71 lines
2.1 KiB
Python
71 lines
2.1 KiB
Python
"""
|
|
=====================
|
|
Monotonic Constraints
|
|
=====================
|
|
|
|
This example illustrates the effect of monotonic constraints on a gradient
|
|
boosting estimator.
|
|
|
|
We build an artificial dataset where the target value is in general
|
|
positively correlated with the first feature (with some random and
|
|
non-random variations), and in general negatively correlated with the second
|
|
feature.
|
|
|
|
By imposing a positive (increasing) or negative (decreasing) constraint on
|
|
the features during the learning process, the estimator is able to properly
|
|
follow the general trend instead of being subject to the variations.
|
|
|
|
This example was inspired by the `XGBoost documentation
|
|
<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.
|
|
"""
|
|
from sklearn.experimental import enable_hist_gradient_boosting # noqa
|
|
from sklearn.ensemble import HistGradientBoostingRegressor
|
|
from sklearn.inspection import plot_partial_dependence
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
print(__doc__)
|
|
|
|
rng = np.random.RandomState(0)
|
|
|
|
n_samples = 5000
|
|
f_0 = rng.rand(n_samples) # positive correlation with y
|
|
f_1 = rng.rand(n_samples) # negative correlation with y
|
|
X = np.c_[f_0, f_1]
|
|
noise = rng.normal(loc=0.0, scale=0.01, size=n_samples)
|
|
y = (5 * f_0 + np.sin(10 * np.pi * f_0) -
|
|
5 * f_1 - np.cos(10 * np.pi * f_1) +
|
|
noise)
|
|
|
|
fig, ax = plt.subplots()
|
|
|
|
|
|
# Without any constraint
|
|
gbdt = HistGradientBoostingRegressor()
|
|
gbdt.fit(X, y)
|
|
disp = plot_partial_dependence(
|
|
gbdt, X, features=[0, 1],
|
|
line_kw={'linewidth': 4, 'label': 'unconstrained'},
|
|
ax=ax)
|
|
|
|
# With positive and negative constraints
|
|
gbdt = HistGradientBoostingRegressor(monotonic_cst=[1, -1])
|
|
gbdt.fit(X, y)
|
|
|
|
plot_partial_dependence(
|
|
gbdt, X, features=[0, 1],
|
|
feature_names=('First feature\nPositive constraint',
|
|
'Second feature\nNegtive constraint'),
|
|
line_kw={'linewidth': 4, 'label': 'constrained'},
|
|
ax=disp.axes_)
|
|
|
|
for f_idx in (0, 1):
|
|
disp.axes_[0, f_idx].plot(X[:, f_idx], y, 'o', alpha=.3, zorder=-1)
|
|
disp.axes_[0, f_idx].set_ylim(-6, 6)
|
|
|
|
plt.legend()
|
|
fig.suptitle("Monotonic constraints illustration")
|
|
|
|
plt.show()
|