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Fix automl settings in scikit-learn pipeline integration example (#602)
* Added test directory and core file to gitignore. Closes #601. * Fixed pipeline fit parameters. Closes #600. * Reverted changes to gitignore.
This commit is contained in:
Zvi Baratz
GitHub
parent
c45741a67b
commit
fefdb48ef3
@ -93,14 +93,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"load dataset from ./openml_ds1169.pkl\n",
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"download dataset from openml\n",
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"Dataset name: airlines\n",
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"X_train.shape: (404537, 7), y_train.shape: (404537,);\n",
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"X_test.shape: (134846, 7), y_test.shape: (134846,)\n"
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@ -115,7 +115,7 @@
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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@ -124,7 +124,7 @@
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"array([ 12., 2648., 4., 15., 4., 450., 67.], dtype=float32)"
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]
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},
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"execution_count": 5,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -142,29 +142,74 @@
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},
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b91d1bdf-ccb8-4fa5-a2d0-67a3538c0afc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b91d1bdf-ccb8-4fa5-a2d0-67a3538c0afc\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
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" ('standardizer', StandardScaler()),\n",
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" ('automl', <flaml.automl.AutoML object at 0x7f046d56fb50>)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a8311733-9e55-4c0c-9c2a-6b9ba6227596\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"a8311733-9e55-4c0c-9c2a-6b9ba6227596\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"52580e54-89ab-4fb7-83a1-ae13962854bb\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"52580e54-89ab-4fb7-83a1-ae13962854bb\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b9fe5397-bf24-491d-a938-c39a780e1ac0\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b9fe5397-bf24-491d-a938-c39a780e1ac0\">AutoML</label><div class=\"sk-toggleable__content\"><pre><flaml.automl.AutoML object at 0x7f046d56fb50></pre></div></div></div></div></div></div></div>"
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"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
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" ('standardizer', StandardScaler()),\n",
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" ('automl',\n",
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" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
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" early_stop=False, ensemble=False, estimator_list='auto',\n",
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" eval_method='auto', fit_kwargs_by_estimator={},\n",
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" hpo_method='auto', keep_search_state=False,\n",
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" learner_selector='sample', log_file_name='',\n",
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" log_training_metric=False, log_type='better',\n",
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" max_iter=None, mem_thres=4294967296, metric='auto',\n",
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" metric_constraints=[], min_sample_size=10000,\n",
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" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
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" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
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" sample=True, split_ratio=0.1, split_type='auto',\n",
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" starting_points='static', task='classification', ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
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" ('standardizer', StandardScaler()),\n",
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" ('automl',\n",
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" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
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" early_stop=False, ensemble=False, estimator_list='auto',\n",
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" eval_method='auto', fit_kwargs_by_estimator={},\n",
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" hpo_method='auto', keep_search_state=False,\n",
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" learner_selector='sample', log_file_name='',\n",
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" log_training_metric=False, log_type='better',\n",
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" max_iter=None, mem_thres=4294967296, metric='auto',\n",
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" metric_constraints=[], min_sample_size=10000,\n",
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" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
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" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
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" sample=True, split_ratio=0.1, split_type='auto',\n",
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" starting_points='static', task='classification', ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AutoML</label><div class=\"sk-toggleable__content\"><pre>AutoML(append_log=False, auto_augment=True, custom_hp={}, early_stop=False,\n",
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" ensemble=False, estimator_list='auto', eval_method='auto',\n",
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" fit_kwargs_by_estimator={}, hpo_method='auto', keep_search_state=False,\n",
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" learner_selector='sample', log_file_name='', log_training_metric=False,\n",
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" log_type='better', max_iter=None, mem_thres=4294967296, metric='auto',\n",
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" metric_constraints=[], min_sample_size=10000, model_history=False,\n",
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" n_concurrent_trials=1, n_jobs=-1, n_splits=5, pred_time_limit=inf,\n",
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" retrain_full=True, sample=True, split_ratio=0.1, split_type='auto',\n",
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" starting_points='static', task='classification', ...)</pre></div></div></div></div></div></div></div>"
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],
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"text/plain": [
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"Pipeline(steps=[('imputuer', SimpleImputer()),\n",
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" ('standardizer', StandardScaler()),\n",
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" ('automl', <flaml.automl.AutoML object at 0x7f046d56fb50>)])"
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" ('automl',\n",
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" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
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" early_stop=False, ensemble=False, estimator_list='auto',\n",
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" eval_method='auto', fit_kwargs_by_estimator={},\n",
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" hpo_method='auto', keep_search_state=False,\n",
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" learner_selector='sample', log_file_name='',\n",
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" log_training_metric=False, log_type='better',\n",
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" max_iter=None, mem_thres=4294967296, metric='auto',\n",
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" metric_constraints=[], min_sample_size=10000,\n",
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" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
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" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
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" sample=True, split_ratio=0.1, split_type='auto',\n",
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" starting_points='static', task='classification', ...))])"
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]
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},
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"execution_count": 6,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import sklearn\n",
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"from sklearn import set_config\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.impute import SimpleImputer\n",
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@ -195,217 +240,177 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"settings = {\n",
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"automl_settings = {\n",
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" \"time_budget\": 60, # total running time in seconds\n",
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" \"metric\": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1','log_loss','mae','mse','r2']\n",
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" \"task\": 'classification', # task type \n",
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" \"estimator_list\":['xgboost','catboost','lgbm'],\n",
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" \"estimator_list\": ['xgboost','catboost','lgbm'],\n",
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" \"log_file_name\": 'airlines_experiment.log', # flaml log file\n",
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"}"
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"}\n",
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"pipeline_settings = {f\"automl__{key}\": value for key, value in automl_settings.items()}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[flaml.automl: 08-22 21:32:13] {1130} INFO - Evaluation method: holdout\n",
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"[flaml.automl: 08-22 21:32:14] {624} INFO - Using StratifiedKFold\n",
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"[flaml.automl: 08-22 21:32:14] {1155} INFO - Minimizing error metric: 1-accuracy\n",
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"[flaml.automl: 08-22 21:32:14] {1175} INFO - List of ML learners in AutoML Run: ['xgboost', 'catboost', 'lgbm']\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 0, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.5s,\tbest xgboost's error=0.3755,\tbest xgboost's error=0.3755\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 1, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.6s,\tbest xgboost's error=0.3755,\tbest xgboost's error=0.3755\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 2, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.6s,\tbest xgboost's error=0.3755,\tbest xgboost's error=0.3755\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 3, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.7s,\tbest xgboost's error=0.3755,\tbest xgboost's error=0.3755\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 4, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.7s,\tbest xgboost's error=0.3679,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 5, current learner lgbm\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.8s,\tbest lgbm's error=0.3811,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 6, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.8s,\tbest xgboost's error=0.3679,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 7, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 0.9s,\tbest xgboost's error=0.3679,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 8, current learner xgboost\n",
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"[flaml.automl: 08-22 21:32:14] {1515} INFO - at 1.0s,\tbest xgboost's error=0.3679,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:14] {1358} INFO - iteration 9, current learner lgbm\n",
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"[flaml.automl: 08-22 21:32:15] {1515} INFO - at 1.1s,\tbest lgbm's error=0.3811,\tbest xgboost's error=0.3679\n",
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"[flaml.automl: 08-22 21:32:15] {1358} INFO - iteration 10, current learner lgbm\n",
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"[flaml.automl: 06-22 08:02:46] {3336} INFO - retrain catboost for 2.8s\n",
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"[flaml.automl: 06-22 08:02:46] {3343} INFO - retrained model: <catboost.core.CatBoostClassifier object at 0x7fbeeb3859d0>\n",
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"[flaml.automl: 06-22 08:02:46] {2636} INFO - fit succeeded\n",
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"[flaml.automl: 06-22 08:02:46] {2637} INFO - Time taken to find the best model: 32.311296463012695\n"
|
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]
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},
|
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{
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"data": {
|
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"text/html": [
|
||||
"<style>div.sk-top-container {color: black;background-color: white;}div.sk-toggleable {background-color: white;}label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}div.sk-estimator:hover {background-color: #d4ebff;}div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}div.sk-item {z-index: 1;}div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}div.sk-parallel-item:only-child::after {width: 0;}div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}div.sk-label-container {position: relative;z-index: 2;text-align: center;}div.sk-container {display: inline-block;position: relative;}</style><div class=\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b994edf1-5e76-4cd3-b719-4a204af673dc\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b994edf1-5e76-4cd3-b719-4a204af673dc\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
|
||||
" ('standardizer', StandardScaler()),\n",
|
||||
" ('automl', <flaml.automl.AutoML object at 0x7f046d56fb50>)])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c94ee64a-d8b1-4cbb-aeca-952bf6963c13\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c94ee64a-d8b1-4cbb-aeca-952bf6963c13\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6a28d11a-19e2-4243-8b85-e3ba5f6f2a7e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"6a28d11a-19e2-4243-8b85-e3ba5f6f2a7e\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"03dcbe59-a8be-4f09-a944-115d90939f81\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"03dcbe59-a8be-4f09-a944-115d90939f81\">AutoML</label><div class=\"sk-toggleable__content\"><pre><flaml.automl.AutoML object at 0x7f046d56fb50></pre></div></div></div></div></div></div></div>"
|
||||
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
|
||||
" ('standardizer', StandardScaler()),\n",
|
||||
" ('automl',\n",
|
||||
" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
|
||||
" early_stop=False, ensemble=False, estimator_list='auto',\n",
|
||||
" eval_method='auto', fit_kwargs_by_estimator={},\n",
|
||||
" hpo_method='auto', keep_search_state=False,\n",
|
||||
" learner_selector='sample', log_file_name='',\n",
|
||||
" log_training_metric=False, log_type='better',\n",
|
||||
" max_iter=None, mem_thres=4294967296, metric='auto',\n",
|
||||
" metric_constraints=[], min_sample_size=10000,\n",
|
||||
" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
|
||||
" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
|
||||
" sample=True, split_ratio=0.1, split_type='auto',\n",
|
||||
" starting_points='static', task='classification', ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('imputuer', SimpleImputer()),\n",
|
||||
" ('standardizer', StandardScaler()),\n",
|
||||
" ('automl',\n",
|
||||
" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
|
||||
" early_stop=False, ensemble=False, estimator_list='auto',\n",
|
||||
" eval_method='auto', fit_kwargs_by_estimator={},\n",
|
||||
" hpo_method='auto', keep_search_state=False,\n",
|
||||
" learner_selector='sample', log_file_name='',\n",
|
||||
" log_training_metric=False, log_type='better',\n",
|
||||
" max_iter=None, mem_thres=4294967296, metric='auto',\n",
|
||||
" metric_constraints=[], min_sample_size=10000,\n",
|
||||
" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
|
||||
" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
|
||||
" sample=True, split_ratio=0.1, split_type='auto',\n",
|
||||
" starting_points='static', task='classification', ...))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AutoML</label><div class=\"sk-toggleable__content\"><pre>AutoML(append_log=False, auto_augment=True, custom_hp={}, early_stop=False,\n",
|
||||
" ensemble=False, estimator_list='auto', eval_method='auto',\n",
|
||||
" fit_kwargs_by_estimator={}, hpo_method='auto', keep_search_state=False,\n",
|
||||
" learner_selector='sample', log_file_name='', log_training_metric=False,\n",
|
||||
" log_type='better', max_iter=None, mem_thres=4294967296, metric='auto',\n",
|
||||
" metric_constraints=[], min_sample_size=10000, model_history=False,\n",
|
||||
" n_concurrent_trials=1, n_jobs=-1, n_splits=5, pred_time_limit=inf,\n",
|
||||
" retrain_full=True, sample=True, split_ratio=0.1, split_type='auto',\n",
|
||||
" starting_points='static', task='classification', ...)</pre></div></div></div></div></div></div></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"Pipeline(steps=[('imputuer', SimpleImputer()),\n",
|
||||
" ('standardizer', StandardScaler()),\n",
|
||||
" ('automl', <flaml.automl.AutoML object at 0x7f046d56fb50>)])"
|
||||
" ('automl',\n",
|
||||
" AutoML(append_log=False, auto_augment=True, custom_hp={},\n",
|
||||
" early_stop=False, ensemble=False, estimator_list='auto',\n",
|
||||
" eval_method='auto', fit_kwargs_by_estimator={},\n",
|
||||
" hpo_method='auto', keep_search_state=False,\n",
|
||||
" learner_selector='sample', log_file_name='',\n",
|
||||
" log_training_metric=False, log_type='better',\n",
|
||||
" max_iter=None, mem_thres=4294967296, metric='auto',\n",
|
||||
" metric_constraints=[], min_sample_size=10000,\n",
|
||||
" model_history=False, n_concurrent_trials=1, n_jobs=-1,\n",
|
||||
" n_splits=5, pred_time_limit=inf, retrain_full=True,\n",
|
||||
" sample=True, split_ratio=0.1, split_type='auto',\n",
|
||||
" starting_points='static', task='classification', ...))])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"automl_pipeline.fit(X_train, y_train, \n",
|
||||
" automl__time_budget=settings['time_budget'],\n",
|
||||
" automl__metric=settings['metric'],\n",
|
||||
" automl__estimator_list=settings['estimator_list'],\n",
|
||||
" automl__log_training_metric=True)"
|
||||
"automl_pipeline.fit(X_train, y_train, **pipeline_settings)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -500,11 +505,9 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "0cfea3304185a9579d09e0953576b57c8581e46e6ebc6dfeb681bc5a511f7544"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.8.0 64-bit ('blend': conda)",
|
||||
"display_name": "Python 3.9.12 64-bit",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
@ -517,7 +520,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.0"
|
||||
"version": "3.9.12"
|
||||
},
|
||||
"vscode": {
|
||||
"interpreter": {
|
||||
"hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
|
||||
}
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@ -37,16 +37,17 @@ automl_pipeline
|
||||
### Run AutoML in the pipeline
|
||||
|
||||
```python
|
||||
settings = {
|
||||
automl_settings = {
|
||||
"time_budget": 60, # total running time in seconds
|
||||
"metric": 'accuracy', # primary metrics can be chosen from: ['accuracy','roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1','log_loss','mae','mse','r2']
|
||||
"task": 'classification', # task type
|
||||
"estimator_list":['xgboost','catboost','lgbm'],
|
||||
"log_file_name": 'airlines_experiment.log', # flaml log file
|
||||
"metric": "accuracy", # primary metrics can be chosen from: ['accuracy','roc_auc', 'roc_auc_ovr', 'roc_auc_ovo', 'f1','log_loss','mae','mse','r2']
|
||||
"task": "classification", # task type
|
||||
"estimator_list": ["xgboost", "catboost", "lgbm"],
|
||||
"log_file_name": "airlines_experiment.log", # flaml log file
|
||||
}
|
||||
automl_pipeline.fit(X_train, y_train,
|
||||
automl__time_budget=60,
|
||||
automl__metric="accuracy")
|
||||
pipeline_settings = {
|
||||
f"automl__{key}": value for key, value in automl_settings.items()
|
||||
}
|
||||
automl_pipeline.fit(X_train, y_train, **pipeline_settings)
|
||||
```
|
||||
|
||||
### Get the automl object from the pipeline
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user