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Pushing the docs to dev/ for branch: main, commit f15ddc05cb4c39b07acf3a950b32471fbaa7f2e2
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dev/_downloads/2f3ef774a6d7e52e1e6b7ccbb75d25f0/plot_gradient_boosting_quantile.py

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@@ -104,12 +104,10 @@ def f(x):
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y_med = all_models["q 0.50"].predict(xx)
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fig = plt.figure(figsize=(10, 10))
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plt.plot(xx, f(xx), "g:", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
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plt.plot(xx, f(xx), "black", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
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plt.plot(X_test, y_test, "b.", markersize=10, label="Test observations")
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plt.plot(xx, y_med, "r-", label="Predicted median")
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plt.plot(xx, y_pred, "r-", label="Predicted mean")
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plt.plot(xx, y_upper, "k-")
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plt.plot(xx, y_lower, "k-")
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plt.plot(xx, y_med, "tab:orange", linewidth=3, label="Predicted median")
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plt.plot(xx, y_pred, "tab:green", linewidth=3, label="Predicted mean")
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plt.fill_between(
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xx.ravel(), y_lower, y_upper, alpha=0.4, label="Predicted 90% interval"
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)
@@ -310,10 +308,8 @@ def coverage_fraction(y, y_low, y_high):
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y_upper = search_95p.predict(xx)
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fig = plt.figure(figsize=(10, 10))
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plt.plot(xx, f(xx), "g:", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
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plt.plot(xx, f(xx), "black", linewidth=3, label=r"$f(x) = x\,\sin(x)$")
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plt.plot(X_test, y_test, "b.", markersize=10, label="Test observations")
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plt.plot(xx, y_upper, "k-")
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plt.plot(xx, y_lower, "k-")
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plt.fill_between(
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xx.ravel(), y_lower, y_upper, alpha=0.4, label="Predicted 90% interval"
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)
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dev/_downloads/b5ac5dfd67b0aab146fcb9faaac8480c/plot_gradient_boosting_quantile.ipynb

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -141,7 +141,7 @@
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},
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"outputs": [],
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"source": [
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"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()"
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"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()"
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]
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},
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{
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},
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"outputs": [],
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"source": [
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"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()"
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"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()"
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]
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},
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{

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