Pushing the docs to dev/ for branch: main, commit f15ddc05cb4c39b07acf3a950b32471fbaa7f2e2

Author: sklearn-ci

dev/_downloads/02fe21a1f5d14bd1ebbe34aa905d9bf6/plot_multilabel.zip

@@ -104,12 +104,10 @@ def f(x):
 y_med = all_models["q 0.50"].predict(xx)
 
 fig = plt.figure(figsize=(10, 10))
-plt.plot(xx, f(xx), "g:", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
+plt.plot(xx, f(xx), "black", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
 plt.plot(X_test, y_test, "b.", markersize=10, label="Test observations")
-plt.plot(xx, y_med, "r-", label="Predicted median")
-plt.plot(xx, y_pred, "r-", label="Predicted mean")
-plt.plot(xx, y_upper, "k-")
-plt.plot(xx, y_lower, "k-")
+plt.plot(xx, y_med, "tab:orange", linewidth=3, label="Predicted median")
+plt.plot(xx, y_pred, "tab:green", linewidth=3, label="Predicted mean")
 plt.fill_between(
     xx.ravel(), y_lower, y_upper, alpha=0.4, label="Predicted 90% interval"
 )
@@ -310,10 +308,8 @@ def coverage_fraction(y, y_low, y_high):
 y_upper = search_95p.predict(xx)
 
 fig = plt.figure(figsize=(10, 10))
-plt.plot(xx, f(xx), "g:", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
+plt.plot(xx, f(xx), "black", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
 plt.plot(X_test, y_test, "b.", markersize=10, label="Test observations")
-plt.plot(xx, y_upper, "k-")
-plt.plot(xx, y_lower, "k-")
 plt.fill_between(
     xx.ravel(), y_lower, y_upper, alpha=0.4, label="Predicted 90% interval"
 )

dev/_downloads/0358b1921962fd7c6f5a94c5abc91476/plot_hashing_vs_dict_vectorizer.zip

@@ -141,7 +141,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\n\ny_pred = all_models[\"mse\"].predict(xx)\ny_lower = all_models[\"q 0.05\"].predict(xx)\ny_upper = all_models[\"q 0.95\"].predict(xx)\ny_med = all_models[\"q 0.50\"].predict(xx)\n\nfig = plt.figure(figsize=(10, 10))\nplt.plot(xx, f(xx), \"g:\", linewidth=3, label=r\"$f(x) = x\\,\\sin(x)$\")\nplt.plot(X_test, y_test, \"b.\", markersize=10, label=\"Test observations\")\nplt.plot(xx, y_med, \"r-\", label=\"Predicted median\")\nplt.plot(xx, y_pred, \"r-\", label=\"Predicted mean\")\nplt.plot(xx, y_upper, \"k-\")\nplt.plot(xx, y_lower, \"k-\")\nplt.fill_between(\n    xx.ravel(), y_lower, y_upper, alpha=0.4, label=\"Predicted 90% interval\"\n)\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$f(x)$\")\nplt.ylim(-10, 25)\nplt.legend(loc=\"upper left\")\nplt.show()"
+        "import matplotlib.pyplot as plt\n\ny_pred = all_models[\"mse\"].predict(xx)\ny_lower = all_models[\"q 0.05\"].predict(xx)\ny_upper = all_models[\"q 0.95\"].predict(xx)\ny_med = all_models[\"q 0.50\"].predict(xx)\n\nfig = plt.figure(figsize=(10, 10))\nplt.plot(xx, f(xx), \"black\", linewidth=3, label=r\"$f(x) = x\\,\\sin(x)$\")\nplt.plot(X_test, y_test, \"b.\", markersize=10, label=\"Test observations\")\nplt.plot(xx, y_med, \"tab:orange\", linewidth=3, label=\"Predicted median\")\nplt.plot(xx, y_pred, \"tab:green\", linewidth=3, label=\"Predicted mean\")\nplt.fill_between(\n    xx.ravel(), y_lower, y_upper, alpha=0.4, label=\"Predicted 90% interval\"\n)\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$f(x)$\")\nplt.ylim(-10, 25)\nplt.legend(loc=\"upper left\")\nplt.show()"
       ]
     },
     {
@@ -267,7 +267,7 @@
       },
       "outputs": [],
       "source": [
-        "y_lower = search_05p.predict(xx)\ny_upper = search_95p.predict(xx)\n\nfig = plt.figure(figsize=(10, 10))\nplt.plot(xx, f(xx), \"g:\", linewidth=3, label=r\"$f(x) = x\\,\\sin(x)$\")\nplt.plot(X_test, y_test, \"b.\", markersize=10, label=\"Test observations\")\nplt.plot(xx, y_upper, \"k-\")\nplt.plot(xx, y_lower, \"k-\")\nplt.fill_between(\n    xx.ravel(), y_lower, y_upper, alpha=0.4, label=\"Predicted 90% interval\"\n)\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$f(x)$\")\nplt.ylim(-10, 25)\nplt.legend(loc=\"upper left\")\nplt.title(\"Prediction with tuned hyper-parameters\")\nplt.show()"
+        "y_lower = search_05p.predict(xx)\ny_upper = search_95p.predict(xx)\n\nfig = plt.figure(figsize=(10, 10))\nplt.plot(xx, f(xx), \"black\", linewidth=3, label=r\"$f(x) = x\\,\\sin(x)$\")\nplt.plot(X_test, y_test, \"b.\", markersize=10, label=\"Test observations\")\nplt.fill_between(\n    xx.ravel(), y_lower, y_upper, alpha=0.4, label=\"Predicted 90% interval\"\n)\nplt.xlabel(\"$x$\")\nplt.ylabel(\"$f(x)$\")\nplt.ylim(-10, 25)\nplt.legend(loc=\"upper left\")\nplt.title(\"Prediction with tuned hyper-parameters\")\nplt.show()"
       ]
     },
     {