ENH Add P2S (Production Press Sensor Data) classification dataset

datasets/p2s.py

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+"""P2S (Production Press Sensor Data) time series classification dataset.
+
+Wraps the ``AIML-TUDA/P2S`` Hugging Face dataset (Normal variant): binary
+classification of whether a deep-drawing press run produced a normal (``0``) or
+defective (``1``) part from its 4096-step force-sensor series.
+
+Data contract output
+--------------------
+X_train : List[np.ndarray (4096, 1)]   one series per training sample
+y_train : np.ndarray (N,) int          class labels (0/1)
+X_test  : List[np.ndarray (4096, 1)]
+y_test  : np.ndarray (M,) int
+task    : "classification"
+metrics : ["accuracy", "balanced_accuracy", "f1_weighted"]
+n_classes : 2
+"""
+
+import numpy as np
+import pandas as pd
+from benchopt import BaseDataset
+
+# Requirement checks
+import fsspec  # noqa: F401
+from huggingface_hub import hf_hub_download  # noqa: F401
+
+
+class Dataset(BaseDataset):
+    """P2S press-sensor binary classification dataset (Normal variant).
+
+    Parameters
+    ----------
+    variant : str
+        Which Hub variant to load. Only ``"Normal"`` is shipped.
+    debug : bool
+        If True, keep only the first 20 samples of each split for fast testing.
+    """
+
+    name = "P2S"
+
+    requirements = ["pip::huggingface_hub", "fsspec"]
+
+    parameters = {
+        "variant": ["Normal"],
+        "debug": [False],
+    }
+
+    test_parameters = {
+        "variant": ["Normal"],
+        "debug": [True],
+    }
+
+    def _load_split(self, split):
+        """Read one split into a list of ``(4096, 1)`` series and int labels."""
+        url = (
+            f"hf://datasets/AIML-TUDA/P2S/{self.variant}/{split}-00000-of-00001.parquet"
+        )
+        df = pd.read_parquet(url, columns=["dowel_deep_drawing_ow", "label"])
+
+        if self.debug:
+            df = df.head(20)
+
+        X = [
+            np.asarray(cell, dtype=np.float32).reshape(-1, 1)
+            for cell in df["dowel_deep_drawing_ow"]
+        ]
+        y = df["label"].to_numpy(dtype=np.int64)
+        return X, y
+
+    def get_data(self):
+        X_train, y_train = self._load_split("train")
+        X_test, y_test = self._load_split("test")
+
+        return dict(
+            X_train=X_train,
+            y_train=y_train,
+            X_test=X_test,
+            y_test=y_test,
+            task="classification",
+            metrics=["accuracy", "balanced_accuracy", "f1_weighted"],
+            n_classes=2,
+        )