IIS_2023_1/antonov_dmitry_lab_1/lab1.py
DmitriyAntonov 8a288f0abf iter3
2023-09-23 20:53:45 +04:00

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import numpy as np
from matplotlib import pyplot as plt
from skimage.metrics import mean_squared_error
from sklearn.datasets import make_moons, make_circles, make_classification
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
)
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
)
"""
Модели:
· Линейную регрессию
· Полиномиальную регрессию (со степенью 3)
· Гребневую полиномиальную регрессию (со степенью 3, alpha = 1.0)
"""
# 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)
# 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)
# 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)
# 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')
# 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()
# 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()
# 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()
# 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)