Metodo de detención temprana

Earlystop.py

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