Pushing the docs to dev/ for branch: master, commit 373946ec9e3c72116bfbf7fab051cf4797d13e0f

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Successive Halving Iterations\n\nThis example illustrates how a successive halving search (\n:class:`~sklearn.model_selection.HalvingGridSearchCV` and\n:class:`~sklearn.model_selection.HalvingRandomSearchCV`) iteratively chooses\nthe best parameter combination out of multiple candidates.\n"
+        "\n# Successive Halving Iterations\n\nThis example illustrates how a successive halving search\n(:class:`~sklearn.model_selection.HalvingGridSearchCV` and\n:class:`~sklearn.model_selection.HalvingRandomSearchCV`)\niteratively chooses the best parameter combination out of\nmultiple candidates.\n"
       ]
     },
     {
@@ -62,7 +62,7 @@
       },
       "outputs": [],
       "source": [
-        "results = pd.DataFrame(rsh.cv_results_)\nresults['params_str'] = results.params.apply(str)\nresults.drop_duplicates(subset=('params_str', 'iter'), inplace=True)\nmean_scores = results.pivot(index='iter', columns='params_str',\n                            values='mean_test_score')\nax = mean_scores.plot(legend=False, alpha=.6)\n\nlabels = [\n    f'iter={i}\\nn_samples={rsh.n_resources_[i]}\\n'\n    f'n_candidates={rsh.n_candidates_[i]}'\n    for i in range(rsh.n_iterations_)\n]\nax.set_xticklabels(labels, rotation=45, multialignment='left')\nax.set_title('Scores of candidates over iterations')\nax.set_ylabel('mean test score', fontsize=15)\nax.set_xlabel('iterations', fontsize=15)\nplt.tight_layout()\nplt.show()"
+        "results = pd.DataFrame(rsh.cv_results_)\nresults['params_str'] = results.params.apply(str)\nresults.drop_duplicates(subset=('params_str', 'iter'), inplace=True)\nmean_scores = results.pivot(index='iter', columns='params_str',\n                            values='mean_test_score')\nax = mean_scores.plot(legend=False, alpha=.6)\n\nlabels = [\n    f'iter={i}\\nn_samples={rsh.n_resources_[i]}\\n'\n    f'n_candidates={rsh.n_candidates_[i]}'\n    for i in range(rsh.n_iterations_)\n]\n\nax.set_xticks(range(rsh.n_iterations_))\nax.set_xticklabels(labels, rotation=45, multialignment='left')\nax.set_title('Scores of candidates over iterations')\nax.set_ylabel('mean test score', fontsize=15)\nax.set_xlabel('iterations', fontsize=15)\nplt.tight_layout()\nplt.show()"
       ]
     },
     {

dev/_downloads/23fb33f64b3c23edf25165a3a4f04237/plot_successive_halving_iterations.ipynb

@@ -2,10 +2,11 @@
 Successive Halving Iterations
 =============================
 
-This example illustrates how a successive halving search (
-:class:`~sklearn.model_selection.HalvingGridSearchCV` and
-:class:`~sklearn.model_selection.HalvingRandomSearchCV`) iteratively chooses
-the best parameter combination out of multiple candidates.
+This example illustrates how a successive halving search
+(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
+:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
+iteratively chooses the best parameter combination out of
+multiple candidates.
 
 """
 import pandas as pd
@@ -60,6 +61,8 @@
     f'n_candidates={rsh.n_candidates_[i]}'
     for i in range(rsh.n_iterations_)
 ]
+
+ax.set_xticks(range(rsh.n_iterations_))
 ax.set_xticklabels(labels, rotation=45, multialignment='left')
 ax.set_title('Scores of candidates over iterations')
 ax.set_ylabel('mean test score', fontsize=15)