78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
"""
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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 positive (increasing) or negative (decreasing) constraint on
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the features during the learning process, the estimator is able to properly
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follow 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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from sklearn.ensemble import HistGradientBoostingRegressor
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from sklearn.inspection import PartialDependenceDisplay
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import numpy as np
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import matplotlib.pyplot as plt
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rng = np.random.RandomState(0)
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n_samples = 5000
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f_0 = rng.rand(n_samples) # positive correlation with y
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f_1 = rng.rand(n_samples) # negative correlation with y
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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 = 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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fig, ax = plt.subplots()
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# Without any constraint
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gbdt = HistGradientBoostingRegressor()
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gbdt.fit(X, y)
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disp = PartialDependenceDisplay.from_estimator(
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gbdt,
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X,
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features=[0, 1],
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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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# With positive and negative constraints
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gbdt = HistGradientBoostingRegressor(monotonic_cst=[1, -1])
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gbdt.fit(X, y)
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PartialDependenceDisplay.from_estimator(
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gbdt,
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X,
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features=[0, 1],
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feature_names=(
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"First feature\nPositive constraint",
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"Second feature\nNegtive constraint",
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),
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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 illustration")
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plt.show()
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