1.0/notebooks/auto_examples/ensemble/plot_monotonic_constraints.ipynb

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        "\n# Monotonic Constraints\n\nThis example illustrates the effect of monotonic constraints on a gradient\nboosting estimator.\n\nWe build an artificial dataset where the target value is in general\npositively correlated with the first feature (with some random and\nnon-random variations), and in general negatively correlated with the second\nfeature.\n\nBy imposing a positive (increasing) or negative (decreasing) constraint on\nthe features during the learning process, the estimator is able to properly\nfollow the general trend instead of being subject to the variations.\n\nThis example was inspired by the `XGBoost documentation\n<https://xgboost.readthedocs.io/en/latest/tutorials/monotonic.html>`_.\n"
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      "source": [
        "from sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.inspection import PartialDependenceDisplay\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nrng = np.random.RandomState(0)\n\nn_samples = 5000\nf_0 = rng.rand(n_samples)  # positive correlation with y\nf_1 = rng.rand(n_samples)  # negative correlation with y\nX = np.c_[f_0, f_1]\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = 5 * f_0 + np.sin(10 * np.pi * f_0) - 5 * f_1 - np.cos(10 * np.pi * f_1) + noise\n\nfig, ax = plt.subplots()\n\n\n# Without any constraint\ngbdt = HistGradientBoostingRegressor()\ngbdt.fit(X, y)\ndisp = PartialDependenceDisplay.from_estimator(\n    gbdt,\n    X,\n    features=[0, 1],\n    line_kw={\"linewidth\": 4, \"label\": \"unconstrained\", \"color\": \"tab:blue\"},\n    ax=ax,\n)\n\n# With positive and negative constraints\ngbdt = HistGradientBoostingRegressor(monotonic_cst=[1, -1])\ngbdt.fit(X, y)\n\nPartialDependenceDisplay.from_estimator(\n    gbdt,\n    X,\n    features=[0, 1],\n    feature_names=(\n        \"First feature\\nPositive constraint\",\n        \"Second feature\\nNegtive constraint\",\n    ),\n    line_kw={\"linewidth\": 4, \"label\": \"constrained\", \"color\": \"tab:orange\"},\n    ax=disp.axes_,\n)\n\nfor f_idx in (0, 1):\n    disp.axes_[0, f_idx].plot(\n        X[:, f_idx], y, \"o\", alpha=0.3, zorder=-1, color=\"tab:green\"\n    )\n    disp.axes_[0, f_idx].set_ylim(-6, 6)\n\nplt.legend()\nfig.suptitle(\"Monotonic constraints illustration\")\n\nplt.show()"
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