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Created March 20, 2021 15:50
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colab_auto_sklearn_profiler.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "colab_auto_sklearn_profiler.ipynb",
"provenance": [],
"collapsed_sections": [],
"mount_file_id": "11_t63audBgQMHVGIIfSgp_Dp1sCbKRHy",
"authorship_tag": "ABX9TyO/+yKS3cAfiIwYjZH3gNkF",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/adamnovotnycom/ffe8e3961fe0207c64a1b9a074883e51/colab_auto_sklearn_profiler.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MXByWp-mQKTp",
"outputId": "996196dc-9e6a-4b17-f7fc-88ae7d2011ed"
},
"source": [
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"import plotly.express as px\n",
"import random\n",
"import sys\n",
"from sklearn.base import BaseEstimator, TransformerMixin\n",
"from sklearn.datasets import make_regression\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.impute import SimpleImputer\n",
"from sklearn.linear_model import ElasticNet\n",
"from sklearn.metrics import r2_score\n",
"from sklearn.model_selection import GridSearchCV, TimeSeriesSplit\n",
"from sklearn.pipeline import FeatureUnion, Pipeline \n",
"from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
"from sklearn.svm import SVR\n",
"print(sys.version)\n",
"print(pd.__version__)"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"3.7.10 (default, Feb 20 2021, 21:17:23) \n",
"[GCC 7.5.0]\n",
"1.1.5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gkl01MkHby2x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2e3c6309-edb8-4681-cf02-be7ee07e76d5"
},
"source": [
"if pd.__version__[0] == \"0\":\n",
" !pip install pandas==1.1.5\n",
" import pandas as pd\n",
"assert pd.__version__[0] == \"1\"\n",
"print(pd.__version__) "
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"1.1.5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "uazEb54Ucs6D",
"outputId": "38c78e1c-58fc-4de7-8e63-1440fbbb90a2"
},
"source": [
"!sudo apt-get install build-essential swig\n",
"!pip install -U tornado\n",
"!pip install dask distributed --upgrade\n",
"!pip install auto-sklearn==0.11.1\n",
"!pip install pipelineprofiler"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Reading package lists... Done\n",
"Building dependency tree \n",
"Reading state information... Done\n",
"build-essential is already the newest version (12.4ubuntu1).\n",
"swig is already the newest version (3.0.12-1).\n",
"0 upgraded, 0 newly installed, 0 to remove and 30 not upgraded.\n",
"Requirement already up-to-date: tornado in /usr/local/lib/python3.7/dist-packages (6.1)\n",
"Requirement already up-to-date: dask in /usr/local/lib/python3.7/dist-packages (2021.3.0)\n",
"Requirement already up-to-date: distributed in /usr/local/lib/python3.7/dist-packages (2021.3.0)\n",
"Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python3.7/dist-packages (from dask) (3.13)\n",
"Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.7/dist-packages (from distributed) (54.1.2)\n",
"Requirement already satisfied, skipping upgrade: click>=6.6 in /usr/local/lib/python3.7/dist-packages (from distributed) (7.1.2)\n",
"Requirement already satisfied, skipping upgrade: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed) (5.4.8)\n",
"Requirement already satisfied, skipping upgrade: toolz>=0.8.2 in /usr/local/lib/python3.7/dist-packages (from distributed) (0.11.1)\n",
"Requirement already satisfied, skipping upgrade: cloudpickle>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from distributed) (1.6.0)\n",
"Requirement already satisfied, skipping upgrade: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed) (1.0.2)\n",
"Requirement already satisfied, skipping upgrade: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed) (1.7.0)\n",
"Requirement already satisfied, skipping upgrade: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed) (2.0.0)\n",
"Requirement already satisfied, skipping upgrade: tornado>=5; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from distributed) (6.1)\n",
"Requirement already satisfied, skipping upgrade: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed) (2.3.0)\n",
"Requirement already satisfied, skipping upgrade: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed) (1.0.1)\n",
"Requirement already satisfied: auto-sklearn==0.11.1 in /usr/local/lib/python3.7/dist-packages (0.11.1)\n",
"Requirement already satisfied: pynisher>=0.6.1 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn==0.11.1) (0.6.4)\n",
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"Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from auto-sklearn==0.11.1) (3.13)\n",
"Requirement already satisfied: liac-arff in /usr/local/lib/python3.7/dist-packages (from auto-sklearn==0.11.1) (2.5.0)\n",
"Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from auto-sklearn==0.11.1) (1.19.5)\n",
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],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bjlx7he7aQ_y",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c12b3c28-6a11-4571-a76d-48fe879bc90b"
},
"source": [
"# Import may fail after the initial instalation of auto-sklearn \n",
"# -> Restart runtime and run all again\n",
"from autosklearn.classification import AutoSklearnClassifier"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/pyparsing.py:3190: FutureWarning:\n",
"\n",
"Possible set intersection at position 3\n",
"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 333
},
"id": "GnxADdQ3NIVp",
"outputId": "b16c9e7d-5520-4afd-ccd2-8b9569f7d904"
},
"source": [
"n_features = 100\n",
"n_samples = 1000\n",
"feature_names = [\"feat_\" + str(i) for i in range(n_features)]\n",
"X, y, coeff = make_regression(\n",
" n_samples=n_samples,\n",
" n_features=100,\n",
" n_informative=5,\n",
" noise=0, \n",
" shuffle=False,\n",
" coef=True\n",
")\n",
"df = pd.DataFrame(X, columns=feature_names)\n",
"df[\"label\"] = y\n",
"df.describe(include=\"all\")"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
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" .dataframe thead th {\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>feat_95</th>\n",
" <th>feat_96</th>\n",
" <th>feat_97</th>\n",
" <th>feat_98</th>\n",
" <th>feat_99</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>...</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.019203</td>\n",
" <td>0.072811</td>\n",
" <td>0.034746</td>\n",
" <td>-0.097877</td>\n",
" <td>-0.011952</td>\n",
" <td>0.066686</td>\n",
" <td>0.030159</td>\n",
" <td>0.000189</td>\n",
" <td>0.002420</td>\n",
" <td>0.017563</td>\n",
" <td>0.006284</td>\n",
" <td>0.031366</td>\n",
" <td>-0.002748</td>\n",
" <td>0.010262</td>\n",
" <td>0.015054</td>\n",
" <td>0.015649</td>\n",
" <td>-0.017908</td>\n",
" <td>0.083927</td>\n",
" <td>0.038492</td>\n",
" <td>-0.005242</td>\n",
" <td>0.017566</td>\n",
" <td>-0.030901</td>\n",
" <td>-0.018557</td>\n",
" <td>-0.045484</td>\n",
" <td>0.006014</td>\n",
" <td>-0.048975</td>\n",
" <td>-0.011360</td>\n",
" <td>0.013926</td>\n",
" <td>0.007330</td>\n",
" <td>0.032466</td>\n",
" <td>0.014782</td>\n",
" <td>0.021690</td>\n",
" <td>0.021282</td>\n",
" <td>-0.024470</td>\n",
" <td>0.012764</td>\n",
" <td>-0.027039</td>\n",
" <td>-0.042040</td>\n",
" <td>0.029412</td>\n",
" <td>-0.004173</td>\n",
" <td>-0.003642</td>\n",
" <td>...</td>\n",
" <td>-0.054103</td>\n",
" <td>0.018199</td>\n",
" <td>0.003166</td>\n",
" <td>-0.013486</td>\n",
" <td>-0.002757</td>\n",
" <td>0.081124</td>\n",
" <td>-0.004794</td>\n",
" <td>-0.053903</td>\n",
" <td>-0.002314</td>\n",
" <td>0.062766</td>\n",
" <td>0.074859</td>\n",
" <td>-0.072353</td>\n",
" <td>-0.048654</td>\n",
" <td>0.035699</td>\n",
" <td>-0.004792</td>\n",
" <td>-0.014287</td>\n",
" <td>0.000113</td>\n",
" <td>-0.069440</td>\n",
" <td>0.006885</td>\n",
" <td>-0.039721</td>\n",
" <td>0.011703</td>\n",
" <td>0.004805</td>\n",
" <td>0.061173</td>\n",
" <td>0.047362</td>\n",
" <td>0.040387</td>\n",
" <td>0.012096</td>\n",
" <td>0.013284</td>\n",
" <td>0.010094</td>\n",
" <td>0.010650</td>\n",
" <td>-0.017618</td>\n",
" <td>-0.017092</td>\n",
" <td>0.017875</td>\n",
" <td>-0.003053</td>\n",
" <td>0.017447</td>\n",
" <td>0.055891</td>\n",
" <td>-0.034754</td>\n",
" <td>0.013203</td>\n",
" <td>-0.008486</td>\n",
" <td>-0.028561</td>\n",
" <td>-0.693929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.973870</td>\n",
" <td>1.004768</td>\n",
" <td>0.999862</td>\n",
" <td>1.007465</td>\n",
" <td>0.998945</td>\n",
" <td>1.031129</td>\n",
" <td>1.048829</td>\n",
" <td>0.985594</td>\n",
" <td>1.050631</td>\n",
" <td>1.013454</td>\n",
" <td>1.008945</td>\n",
" <td>0.975183</td>\n",
" <td>0.992397</td>\n",
" <td>0.985975</td>\n",
" <td>0.963173</td>\n",
" <td>1.034716</td>\n",
" <td>0.956639</td>\n",
" <td>0.929459</td>\n",
" <td>1.003659</td>\n",
" <td>0.991913</td>\n",
" <td>1.004515</td>\n",
" <td>1.000251</td>\n",
" <td>0.980971</td>\n",
" <td>1.020970</td>\n",
" <td>1.019893</td>\n",
" <td>1.009620</td>\n",
" <td>1.012069</td>\n",
" <td>0.998826</td>\n",
" <td>1.015051</td>\n",
" <td>0.980708</td>\n",
" <td>1.044498</td>\n",
" <td>0.973193</td>\n",
" <td>1.018589</td>\n",
" <td>0.990067</td>\n",
" <td>1.009618</td>\n",
" <td>0.958499</td>\n",
" <td>1.028147</td>\n",
" <td>0.965750</td>\n",
" <td>1.007875</td>\n",
" <td>1.032366</td>\n",
" <td>...</td>\n",
" <td>0.990841</td>\n",
" <td>0.962328</td>\n",
" <td>1.009747</td>\n",
" <td>1.005100</td>\n",
" <td>0.962815</td>\n",
" <td>1.052184</td>\n",
" <td>0.971594</td>\n",
" <td>0.990236</td>\n",
" <td>1.010134</td>\n",
" <td>1.018775</td>\n",
" <td>1.009757</td>\n",
" <td>0.970031</td>\n",
" <td>0.997735</td>\n",
" <td>0.955657</td>\n",
" <td>1.003400</td>\n",
" <td>0.993153</td>\n",
" <td>1.030896</td>\n",
" <td>0.969546</td>\n",
" <td>1.004345</td>\n",
" <td>0.975442</td>\n",
" <td>0.976086</td>\n",
" <td>0.984694</td>\n",
" <td>0.987340</td>\n",
" <td>0.977618</td>\n",
" <td>1.021386</td>\n",
" <td>0.992606</td>\n",
" <td>1.019135</td>\n",
" <td>0.996453</td>\n",
" <td>0.996129</td>\n",
" <td>1.021785</td>\n",
" <td>1.037741</td>\n",
" <td>1.028137</td>\n",
" <td>0.978914</td>\n",
" <td>1.018245</td>\n",
" <td>1.042289</td>\n",
" <td>1.003997</td>\n",
" <td>0.991046</td>\n",
" <td>0.995290</td>\n",
" <td>0.995240</td>\n",
" <td>156.795949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-2.765573</td>\n",
" <td>-2.731727</td>\n",
" <td>-2.846354</td>\n",
" <td>-3.033817</td>\n",
" <td>-2.848870</td>\n",
" <td>-3.315968</td>\n",
" <td>-3.033812</td>\n",
" <td>-3.286159</td>\n",
" <td>-3.054974</td>\n",
" <td>-2.991715</td>\n",
" <td>-3.027087</td>\n",
" <td>-2.849216</td>\n",
" <td>-3.058706</td>\n",
" <td>-3.838618</td>\n",
" <td>-2.782878</td>\n",
" <td>-3.665172</td>\n",
" <td>-3.711044</td>\n",
" <td>-2.769722</td>\n",
" <td>-3.587558</td>\n",
" <td>-3.183253</td>\n",
" <td>-3.352504</td>\n",
" <td>-3.310045</td>\n",
" <td>-2.600647</td>\n",
" <td>-3.469872</td>\n",
" <td>-3.204222</td>\n",
" <td>-2.915251</td>\n",
" <td>-2.884871</td>\n",
" <td>-3.663712</td>\n",
" <td>-2.929196</td>\n",
" <td>-3.483136</td>\n",
" <td>-3.509528</td>\n",
" <td>-3.102396</td>\n",
" <td>-3.193426</td>\n",
" <td>-2.658238</td>\n",
" <td>-2.812689</td>\n",
" <td>-2.972708</td>\n",
" <td>-3.535813</td>\n",
" <td>-3.205097</td>\n",
" <td>-3.539630</td>\n",
" <td>-3.278193</td>\n",
" <td>...</td>\n",
" <td>-2.892609</td>\n",
" <td>-2.577924</td>\n",
" <td>-4.020972</td>\n",
" <td>-3.135617</td>\n",
" <td>-3.496491</td>\n",
" <td>-3.720986</td>\n",
" <td>-3.453112</td>\n",
" <td>-3.493723</td>\n",
" <td>-3.190831</td>\n",
" <td>-2.975431</td>\n",
" <td>-3.349800</td>\n",
" <td>-3.449818</td>\n",
" <td>-3.261018</td>\n",
" <td>-3.328669</td>\n",
" <td>-3.197572</td>\n",
" <td>-3.133965</td>\n",
" <td>-2.913934</td>\n",
" <td>-3.480351</td>\n",
" <td>-3.196015</td>\n",
" <td>-4.649858</td>\n",
" <td>-2.795095</td>\n",
" <td>-3.212514</td>\n",
" <td>-2.777956</td>\n",
" <td>-3.558587</td>\n",
" <td>-3.486024</td>\n",
" <td>-3.088846</td>\n",
" <td>-3.052082</td>\n",
" <td>-2.798971</td>\n",
" <td>-2.848340</td>\n",
" <td>-3.539618</td>\n",
" <td>-3.340491</td>\n",
" <td>-2.974426</td>\n",
" <td>-3.196331</td>\n",
" <td>-3.280777</td>\n",
" <td>-3.427735</td>\n",
" <td>-3.194310</td>\n",
" <td>-2.827028</td>\n",
" <td>-3.098474</td>\n",
" <td>-2.897833</td>\n",
" <td>-552.197622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>-0.676321</td>\n",
" <td>-0.629661</td>\n",
" <td>-0.680625</td>\n",
" <td>-0.807015</td>\n",
" <td>-0.664607</td>\n",
" <td>-0.671336</td>\n",
" <td>-0.718173</td>\n",
" <td>-0.676454</td>\n",
" <td>-0.705811</td>\n",
" <td>-0.650574</td>\n",
" <td>-0.693801</td>\n",
" <td>-0.589354</td>\n",
" <td>-0.643861</td>\n",
" <td>-0.679057</td>\n",
" <td>-0.640994</td>\n",
" <td>-0.673618</td>\n",
" <td>-0.646520</td>\n",
" <td>-0.579014</td>\n",
" <td>-0.663780</td>\n",
" <td>-0.684801</td>\n",
" <td>-0.667897</td>\n",
" <td>-0.731198</td>\n",
" <td>-0.674951</td>\n",
" <td>-0.753102</td>\n",
" <td>-0.686039</td>\n",
" <td>-0.748897</td>\n",
" <td>-0.735551</td>\n",
" <td>-0.614446</td>\n",
" <td>-0.638776</td>\n",
" <td>-0.648189</td>\n",
" <td>-0.652329</td>\n",
" <td>-0.616909</td>\n",
" <td>-0.632700</td>\n",
" <td>-0.717579</td>\n",
" <td>-0.697392</td>\n",
" <td>-0.671472</td>\n",
" <td>-0.733369</td>\n",
" <td>-0.578196</td>\n",
" <td>-0.699989</td>\n",
" <td>-0.686083</td>\n",
" <td>...</td>\n",
" <td>-0.727118</td>\n",
" <td>-0.675894</td>\n",
" <td>-0.704584</td>\n",
" <td>-0.701404</td>\n",
" <td>-0.686563</td>\n",
" <td>-0.602587</td>\n",
" <td>-0.643403</td>\n",
" <td>-0.684945</td>\n",
" <td>-0.640580</td>\n",
" <td>-0.638519</td>\n",
" <td>-0.616474</td>\n",
" <td>-0.765868</td>\n",
" <td>-0.747456</td>\n",
" <td>-0.569431</td>\n",
" <td>-0.662788</td>\n",
" <td>-0.686248</td>\n",
" <td>-0.682736</td>\n",
" <td>-0.745134</td>\n",
" <td>-0.656286</td>\n",
" <td>-0.665905</td>\n",
" <td>-0.661333</td>\n",
" <td>-0.643449</td>\n",
" <td>-0.585665</td>\n",
" <td>-0.612435</td>\n",
" <td>-0.686181</td>\n",
" <td>-0.660987</td>\n",
" <td>-0.652181</td>\n",
" <td>-0.699866</td>\n",
" <td>-0.630471</td>\n",
" <td>-0.686378</td>\n",
" <td>-0.697847</td>\n",
" <td>-0.661811</td>\n",
" <td>-0.662083</td>\n",
" <td>-0.632202</td>\n",
" <td>-0.618930</td>\n",
" <td>-0.731618</td>\n",
" <td>-0.676630</td>\n",
" <td>-0.668744</td>\n",
" <td>-0.689677</td>\n",
" <td>-98.326307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>-0.038091</td>\n",
" <td>0.095016</td>\n",
" <td>0.029457</td>\n",
" <td>-0.073810</td>\n",
" <td>-0.004831</td>\n",
" <td>0.078501</td>\n",
" <td>0.009822</td>\n",
" <td>-0.008720</td>\n",
" <td>0.011434</td>\n",
" <td>-0.008726</td>\n",
" <td>-0.001878</td>\n",
" <td>0.014341</td>\n",
" <td>0.033412</td>\n",
" <td>0.006637</td>\n",
" <td>0.052220</td>\n",
" <td>-0.010368</td>\n",
" <td>-0.014754</td>\n",
" <td>0.067721</td>\n",
" <td>0.035848</td>\n",
" <td>-0.013227</td>\n",
" <td>0.005184</td>\n",
" <td>-0.073271</td>\n",
" <td>-0.026870</td>\n",
" <td>-0.058872</td>\n",
" <td>0.019228</td>\n",
" <td>-0.041615</td>\n",
" <td>-0.006941</td>\n",
" <td>0.052769</td>\n",
" <td>0.030802</td>\n",
" <td>-0.008860</td>\n",
" <td>-0.017476</td>\n",
" <td>0.000332</td>\n",
" <td>0.027341</td>\n",
" <td>-0.032113</td>\n",
" <td>0.002343</td>\n",
" <td>-0.044942</td>\n",
" <td>-0.045441</td>\n",
" <td>0.055692</td>\n",
" <td>0.008169</td>\n",
" <td>-0.033074</td>\n",
" <td>...</td>\n",
" <td>-0.049789</td>\n",
" <td>0.022649</td>\n",
" <td>-0.013231</td>\n",
" <td>-0.016607</td>\n",
" <td>-0.011069</td>\n",
" <td>0.070115</td>\n",
" <td>-0.000556</td>\n",
" <td>-0.031849</td>\n",
" <td>0.024749</td>\n",
" <td>0.004671</td>\n",
" <td>0.094692</td>\n",
" <td>-0.069446</td>\n",
" <td>-0.053199</td>\n",
" <td>0.032946</td>\n",
" <td>-0.018742</td>\n",
" <td>-0.050370</td>\n",
" <td>-0.054469</td>\n",
" <td>-0.020724</td>\n",
" <td>0.021914</td>\n",
" <td>0.009400</td>\n",
" <td>-0.021129</td>\n",
" <td>0.033363</td>\n",
" <td>0.071212</td>\n",
" <td>0.030248</td>\n",
" <td>0.072361</td>\n",
" <td>-0.008894</td>\n",
" <td>0.001344</td>\n",
" <td>0.008644</td>\n",
" <td>0.025936</td>\n",
" <td>-0.003293</td>\n",
" <td>-0.009914</td>\n",
" <td>0.019698</td>\n",
" <td>0.008743</td>\n",
" <td>0.056183</td>\n",
" <td>0.093708</td>\n",
" <td>-0.033623</td>\n",
" <td>0.045236</td>\n",
" <td>0.000043</td>\n",
" <td>-0.031789</td>\n",
" <td>-0.043498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.633403</td>\n",
" <td>0.777429</td>\n",
" <td>0.707360</td>\n",
" <td>0.595601</td>\n",
" <td>0.671508</td>\n",
" <td>0.758155</td>\n",
" <td>0.744497</td>\n",
" <td>0.666768</td>\n",
" <td>0.736434</td>\n",
" <td>0.712700</td>\n",
" <td>0.688065</td>\n",
" <td>0.692900</td>\n",
" <td>0.605220</td>\n",
" <td>0.668602</td>\n",
" <td>0.652184</td>\n",
" <td>0.724633</td>\n",
" <td>0.663859</td>\n",
" <td>0.745836</td>\n",
" <td>0.702741</td>\n",
" <td>0.665913</td>\n",
" <td>0.679483</td>\n",
" <td>0.659118</td>\n",
" <td>0.621244</td>\n",
" <td>0.619366</td>\n",
" <td>0.705254</td>\n",
" <td>0.643611</td>\n",
" <td>0.677285</td>\n",
" <td>0.636778</td>\n",
" <td>0.690061</td>\n",
" <td>0.712487</td>\n",
" <td>0.695342</td>\n",
" <td>0.646868</td>\n",
" <td>0.678055</td>\n",
" <td>0.654483</td>\n",
" <td>0.702076</td>\n",
" <td>0.626822</td>\n",
" <td>0.651463</td>\n",
" <td>0.686961</td>\n",
" <td>0.688609</td>\n",
" <td>0.677129</td>\n",
" <td>...</td>\n",
" <td>0.642709</td>\n",
" <td>0.665944</td>\n",
" <td>0.695515</td>\n",
" <td>0.710975</td>\n",
" <td>0.655329</td>\n",
" <td>0.786996</td>\n",
" <td>0.671391</td>\n",
" <td>0.603326</td>\n",
" <td>0.674047</td>\n",
" <td>0.767700</td>\n",
" <td>0.736179</td>\n",
" <td>0.610128</td>\n",
" <td>0.630390</td>\n",
" <td>0.638956</td>\n",
" <td>0.665782</td>\n",
" <td>0.709193</td>\n",
" <td>0.727619</td>\n",
" <td>0.576610</td>\n",
" <td>0.698628</td>\n",
" <td>0.600495</td>\n",
" <td>0.653422</td>\n",
" <td>0.644439</td>\n",
" <td>0.673071</td>\n",
" <td>0.706484</td>\n",
" <td>0.690993</td>\n",
" <td>0.703874</td>\n",
" <td>0.691476</td>\n",
" <td>0.694085</td>\n",
" <td>0.657737</td>\n",
" <td>0.637031</td>\n",
" <td>0.684598</td>\n",
" <td>0.669994</td>\n",
" <td>0.631924</td>\n",
" <td>0.696028</td>\n",
" <td>0.772634</td>\n",
" <td>0.633767</td>\n",
" <td>0.640201</td>\n",
" <td>0.725448</td>\n",
" <td>0.631506</td>\n",
" <td>103.499420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3.026795</td>\n",
" <td>3.390151</td>\n",
" <td>2.991081</td>\n",
" <td>3.174868</td>\n",
" <td>3.684328</td>\n",
" <td>3.361824</td>\n",
" <td>3.481438</td>\n",
" <td>3.051310</td>\n",
" <td>3.255389</td>\n",
" <td>3.177705</td>\n",
" <td>2.585613</td>\n",
" <td>2.865223</td>\n",
" <td>2.953114</td>\n",
" <td>3.166652</td>\n",
" <td>3.765819</td>\n",
" <td>3.553199</td>\n",
" <td>2.955398</td>\n",
" <td>2.700445</td>\n",
" <td>2.996122</td>\n",
" <td>3.082052</td>\n",
" <td>3.330310</td>\n",
" <td>3.524104</td>\n",
" <td>2.936346</td>\n",
" <td>3.283567</td>\n",
" <td>2.804219</td>\n",
" <td>3.405304</td>\n",
" <td>3.071213</td>\n",
" <td>2.903830</td>\n",
" <td>2.981178</td>\n",
" <td>3.079457</td>\n",
" <td>3.836762</td>\n",
" <td>2.960405</td>\n",
" <td>3.520389</td>\n",
" <td>2.755024</td>\n",
" <td>3.615282</td>\n",
" <td>2.918236</td>\n",
" <td>3.208628</td>\n",
" <td>3.176017</td>\n",
" <td>3.036659</td>\n",
" <td>2.853665</td>\n",
" <td>...</td>\n",
" <td>3.229805</td>\n",
" <td>2.714358</td>\n",
" <td>2.831686</td>\n",
" <td>3.870396</td>\n",
" <td>3.137382</td>\n",
" <td>3.400247</td>\n",
" <td>3.053751</td>\n",
" <td>3.285882</td>\n",
" <td>3.274251</td>\n",
" <td>3.409249</td>\n",
" <td>3.412608</td>\n",
" <td>2.636035</td>\n",
" <td>3.254553</td>\n",
" <td>3.326257</td>\n",
" <td>3.036208</td>\n",
" <td>2.822060</td>\n",
" <td>3.275174</td>\n",
" <td>2.786524</td>\n",
" <td>3.337405</td>\n",
" <td>3.479219</td>\n",
" <td>3.286041</td>\n",
" <td>3.251010</td>\n",
" <td>2.984686</td>\n",
" <td>3.635974</td>\n",
" <td>3.551308</td>\n",
" <td>3.467748</td>\n",
" <td>3.594626</td>\n",
" <td>3.199389</td>\n",
" <td>3.857585</td>\n",
" <td>3.861245</td>\n",
" <td>3.342104</td>\n",
" <td>3.840093</td>\n",
" <td>3.365860</td>\n",
" <td>3.294649</td>\n",
" <td>3.314006</td>\n",
" <td>3.868654</td>\n",
" <td>2.970025</td>\n",
" <td>3.187762</td>\n",
" <td>3.016280</td>\n",
" <td>462.072385</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 101 columns</p>\n",
"</div>"
],
"text/plain": [
" feat_0 feat_1 ... feat_99 label\n",
"count 1000.000000 1000.000000 ... 1000.000000 1000.000000\n",
"mean -0.019203 0.072811 ... -0.028561 -0.693929\n",
"std 0.973870 1.004768 ... 0.995240 156.795949\n",
"min -2.765573 -2.731727 ... -2.897833 -552.197622\n",
"25% -0.676321 -0.629661 ... -0.689677 -98.326307\n",
"50% -0.038091 0.095016 ... -0.031789 -0.043498\n",
"75% 0.633403 0.777429 ... 0.631506 103.499420\n",
"max 3.026795 3.390151 ... 3.016280 462.072385\n",
"\n",
"[8 rows x 101 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
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"colab": {
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"id": "hb3BsHb9N68F",
"outputId": "e1e0f919-896b-4200-a5af-194c965251fd"
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"source": [
"coef_df = pd.DataFrame(index=feature_names)\n",
"coef_df[\"true_coefficients\"] = coeff\n",
"significant_coeffs = coef_df.loc[coef_df[\"true_coefficients\"]>0, :].index\n",
"coef_df.loc[significant_coeffs, :]"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
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" <tr>\n",
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"text/plain": [
" true_coefficients\n",
"feat_0 35.337174\n",
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"metadata": {
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"execution_count": 7,
"outputs": [
{
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" feat_0 feat_1 feat_2 feat_3 feat_4 label\n",
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]
},
"metadata": {
"tags": []
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"execution_count": 7
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},
{
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"id": "Oxw49kkGF-Ed",
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"source": [
"fig = px.scatter_matrix(df.loc[:, list(significant_coeffs) + \n",
" [\"label\"] + [\"feat_5\", \"feat_6\", \"feat_7\"]])\n",
"fig.update_layout(width=900,\n",
" height=700)\n",
"fig.update_traces(marker=dict(size=2))\n",
"fig.show()"
],
"execution_count": 8,
"outputs": [
{
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},
{
"cell_type": "markdown",
"metadata": {
"id": "ytlIbGY67RGU"
},
"source": [
"Include meaningless categorical features"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 422
},
"id": "uuPXgKUMoOCx",
"outputId": "f453a479-22d6-4173-85fe-417e6ca9050e"
},
"source": [
"states = [\"CA\", \"NY\", \"TX\"]\n",
"df[\"state\"] = random.choices(states, k=n_samples)\n",
"hour = [\"1\", \"2\"]\n",
"df[\"hour\"] = random.choices(hour, k=n_samples)\n",
"df.describe(include=\"all\")"
],
"execution_count": 9,
"outputs": [
{
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>371</td>\n",
" <td>525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>-0.019203</td>\n",
" <td>0.072811</td>\n",
" <td>0.034746</td>\n",
" <td>-0.097877</td>\n",
" <td>-0.011952</td>\n",
" <td>0.066686</td>\n",
" <td>0.030159</td>\n",
" <td>0.000189</td>\n",
" <td>0.002420</td>\n",
" <td>0.017563</td>\n",
" <td>0.006284</td>\n",
" <td>0.031366</td>\n",
" <td>-0.002748</td>\n",
" <td>0.010262</td>\n",
" <td>0.015054</td>\n",
" <td>0.015649</td>\n",
" <td>-0.017908</td>\n",
" <td>0.083927</td>\n",
" <td>0.038492</td>\n",
" <td>-0.005242</td>\n",
" <td>0.017566</td>\n",
" <td>-0.030901</td>\n",
" <td>-0.018557</td>\n",
" <td>-0.045484</td>\n",
" <td>0.006014</td>\n",
" <td>-0.048975</td>\n",
" <td>-0.011360</td>\n",
" <td>0.013926</td>\n",
" <td>0.007330</td>\n",
" <td>0.032466</td>\n",
" <td>0.014782</td>\n",
" <td>0.021690</td>\n",
" <td>0.021282</td>\n",
" <td>-0.024470</td>\n",
" <td>0.012764</td>\n",
" <td>-0.027039</td>\n",
" <td>-0.042040</td>\n",
" <td>0.029412</td>\n",
" <td>-0.004173</td>\n",
" <td>-0.003642</td>\n",
" <td>...</td>\n",
" <td>0.003166</td>\n",
" <td>-0.013486</td>\n",
" <td>-0.002757</td>\n",
" <td>0.081124</td>\n",
" <td>-0.004794</td>\n",
" <td>-0.053903</td>\n",
" <td>-0.002314</td>\n",
" <td>0.062766</td>\n",
" <td>0.074859</td>\n",
" <td>-0.072353</td>\n",
" <td>-0.048654</td>\n",
" <td>0.035699</td>\n",
" <td>-0.004792</td>\n",
" <td>-0.014287</td>\n",
" <td>0.000113</td>\n",
" <td>-0.069440</td>\n",
" <td>0.006885</td>\n",
" <td>-0.039721</td>\n",
" <td>0.011703</td>\n",
" <td>0.004805</td>\n",
" <td>0.061173</td>\n",
" <td>0.047362</td>\n",
" <td>0.040387</td>\n",
" <td>0.012096</td>\n",
" <td>0.013284</td>\n",
" <td>0.010094</td>\n",
" <td>0.010650</td>\n",
" <td>-0.017618</td>\n",
" <td>-0.017092</td>\n",
" <td>0.017875</td>\n",
" <td>-0.003053</td>\n",
" <td>0.017447</td>\n",
" <td>0.055891</td>\n",
" <td>-0.034754</td>\n",
" <td>0.013203</td>\n",
" <td>-0.008486</td>\n",
" <td>-0.028561</td>\n",
" <td>-0.693929</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.973870</td>\n",
" <td>1.004768</td>\n",
" <td>0.999862</td>\n",
" <td>1.007465</td>\n",
" <td>0.998945</td>\n",
" <td>1.031129</td>\n",
" <td>1.048829</td>\n",
" <td>0.985594</td>\n",
" <td>1.050631</td>\n",
" <td>1.013454</td>\n",
" <td>1.008945</td>\n",
" <td>0.975183</td>\n",
" <td>0.992397</td>\n",
" <td>0.985975</td>\n",
" <td>0.963173</td>\n",
" <td>1.034716</td>\n",
" <td>0.956639</td>\n",
" <td>0.929459</td>\n",
" <td>1.003659</td>\n",
" <td>0.991913</td>\n",
" <td>1.004515</td>\n",
" <td>1.000251</td>\n",
" <td>0.980971</td>\n",
" <td>1.020970</td>\n",
" <td>1.019893</td>\n",
" <td>1.009620</td>\n",
" <td>1.012069</td>\n",
" <td>0.998826</td>\n",
" <td>1.015051</td>\n",
" <td>0.980708</td>\n",
" <td>1.044498</td>\n",
" <td>0.973193</td>\n",
" <td>1.018589</td>\n",
" <td>0.990067</td>\n",
" <td>1.009618</td>\n",
" <td>0.958499</td>\n",
" <td>1.028147</td>\n",
" <td>0.965750</td>\n",
" <td>1.007875</td>\n",
" <td>1.032366</td>\n",
" <td>...</td>\n",
" <td>1.009747</td>\n",
" <td>1.005100</td>\n",
" <td>0.962815</td>\n",
" <td>1.052184</td>\n",
" <td>0.971594</td>\n",
" <td>0.990236</td>\n",
" <td>1.010134</td>\n",
" <td>1.018775</td>\n",
" <td>1.009757</td>\n",
" <td>0.970031</td>\n",
" <td>0.997735</td>\n",
" <td>0.955657</td>\n",
" <td>1.003400</td>\n",
" <td>0.993153</td>\n",
" <td>1.030896</td>\n",
" <td>0.969546</td>\n",
" <td>1.004345</td>\n",
" <td>0.975442</td>\n",
" <td>0.976086</td>\n",
" <td>0.984694</td>\n",
" <td>0.987340</td>\n",
" <td>0.977618</td>\n",
" <td>1.021386</td>\n",
" <td>0.992606</td>\n",
" <td>1.019135</td>\n",
" <td>0.996453</td>\n",
" <td>0.996129</td>\n",
" <td>1.021785</td>\n",
" <td>1.037741</td>\n",
" <td>1.028137</td>\n",
" <td>0.978914</td>\n",
" <td>1.018245</td>\n",
" <td>1.042289</td>\n",
" <td>1.003997</td>\n",
" <td>0.991046</td>\n",
" <td>0.995290</td>\n",
" <td>0.995240</td>\n",
" <td>156.795949</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>-2.765573</td>\n",
" <td>-2.731727</td>\n",
" <td>-2.846354</td>\n",
" <td>-3.033817</td>\n",
" <td>-2.848870</td>\n",
" <td>-3.315968</td>\n",
" <td>-3.033812</td>\n",
" <td>-3.286159</td>\n",
" <td>-3.054974</td>\n",
" <td>-2.991715</td>\n",
" <td>-3.027087</td>\n",
" <td>-2.849216</td>\n",
" <td>-3.058706</td>\n",
" <td>-3.838618</td>\n",
" <td>-2.782878</td>\n",
" <td>-3.665172</td>\n",
" <td>-3.711044</td>\n",
" <td>-2.769722</td>\n",
" <td>-3.587558</td>\n",
" <td>-3.183253</td>\n",
" <td>-3.352504</td>\n",
" <td>-3.310045</td>\n",
" <td>-2.600647</td>\n",
" <td>-3.469872</td>\n",
" <td>-3.204222</td>\n",
" <td>-2.915251</td>\n",
" <td>-2.884871</td>\n",
" <td>-3.663712</td>\n",
" <td>-2.929196</td>\n",
" <td>-3.483136</td>\n",
" <td>-3.509528</td>\n",
" <td>-3.102396</td>\n",
" <td>-3.193426</td>\n",
" <td>-2.658238</td>\n",
" <td>-2.812689</td>\n",
" <td>-2.972708</td>\n",
" <td>-3.535813</td>\n",
" <td>-3.205097</td>\n",
" <td>-3.539630</td>\n",
" <td>-3.278193</td>\n",
" <td>...</td>\n",
" <td>-4.020972</td>\n",
" <td>-3.135617</td>\n",
" <td>-3.496491</td>\n",
" <td>-3.720986</td>\n",
" <td>-3.453112</td>\n",
" <td>-3.493723</td>\n",
" <td>-3.190831</td>\n",
" <td>-2.975431</td>\n",
" <td>-3.349800</td>\n",
" <td>-3.449818</td>\n",
" <td>-3.261018</td>\n",
" <td>-3.328669</td>\n",
" <td>-3.197572</td>\n",
" <td>-3.133965</td>\n",
" <td>-2.913934</td>\n",