91 lines
2.6 KiB
Python
91 lines
2.6 KiB
Python
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"""
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=================================================
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Plot individual and voting regression predictions
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=================================================
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.. currentmodule:: sklearn
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A voting regressor is an ensemble meta-estimator that fits several base
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regressors, each on the whole dataset. Then it averages the individual
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predictions to form a final prediction.
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We will use three different regressors to predict the data:
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:class:`~ensemble.GradientBoostingRegressor`,
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:class:`~ensemble.RandomForestRegressor`, and
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:class:`~linear_model.LinearRegression`).
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Then the above 3 regressors will be used for the
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:class:`~ensemble.VotingRegressor`.
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Finally, we will plot the predictions made by all models for comparison.
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We will work with the diabetes dataset which consists of 10 features
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collected from a cohort of diabetes patients. The target is a quantitative
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measure of disease progression one year after baseline.
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"""
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import matplotlib.pyplot as plt
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from sklearn.datasets import load_diabetes
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from sklearn.ensemble import (
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GradientBoostingRegressor,
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RandomForestRegressor,
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VotingRegressor,
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)
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from sklearn.linear_model import LinearRegression
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# %%
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# Training classifiers
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# --------------------------------
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#
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# First, we will load the diabetes dataset and initiate a gradient boosting
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# regressor, a random forest regressor and a linear regression. Next, we will
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# use the 3 regressors to build the voting regressor:
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X, y = load_diabetes(return_X_y=True)
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# Train classifiers
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reg1 = GradientBoostingRegressor(random_state=1)
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reg2 = RandomForestRegressor(random_state=1)
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reg3 = LinearRegression()
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reg1.fit(X, y)
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reg2.fit(X, y)
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reg3.fit(X, y)
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ereg = VotingRegressor([("gb", reg1), ("rf", reg2), ("lr", reg3)])
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ereg.fit(X, y)
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# %%
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# Making predictions
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# --------------------------------
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#
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# Now we will use each of the regressors to make the 20 first predictions.
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xt = X[:20]
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pred1 = reg1.predict(xt)
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pred2 = reg2.predict(xt)
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pred3 = reg3.predict(xt)
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pred4 = ereg.predict(xt)
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# %%
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# Plot the results
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# --------------------------------
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#
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# Finally, we will visualize the 20 predictions. The red stars show the average
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# prediction made by :class:`~ensemble.VotingRegressor`.
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plt.figure()
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plt.plot(pred1, "gd", label="GradientBoostingRegressor")
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plt.plot(pred2, "b^", label="RandomForestRegressor")
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plt.plot(pred3, "ys", label="LinearRegression")
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plt.plot(pred4, "r*", ms=10, label="VotingRegressor")
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plt.tick_params(axis="x", which="both", bottom=False, top=False, labelbottom=False)
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plt.ylabel("predicted")
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plt.xlabel("training samples")
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plt.legend(loc="best")
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plt.title("Regressor predictions and their average")
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plt.show()
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