTJtIhHUKd2TXQwEvjUWCk34V+NN9Iu3ahGjYMoyEyS5Y5enOOq0emu7p5TEy3jPE0dZqrN2a7WrLU0aZNKSkimRaMABVFoPAECIIUXptTYwQQQAABBPJEIHEmmSc1oRoI2BCZPX8dRnee6dHN15BBvoZfE1fv83x1PjbQPnuHqltX+vdaR5986urFl1098LCnG0fFdOeEmB5/0tO78xx9860UBHhWSIBECCBQkAIEQQqy2ak0AggggAAC+SDA1Xb50IrUoXyBeomgR5t9Q53QOdD5vX1dmwiK/Gmgr25n+zq8Y6DmzULZ5KyrVknzP3D01NOe/joxFgVG7nvQi27bu2Chox9/2jpgWP5R2YIAAghUQyAH/jQTBKlG+7IrAggggAACCCCAQM4J5HSBGzcKddBvQp1+aqD+/YrmF+l/sa/TEq8PbB9ql8T2zZulZV85mvO2q2mPebrtdk+jb4tp8iOuXnvD1RdfOvqFYTQ53Q8oPAJZK5ADMVeCIFnbeygYAggggAACCCBQ0wLkl28CMU9qvkeoIzoGOud3vgYP9HXd0Lj69PJ1wnGBWrcOVa+e9PPP0qLPXL38qquH/ubpxpEx3THe04wnPM19z9WKFZIf5JsO9UEAAQS2FiAIsrUJaxBAAAEEalAgB66KrMHaklVWC1A4BApEoE4dqfU+oTongiB9evoafnVcgwf56t7N15GHB2rePFQsJn2/2tEHHzqa+YyrCffGosDIxPs9Pfu8q48WOPphTQ58pVsgbUo1EUCg5gQIgtScJTkhgAACCJQhwEfoMlAysIpDIoBAYQvs0jBU+3ahfntKoP4X+dHdaAb0i6vraYEOOjCUDbOJx6Xlyx299Y6r6TM8jR3naeQtMU2a6mnWa64Wf+5o48bCdqT2CCCQ+wIEQXK/DakBAggggMC2BdiKAAIIIFBKwEucBTRrJnU8NFC3s3zZhKvXD4/rgj/4OvH4QG32DbTjjtL69dLixY5eSQRBJk3xNGJ0TGPHe3rsH57enutq+deO4n6pzHmJAAIIZLFA4u0vi0tH0RBAAAEEqinA7ggggAACCFRMYIfa0t57hTrumEC9zws09Mq4rrzCV49zfHU6IlCLPUPVqiX9sNrRhx85euY5VxPvK5pfZOK9np5OvP7gn040zKZiRyQVAgggkH4BgiDpN+eICCCQLgGOgwACCCCAAALVEth5p1Dt2oY6tUugi/v6un5YXH/sH9cZp/vqcFCoJk2kIJCWr3D0zlxXMx73oglXb7o5pocne3pllqtFnzlav6FaxWBnBBBAoMYECILUGCUZIZBdApQGAQQQQACB0gJMVFxahNeVFXATZw+77yYddkios8/0dfmAeBQYufB8XyefGGj//QL9qkGoDYmgx+dfOJr1uqvJj3gamQiKjBnnadoML7p171f/cmRzkFT2+KRHAAEEqiuQeBurbhbsj0DWCVAgBCokwMlAhZhIhAACeSTARMV51JhZVJXataVWLUMdc1Sgnj0CXT3E1zVD4jqve6CjOwVq1SJU7VrSmjWOFixw9NwLru59oGgYzYR7PD31tBvdpea77x2F/HHOopalKIUskM+/igURBLlv6jNq27lv8WKvS3bo71b/qFN7DS3e/uqc90tulqVP7n/RkFFav2HjFtuz6wWlQQCBigpwMlBRKdIhgAACCCBQOYEGDaQD9g/U5aRAF17gR3ejGTggrrO6+jq0Q6CmTaUgcZa14htH785zNeMJT+PuKgqMPPCQpxdecvXxJ67Wrq3ccUmNAAI1I5DPn5PzKwhSRntbwOKLpV9r1mNjtXDWg9Hj9KdmKRnosO3D/nK3up/ROdo+7e4bNHLcFC1ctDTKzdJZ+uT+uzXZRX8ZOynaxg8EEEAAAQQQQAABBBDYvoCTOKNq2kQ6pEOoM7sGGnhpPAqMXJQIkJxycqC2BwTaaadQmzZLS5Y5enOOq0emu7p5TEy3jPE0dZqrN2a7WrLU0aZN2z8eKRBAoAAEqljFvA+C1KtbRyOuvUS7Nto5Iqpfr46aNW2kL5d9E71e8tW3Wrtug87s0il63WrP3bTHbo319vyPo9cvvvZeFCBJ7n/ycYdq/keLZVePRAn4gQACCCCAAAIIIIAAApUWsCEyLVuEOurIQOf+PtBVV/gadlVcvc4NdOzRgfZqFarODtK/1zr65FNXL77s6oGHPd04KqY7J8T0+JOe3p3n6JtvJZuctdIFYAcEcliAolddIO+DIKVpLOjxyeJl2qvF7tGmVavXaO3P66Pn9sOCJna1h109YleJfLvqB1tdvDRp1FBhGGrV9z9G62xyKBapcA2cRN1ZXLcwDbxEvVkcYYBBVfqAm/gEUpX92If+Rh/I/T7gJv5+lrc0aODogP1DdTkp1EUXBLp+uK8rBgY65+xAHQ8N1axZ4nOnI61aJc3/wNFTT3v668RYFBi570FPz7/o6uOPHf201uUz2jacy/NPz3ol2qZaS8HvL/5VSyDxEaRa++fMznblhs370ePSG9Svd1cd3+ng4rLblSF2hUjxilJPkgGTUqujl7v+qo5YCtmgdqL9WXb9VWEaNErUm6W2MMCgKn1gp3q16Du8h9AHCrQPVOZzQ5OdauuAvWupy7G1dHGvmP7f1Z7uGOnpmkGuup/l6tCDHTVuJG3eLC37ytHst1w98ndPt45xNfpWT9Omx/TuO7X0/be11GCH2nnwuS0f6lDI5w41U/foRJQfVRZwq7xnju1ow1memzJa7z47QbPnfhRNdpqswoqVq7VuffmTnSaHziTTl3xc+eNGsRSywS+J9mdZ+WNhGqxK1JvlF2GAQVX6wJqfN9N3eA+hDxRoH6jy54aEl+27+udf1KDhJrU/cJPOPGOz/jQwruHXxNX7PF+djw20z96h6taV/r1W+ufCUE88G2jshEBXXOvr+hvj+utDcT310ma9//EvWvFDYX6GMcfMLRsTn59ZVlbjPLLk+SjPKy9QMEGQJI0NdzmqY3vZcBdbZ8NbGuxYz55GS3IIzN4t95CltaEx0Yb//LDhM47jqEnjojlG/rOaBwQQQAABBBBAAAEEUiZAxtsWqJcIerTZN9QJnQOd39vXtYmgyJ8G+up2tq/DOwZq3iyU50nfr3b0wYeOZj7jasK9Md04MqaJ93t69nlXHy1w9MMaZ9sHYmtKBcKU5k7mCBQJ5H0QxIbBXHfTPbLghlXZXtvdXizIYa9b7bmbGtSvqydfmGMvZXOGfP3t9zqiwwHR65OPO1SW3vazFTZRaof2rYsnWrV1LAhEAvzNjBj4gQACCCCAQA0LkB0CVRJo3CjUQb8Jdfqpgfr383X98LgG9Iur62mBDjowlG2Px6Xlyx299Y6r6TM8jR3naeQtMU2a6mnWa64Wf+5oY/kXjFepXOxUvgAfp8u3YUvNCeR9EMSGwRjXYb8doLad+6rzOYOju71c3PM0Wx1d7THqz5dGgQ7bbnOGDB/US23btIy2H9/p4Ci97de2c1/ZRKl/Htwn2sYPBLYQIHS9BQcvEEAAAQSqKlDyNKCqebAfAgiUFoh5kk2s2vHQQN3O8mVXilhg5II/+Drx+EBt9g20447S+vXS4sWOXkkEQSZN8TRidExjx3t67B+e3p7ravnXjuJ+6dx5jQACuSKQ90EQawi7Re7CWQ8quSQDILbNFguU2Hwhye0W+LD1ycXSJ7fdP2ZYFDhJbuMRAQQQQAABBFIkULDZElUv2Kan4mkX2KG2tPdeoY47JlDv8wINvTKuK6/w1eMcX52OCNRiz1C1akk/rHb04UeOnnnO1cT7vKJhNPd6ejrx+oN/OtEwm7QXngMigECVBAoiCFIlGXbKPwG+WMu/NqVGCOSxQNWqxhtd1dzYCwEEEPivwM47hWrXNtSpXQJd3NfX9cPi+mP/uM443VeHg0I1aSIFgbR8haN35rqa8binO8Z7uunmmB6e7OmVWa4WfeZo/Yb/5skzBBDIHgGCINnTFpQk1QJ8sZZq4TLzh71MFlZuW4CtVRbgN67KdOyIAAIIlCPgJs6Ydt9NOuyQUGef6evyAfEoMHLh+b5OPjHQ/vsF+lWDUBsSQY/Pv3A063VXkx/xNDIRFBkzztO0GZ7mvO3qq385sjlIyjlMlVfzzl9lOnYsUIHEr3SB1rycavMmUg4MqxGoogDfS1cWjvQI5K4Af0Nzt+0oOQIIVE6gdm2pVctQxxwVqGePQFcP8XXNkLjO6x7o6E6BWrUIVbuWtGaNowULHD33gqt7HygaRjPhHk9PPe1Gd6n57ntHYTXfPPmsVbm2IzUCBEFK9QHeREqB8BKBdApwLAQQyGkB/obmdPNReAQQqKZAgwbSAfsH6nJSoAsv8KO70QwcENdZXX0d2iFQ06ZSkAh4rPjG0bvzXM14wtO4uzzdNDqmByd5eukVV58ucrVuXTULwu4IILBNAYIg2+TJro2J98zsKhCl2UqgOm20VWasQAABBBBAAAEEEMhZAScRGW7aRDqkQ6gzuwYaeGk8CoxclAiQnHJyoLYHBNppp1Abf5G+XOLo9TddTXnU1ahbY7r1dk+P/t3V7LdcLV3maNPmnGWg4AhknQBBkKxrkvILlHgfLX9jbm/Jm9LTRnnTlFQEAQQQQAABBBCocQEbItOyRaijjgx09RibcAAAEABJREFU7u8DXXWFr2FXxdXr3EDHHh1or1ah6uwg/fSTo4Ufu3r+RVf3P1Q0jGb83TE9OdPVvPmOVq5StYfR1HjlyBCBHBEgCJLxhqIACCCAAAII5LcAV8nld/tmT+34KiJ72oKSVEagfn1pvzaBTjohUN8+vq4dGtegy3x1O8vXYYcEarZ7KDfRvVeulN6b7+qJmZ7GT4hFt+l9IBEgeeElVx9/4mrt2socNY/TJqzyuHZUrQYEMhsEqYEKkAUCCCCAAAIIZLcAn0ezu33yp3TZHG7jtyB/+lnqa2LDaHZtHOqgA0OdcXqgAZcUzS/S70I/um1vu3ahGjYMoyEyS5Y5enOOq0emu7p5TEy3jPE0dZqrN2a7WrLU0aZNqS9v1h0hm98Ksg4rzQXKksMRBMmShqAYCCCAAAIIIIAAAvkqwFlZ/rVsegNbsZi0569DdToiUI9uvoYM8jX8mrh6n+er87GB9tk7VN260r/XOvrkU1cvvuzqgYc93TgqpjsnxPT4k57enefom28lP8i/1siFGlHG7BEgCJI9bUFJEEAAAQQQQAABBBBAICcEMh/YqpcIerTZN9QJnQOd39vXtYmgyJ8G+up2tq/DOwZq3iyUmzjbW7VKmv+Bo6ee9vTXiUXDaCbe7+nZ5119tMDRD2tSHtDJiRalkIUjkPi1KJzKUlMEECgpwB+8kho8RwABBBBAAAEEal4gvTk2bhTqoN+EOv3UQP37FQ2j6X+xr9MSrw9sH2qXxPZ4XFq+3NFb77iaPsPT2HGeRt4S06Spnma95mrx5442bkxvuTkaAukUIAiSTm2OhUBWCWT+G4ys4qAwCCCAAAIIIFCzAuSWcYGYJzXfI9QRHQOd8ztfgwf6um5oXH16+TrhuECtW4eqV09av15avNjRK4kgyKQpnkaMjmnseE+P/cPT23NdLf/aUdzPeHUoAAI1IkAQpEYYyQQBBBBAAAEEEEAAgf8K8AyBbBWoU0dqvU+ozokgSJ+evoZfHdfgQb66d/N15OGBmjcPZXOQ/LDa0YcfOXrmOVcT7/N048iYJt7r6enE6w/+6ej7xPZsrSPlQmBbAgRBtqXDNgQQQAABBBDISgGuZcvKZkkWikcEEMgxgV0ahmrfLtRvTwnU/yJf1w+Pa0C/uLqeFsjuUmPDbHxfWr7C0TtzXc143NMd4z3ddHNMD0/29MosV4s+c7R+Q45VnOIWpABBkIJsdiqNAAIIIIBAbgtk76xGue1K6RFAAAET8BJnic2aSR0PDdTtLF824aoFRi74g68Tjw/UZt9AO+4obUgEPT7/wtGs111NfsTTyERQZMw4T9NmeJrztquv/uXI5iCxPFkQyBaBRPfOlqJQDgQQQAABBApYIB/O6gu4+ag6AgggkO8CO9SW9t4r1HHHBOp9XqChV8Z15RW+epzjq9MRgVrsGapWLWnNGkcLFjh67gVX9z5QNIxmwj2ennra1QcfOvrue0dhdDkff/jyvc9ka/0IgmRry1SxXNH7SRX3ZTcEEEAAgaoLVHtP3sCrTUgGCCCAAALpFdh5p1Dt2oY6tUugi/v6un5YXAMHxHVWV1+HdgjUtKkUJP6+rfjG0bvzXM14wtO4uzzdNDqmBye5eukVV58ucrVuXXrLzdEKW4AgSJ61P/HUPGtQqoNAbghQSgQQQAABBBBAQG7i7LJpE+mQDqHO7Bpo4KXxaH6Riy7wdcrJgdoeEGinROBk4y/Sl0scvf6mqymPuhp1a0y33u7p0b+7mv2Wq6XLHG3aDCgCqRFIdNPUZEyuCCCAQGEIUEsEEEAAAQQQQACB8gRq15Jatgh11JGBzv19oKuu8DXsqrh6nRvo2KMD7dUqVJ0dpJ9+crTwY1fPv+jq/oeKhtGMvzumJ2e6mjff0cpV+s8wmvKOxHoEKiZAEKRiTjmfKsz5GlCBrBSgUAgggAACCCCAAAIIVFKgfn1pvzaBTjohUN8+vq4dGtegy/xoEtbDDgnUbPdQriOtXCm9N9/VEzM9jZ8Qi27T+0AiQPLCS64+/sTV2rWVPDDJEUgIEARJIGT//8Q7QDULWf0cqlmAPNydKiGAAAIIIIAAAggggED1BZzEycqujUPZ7XjPOD3QgEv8aBhNvwv9aL6Rdu1CNWwYRkNklixz9OYcV49Md3XzmJhuGeNp6jRXb8x2tWSpo02bql8ecshvAYIgOdG+WXcdR06oUUgEEEAAAQQQQAABBBDITYFYTNrz12F055ke3XwNGeRr+DVx9T7PV+djA+2zd6i6daV/r3X0yaeuXnzZ1QMPe7pxVEx3Tojp8Sc9vTvP0TffSkGQmwaZKXX+n3sSBKl0z2IHBBBAAAEEEEAAAQQQQACBdAvUSwQ92uwb6oTOgc7v7evaRFDkTwN9dTvb1+EdAzVvFkaTs65aJc3/wNFTT3v668RYFBi570Evum3vgoWOfvzJSXfRc+h4+W9TuSBIDjUdRUUg9QL5/waRekOOgAACCCCAAAIIIIBA1QUaNwp10G9CnX5qoP79iobR9L/Y12mJ1we2D7VLYvvmzdKyrxzNedvVtMc83Xa7p9G3xTT5EVevveHqiy8d/cIwmq0bIU/XEATJ04alWukQyP9LxdKhyDG2JUCgbVs6bEMAAQQQQAABBEoLxDyp+R6hjugY6Jzf+Ro80Nd1Q+Pq08vXCccFat06VL160s8/S4s+c/Xyq64e+punG0fGdMd4TzOe8DT3PVcrVkh+UDp3XueDAEGQfGhF6oAAAnkqQKAtTxuWaiGAAAIIIJDNAnlXtjp1pNb7hOqcCIL06elr+NVxDR7kq3s3X0ceHqh581A2B8n3qx198KGjmc+4mnBvLAqMTLzf07PPu/pogaMf1vAFVT50DoIg+dCK1AEBBBBAAAEEEEAAAQRqQIAsCkVgl4ah2rcL9dtTAvW/yI/uRjOgX1xdTwtkd6mxYTbxuLR8uaO33nE1fYanseM8jbwlpklTPc16zdXizx1t3FgoYvlTT4Ig+dOW1AQBBBBAAAEEEEAAgaoLsCcCBSzgJc6MmzWTOh4aqNtZvmzC1euHx3XBH3ydeHygNvsG2nFHaf16afFiR68kgiCTpngaMTqmseM9PfYPT2/PdbX8a0cWPClgyqyveqKps76MFBABBBBAAAEEEEAAgZQKkDkCCCBQWmCH2tLee4U67phAvc8LNPTKuK68wlePc3x1OiJQiz1D1aol/bDa0YcfOXrmOVcT7yuaX2TCPZ6eetqNhtd8972jkFHOpXkz9roggiDX3XSP2nbuW7zcN/WZLcBLb7e0JdPYc1tny0VDRmn9ho1b7M8LBBBAAAEEEEAghwUoOgIIIIBABQV23ilUu7ahTu0S6OK+vq4fFtcf+8d1xum+OhwUqkkTKUgEPFZ84+jdea5sotVxd3m6aXRMD07y9NIrrj5d5GrdugoekGQ1LpD3QZBkwGLWY2O1cNaDmnb3Dbp38ky9Ouf9LTDPOuWoaLulseXinqdF2y3d9KdmKbn/bk120V/GToq28QMBBBBAAAEEcl2A8iOAAAIIIFB1ATdxRr37btJhh4Q6+0xflw+IR4GRC8/3dfKJgfbfL9CvGoTa+Iv05RJHr7/pasqjrkbdGtOtt3t69O+uZr/laukyR5s2V70c7FlxgUSTVTxxLqasV7eORlx7iXZttHNU/FZ77qb9W7fQl8u+iV5v78eLr72n7md0Lt7/5OMO1fyPFuu71T9ub1e2I5AiAWalThEs2SJQeALUGAEEEEAAAQRqXKB2balVy1DHHBWoZ49AVw/xdc2QuM7rHujoToFatQhVu5b000+OFn7s6vkXXd3/UNEwmvF3x/TkTFfz5jtauUoMo6nx1pHyPghS2mzd+o1asXK19mqx+xabnnh+9lbDZewqkm9X/bBFuiaNGiY6YqhV3xME2QKGF2kUCNN4rPIO5ZS3gfUI5IwABUUAAQQQQAABBNIl0KCBdMD+gbqcFOjCC/zobjQDB8R1Vldfh3YI1LRpUUlWrpTem+/qiZmexk+IRbfpfSARIHnhJVcff+Jq7dqidPysukDBBUHGTJyuDu1b6/hOBxer2ZUiNgTGlrKGy5QOmBTvmHjSoG5MLBgUXh/w6Pe5/btP+9F+Ge8D9WvzPlJ4fzv4vECb0wfoA/SBZB/4Vb2Y9mkRU+ejPPXu7mr4YGnk/0iX95fO/K10UPtQDXdWNERmyTJHb85x9ch0VzePiSXOQvPxf/q+6HXzka+8OtkEqHZlx58H9ykvidq2aanjjzp4i+Ey2xw64yS+EWeRMMAgZ/oAv7P8vtIHsqIPuLRDVrQD7938/aYP0AfoA1nTB2rXdrT3Xo6OP87RBb1d/d/hjv6/Pzu6+HxHJx0v7buPtGtj5em/xOeCNNWsYIIgyQDInSOukM0TUhFfS2cToZZMu2r1msRnFkdNGu8crV67frNYMMiZPkB/5feVPkAfyJI+sG5jnLbIkrbgbxifY+gD9AH6QPb2gdDdrBYtN+vYY+L6Q6+4Bl0Wj85D+VF1gYIIglgAxIjuHzNsqwCITXB6x32P2eZoWbhoqeb98zMd0eGA6LVNhGp3h7F0tsImSrXhNMmJVm0dS24IUEoEEEAAgXQIpO+bnHTUhmMggAACCCCAQH4J5H0QxIIXdjeXkhOftu3cVxcNGSWb+LR+vTr6YMHnsnW29Lj0Bg0f1CsaFmNNbXOH2N1hOp8zOEqzveE0tk8WLnlbpPSNHMtbQiqGAAII1LAA78w1DEp2CCCAAAIIIFCDAnkfBNm10c56bspoLZz14BZL8qoQG/Jiz0tut8BHSeOLe55WvK+ltX1Kbud55gT4vjFz9hwZAQQQQAABBBBAAAEEEMguge2XJu+DINsnIAUCCCCAAAIIIIAAAggggAACOS5A8SskQBCkQkwkQgABBBBAAAEEEEAAAQQQyFYByoVARQUIglRUinQIIIAAAgggkCEBBj9mCJ7DIoBAbghQSgQQqIQAQZBKYJEUAQQQQAABBDIhwGSrmVDnmAjkhgClRACBKgsU6J9XgiBV7jHsiAACCCCAAAIIIIBABgU4NAIIIFAdgQK90JIgSHU6DfsigAACCCCAAAIIZESAgyKAAAIIIFAVAYIgVVErsU+zRnXFggF9gD5AH6AP0AfoA2nsA3z24PMXfSCb+8AuvB/yfpjaPlDidJSnVRAgCFIFNHZBAAEEEEAAgUwJcFwEEEAgOwWKp1co0CEG2dkqlAqBLQXs95QgyJYmvEIAAQQQQCB7BSgZAggggEDWChD7yNqmoWDpFLAoQzqPV8lj2e8pQZBKopEcAQQQQCAzAhwVAQQQQAABBBBAIMsFLMqQ5UUkCFKFBnp1zvtq27nvFsuRXS/TwkVLxT8EEEAgBQJkiQACCCCAAAIIIFCAAus3bNRFQ0bp1F5D9d3qH4sF7Lmts8WeF2/gyXYFCIJsl6jsBL9u1kSzHhurhbMejJa3Zt6ltm1allBnpaMAAAzqSURBVJ2YtQggUA0BdkUAgawSyPLLXLPKisIggAACCCBQQwI71q+rJ1+YU5ybPbd1xSuq+6SA/r4TBKluZ2F/BFIpQN5VECigd/Aq6LALAtUWyIHLXKtdRzJAAAEEEEAgywS6dz1Os+d+FF0NYld+2HNbV2PFzJK/7+n4JE8QpMZ6DRnVtAD5IVA1gSx5B69a4dkLAQQQQAABBBBAAIGtBJrs2lBHdWwfXQ2yYNGS6Lmt2yphjq9Ixyd5giBV7CT/WrFKnc8ZXDwvyH1Tn6liTmXuxkoEEEAAAQQQQAABBBBAAAEEigXO7NJJz706V5OmvyB7XryBJ5USyMIgSKXKn7HEpecEubjnaRkrCwdGAAEEck4gHdc65hwKBUYAAQQQQAABBMoX2LXRzmrdao/oKhB7Xn7KXNqS/rK66T8kR0QAAQQQKHiBdFzrWPDIACCAAAIIIIBAVgtUoXAjrr1EfAFfBbgSuxAEKYHBUwQQQAABBBBAAAEEEEAAgdQLcAQEMiVAECRT8hy3SIBL4osc+IkAAggggAACCCBQKALUEwEEMihAEKQK+Md3OljPTRktxmFVAa/0LlwSX1qE1wgggAACCCCAQB4LUDUEEKiMQL26dXT/mGGyc9DS+9k6zktLq2z/dfqDIHzzv/1WIQUCCCCAAAIIIIBA/glQIwQQQACBjAukPwjCN/8Zb3QKgAACCCCAAAIIpFuA4yGAAAIIIJANAukPgqS51lx4kmZwDocAAnkmwLtonjUo1cmMAEdFAIG8FuBvZV43L5XLO4G8D4Jw4Une9VkqhAACaRXgXTSt3Hl5MCqFAAII5LsAfyvzvYWpX34J5H0QJL+ai9oggAACCOSUAIVFAAEEEEAAAQQQyCoBgiBZ1RwUBgEEEMgfAWqCAAIIIIAAAggggEC2CRQFQRjGlm3tQnkQQCC3BSg9AggggAACCCCAAAJZIMDJfulGKAqCMIyttAuvEUCgygLsiAACCCCAAAIIIIAAAtkhwMl+6XYoCoKUXstrBBComgB7IYAAAggggAACCCCAAAIIZK0AQZCsbZrcKxglRgABBBBAAAEEEEAAAQQQQCCbBQiC1EzrkAsCCJQlwBDEslRYhwACCCCAAAIIIIAAAhkSqIEgSIZKzmERQCD7BRiCmP1tRAnTIEA0MA3IWXsIWj9rm4aCIYAAAghUSSD3dyIIkvttSA0QQAABBLJagGhgVjdPigtH66cYmOwRQACBdApwrLwQKMwgCF/LZFXnLa85ylufVYWnMAgggAACCCCAAAIIFIAAVUQgXwQKMwjC1zJZ1X/La47y1mdV4SkMArkiQFQxV1qKciKAAAIIZJ8AJUq3AJ9b0i1eUMcrzCBIrjQxv/y50lKUM50C/F5UTZuoYtXc2CsjAus3bNRFQ0bpvqnPZOT4HBQBBEoK8ByBDAjwuSUD6IVzSIIg2dzW/PJnc+tQtkwJ8HuRKXmOiwACCBSeADVGAAEEEMg7AYIgedekVAgBBBBAAAEEEKi+ADkggAACCCCQjwIEQfKxVakTAggggAACNSRgQ1Ladu6rkoutK5n9dTfdU7z9yK6XaeGipbJ/JYe1lExjQ11sm6VJLpZn8hiH/XaA3nn/k+Sm4seSeWzvOKf2GqrvVv9YvG8ln5AcAQQQQAABBPJUgCBInjasmDchX1uWeiGAAALVFNjeH4j/Zm+BielPzdKsx8Zq4awH9e6zE3T4wfv/N0HimQUmEg/Rdksz4rpL1O+q0cWBENt2293TdPJxh0ZpLK8VK1dr6uOv2KZoScVxnpsyWrs22jnKnx8IIIAAAggggEBSgCBIUiLfHpk3Id9alPoggEA6BAriGBX7A2FXUVgAZNjlPcsNJtgVH4uXfK0h/bsXy1mQZP/WLfT2/I+L1115aQ8d3+ng6LUFJjq0b60vln4dvU7VcaLM+YEAAggggAACCJQSIAhSCoSXCCCAQKEKUG8ESgqs+v5HhWGoJo0ally9xfNVq9fo48+WqvM5g4uHw5Q3lGWLHUu8SNdxShySpwgggAACCCBQwAIEQQq48al6tghU/NL0bClxHpaDKlVXgG5cXcGs3P9XDeqrSeNtDyn5dbMmxcNlbDhMcrm452mq6L90Haei5SEdAggggAACCOSvQMaDIHxuzt/ORc0qKlCxS9Mrmlvl07EHAjUgQDeuAcTsy+Lfa9fJrtQor2R2lYhdLbKtNOXtW3J9uo5T8pg8RwABBBBAAIHCFMh4EITPzYXZ8bKm1hQEAQQQQKBMgVZ77qY9dmusyTNeLN5ud2yxJbkimeaq/7lrizux2ESnr855P5lsm49t27TUIb/Zd4vj2KSpNX2cbRaCjQgggAACCCBQMAIZD4IUjHQWVpQiIYAAAgggUJ5Avbp1dOeIK/Ttqh+K5/v4x7NvbHF3GEtz/5hhsolOS84LYhOqtmvTqryst1r/58F9tjjOz+s2pOQ40YG5BDVi4AcCCCCAAAKFKlCoQZBCbW/qjQACCCCAQIUFkkGO5DwfI6/rH+27V4vdo8fkjxHXXhLd/jaZLnl72uT+pecHsfS2JPdPpkvuf0W/c2TBlbL2S6axx+0dJ5n/Fo9cgroFBy8QQAABBBAoAIEtqkgQZAsOXiCAAAIIIIBAUqD0sBYborJi5WpV5iqPZF48IoAAAggggEAmBDhmaQGCIKVFeI0AAgggkJsCDHNISbtdft3txcNhRt05VZPGXaddG237jjEpKQiZIoAAAgggUFkB0iNQhgBBkDJQWIUAAgggkIMCDHOo8Uaz4Sg27CS5JIef1PiByBABBBBAoMYFyBABBMoWIAhStgtrEUAAAQQQQAABBBBAIDcFKDUCCCBQrgBBkHJp2IAAAggggAACCCCAQK4JUF4EEEAAgW0JEATZlg7bEEAAAQQQQAABBHJHgJIigAACCCCwHYEcCoIw49122jLvNtPiedekVAgBBBBAIIUCZI0AAggggAAC2xfIoSAIM95tvznzKwUtnl/tmZ+1IVSXn+1KrXJQgCIjgAACCCCAAAIVEsihIEiF6kMiBBBAII0ChOrSiM2hyhVgAwIIIIAAAggggEBFBQiCVFSKdAgggAACWSYQSllWIoqDAAIIIIAAAgggkN0CBEGyu30oHQIIIFCuABu4Eoc+gAACCCCAAAIIIFA5AYIglfMiNQIIZIcApUAAAQQQQAABBBBAAAEEKi1AEKTSZOyAQKYFOD4CCCCAQHUEmNK4Onrsi0ChCfCOUWgtTn3zX4AgSP63cX7VkNoggAACCCBQTQEGUlUTkN0RKCgB3jEKqrmpbEEIEATJoWamqDkkkIVfGmRhkXKoQSkqAggggAACCCCAAAII5INArgRB8sGaOhSSQBZ+aZCFRSqkHkFdEUAAAQQQQMAE+FbGFFgQQGDbAindShAkpbxkjgACCCCAAAIIIIAAAsUCfCtTTMETBMoWYG2qBQiCpFqY/BFAAAEEEEAAAQQQQAABBLYvQAoE0iBAECQNyBwCAQQQQAABBBBAAAEEENiWANsQQCA9AgRB0uPMURBAAAEEEEAAAQQQQKBsAdYigAACaRMgCJI2ag5UyALMAVbIrU/dEUAAAQTyXqBaf+jzXocKIoAAAlklQBAkq5qDwuSrAHOA5WvLUi8EEEAAgWoJ5MvO/KHPl5akHgggUAACBEEKoJGpIgIIIIAAAgUhkGPfxtdomxRy3WsUkswQQAABBPJdgCBIvrcw9UMAAQQQQCD7BFJTokL+Nr6Q656a3kSuCCCAAAJ5KkAQJE8blmpluwBf2WV7C1E+BFInQM4IIIAAAggggEABC2T4VIggSAH3PaqeSQG+ssukPsfOoACHRgABBBBAAAEEEChsgQyfChEEKezuR+0RQCCNAhwKAQQQQAABBBBAAAEEMitAECSz/hwdgUIRoJ4IIIAAAggggAACCCCAQMYFCIJkvAkoQP4LUEMEEEAAAQQQQAABBBBAAIFsECAIkg2tkM9loG4IIIAAAggUpECGZ31LgXn+1SgFSGSJAAIIIJD1AgRBUthEZI0AAggggAAChSqQ4VnfUsCefzVKARJZIoAAAghkvUCqgiBZX3EKiAACCCCAAAIIIIAAAggggAAC1RbIqQwIguRUc1FYBBBAAAEEEEAAAQQQQACB7BGgJLkmQBAk11qM8iKAAAIIIIAAAggggAAC2SBAGRDIQQGCIDnYaBQZAQQQQAABBBBAAAEEMivA0RFAIDcFCILkZrtR6rQKMB9+Wrk5GAIIIIAAAghkuwDlQwABBHJWgCBIzjYdBU+fAPPhp8+aIyGAAAIIIJDtApQPAQQQQCCXBQiC5HLrUXYEEEAAAQQQQCCdAhwLAQQQ2EKAK6a34OBFTggQBMmJZqKQCCCAAAIIIJBpAY6PAAIIIFBagCumS4vwOvsF/n8AAAD//91UTkMAAAAGSURBVAMAl3wv2FEKQcwAAAAASUVORK5CYII=", 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", 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", + "text/html": [ + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "charters.target(\n", + " df=pdp_data,\n", + " target=\"risk\",\n", + " features=[\"exercise\", \"healthy\"],\n", + " numerator=[\"rate_weight\"],\n", + " denominator=[\"weight\"],\n", + " generate_pairwise=True,\n", + ")" + ] } ], "metadata": { diff --git a/notebooks/tutorials/experience_study.ipynb b/notebooks/tutorials/experience_study.ipynb index 145d28b..1b6b2d1 100644 --- a/notebooks/tutorials/experience_study.ipynb +++ b/notebooks/tutorials/experience_study.ipynb @@ -42,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "from morai.experience import charters, experience" + "from morai.experience import charters, credibility, experience, tables" ] }, { @@ -91,7 +91,7 @@ "A central rate (mx) can be used to approximate initial rate (qx) using the average force of mortality (ux). Described in more detail in section 8:\n", "- mx ≈ ux\n", "- qx = 1 - exp(-ux)\n", - "- ux = -log(1-ux)" + "- ux = -log(1-qx)" ] }, { @@ -272,16 +272,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m formatting df... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study periods: `2010-01-01` to `2014-12-31` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study frequency: `annually` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m shape before: (6, 8), shape_after: (38, 13) \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `annual` - rate type: `qx` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `False` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting actuals... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m total actuals: 3 \u001b[0m\n" + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m formatting df... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study periods: `2010-01-01` to `2014-12-31` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study frequency: `annually` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m shape before: (6, 8), shape_after: (38, 13) \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `annual` - rate type: `qx` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `False` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting actuals... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:22 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m total actuals: 3 \u001b[0m\n" ] } ], @@ -298,7 +298,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "b9507d9b-dcd2-4e37-a749-2e1da049fd23", "metadata": { "scrolled": true @@ -412,7 +412,7 @@ "6 0.51 NaN NaN NaN NaN NaN" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -421,7 +421,7 @@ "study_df.pivot_table(\n", " index=\"id\",\n", " columns=\"policy_dur\",\n", - " values=\"exposure\",\n", + " values=\"exposure_cnt\",\n", " aggfunc=\"sum\",\n", ")" ] @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "3e759ec4-d205-4c78-8901-bc77ce662992", "metadata": {}, "outputs": [], @@ -472,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "72dca44c-2bec-408a-b719-dea25c8d0c6e", "metadata": {}, "outputs": [ @@ -480,28 +480,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m formatting df... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study periods: `2023-01-01` to `2023-12-31` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:32 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study frequency: `monthly` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m shape before: (10000, 4), shape_after: (120000, 9) \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting actuals... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m total actuals: 1,207 \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `annual` - rate type: `qx` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `distributed` - rate type: `qx` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `exact` - rate type: `ux` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", - "\u001b[37m 2026-04-19 14:19:33 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n" + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m formatting df... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study periods: `2023-01-01` to `2023-12-31` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study frequency: `monthly` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m shape before: (10000, 4), shape_after: (120000, 9) \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting actuals... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m total actuals: 1,207 \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `annual` - rate type: `qx` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:02:59 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `distributed` - rate type: `qx` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m getting exposures... \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m exposure method: `exact` - rate type: `mx` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m calendar exposure: `True` \u001b[0m\n", + "\u001b[37m 2026-05-24 08:03:00 \u001b[0m|\u001b[37m morai.experience.experience \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m study decrement: `D` \u001b[0m\n" ] } ], "source": [ + "# calculate exposures 3 different ways\n", "study_df = experience.create_study(\n", " df=soa_compare_data,\n", " bos=\"2023-01-01\",\n", @@ -534,7 +535,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "9566eac5-74d9-4abe-ab9f-4bed96311202", "metadata": {}, "outputs": [ @@ -560,7 +561,7 @@ " \n", " \n", " eos_date\n", - " actuals\n", + " actual_cnt\n", " exposure_annual\n", " exposure_dist\n", " exposure_exact\n", @@ -732,7 +733,7 @@ "" ], "text/plain": [ - " eos_date actuals exposure_annual exposure_dist \\\n", + " eos_date actual_cnt exposure_annual exposure_dist \\\n", "0 2023-01-31 00:00:00 108 948.1425 849.3151 \n", "1 2023-02-28 00:00:00 97 840.1589 767.1233 \n", "2 2023-03-31 00:00:00 106 911.7671 849.3151 \n", @@ -763,30 +764,31 @@ "12 9,434.6438 0.1207 0.1207 0.1279 0.1201 " ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# group by month and then create qx\n", "compare_df = (\n", " study_df.groupby(\"eos_date\")[\n", - " [\"actuals\", \"exposure_annual\", \"exposure_dist\", \"exposure_exact\"]\n", + " [\"actual_cnt\", \"exposure_annual\", \"exposure_dist\", \"exposure_exact\"]\n", " ]\n", " .sum()\n", " .reset_index()\n", ")\n", - "compare_df[\"qx_annual\"] = compare_df[\"actuals\"] / compare_df[\"exposure_annual\"]\n", - "compare_df[\"qx_dist\"] = compare_df[\"actuals\"] / compare_df[\"exposure_dist\"]\n", - "compare_df[\"ux_exact\"] = compare_df[\"actuals\"] / compare_df[\"exposure_exact\"]\n", + "compare_df[\"qx_annual\"] = compare_df[\"actual_cnt\"] / compare_df[\"exposure_annual\"]\n", + "compare_df[\"qx_dist\"] = compare_df[\"actual_cnt\"] / compare_df[\"exposure_dist\"]\n", + "compare_df[\"ux_exact\"] = compare_df[\"actual_cnt\"] / compare_df[\"exposure_exact\"]\n", "compare_df[\"qx_exact\"] = 1 - np.exp(-compare_df[\"ux_exact\"])\n", "\n", "# create total row\n", "total_df = compare_df.copy()\n", "total = total_df.sum(numeric_only=True)\n", - "total[\"qx_annual\"] = total[\"actuals\"] / total[\"exposure_annual\"]\n", - "total[\"qx_dist\"] = total[\"actuals\"] / total[\"exposure_dist\"]\n", - "total[\"ux_exact\"] = total[\"actuals\"] / total[\"exposure_exact\"]\n", + "total[\"qx_annual\"] = total[\"actual_cnt\"] / total[\"exposure_annual\"]\n", + "total[\"qx_dist\"] = total[\"actual_cnt\"] / total[\"exposure_dist\"]\n", + "total[\"ux_exact\"] = total[\"actual_cnt\"] / total[\"exposure_exact\"]\n", "total[\"qx_exact\"] = 1 - np.exp(-total[\"ux_exact\"])\n", "total[\"eos_date\"] = \"total\"\n", "total_df = pd.concat([total_df, total.to_frame().T], ignore_index=True)\n", @@ -797,7 +799,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "d2cea50c-8bd6-4d12-a63c-849d222eb79a", "metadata": { "scrolled": true @@ -1705,12 +1707,12 @@ } } }, - "image/png": 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", + "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.pdp(\n", - " model=model,\n", - " df=md_encoded,\n", - " x_axis=\"exercise\",\n", - " weight=\"weight\",\n", - " mapping=mapping,\n", - " display=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "6051cc4e-885b-4b0f-a2c7-970afe936c50", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[37m 2025-08-03 23:22:19 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Model: [CatBoostRegressor] for partial dependence plot. \u001b[0m\n", - "\u001b[37m 2025-08-03 23:22:19 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Weights: [weight] \u001b[0m\n", - "\u001b[37m 2025-08-03 23:22:19 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m x_axis: [healthy] type: [passthrough] center: [global] \u001b[0m\n", - "\u001b[37m 2025-08-03 23:22:19 \u001b[0m|\u001b[37m morai.experience.charters \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Creating 2 predictions. \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0103487f2e2e4652bd5868273740675c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing: 0%| | 0/2 [00:00 \n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.pdp(\n", - " model=model,\n", - " df=md_encoded,\n", - " x_axis=\"healthy\",\n", - " weight=\"weight\",\n", - " mapping=mapping,\n", - " display=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "239e75ac-19be-477d-a0bf-e1826ba591c9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 2025-08-03 23:32:28 | morai.experience.charters | INFO | Generating `1` pairwise plots from features \n", - " 2025-08-03 23:32:28 | morai.experience.charters | INFO | Creating '1' pairwise plots. \n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "exercise=0
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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "charters.target(\n", - " df=pdp_data,\n", - " target=\"risk\",\n", - " features=[\"exercise\", \"healthy\"],\n", - " numerator=[\"rate_weight\"],\n", - " denominator=[\"weight\"],\n", - " generate_pairwise=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "456439e1-3d90-4cf0-b4a8-c0cc624ce568", - "metadata": {}, - "source": [ - "## Reload" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "c809667f-7460-4ade-8b46-9ac35a69d0a8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import importlib\n", - "\n", - "importlib.reload(charters)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0f971982-7829-47cd-9e6e-e9adae237ed6", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "morai", - "language": "python", - "name": "morai" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.7" - }, - "toc-autonumbering": true - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/tutorials/tables.ipynb b/notebooks/tutorials/tables.ipynb index 7862d26..5fda331 100644 --- a/notebooks/tutorials/tables.ipynb +++ b/notebooks/tutorials/tables.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "8aeed43f-4fc4-4a6b-a2aa-78fc4e157fbf", "metadata": {}, "outputs": [], @@ -37,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "898a8feb-f241-4d9a-8d27-873cd899ae77", "metadata": {}, "outputs": [], @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "e2f06050-290c-461e-8acd-1bd7977b1ad3", "metadata": {}, "outputs": [ @@ -64,9 +64,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2025-09-08 23:44:17 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Created table that has the following dims: {'issue_age': range(0, 122), 'duration': range(1, 123)} \u001b[0m\n", - "\u001b[37m 2025-09-08 23:44:17 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Table has 7503 cells. \u001b[0m\n", - "\u001b[37m 2025-09-08 23:44:17 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m tables: [997] \u001b[0m\n" + "\u001b[37m 2026-05-12 23:07:46 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Created table that has the following dims: {'issue_age': range(0, 122), 'duration': range(1, 123)} \u001b[0m\n", + "\u001b[37m 2026-05-12 23:07:46 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Table has 7503 cells. \u001b[0m\n", + "\u001b[37m 2026-05-12 23:07:46 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m tables: [997] \u001b[0m\n" ] } ], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "8501a274-665e-4885-a8a2-54d14819de29", "metadata": {}, "outputs": [ @@ -208,7 +208,7 @@ "[7503 rows x 4 columns]" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -219,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "id": "249e9531-8324-43b4-8f42-a034d28b4b0f", "metadata": {}, "outputs": [], @@ -229,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "id": "072380db-8054-42b6-aef4-fe5b9062d347", "metadata": {}, "outputs": [ @@ -239,7 +239,7 @@ "'2008 Value Basic Table (VBT) Primary Table - Female, Non-Smoker, Age Nearest Birthday. Minimum Age: 0. Maximum Age: 90'" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +250,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "id": "481e3b10-0c22-4fce-9346-a1c315c5c2eb", "metadata": {}, "outputs": [ @@ -260,7 +260,7 @@ "'2008 VBT-Primary Female Non-Smoker ANB'" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -271,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "id": "474007b9-429d-4f6d-8d9c-48bf977c2007", "metadata": { "scrolled": true @@ -281,11 +281,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2025-07-09 00:32:07 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Created table that has the following dims: {'issue_age': range(0, 122), 'duration': range(1, 123), 'sex': ['F', 'M'], 'smoker_status': ['NS', 'S'], 'year': [2015]} \u001b[0m\n", - "\u001b[37m 2025-07-09 00:32:07 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Table has 30012 cells. \u001b[0m\n", - "\u001b[37m 2025-07-09 00:32:07 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m tables: [3224, 3234, 3252, 3262] \u001b[0m\n", - "\u001b[37m 2025-07-09 00:32:07 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m juveniles: [3273, 3273, 3274, 3274] \u001b[0m\n", - "\u001b[37m 2025-07-09 00:32:07 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m extend: True, filled in 488 missing values. \u001b[0m\n" + "\u001b[37m 2026-05-12 23:08:00 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Created table that has the following dims: {'issue_age': range(0, 122), 'duration': range(1, 123), 'sex': ['F', 'M'], 'smoker_status': ['NS', 'S'], 'year': [2015]} \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:00 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m Table has 30012 cells. \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:00 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m tables: [3224, 3234, 3252, 3262] \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:00 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m juveniles: [3273, 3273, 3274, 3274] \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:00 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m extend: True, filled in 488 missing values. \u001b[0m\n" ] } ], @@ -308,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "cbd7316c-2d50-4864-b06b-f53473424797", "metadata": {}, "outputs": [], @@ -318,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "3dcab593-f29b-4ed4-b786-1b731369fdc5", "metadata": {}, "outputs": [], @@ -331,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "49cba4ea-df95-4b51-a3ef-0318308ecf7d", "metadata": {}, "outputs": [ @@ -405,7 +405,7 @@ "4 binned_face 05: 5,000,000+ 0.889072" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -424,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "cb5ccf5a-5c56-41be-a31f-5c6cf7530f5e", "metadata": {}, "outputs": [], @@ -434,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "a2ae20f5-f219-4e57-8ef1-9a40781a6820", "metadata": {}, "outputs": [ @@ -442,8 +442,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2025-04-27 22:39:18 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m loading 'glm_mults' from mapping file: C:\\Users\\johnk\\Desktop\\github\\morai\\files\\rates\\rate_map.yaml \u001b[0m\n", - "\u001b[37m 2025-04-27 22:39:18 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m building table for rate: 'glm_mults' with format: 'workbook' \u001b[0m\n" + "\u001b[37m 2026-05-12 23:08:25 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m loading 'glm_mults' from mapping file: C:\\Users\\johnk\\Desktop\\github\\configs\\morai\\files\\rates\\rate_map.yaml \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:25 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m building table for rate: 'glm_mults' with format: 'workbook' \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:30 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m loading mi_table from file: mi.csv \u001b[0m\n" ] } ], @@ -453,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "cf740cab-ace4-4d9d-bc21-28b4c9ff40a7", "metadata": {}, "outputs": [ @@ -461,8 +462,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[37m 2025-04-27 22:39:44 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m derived table using multiplier: `0.97` \u001b[0m\n", - "\u001b[37m 2025-04-27 22:39:44 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m used the following subcategories: `[['01: 0 - 24,999'], ['2_1', '3_2'], ['test'], [2012]]` \u001b[0m\n" + "\u001b[37m 2026-05-12 23:08:30 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m derived table average multiplier: `0.94` \u001b[0m\n", + "\u001b[37m 2026-05-12 23:08:31 \u001b[0m|\u001b[37m morai.experience.tables \u001b[0m|\u001b[32m INFO \u001b[0m|\u001b[32m used the following subcategories with mult: `['01: 0 - 24,999', '2_1', '3_2', 2012]` \u001b[0m\n" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 953710a..67eca48 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -78,7 +78,6 @@ morai = "morai.utils.cli:cli" # the entry points provide console scripts at comm homepage = "https://github.com/jkoestner/morai" repository = "https://github.com/jkoestner/morai" -# TODO: Remove after setuptools support is dropped. [tool.setuptools] include-package-data = true # include files from MANIFEST.in @@ -191,7 +190,7 @@ exclude = ["*.ipynb", "__init__.py"] pylint.max-args = 10 [tool.ruff.lint.per-file-ignores] -"morai/dashboard/pages/*.py" = ["ANN", "E501"] +"morai/dashboard/*/*.py" = ["ANN", "E501"] "tests/*.py" = ["ANN001","ANN201"] [tool.mypy] diff --git a/tests/files/experience/sample_experience_data.csv b/tests/files/experience/sample_experience_data.csv deleted file mode 100644 index 43fccf0..0000000 --- a/tests/files/experience/sample_experience_data.csv +++ /dev/null @@ -1,101 +0,0 @@ -id,gender,smoker,issue_date, amount ,termination_date,termination_reason,notes -1,F,NS,6/30/2015,"50,000",7/17/2022,L, -2,M,NS,8/18/2017,"100,000",4/9/2024,L, -3,M,NS,11/26/2019,"100,000",,,inforce -4,M,NS,8/19/2017,"50,000",,, -5,M,NS,5/12/2014,"50,000",,, -6,F,NS,6/15/2015,"100,000",,, -7,F,NS,4/9/2013,"100,000",10/23/2022,L,not in the study -8,F,NS,9/22/2018,"50,000",5/11/2024,L, -9,M,NS,12/28/2020,"100,000",,, -10,M,NS,6/13/2015,"50,000",,, -11,M,NS,10/23/2023,"100,000",,, -12,M,NS,12/27/2020,"50,000",,, -13,F,S,5/12/2014,"100,000",,, -14,M,NS,2/6/2012,"100,000",,, -15,M,NS,8/20/2017,"50,000",4/10/2024,L, -16,F,NS,3/7/2013,"50,000",4/8/2024,L,partial year -17,M,NS,11/27/2020,"50,000",3/7/2024,D,partial year -18,F,NS,2/4/2011,"100,000",,, -19,M,NS,1/3/2011,"100,000",,, -20,M,NS,11/25/2019,"50,000",,, -21,F,NS,12/28/2020,"100,000",,, -22,F,NS,7/15/2016,"50,000",,, -23,F,NS,1/2/2011,"100,000",,, -24,F,NS,8/19/2017,"50,000",,, -25,M,NS,8/20/2017,"50,000",11/30/2024,L, -26,F,NS,4/9/2013,"50,000",4/9/2023,L,lapse on anniversary -27,M,NS,11/25/2019,"100,000",,, -28,M,NS,7/17/2016,"100,000",,, -29,M,NS,12/27/2020,"100,000",,, -30,M,NS,2/5/2012,"100,000",1/2/2024,L, -31,M,S,8/19/2017,"50,000",,, -32,M,NS,7/16/2016,"100,000",,, -33,F,NS,1/3/2011,"50,000",,, -34,F,NS,11/26/2023,"100,000",12/10/2024,D, -35,F,NS,3/7/2012,"50,000",,, -36,F,S,7/17/2016,"50,000",,, -37,F,NS,4/9/2013,"100,000",,, -38,F,NS,11/30/2009,"100,000",,, -39,F,NS,10/24/2019,"100,000",,, -40,F,NS,7/16/2016,"50,000",9/20/2022,L, -41,F,NS,2/4/2011,"50,000",1/2/2024,L, -42,F,NS,4/8/2013,"50,000",,, -43,F,NS,10/24/2019,"50,000",,, -44,F,NS,12/28/2020,"50,000",,, -45,M,NS,7/15/2015,"100,000",5/13/2024,L, -46,M,NS,10/23/2018,"100,000",,, -47,F,NS,1/2/2011,"50,000",,, -48,M,NS,5/11/2014,"50,000",1/2/2023,D,death before anniversary -49,F,S,8/19/2017,"50,000",,, -50,F,NS,1/2/2011,"100,000",,, -51,F,NS,5/11/2014,"50,000",,, -52,F,NS,3/7/2012,"50,000",,, -53,F,NS,4/8/2013,"50,000",5/12/2024,L, -54,F,NS,11/26/2019,"50,000",,, -55,F,NS,12/27/2020,"50,000",,, -56,F,NS,4/10/2014,"100,000",,, -57,F,S,10/24/2019,"100,000",,, -58,F,S,7/17/2016,"50,000",,, -59,F,NS,1/2/2011,"100,000",,, -60,F,NS,11/27/2020,"50,000",11/25/2023,L,lapse before anniversary -61,M,NS,11/25/2019,"50,000",3/6/2021,D,not in the study -62,M,NS,5/12/2014,"50,000",,, -63,F,NS,4/10/2014,"50,000",,, -64,F,NS,12/27/2020,"50,000",,, -65,M,NS,3/7/2013,"100,000",3/7/2023,D,death on anniversary -66,F,NS,6/15/2015,"50,000",,, -67,M,NS,9/22/2018,"100,000",,, -68,F,S,4/10/2013,"100,000",,, -69,F,NS,9/21/2018,"100,000",7/17/2022,L, -70,F,NS,12/1/2009,"100,000",,, -71,F,NS,7/16/2016,"100,000",,, -72,F,NS,2/5/2012,"50,000",,, -73,M,S,2/6/2012,"50,000",6/14/2022,D, -74,M,NS,3/6/2012,"50,000",,, -75,F,NS,11/26/2019,"100,000",12/28/2023,D,death after anniversary -76,M,NS,9/21/2018,"100,000",,, -77,F,NS,3/7/2013,"100,000",,, -78,M,NS,12/28/2020,"100,000",,, -79,F,NS,7/16/2016,"100,000",12/28/2023,D, -80,M,NS,12/28/2020,"100,000",,, -81,F,NS,7/16/2016,"100,000",,, -82,M,NS,4/9/2013,"100,000",,, -83,F,NS,9/20/2017,"100,000",,, -84,M,NS,3/6/2012,"50,000",,, -85,M,NS,11/25/2019,"50,000",,, -86,M,NS,6/15/2015,"50,000",9/20/2022,D, -87,M,NS,1/2/2011,"100,000",,, -88,M,NS,1/2/2011,"50,000",,, -89,M,NS,12/27/2020,"50,000",,, -90,F,S,7/17/2016,"50,000",10/24/2023,L,lapse after anniversary -91,M,NS,7/17/2016,"50,000",,, -92,F,NS,7/17/2016,"50,000",,, -93,F,NS,6/15/2015,"50,000",,, -94,F,NS,6/15/2015,"100,000",,, -95,M,NS,2/4/2011,"50,000",,, -96,F,NS,6/13/2015,"100,000",,, -97,F,NS,6/14/2015,"50,000",,, -98,F,NS,6/15/2015,"100,000",,, -99,F,NS,11/30/2009,"100,000",11/26/2023,L, -100,F,NS,7/15/2015,"50,000",,, diff --git a/tests/files/integrations/cdc/cdc.sql b/tests/files/integrations/cdc/cdc.sql index a7b9e8a..f764579 100644 Binary files a/tests/files/integrations/cdc/cdc.sql and b/tests/files/integrations/cdc/cdc.sql differ diff --git a/tests/test_cdc.py b/tests/test_cdc.py index b62d4db..b36fbeb 100644 --- a/tests/test_cdc.py +++ b/tests/test_cdc.py @@ -57,8 +57,8 @@ def test_get_cdc_data_xml(mock_post, tmp_path): df = cdc.get_cdc_data_xml(xml_filename="cdc_d176.xml", parse_date_col="Month") assert isinstance(df, pd.DataFrame) - assert df.shape == (93, 6) - assert df.iloc[-1]["month"] == pd.Timestamp("2025-09-01") + assert df.shape == (93, 7) + assert df.iloc[-1]["month"] == 9 assert df.iloc[-1]["year"] == 2025 @@ -86,7 +86,7 @@ def test_get_cdc_data_sql(): """Tests getting cdc data from sql.""" df = cdc.get_cdc_data_sql(db_filepath=test_sql_path, table_name="mcd18_monthly") assert isinstance(df, pd.DataFrame) - assert df.shape == (92, 7) + assert df.shape == (100, 6) def test_get_last_updated(): @@ -96,8 +96,8 @@ def test_get_last_updated(): Patched the files path to the tests path. """ with patch("morai.utils.helpers.FILES_PATH", helpers.TESTS_PATH): - last_updated = cdc.get_last_updated(table_name="mcd18_monthly") - assert last_updated == "2025-10-14 23:13:03" + last_updated_meta = cdc.get_last_updated(table_name="mcd18_monthly") + assert last_updated_meta["last_updated"] == "2026-05-03 23:36:09" def test_get_cdc_reference(): @@ -185,14 +185,21 @@ def test_calc_mi(): mi_2019 = mi_df.loc[mi_df["year"] == 2019, "crude_adj"].iloc[0] mi_2018 = mi_df.loc[mi_df["year"] == 2018, "crude_adj"].iloc[0] mi_1_year = 1 - (mi_2019 / mi_2018) - mi_10_year = mi_df["1_year_mi"].rolling(window=10).mean().iloc[-1] + mi_10_year = mi_df["1_year_mi_pct"].rolling(window=10).mean().iloc[-1] assert isinstance(mi_df, pd.DataFrame) assert all( col in mi_df.columns - for col in ["year", "crude_adj", "deaths", "1_year_mi", "10_year_mi", "whl_3"] + for col in [ + "year", + "crude_adj", + "deaths", + "1_year_mi_pct", + "10_year_mi_pct", + "whl_3_pct", + ] ) - assert mi_df["1_year_mi"].iloc[-1] == mi_1_year - assert mi_df["10_year_mi"].iloc[-1] == mi_10_year + assert mi_df["1_year_mi_pct"].iloc[-1] == mi_1_year + assert mi_df["10_year_mi_pct"].iloc[-1] == mi_10_year def test_compare_df(): diff --git a/tests/test_credibility.py b/tests/test_credibility.py index 37867d6..af09481 100644 --- a/tests/test_credibility.py +++ b/tests/test_credibility.py @@ -23,8 +23,8 @@ def test_limited_fluctuation(): sd=1, u=1, ) - assert partial.iloc[0]["credibility_lf"] == approx(0.3040, abs=1e-4) - assert full.iloc[0]["credibility_lf"] == approx(1.00, abs=1e-2) + assert partial[0] == approx(0.3040, abs=1e-4) + assert full[0] == approx(1.00, abs=1e-2) def test_asymptotic(): @@ -33,15 +33,18 @@ def test_asymptotic(): df=pd.DataFrame([{"lapses": 100}]), measure="lapses", k=270 ) test_partial = 100 / (100 + 270) - assert partial.iloc[0]["credibility_as"] == test_partial + assert partial[0] == test_partial def test_vm20_buhlmann(): """Checks vm20 buhlmann credibility.""" partial = credibility.vm20_buhlmann( - df=cred_df, amount_col="amount", rate_col="rate", exposure_col="exposure" + seriatim_df=cred_df, + amount_col="amount", + rate_col="rate", + exposure_col="exposure", ) - assert partial.iloc[0]["credibility_vm20"] == approx(0.1682, abs=1e-4) + assert partial[0] == approx(0.1682, abs=1e-4) def test_vm20_buhlmann_approx(): @@ -56,7 +59,7 @@ def test_vm20_buhlmann_approx(): df=cred_df, a_col="a", b_col="b", c_col="c" ) - assert partial.iloc[0]["credibility_vm20_approx"] == approx(0.1682, abs=1e-4) + assert partial[0] == approx(0.1682, abs=1e-4) def test_buhlmann(): @@ -65,4 +68,4 @@ def test_buhlmann(): df=pd.DataFrame([{"lapses": 100}]), measure="lapses", k=270 ) test_partial = 100 / (100 + 270) - assert partial.iloc[0]["credibility_bu"] == test_partial + assert partial[0] == test_partial diff --git a/tests/test_experience.py b/tests/test_experience.py index b5b8eb3..c8d2038 100644 --- a/tests/test_experience.py +++ b/tests/test_experience.py @@ -328,7 +328,7 @@ def test_exposure_policy() -> None: (test_df["id"] == 1) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 70 / 366 ), "Expected exposure to be using 366 days for policy year as in leap year." @@ -346,7 +346,7 @@ def test_exposure_annual_calendar() -> None: get_exposures=True, get_actuals=False, ) - test_df = test_df[test_df["exposure"] != 0] + test_df = test_df[test_df["exposure_cnt"] != 0] # not in study period # issued after study period @@ -369,7 +369,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be 365 for inforce policy in total" assert ( @@ -377,7 +377,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be 365 for inforce policy in total" # issued during study period @@ -391,7 +391,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 5) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 70 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -408,7 +408,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 6) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -425,7 +425,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 7) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -438,7 +438,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 8) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert test_df[ @@ -455,7 +455,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 99 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -463,7 +463,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 1 / 365 ), ( "Expected exposure to be up until termination for decrement not under study, " @@ -475,7 +475,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -483,7 +483,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 8) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 100 / 365 ), "Expected exposure to be up until termination for decrement not under study" # in the future @@ -492,7 +492,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 6) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -500,7 +500,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 168 / 365 ), "Expected exposure to be same as inforce policy" @@ -511,7 +511,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 11) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 130 / 365 ), "Expected exposure to be up until anniversary for decrement under study" assert test_df[ @@ -528,7 +528,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 65 / 365 ), "Expected exposure to be up until anniversary for decrement under study" assert ( @@ -536,7 +536,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 365 / 365 ), ( "Expected exposure to be up until anniversary for decrement under study, " @@ -549,7 +549,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement under study" assert ( @@ -557,7 +557,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 365 / 365 ), ( "Expected exposure to be up until termination for decrement under study, " @@ -570,7 +570,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -578,7 +578,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be same as inforce policy" @@ -589,7 +589,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 66 / 366 ), ( "Expected exposure to be up until min(termination, anniversary, eos) for " @@ -600,7 +600,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 12) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 25 / 366 ), ( "Expected exposure to be up until min(termination, eos) for " @@ -612,7 +612,7 @@ def test_exposure_annual_calendar() -> None: (test_df["id"] == 15) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 331 / 366 ), "Expected exposure to be up until anniversary for decrement under study" assert test_df[ @@ -636,7 +636,7 @@ def test_exposure_annual_calendar() -> None: get_actuals=False, ) - assert frequency_df[frequency_df["exposure"] < 0].empty, ( + assert frequency_df[frequency_df["exposure_cnt"] < 0].empty, ( "There should be no negative exposures" ) @@ -645,7 +645,7 @@ def test_exposure_annual_calendar() -> None: (frequency_df["id"] == 12) & (frequency_df["bos_date"] == "3/1/2023") & (frequency_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 365 / 365 ), "Expected exposure to be from bos to anniversary" @@ -654,7 +654,7 @@ def test_exposure_annual_calendar() -> None: (frequency_df["id"] == 12) & (frequency_df["bos_date"] == "4/1/2023") & (frequency_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 0 / 365 ), "Expected exposure to be from bos to anniversary" @@ -663,7 +663,7 @@ def test_exposure_annual_calendar() -> None: (frequency_df["id"] == 11) & (frequency_df["bos_date"] == "1/1/2023") & (frequency_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 31 / 365 ), "Expected exposure to be from bos to eos" @@ -681,7 +681,7 @@ def test_exposure_distributed_calendar() -> None: get_exposures=True, get_actuals=False, ) - test_df = test_df[test_df["exposure"] != 0] + test_df = test_df[test_df["exposure_cnt"] != 0] # not in study period # issued after study period @@ -700,7 +700,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be 365 for inforce policy in total" assert ( @@ -708,7 +708,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be 365 for inforce policy in total" # issued during study period @@ -722,7 +722,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 5) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 70 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -739,7 +739,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 6) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -756,7 +756,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 7) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -769,7 +769,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 8) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert test_df[ @@ -786,7 +786,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 99 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -794,7 +794,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 1 / 365 ), ( "Expected exposure to be up until termination for decrement not under study, " @@ -806,7 +806,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -814,7 +814,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 8) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 100 / 365 ), "Expected exposure to be up until termination for decrement not under study" # in the future @@ -823,7 +823,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 6) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -831,7 +831,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 168 / 365 ), "Expected exposure to be same as inforce policy" @@ -842,7 +842,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 11) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 130 / 365 ), "Expected exposure to be up until anniversary for decrement under study" assert test_df[ @@ -859,7 +859,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 65 / 365 ), "Expected exposure to be up until anniversary for decrement under study" assert ( @@ -867,7 +867,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 300 / 365 ), ( "Expected exposure to be up until anniversary for decrement under study, " @@ -880,7 +880,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement under study" assert ( @@ -888,7 +888,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until termination for decrement under study, " @@ -901,7 +901,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -909,7 +909,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be same as inforce policy" # terminated before study period @@ -918,7 +918,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 2) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 328 / 365 ), "Expected exposure to be up until anniversary for decrement under study" assert test_df[ @@ -938,7 +938,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 66 / 366 ), ( "Expected exposure to be up until min(termination, anniversary, eos) for " @@ -949,7 +949,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 12) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 25 / 366 ), ( "Expected exposure to be up until min(termination, eos) for " @@ -961,7 +961,7 @@ def test_exposure_distributed_calendar() -> None: (test_df["id"] == 15) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 91 / 366 ), "Expected exposure to be up until anniversary for decrement under study" assert test_df[ @@ -985,7 +985,7 @@ def test_exposure_distributed_calendar() -> None: get_actuals=False, ) - assert frequency_df[frequency_df["exposure"] < 0].empty, ( + assert frequency_df[frequency_df["exposure_cnt"] < 0].empty, ( "There should be no negative exposures" ) @@ -994,7 +994,7 @@ def test_exposure_distributed_calendar() -> None: (frequency_df["id"] == 12) & (frequency_df["bos_date"] == "3/1/2023") & (frequency_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 25 / 365 ), "Expected exposure to be from bos to anniversary" @@ -1003,7 +1003,7 @@ def test_exposure_distributed_calendar() -> None: (frequency_df["id"] == 12) & (frequency_df["bos_date"] == "4/1/2023") & (frequency_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 30 / 365 ), "Expected exposure to be from bos to anniversary" @@ -1012,7 +1012,7 @@ def test_exposure_distributed_calendar() -> None: (frequency_df["id"] == 11) & (frequency_df["bos_date"] == "1/1/2023") & (frequency_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 31 / 365 ), "Expected exposure to be from bos to eos" @@ -1030,7 +1030,7 @@ def test_exposure_exact_calendar() -> None: get_exposures=True, get_actuals=False, ) - test_df = test_df[test_df["exposure"] != 0] + test_df = test_df[test_df["exposure_cnt"] != 0] # not in study period # issued after study period @@ -1053,7 +1053,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be 365 for inforce policy in total" assert ( @@ -1061,7 +1061,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 4) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be 365 for inforce policy in total" # issued during study period @@ -1075,7 +1075,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 5) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 70 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -1092,7 +1092,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 6) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -1109,7 +1109,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 7) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 1) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), ( "Expected exposure to be up until eos for inforce policy issued during " @@ -1122,7 +1122,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 8) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert test_df[ @@ -1139,7 +1139,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 99 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -1147,7 +1147,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 9) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 1 / 365 ), ( "Expected exposure to be up until termination for decrement not under study, " @@ -1159,7 +1159,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be up until termination for decrement not under study" assert ( @@ -1167,7 +1167,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 8) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 100 / 365 ), "Expected exposure to be up until termination for decrement not under study" # in the future @@ -1176,7 +1176,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 6) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 197 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -1184,7 +1184,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 10) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 7) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 168 / 365 ), "Expected exposure to be same as inforce policy" @@ -1195,7 +1195,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 11) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 33 / 365 ), "Expected exposure to be up until date of decrement for decrement under study" assert test_df[ @@ -1212,7 +1212,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 10) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 65 / 365 ), "Expected exposure to be up until date of decrement for decrement under study" assert ( @@ -1220,7 +1220,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 12) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 1 / 365 ), ( "Expected exposure to be up until date of decrement for decrement under study, " @@ -1233,7 +1233,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be up until termination for decrement under study" assert ( @@ -1241,7 +1241,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2023") & (test_df["policy_dur"] == 5) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 33 / 365 ), "Expected exposure to be up until termination for decrement under study." # in the future @@ -1250,7 +1250,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 3) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 329 / 365 ), "Expected exposure to be same as inforce policy" assert ( @@ -1258,7 +1258,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 13) & (test_df["bos_date"] == "1/1/2022") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 36 / 365 ), "Expected exposure to be same as inforce policy" @@ -1269,7 +1269,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 66 / 366 ), ( "Expected exposure to be up until min(termination, anniversary, eos) for " @@ -1280,7 +1280,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 14) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 12) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 25 / 366 ), ( "Expected exposure to be up until min(termination, eos) for " @@ -1292,7 +1292,7 @@ def test_exposure_exact_calendar() -> None: (test_df["id"] == 15) & (test_df["bos_date"] == "1/1/2024") & (test_df["policy_dur"] == 4) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 67 / 366 ), "Expected exposure to be up until decrement for decrement under study" assert test_df[ @@ -1316,7 +1316,7 @@ def test_exposure_exact_calendar() -> None: get_actuals=False, ) - assert frequency_df[frequency_df["exposure"] < 0].empty, ( + assert frequency_df[frequency_df["exposure_cnt"] < 0].empty, ( "There should be no negative exposures" ) @@ -1325,7 +1325,7 @@ def test_exposure_exact_calendar() -> None: (frequency_df["id"] == 12) & (frequency_df["bos_date"] == "3/1/2023") & (frequency_df["policy_dur"] == 11) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 1 / 365 ), "Expected exposure to be from bos to anniversary" @@ -1334,6 +1334,6 @@ def test_exposure_exact_calendar() -> None: (frequency_df["id"] == 11) & (frequency_df["bos_date"] == "1/1/2023") & (frequency_df["policy_dur"] == 9) - ]["exposure"].iloc[0] + ]["exposure_cnt"].iloc[0] == 31 / 365 ), "Expected exposure to be from bos to eos"