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72 lines
2.0 KiB
72 lines
2.0 KiB
from sklearn.base import clone
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from sklearn.pipeline import Pipeline
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from sklearn import preprocessing
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from sklearn.preprocessing import PolynomialFeatures
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.linear_model import SGDRegressor
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from sklearn.metrics import mean_squared_error
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import train_test_split
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np.random.seed(42)
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m = 100
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X = 6 * np.random.rand(m, 1) - 3
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y = 2 + X + 0.5 * X**2 + np.random.randn(m, 1)
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plt.plot(X,y,".", label = "Datos originales")
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X_train, X_val, y_train, y_val = train_test_split(X[:50], y[:50].ravel(), test_size=0.5, random_state=10)
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poly_scaler = Pipeline([
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("poly_features", PolynomialFeatures(degree=90, include_bias=False)),
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("std_scaler", StandardScaler()),
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])
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X_train_poly_scaled = poly_scaler.fit_transform(X_train)
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X_val_poly_scaled = poly_scaler.transform(X_val)
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sgd_reg = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True, penalty=None,
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learning_rate="constant", eta0=0.0005, random_state=42)
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print(sgd_reg)
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minimum_val_error = float("inf")
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best_epoch = None
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best_model = None
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for epoch in range(1000):
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sgd_reg.fit(X_train_poly_scaled, y_train) # continues where it left off
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y_val_predict = sgd_reg.predict(X_val_poly_scaled)
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val_error = mean_squared_error(y_val, y_val_predict)
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if val_error < minimum_val_error:
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minimum_val_error = val_error
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best_epoch = epoch
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best_model = clone(sgd_reg)
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print(best_epoch)
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sgd_reg = SGDRegressor(max_iter=best_epoch, tol=-np.infty, warm_start=True, penalty=None,
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learning_rate="constant", eta0=0.0005, random_state=42)
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poly_features = PolynomialFeatures(degree=2, include_bias=False)
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X_pol = poly_features.fit_transform(X)
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sgd_reg.fit(X_pol,y.ravel())
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yout=sgd_reg.predict(X_pol)
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plt.plot(X,yout,"*", label = "Predicciones")
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# naming the x axis
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plt.xlabel('Eje X')
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# naming the y axis
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plt.ylabel('Eje Y')
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# giving a title to my graph
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plt.legend()
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plt.show()
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