Pushing the docs to dev/ for branch: main, commit 43550f011a3adcb86fa09a7d508a9929ab87e49d

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -54,8 +54,10 @@
 
 def make_heatmap(ax, gs, is_sh=False, make_cbar=False):
     """Helper to make a heatmap."""
-    results = pd.DataFrame.from_dict(gs.cv_results_)
-    results["params_str"] = results.params.apply(str)
+    results = pd.DataFrame(gs.cv_results_)
+    results[["param_C", "param_gamma"]] = results[["param_C", "param_gamma"]].astype(
+        np.float64
+    )
     if is_sh:
         # SH dataframe: get mean_test_score values for the highest iter
         scores_matrix = results.sort_values("iter").pivot_table(

dev/_downloads/6383d955c013c730f9d211f15e261f38/plot_successive_halving_heatmap.py

@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "def make_heatmap(ax, gs, is_sh=False, make_cbar=False):\n    \"\"\"Helper to make a heatmap.\"\"\"\n    results = pd.DataFrame.from_dict(gs.cv_results_)\n    results[\"params_str\"] = results.params.apply(str)\n    if is_sh:\n        # SH dataframe: get mean_test_score values for the highest iter\n        scores_matrix = results.sort_values(\"iter\").pivot_table(\n            index=\"param_gamma\",\n            columns=\"param_C\",\n            values=\"mean_test_score\",\n            aggfunc=\"last\",\n        )\n    else:\n        scores_matrix = results.pivot(\n            index=\"param_gamma\", columns=\"param_C\", values=\"mean_test_score\"\n        )\n\n    im = ax.imshow(scores_matrix)\n\n    ax.set_xticks(np.arange(len(Cs)))\n    ax.set_xticklabels([\"{:.0E}\".format(x) for x in Cs])\n    ax.set_xlabel(\"C\", fontsize=15)\n\n    ax.set_yticks(np.arange(len(gammas)))\n    ax.set_yticklabels([\"{:.0E}\".format(x) for x in gammas])\n    ax.set_ylabel(\"gamma\", fontsize=15)\n\n    # Rotate the tick labels and set their alignment.\n    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n\n    if is_sh:\n        iterations = results.pivot_table(\n            index=\"param_gamma\", columns=\"param_C\", values=\"iter\", aggfunc=\"max\"\n        ).values\n        for i in range(len(gammas)):\n            for j in range(len(Cs)):\n                ax.text(\n                    j,\n                    i,\n                    iterations[i, j],\n                    ha=\"center\",\n                    va=\"center\",\n                    color=\"w\",\n                    fontsize=20,\n                )\n\n    if make_cbar:\n        fig.subplots_adjust(right=0.8)\n        cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n        fig.colorbar(im, cax=cbar_ax)\n        cbar_ax.set_ylabel(\"mean_test_score\", rotation=-90, va=\"bottom\", fontsize=15)\n\n\nfig, axes = plt.subplots(ncols=2, sharey=True)\nax1, ax2 = axes\n\nmake_heatmap(ax1, gsh, is_sh=True)\nmake_heatmap(ax2, gs, make_cbar=True)\n\nax1.set_title(\"Successive Halving\\ntime = {:.3f}s\".format(gsh_time), fontsize=15)\nax2.set_title(\"GridSearch\\ntime = {:.3f}s\".format(gs_time), fontsize=15)\n\nplt.show()"
+        "def make_heatmap(ax, gs, is_sh=False, make_cbar=False):\n    \"\"\"Helper to make a heatmap.\"\"\"\n    results = pd.DataFrame(gs.cv_results_)\n    results[[\"param_C\", \"param_gamma\"]] = results[[\"param_C\", \"param_gamma\"]].astype(\n        np.float64\n    )\n    if is_sh:\n        # SH dataframe: get mean_test_score values for the highest iter\n        scores_matrix = results.sort_values(\"iter\").pivot_table(\n            index=\"param_gamma\",\n            columns=\"param_C\",\n            values=\"mean_test_score\",\n            aggfunc=\"last\",\n        )\n    else:\n        scores_matrix = results.pivot(\n            index=\"param_gamma\", columns=\"param_C\", values=\"mean_test_score\"\n        )\n\n    im = ax.imshow(scores_matrix)\n\n    ax.set_xticks(np.arange(len(Cs)))\n    ax.set_xticklabels([\"{:.0E}\".format(x) for x in Cs])\n    ax.set_xlabel(\"C\", fontsize=15)\n\n    ax.set_yticks(np.arange(len(gammas)))\n    ax.set_yticklabels([\"{:.0E}\".format(x) for x in gammas])\n    ax.set_ylabel(\"gamma\", fontsize=15)\n\n    # Rotate the tick labels and set their alignment.\n    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\", rotation_mode=\"anchor\")\n\n    if is_sh:\n        iterations = results.pivot_table(\n            index=\"param_gamma\", columns=\"param_C\", values=\"iter\", aggfunc=\"max\"\n        ).values\n        for i in range(len(gammas)):\n            for j in range(len(Cs)):\n                ax.text(\n                    j,\n                    i,\n                    iterations[i, j],\n                    ha=\"center\",\n                    va=\"center\",\n                    color=\"w\",\n                    fontsize=20,\n                )\n\n    if make_cbar:\n        fig.subplots_adjust(right=0.8)\n        cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n        fig.colorbar(im, cax=cbar_ax)\n        cbar_ax.set_ylabel(\"mean_test_score\", rotation=-90, va=\"bottom\", fontsize=15)\n\n\nfig, axes = plt.subplots(ncols=2, sharey=True)\nax1, ax2 = axes\n\nmake_heatmap(ax1, gsh, is_sh=True)\nmake_heatmap(ax2, gs, make_cbar=True)\n\nax1.set_title(\"Successive Halving\\ntime = {:.3f}s\".format(gsh_time), fontsize=15)\nax2.set_title(\"GridSearch\\ntime = {:.3f}s\".format(gs_time), fontsize=15)\n\nplt.show()"
       ]
     },
     {