Pushing the docs to dev/ for branch: main, commit e54a077f2d17f61c7f21404226dba04585da6a44

Author: dirty-cat-ci

dev/_downloads/198ad5d07740cb53545df898265e9ffe/02_text_with_string_encoders.py

@@ -295,6 +295,7 @@ def plot_performance_tradeoff(results):
             c="k",
             capsize=2,
         )
+        ax.set_xscale("log")
 
         ax.set_xlabel("Time to fit (seconds)")
         ax.set_ylabel("ROC AUC")
@@ -306,8 +307,8 @@ def plot_performance_tradeoff(results):
         xytext=(8.5, 0.90),
         arrowprops=dict(arrowstyle="->", mutation_scale=15),
     )
-    ax.text(8, 0.86, "Best time / \nperformance trade-off")
-    ax.legend(bbox_to_anchor=(1, 0.3))
+    ax.text(5.8, 0.86, "Best time / \nperformance trade-off")
+    ax.legend(bbox_to_anchor=(1.02, 0.3))
     plt.show()
 
 

dev/_downloads/1beb4a6828e42ba7f09ae925e26e794b/02_text_with_string_encoders.zip

@@ -166,7 +166,7 @@
       },
       "outputs": [],
       "source": [
-        "def plot_performance_tradeoff(results):\n    fig, ax = plt.subplots(figsize=(5, 4), dpi=200)\n    markers = [\"s\", \"o\", \"^\", \"x\"]\n    for idx, (name, result) in enumerate(results):\n        ax.scatter(\n            result[\"fit_time\"],\n            result[\"test_score\"],\n            label=name,\n            marker=markers[idx],\n        )\n        mean_fit_time = np.mean(result[\"fit_time\"])\n        mean_score = np.mean(result[\"test_score\"])\n        ax.scatter(\n            mean_fit_time,\n            mean_score,\n            color=\"k\",\n            marker=markers[idx],\n        )\n        std_fit_time = np.std(result[\"fit_time\"])\n        std_score = np.std(result[\"test_score\"])\n        ax.errorbar(\n            x=mean_fit_time,\n            y=mean_score,\n            yerr=std_score,\n            fmt=\"none\",\n            c=\"k\",\n            capsize=2,\n        )\n        ax.errorbar(\n            x=mean_fit_time,\n            y=mean_score,\n            xerr=std_fit_time,\n            fmt=\"none\",\n            c=\"k\",\n            capsize=2,\n        )\n\n        ax.set_xlabel(\"Time to fit (seconds)\")\n        ax.set_ylabel(\"ROC AUC\")\n        ax.set_title(\"Prediction performance / training time trade-off\")\n\n    ax.annotate(\n        \"\",\n        xy=(1.5, 0.98),\n        xytext=(8.5, 0.90),\n        arrowprops=dict(arrowstyle=\"->\", mutation_scale=15),\n    )\n    ax.text(8, 0.86, \"Best time / \\nperformance trade-off\")\n    ax.legend(bbox_to_anchor=(1, 0.3))\n    plt.show()\n\n\nplot_performance_tradeoff(results)"
+        "def plot_performance_tradeoff(results):\n    fig, ax = plt.subplots(figsize=(5, 4), dpi=200)\n    markers = [\"s\", \"o\", \"^\", \"x\"]\n    for idx, (name, result) in enumerate(results):\n        ax.scatter(\n            result[\"fit_time\"],\n            result[\"test_score\"],\n            label=name,\n            marker=markers[idx],\n        )\n        mean_fit_time = np.mean(result[\"fit_time\"])\n        mean_score = np.mean(result[\"test_score\"])\n        ax.scatter(\n            mean_fit_time,\n            mean_score,\n            color=\"k\",\n            marker=markers[idx],\n        )\n        std_fit_time = np.std(result[\"fit_time\"])\n        std_score = np.std(result[\"test_score\"])\n        ax.errorbar(\n            x=mean_fit_time,\n            y=mean_score,\n            yerr=std_score,\n            fmt=\"none\",\n            c=\"k\",\n            capsize=2,\n        )\n        ax.errorbar(\n            x=mean_fit_time,\n            y=mean_score,\n            xerr=std_fit_time,\n            fmt=\"none\",\n            c=\"k\",\n            capsize=2,\n        )\n        ax.set_xscale(\"log\")\n\n        ax.set_xlabel(\"Time to fit (seconds)\")\n        ax.set_ylabel(\"ROC AUC\")\n        ax.set_title(\"Prediction performance / training time trade-off\")\n\n    ax.annotate(\n        \"\",\n        xy=(1.5, 0.98),\n        xytext=(8.5, 0.90),\n        arrowprops=dict(arrowstyle=\"->\", mutation_scale=15),\n    )\n    ax.text(5.8, 0.86, \"Best time / \\nperformance trade-off\")\n    ax.legend(bbox_to_anchor=(1.02, 0.3))\n    plt.show()\n\n\nplot_performance_tradeoff(results)"
       ]
     },
     {