Upload scikit-learn/Test_ScikitLearn.py with huggingface_hub

scikit-learn/Test_ScikitLearn.py

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+"""
+On-device verification for the cross-compiled scikit-learn wheel.
+
+Run after installing:
+    pip install scikit_learn-1.7.1-cp312-cp312-android_24_x86_64.whl
+
+Usage:
+    python Test_ScikitLearn.py [--quick]
+
+Exit code 0 = everything required PASSed.
+Requires: numpy, scipy, joblib, threadpoolctl at runtime.
+
+Generated by RIMI
+"""
+import sys
+
+RESULTS = []
+
+
+def test(name, fn):
+    try:
+        fn()
+        RESULTS.append((name, "PASS", None))
+    except NotImplementedError as exc:
+        RESULTS.append((name, "SKIP", str(exc)))
+    except Exception as exc:
+        RESULTS.append((name, "FAIL", "%s: %s" % (type(exc).__name__, exc)))
+        print("    ! %s -> %s: %s" % (name, type(exc).__name__, exc))
+
+
+def section(title):
+    print("=" * 60)
+    print(title)
+    print("=" * 60)
+
+
+# ---------------------------------------------------------------------------
+# 1. import / version
+# ---------------------------------------------------------------------------
+def import_sklearn():
+    import sklearn
+    print("    sklearn", sklearn.__version__)
+    assert hasattr(sklearn, "__version__")
+    assert hasattr(sklearn, "show_versions")
+
+def check_c_extension():
+    import sklearn
+    # Check that at least one Cython extension loads (tree, metrics, etc.)
+    from sklearn.tree import _tree
+    assert hasattr(_tree, "Tree")
+
+
+# ---------------------------------------------------------------------------
+# 2. datasets
+# ---------------------------------------------------------------------------
+def load_iris():
+    from sklearn.datasets import load_iris
+    X, y = load_iris(return_X_y=True)
+    assert X.shape == (150, 4)
+    assert y.shape == (150,)
+
+def load_digits():
+    from sklearn.datasets import load_digits
+    X, y = load_digits(return_X_y=True)
+    assert X.shape[0] == 1797
+
+
+# ---------------------------------------------------------------------------
+# 3. preprocessing
+# ---------------------------------------------------------------------------
+def scaler_standard():
+    from sklearn.preprocessing import StandardScaler
+    import numpy as np
+    X = np.array([[1, 2], [3, 4], [5, 6]], dtype=np.float64)
+    scaler = StandardScaler()
+    Xt = scaler.fit_transform(X)
+    assert Xt.shape == X.shape
+    assert abs(Xt.mean()) < 1e-6
+
+def scaler_minmax():
+    from sklearn.preprocessing import MinMaxScaler
+    import numpy as np
+    X = np.array([[1, 2], [3, 4]], dtype=np.float64)
+    scaler = MinMaxScaler()
+    Xt = scaler.fit_transform(X)
+    assert Xt.min() >= 0 and Xt.max() <= 1
+
+
+# ---------------------------------------------------------------------------
+# 4. decomposition
+# ---------------------------------------------------------------------------
+def pca_test():
+    from sklearn.decomposition import PCA
+    import numpy as np
+    rng = np.random.default_rng(42)
+    X = rng.random((50, 10))
+    pca = PCA(n_components=2)
+    Xt = pca.fit_transform(X)
+    assert Xt.shape == (50, 2)
+
+
+# ---------------------------------------------------------------------------
+# 5. cluster
+# ---------------------------------------------------------------------------
+def kmeans_test():
+    from sklearn.cluster import KMeans
+    import numpy as np
+    rng = np.random.default_rng(42)
+    X = rng.random((30, 2))
+    km = KMeans(n_clusters=3, n_init=10, random_state=42)
+    labels = km.fit_predict(X)
+    assert labels.shape == (30,)
+    assert len(set(labels)) == 3
+
+
+# ---------------------------------------------------------------------------
+# 6. linear model
+# ---------------------------------------------------------------------------
+def logistic_regression():
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.datasets import load_iris
+    X, y = load_iris(return_X_y=True)
+    # binary iris (first 100 samples, 2 classes)
+    Xb, yb = X[:100], y[:100]
+    clf = LogisticRegression(max_iter=200)
+    clf.fit(Xb, yb)
+    pred = clf.predict(Xb[:5])
+    assert pred.shape == (5,)
+
+def linear_regression():
+    from sklearn.linear_model import LinearRegression
+    import numpy as np
+    X = np.array([[1], [2], [3], [4]], dtype=np.float64)
+    y = np.array([2, 4, 6, 8], dtype=np.float64)
+    reg = LinearRegression()
+    reg.fit(X, y)
+    pred = reg.predict([[5]])
+    assert abs(pred[0] - 10) < 1e-3
+
+
+# ---------------------------------------------------------------------------
+# 7. ensemble
+# ---------------------------------------------------------------------------
+def random_forest():
+    from sklearn.ensemble import RandomForestClassifier
+    from sklearn.datasets import load_iris
+    X, y = load_iris(return_X_y=True)
+    clf = RandomForestClassifier(n_estimators=10, random_state=42)
+    clf.fit(X[:100], y[:100])
+    pred = clf.predict(X[:5])
+    assert pred.shape == (5,)
+
+
+# ---------------------------------------------------------------------------
+# 8. metrics
+# ---------------------------------------------------------------------------
+def metrics_test():
+    from sklearn.metrics import accuracy_score, mean_squared_error
+    import numpy as np
+    y_true = np.array([0, 1, 1, 0])
+    y_pred = np.array([0, 1, 0, 0])
+    acc = accuracy_score(y_true, y_pred)
+    assert 0 <= acc <= 1
+    mse = mean_squared_error([1, 2, 3], [1, 2, 3])
+    assert mse == 0
+
+
+# ---------------------------------------------------------------------------
+# 9. model_selection
+# ---------------------------------------------------------------------------
+def train_test_split():
+    from sklearn.model_selection import train_test_split
+    import numpy as np
+    X = np.random.rand(20, 4)
+    y = np.random.randint(0, 2, 20)
+    Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=42)
+    assert Xtr.shape[0] == 15 and Xte.shape[0] == 5
+
+def cross_val():
+    from sklearn.model_selection import cross_val_score
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.datasets import load_iris
+    X, y = load_iris(return_X_y=True)
+    clf = LogisticRegression(max_iter=200)
+    scores = cross_val_score(clf, X[:100], y[:100], cv=3)
+    assert len(scores) == 3
+
+
+# ---------------------------------------------------------------------------
+def main():
+    quick = "--quick" in sys.argv
+    section("1. import / version")
+    test("import sklearn", import_sklearn)
+    test("C extension _tree", check_c_extension)
+
+    section("2. datasets")
+    test("load_iris", load_iris)
+    test("load_digits", load_digits)
+
+    section("3. preprocessing")
+    test("StandardScaler", scaler_standard)
+    test("MinMaxScaler", scaler_minmax)
+
+    section("4. decomposition")
+    test("PCA", pca_test)
+
+    section("5. cluster")
+    test("KMeans", kmeans_test)
+
+    section("6. linear_model")
+    test("LogisticRegression", logistic_regression)
+    test("LinearRegression", linear_regression)
+
+    section("7. ensemble")
+    test("RandomForest", random_forest)
+
+    section("8. metrics")
+    test("metrics", metrics_test)
+
+    section("9. model_selection")
+    test("train_test_split", train_test_split)
+    test("cross_val_score", cross_val)
+
+    print()
+    print("=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    fails = 0
+    skips = 0
+    for name, status, why in RESULTS:
+        mark = "  OK" if status == "PASS" else (" SKIP" if status == "SKIP" else "FAIL")
+        print("%s %s" % (mark, name))
+        if why:
+            print("        -> %s" % why)
+        if status == "FAIL":
+            fails += 1
+        elif status == "SKIP":
+            skips += 1
+    print()
+    passed = len(RESULTS) - fails - skips
+    print("passed=%d skipped=%d failed=%d" % (passed, skips, fails))
+    if fails:
+        print("RESULT: FAILED")
+    elif skips and not quick:
+        print("RESULT: PASSED (with informational skips)")
+    else:
+        print("RESULT: PASSED")
+    sys.exit(1 if fails else 0)
+
+
+if __name__ == "__main__":
+    main()