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* MAINT: Used f-string method Updated the code with f-string methods wherever required for a better and cleaner understanding of the code. * Updated files with f-string method * Update rsa_key_generator.py * Update rsa_key_generator.py * Update elgamal_key_generator.py * Update lru_cache.py I don't think this change is efficient but it might tackle the error as the error was due to using long character lines. * Update lru_cache.py * Update lru_cache.py Co-authored-by: cyai <seriesscar@gmail.com> Co-authored-by: Christian Clauss <cclauss@me.com>
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
"""Implementation of GradientBoostingRegressor in sklearn using the
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boston dataset which is very popular for regression problem to
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predict house price.
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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_boston
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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 the sklearn
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df = load_boston()
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print(df.keys())
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# now let construct a data frame
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df_boston = pd.DataFrame(df.data, columns=df.feature_names)
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# let add the target to the dataframe
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df_boston["Price"] = df.target
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# print the first five rows using the head function
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print(df_boston.head())
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# Summary statistics
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print(df_boston.describe().T)
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# Feature selection
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X = df_boston.iloc[:, :-1]
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y = df_boston.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 evaluation 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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