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priya-sundaram-dev
35b7074d2d
Re-enable the four scikit-learn machine-learning examples and the
neural-network perceptron that had been disabled (renamed to
.broken.txt / .DISABLED), and modernize them so they import and run
cleanly on current scikit-learn and pass the doctest CI:
machine_learning/gaussian_naive_bayes.py
machine_learning/random_forest_classifier.py
- Replace the removed sklearn.metrics.plot_confusion_matrix with
ConfusionMatrixDisplay.from_estimator (removed in scikit-learn 1.2).
- Drop the artificial time.sleep() calls.
machine_learning/gradient_boosting_regressor.py
machine_learning/random_forest_regressor.py
- Replace the removed load_boston dataset (removed in scikit-learn
1.2 for ethical reasons) with the bundled load_diabetes dataset so
the examples run offline.
- Avoid an unused-variable lint (RUF059).
neural_network/perceptron.py
- Use a dedicated seeded random.Random instance instead of the global
random state, so training is reproducible and thread-safe under the
parallel test runner.
- Cap training at epoch_number epochs so it always terminates even on
non-linearly-separable data (previously an unbounded while True).
- Have training() and sort() return their results instead of printing,
per the contribution guidelines, and update the doctests accordingly.
Requested by @cclauss in #8029; perceptron follow-up to #15206.
71 lines
2.5 KiB
Python
71 lines
2.5 KiB
Python
"""Implementation of GradientBoostingRegressor in sklearn using the
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diabetes dataset, a popular regression problem used to predict
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disease progression one year after baseline.
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Note: this example previously used the Boston house-price dataset,
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which was removed from scikit-learn (>=1.2) for ethical reasons.
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``load_diabetes`` is a drop-in bundled alternative that ships with
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scikit-learn, so the example runs offline.
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"""
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import matplotlib.pyplot as plt
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import pandas as pd
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from sklearn.datasets import load_diabetes
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split
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def main():
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# loading the dataset from sklearn
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df = load_diabetes()
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print(df.keys())
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# now let's construct a data frame
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df_data = pd.DataFrame(df.data, columns=df.feature_names)
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# let's add the target to the dataframe
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df_data["Target"] = df.target
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# print the first five rows using the head function
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print(df_data.head())
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# Summary statistics
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print(df_data.describe().T)
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# Feature selection
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x = df_data.iloc[:, :-1]
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y = df_data.iloc[:, -1] # target variable
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# split the data with 75% train and 25% test sets.
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x_train, x_test, y_train, y_test = train_test_split(
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x, y, random_state=0, test_size=0.25
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)
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model = GradientBoostingRegressor(
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n_estimators=500, max_depth=5, min_samples_split=4, learning_rate=0.01
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)
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# training the model
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model.fit(x_train, y_train)
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# to see how good the model fit the data
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training_score = model.score(x_train, y_train).round(3)
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test_score = model.score(x_test, y_test).round(3)
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print("Training score of GradientBoosting is :", training_score)
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print("The test score of GradientBoosting is :", test_score)
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# Let us evaluate the model by finding the errors
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y_pred = model.predict(x_test)
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# The mean squared error
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print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
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# Explained variance score: 1 is perfect prediction
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print(f"Test Variance score: {r2_score(y_test, y_pred):.2f}")
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# So let's run the model against the test data
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_fig, ax = plt.subplots()
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ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0))
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ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "k--", lw=4)
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ax.set_xlabel("Actual")
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ax.set_ylabel("Predicted")
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ax.set_title("Truth vs Predicted")
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# this show function will display the plotting
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plt.show()
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if __name__ == "__main__":
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main()
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