IIS_2023_1/alexandrov_dmitrii_lab_1/lab1.py

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import random
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.datasets import make_moons
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import Pipeline
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rs = random.randrange(50)
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X, y = make_moons(n_samples=250, noise=0.3, random_state=rs)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)
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figure = plt.figure(1, figsize=(16, 9))
axis = figure.subplots(4, 3)
cm = ListedColormap(['#FF0000', "#0000FF"])
arr_res = list(range(len(y_test)))
X_scale = list(range(len(y_test)))
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def test(col, model):
global axis
global arr_res
global X_test
global X_train
global y_train
global y_test
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model.fit(X_train, y_train)
res_y = model.predict(X_test)
print(model.score(X_test, y_test))
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axis[0, col].scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm)
axis[1, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm)
axis[2, col].scatter(X_test[:, 0], X_test[:, 1], c=res_y, cmap=cm)
axis[3, col].plot([i for i in range(len(res_y))], y_test, c="g")
axis[3, col].plot([i for i in range(len(res_y))], res_y, c="r")
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def start():
lin = LinearRegression()
poly = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression())])
ridge = Pipeline([('poly', PolynomialFeatures(degree=3)),
('ridge', Ridge(alpha=1.0))])
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test(0, lin)
test(1, poly)
test(2, ridge)
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plt.show()
start()