Glm5

tests/conftest.py

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+# -*- coding: utf-8 -*-
+"""
+Shared fixtures for machine learning algorithm tests.
+This module provides common test data and utilities across all test modules.
+"""
+import os
+import sys
+import numpy as np
+import pytest
+import scipy.io as spio
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, PROJECT_ROOT)
+
+LINEAR_REGRESSION_DIR = os.path.join(PROJECT_ROOT, 'LinearRegression')
+LOGISTIC_REGRESSION_DIR = os.path.join(PROJECT_ROOT, 'LogisticRegression')
+NEURAL_NETWORK_DIR = os.path.join(PROJECT_ROOT, 'NeuralNetwok')
+SVM_DIR = os.path.join(PROJECT_ROOT, 'SVM')
+KMEANS_DIR = os.path.join(PROJECT_ROOT, 'K-Means')
+PCA_DIR = os.path.join(PROJECT_ROOT, 'PCA')
+ANOMALY_DETECTION_DIR = os.path.join(PROJECT_ROOT, 'AnomalyDetection')
+
+
+@pytest.fixture
+def linear_regression_data():
+    """Load linear regression data from data.txt"""
+    data_path = os.path.join(LINEAR_REGRESSION_DIR, 'data.txt')
+    if os.path.exists(data_path):
+        data = np.loadtxt(data_path, delimiter=',', dtype=np.float64)
+        X = data[:, 0:-1]
+        y = data[:, -1]
+        return {'X': X, 'y': y, 'data': data}
+    return None
+
+
+@pytest.fixture
+def logistic_regression_data1():
+    """Load logistic regression data1"""
+    data_path = os.path.join(LOGISTIC_REGRESSION_DIR, 'data1.txt')
+    if os.path.exists(data_path):
+        data = np.loadtxt(data_path, delimiter=',', dtype=np.float64)
+        X = data[:, 0:-1]
+        y = data[:, -1]
+        return {'X': X, 'y': y, 'data': data}
+    return None
+
+
+@pytest.fixture
+def logistic_regression_data2():
+    """Load logistic regression data2 for polynomial features"""
+    data_path = os.path.join(LOGISTIC_REGRESSION_DIR, 'data2.txt')
+    if os.path.exists(data_path):
+        data = np.loadtxt(data_path, delimiter=',', dtype=np.float64)
+        X = data[:, 0:-1]
+        y = data[:, -1]
+        return {'X': X, 'y': y, 'data': data}
+    return None
+
+
+@pytest.fixture
+def digits_data():
+    """Load handwritten digits data for OneVsAll and Neural Network"""
+    data_path = os.path.join(LOGISTIC_REGRESSION_DIR, 'data_digits.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X'], 'y': data['y']}
+    return None
+
+
+@pytest.fixture
+def svm_data1():
+    """Load SVM data1 for linear classification"""
+    data_path = os.path.join(SVM_DIR, 'data1.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X'], 'y': np.ravel(data['y'])}
+    return None
+
+
+@pytest.fixture
+def svm_data2():
+    """Load SVM data2 for non-linear classification"""
+    data_path = os.path.join(SVM_DIR, 'data2.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X'], 'y': np.ravel(data['y'])}
+    return None
+
+
+@pytest.fixture
+def kmeans_data():
+    """Load K-Means clustering data"""
+    data_path = os.path.join(KMEANS_DIR, 'data.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X']}
+    return None
+
+
+@pytest.fixture
+def pca_data():
+    """Load PCA 2D data"""
+    data_path = os.path.join(PCA_DIR, 'data.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X']}
+    return None
+
+
+@pytest.fixture
+def pca_face_data():
+    """Load PCA face image data"""
+    data_path = os.path.join(PCA_DIR, 'data_faces.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {'X': data['X']}
+    return None
+
+
+@pytest.fixture
+def anomaly_detection_data():
+    """Load anomaly detection data"""
+    data_path = os.path.join(ANOMALY_DETECTION_DIR, 'data1.mat')
+    if os.path.exists(data_path):
+        data = spio.loadmat(data_path)
+        return {
+            'X': data['X'],
+            'Xval': data['Xval'],
+            'yval': data['yval']
+        }
+    return None
+
+
+@pytest.fixture
+def synthetic_regression_data():
+    """Generate synthetic regression data for testing"""
+    np.random.seed(42)
+    n_samples = 100
+    n_features = 3
+    X = np.random.randn(n_samples, n_features)
+    true_theta = np.array([1.5, -2.0, 1.0])
+    y = np.dot(X, true_theta) + np.random.randn(n_samples) * 0.1
+    return {'X': X, 'y': y, 'true_theta': true_theta}
+
+
+@pytest.fixture
+def synthetic_classification_data():
+    """Generate synthetic classification data for testing"""
+    np.random.seed(42)
+    n_samples = 100
+    n_features = 2
+    X = np.random.randn(n_samples, n_features)
+    y = (X[:, 0] + X[:, 1] > 0).astype(np.float64)
+    return {'X': X, 'y': y}
+
+
+@pytest.fixture
+def synthetic_clustering_data():
+    """Generate synthetic clustering data for testing"""
+    np.random.seed(42)
+    n_samples = 150
+    centers = np.array([[0, 0], [5, 5], [0, 5]])
+    X = np.vstack([
+        np.random.randn(50, 2) + centers[0],
+        np.random.randn(50, 2) + centers[1],
+        np.random.randn(50, 2) + centers[2]
+    ])
+    return {'X': X, 'n_clusters': 3}
+
+
+@pytest.fixture
+def small_neural_network_params():
+    """Parameters for small neural network testing"""
+    return {
+        'input_layer_size': 3,
+        'hidden_layer_size': 5,
+        'num_labels': 3,
+        'm': 5
+    }
+
+
+def assert_array_almost_equal_custom(actual, desired, decimal=6):
+    """Custom assertion for numpy arrays with better error messages"""
+    np.testing.assert_array_almost_equal(
+        actual, desired, decimal=decimal,
+        err_msg=f"Arrays are not almost equal up to {decimal} decimals"
+    )
+
+
+def assert_shape(actual, expected_shape):
+    """Assert that array has expected shape"""
+    assert actual.shape == expected_shape, \
+        f"Expected shape {expected_shape}, got {actual.shape}"

tests/requirements.txt

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+pytest>=7.0.0
+pytest-cov>=4.0.0
+numpy>=1.20.0
+scipy>=1.7.0
+scikit-learn>=1.0.0