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+15-15 b/Diff for: ‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
+15-15
@@ -51,7 +51,7 @@
       },
       "outputs": [],
       "source": [
-        "from time import time\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import QuantileTransformer\nfrom sklearn.neural_network import MLPRegressor\n\nprint(\"Training MLPRegressor...\")\ntic = time()\nest = make_pipeline(\n    QuantileTransformer(),\n    MLPRegressor(\n        hidden_layer_sizes=(50, 50), learning_rate_init=0.01, early_stopping=True\n    ),\n)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
+        "from time import time\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import QuantileTransformer\nfrom sklearn.neural_network import MLPRegressor\n\nprint(\"Training MLPRegressor...\")\ntic = time()\nest = make_pipeline(\n    QuantileTransformer(),\n    MLPRegressor(\n        hidden_layer_sizes=(30, 15),\n        learning_rate_init=0.01,\n        early_stopping=True,\n        random_state=0,\n    ),\n)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "from sklearn.ensemble import HistGradientBoostingRegressor\n\nprint(\"Training HistGradientBoostingRegressor...\")\ntic = time()\nest = HistGradientBoostingRegressor()\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
+        "from sklearn.ensemble import HistGradientBoostingRegressor\n\nprint(\"Training HistGradientBoostingRegressor...\")\ntic = time()\nest = HistGradientBoostingRegressor(random_state=0)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
       ]
     },
     {
@@ -130,7 +130,7 @@
       },
       "outputs": [],
       "source": [
-        "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features,\n    kind=\"average\",\n    n_jobs=3,\n    grid_resolution=20,\n    ax=ax,\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)"
+        "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n    est,\n    X_train,\n    features,\n    kind=\"average\",\n    n_jobs=2,\n    grid_resolution=10,\n    ax=ax,\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)"
       ]
     },
     {
@@ -148,7 +148,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n    est, X_train, features=features, kind=\"average\", grid_resolution=20\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n    \"Partial dependence of house value on median\\n\"\n    \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
+        "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n    est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n    \"Partial dependence of house value on median\\n\"\n    \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
       ]
     }
   ],
 
@@ -80,7 +80,10 @@
 est = make_pipeline(
     QuantileTransformer(),
     MLPRegressor(
-        hidden_layer_sizes=(50, 50), learning_rate_init=0.01, early_stopping=True
+        hidden_layer_sizes=(30, 15),
+        learning_rate_init=0.01,
+        early_stopping=True,
+        random_state=0,
     ),
 )
 est.fit(X_train, y_train)
@@ -145,7 +148,7 @@
 
 print("Training HistGradientBoostingRegressor...")
 tic = time()
-est = HistGradientBoostingRegressor()
+est = HistGradientBoostingRegressor(random_state=0)
 est.fit(X_train, y_train)
 print(f"done in {time() - tic:.3f}s")
 print(f"Test R2 score: {est.score(X_test, y_test):.2f}")
@@ -233,8 +236,8 @@
     X_train,
     features,
     kind="average",
-    n_jobs=3,
-    grid_resolution=20,
+    n_jobs=2,
+    grid_resolution=10,
     ax=ax,
 )
 print(f"done in {time() - tic:.3f}s")
@@ -265,12 +268,13 @@
 
 features = ("AveOccup", "HouseAge")
 pdp = partial_dependence(
-    est, X_train, features=features, kind="average", grid_resolution=20
+    est, X_train, features=features, kind="average", grid_resolution=10
 )
 XX, YY = np.meshgrid(pdp["values"][0], pdp["values"][1])
 Z = pdp.average[0].T
 ax = Axes3D(fig)
 fig.add_axes(ax)
+
 surf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor="k")
 ax.set_xlabel(features[0])
 ax.set_ylabel(features[1])
Original file line number	Diff line number	Diff line change
`@@ -51,7 +51,7 @@`
`51`	`51`	`},`
`52`	`52`	`"outputs": [],`
`53`	`53`	`"source": [`
`54`		- "from time import time\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import QuantileTransformer\nfrom sklearn.neural_network import MLPRegressor\n\nprint(\"Training MLPRegressor...\")\ntic = time()\nest = make_pipeline(\n QuantileTransformer(),\n MLPRegressor(\n hidden_layer_sizes=(50, 50), learning_rate_init=0.01, early_stopping=True\n ),\n)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
	`54`	+ "from time import time\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import QuantileTransformer\nfrom sklearn.neural_network import MLPRegressor\n\nprint(\"Training MLPRegressor...\")\ntic = time()\nest = make_pipeline(\n QuantileTransformer(),\n MLPRegressor(\n hidden_layer_sizes=(30, 15),\n learning_rate_init=0.01,\n early_stopping=True,\n random_state=0,\n ),\n)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"
`55`	`55`	`]`
`56`	`56`	`},`
`57`	`57`	`{`
`@@ -87,7 +87,7 @@`
`87`	`87`	`},`
`88`	`88`	`"outputs": [],`
`89`	`89`	`"source": [`
`90`		`- "from sklearn.ensemble import HistGradientBoostingRegressor\n\nprint(\"Training HistGradientBoostingRegressor...\")\ntic = time()\nest = HistGradientBoostingRegressor()\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"`
	`90`	`+ "from sklearn.ensemble import HistGradientBoostingRegressor\n\nprint(\"Training HistGradientBoostingRegressor...\")\ntic = time()\nest = HistGradientBoostingRegressor(random_state=0)\nest.fit(X_train, y_train)\nprint(f\"done in {time() - tic:.3f}s\")\nprint(f\"Test R2 score: {est.score(X_test, y_test):.2f}\")"`
`91`	`91`	`]`
`92`	`92`	`},`
`93`	`93`	`{`
`@@ -130,7 +130,7 @@`
`130`	`130`	`},`
`131`	`131`	`"outputs": [],`
`132`	`132`	`"source": [`
`133`		- "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features,\n kind=\"average\",\n n_jobs=3,\n grid_resolution=20,\n ax=ax,\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)"
	`133`	+ "features = [\"AveOccup\", \"HouseAge\", (\"AveOccup\", \"HouseAge\")]\nprint(\"Computing partial dependence plots...\")\ntic = time()\n_, ax = plt.subplots(ncols=3, figsize=(9, 4))\ndisplay = PartialDependenceDisplay.from_estimator(\n est,\n X_train,\n features,\n kind=\"average\",\n n_jobs=2,\n grid_resolution=10,\n ax=ax,\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)"
`134`	`134`	`]`
`135`	`135`	`},`
`136`	`136`	`{`
`@@ -148,7 +148,7 @@`
`148`	`148`	`},`
`149`	`149`	`"outputs": [],`
`150`	`150`	`"source": [`
`151`		- "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n est, X_train, features=features, kind=\"average\", grid_resolution=20\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n \"Partial dependence of house value on median\\n\"\n \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
	`151`	+ "import numpy as np\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\n\nfeatures = (\"AveOccup\", \"HouseAge\")\npdp = partial_dependence(\n est, X_train, features=features, kind=\"average\", grid_resolution=10\n)\nXX, YY = np.meshgrid(pdp[\"values\"][0], pdp[\"values\"][1])\nZ = pdp.average[0].T\nax = Axes3D(fig)\nfig.add_axes(ax)\n\nsurf = ax.plot_surface(XX, YY, Z, rstride=1, cstride=1, cmap=plt.cm.BuPu, edgecolor=\"k\")\nax.set_xlabel(features[0])\nax.set_ylabel(features[1])\nax.set_zlabel(\"Partial dependence\")\n# pretty init view\nax.view_init(elev=22, azim=122)\nplt.colorbar(surf)\nplt.suptitle(\n \"Partial dependence of house value on median\\n\"\n \"age and average occupancy, with Gradient Boosting\"\n)\nplt.subplots_adjust(top=0.9)\nplt.show()"
`152`	`152`	`]`
`153`	`153`	`}`
`154`	`154`	`],`