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* Added Gradient Boosting Classifier * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update gradient_boosting_classifier.py * Update gradient_boosting_classifier.py * Update gradient_boosting_classifier.py * Update gradient_boosting_classifier.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
119 lines
4.2 KiB
Python
119 lines
4.2 KiB
Python
import numpy as np
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from sklearn.datasets import load_iris
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeRegressor
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class GradientBoostingClassifier:
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def __init__(self, n_estimators: int = 100, learning_rate: float = 0.1) -> None:
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"""
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Initialize a GradientBoostingClassifier.
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Parameters:
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- n_estimators (int): The number of weak learners to train.
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- learning_rate (float): The learning rate for updating the model.
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Attributes:
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- n_estimators (int): The number of weak learners.
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- learning_rate (float): The learning rate.
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- models (list): A list to store the trained weak learners.
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"""
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self.n_estimators = n_estimators
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self.learning_rate = learning_rate
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self.models: list[tuple[DecisionTreeRegressor, float]] = []
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def fit(self, features: np.ndarray, target: np.ndarray) -> None:
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"""
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Fit the GradientBoostingClassifier to the training data.
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Parameters:
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- features (np.ndarray): The training features.
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- target (np.ndarray): The target values.
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Returns:
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None
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>>> import numpy as np
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>>> from sklearn.datasets import load_iris
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>>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
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>>> iris = load_iris()
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>>> X, y = iris.data, iris.target
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>>> clf.fit(X, y)
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>>> # Check if the model is trained
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>>> len(clf.models) == 100
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True
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"""
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for _ in range(self.n_estimators):
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# Calculate the pseudo-residuals
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residuals = -self.gradient(target, self.predict(features))
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# Fit a weak learner (e.g., decision tree) to the residuals
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model = DecisionTreeRegressor(max_depth=1)
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model.fit(features, residuals)
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# Update the model by adding the weak learner with a learning rate
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self.models.append((model, self.learning_rate))
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def predict(self, features: np.ndarray) -> np.ndarray:
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"""
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Make predictions on input data.
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Parameters:
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- features (np.ndarray): The input data for making predictions.
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Returns:
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- np.ndarray: An array of binary predictions (-1 or 1).
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>>> import numpy as np
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>>> from sklearn.datasets import load_iris
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>>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
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>>> iris = load_iris()
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>>> X, y = iris.data, iris.target
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>>> clf.fit(X, y)
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>>> y_pred = clf.predict(X)
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>>> # Check if the predictions have the correct shape
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>>> y_pred.shape == y.shape
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True
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"""
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# Initialize predictions with zeros
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predictions = np.zeros(features.shape[0])
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for model, learning_rate in self.models:
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predictions += learning_rate * model.predict(features)
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return np.sign(predictions) # Convert to binary predictions (-1 or 1)
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def gradient(self, target: np.ndarray, y_pred: np.ndarray) -> np.ndarray:
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"""
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Calculate the negative gradient (pseudo-residuals) for logistic loss.
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Parameters:
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- target (np.ndarray): The target values.
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- y_pred (np.ndarray): The predicted values.
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Returns:
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- np.ndarray: An array of pseudo-residuals.
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>>> import numpy as np
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>>> clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
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>>> target = np.array([0, 1, 0, 1])
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>>> y_pred = np.array([0.2, 0.8, 0.3, 0.7])
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>>> residuals = clf.gradient(target, y_pred)
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>>> # Check if residuals have the correct shape
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>>> residuals.shape == target.shape
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True
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"""
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return -target / (1 + np.exp(target * y_pred))
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if __name__ == "__main__":
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iris = load_iris()
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X, y = iris.data, iris.target
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_size=0.2, random_state=42
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)
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clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)
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clf.fit(X_train, y_train)
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y_pred = clf.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print(f"Accuracy: {accuracy:.2f}")
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