Doubao dogfood

tests/__init__.py

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+# -*- coding: utf-8 -*-
+"""
+Tests package for Machine Learning algorithms.
+"""

tests/conftest.py

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+# -*- coding: utf-8 -*-
+"""
+Shared fixtures for machine learning algorithm tests.
+"""
+import os
+import sys
+import numpy as np
+from scipy import io as spio
+import pytest
+
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, PROJECT_ROOT)
+
+
+@pytest.fixture
+def project_root():
+    return PROJECT_ROOT
+
+
+@pytest.fixture
+def linear_regression_data_path(project_root):
+    return os.path.join(project_root, "LinearRegression", "data.txt")
+
+
+@pytest.fixture
+def logistic_regression_data1_path(project_root):
+    return os.path.join(project_root, "LogisticRegression", "data1.txt")
+
+
+@pytest.fixture
+def logistic_regression_data2_path(project_root):
+    return os.path.join(project_root, "LogisticRegression", "data2.txt")
+
+
+@pytest.fixture
+def logistic_regression_digits_path(project_root):
+    return os.path.join(project_root, "LogisticRegression", "data_digits.mat")
+
+
+@pytest.fixture
+def neural_network_digits_path(project_root):
+    return os.path.join(project_root, "NeuralNetwok", "data_digits.mat")
+
+
+@pytest.fixture
+def svm_data1_path(project_root):
+    return os.path.join(project_root, "SVM", "data1.mat")
+
+
+@pytest.fixture
+def svm_data2_path(project_root):
+    return os.path.join(project_root, "SVM", "data2.mat")
+
+
+@pytest.fixture
+def svm_data3_path(project_root):
+    return os.path.join(project_root, "SVM", "data3.mat")
+
+
+@pytest.fixture
+def kmeans_data_path(project_root):
+    return os.path.join(project_root, "K-Means", "data.mat")
+
+
+@pytest.fixture
+def pca_data_path(project_root):
+    return os.path.join(project_root, "PCA", "data.mat")
+
+
+@pytest.fixture
+def pca_faces_data_path(project_root):
+    return os.path.join(project_root, "PCA", "data_faces.mat")
+
+
+@pytest.fixture
+def anomaly_detection_data1_path(project_root):
+    return os.path.join(project_root, "AnomalyDetection", "data1.mat")
+
+
+@pytest.fixture
+def anomaly_detection_data2_path(project_root):
+    return os.path.join(project_root, "AnomalyDetection", "data2.mat")
+
+
+@pytest.fixture
+def load_txt_data():
+    def _load(path, delimiter=",", dtype=np.float64):
+        return np.loadtxt(path, delimiter=delimiter, dtype=dtype)
+    return _load
+
+
+@pytest.fixture
+def load_mat_data():
+    def _load(path):
+        return spio.loadmat(path)
+    return _load
+
+
+@pytest.fixture
+def simple_regression_data():
+    np.random.seed(42)
+    X = np.random.randn(100, 2)
+    true_theta = np.array([[3.0], [2.0], [1.0]])
+    X_with_bias = np.hstack((np.ones((100, 1)), X))
+    y = np.dot(X_with_bias, true_theta) + np.random.randn(100, 1) * 0.1
+    return X, y, true_theta
+
+
+@pytest.fixture
+def simple_classification_data():
+    np.random.seed(42)
+    X = np.random.randn(100, 2)
+    y = (X[:, 0] + X[:, 1] > 0).astype(int)
+    return X, y
+
+
+@pytest.fixture
+def simple_multiclass_data():
+    np.random.seed(42)
+    X = np.random.randn(150, 2)
+    y = np.zeros(150)
+    y[50:100] = 1
+    y[100:] = 2
+    X[:50] += np.array([2, 2])
+    X[50:100] += np.array([-2, 2])
+    X[100:] += np.array([0, -2])
+    return X, y
+
+
+@pytest.fixture
+def simple_clustering_data():
+    np.random.seed(42)
+    cluster1 = np.random.randn(30, 2) + np.array([0, 0])
+    cluster2 = np.random.randn(30, 2) + np.array([5, 5])
+    cluster3 = np.random.randn(30, 2) + np.array([5, 0])
+    X = np.vstack((cluster1, cluster2, cluster3))
+    return X
+
+
+@pytest.fixture
+def simple_pca_data():
+    np.random.seed(42)
+    X = np.random.randn(100, 5)
+    return X
+
+
+@pytest.fixture
+def simple_anomaly_data():
+    np.random.seed(42)
+    X_normal = np.random.randn(100, 2)
+    X_anomaly = np.random.randn(10, 2) + np.array([5, 5])
+    X = np.vstack((X_normal, X_anomaly))
+    y = np.zeros(110)
+    y[100:] = 1
+    return X, y