Adaboost(Regression)

import numpy as np
import matplotlib.pyplot as plt
import japanize_matplotlib
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor

# NOTE: This model is to check the model's sample_weight
class DummyRegressor:
    def __init__(self):
        self.model = DecisionTreeRegressor(max_depth=5)
        self.error_vector = None
        self.X_for_plot = None
        self.y_for_plot = None

    def fit(self, X, y):
        self.model.fit(X, y)
        y_pred = self.model.predict(X)

        # Weights are calculated based on regression error.
        # https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/ensemble/_weight_boosting.py#L1130
        self.error_vector = np.abs(y_pred - y)
        self.X_for_plot = X.copy()
        self.y_for_plot = y.copy()
        return self.model

    def predict(self, X, check_input=True):
        return self.model.predict(X)

    def get_params(self, deep=False):
        return {}

    def set_params(self, deep=False):
        return {}

# training dataset
X = np.linspace(-10, 10, 500)[:, np.newaxis]
y = (np.sin(X).ravel() + np.cos(4 * X).ravel()) * 10 + 10 + np.linspace(-2, 2, 500)

## train Adaboost regressor
reg = AdaBoostRegressor(
    DummyRegressor(),
    n_estimators=100,
    random_state=100,
    loss="linear",
    learning_rate=0.8,
)
reg.fit(X, y)
y_pred = reg.predict(X)

# Check the fitting to the training data.
plt.figure(figsize=(10, 5))
plt.scatter(X, y, c="k", marker="x", label="訓練データ")
plt.plot(X, y_pred, c="r", label="prediction", linewidth=1)
plt.xlabel("x")
plt.ylabel("y")
plt.legend()
plt.show()

def visualize_weight(reg, X, y, y_pred):
    """Function for plotting the value (number of times sampled) corresponding to the weights of the sample

    Parameters
    ----------
    reg : sklearn.ensemble._weight_boosting
        boosting model
    X : numpy.ndarray
        training dataset
    y : numpy.ndarray
        target
    y_pred:
        prediction
    """
    assert reg.estimators_ is not None, "len(reg.estimators_) > 0"

    for i, estimators_i in enumerate(reg.estimators_):
        if i % 100 == 0:
            # Count how many times the data appears in the data used to create the i-th model
            weight_dict = {xi: 0 for xi in X.ravel()}
            for xi in estimators_i.X_for_plot.ravel():
                weight_dict[xi] += 1

            # Plot the number of occurrences as orange circles on the graph (the more the number, the larger the circle)
            weight_x_sorted = sorted(weight_dict.items(), key=lambda x: x[0])
            weight_vec = np.array([s * 100 for xi, s in weight_x_sorted])

            # plot graph
            plt.figure(figsize=(10, 5))
            plt.title(f"Visualization of the weighted sample after creating the {i}-th model, loss={reg.loss}")
            plt.scatter(X, y, c="k", marker="x", label="training data")
            plt.scatter(
                estimators_i.X_for_plot,
                estimators_i.y_for_plot,
                marker="o",
                alpha=0.2,
                c="orange",
                s=weight_vec,
            )
            plt.plot(X, y_pred, c="r", label="prediction", linewidth=2)
            plt.legend(loc="upper right")
            plt.show()


## Create a regression model with loss="linear
reg = AdaBoostRegressor(
    DummyRegressor(),
    n_estimators=101,
    random_state=100,
    loss="linear",
    learning_rate=1,
)
reg.fit(X, y)
y_pred = reg.predict(X)
visualize_weight(reg, X, y, y_pred)

## Create regression model with loss="square
reg = AdaBoostRegressor(
    DummyRegressor(),
    n_estimators=101,
    random_state=100,
    loss="square",
    learning_rate=1,
)
reg.fit(X, y)
y_pred = reg.predict(X)
visualize_weight(reg, X, y, y_pred)