110 lines
3.2 KiB
Python
110 lines
3.2 KiB
Python
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"""
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=====================
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Monotonic Constraints
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=====================
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This example illustrates the effect of monotonic constraints on a gradient
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boosting estimator.
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We build an artificial dataset where the target value is in general
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positively correlated with the first feature (with some random and
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non-random variations), and in general negatively correlated with the second
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feature.
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By imposing a monotonic increase or a monotonic decrease constraint, respectively,
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on the features during the learning process, the estimator is able to properly follow
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the general trend instead of being subject to the variations.
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This example was inspired by the `XGBoost documentation
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<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.
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"""
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# %%
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.inspection import PartialDependenceDisplay
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rng = np.random.RandomState(0)
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n_samples = 1000
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f_0 = rng.rand(n_samples)
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f_1 = rng.rand(n_samples)
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X = np.c_[f_0, f_1]
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noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
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# y is positively correlated with f_0, and negatively correlated with f_1
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y = 5 * f_0 + np.sin(10 * np.pi * f_0) - 5 * f_1 - np.cos(10 * np.pi * f_1) + noise
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# %%
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# Fit a first model on this dataset without any constraints.
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gbdt_no_cst = HistGradientBoostingRegressor()
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gbdt_no_cst.fit(X, y)
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# %%
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# Fit a second model on this dataset with monotonic increase (1)
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# and a monotonic decrease (-1) constraints, respectively.
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gbdt_with_monotonic_cst = HistGradientBoostingRegressor(monotonic_cst=[1, -1])
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gbdt_with_monotonic_cst.fit(X, y)
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# %%
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# Let's display the partial dependence of the predictions on the two features.
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fig, ax = plt.subplots()
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disp = PartialDependenceDisplay.from_estimator(
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gbdt_no_cst,
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X,
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features=[0, 1],
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feature_names=(
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"First feature",
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"Second feature",
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),
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line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"},
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ax=ax,
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)
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PartialDependenceDisplay.from_estimator(
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gbdt_with_monotonic_cst,
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X,
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features=[0, 1],
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line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"},
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ax=disp.axes_,
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)
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for f_idx in (0, 1):
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disp.axes_[0, f_idx].plot(
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X[:, f_idx], y, "o", alpha=0.3, zorder=-1, color="tab:green"
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)
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disp.axes_[0, f_idx].set_ylim(-6, 6)
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plt.legend()
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fig.suptitle("Monotonic constraints effect on partial dependences")
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plt.show()
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# %%
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# We can see that the predictions of the unconstrained model capture the
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# oscillations of the data while the constrained model follows the general
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# trend and ignores the local variations.
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# %%
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# .. _monotonic_cst_features_names:
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#
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# Using feature names to specify monotonic constraints
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# ----------------------------------------------------
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#
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# Note that if the training data has feature names, it's possible to specify the
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# monotonic constraints by passing a dictionary:
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import pandas as pd
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X_df = pd.DataFrame(X, columns=["f_0", "f_1"])
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gbdt_with_monotonic_cst_df = HistGradientBoostingRegressor(
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monotonic_cst={"f_0": 1, "f_1": -1}
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).fit(X_df, y)
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np.allclose(
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gbdt_with_monotonic_cst_df.predict(X_df), gbdt_with_monotonic_cst.predict(X)
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)
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