Pushing the docs to dev/ for branch: master, commit 9358a94aa7c8bb887bd64ae156e0d0a7ee637d1c

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -26,7 +26,7 @@
       },
       "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()"
+        "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,\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\nplot_partial_dependence(\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()"
       ]
     }
   ],

dev/_downloads/215c560d29193ab9b0a495609bc74802/plot_monotonic_constraints.ipynb

@@ -80,7 +80,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import partial_dependence\nfrom sklearn.inspection import plot_partial_dependence\n\nprint('Computing partial dependence plots...')\ntic = time()\nfeatures = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms']\ndisplay = plot_partial_dependence(\n       est, X_train, features, kind=\"both\", subsample=50,\n       n_jobs=3, grid_resolution=20\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    'Partial dependence of house value on non-___location features\\n'\n    'for the California housing dataset, with MLPRegressor'\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
+        "import matplotlib.pyplot as plt\nfrom sklearn.inspection import partial_dependence\nfrom sklearn.inspection import plot_partial_dependence\n\nprint('Computing partial dependence plots...')\ntic = time()\nfeatures = ['MedInc', 'AveOccup', 'HouseAge', 'AveRooms']\ndisplay = plot_partial_dependence(\n       est, X_train, features, kind=\"both\", subsample=50,\n       n_jobs=3, grid_resolution=20, random_state=0\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    'Partial dependence of house value on non-___location features\\n'\n    'for the California housing dataset, with MLPRegressor'\n)\ndisplay.figure_.subplots_adjust(hspace=0.3)"
       ]
     },
     {
@@ -116,7 +116,7 @@
       },
       "outputs": [],
       "source": [
-        "print('Computing partial dependence plots...')\ntic = time()\ndisplay = plot_partial_dependence(\n    est, X_train, features, kind=\"both\", subsample=50,\n    n_jobs=3, grid_resolution=20\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    'Partial dependence of house value on non-___location features\\n'\n    'for the California housing dataset, with Gradient Boosting'\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
+        "print('Computing partial dependence plots...')\ntic = time()\ndisplay = plot_partial_dependence(\n    est, X_train, features, kind=\"both\", subsample=50,\n    n_jobs=3, grid_resolution=20, random_state=0\n)\nprint(f\"done in {time() - tic:.3f}s\")\ndisplay.figure_.suptitle(\n    'Partial dependence of house value on non-___location features\\n'\n    'for the California housing dataset, with Gradient Boosting'\n)\ndisplay.figure_.subplots_adjust(wspace=0.4, hspace=0.3)"
       ]
     },
     {

dev/_downloads/21b82d82985712b5de6347f382c77c86/plot_partial_dependence.ipynb

@@ -138,10 +138,13 @@
 
 disp = plot_partial_dependence(
     gbdt_no_cst, X, features=[0], feature_names=['feature 0'],
-    line_kw={'linewidth': 4, 'label': 'unconstrained'})
+    line_kw={'linewidth': 4, 'label': 'unconstrained', "color": "tab:blue"})
 plot_partial_dependence(gbdt_cst, X, features=[0],
-    line_kw={'linewidth': 4, 'label': 'constrained'}, ax=disp.axes_)
-disp.axes_[0, 0].plot(X[:, 0], y, 'o', alpha=.5, zorder=-1, label='samples')
+    line_kw={'linewidth': 4, 'label': 'constrained', "color": "tab:orange"},
+    ax=disp.axes_)
+disp.axes_[0, 0].plot(
+    X[:, 0], y, 'o', alpha=.5, zorder=-1, label='samples', color="tab:green"
+)
 disp.axes_[0, 0].set_ylim(-3, 3); disp.axes_[0, 0].set_xlim(-1, 1)
 plt.legend()
 plt.show()

dev/_downloads/4f07b03421908788913e044918d8ed1e/plot_release_highlights_0_23_0.py

@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.inspection import plot_partial_dependence\nfrom sklearn.experimental import enable_hist_gradient_boosting  # noqa\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = (5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise)\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = plot_partial_dependence(\n    gbdt_no_cst, X, features=[0], feature_names=['feature 0'],\n    line_kw={'linewidth': 4, 'label': 'unconstrained'})\nplot_partial_dependence(gbdt_cst, X, features=[0],\n    line_kw={'linewidth': 4, 'label': 'constrained'}, ax=disp.axes_)\ndisp.axes_[0, 0].plot(X[:, 0], y, 'o', alpha=.5, zorder=-1, label='samples')\ndisp.axes_[0, 0].set_ylim(-3, 3); disp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
+        "import numpy as np\nfrom matplotlib import pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.inspection import plot_partial_dependence\nfrom sklearn.experimental import enable_hist_gradient_boosting  # noqa\nfrom sklearn.ensemble import HistGradientBoostingRegressor\n\nn_samples = 500\nrng = np.random.RandomState(0)\nX = rng.randn(n_samples, 2)\nnoise = rng.normal(loc=0.0, scale=0.01, size=n_samples)\ny = (5 * X[:, 0] + np.sin(10 * np.pi * X[:, 0]) - noise)\n\ngbdt_no_cst = HistGradientBoostingRegressor().fit(X, y)\ngbdt_cst = HistGradientBoostingRegressor(monotonic_cst=[1, 0]).fit(X, y)\n\ndisp = plot_partial_dependence(\n    gbdt_no_cst, X, features=[0], feature_names=['feature 0'],\n    line_kw={'linewidth': 4, 'label': 'unconstrained', \"color\": \"tab:blue\"})\nplot_partial_dependence(gbdt_cst, X, features=[0],\n    line_kw={'linewidth': 4, 'label': 'constrained', \"color\": \"tab:orange\"},\n    ax=disp.axes_)\ndisp.axes_[0, 0].plot(\n    X[:, 0], y, 'o', alpha=.5, zorder=-1, label='samples', color=\"tab:green\"\n)\ndisp.axes_[0, 0].set_ylim(-3, 3); disp.axes_[0, 0].set_xlim(-1, 1)\nplt.legend()\nplt.show()"
       ]
     },
     {

dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip

@@ -45,23 +45,33 @@
 gbdt = HistGradientBoostingRegressor()
 gbdt.fit(X, y)
 disp = plot_partial_dependence(
-    gbdt, X, features=[0, 1],
-    line_kw={'linewidth': 4, 'label': 'unconstrained'},
-    ax=ax)
+    gbdt,
+    X,
+    features=[0, 1],
+    line_kw={"linewidth": 4, "label": "unconstrained", "color": "tab:blue"},
+    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_)
+    gbdt,
+    X,
+    features=[0, 1],
+    feature_names=(
+        "First feature\nPositive constraint",
+        "Second feature\nNegtive constraint",
+    ),
+    line_kw={"linewidth": 4, "label": "constrained", "color": "tab:orange"},
+    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].plot(
+        X[:, f_idx], y, "o", alpha=0.3, zorder=-1, color="tab:green"
+    )
     disp.axes_[0, f_idx].set_ylim(-6, 6)
 
 plt.legend()