antonov_dmitry_lab_1/lab1.py

@@ -1,59 +1,97 @@
 import numpy as np
 from matplotlib import pyplot as plt
-from matplotlib.colors import ListedColormap
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import StandardScaler
+from skimage.metrics import mean_squared_error
 from sklearn.datasets import make_moons, make_circles, make_classification
-from sklearn.neural_network import MLPClassifier
+from sklearn.linear_model import LinearRegression, Ridge
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler, PolynomialFeatures
+
+X, y = make_classification(
+    n_features=2,
+    n_redundant=0,
+    n_informative=2,
+    random_state=0,
+    n_clusters_per_class=1
+)
 
-X, y = make_classification(n_features=2, n_redundant=0, n_informative=2, random_state=0, n_clusters_per_class=1)
 rng = np.random.RandomState(2)
 X += 2 * rng.uniform(size=X.shape)
 linearly_dataset = (X, y)
 moon_dataset = make_moons(noise=0.3, random_state=0)
 circles_dataset = make_circles(noise=0.2, factor=0.5, random_state=1)
-
 datasets = [moon_dataset, circles_dataset, linearly_dataset]
+
+"""
+Данные:
+· moon_dataset
+· circles_dataset
+· linearly_dataset
+"""
 for ds_cnt, ds in enumerate(datasets):
     X, y = ds
     X = StandardScaler().fit_transform(X)
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4, random_state=42)
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=.4, random_state=42
+    )
+    """
+    Модели:
+    · Линейную регрессию
+    · Полиномиальную регрессию (со степенью 3)
+    · Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
+    """
 
-    alphas = np.logspace(-5, 3, 5)
+    # Линейная
+    linear_regression = LinearRegression()
+    linear_regression.fit(X_train, y_train)
+    linear_predictions = linear_regression.predict(X_test)
+    linear_mse = mean_squared_error(y_test, linear_predictions)
 
-    current_subplot = plt.subplot(3, 5 + 1, i)
+    # Полиномиальная (degree=3)
+    poly_regression = make_pipeline(PolynomialFeatures(degree=3), LinearRegression())
+    poly_regression.fit(X_train, y_train)
+    poly_predictions = poly_regression.predict(X_test)
+    poly_mse = mean_squared_error(y_test, poly_predictions)
 
-current_subplot.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
-current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
+    # Гребневая (degree=3, alpha=1.0)
+    poly_regression_alpha = make_pipeline(PolynomialFeatures(degree=3), Ridge(alpha=1.0))
+    poly_regression_alpha.fit(X_train, y_train)
+    poly_alpha_predictions = poly_regression_alpha.predict(X_test)
+    poly_alpha_mse = mean_squared_error(y_test, poly_alpha_predictions)
 
-cm = plt.cm.RdBu
-cm_bright = ListedColormap(['#FF0000', '#0000FF'])
+    # График данных
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap='coolwarm')
+    plt.title('Датасет №' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
 
-h = .02  # шаг регулярной сетки
-x0_min, x0_max = X[:, 0].min() - .5, X[:, 0].max() + .5
-x1_min, x1_max = X[:, 1].min() - .5, X[:, 1].max() + .5
-xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h))
+    # График линейной модели
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=linear_predictions, cmap='coolwarm')
+    plt.title('Линейная ds'+ str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
 
-Z = clf.decision_function(np.c_[xx0.ravel(), xx1.ravel()])  # 1
-Z = clf.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1]  # 2
-Z = clf.predict(np.c_[xx0.ravel(), xx1.ravel()])  # 3
+    # График полиномиальной модели (degree=3)
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_predictions, cmap='coolwarm')
+    plt.title('Полиномиальная (degree=3) ds' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
 
-hasattr(clf, "decision_function")
+    # График гребневой модели (degree=3, alpha=1.0)
+    plt.figure(figsize=(10, 6))
+    plt.scatter(X_test[:, 0], X_test[:, 1], c=poly_alpha_predictions, cmap='coolwarm')
+    plt.title('Гребневая (degree=3, alpha=1.0) ds' + str(ds_cnt))
+    plt.xlabel('X')
+    plt.ylabel('Y')
+    plt.show()
 
-Z = Z.reshape(xx0.shape)
-current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
-current_subplot.set_xlim(xx0.min(), xx0.max())
-current_subplot.set_ylim(xx0.min(), xx1.max())
-current_subplot.set_xticks(())
-current_subplot.set_yticks(())
-current_subplot.set_title(name)
-current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'),
-                     size=15, horizontalalignment='right')
+    # Сравнение качества
+    print('Линейная MSE:', linear_mse)
+    print('Полиномиальная (degree=3) MSE:', poly_mse)
+    print('Гребневая (degree=3, alpha=1.0) MSE:', poly_alpha_mse)
 
-current_subplot.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
-
-figure.subplots_adjust(left=.02, right=.98)
-
-current_subplot.set_title("Input data")
-current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score).lstrip('0'), size=15,
-                     horizontalalignment='right')