results-anova.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train an ANOVA model on the configurations of the pipeline and their respective drug-agnostic $ r^2 $ to determine which factors lead to a bigger improvement in performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean = pd.read_csv('data/runs/clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(119804, 77)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">r2_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Selumetinib</th>\n",
       "      <td>4512.0</td>\n",
       "      <td>0.189548</td>\n",
       "      <td>0.177463</td>\n",
       "      <td>-0.586784</td>\n",
       "      <td>0.003512</td>\n",
       "      <td>0.215650</td>\n",
       "      <td>0.337072</td>\n",
       "      <td>0.598656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nutlin-3</th>\n",
       "      <td>4480.0</td>\n",
       "      <td>0.042574</td>\n",
       "      <td>0.222276</td>\n",
       "      <td>-1.956753</td>\n",
       "      <td>-0.029524</td>\n",
       "      <td>0.013063</td>\n",
       "      <td>0.129311</td>\n",
       "      <td>0.738862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paclitaxel</th>\n",
       "      <td>4479.0</td>\n",
       "      <td>0.114114</td>\n",
       "      <td>0.157428</td>\n",
       "      <td>-0.866935</td>\n",
       "      <td>-0.003286</td>\n",
       "      <td>0.121838</td>\n",
       "      <td>0.223766</td>\n",
       "      <td>0.479926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Saracatinib</th>\n",
       "      <td>4452.0</td>\n",
       "      <td>0.089953</td>\n",
       "      <td>0.155960</td>\n",
       "      <td>-1.412134</td>\n",
       "      <td>-0.006891</td>\n",
       "      <td>0.083961</td>\n",
       "      <td>0.181256</td>\n",
       "      <td>0.586263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TAE684</th>\n",
       "      <td>4451.0</td>\n",
       "      <td>0.054032</td>\n",
       "      <td>0.166655</td>\n",
       "      <td>-0.774732</td>\n",
       "      <td>-0.022764</td>\n",
       "      <td>0.016381</td>\n",
       "      <td>0.126441</td>\n",
       "      <td>0.693535</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            r2_score                                                    \\\n",
       "               count      mean       std       min       25%       50%   \n",
       "Drug                                                                     \n",
       "Selumetinib   4512.0  0.189548  0.177463 -0.586784  0.003512  0.215650   \n",
       "Nutlin-3      4480.0  0.042574  0.222276 -1.956753 -0.029524  0.013063   \n",
       "Paclitaxel    4479.0  0.114114  0.157428 -0.866935 -0.003286  0.121838   \n",
       "Saracatinib   4452.0  0.089953  0.155960 -1.412134 -0.006891  0.083961   \n",
       "TAE684        4451.0  0.054032  0.166655 -0.774732 -0.022764  0.016381   \n",
       "\n",
       "                                 \n",
       "                  75%       max  \n",
       "Drug                             \n",
       "Selumetinib  0.337072  0.598656  \n",
       "Nutlin-3     0.129311  0.738862  \n",
       "Paclitaxel   0.223766  0.479926  \n",
       "Saracatinib  0.181256  0.586263  \n",
       "TAE684       0.126441  0.693535  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean[['Drug', 'r2_score']].groupby('Drug').describe().sort_values(('r2_score', 'count'), ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['C', 'Drug', 'algorithm', 'alpha', 'bootstrap', 'cache_size', 'coef0',\n",
       "       'copy_X', 'criterion', 'degree', 'epsilon', 'fit_intercept', 'gamma',\n",
       "       'kernel', 'l1_ratio', 'leaf_size', 'max_depth', 'max_features',\n",
       "       'max_iter', 'max_leaf_nodes', 'mean_absolute_error',\n",
       "       'mean_squared_error', 'median_absolute_error', 'metric',\n",
       "       'metric_params', 'min_impurity_decrease', 'min_impurity_split',\n",
       "       'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf',\n",
       "       'n_estimators', 'n_jobs', 'n_neighbors', 'normalize', 'oob_score', 'p',\n",
       "       'positive', 'precompute', 'presort', 'r2_score', 'random_state', 'run',\n",
       "       'selection', 'shrinking', 'splitter', 'tol', 'verbose', 'warm_start',\n",
       "       'weights', 'operation', 'started', 'time', 'status', 'label', 'ajive',\n",
       "       'ccle', 'ctrp', 'cutoff', 'drugs', 'feda', 'fs', 'gdsc', 'metric_drug',\n",
       "       'model', 'n', 'norm', 'test', 'threshold', 'step', 'r_2_mean',\n",
       "       'r_2_std', 'seconds', 'datasets', 'Quality', 'r2', 'da', 'catn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by determining the factors and interaction factors using the patsy formula api"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "f = 'r2 ~catn +Quality + fs:model + model +catn:model+ +norm:model+da:model+da+ctrp + gdsc + norm+C(datasets)+metric_drug'\n",
    "y, X = patsy.dmatrices(f,clean.drop('C', axis=1), return_type='dataframe')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop factors that either don't exist on our model aand those that would cause multicolinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.drop(['fs[T.f_regression]:model[DecisionTreeRegressor]', 'fs[T.ElasticNet]:model[ElasticNet]', 'fs[T.RandomForestRegressor]:model[RandomForestRegressor]'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We turn the results of the OLS from a `SimpleTable` into a `DataFrame`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = sm.OLS(y, X).fit()\n",
    "results = pd.DataFrame(mod.summary(alpha=0.01).tables[1].data).set_index(0)\n",
    "results = results.rename(columns=results.iloc[0])[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "results['[0.005'] = pd.to_numeric(results['[0.005'])\n",
    "results['0.995]'] = pd.to_numeric(results['0.995]'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We plot the factors for which 0 is not within the 99% confidence interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.005</th>\n",
       "      <th>0.995]</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>C(datasets)[T.2]</th>\n",
       "      <td>0.0165</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7.532</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>da[T.none]</th>\n",
       "      <td>0.0173</td>\n",
       "      <td>0.005</td>\n",
       "      <td>3.775</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fs[T.Lasso]:model[RandomForestRegressor]</th>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2.833</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catn[T.mid]</th>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.005</td>\n",
       "      <td>4.014</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fs[T.ElasticNet]:model[SVR]</th>\n",
       "      <td>0.0217</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2.955</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.041</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                coef    std err          t  \\\n",
       "0                                                                            \n",
       "C(datasets)[T.2]                              0.0165      0.002      7.532   \n",
       "da[T.none]                                    0.0173      0.005      3.775   \n",
       "fs[T.Lasso]:model[RandomForestRegressor]      0.0180      0.006      2.833   \n",
       "catn[T.mid]                                   0.0199      0.005      4.014   \n",
       "fs[T.ElasticNet]:model[SVR]                   0.0217      0.007      2.955   \n",
       "\n",
       "                                           P>|t|  [0.005  0.995]  \n",
       "0                                                                 \n",
       "C(datasets)[T.2]                           0.000   0.011   0.022  \n",
       "da[T.none]                                 0.000   0.005   0.029  \n",
       "fs[T.Lasso]:model[RandomForestRegressor]   0.005   0.002   0.034  \n",
       "catn[T.mid]                                0.000   0.007   0.033  \n",
       "fs[T.ElasticNet]:model[SVR]                0.003   0.003   0.041  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[results['[0.005']*results['0.995]'] > 0].sort_values('coef').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83, 6)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see 42 interaction factors are not significant (0 is within their 99% confidence interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36, 6)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[results['[0.005']*results['0.995]'] > 0].sort_values('coef').shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We drop non-significant factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop = list(results[results['[0.005']*results['0.995]'] < 0].index)\n",
    "drop = [i for i in drop if ':' in i]\n",
    "X = X.drop(drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then retrain the OLS model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = sm.OLS(y, X).fit()\n",
    "results = pd.DataFrame(mod.summary(alpha=0.01).tables[1].data).set_index(0)\n",
    "results = results.rename(columns=results.iloc[0])[1:]\n",
    "results['[0.005'] = pd.to_numeric(results['[0.005'])\n",
    "results['0.995]'] = pd.to_numeric(results['0.995]'])\n",
    "results['coef'] = pd.to_numeric(results['coef'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "anova = results[results['[0.005']*results['0.995]'] > 0]\n",
    "anova = anova.sort_values('coef').reset_index().rename(columns={0:'factor'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor</th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.005</th>\n",
       "      <th>0.995]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-0.1704</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-37.700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>-0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>da[T.feda]</td>\n",
       "      <td>-0.0872</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-20.375</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.SVR]</td>\n",
       "      <td>-0.0850</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-7.889</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.RandomForestReg...</td>\n",
       "      <td>-0.0824</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-8.569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.KNeighborsRegre...</td>\n",
       "      <td>-0.0814</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-6.538</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-9.585</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>da[T.feda]:model[T.SVR]</td>\n",
       "      <td>-0.0531</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-8.346</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>-0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>da[T.feda]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0483</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-6.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>norm[T.None]:model[T.RandomForestRegressor]</td>\n",
       "      <td>-0.0431</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-7.395</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>catn[T.low]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0341</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-4.703</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>catn[T.mid]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0332</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-4.427</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>da[T.feda]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0313</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-5.458</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>catn[T.very low]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0267</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-3.721</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.045</td>\n",
       "      <td>-0.008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>da[T.feda]:model[T.RandomForestRegressor]</td>\n",
       "      <td>-0.0262</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-4.069</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>norm[T.None]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0260</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-3.415</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>da[T.none]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0245</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-3.493</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.043</td>\n",
       "      <td>-0.006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>catn[T.very low]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0233</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-4.936</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.035</td>\n",
       "      <td>-0.011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>catn[T.mid]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0173</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-3.658</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.030</td>\n",
       "      <td>-0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>da[T.none]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0126</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-2.692</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>catn[T.very low]</td>\n",
       "      <td>-0.0091</td>\n",
       "      <td>0.003</td>\n",
       "      <td>-2.846</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-0.017</td>\n",
       "      <td>-0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>norm[T.QuantileTransformer]</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.003</td>\n",
       "      <td>2.955</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>da[T.none]</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4.323</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>catn[T.mid]</td>\n",
       "      <td>0.0145</td>\n",
       "      <td>0.003</td>\n",
       "      <td>4.608</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>fs[T.ElasticNet]:model[SVR]</td>\n",
       "      <td>0.0162</td>\n",
       "      <td>0.006</td>\n",
       "      <td>2.872</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>C(datasets)[T.2]</td>\n",
       "      <td>0.0163</td>\n",
       "      <td>0.002</td>\n",
       "      <td>7.483</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.011</td>\n",
       "      <td>0.022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>fs[T.RandomForestRegressor]:model[SVR]</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.006</td>\n",
       "      <td>3.322</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>ctrp[T.True]</td>\n",
       "      <td>0.0261</td>\n",
       "      <td>0.002</td>\n",
       "      <td>11.970</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>fs[T.mutual_info_regression]:model[RandomFores...</td>\n",
       "      <td>0.0269</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4.624</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>norm[T.MinMaxScaler]:model[T.SVR]</td>\n",
       "      <td>0.0273</td>\n",
       "      <td>0.007</td>\n",
       "      <td>4.022</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>fs[T.mutual_info_regression]:model[SVR]</td>\n",
       "      <td>0.0277</td>\n",
       "      <td>0.006</td>\n",
       "      <td>4.460</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.012</td>\n",
       "      <td>0.044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>fs[T.f_regression]:model[RandomForestRegressor]</td>\n",
       "      <td>0.0282</td>\n",
       "      <td>0.006</td>\n",
       "      <td>5.028</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.014</td>\n",
       "      <td>0.043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>norm[T.None]</td>\n",
       "      <td>0.0297</td>\n",
       "      <td>0.003</td>\n",
       "      <td>8.711</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>norm[T.StandardScaler]</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4.305</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>gdsc[T.True]</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.002</td>\n",
       "      <td>24.149</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>model[T.SVR]</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>0.005</td>\n",
       "      <td>24.542</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>model[T.RandomForestRegressor]</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.005</td>\n",
       "      <td>40.697</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>model[T.ElasticNet]</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.004</td>\n",
       "      <td>50.396</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>model[T.KNeighborsRegressor]</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.009</td>\n",
       "      <td>25.580</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               factor    coef    std err  \\\n",
       "0                                           Intercept -0.1704      0.005   \n",
       "1                                          da[T.feda] -0.0872      0.004   \n",
       "2                 norm[T.StandardScaler]:model[T.SVR] -0.0850      0.011   \n",
       "3   norm[T.StandardScaler]:model[T.RandomForestReg... -0.0824      0.010   \n",
       "4   norm[T.StandardScaler]:model[T.KNeighborsRegre... -0.0814      0.012   \n",
       "5          norm[T.StandardScaler]:model[T.ElasticNet] -0.0785      0.008   \n",
       "6                             da[T.feda]:model[T.SVR] -0.0531      0.006   \n",
       "7             da[T.feda]:model[T.KNeighborsRegressor] -0.0483      0.008   \n",
       "8         norm[T.None]:model[T.RandomForestRegressor] -0.0431      0.006   \n",
       "9            catn[T.low]:model[T.KNeighborsRegressor] -0.0341      0.007   \n",
       "10           catn[T.mid]:model[T.KNeighborsRegressor] -0.0332      0.008   \n",
       "11                     da[T.feda]:model[T.ElasticNet] -0.0313      0.006   \n",
       "12      catn[T.very low]:model[T.KNeighborsRegressor] -0.0267      0.007   \n",
       "13          da[T.feda]:model[T.RandomForestRegressor] -0.0262      0.006   \n",
       "14          norm[T.None]:model[T.KNeighborsRegressor] -0.0260      0.008   \n",
       "15            da[T.none]:model[T.KNeighborsRegressor] -0.0245      0.007   \n",
       "16               catn[T.very low]:model[T.ElasticNet] -0.0233      0.005   \n",
       "17                    catn[T.mid]:model[T.ElasticNet] -0.0173      0.005   \n",
       "18                     da[T.none]:model[T.ElasticNet] -0.0126      0.005   \n",
       "19                                   catn[T.very low] -0.0091      0.003   \n",
       "20                        norm[T.QuantileTransformer]  0.0094      0.003   \n",
       "21                                         da[T.none]  0.0126      0.003   \n",
       "22                                        catn[T.mid]  0.0145      0.003   \n",
       "23                        fs[T.ElasticNet]:model[SVR]  0.0162      0.006   \n",
       "24                                   C(datasets)[T.2]  0.0163      0.002   \n",
       "25             fs[T.RandomForestRegressor]:model[SVR]  0.0205      0.006   \n",
       "26                                       ctrp[T.True]  0.0261      0.002   \n",
       "27  fs[T.mutual_info_regression]:model[RandomFores...  0.0269      0.006   \n",
       "28                  norm[T.MinMaxScaler]:model[T.SVR]  0.0273      0.007   \n",
       "29            fs[T.mutual_info_regression]:model[SVR]  0.0277      0.006   \n",
       "30    fs[T.f_regression]:model[RandomForestRegressor]  0.0282      0.006   \n",
       "31                                       norm[T.None]  0.0297      0.003   \n",
       "32                             norm[T.StandardScaler]  0.0332      0.008   \n",
       "33                                       gdsc[T.True]  0.0500      0.002   \n",
       "34                                       model[T.SVR]  0.1106      0.005   \n",
       "35                     model[T.RandomForestRegressor]  0.2122      0.005   \n",
       "36                                model[T.ElasticNet]  0.2166      0.004   \n",
       "37                       model[T.KNeighborsRegressor]  0.2416      0.009   \n",
       "\n",
       "            t   P>|t|  [0.005  0.995]  \n",
       "0     -37.700   0.000  -0.182  -0.159  \n",
       "1     -20.375   0.000  -0.098  -0.076  \n",
       "2      -7.889   0.000  -0.113  -0.057  \n",
       "3      -8.569   0.000  -0.107  -0.058  \n",
       "4      -6.538   0.000  -0.113  -0.049  \n",
       "5      -9.585   0.000  -0.100  -0.057  \n",
       "6      -8.346   0.000  -0.069  -0.037  \n",
       "7      -6.006   0.000  -0.069  -0.028  \n",
       "8      -7.395   0.000  -0.058  -0.028  \n",
       "9      -4.703   0.000  -0.053  -0.015  \n",
       "10     -4.427   0.000  -0.053  -0.014  \n",
       "11     -5.458   0.000  -0.046  -0.017  \n",
       "12     -3.721   0.000  -0.045  -0.008  \n",
       "13     -4.069   0.000  -0.043  -0.010  \n",
       "14     -3.415   0.001  -0.046  -0.006  \n",
       "15     -3.493   0.000  -0.043  -0.006  \n",
       "16     -4.936   0.000  -0.035  -0.011  \n",
       "17     -3.658   0.000  -0.030  -0.005  \n",
       "18     -2.692   0.007  -0.025  -0.001  \n",
       "19     -2.846   0.004  -0.017  -0.001  \n",
       "20      2.955   0.003   0.001   0.018  \n",
       "21      4.323   0.000   0.005   0.020  \n",
       "22      4.608   0.000   0.006   0.023  \n",
       "23      2.872   0.004   0.002   0.031  \n",
       "24      7.483   0.000   0.011   0.022  \n",
       "25      3.322   0.001   0.005   0.036  \n",
       "26     11.970   0.000   0.021   0.032  \n",
       "27      4.624   0.000   0.012   0.042  \n",
       "28      4.022   0.000   0.010   0.045  \n",
       "29      4.460   0.000   0.012   0.044  \n",
       "30      5.028   0.000   0.014   0.043  \n",
       "31      8.711   0.000   0.021   0.038  \n",
       "32      4.305   0.000   0.013   0.053  \n",
       "33     24.149   0.000   0.045   0.055  \n",
       "34     24.542   0.000   0.099   0.122  \n",
       "35     40.697   0.000   0.199   0.226  \n",
       "36     50.396   0.000   0.206   0.228  \n",
       "37     25.580   0.000   0.217   0.266  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor</th>\n",
       "      <th>coef</th>\n",
       "      <th>std err</th>\n",
       "      <th>t</th>\n",
       "      <th>P&gt;|t|</th>\n",
       "      <th>[0.005</th>\n",
       "      <th>0.995]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-0.1704</td>\n",
       "      <td>0.005</td>\n",
       "      <td>-37.700</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.182</td>\n",
       "      <td>-0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>da[T.feda]</td>\n",
       "      <td>-0.0872</td>\n",
       "      <td>0.004</td>\n",
       "      <td>-20.375</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.098</td>\n",
       "      <td>-0.076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.SVR]</td>\n",
       "      <td>-0.0850</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-7.889</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.RandomForestReg...</td>\n",
       "      <td>-0.0824</td>\n",
       "      <td>0.010</td>\n",
       "      <td>-8.569</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.KNeighborsRegre...</td>\n",
       "      <td>-0.0814</td>\n",
       "      <td>0.012</td>\n",
       "      <td>-6.538</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>norm[T.StandardScaler]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0785</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-9.585</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.100</td>\n",
       "      <td>-0.057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>da[T.feda]:model[T.SVR]</td>\n",
       "      <td>-0.0531</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-8.346</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>-0.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>da[T.feda]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0483</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-6.006</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>norm[T.None]:model[T.RandomForestRegressor]</td>\n",
       "      <td>-0.0431</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-7.395</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.058</td>\n",
       "      <td>-0.028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>catn[T.low]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0341</td>\n",
       "      <td>0.007</td>\n",
       "      <td>-4.703</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>catn[T.mid]:model[T.KNeighborsRegressor]</td>\n",
       "      <td>-0.0332</td>\n",
       "      <td>0.008</td>\n",
       "      <td>-4.427</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.053</td>\n",
       "      <td>-0.014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>da[T.feda]:model[T.ElasticNet]</td>\n",
       "      <td>-0.0313</td>\n",
       "      <td>0.006</td>\n",
       "      <td>-5.458</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-0.046</td>\n",
       "      <td>-0.017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>norm[T.StandardScaler]</td>\n",
       "      <td>0.0332</td>\n",
       "      <td>0.008</td>\n",
       "      <td>4.305</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.013</td>\n",
       "      <td>0.053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>gdsc[T.True]</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>0.002</td>\n",
       "      <td>24.149</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>model[T.SVR]</td>\n",
       "      <td>0.1106</td>\n",
       "      <td>0.005</td>\n",
       "      <td>24.542</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.099</td>\n",
       "      <td>0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>model[T.RandomForestRegressor]</td>\n",
       "      <td>0.2122</td>\n",
       "      <td>0.005</td>\n",
       "      <td>40.697</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>model[T.ElasticNet]</td>\n",
       "      <td>0.2166</td>\n",
       "      <td>0.004</td>\n",
       "      <td>50.396</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>model[T.KNeighborsRegressor]</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.009</td>\n",
       "      <td>25.580</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.217</td>\n",
       "      <td>0.266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               factor    coef    std err  \\\n",
       "0                                           Intercept -0.1704      0.005   \n",
       "1                                          da[T.feda] -0.0872      0.004   \n",
       "2                 norm[T.StandardScaler]:model[T.SVR] -0.0850      0.011   \n",
       "3   norm[T.StandardScaler]:model[T.RandomForestReg... -0.0824      0.010   \n",
       "4   norm[T.StandardScaler]:model[T.KNeighborsRegre... -0.0814      0.012   \n",
       "5          norm[T.StandardScaler]:model[T.ElasticNet] -0.0785      0.008   \n",
       "6                             da[T.feda]:model[T.SVR] -0.0531      0.006   \n",
       "7             da[T.feda]:model[T.KNeighborsRegressor] -0.0483      0.008   \n",
       "8         norm[T.None]:model[T.RandomForestRegressor] -0.0431      0.006   \n",
       "9            catn[T.low]:model[T.KNeighborsRegressor] -0.0341      0.007   \n",
       "10           catn[T.mid]:model[T.KNeighborsRegressor] -0.0332      0.008   \n",
       "11                     da[T.feda]:model[T.ElasticNet] -0.0313      0.006   \n",
       "32                             norm[T.StandardScaler]  0.0332      0.008   \n",
       "33                                       gdsc[T.True]  0.0500      0.002   \n",
       "34                                       model[T.SVR]  0.1106      0.005   \n",
       "35                     model[T.RandomForestRegressor]  0.2122      0.005   \n",
       "36                                model[T.ElasticNet]  0.2166      0.004   \n",
       "37                       model[T.KNeighborsRegressor]  0.2416      0.009   \n",
       "\n",
       "            t   P>|t|  [0.005  0.995]  \n",
       "0     -37.700   0.000  -0.182  -0.159  \n",
       "1     -20.375   0.000  -0.098  -0.076  \n",
       "2      -7.889   0.000  -0.113  -0.057  \n",
       "3      -8.569   0.000  -0.107  -0.058  \n",
       "4      -6.538   0.000  -0.113  -0.049  \n",
       "5      -9.585   0.000  -0.100  -0.057  \n",
       "6      -8.346   0.000  -0.069  -0.037  \n",
       "7      -6.006   0.000  -0.069  -0.028  \n",
       "8      -7.395   0.000  -0.058  -0.028  \n",
       "9      -4.703   0.000  -0.053  -0.015  \n",
       "10     -4.427   0.000  -0.053  -0.014  \n",
       "11     -5.458   0.000  -0.046  -0.017  \n",
       "32      4.305   0.000   0.013   0.053  \n",
       "33     24.149   0.000   0.045   0.055  \n",
       "34     24.542   0.000   0.099   0.122  \n",
       "35     40.697   0.000   0.199   0.226  \n",
       "36     50.396   0.000   0.206   0.228  \n",
       "37     25.580   0.000   0.217   0.266  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova[(anova['coef']>0.03)|(anova['coef']<-0.03)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38, 7)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x72 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.palplot(sns.color_palette(\"seismic\", n_colors=14)[:-3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "g = sns.catplot(x='factor', y='coef', data=anova[(anova['coef']>0.03)|(anova['coef']<-0.03)],height=5, kind='bar', palette=sns.color_palette(\"seismic\", n_colors=22)[:-4], aspect=2.1)\n",
    "#labels = ['Intercept', 'FEDA','SS:SVR', 'SS:RF','SS:KNN','SS:EN', 'FEDA:SVR', 'FEDA:KNN', 'NoNorm:RF', 'Low:KNN', 'Mid:KNN', 'FEDA:EN', 'VLow:KNN','FEDA:RF', 'NoNorm:KNN', 'NoDA:KNN', 'VLow:EN','RF:SVR', 'CTRP', 'MI:RF', 'MinMax:SVR', 'MI:SVR', 'F:RF', 'NoNorm','SS', 'GDSC', 'SVR', 'RF', 'EN', 'KNN']\n",
    "labels = ['Intercept', 'FEDA','SS:SVR', 'SS:RF','SS:KNN','SS:EN', 'FEDA:SVR', 'FEDA:KNN', 'NoNorm:RF', 'Low:KNN', 'Mid:KNN', 'FEDA:EN', 'SS', 'GDSC', 'SVR', 'RF', 'EN', 'KNN']\n",
    "\n",
    "\n",
    "g.set_xticklabels(labels, rotation=45)\n",
    "g.set(ylabel='coefficient')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('anova2.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "g = sns.catplot(x='factor', y='coef', data=anova[(anova['coef']>0.05)|(anova['coef']<-0.05)],height=5, kind='bar', palette=sns.color_palette(\"seismic\", n_colors=14)[:-3], aspect=1.9)\n",
    "labels = ['Intercept', 'FEDA','SS:SVR', 'SS:RF','SS:KNN','SS:EN', 'FEDA:SVR', \n",
    "          'SVR', 'RF', 'EN', 'KNN']\n",
    "g.set_xticklabels(labels,rotation=45)\n",
    "g.set(ylabel='coefficient')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/anova.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we calculate the p values and store them in an excel file. These will be used to determine if certain characteristics outperform others. Having statistically significant differences in their means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['datasets'] = sp.posthoc_ttest(clean, val_col='r2', group_col='datasets', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['datime'] = sp.posthoc_ttest(clean, val_col='seconds', group_col='da',p_adjust='bonferroni', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['dar2'] = sp.posthoc_ttest(clean, val_col='r2', group_col='da',p_adjust='bonferroni', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['drug_metric']= sp.posthoc_ttest(clean, val_col='r2', group_col='metric_drug',p_adjust='bonferroni', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['ajive'] = sp.posthoc_ttest(clean[clean['ajive'].notnull()], val_col='r2', group_col='ajive',p_adjust='bonferroni', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['model']= sp.posthoc_ttest(clean, val_col='r2', group_col='model',p_adjust='bonferroni', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['norm']=sp.posthoc_ttest(clean, val_col='r2', group_col='norm', equal_var=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean['catnmodel'] = clean['model']+clean['catn'].astype(str)\n",
    "data['catnmodel'] = sp.posthoc_ttest(clean, val_col='r2', group_col='catnmodel', equal_var=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean['damodel'] = clean['da']+clean['model']\n",
    "data['damodel'] = sp.posthoc_ttest(clean, val_col='r2', group_col='damodel', equal_var=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean['normodel'] = clean['model']+clean['norm']\n",
    "data['normodel'] = sp.posthoc_ttest(clean, val_col='r2', group_col='normodel',p_adjust='bonferroni', equal_var=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['norm'] = sp.posthoc_ttest(clean, val_col='r2', group_col='norm',p_adjust='bonferroni', equal_var=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean['fsmodel'] = clean['model']+clean['fs']\n",
    "data['fsmodel'] = sp.posthoc_ttest(clean, val_col='r2', group_col='fsmodel', equal_var=False).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('pvalues.xlsx') as writer:\n",
    "    for k,v in data.items():\n",
    "        v.to_excel(writer, sheet_name=k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Odds Ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OR(indices, rest, top, group):\n",
    "    li = []\n",
    "    for i in indices:\n",
    "        model_t = top[(top[group[0]]==i[0])&(top[group[1]]==i[1])].shape[0]\n",
    "        nmodel_t = top[(top[group[0]]!=i[0])|(top[group[1]]!=i[1])].shape[0]\n",
    "        model_r = rest[(rest[group[0]]==i[0])&(rest[group[1]]==i[1])].shape[0]\n",
    "        nmodel_r = rest[(rest[group[0]]!=i[0])|(rest[group[1]]!=i[1])].shape[0]\n",
    "        if model_r > 0 and nmodel_r > 0:\n",
    "            li.append((model_t/model_r)/(nmodel_t/nmodel_r))\n",
    "        else:\n",
    "            li.append(0)\n",
    "    return li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def top_OR(data, group, q=0.95, score='r2_score'):\n",
    "    top = data[data[score] >= data[score].quantile(q)]\n",
    "    counts = data[group +[score]].groupby(group).count()\n",
    "    topcounts = top[group +[score]].groupby(group).count()\n",
    "    topcounts['OR'] = OR(topcounts.index, data[data[score]<data[score].quantile(q)], top, group)\n",
    "    return topcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ORone(indices, rest, top, group):\n",
    "    li = []\n",
    "    for i in indices:\n",
    "        model_t = top[(top[group[0]]==i)].shape[0]\n",
    "        nmodel_t = top[(top[group[0]]!=i)].shape[0]\n",
    "        model_r = rest[(rest[group[0]]==i)].shape[0]\n",
    "        nmodel_r = rest[(rest[group[0]]!=i)].shape[0]\n",
    "        if model_r > 0 and nmodel_r > 0:\n",
    "            li.append((model_t/model_r)/(nmodel_t/nmodel_r))\n",
    "        else:\n",
    "            li.append(0)\n",
    "    return li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_OR(data, group, q=0.95, score='r2_score'):\n",
    "    top = data[data[score] >= data[score].quantile(q)]\n",
    "    counts = data[group +[score]].groupby(group).count()\n",
    "    topcounts = top[group +[score]].groupby(group).count()\n",
    "    topcounts['OR'] = ORone(topcounts.index, data[data[score]<data[score].quantile(q)], top, group)\n",
    "    return topcounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hyper parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here some initial exploration of the hyper parameters of the models is performed "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = clean[clean['model']=='KNeighborsRegressor'][['Drug', 'fs', 'da','ajive', 'catn', 'n_neighbors', 'weights', 'algorithm', 'p','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>Drug</th>\n",
       "      <th>fs</th>\n",
       "      <th>da</th>\n",
       "      <th>ajive</th>\n",
       "      <th>catn</th>\n",
       "      <th>n_neighbors</th>\n",
       "      <th>weights</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>p</th>\n",
       "      <th>r2_score</th>\n",
       "      <th>r2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>AZD6482</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>29.0</td>\n",
       "      <td>uniform</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.416520</td>\n",
       "      <td>0.236597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>AZD7762</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>13.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.377099</td>\n",
       "      <td>0.109259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>BMS-754807</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>20.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.112876</td>\n",
       "      <td>0.033824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>Dasatinib</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>14.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.427305</td>\n",
       "      <td>0.157184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>none</td>\n",
       "      <td>NaN</td>\n",
       "      <td>low</td>\n",
       "      <td>24.0</td>\n",
       "      <td>distance</td>\n",
       "      <td>kd_tree</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.107432</td>\n",
       "      <td>-0.010444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Drug     fs    da  ajive catn  n_neighbors   weights  algorithm  \\\n",
       "207     AZD6482  Lasso  none    NaN  low         29.0   uniform  ball_tree   \n",
       "208     AZD7762  Lasso  none    NaN  low         13.0  distance  ball_tree   \n",
       "209  BMS-754807  Lasso  none    NaN  low         20.0  distance  ball_tree   \n",
       "210   Dasatinib  Lasso  none    NaN  low         14.0  distance  ball_tree   \n",
       "211   Docetaxel  Lasso  none    NaN  low         24.0  distance    kd_tree   \n",
       "\n",
       "       p  r2_score        r2  \n",
       "207  1.0  0.416520  0.236597  \n",
       "208  1.0  0.377099  0.109259  \n",
       "209  2.0  0.112876  0.033824  \n",
       "210  1.0  0.427305  0.157184  \n",
       "211  1.0  0.107432 -0.010444  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='weights', y='r2', hue='algorithm', data=knn[knn['n_neighbors']>5], kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "knn['ajive'] = knn['ajive'].fillna(0)\n",
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "\n",
    "g = sns.catplot(x='catn', y='r2',order=order, hue='ajive', data=knn, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "g = sns.catplot(x='catn', y='r2',hue='da', data=knn,order=order, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1240f5c88>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "order= ['very low', 'low', 'mid', 'high', 'very high']\n",
    "g = sns.catplot(x='catn', y='r2', hue='p', data=knn,order=order, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower left')\n",
    "plt.tight_layout()\n",
    "g.set(xlabel='features')\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'catn', y='OR', hue='da',order=order,data=top_OR(knn, ['da', 'catn'], score='r2').reset_index(), kind='bar', palette='Greens', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='upper center')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = clean[clean['model']=='RandomForestRegressor'][['Drug', 'fs', 'da', 'catn', 'n_estimators', 'max_depth', 'max_features','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'max_depth', y='OR', hue='max_features',data=top_OR(rf, ['max_depth', 'max_features'], score='r2').reset_index(), kind='bar', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "\n",
    "g = sns.catplot(x='n_estimators', y='r2', hue='max_depth', data=rf, kind='bar',palette='Greens',height=5, aspect=1.9, legend=False)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "plt.legend(title='max_depth',ncol=2)\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-depth.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='max_features', y='r2', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-depth.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='max_depth', y='r2', hue='fs', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XV1991aSDFxcXa8aMGVq2bJm2bdum7t27KzMz06WN1WpVenq6ZsyYoT/84Q8aOXKkpk+f3qTjehPXLwIAADTMo1tab7/9dv3qV7/S3//+d5WUlOjMmTPOX02xa9cu9ezZU127dpV0YQqmLVu2yGazOdscOHBALVq00LXXXitJGjVqlA4cOKD8/PwmHRsAAAC+Y3J4MO9R3759ncPrP9yx7nA4ZDKZdPjw4Ys++IoVK3T8+HHNnz/fuS45OVnbt29XfHy8JGnLli3Kzs7W6tWrnW1uu+02LV68WH379r3oY3vKWl0jS0R4ndsnTJigkydPqlOnTlqzZo3bNjXVVoVHWJq0D0my2qyymN3vx9N91FitCrc0rZbqapsiIuqe+MDTWmzVVpnr+XPxhN1WozCz+78fT+uobx9Gaqhveaqh/uVRLfX0LY/raKBveaqh/uUJo/uWP/bTWN7oX97oW1Lg9K9A6VuSd/pFMPctic8ud5r7Z5dHfzo/nRTeW+x2u9v1YWFhjWrjidOny2W3N35e0bi4aI2bvrbO7UVFZyVJeUVn62y3bvF4FRaerXMfFkukc1lfu7i4aD206hm32/LLCp3LutpI0uqJy7R1wsQ6t1fk5TuXdbUbvmaVFry4oc59FJ8udy7ra/fC/NH1/n49ERcXra/f3O52m7W00rmsq40k9Zk0pMl1XIyG+panGupfnqsKkH14SyDV4nve6F/e61tS8+pfgVKHf/DZZbRAquXixMVFu13vUejs1KmTV4v5QVJSkvbu/fGu76KiIkVERKhdu3Yubf59KN1ut6uoqEiJiYmG1OQPY8aM0+bNHyk19U5/lwIAAGAIvz6mZNCgQTp48KCOHj0qSVq/fr2GDBkis/nHLNy7d2+dO3dOe/bskSTl5OSoW7duSkhI8EvNRujXr7/mzMng6T0AfKaq9ISKj2xVVekJf5eCZoa+hbo07eKDJoqJidGSJUs0depUWa1WJSUlacmSJcrPz9djjz2mFStWKCEhQcuXL9e8efNUUVGh6OhoZWVl+bNswCeqSk/oXN4BtUq8SpFtf+bvctDMlOfuk63itMpzq+lf8Cr6Furi19ApSYMHD9bgwYNrrc/JyXH+fNVVVyk7O9uXZQWkKqtVqycuc7ttwv+doJNnTyqhTVydbRBc+OCGkRw11S5LwFvoW6iL30MnPFdWWqW6LjCuqXE4lw3djITgwAc3AKA5IXQ24HxVtdYtHl/n9gkTtunkyTIlxkZrTR3tzlcRGgAAQGgjdDbgbNl5nVXdTxvy9AwjAABAKCN0AgAAj3ljBPCH/SC0EDoBoBni0iAYhRFAXCxCJ+AnnC2AkQgGAAINoRPwE0IBACCU+PWJRAAAAAgNhE4AAOA1UVEtXJbADwidAADAa8aMGafk5F4aM2acv0tBgOGazibiGx2MQt+CkehfMEq/fv3Vr19/f5eBAETobKIxY8Zp8+aPlJp6p79LQTND3wocNTU2lZQUymaz+rsUr3niiSdVWVmhqKiWyss77lxvNlvUvn2cwsP57wGAd/Gp0kR8o4NR6FuBo6SkUC1atFSrVokymUz+LscwDodD586VqaSkULGxHf1dDoBmhms6AaABNptVrVq1adaBU5JMJpNatWrTrM7oAggcnOkMMdXnqzR8zao6t6+fMEElJ0+qZWJCve2AUNPcA+cPQuX3CcD3CJ0h5sxZq3S27rMYnkxIHhcXbUhtQLCIbtNCLSIjDD3G+apqnS2r++EBABBsCJ0A0EgtIiM0bvpaQ4+xbvH4ep9YBQDBhtAJAEHuj3/covfee1cmk0ktWrTQs89OU48eyS5tDh06qKVLF6uyskIxMR00e/Y8xccn+KliAKGIG4maCebcA0LTP/95TG+++bpeeeUNrV69Tg8++LCef36aS5vq6mrNmjVDTz31jNau3aChQ4crI2OunyoGEKoInc0ET4AAQpPFYtHMmbMVGxsrSerRI1nFxadVVfXj0Pzhw98oMjJSffteLUkaNixVhw9/o8LCAr/UDCA0MbzeTDCnIxCakpI6KSmpk6QL82y+8carGjhwsCIjfxz1KCjIV3x8ovN1eHi4YmI6KD8/T3Fx8T6vGUBoInQCQDNQWVmp+fNfUkFBvl555Q2XbXa7w+17TCYGuwD4Dp84ABDk8vJO6YknHlZ4eJjeeOMtRUe7TmuWmJiooqIfh9LtdruKi08rPp6znAB8h9AJAEHszJkzeuqpR3XjjTfp5ZczXYbVf5Cc3EsVFRXat2+vJGnbtq3q0uUyhtYB+BTD6wDQSOerqrVu8XjDj+GJjRt/r8LCAu3cuV07d253rl+4cKlmzpyqrKxlio2NU2Zmll55ZZEqKysVHd1ac+ZkGFQ5ALhH6ASARjpbdj5gJm5/+OHH9PDDj7ndtnr1OufPPXoka+XK3/qqLACoheF1AAAAGI4znWg0a5VNL8wfXef2CRM+1smTpYrp0LredtYqmxHlAQCAAEToRKOVllXWu72mxuFcFhae9UVJAAAgwDG8DgAAAMMROgEAAGA4QicAAAAMxzWdANBI7dtaZLZEGnoMm7VKJaVWQ48BAL5E6ASARjJbIvWXxY8Yeoyrp78jybPQuX79u9q8+WNJUufOnTV9+ouKieng0mb37l16661fyWq16rLLuun55+fUelwmABiJ4XUACGJ//es+ffTRB1qxYrXefff36tSps95+e7lLm5KSEs2f/5J++cuFeu+9jeratZveeGOpnyoGEKoInQAQxH7xi356991stWzZUlVVVSoqKlK7du1d2nz11Rf6+c976JJLukiSRo0arc8++0Q2G3PlAvAdQicABDmz2az//u/PdNddw/X11/9PqakjXbbn5+crPj7B+bpDh1jV1Nh05swZX5cKIIQROgGgGbj55lu1ZcvnevDB/9TUqemy2+3ObQ6H3e17wsJMvioPAAidABDMvvvunzpw4Gvn69TUkcrLy9XZs2XOdYmJHVVYWOh8XVx8WmazWW3atPVprQBCG6ETAILYqVOn9PLLs1RWdiFk/uEPm9W16+Vq27ads8011wzQkSOH9M9/HpMkffTRBxo4cLDMZiYwAeA7fOIAQCPZrFX/mtLI2GN4IiXlOo0ZM1aTJj2i8PBwxcfHKzMzS99+e0gLF2Zo9ep1at++vWbP/qVeeulFVVdblZDQUbNnzzO0fgD4KUInADTShUnbA2fi9nvvHa977x3vsi4pqZNWr17nfJ2Scp1SUq7zdWkA4MTwOgAAAAxH6AQAAIDhCJ0AAAAwHKETAAAAhuNGIgQ1m9WmPpOGuN1m+eI3UrlkaRtVZ5sf9gEAAIxF6ERQKymtrHNbTY3DuSwsPOurkgAAgBsMrwMAAMBwnOkEgEZq0zZSkRaLoceoslpVVurZBPEAEAwInQDQSJEWix5a9Yyhx1g9cZkkz0Pnzp3b9fLLL+rzz/9Ua9uhQwe1dOliVVZWKCamg2bPnqf4+AQvVgsADfPr8Pr+/ft19913a9iwYXrggQeUl5dXb/tdu3YpNTXVR9UBQHA4ceI7LV/+mhwOR61t1dXVmjVrhp566hmtXbtBQ4cOV0bGXD9UCSDU+S10Wq1Wpaena8aMGfrDH/6gkSNHavr06W7bVlZWaunSpZoyZYpsNu40BoAfnD9/XvPmzVZ6+hS32w8f/kaRkZHq2/dqSdKwYak6fPgbFRYW+LJMAPBf6Dxw4IBatGiha6+9VpI0atQoHThwQPn5+bXa7ty5U1arVQsXLvR1mQAQ0JYsma+RI+9St27d3W4vKMhXfHyi83V4eLhiYjooP7/+kSUA8DbDr+nctGmTZsyYUWv9ggUL1LFjR+fr8PBwxcbGKjc3VwkJrtcaDR06VEOHDtWePXsuuo4OHVpf9HtDSXi4ybmMi4v22z68IVDqQPArKAiT2ez77+gNHXPDht/LbDbrzjtHKTc31+17TKYLv/59/YXX4XXuPywsjH8zALzO8NCZlpamtLS0Wus3bdrktn1YmDEf7KdPl8tur329E1x5Y27LQJkfM1DqQPCz2+2y2ew+P25Dx9yy5WOdP39e998/VjZbtaqrq3X//WO1aNFSJSRcOLsZF5egwsIC577sdrtOnz6tDh3i6ty/3W7n3wyAi1bXl1a/3b2elJTkMpRut9tVVFSkxMTEet4FAPjBypVrnD+fOpWr8eNHa/XqdS5tkpN7qaKiQvv27VW/fv21bdtWdelymeLi4n1dLoAQ57fQ2bt3b507d0579uxRSkqKcnJy1K1bt1pD6wAQaKqs1n9NaWTsMS5WUVGhpk17RllZyxQbG6fMzCy98soiVVZWKjq6tebMyfBipQDgGb+FzoiICC1fvlzz5s1TRUWFoqOjlZWV5dw+cuRIZWRk6KqrrvJXiQDg1oVJ2wNr4vaOHZP03//9Z0lSbGycyxnPHj2StXLlb/1VGgBI8vPk8FdddZWys7PdbsvJyam1LiUlRX/84x+NLgsAAABexrPXAQAAYDhCJ1xERbVwWQIAAHgDoRMuxowZp+TkXhozZpy/SwEAAM2IX6/pRODp16+/+vXr7+8yAABAM8OZTgAAABiOM50A0Ejtoi2KaBFp6DGqz1fpzNmLn6sTAAINoRMAGimiRaS2Tpho6DGGr1kleRg6s7IWavfuXYqObiNJ6ty5szIyFru0OXTooJYuXazKygrFxHTQ7NnzFB/PwzgA+A6hEwCC3IEDX2v+/MXq0SPZ7fbq6mrNmjVDs2fPU9++V2vz5hxlZMzV66+/5eNKAYQyrukEgCB27ly5jh8/pt/9bpUefHCsXnzxOeXl5bm0OXz4G0VGRqpv36slScOGperw4W9UWFjgj5IBhChCJwAEscLCQl177QBNmvSMVq9+Tz17XqWZM6fKbrc72xQU5Cs+PtH5Ojw8XDExHZSfn+dulwBgCEInAASxLl0u0+LFr6lTp84ymUy6774HlJt7UidPfu9sY7c73L7XZOK/AAC+wycOAASxb789rE8++aPLOofDIbP5x0v2ExMTVVT041C63W5XcfFpxcfH+6xOACB0AkAQczjseu21Jc7rOD/8cIO6dLlMHTsmOdskJ/dSRUWF9u3bK0natm2runS5THFxhE4AvsPd6wDQSNXnqy5MaWTwMTxx5ZU9NWnS05o27Wk5HHbFxcVr3rxMFRUVatq0Z5SVtUyxsXHKzMzSK68sUmVlpaKjW2vOnAxD6weAnyJ0AkAjnTlr9XgOTV9ITR2p1NSRtdavXr3O+XOPHslaufK3viwLAFwwvA4AAADDEToBAABgOEInAAAADEfoBAAAgOEInfC6qKgWLksAAABCJ7xuzJhxSk7upTFjxvm7FAAAECCYMgle169ff/Xr19/fZQAAgABC6ASARmrbJkqWSGM/Pq1VNpWWVRp6DADwJUInADSSJdKsBS9uMPQYL8wf7XHbY8f+oaVLF6m8/KxMJpOmTJmuq67q49Lm0KGDWrp0sSorKxQT00GzZ89TfHyCt8sGgDpxTScABLGqqvOaMuUp3XvvOK1atU6PPjpJc+e+IIfD4WxTXV2tWbNm6KmnntHatRs0dOhwZWTM9WPVAEIRoRMAgtiXX36h+PgEDRp0oyTpuuuu1+LFr7mEzsOHv1FkZKT69r1akjRsWKoOH/5GhYUFfqkZQGhieB0Agth33x1Xhw6xWrQoQ3/72xG1atVKTzwxWWFhP55TKCjIV3x8ovN1eHi4YmI6KD8/T3Fx8f4oG0AI4kwnAAQxm82mL7/creHD0/TrX/9O48ZN0PTpz6qi4pyzjd3ucPtek4n/AgD4Dp84ABDE4uLidemlXZw3Dg0YMFAWS6SOH/+ns01iYqKKin4cSrfb7SouPq34eM5yAvAdQicABLEBAwYqNzdX33xzUJJ08OABnT9/Xpde2sXZJjm5lyoqKrRv315J0rZtW9Wly2UMrQPwKa7pBIBGslbZGjWl0cUewxMxMR20ePGrWrYsS5WVFQoPNysjY5EqKio0adKjyspaptjYOGVmZumVVxapsrJS0dGtNWdOhqH1A8BPEToBoJECbdL23r1/oRUrVtdav3r1OufPPXoka+XK3/qwKgBwxfA6AAAADEfoBAAAgOEInQAAADAcoRMAAMfyZHIAABGZSURBVACGI3QCAADAcIROAAAAGI4pkwCgkdq2scgSGWnoMaxVVSotsxp6DADwJUInADSSJTJSS59/3NBjTM18W1LDoXPLlo+Vnb3e+frcuXIVFOTrww+3Kiamg3P97t279NZbv5LVatVll3XT88/PUXR0tBGlA4BbhE4ACGJ33DFCd9wxQpJks9k0efJjGj9+gkvgLCkp0fz5L+nNN9/RJZd00TvvvKU33liqF16Y66+yAYQgrukEgGbivffeVZs2bXTnna6P6Pzqqy/085/30CWXdJEkjRo1Wp999olsNs8etQkA3kDoBIBmoLT0jNau/a2efvr/1NqWn5+v+PgE5+sOHWJVU2PTmTNnfFkigBBH6ASAZuDjjz/UwIGD1Lnzz2ptczjsbt8TFmYyuiwAcCJ0AkAz8Nlnn2j48DS32xITO6qwsND5urj4tMxms9q0aeur8gCA0AkAwa6srEwnThxXnz593W6/5poBOnLkkP75z2OSpI8++kADBw6W2cy9pAB8h9AJAEHu5MkTionpoIiICOe6b789pIceGidJat++vWbP/qVeeulFjR8/WgcPHtDUqTP8VS6AEGVyOBwOfxfhC6dPl8tuD4nfKv7l2WcnKS8vV4mJSXrttTf9XQ6CWF7ecSUmXup83dwnh//p7xcAGiMuzv0cwIytAEAjXQiDPC0IABqD4XUAAAAYzq+hc//+/br77rs1bNgwPfDAA8rLy6uz3dixYzVy5EilpaUpJyfHx5UCAACgKfw2vG61WpWenq4lS5bo2muv1YYNGzR9+nStWbPGpZ3dbtdTTz2lRYsWaeDAgcrLy9Odd96pXr16qVu3bn6qHgAAAI3htzOdBw4cUIsWLXTttddKkkaNGqUDBw4oPz/fpV1VVZWefvppDRw4UJKUmJiomJiYOs+KAgAAIPAYHjo3bdqk5OTkWr9OnDihjh07OtuFh4crNjZWubm5Lu+PiorSmDFjnK/ff/99lZeXq29f9/PRAQAAIPAYPryelpamtLTaT8nYtGmT2/ZhYXXn4BUrVmjNmjV655131LJly0bV0aFD60a1R/ALDzc5l3VN3wB4oqAgTGZz6Nx3GRYWxr8ZAF7nt2s6k5KSXIbS7Xa7ioqKlJiYWKttVVWVZs6cqaNHj2r9+vXq3Llzo4/HPJ2hp6bG4VwWFp71czUIZna7XTbbj88vb982SmaLsR+fNqtNJaWVhh6jLna7nX8zAC5awM3T2bt3b507d0579uxRSkqKcnJy1K1bNyUkJLi0czgcSk9PlyStX7++0Wc4AcDbzBazvn5zu6HH6DNpiMdtd+7crl//+i2ZTGFq1aqVnnvuBXXpcplLm0OHDmrp0sWqrKxQTEwHzZ49T/HxCXXsEQC8z2+hMyIiQsuXL9e8efNUUVGh6OhoZWVlObePHDlSGRkZqqys1I4dO9SlSxfdd999zu1Tp07VjTfe6I/SASBgVFWd18svv6hVq9bqkku66MMPNygrK1O/+tUKZ5vq6mrNmjVDs2fPU9++V2vz5hxlZMzV66+/5cfKAYQavz6R6KqrrlJ2drbbbf8+F+eRI0d8VRIABJWamgvD/mfPlkuSKisrZLFYXNocPvyNIiMj1bfv1ZKkYcNStWxZlgoLCxQXF+/bggGELB6DCQBBrGXLlpo27Xk99dQjat8+RlZrld54422XNgUF+YqP//F6+fDwcMXEdFB+fh6hE4DPhM7tmADQDB09+r/6zW9W6ne/+70+/HCr0tOnavr0KbJaf3w2fF03UZpM/BcAwHf4xAGAILZnz2717NlLP/vZJZKk22+/Q5WVFfr++++cbRITE1VUVOB8bbfbVVx8WvHxnOUE4DuETgAIYj16XKmvv/5/KioqlCTt27dXYWHh6tTpx6nlkpN7qaKiQvv27ZUkbdu2VV26XMbQOgCf4ppONFtRUS1cloC32Ky2Rk1pdLHH8ES/fv314IMP65lnnpTZbFbLlq20ePGrOnv2rB5//GFlZS1TbGycMjOz9Mori1RZWano6NaaMyfD0PoB4KdMDocjJGZMZ3L40LNv315t3vyRUlPvVL9+/f1dDoJYXt5xJSZe6u8yfCbUfr8AvCvgJocHjNavX3/CJgAAAYJrOgEAAGA4QicAeCBErkQKmd8nAN8jdAJAA8LCwlVT49mNPcGupsamsLBwf5cBoBkidAJAA6KiWuvs2TNyOOz+LsVQDoddZ8+WKCqqtb9LAdAMcSMRADSgdeu2KikpVH7+95Ka8/CzSRZLC7Vu3dbfhQBohgidANAAk8mkmBgmUgeApmB4HQAAAIYjdAIAAMBwhE4AAAAYLmSu6QwLM/m7BAAAgJAVMs9eBwAAgP8wvA4AAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADAcoRMAAACGI3QCAADAcIROAAAAGI7QCQAAAMOFzGMwccGyZct0+vRpzZs3T5K0ceNGvfPOO7LZbEpJSdHs2bNlsVhqvW/Hjh3KysqS1WpV9+7dtWDBArVp08bX5SNAvfTSS9qxY4ezT1x66aV6/fXXtWLFCm3cuFE1NTUaPny4nnnmGYWF1f6u62k/RGjx9POqpqZGixYt0s6dO1VTU6MJEybogQcecLtPT/skmref9q1HH31Ux48fV1RUlCTpmmuu0axZs+hb3uZASDh58qQjPT3d0adPH8fs2bMdDofDceTIEcegQYMchYWFDrvd7pgxY4bjjTfeqPXe06dPO1JSUhxHjx51OBwOx2uvveaYOXOmT+tHYEtLS3Ps37/fZd327dsdqampjvLyckdVVZXjoYcecnz44Ye13utpP0ToaOzn1bvvvut4+OGHHdXV1Y6ysjJHamqq48svv6y1X0/7JJovd33Lbrc7rrnmGkdRUVGt9vQt7yKCh4js7GylpKRo4sSJznWff/65brrpJsXGxspkMmns2LH6+OOPa713165d6tmzp7p27SpJGjdunLZs2SKbzeaz+hG4ysvL9Y9//EMrVqxQWlqa0tPTlZubq08//VSpqalq1aqVLBaLRo8erZycnFrv97QfInQ09vPqs88+06hRo2Q2mxUdHa20tDS3fc3TPonmy13f+vvf/y6Hw6EXXnhBaWlpev7553XmzBlJ9C1vI3SGiGeeeUbjx493OdV/6tQpJSYmOl8nJiYqNze31nvz8vJc2sXFxclms6m4uNjYohEU8vPzdf311+u5557Txx9/rD59+ujJJ59Ubm6uS79JSEhw27887YcIHY39vPrptoSEBJ06darWft21o6+FFnd9q6SkRNddd50WLFigjz76SC1bttTMmTMl0be8jWs6Q5jD4ai1zt31J3a73e37uVYFktStWze9/fbbztf/+Z//qTfffNPtNb/u+oyn/RChrb5+4m6byWRq1D4QulJSUpSSkuJ8PXnyZF1//fWqqqqib3kZfyIhLCkpSfn5+c7X+fn5Lt/U6mpXVFSkiIgItWvXzid1IrAdPHhQmzZtclnncDhkNptd+k1BQYFH/auufojQVl8/SUpKUkFBgdtt9e2jrj6J0LJ7925t377d+drhcMhkMiksLIy+5WWEzhB28803a/v27SooKJDD4dD69et166231mo3aNAgHTx4UEePHpUkrV+/XkOGDJHZzIlyXDgTnpGR4RxKWrdunS6//HI9+OCD2rx5s8rLy2W1WvXBBx+47V+e9kOEtvr6ya233qoPPvhA1dXVKi8v1+bNm3XbbbfV2sett97qUZ9EaCkrK1NGRobKysokSe+8845uu+02RURE0Le8jNQQwq644gpNmTJFEydOlM1mU69evZSeni5JOnDggGbNmqWcnBzFxMRoyZIlmjp1qqxWq5KSkrRkyRI/V49A0bt3bz333HN69NFHZbfblZiYqFdffVWdO3fW3/72N91zzz2y2Wy64YYbNHbsWEkXbgpZv369Vq5cWW8/BH5QXz8ZO3asTpw4oZEjR8pms2nUqFG68cYbJUnvvfeeDh48qPnz52vIkCF19kmErqFDh+rYsWO69957ZbfbdcUVVzinUqJveZfJ4e5CBAAAAMCLGF4HAACA4QidAAAAMByhEwAAAIYjdAIAAMBwhE4AAAAYjtAJAH6Sn58vq9Vq6DHKyspUWlpq6DEAwBOETgDwg6KiIt1+++0qLy+XJM2ZM0cLFy70+nFuu+02fffdd17fLwA0FpPDA4AfnD9/XhUVFc7XP0xG7W1nzpwxZL8A0Fic6QQASd9//7369u2r1atXa/DgwUpJSdHMmTNVXV3d4Hs///xzjRgxQv3799fYsWN14MAB57b/+q//0g033KCUlBTdd9992r9/vyRp5MiRkqRbbrlFe/fu1cyZM53Bc+bMmcrMzNT999+vX/ziF7rnnnu0f/9+PfTQQ+rbt6/uuecenTp1SpJUWlqq5557TjfffLP69Omj4cOH609/+pMkacSIEZKk+++/Xx9//LEkae3atfqP//gPXX311S71SHI+ieWaa65RVlaWjh07pvHjx6t///665ZZbtGjRItnt9qb+UQMIUYROAPiXiooKHT58WJ988onWrl2rzz//XJ988km97zlw4ICmTZum559/Xl988YXuu+8+PfLIIyotLdXBgwf129/+VtnZ2dq9e7cGDBigBQsWSJJycnIkXQis/fv3r7XfjRs3atasWfriiy9ktVo1ceJETZs2TX/+858VHh6uNWvWSJKWLFmiqqoqbdmyRX/5y1904403KiMjQ5KcQfPdd9/ViBEjlJ2dreXLlysrK0t79uzRnXfeqYkTJyo/P9953PLycv3pT3/SE088oUWLFql///766quv9Lvf/U5btmzRrl27mv4HDSAkEToB4N88+uijioqK0uWXX67evXvr2LFj9bbfsGGDUlNTdd1118lsNmvkyJHq0qWLtm3bpoiICJWXlys7O1t/+9vflJ6ervXr13tUx+DBg9WjRw+1aNFCffr0UUpKinr16qWoqChdc801ys3NlSQ9++yzysjIUEREhHJzc9W6dWuXEPnvPvroI91///3q3bu3zGaz7r33XnXr1k3btm1ztrn99ttlsVjUunVrWSwW7d69W59++qmio6O1fft23XDDDR7+SQKAK0InAPybmJgY589ms1kOh6Pe9rm5ucrJyVH//v2dv44cOaLc3FxdccUVWr58uf7yl7/o3nvv1U033aTs7GyP6mjbtq3z5/DwcLVp08b52mQyOesqKCjQk08+qYEDB+q5557ToUOH6qy5qKhInTp1clnXqVMn51C9JMXGxjp/XrBggXr37q3FixdrwIABmjRpUp2BFgAaQugEgCaIj4/X+PHjtXfvXuevTZs26ZFHHlFBQYHat2+vVatW6csvv9TUqVM1a9YsnThxosH9mkwmj44/ZcoUDR48WF988YXef/9957Wi7iQlJen77793WXfixAmXoPnvx/3222/19NNP67PPPtPWrVt17tw5vfrqqx7VBQA/RegEgCYYNWqUNm7cqL/+9a9yOBzavXu3UlNTdfDgQR09elSPPPKIvv32W0VGRqp9+/ayWCxq1aqVLBaLJOncuXNNOn55eblatGihsLAwHT9+XG+99ZbLzU8/DPFL0l133aW1a9dq//79stlsWr9+vf73f/9XQ4cOdbvvpUuX6tVXX5XValVsbKzCw8PVrl27JtULIHQxZRIANEH//v310ksvafbs2Tp58qTi4uI0Z84cDRgwQJL0xBNP6Mknn1RJSYmSkpL02muvKSYmRg6HQzfddJPS0tKadPZw/vz5WrBggZYtW6b4+HiNHTtWWVlZOnHihH72s59p9OjReuKJJzR9+nSNHz/eebd7QUGBLr/8cq1cuVKdO3d2u++FCxdq7ty5GjhwoEwmk4YMGaLJkydfdK0AQpvJ0dAFSwAAAEATMbwOAAAAwzG8DgD1GD16tI4ePep2W3x8vMt0QwCAujG8DgAAAMMxvA4AAADDEToBAABgOEInAAAADEfoBAAAgOEInQAAADDc/wepThPVx7ZgpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='n_estimators', y='r2', hue='max_depth', data=rf, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "en = clean[clean['model']=='ElasticNet'][['Drug', 'fs', 'da', 'catn', 'alpha', 'l1_ratio','r2_score', 'r2']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#hue = ['DecisionTreeRegressor', 'mutual_info_regression', 'ElasticNet', 'f_regression', 'Lasso', 'RandomForestRegressor', ]\n",
    "g = sns.catplot(x = 'alpha', y='OR', hue='l1_ratio',data=top_OR(en, ['l1_ratio', 'alpha'], score='r2').reset_index(), kind='bar', palette='Greens', height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "g.set_ylabels('Odds Ratio')\n",
    "plt.legend(loc='lower right')\n",
    "plt.tight_layout()\n",
    "#plt.savefig('graphs/adjusted-top-n.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 684x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1)\n",
    "#order= ['DecisionTreeRegressor', 'ElasticNet', 'KNeighborsRegressor', 'RandomForestRegressor', 'SVR']\n",
    "g = sns.catplot(x='alpha', y='r2', data=en, kind='bar',height=5, aspect=1.9, legend=False)\n",
    "#g.set_xticklabels(['Decision Tree', 'Elastic Net', 'KNN', 'Random Forest', 'SVR'])\n",
    "\n",
    "#plt.legend(loc='lower center')\n",
    "plt.tight_layout()\n",
    "#g.set(yscale='symlog')\n",
    "#plt.savefig('graphs/adjusted-n.eps', format='eps')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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