Pushing the docs to dev/ for branch: master, commit 36ebf3e371a10ba2377199f04bf5c626b0fbc43c

Author: sklearn-ci

dev/_downloads/02c11153da84f0c2add6642233ca1ed5/plot_monotonic_constraints.py

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+"""
+=====================
+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()

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Monotonic Constraints\n\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"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.experimental import enable_hist_gradient_boosting  # noqa\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.inspection import plot_partial_dependence\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nprint(__doc__)\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) -\n     5 * f_1 - np.cos(10 * np.pi * f_1) +\n     noise)\n\nfig, ax = plt.subplots()\n\n\n# Without any constraint\ngbdt = HistGradientBoostingRegressor()\ngbdt.fit(X, y)\ndisp = plot_partial_dependence(\n    gbdt, X, features=[0, 1],\n    line_kw={'linewidth': 4, 'label': 'unconstrained'},\n    ax=ax)\n\n# With positive and negative constraints\ngbdt = HistGradientBoostingRegressor(monotonic_cst=[1, -1])\ngbdt.fit(X, y)\n\nplot_partial_dependence(\n    gbdt, X, features=[0, 1],\n    feature_names=('First feature\\nPositive constraint',\n                   'Second feature\\nNegtive constraint'),\n    line_kw={'linewidth': 4, 'label': 'constrained'},\n    ax=disp.axes_)\n\nfor f_idx in (0, 1):\n    disp.axes_[0, f_idx].plot(X[:, f_idx], y, 'o', alpha=.3, zorder=-1)\n    disp.axes_[0, f_idx].set_ylim(-6, 6)\n\nplt.legend()\nfig.suptitle(\"Monotonic constraints illustration\")\n\nplt.show()"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.1"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}