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78 lines
2.4 KiB
78 lines
2.4 KiB
from sklearn import datasets
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from sklearn.linear_model import LogisticRegression
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import numpy as np
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import matplotlib.pyplot as plt
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iris = datasets.load_iris()
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list(iris.keys())
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print(iris.DESCR)
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X = iris["data"][:, 3:] # petal width
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y = (iris["target"] == 2).astype(np.int) # 1 if Iris-Virginica, else 0
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log_reg = LogisticRegression(solver="liblinear", random_state=42)
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log_reg.fit(X, y)
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X_new = np.linspace(0, 3, 1000).reshape(-1, 1)
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y_proba = log_reg.predict_proba(X_new)
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decision_boundary = X_new[y_proba[:, 1] >= 0.5][0]
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plt.figure(figsize=(8, 3))
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plt.plot(X[y==0], y[y==0], "bs")
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plt.plot(X[y==1], y[y==1], "g^")
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plt.plot([decision_boundary, decision_boundary], [-1, 2], "k:", linewidth=2)
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plt.plot(X_new, y_proba[:, 1], "g-", linewidth=2, label="Iris-Virginica")
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plt.plot(X_new, y_proba[:, 0], "b--", linewidth=2, label="Not Iris-Virginica")
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plt.text(decision_boundary+0.02, 0.15, "Decision boundary", fontsize=14, color="k", ha="center")
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plt.arrow(decision_boundary, 0.08, -0.3, 0, head_width=0.05, head_length=0.1, fc='b', ec='b')
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plt.arrow(decision_boundary, 0.92, 0.3, 0, head_width=0.05, head_length=0.1, fc='g', ec='g')
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plt.xlabel("Ancho de petalo (cm)", fontsize=14)
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plt.ylabel("Probabilidad", fontsize=14)
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plt.legend(loc="center left", fontsize=14)
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plt.axis([0, 3, -0.02, 1.02])
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X = iris["data"][:, (2, 3)] # petal length, petal width
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y = iris["target"]
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softmax_reg = LogisticRegression(multi_class="multinomial",solver="lbfgs", C=10, random_state=42)
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softmax_reg.fit(X, y)
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x0, x1 = np.meshgrid(
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np.linspace(0, 8, 500).reshape(-1, 1),
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np.linspace(0, 3.5, 200).reshape(-1, 1),
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)
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X_new = np.c_[x0.ravel(), x1.ravel()]
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y_proba = softmax_reg.predict_proba(X_new)
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y_predict = softmax_reg.predict(X_new)
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zz1 = y_proba[:, 1].reshape(x0.shape)
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zz = y_predict.reshape(x0.shape)
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plt.figure(figsize=(10, 4))
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plt.plot(X[y==2, 0], X[y==2, 1], "g^", label="Iris-Virginica")
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plt.plot(X[y==1, 0], X[y==1, 1], "bs", label="Iris-Versicolor")
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plt.plot(X[y==0, 0], X[y==0, 1], "yo", label="Iris-Setosa")
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from matplotlib.colors import ListedColormap
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custom_cmap = ListedColormap(['#fafab0','#9898ff','#a0faa0'])
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plt.contourf(x0, x1, zz, cmap=custom_cmap)
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contour = plt.contour(x0, x1, zz1, cmap=plt.cm.brg)
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plt.clabel(contour, inline=1, fontsize=12)
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plt.xlabel("Largo de petalo", fontsize=14)
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plt.ylabel("ancho de petalo", fontsize=14)
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plt.legend(loc="center left", fontsize=14)
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plt.axis([0, 7, 0, 3.5])
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plt.show()
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