Delete scikit-learn/Test_ScikitLearn.py

scikit-learn/Test_ScikitLearn.py

@@ -1,252 +0,0 @@
-"""
-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()