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Last active October 17, 2020 00:56
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results.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.experimental import enable_hist_gradient_boosting\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.base import BaseEstimator\n",
"from sklearn.compose import ColumnTransformer\n",
"from sklearn.preprocessing import OrdinalEncoder\n",
"from sklearn.ensemble import HistGradientBoostingClassifier\n",
"from sklearn.ensemble import HistGradientBoostingRegressor\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.compose import make_column_selector\n",
"from sklearn.impute import SimpleImputer\n",
"from sksearchspace import AutoHalvingRandomSearchCV\n",
"import json\n",
"import pandas as pd\n",
"\n",
"import sklearn\n",
"sklearn.set_config(display='diagram')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overview of D3M with AutoHalvingRandomSearchCV"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we use a really simple pipeline: "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"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=\"08d1212c-93a7-40e8-bad1-426c8d2439c5\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"08d1212c-93a7-40e8-bad1-426c8d2439c5\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('num', 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])),\n",
" ('clf', HistGradientBoostingClassifier())])</pre></div></div></div><div class=\"sk-serial\"><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=\"4dee3336-106c-47bd-b376-ab45a12ab13d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4dee3336-106c-47bd-b376-ab45a12ab13d\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('num', 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"3a20a871-98e5-4dbc-b58d-c3060df55b57\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"3a20a871-98e5-4dbc-b58d-c3060df55b57\">num</label><div class=\"sk-toggleable__content\"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0></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=\"97116b8e-be0b-4639-a1ea-271c0f164e32\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"97116b8e-be0b-4639-a1ea-271c0f164e32\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f095b2a1-d22f-43d6-a797-f54ddcea79ec\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"f095b2a1-d22f-43d6-a797-f54ddcea79ec\">cat</label><div class=\"sk-toggleable__content\"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"43f749eb-9d7a-4533-b4e1-60084cec4faa\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"43f749eb-9d7a-4533-b4e1-60084cec4faa\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value='sk_missing', strategy='constant')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b73ec1e8-063c-4229-8d2b-74eb9d50ade3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b73ec1e8-063c-4229-8d2b-74eb9d50ade3\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"53cbfb64-4791-4b7e-8914-9d1d75650559\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"53cbfb64-4791-4b7e-8914-9d1d75650559\">HistGradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div>"
],
"text/plain": [
"Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('num', 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])),\n",
" ('clf', HistGradientBoostingClassifier())])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cat_prep = Pipeline([\n",
"\n",
" ('impute', SimpleImputer(strategy='constant', fill_value='sk_missing')),\n",
" ('encoder', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))\n",
"])\n",
"\n",
"ct = ColumnTransformer([\n",
" ('num', 'passthrough', make_column_selector(dtype_include=['number'])),\n",
" ('cat', cat_prep, make_column_selector(dtype_include=['object', 'category']))\n",
"])\n",
"\n",
"pipe = Pipeline(\n",
" [('preprocess', ct),\n",
" ('clf', HistGradientBoostingClassifier())]\n",
")\n",
"pipe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Example of using `AutoHalvingRandomSearchCV` is shown in the [README](https://github.com/thomasjpfan/sksearchspace/tree/AutoHalvingRandomSearchCV_0.24.dev0#auto-halving-search) of `sksearchspace`"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"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=\"1c2d02f9-ba6b-41ba-8d15-11db32ebb961\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"1c2d02f9-ba6b-41ba-8d15-11db32ebb961\">AutoHalvingRandomSearchCV</label><div class=\"sk-toggleable__content\"><pre>AutoHalvingRandomSearchCV(estimator=Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('num',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])),\n",
" ('clf',\n",
" HistGradientBoostingClassifier())]),\n",
" random_state=0,\n",
" refit=<function _refit_callable at 0x7fc1adf78af0>,\n",
" verbose=1)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><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=\"dddb0af0-5f01-442f-b45f-b1586b7c0239\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"dddb0af0-5f01-442f-b45f-b1586b7c0239\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[('num', 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b9eabb71-90be-4cf6-a8d7-7ed9e8952be8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b9eabb71-90be-4cf6-a8d7-7ed9e8952be8\">num</label><div class=\"sk-toggleable__content\"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0></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=\"d7e8dda1-c78d-4d1d-95e7-352f5e92b2f8\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"d7e8dda1-c78d-4d1d-95e7-352f5e92b2f8\">passthrough</label><div class=\"sk-toggleable__content\"><pre>passthrough</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"08533656-786d-4269-9e71-92b6296a4d5d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"08533656-786d-4269-9e71-92b6296a4d5d\">cat</label><div class=\"sk-toggleable__content\"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90></pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"5b2fbbe3-3afb-45fc-b551-a4cc7c040078\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"5b2fbbe3-3afb-45fc-b551-a4cc7c040078\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer(fill_value='sk_missing', strategy='constant')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c6696af1-4e9a-4a52-b0ae-8b18e50ae707\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c6696af1-4e9a-4a52-b0ae-8b18e50ae707\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c9181462-db81-4b45-bb16-03404e6797f3\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"c9181462-db81-4b45-bb16-03404e6797f3\">HistGradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
],
"text/plain": [
"AutoHalvingRandomSearchCV(estimator=Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('num',\n",
" 'passthrough',\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70c7f0>),\n",
" ('cat',\n",
" Pipeline(steps=[('impute',\n",
" SimpleImputer(fill_value='sk_missing',\n",
" strategy='constant')),\n",
" ('encoder',\n",
" OrdinalEncoder(handle_unknown='use_encoded_value',\n",
" unknown_value=-1))]),\n",
" <sklearn.compose._column_transformer.make_column_selector object at 0x7fc1ae70cd90>)])),\n",
" ('clf',\n",
" HistGradientBoostingClassifier())]),\n",
" random_state=0,\n",
" refit=<function _refit_callable at 0x7fc1adf78af0>,\n",
" verbose=1)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"search_cv = AutoHalvingRandomSearchCV(pipe, verbose=1, random_state=0)\n",
"search_cv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Results for tabular D3M datasets compared to Winter2020 dry run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here are the results for datasets with the time it took to train and the relative rank compared to the other teams."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>time</th>\n",
" <th>rank</th>\n",
" <th>dataset</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5.768322</td>\n",
" <td>1</td>\n",
" <td>299_libras_move</td>\n",
" <td>9.413095e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>26.817153</td>\n",
" <td>2</td>\n",
" <td>LL0_186_braziltourism</td>\n",
" <td>3.381186e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>3.592759</td>\n",
" <td>3</td>\n",
" <td>LL0_207_autoPrice</td>\n",
" <td>-5.178898e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>67.041031</td>\n",
" <td>3</td>\n",
" <td>196_autoMpg</td>\n",
" <td>-6.421878e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>111.016912</td>\n",
" <td>3</td>\n",
" <td>57_hypothyroid</td>\n",
" <td>9.866451e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>31.862460</td>\n",
" <td>3</td>\n",
" <td>534_cps_85_wages</td>\n",
" <td>-2.018426e+01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>138.088802</td>\n",
" <td>5</td>\n",
" <td>26_radon_seed</td>\n",
" <td>-2.020705e-02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>44.006810</td>\n",
" <td>10</td>\n",
" <td>LL0_1100_popularkids</td>\n",
" <td>3.727642e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>62.725310</td>\n",
" <td>11</td>\n",
" <td>185_baseball</td>\n",
" <td>6.735195e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>668.855906</td>\n",
" <td>11</td>\n",
" <td>27_wordLevels</td>\n",
" <td>2.155993e-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>95.491247</td>\n",
" <td>11</td>\n",
" <td>1491_one_hundred_plants_margin</td>\n",
" <td>6.894127e-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" time rank dataset score\n",
"2 5.768322 1 299_libras_move 9.413095e-01\n",
"10 26.817153 2 LL0_186_braziltourism 3.381186e-01\n",
"1 3.592759 3 LL0_207_autoPrice -5.178898e+06\n",
"5 67.041031 3 196_autoMpg -6.421878e+00\n",
"6 111.016912 3 57_hypothyroid 9.866451e-01\n",
"9 31.862460 3 534_cps_85_wages -2.018426e+01\n",
"3 138.088802 5 26_radon_seed -2.020705e-02\n",
"4 44.006810 10 LL0_1100_popularkids 3.727642e-01\n",
"0 62.725310 11 185_baseball 6.735195e-01\n",
"7 668.855906 11 27_wordLevels 2.155993e-01\n",
"8 95.491247 11 1491_one_hundred_plants_margin 6.894127e-01"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = pd.read_csv(\"results.csv\")\n",
"results.sort_values(\"rank\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "sksearchspace",
"language": "python",
"name": "conda-env-sksearchspace-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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