72 lines
3.1 KiB
Python
72 lines
3.1 KiB
Python
import numpy as np
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from matplotlib.colors import ListedColormap
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from matplotlib.axes import Axes
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from matplotlib import pyplot as plt
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TRAIN_DATA_ROW_LENGTH = 3
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TEST_DATA_ROW_LENGTH = 6
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LINEAR_REGRESSION_PLOT_INDEX = 6
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PERCEPTRON_REGRESSION_PLOT_INDEX = 7
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RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX = 8
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def show_plot(x, x_train, x_test, y_train, y_test, my_linear_model, linear_model_score, my_perceptron_model, perceptron_model_score, pipeline, polynomial_model_score):
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h = .02 # шаг регулярной сетки
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x0_min, x0_max = x[:, 0].min() - .5, x[:, 0].max() + .5
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x1_min, x1_max = x[:, 1].min() - .5, x[:, 1].max() + .5
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xx0, xx1 = np.meshgrid(np.arange(x0_min, x0_max, h),
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np.arange(x1_min, x1_max, h))
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cm = plt.cm.RdBu
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cm_bright = ListedColormap(['#FF0000', '#0000FF'])
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for i in range(9):
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current_subplot = plt.subplot(3, 3, i+1)
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if i < TRAIN_DATA_ROW_LENGTH:
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current_subplot.scatter(
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x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
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elif i < TEST_DATA_ROW_LENGTH:
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current_subplot.scatter(
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x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
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else:
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if i == LINEAR_REGRESSION_PLOT_INDEX:
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show_gradient(my_linear_model, current_subplot=current_subplot,
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title='LinearRegression', score=linear_model_score, xx0=xx0, xx1=xx1, cm=cm)
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elif i == PERCEPTRON_REGRESSION_PLOT_INDEX:
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show_gradient(my_perceptron_model, current_subplot=current_subplot,
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title='Perceptron', score=perceptron_model_score, xx0=xx0, xx1=xx1, cm=cm)
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elif i == RIDGE_POLY_REGRESSION_REGRESSION_PLOT_INDEX:
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current_subplot.set_title('RidgePolyRegression')
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show_gradient(pipeline, current_subplot=current_subplot,
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title='RidgePolyRegression', score=polynomial_model_score, xx0=xx0, xx1=xx1, cm=cm)
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current_subplot.scatter(
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x_train[:, 0], x_train[:, 1], c=y_train, cmap=cm_bright)
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current_subplot.scatter(
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x_test[:, 0], x_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
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plt.show()
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def show_gradient(model, current_subplot: Axes, title: str, score: float, xx0, xx1, cm):
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current_subplot.set_title(title)
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if hasattr(model, "decision_function"):
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Z = model.decision_function(np.c_[xx0.ravel(), xx1.ravel()])
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elif hasattr(model, "predict_proba"):
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Z = model.predict_proba(np.c_[xx0.ravel(), xx1.ravel()])[:, 1]
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elif hasattr(model, "predict"):
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Z = model.predict(np.c_[xx0.ravel(), xx1.ravel()])
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else:
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return
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Z = Z.reshape(xx0.shape)
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current_subplot.contourf(xx0, xx1, Z, cmap=cm, alpha=.8)
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current_subplot.set_xlim(xx0.min(), xx0.max())
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current_subplot.set_ylim(xx0.min(), xx1.max())
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current_subplot.set_xticks(())
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current_subplot.set_yticks(())
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current_subplot.text(xx0.max() - .3, xx1.min() + .3, ('%.2f' % score),
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size=15, horizontalalignment='left')
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