Scripts/2018_19_season/mvp_predictions/Predicting MVP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import time\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data will be from 1980-81 up to past season for train and validation splits. That is because prior to that season voting was done by players."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def work_player_profile(param, season):\n",
    "    url = \"https://www.basketball-reference.com\" + param\n",
    "    res = requests.get(url)\n",
    "    soup = BeautifulSoup(res.text)\n",
    "    \n",
    "    data_dict = {}\n",
    "    \n",
    "    per_game = soup.find(attrs={'id': 'all_per_game'})\n",
    "    for row in per_game.findAll(\"tr\"):\n",
    "        if 'id' in row.attrs and row.attrs['id'] == \"per_game.\" + season:\n",
    "            data_dict['fga'] = float(row.find('td', attrs={'data-stat': 'fga_per_g'}).text)\n",
    "            data_dict['fg3a'] = float(row.find('td', attrs={'data-stat': 'fg3a_per_g'}).text)\n",
    "            data_dict['fta'] = float(row.find('td', attrs={'data-stat': 'fta_per_g'}).text)\n",
    "            break\n",
    "    \n",
    "    advanced_table = soup.find(attrs={'id': 'all_advanced'})\n",
    "    for child in advanced_table.children:\n",
    "        if \"table_outer_container\" in child:\n",
    "            other_soup = BeautifulSoup(child)\n",
    "            rows = other_soup.findAll(\"tr\")\n",
    "    for row in rows:\n",
    "        if 'id' in row.attrs and row.attrs['id'] == \"advanced.\" + season:\n",
    "            data_dict.update(\n",
    "                {\n",
    "                    'per': float(row.find('td', attrs={'data-stat': 'per'}).text),\n",
    "                    'ts_pct': float(row.find('td', attrs={'data-stat': 'ts_pct'}).text),\n",
    "                    'usg_pct': float(row.find('td', attrs={'data-stat': 'usg_pct'}).text),\n",
    "                    'bpm': float(row.find('td', attrs={'data-stat': 'bpm'}).text),\n",
    "                    'season': str(int(season)-1) + \"-\" + season[-2:],\n",
    "                }\n",
    "            )\n",
    "            return data_dict\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_stats_of_voting(url):\n",
    "    res = requests.get(url)\n",
    "    soup = BeautifulSoup(res.text)\n",
    "    item = soup.find(attrs={'class': 'stats_table'})\n",
    "    rows = item.findAll(\"tr\")\n",
    "    \n",
    "    season = url.split(\".html\")[0][-4:]\n",
    "    \n",
    "    print(f\"Current season: {season}\")\n",
    "    \n",
    "    players_stats = defaultdict(list)\n",
    "    \n",
    "    for index, row in enumerate(rows):\n",
    "        \n",
    "        print(f\"\\tCurrent index: {index} of {len(rows)}\")\n",
    "        header_cells = row.findAll(\"th\")\n",
    "        for header_cell in header_cells:\n",
    "            if 'data-stat' in header_cell.attrs and header_cell['data-stat'] == 'ranker' and 'csk' in header_cell.attrs:\n",
    "                rank = int(header_cell.getText())\n",
    "        td_cells = row.findAll(\"td\")\n",
    "        if not td_cells:\n",
    "            continue\n",
    "        for cell in td_cells:\n",
    "            if 'data-stat' not in cell.attrs:\n",
    "                continue\n",
    "            if cell['data-stat'] == 'age':\n",
    "                continue\n",
    "            if cell['data-stat'] == 'team_id':\n",
    "                base = \"https://www.basketball-reference.com\"\n",
    "                try:\n",
    "                    link = cell.find(\"a\")['href']\n",
    "                except Exception:\n",
    "                    players_stats['win_pct'].append(0.5)  # average\n",
    "                    continue\n",
    "                url = base + link\n",
    "                time.sleep(1)\n",
    "                soup = BeautifulSoup(requests.get(url).text)\n",
    "                for item in soup.findAll(\"p\"):\n",
    "                    if \"Record\" in item.text:\n",
    "                        record = re.findall(\"\\d+\\-\\d+\", item.text)[0]\n",
    "                        splitted = record.split(\"-\")\n",
    "                        players_stats['win_pct'].append(float(splitted[0]) / (float(splitted[1]) + float(splitted[0])))\n",
    "                        break\n",
    "                continue\n",
    "            if cell['data-stat'] == 'player':\n",
    "                time.sleep(1)\n",
    "                advanced_dict = work_player_profile(cell.find(\"a\")['href'], season)\n",
    "                for key in advanced_dict:\n",
    "                    players_stats[key].append(advanced_dict[key])\n",
    "                players_stats[cell['data-stat']].append(cell.getText())\n",
    "            else:\n",
    "                text = cell.getText() or \"0\"\n",
    "                players_stats[cell['data-stat']].append(float(text))\n",
    "    return players_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "seasons = range(2016, 2019)\n",
    "\n",
    "new_data = defaultdict(list)\n",
    "\n",
    "for season in seasons:\n",
    "    full_url = f\"https://www.basketball-reference.com/awards/awards_{str(season)}.html\"\n",
    "    cur_season_dict = get_stats_of_voting(full_url)\n",
    "    for key in cur_season_dict:\n",
    "        new_data[key].extend(cur_season_dict[key])\n",
    "        \n",
    "data_frame = pd.DataFrame(data_mvp)\n",
    "data_frame.to_csv(\"mvp_votings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key in new_data:\n",
    "    data_mvp[key].extend(new_data[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.DataFrame(data_mvp)\n",
    "data_frame.to_csv(\"mvp_votings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.read_csv(\"mvp_votings.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding best features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, mutual_info_regression, f_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_features = ['fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
    "       'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "       'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct', 'ws',\n",
    "       'ws_per_48', 'win_pct']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data = data_frame[orig_features].to_numpy()\n",
    "y_data = data_frame[['award_share']].to_numpy()\n",
    "y_data = y_data.reshape(y_data.shape[0], )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Random forest to check most significant features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "regressor = RandomForestRegressor(n_estimators=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_indices = np.argsort(regressor.feature_importances_)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ws: 0.40651032881353694\n",
      "win_pct: 0.12516923391566231\n",
      "per: 0.09620828488856095\n",
      "bpm: 0.05180647439660696\n",
      "ws_per_48: 0.037306876283414836\n",
      "fga: 0.03628823279036492\n",
      "usg_pct: 0.02922362860650428\n",
      "ft_pct: 0.027996304407172944\n",
      "ast_per_g: 0.024380031499797675\n",
      "fg_pct: 0.022485588465302166\n",
      "mp_per_g: 0.022081776438384656\n",
      "fta: 0.02187362197773666\n",
      "pts_per_g: 0.018496710202897785\n",
      "trb_per_g: 0.01616551911325858\n",
      "fg3_pct: 0.016158514189164693\n",
      "ts_pct: 0.015414174749726362\n",
      "blk_per_g: 0.011512056523768808\n",
      "stl_per_g: 0.010514733414184891\n",
      "fg3a: 0.010407909323953446\n"
     ]
    }
   ],
   "source": [
    "for index in sorted_indices:\n",
    "    print(f\"{orig_features[index]}: {regressor.feature_importances_[index]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It kinda seems that only using Win shares we can conclude which player won the most points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = SelectKBest(mutual_info_regression, k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_feat = selector.fit_transform(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_indices = np.argsort(selector.scores_)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ws: 0.3037\n",
      "per: 0.2628\n",
      "ws_per_48: 0.2598\n",
      "bpm: 0.1992\n",
      "pts_per_g: 0.1482\n",
      "win_pct: 0.1152\n",
      "usg_pct: 0.1012\n",
      "fta: 0.0967\n",
      "fga: 0.0875\n",
      "ts_pct: 0.0773\n",
      "trb_per_g: 0.0702\n",
      "mp_per_g: 0.0626\n",
      "stl_per_g: 0.0467\n",
      "fg3a: 0.0269\n",
      "ast_per_g: 0.0251\n",
      "ft_pct: 0.0245\n",
      "blk_per_g: 0.0168\n",
      "fg_pct: 0.0127\n",
      "fg3_pct: 0.0000\n"
     ]
    }
   ],
   "source": [
    "for index in sorted_indices:\n",
    "    print(f\"{orig_features[index]}: {selector.scores_[index]:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same goes here, Win shares are at the top again."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_frame[orig_features + [\"award_share\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f18047db940>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 16))\n",
    "\n",
    "fig.set_facecolor('white')\n",
    "\n",
    "sns.heatmap(data.corr(), annot=True, cmap=\"RdYlGn\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BPM and PER represent similar stat. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Points per game is directly connected with usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will remove now all percentages because true shooting percentage is enough. Also, WS per 48 can be removed since WS are better and that is just scaled value.\n",
    "\n",
    "Also, the attempts will be removed since they are included in usage stat."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since per and BPM model similiar stat, I will use BPM only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'win_pct']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data_frame[features + [\"award_share\"]]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "\n",
    "fig.set_facecolor('white')\n",
    "\n",
    "sns.heatmap(data.corr(), annot=True, cmap=\"RdYlGn\")\n",
    "_ = plt.yticks(rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_frame[orig_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformed = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "decomposed = pca.fit_transform(transformed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f1801abb400>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 14))\n",
    "\n",
    "plt.scatter(decomposed, data_frame.award_share)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the data doesn't really fit that well the regression task, but we gotta work with what we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Award share')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "plt.hist(data_frame.award_share, bins=20, edgecolor='#F5F5F5')\n",
    "plt.title(\"Number of occurrences of award shares\", size=16)\n",
    "plt.ylabel(\"Number of occurrences\", size=12)\n",
    "plt.xlabel(\"Award share\", size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Defining the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures, normalize, StandardScaler, MinMaxScaler\n",
    "from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, BaggingRegressor, AdaBoostRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(data_frame, estimators, params, filename, poly_fit = None, scaler = None):\n",
    "    \n",
    "    unique_seasons = data_frame.season.unique()\n",
    "    train_columns = ['ts_pct', 'bpm', 'usg_pct', 'pts_per_g', 'trb_per_g', 'per', 'ws','ws_per_48', 'win_pct']\n",
    "    target_columns = ['award_share']\n",
    "    \n",
    "    for handler in logging.root.handlers[:]:\n",
    "        logging.root.removeHandler(handler)\n",
    "    logging.basicConfig(filename=filename, filemode='w', level=logging.INFO)\n",
    "    logger = logging.getLogger()\n",
    "    \n",
    "    minimal_error, best_estimator = None, None\n",
    "    \n",
    "    for estimator in estimators:\n",
    "        \n",
    "        try: \n",
    "            print(f\"Starting with estimator: {estimator.__name__}\")\n",
    "            logging.info(f\"Starting with estimator: {estimator.__name__}\")\n",
    "\n",
    "            for index, cur_params in enumerate(params[estimator.__name__]):\n",
    "\n",
    "                regressor = estimator(**cur_params)\n",
    "\n",
    "                # To collect MSE over each split\n",
    "                errors = []\n",
    "\n",
    "                # to collect accuracies\n",
    "                accuracies = []\n",
    "                top_1_accs = []\n",
    "\n",
    "                for season in unique_seasons:\n",
    "                    \n",
    "                    train_data = data_frame.loc[data_frame.season != season]\n",
    "                    validation_data = data_frame.loc[data_frame.season == season]\n",
    "                    validation_data = validation_data.sort_values(by='award_share', ascending=False)\n",
    "\n",
    "                    # Get train data\n",
    "                    train_x = train_data[train_columns].to_numpy()\n",
    "                    train_y = train_data[target_columns].to_numpy()\n",
    "                    train_y = train_y.reshape(train_y.shape[0], )\n",
    "\n",
    "                    # Validate over one season only\n",
    "                    val_x = validation_data[train_columns].to_numpy()\n",
    "                    val_y = validation_data[target_columns].to_numpy()\n",
    "                    val_y = val_y.reshape(val_y.shape[0], )\n",
    "\n",
    "                    if poly_fit is not None:\n",
    "                        # poly_fit = PolynomialFeatures(degree=2,interaction_only=False)\n",
    "                        train_x = poly_fit.fit_transform(train_x)\n",
    "                        val_x = poly_fit.fit_transform(val_x)\n",
    "\n",
    "                    if scaler is not None:\n",
    "                        train_x = scaler.fit_transform(train_x)\n",
    "                        val_x = scaler.fit_transform(val_x)\n",
    "                        \n",
    "                    shuffle_x, shuffle_y = shuffle(train_x, train_y)\n",
    "\n",
    "                    regressor.fit(shuffle_x, shuffle_y)\n",
    "                    predicted_y = regressor.predict(val_x)\n",
    "\n",
    "                    sorted_indices = np.argsort(predicted_y)[::-1]\n",
    "                    correct_indices = np.arange(len(val_y))\n",
    "\n",
    "                    accuracy = np.sum(sorted_indices[:5] == correct_indices[:5]) / len(correct_indices[:5])\n",
    "                    accuracies.append(accuracy)\n",
    "                    \n",
    "                    top_1_accs.append(\n",
    "                        np.sum(sorted_indices[:1] == correct_indices[:1]) / len(correct_indices[:1])\n",
    "                    )\n",
    "                    curr_error = mean_squared_error(val_y, predicted_y)\n",
    "                    errors.append(curr_error)\n",
    "\n",
    "                mean_error = np.average(errors)\n",
    "                mean_accuracy = np.average(accuracies)\n",
    "                mean_acc_at_1 = np.average(top_1_accs)\n",
    "                logging.info(f\"Params: {cur_params}, MSE over all splits is: {mean_error:.4f}, Mean acc at 5: {mean_accuracy:.4f}, Mean acc at 1: {mean_acc_at_1:.4f}\")\n",
    "                print(\n",
    "                    f\"Params: {cur_params}, MSE over all splits is: {mean_error:.4f}, Mean acc at 5: {mean_accuracy:.4f}, Mean acc at 1: {mean_acc_at_1:.4f}\"\n",
    "                )\n",
    "\n",
    "\n",
    "                if minimal_error is None or mean_error < minimal_error:\n",
    "                    minimal_error = mean_error\n",
    "                    best_estimator = estimator(*cur_params)\n",
    "        except Exception:\n",
    "            print(f\"Exception: {estimator}\")\n",
    "            continue\n",
    "            \n",
    "    return best_estimator\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_rbf_svr():\n",
    "    dicts = []\n",
    "    for C in [0.1, 1, 10, 50, 100]:\n",
    "        for gamma in [0.00001, 0.0001, 0.001, 0.01, 0.1]:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'kernel': 'rbf',\n",
    "                    'C': C,\n",
    "                    'gamma': gamma\n",
    "                }\n",
    "            )\n",
    "    return dicts\n",
    "\n",
    "def define_poly_svr():\n",
    "    dicts = []\n",
    "    for C in [0.1, 1, 10, 100]:\n",
    "        for gamma in [0.0001, 0.001, 0.01, 0.1]:\n",
    "            for degree in [1, 2, 3]:\n",
    "                dicts.append(\n",
    "                    {\n",
    "                        'kernel': 'poly',\n",
    "                        'C': C,\n",
    "                        'gamma': gamma,\n",
    "                        'degree': degree\n",
    "                    }\n",
    "                )\n",
    "    return dicts\n",
    "\n",
    "def define_linear_svr():\n",
    "    dicts = []\n",
    "    for C in [0.1, 1, 10, 100]:\n",
    "        dicts.append(\n",
    "            {\n",
    "                'kernel': 'linear',\n",
    "                'C': C\n",
    "            }\n",
    "        )\n",
    "    return dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "def define_gradient_boosting_regressor():\n",
    "    dicts = []\n",
    "    for estimator in [10, 50, 100, 200]:\n",
    "        for learning_rate in [0.1, 0.01, 0.001, 0.0001, 1]:\n",
    "            for subsample in [0.5, 1.0]:\n",
    "                dicts.append(\n",
    "                    {\n",
    "                        'n_estimators': estimator,\n",
    "                        'learning_rate': learning_rate,\n",
    "                        'subsample': subsample,\n",
    "                    }\n",
    "                )\n",
    "    return dicts\n",
    "\n",
    "def define_adaboost_regressor():\n",
    "    dicts = []\n",
    "    BaggingRegressor\n",
    "    for n_estimators in [10, 50, 100, 200]:\n",
    "        for learning_rate in [0.0001, 0.001, 0.01, 0.1, 1]:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'base_estimator': Ridge(alpha=10.0),\n",
    "                    'n_estimators': n_estimators,\n",
    "                }\n",
    "            )\n",
    "    return dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_elastic_net_params():\n",
    "    dicts = []\n",
    "    for alpha in [0.2, 0.5, 1.0, 2.0, 5.0]:\n",
    "        for ratio in [0.2, 0.4, 0.5, 0.6, 0.8]:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'alpha': alpha,\n",
    "                    'l1_ratio': ratio\n",
    "                }\n",
    "            )\n",
    "    return dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [\n",
    "    LinearRegression,\n",
    "    Ridge,\n",
    "    RandomForestRegressor,\n",
    "    #ElasticNet,\n",
    "    # AdaBoostRegressor,\n",
    "    GradientBoostingRegressor,\n",
    "    SVR,\n",
    "    # MLPRegressor,\n",
    "]\n",
    "params = {\n",
    "    LinearRegression.__name__: [\n",
    "        {\n",
    "            'n_jobs': -1,\n",
    "        },\n",
    "        {\n",
    "            'n_jobs': -1,\n",
    "            'normalize': True\n",
    "        },\n",
    "    ],\n",
    "    Ridge.__name__: [\n",
    "        {\n",
    "            'alpha': 1.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 10.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 50.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 100.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 200.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 500.0\n",
    "        },\n",
    "    ],\n",
    "    ElasticNet.__name__: [\n",
    "        *get_elastic_net_params()\n",
    "    ],\n",
    "    RandomForestRegressor.__name__: [\n",
    "        {\n",
    "            'n_estimators': 10,\n",
    "            'n_jobs': -1\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 100,\n",
    "            'n_jobs': -1\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 50,\n",
    "            'n_jobs': -1\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 50,\n",
    "            'n_jobs': -1,\n",
    "            'min_samples_split': 3\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 100,\n",
    "            'n_jobs': -1,\n",
    "            'min_samples_split': 3\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 200,\n",
    "            'n_jobs': -1,\n",
    "            'min_samples_split': 3\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 200,\n",
    "            'n_jobs': -1,\n",
    "        },\n",
    "    ],\n",
    "    SVR.__name__: [\n",
    "        # *define_poly_svr(),\n",
    "        # *define_linear_svr(),\n",
    "        *define_rbf_svr(),\n",
    "    ],\n",
    "    AdaBoostRegressor.__name__: [\n",
    "        *define_adaboost_regressor()\n",
    "    ],\n",
    "    GradientBoostingRegressor.__name__: [\n",
    "        *define_gradient_boosting_regressor()\n",
    "    ],\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LAD\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params: {'epsilon': 0.001, 'max_iter': 5000}, MSE over all splits is: 0.0892, Mean acc at 5: 0.0632, Mean acc at 1: 0.2632\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/.local/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"temp.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=False),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 0.0350, Mean acc at 5: 0.2421, Mean acc at 1: 0.6316\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 0.0276, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0269, Mean acc at 5: 0.3105, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0270, Mean acc at 5: 0.3526, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0275, Mean acc at 5: 0.3316, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0280, Mean acc at 5: 0.3263, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0285, Mean acc at 5: 0.3105, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0291, Mean acc at 5: 0.3158, Mean acc at 1: 0.6316\n",
      "Starting with estimator: RandomForestRegressor\n",
      "Params: {'n_estimators': 10, 'n_jobs': -1}, MSE over all splits is: 0.0284, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1}, MSE over all splits is: 0.0263, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1}, MSE over all splits is: 0.0269, Mean acc at 5: 0.3158, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0272, Mean acc at 5: 0.3000, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0265, Mean acc at 5: 0.3316, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1}, MSE over all splits is: 0.0263, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0315, Mean acc at 5: 0.2947, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0309, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0618, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0617, Mean acc at 5: 0.2579, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.3158, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2316, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2526, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.0787, Mean acc at 5: 0.2526, Mean acc at 1: 0.4737\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0325, Mean acc at 5: 0.2947, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0267, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0259, Mean acc at 5: 0.2842, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0418, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0427, Mean acc at 5: 0.2895, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0653, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0653, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0689, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0689, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.1773, Mean acc at 5: 0.1789, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0386, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0270, Mean acc at 5: 0.2789, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0254, Mean acc at 5: 0.3105, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0320, Mean acc at 5: 0.2526, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0320, Mean acc at 5: 0.2737, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0617, Mean acc at 5: 0.2947, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0617, Mean acc at 5: 0.2684, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2526, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2316, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.2237, Mean acc at 5: 0.1632, Mean acc at 1: 0.3158\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0412, Mean acc at 5: 0.2947, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0294, Mean acc at 5: 0.2421, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0268, Mean acc at 5: 0.3053, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0265, Mean acc at 5: 0.2632, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0554, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0556, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0677, Mean acc at 5: 0.2842, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0677, Mean acc at 5: 0.2368, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.1785, Mean acc at 5: 0.1368, Mean acc at 1: 0.2895\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0415, Mean acc at 5: 0.2895, Mean acc at 1: 0.6316\n",
      "Starting with estimator: SVR\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 1e-05}, MSE over all splits is: 0.0369, Mean acc at 5: 0.2684, Mean acc at 1: 0.5526\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.0001}, MSE over all splits is: 0.0592, Mean acc at 5: 0.2211, Mean acc at 1: 0.3421\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.001}, MSE over all splits is: 0.0692, Mean acc at 5: 0.1105, Mean acc at 1: 0.1579\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.01}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0316, Mean acc at 1: 0.0789\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.1}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 1e-05}, MSE over all splits is: 0.0380, Mean acc at 5: 0.2684, Mean acc at 1: 0.4737\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}, MSE over all splits is: 0.0518, Mean acc at 5: 0.1789, Mean acc at 1: 0.3421\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}, MSE over all splits is: 0.0698, Mean acc at 5: 0.0895, Mean acc at 1: 0.1579\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0421, Mean acc at 1: 0.0789\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 1e-05}, MSE over all splits is: 0.0443, Mean acc at 5: 0.2789, Mean acc at 1: 0.5526\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}, MSE over all splits is: 0.0520, Mean acc at 5: 0.1737, Mean acc at 1: 0.3421\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}, MSE over all splits is: 0.0698, Mean acc at 5: 0.0895, Mean acc at 1: 0.1579\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0421, Mean acc at 1: 0.0789\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 1e-05}, MSE over all splits is: 0.0518, Mean acc at 5: 0.2737, Mean acc at 1: 0.6053\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.0001}, MSE over all splits is: 0.0520, Mean acc at 5: 0.1737, Mean acc at 1: 0.3421\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.001}, MSE over all splits is: 0.0698, Mean acc at 5: 0.0895, Mean acc at 1: 0.1579\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0421, Mean acc at 1: 0.0789\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 1e-05}, MSE over all splits is: 0.0578, Mean acc at 5: 0.2526, Mean acc at 1: 0.5789\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}, MSE over all splits is: 0.0520, Mean acc at 5: 0.1737, Mean acc at 1: 0.3421\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}, MSE over all splits is: 0.0698, Mean acc at 5: 0.0895, Mean acc at 1: 0.1579\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0421, Mean acc at 1: 0.0789\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_poly_2.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 0.0382, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 0.0335, Mean acc at 5: 0.2632, Mean acc at 1: 0.6579\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0270, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0272, Mean acc at 5: 0.3263, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0276, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0280, Mean acc at 5: 0.2895, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0283, Mean acc at 5: 0.3105, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0288, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Starting with estimator: RandomForestRegressor\n",
      "Params: {'n_estimators': 10, 'n_jobs': -1}, MSE over all splits is: 0.0289, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1}, MSE over all splits is: 0.0261, Mean acc at 5: 0.2684, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1}, MSE over all splits is: 0.0265, Mean acc at 5: 0.3211, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0265, Mean acc at 5: 0.2895, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0322, Mean acc at 5: 0.3053, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0309, Mean acc at 5: 0.2842, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0615, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0617, Mean acc at 5: 0.2579, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2947, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2316, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3263, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2526, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.0687, Mean acc at 5: 0.2158, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0338, Mean acc at 5: 0.2684, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0275, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0260, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0423, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0426, Mean acc at 5: 0.2895, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0653, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0653, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0689, Mean acc at 5: 0.3105, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0689, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.1540, Mean acc at 5: 0.1895, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0380, Mean acc at 5: 0.3053, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0280, Mean acc at 5: 0.3000, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0255, Mean acc at 5: 0.3053, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0324, Mean acc at 5: 0.3105, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0320, Mean acc at 5: 0.2737, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0617, Mean acc at 5: 0.3000, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0617, Mean acc at 5: 0.2684, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.2316, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.1668, Mean acc at 5: 0.1789, Mean acc at 1: 0.3684\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0396, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0295, Mean acc at 5: 0.2526, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0265, Mean acc at 5: 0.3105, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0268, Mean acc at 5: 0.2474, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0264, Mean acc at 5: 0.3000, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0554, Mean acc at 5: 0.2684, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0556, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0677, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0677, Mean acc at 5: 0.2368, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.3045, Mean acc at 5: 0.1579, Mean acc at 1: 0.2632\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0396, Mean acc at 5: 0.3211, Mean acc at 1: 0.6579\n",
      "Starting with estimator: SVR\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 1e-05}, MSE over all splits is: 0.0400, Mean acc at 5: 0.2368, Mean acc at 1: 0.4737\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.0001}, MSE over all splits is: 0.0642, Mean acc at 5: 0.1053, Mean acc at 1: 0.2105\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.001}, MSE over all splits is: 0.0695, Mean acc at 5: 0.1053, Mean acc at 1: 0.1316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.01}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0105, Mean acc at 1: 0.0263\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.1}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 1e-05}, MSE over all splits is: 0.0406, Mean acc at 5: 0.2526, Mean acc at 1: 0.5263\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}, MSE over all splits is: 0.0591, Mean acc at 5: 0.1316, Mean acc at 1: 0.2368\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}, MSE over all splits is: 0.0707, Mean acc at 5: 0.1158, Mean acc at 1: 0.2105\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0105, Mean acc at 1: 0.0263\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 1e-05}, MSE over all splits is: 0.0521, Mean acc at 5: 0.2421, Mean acc at 1: 0.4474\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}, MSE over all splits is: 0.0591, Mean acc at 5: 0.1316, Mean acc at 1: 0.2368\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}, MSE over all splits is: 0.0707, Mean acc at 5: 0.1158, Mean acc at 1: 0.2105\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0105, Mean acc at 1: 0.0263\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 1e-05}, MSE over all splits is: 0.0580, Mean acc at 5: 0.2158, Mean acc at 1: 0.5526\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.0001}, MSE over all splits is: 0.0591, Mean acc at 5: 0.1316, Mean acc at 1: 0.2368\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.001}, MSE over all splits is: 0.0707, Mean acc at 5: 0.1158, Mean acc at 1: 0.2105\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0105, Mean acc at 1: 0.0263\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 1e-05}, MSE over all splits is: 0.0607, Mean acc at 5: 0.2053, Mean acc at 1: 0.5526\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}, MSE over all splits is: 0.0591, Mean acc at 5: 0.1316, Mean acc at 1: 0.2368\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}, MSE over all splits is: 0.0707, Mean acc at 5: 0.1158, Mean acc at 1: 0.2105\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0105, Mean acc at 1: 0.0263\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_poly_2_all.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=False),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 0.0673, Mean acc at 5: 0.2368, Mean acc at 1: 0.5789\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 0.0451, Mean acc at 5: 0.2316, Mean acc at 1: 0.5526\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0311, Mean acc at 5: 0.2684, Mean acc at 1: 0.6053\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0299, Mean acc at 5: 0.2737, Mean acc at 1: 0.6053\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0290, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0287, Mean acc at 5: 0.2947, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0284, Mean acc at 5: 0.3053, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0281, Mean acc at 5: 0.3211, Mean acc at 1: 0.6316\n",
      "Starting with estimator: RandomForestRegressor\n",
      "Params: {'n_estimators': 10, 'n_jobs': -1}, MSE over all splits is: 0.0306, Mean acc at 5: 0.3158, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1}, MSE over all splits is: 0.0269, Mean acc at 5: 0.3158, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1}, MSE over all splits is: 0.0269, Mean acc at 5: 0.3158, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0268, Mean acc at 5: 0.3368, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0271, Mean acc at 5: 0.3421, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1, 'min_samples_split': 3}, MSE over all splits is: 0.0265, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'n_jobs': -1}, MSE over all splits is: 0.0264, Mean acc at 5: 0.3474, Mean acc at 1: 0.7105\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0311, Mean acc at 5: 0.3105, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0309, Mean acc at 5: 0.3053, Mean acc at 1: 0.7632\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0613, Mean acc at 5: 0.3053, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0618, Mean acc at 5: 0.2947, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.3158, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3158, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3263, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.0952, Mean acc at 5: 0.2105, Mean acc at 1: 0.5263\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0429, Mean acc at 5: 0.2895, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0278, Mean acc at 5: 0.2632, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0264, Mean acc at 5: 0.2842, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0414, Mean acc at 5: 0.2895, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0417, Mean acc at 5: 0.3105, Mean acc at 1: 0.7632\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0652, Mean acc at 5: 0.2895, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0654, Mean acc at 5: 0.3158, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0689, Mean acc at 5: 0.3158, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0689, Mean acc at 5: 0.3263, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.1812, Mean acc at 5: 0.1842, Mean acc at 1: 0.3684\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0494, Mean acc at 5: 0.2158, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0287, Mean acc at 5: 0.3000, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0269, Mean acc at 5: 0.2526, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0308, Mean acc at 5: 0.3000, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0313, Mean acc at 5: 0.2947, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0614, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0618, Mean acc at 5: 0.2842, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0685, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0685, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.2492, Mean acc at 5: 0.1211, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0477, Mean acc at 5: 0.2211, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0291, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0278, Mean acc at 5: 0.2474, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0262, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0261, Mean acc at 5: 0.3105, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0548, Mean acc at 5: 0.3000, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0552, Mean acc at 5: 0.3053, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0676, Mean acc at 5: 0.3368, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0677, Mean acc at 5: 0.3053, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.2431, Mean acc at 5: 0.1579, Mean acc at 1: 0.3158\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.0484, Mean acc at 5: 0.2474, Mean acc at 1: 0.5526\n",
      "Starting with estimator: SVR\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 1e-05}, MSE over all splits is: 0.0696, Mean acc at 5: 0.1000, Mean acc at 1: 0.1579\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.0001}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.001}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.01}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 0.1, 'gamma': 0.1}, MSE over all splits is: 0.0696, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 1e-05}, MSE over all splits is: 0.0710, Mean acc at 5: 0.1211, Mean acc at 1: 0.1842\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 1, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 1e-05}, MSE over all splits is: 0.0710, Mean acc at 5: 0.1211, Mean acc at 1: 0.1842\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 10, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 1e-05}, MSE over all splits is: 0.0710, Mean acc at 5: 0.1211, Mean acc at 1: 0.1842\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.0001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 50, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 1e-05}, MSE over all splits is: 0.0710, Mean acc at 5: 0.1211, Mean acc at 1: 0.1842\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.01}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'kernel': 'rbf', 'C': 100, 'gamma': 0.1}, MSE over all splits is: 0.0711, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_poly_3.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=3, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [\n",
    "    LinearRegression,\n",
    "    Ridge,\n",
    "    Lasso,\n",
    "    # RandomForestRegressor,\n",
    "    GradientBoostingRegressor,\n",
    "    #SVR,\n",
    "    # MLPRegressor,\n",
    "]\n",
    "params = {\n",
    "    LinearRegression.__name__: [\n",
    "        {\n",
    "            'n_jobs': -1,\n",
    "        },\n",
    "        {\n",
    "            'n_jobs': -1,\n",
    "            'normalize': True\n",
    "        },\n",
    "    ],\n",
    "    Ridge.__name__: [\n",
    "        {\n",
    "            'alpha': 1.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 10.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 50.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 100.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 200.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 500.0\n",
    "        },\n",
    "    ],\n",
    "    Lasso.__name__: [\n",
    "        {\n",
    "            'alpha': 1.0,\n",
    "        },\n",
    "        {\n",
    "            'alpha': 10.0\n",
    "        },\n",
    "        {\n",
    "            'alpha': 100.0\n",
    "        },\n",
    "    ],\n",
    "    RandomForestRegressor.__name__: [\n",
    "        {\n",
    "            'n_estimators': 10,\n",
    "            'n_jobs': -1\n",
    "        },\n",
    "        {\n",
    "            'n_estimators': 50,\n",
    "            'n_jobs': -1\n",
    "        },\n",
    "        \n",
    "    ],\n",
    "    SVR.__name__: [\n",
    "        *define_poly_svr(),\n",
    "        *define_linear_svr(),\n",
    "        #*define_rbf_svr(),\n",
    "    ],\n",
    "    GradientBoostingRegressor.__name__: [\n",
    "        *define_gradient_boosting_regressor()\n",
    "    ]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 0.0500, Mean acc at 5: 0.3316, Mean acc at 1: 0.6842\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 0.0500, Mean acc at 5: 0.3316, Mean acc at 1: 0.6842\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0485, Mean acc at 5: 0.3421, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0414, Mean acc at 5: 0.3474, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0378, Mean acc at 5: 0.3579, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0413, Mean acc at 5: 0.3316, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0482, Mean acc at 5: 0.3263, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0578, Mean acc at 5: 0.3211, Mean acc at 1: 0.6316\n",
      "Starting with estimator: Lasso\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0364, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0359, Mean acc at 5: 0.3000, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0610, Mean acc at 5: 0.3211, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0607, Mean acc at 5: 0.3211, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0684, Mean acc at 5: 0.3211, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0683, Mean acc at 5: 0.3158, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2632, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3053, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.2514, Mean acc at 5: 0.1842, Mean acc at 1: 0.3947\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1339, Mean acc at 5: 0.2211, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0574, Mean acc at 5: 0.2474, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0483, Mean acc at 5: 0.2632, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0422, Mean acc at 5: 0.2684, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0424, Mean acc at 5: 0.3263, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0649, Mean acc at 5: 0.3000, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0648, Mean acc at 5: 0.3053, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0688, Mean acc at 5: 0.3053, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0688, Mean acc at 5: 0.3053, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.6333, Mean acc at 5: 0.1263, Mean acc at 1: 0.2895\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1694, Mean acc at 5: 0.1947, Mean acc at 1: 0.5263\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0656, Mean acc at 5: 0.3000, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0581, Mean acc at 5: 0.2684, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0358, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0362, Mean acc at 5: 0.3105, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0609, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0607, Mean acc at 5: 0.3263, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0684, Mean acc at 5: 0.3105, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0683, Mean acc at 5: 0.3158, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.7704, Mean acc at 5: 0.1526, Mean acc at 1: 0.2105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1679, Mean acc at 5: 0.2368, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0714, Mean acc at 5: 0.2737, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0674, Mean acc at 5: 0.2842, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0390, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0377, Mean acc at 5: 0.3000, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0543, Mean acc at 5: 0.3105, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0541, Mean acc at 5: 0.3105, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0675, Mean acc at 5: 0.3053, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0674, Mean acc at 5: 0.3158, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.0733, Mean acc at 5: 0.1105, Mean acc at 1: 0.1842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1629, Mean acc at 5: 0.1895, Mean acc at 1: 0.5000\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_scaler.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    # poly_fit=PolynomialFeatures(degree=3, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 1.3333, Mean acc at 5: 0.2474, Mean acc at 1: 0.5263\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 1.1544, Mean acc at 5: 0.2632, Mean acc at 1: 0.5526\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0850, Mean acc at 5: 0.3368, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0503, Mean acc at 5: 0.3579, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0424, Mean acc at 5: 0.3474, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0390, Mean acc at 5: 0.3421, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0363, Mean acc at 5: 0.3211, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0374, Mean acc at 5: 0.3211, Mean acc at 1: 0.6579\n",
      "Starting with estimator: Lasso\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0409, Mean acc at 5: 0.2474, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0387, Mean acc at 5: 0.3000, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0597, Mean acc at 5: 0.3211, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0597, Mean acc at 5: 0.3000, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2579, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3263, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.3313, Mean acc at 5: 0.1421, Mean acc at 1: 0.2895\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1514, Mean acc at 5: 0.2316, Mean acc at 1: 0.4211\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0743, Mean acc at 5: 0.2789, Mean acc at 1: 0.5263\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0725, Mean acc at 5: 0.2895, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0412, Mean acc at 5: 0.2842, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0408, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0642, Mean acc at 5: 0.3316, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0642, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0688, Mean acc at 5: 0.3368, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0688, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.9292, Mean acc at 5: 0.1211, Mean acc at 1: 0.1842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1931, Mean acc at 5: 0.1789, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0856, Mean acc at 5: 0.2211, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0918, Mean acc at 5: 0.2368, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0395, Mean acc at 5: 0.2632, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0387, Mean acc at 5: 0.3053, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0597, Mean acc at 5: 0.3053, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0598, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.3263, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.2326, Mean acc at 5: 0.1158, Mean acc at 1: 0.3158\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1919, Mean acc at 5: 0.1474, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.1016, Mean acc at 5: 0.2579, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.1041, Mean acc at 5: 0.2737, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0520, Mean acc at 5: 0.2684, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0499, Mean acc at 5: 0.3158, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0525, Mean acc at 5: 0.2842, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0525, Mean acc at 5: 0.2684, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0672, Mean acc at 5: 0.3316, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0672, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.2010, Mean acc at 5: 0.1158, Mean acc at 1: 0.2895\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1963, Mean acc at 5: 0.1526, Mean acc at 1: 0.3684\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_scaler_poly_2.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 1.3578, Mean acc at 5: 0.2526, Mean acc at 1: 0.4474\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 1.6867, Mean acc at 5: 0.2579, Mean acc at 1: 0.3684\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.1061, Mean acc at 5: 0.3211, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0548, Mean acc at 5: 0.3526, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0439, Mean acc at 5: 0.3526, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0400, Mean acc at 5: 0.3474, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0367, Mean acc at 5: 0.3263, Mean acc at 1: 0.6316\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0365, Mean acc at 5: 0.3263, Mean acc at 1: 0.6579\n",
      "Starting with estimator: Lasso\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0420, Mean acc at 5: 0.2316, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0388, Mean acc at 5: 0.3000, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0597, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0597, Mean acc at 5: 0.2947, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2789, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.3053, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.3052, Mean acc at 5: 0.1737, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1572, Mean acc at 5: 0.2158, Mean acc at 1: 0.4474\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0781, Mean acc at 5: 0.2684, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0737, Mean acc at 5: 0.2632, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0411, Mean acc at 5: 0.3316, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0408, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0642, Mean acc at 5: 0.3053, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0642, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0688, Mean acc at 5: 0.3421, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0688, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.9811, Mean acc at 5: 0.1684, Mean acc at 1: 0.2632\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1854, Mean acc at 5: 0.1368, Mean acc at 1: 0.3158\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0863, Mean acc at 5: 0.2474, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0908, Mean acc at 5: 0.2632, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0396, Mean acc at 5: 0.2632, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0388, Mean acc at 5: 0.3158, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0597, Mean acc at 5: 0.2842, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0597, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.5011, Mean acc at 5: 0.1421, Mean acc at 1: 0.2895\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1914, Mean acc at 5: 0.2000, Mean acc at 1: 0.3684\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0993, Mean acc at 5: 0.2632, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.1045, Mean acc at 5: 0.2474, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0527, Mean acc at 5: 0.2579, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0503, Mean acc at 5: 0.3263, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0526, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0524, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0672, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0672, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.7162, Mean acc at 5: 0.1105, Mean acc at 1: 0.2105\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1920, Mean acc at 5: 0.2158, Mean acc at 1: 0.4211\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_scaler_poly_2_all.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=False),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LinearRegression\n",
      "Params: {'n_jobs': -1}, MSE over all splits is: 72.1856, Mean acc at 5: 0.0421, Mean acc at 1: 0.0789\n",
      "Params: {'n_jobs': -1, 'normalize': True}, MSE over all splits is: 72.6812, Mean acc at 5: 0.0632, Mean acc at 1: 0.1842\n",
      "Starting with estimator: Ridge\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.1379, Mean acc at 5: 0.3368, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0787, Mean acc at 5: 0.3421, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 50.0}, MSE over all splits is: 0.0584, Mean acc at 5: 0.3421, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0526, Mean acc at 5: 0.3579, Mean acc at 1: 0.6842\n",
      "Params: {'alpha': 200.0}, MSE over all splits is: 0.0471, Mean acc at 5: 0.3474, Mean acc at 1: 0.6579\n",
      "Params: {'alpha': 500.0}, MSE over all splits is: 0.0398, Mean acc at 5: 0.3316, Mean acc at 1: 0.6579\n",
      "Starting with estimator: Lasso\n",
      "Params: {'alpha': 1.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 10.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Params: {'alpha': 100.0}, MSE over all splits is: 0.0693, Mean acc at 5: 0.0000, Mean acc at 1: 0.0000\n",
      "Starting with estimator: GradientBoostingRegressor\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0476, Mean acc at 5: 0.2842, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0456, Mean acc at 5: 0.3158, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0596, Mean acc at 5: 0.3368, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0596, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.3158, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2684, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0692, Mean acc at 5: 0.2684, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 0.3806, Mean acc at 5: 0.1263, Mean acc at 1: 0.2368\n",
      "Params: {'n_estimators': 10, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1593, Mean acc at 5: 0.2211, Mean acc at 1: 0.4737\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.0886, Mean acc at 5: 0.2316, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.0925, Mean acc at 5: 0.2526, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0427, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0426, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0640, Mean acc at 5: 0.2789, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0641, Mean acc at 5: 0.3000, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0687, Mean acc at 5: 0.3105, Mean acc at 1: 0.7368\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0688, Mean acc at 5: 0.2632, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.3734, Mean acc at 5: 0.0947, Mean acc at 1: 0.2105\n",
      "Params: {'n_estimators': 50, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.1914, Mean acc at 5: 0.2000, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.1002, Mean acc at 5: 0.2368, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.1079, Mean acc at 5: 0.2684, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0455, Mean acc at 5: 0.2632, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0448, Mean acc at 5: 0.3053, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0595, Mean acc at 5: 0.2895, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0596, Mean acc at 5: 0.2842, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2895, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0682, Mean acc at 5: 0.2789, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.4762, Mean acc at 5: 0.0947, Mean acc at 1: 0.1579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.2001, Mean acc at 5: 0.1789, Mean acc at 1: 0.3421\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 0.5}, MSE over all splits is: 0.1077, Mean acc at 5: 0.2158, Mean acc at 1: 0.5000\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.1, 'subsample': 1.0}, MSE over all splits is: 0.1199, Mean acc at 5: 0.2263, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 0.5}, MSE over all splits is: 0.0636, Mean acc at 5: 0.2474, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.01, 'subsample': 1.0}, MSE over all splits is: 0.0632, Mean acc at 5: 0.2632, Mean acc at 1: 0.5263\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 0.5}, MSE over all splits is: 0.0525, Mean acc at 5: 0.2947, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.001, 'subsample': 1.0}, MSE over all splits is: 0.0526, Mean acc at 5: 0.3211, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 0.5}, MSE over all splits is: 0.0671, Mean acc at 5: 0.2737, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 200, 'learning_rate': 0.0001, 'subsample': 1.0}, MSE over all splits is: 0.0671, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 0.5}, MSE over all splits is: 1.4785, Mean acc at 5: 0.1211, Mean acc at 1: 0.3684\n",
      "Params: {'n_estimators': 200, 'learning_rate': 1, 'subsample': 1.0}, MSE over all splits is: 0.2023, Mean acc at 5: 0.2053, Mean acc at 1: 0.3947\n"
     ]
    }
   ],
   "source": [
    "best_estimator = pipeline(\n",
    "    data_frame=data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_reg_scaler_poly_3.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=3, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "        'stl_per_g', 'blk_per_g', 'ws', 'win_pct']\n",
    "target_columns = ['award_share']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "season = \"2015-16\"\n",
    "#'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}\n",
    "# ''n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5, 'loss': 'huber'\n",
    "model = GradientBoostingRegressor(n_estimators=50, learning_rate= 0.1, subsample=1.0)\n",
    "model = RandomForestRegressor(n_estimators=50, n_jobs=-1)\n",
    "# model = Ridge(alpha=0.1)\n",
    "poly_fit = PolynomialFeatures(degree=3, interaction_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = data_frame.loc[data_frame.season != season]\n",
    "validation_data = data_frame.loc[data_frame.season == season]\n",
    "#validation_data = validation_data.sort_values(by='award_share')\n",
    "\n",
    "# Get train data\n",
    "train_x = train_data[train_columns].to_numpy()\n",
    "#train_x = poly_fit.fit_transform(train_x)\n",
    "train_y = train_data[target_columns].to_numpy()\n",
    "train_y = train_y.reshape(train_y.shape[0], )\n",
    "\n",
    "# Validate over one season only\n",
    "val_x = validation_data[train_columns].to_numpy()\n",
    "#val_x = poly_fit.fit_transform(val_x)\n",
    "val_y = validation_data[target_columns].to_numpy()\n",
    "val_y = val_y.reshape(val_y.shape[0], )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.82988 , 0.33518 , 0.52746 , 0.47618 , 0.25812 , 0.18638 ,\n",
       "       0.104882, 0.05074 , 0.12348 , 0.051238])"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y)\n",
    "predict_y = model.predict(val_x)\n",
    "predict_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.   , 0.484, 0.482, 0.371, 0.112, 0.082, 0.038, 0.02 , 0.007,\n",
       "       0.005])"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Results Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = os.listdir('logs/')\n",
    "result_list = []\n",
    "for name in results:\n",
    "    if 'log_final_features' in name:\n",
    "        added_name = name.split(\".txt\")[0].split(\"log_final_features_\")[1]\n",
    "        estimator = None\n",
    "        with open(\"logs/\" + name) as f:\n",
    "            lines = f.readlines()\n",
    "        for line in lines:\n",
    "            if \"{\" not in line:\n",
    "                estimator = line.split(\" \")[-1].strip()\n",
    "                continue\n",
    "            params = line.split(\"{\")[1].split(\"}\")[0].strip().replace(\"'\", \"\")\n",
    "            mse = float(line.split(\"MSE over all splits is: \")[1].split(\",\")[0].strip())\n",
    "            acc_top_1 = float(line.split(\"Mean acc at 1: \")[1].strip())\n",
    "            acc_top_5 = float(line.split(\"Mean acc at 5: \")[1].split(\",\")[0].strip())\n",
    "            result_list.append((added_name,estimator, params, acc_top_5, acc_top_1, mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_results = sorted(result_list, key=lambda item: item[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open('reg_results_mse_sorted.txt', 'w') as f:\n",
    "    for line in sorted_results:\n",
    "        string = \"\"\n",
    "        for item in line:\n",
    "            string = string + str(item) + \"|\"\n",
    "        string += \"\\n\"\n",
    "        f.write(string)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the results of couple of simple regressors, the best one is Ridge regressor with poly level 2 and all polynoms (not only interacitons). Alpha is set to 50 or 100."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cls attempt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is more natural to approach this problem as a classification problem, since we need to rank players, in other words, we need to give them a class. Minimum number of players for a season is 10, so I'll limit each season to only ten players, and I'll let the classifier decide top 10 ranks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_data_frame = None\n",
    "ranks = []\n",
    "minimum = None\n",
    "for season in data_frame.season.unique():\n",
    "    season_data = data_frame.loc[data_frame.season == season]\n",
    "    num_pos = len(season_data)\n",
    "    ranks = list(range(1, num_pos+1))\n",
    "    season_data = season_data.sort_values(by='award_share', ascending=False)\n",
    "    is_mvp = [1 if rank == 1 else 0 for rank in ranks]\n",
    "    season_data['rank'] = ranks\n",
    "    season_data['is_mvp'] = is_mvp\n",
    "    season_data = season_data[:10]\n",
    "    if cls_data_frame is None:\n",
    "        cls_data_frame = season_data\n",
    "    else:\n",
    "        cls_data_frame = cls_data_frame.append(season_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_data_frame.to_csv('cls_data_frame.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
    "       'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "       'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct', 'ws',\n",
    "       'ws_per_48', 'win_pct']\n",
    "x_data = cls_data_frame[features].to_numpy()\n",
    "y_data = cls_data_frame[['rank']].to_numpy()\n",
    "scaler = MinMaxScaler()\n",
    "x_data = scaler.fit_transform(x_data)\n",
    "y_data = y_data.reshape(y_data.shape[0], )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = SelectKBest(mutual_info_classif, k=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_feat = selector.fit_transform(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_indices = np.argsort(selector.scores_)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "per: 0.2472\n",
      "bpm: 0.2130\n",
      "ws_per_48: 0.2024\n",
      "ws: 0.1613\n",
      "win_pct: 0.0878\n",
      "trb_per_g: 0.0635\n",
      "blk_per_g: 0.0535\n",
      "stl_per_g: 0.0518\n",
      "ts_pct: 0.0419\n",
      "usg_pct: 0.0402\n",
      "pts_per_g: 0.0392\n",
      "fta: 0.0381\n",
      "ast_per_g: 0.0368\n",
      "ft_pct: 0.0360\n",
      "fg_pct: 0.0211\n",
      "fg3a: 0.0190\n",
      "mp_per_g: 0.0000\n",
      "fg3_pct: 0.0000\n",
      "fga: 0.0000\n"
     ]
    }
   ],
   "source": [
    "for index in sorted_indices:\n",
    "    print(f\"{features[index]}: {selector.scores_[index]:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "win_pct: 0.07045285760530273\n",
      "ws_per_48: 0.0692605957927698\n",
      "ws: 0.06797313417636797\n",
      "bpm: 0.06376022441733051\n",
      "per: 0.060702595278032165\n",
      "pts_per_g: 0.05464115358658442\n",
      "ft_pct: 0.052663705715118636\n",
      "ts_pct: 0.0506437427328556\n",
      "fga: 0.04964946440566086\n",
      "fg_pct: 0.04909896844957942\n",
      "mp_per_g: 0.049053654632370554\n",
      "ast_per_g: 0.04893833368148348\n",
      "fta: 0.04884429460438513\n",
      "trb_per_g: 0.04833394179969547\n",
      "usg_pct: 0.046024240524036156\n",
      "blk_per_g: 0.044444415935090895\n",
      "fg3_pct: 0.04439268040673557\n",
      "stl_per_g: 0.041658569471196755\n",
      "fg3a: 0.03946342678540384\n"
     ]
    }
   ],
   "source": [
    "sorted_indices = np.argsort(clf.feature_importances_)[::-1]\n",
    "for index in sorted_indices:\n",
    "    print(f\"{features[index]}: {clf.feature_importances_[index]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For random forest classifier there isn't any feature which really jumps out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_max_prob_for_rank(predictions):\n",
    "    indices = []\n",
    "    visited = []\n",
    "    # return np.argsort(predictions[:, 0])[::-1]\n",
    "    return np.argsort(predictions[:, 0])[::-1] + 1\n",
    "    for i in range(len(predictions)):\n",
    "        rank_i_values = predictions[:,i]\n",
    "        for top_rank in np.argsort(predictions[:,i])[::-1]:\n",
    "            if top_rank in visited:\n",
    "                continue\n",
    "            indices.append(top_rank+1)\n",
    "            visited.append(top_rank)\n",
    "            break\n",
    "    return indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mean_reciprocal_rank(preds, ground_truths):\n",
    "    total_sum = 0.0\n",
    "    for i in range(len(ground_truths)):\n",
    "        index = np.where(preds == ground_truths)[0]\n",
    "        if len(index) == 0:\n",
    "            total_sum += 0.0\n",
    "            continue\n",
    "        total_sum += 1. / (index[0]+1)\n",
    "    return total_sum / len(ground_truths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures, normalize, StandardScaler, MinMaxScaler\n",
    "from sklearn.utils import shuffle\n",
    "import logging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cls_pipeline(data_frame, estimators, params, filename, poly_fit = None, scaler = None):\n",
    "    \n",
    "    unique_seasons = data_frame.season.unique()\n",
    "    \"\"\"\n",
    "    train_columns = [\n",
    "        'fga', 'fg3a', 'fta', 'fg_pct', 'fg3_pct', 'ft_pct', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
    "        'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g', 'stl_per_g', 'blk_per_g', 'ws_per_48'            \n",
    "    ]\n",
    "    \n",
    "    train_columns = [\n",
    "        'fga', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm', 'pts_per_g','ws_per_48'            \n",
    "    ]\n",
    "    \"\"\"\n",
    "    train_columns = ['ts_pct', 'bpm', 'per', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'usg_pct', 'win_pct']\n",
    "    target_columns = ['rank']\n",
    "    \n",
    "    for handler in logging.root.handlers[:]:\n",
    "        logging.root.removeHandler(handler)\n",
    "    logging.basicConfig(filename=filename, filemode='w', level=logging.INFO)\n",
    "    logger = logging.getLogger()\n",
    "    \n",
    "    maximal_acc, best_estimator = None, None\n",
    "    \n",
    "    for estimator in estimators:\n",
    "        \n",
    "        \n",
    "            print(f\"Starting with estimator: {estimator.__name__}\")\n",
    "            logging.info(f\"Starting with estimator: {estimator.__name__}\")\n",
    "\n",
    "            for index, cur_params in enumerate(params[estimator.__name__]):\n",
    "\n",
    "                classifier = estimator(**cur_params)\n",
    "\n",
    "                # To collect MSE over each split\n",
    "                total_accuracies = []\n",
    "\n",
    "                # to collect accuracies\n",
    "                accuracies = []\n",
    "                top_1_accs = []\n",
    "\n",
    "                for season in unique_seasons:\n",
    "\n",
    "                    train_data = data_frame.loc[data_frame.season != season]\n",
    "                    validation_data = data_frame.loc[data_frame.season == season]\n",
    "                    validation_data = validation_data.sort_values(by='award_share', ascending=False)\n",
    "\n",
    "                    # Get train data\n",
    "                    train_x = train_data[train_columns].to_numpy()\n",
    "                    train_y = train_data[target_columns].to_numpy()\n",
    "                    train_y = train_y.reshape(train_y.shape[0], )\n",
    "\n",
    "                    # Validate over one season only\n",
    "                    val_x = validation_data[train_columns].to_numpy()\n",
    "                    val_y = validation_data[target_columns].to_numpy()\n",
    "                    val_y = val_y.reshape(val_y.shape[0], )\n",
    "\n",
    "                    if poly_fit is not None:\n",
    "                        # poly_fit = PolynomialFeatures(degree=2,interaction_only=False)\n",
    "                        train_x = poly_fit.fit_transform(train_x)\n",
    "                        val_x = poly_fit.fit_transform(val_x)\n",
    "\n",
    "                    if scaler is not None:\n",
    "                        train_x = scaler.fit_transform(train_x)\n",
    "                        val_x = scaler.fit_transform(val_x)\n",
    "\n",
    "                    shuffle_x, shuffle_y = shuffle(train_x, train_y)\n",
    "\n",
    "                    classifier.fit(shuffle_x, shuffle_y)\n",
    "                    predicted_y = classifier.predict_proba(val_x)\n",
    "\n",
    "                    # sorted_indices = np.argsort(predicted_y)[::-1]\n",
    "                    # correct_indices = np.arange(len(val_y))\n",
    "                    # print(predict_y)\n",
    "                    indices = get_max_prob_for_rank(predicted_y)\n",
    "                    \n",
    "                    accuracy = np.sum(indices[:5] == val_y[:5]) / len(val_y[:5])\n",
    "                    accuracies.append(accuracy)\n",
    "                    \n",
    "                    top_1_accs.append(\n",
    "                        np.sum(indices[:1] == val_y[:1]) / len(val_y[:1])\n",
    "                    )\n",
    "                    #curr_error = mean_squared_error(y_true=list(val_y[:5]) + list(val_y[-3:]),\n",
    "                    #                                y_pred=list(predicted_y[:5]) + list(predicted_y[-3:])\n",
    "                    total_accuracies.append(mean_reciprocal_rank(preds=indices[:5], ground_truths=val_y[:5]))\n",
    "\n",
    "                mean_total_acc = np.average(total_accuracies)\n",
    "                mean_accuracy = np.average(accuracies)\n",
    "                mean_acc_at_1 = np.average(top_1_accs)\n",
    "                logging.info(f\"Params: {cur_params}, MRR top 5 is: {mean_total_acc:.4f}, Mean acc at 5: {mean_accuracy:.4f}, Mean acc at 1: {mean_acc_at_1:.4f}\")\n",
    "                print(\n",
    "                    f\"Params: {cur_params}, MRR top 5 is: {mean_total_acc:.4f}, Mean acc at 5: {mean_accuracy:.4f}, Mean acc at 1: {mean_acc_at_1:.4f}\"\n",
    "                )\n",
    "\n",
    "\n",
    "                if maximal_acc is None or mean_total_acc > maximal_acc:\n",
    "                    maximal_acc = mean_total_acc\n",
    "                    best_estimator = estimator(*cur_params)\n",
    "\n",
    "            \n",
    "    return best_estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_log_reg():\n",
    "    dicts = []\n",
    "    for C in [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]:\n",
    "        for solver in ['newton-cg', 'lbfgs']:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'multi_class': 'multinomial',\n",
    "                    'solver': solver,\n",
    "                    'C': C,\n",
    "                    'n_jobs': -1,\n",
    "                    'max_iter': 2000\n",
    "                }\n",
    "            )\n",
    "    return dicts\n",
    "            \n",
    "def get_svc():\n",
    "    dicts = []\n",
    "    for C in [0.001, 0.01, 0.1, 1.0, 10, 100]:\n",
    "        for gamma in [0.0001, 0.001, 0.01, 0.1, 1.0]:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'C': C,\n",
    "                    'gamma': gamma,\n",
    "                    'probability': True\n",
    "                }\n",
    "            )\n",
    "    return dicts\n",
    "\n",
    "def get_rf_cls():\n",
    "    dicts = []\n",
    "    for n_estimators in [10, 100, 200]:\n",
    "        for min_samples_split in [2, 3]:\n",
    "            dicts.append(\n",
    "                {\n",
    "                    'n_estimators': n_estimators,\n",
    "                    'min_samples_split': min_samples_split,\n",
    "                    'n_jobs': -1\n",
    "                }\n",
    "            )\n",
    "    return dicts\n",
    "\n",
    "def define_gradient_boosting_cls():\n",
    "    dicts = []\n",
    "    for estimator in [10, 50, 100]:\n",
    "        for learning_rate in [0.1, 0.01, 0.001, 0.0001]:\n",
    "            for subsample in [0.5, 1.0]:\n",
    "                dicts.append(\n",
    "                    {\n",
    "                        'n_estimators': estimator,\n",
    "                        'learning_rate': learning_rate,\n",
    "                        'subsample': subsample,\n",
    "                    }\n",
    "                )\n",
    "    return dicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = [\n",
    "    LogisticRegression,\n",
    "    RandomForestClassifier,\n",
    "    GradientBoostingClassifier,\n",
    "    SVC,\n",
    "]\n",
    "params = {\n",
    "    LogisticRegression.__name__: [\n",
    "        *get_log_reg()\n",
    "    ],\n",
    "    GradientBoostingClassifier.__name__: [\n",
    "        *define_gradient_boosting_cls()\n",
    "    ],\n",
    "    SVC.__name__: [\n",
    "        *get_svc()\n",
    "    ],\n",
    "    RandomForestClassifier.__name__ : [\n",
    "        *get_rf_cls()\n",
    "    ]\n",
    "\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LogisticRegression\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.001, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6632, Mean acc at 5: 0.3053, Mean acc at 1: 0.6053\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.001, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6632, Mean acc at 5: 0.3053, Mean acc at 1: 0.6053\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.01, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6632, Mean acc at 5: 0.3105, Mean acc at 1: 0.6053\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.01, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6632, Mean acc at 5: 0.3105, Mean acc at 1: 0.6053\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.1, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6873, Mean acc at 5: 0.3368, Mean acc at 1: 0.6316\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.1, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6873, Mean acc at 5: 0.3368, Mean acc at 1: 0.6316\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 1.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.7509, Mean acc at 5: 0.3421, Mean acc at 1: 0.7105\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 1.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.7509, Mean acc at 5: 0.3421, Mean acc at 1: 0.7105\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 10.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.7127, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 10.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.7127, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 100.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6930, Mean acc at 5: 0.3105, Mean acc at 1: 0.6316\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 100.0, 'n_jobs': -1, 'max_iter': 2000}, MRR top 5 is: 0.6930, Mean acc at 5: 0.3105, Mean acc at 1: 0.6316\n",
      "Starting with estimator: RandomForestClassifier\n",
      "Params: {'n_estimators': 10, 'min_samples_split': 2, 'n_jobs': -1}, MRR top 5 is: 0.6706, Mean acc at 5: 0.2368, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'min_samples_split': 3, 'n_jobs': -1}, MRR top 5 is: 0.6404, Mean acc at 5: 0.2211, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'min_samples_split': 2, 'n_jobs': -1}, MRR top 5 is: 0.7254, Mean acc at 5: 0.2895, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'min_samples_split': 3, 'n_jobs': -1}, MRR top 5 is: 0.7522, Mean acc at 5: 0.3211, Mean acc at 1: 0.7105\n",
      "Params: {'n_estimators': 200, 'min_samples_split': 2, 'n_jobs': -1}, MRR top 5 is: 0.6719, Mean acc at 5: 0.2474, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 200, 'min_samples_split': 3, 'n_jobs': -1}, MRR top 5 is: 0.6798, Mean acc at 5: 0.2632, Mean acc at 1: 0.6316\n",
      "Starting with estimator: GradientBoostingClassifier\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 0.5}, MRR top 5 is: 0.6754, Mean acc at 5: 0.3211, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.1, 'subsample': 1.0}, MRR top 5 is: 0.6754, Mean acc at 5: 0.2368, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 0.5}, MRR top 5 is: 0.6917, Mean acc at 5: 0.2316, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.01, 'subsample': 1.0}, MRR top 5 is: 0.6623, Mean acc at 5: 0.2000, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 0.5}, MRR top 5 is: 0.7061, Mean acc at 5: 0.2789, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.001, 'subsample': 1.0}, MRR top 5 is: 0.6338, Mean acc at 5: 0.2316, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 0.5}, MRR top 5 is: 0.6667, Mean acc at 5: 0.2632, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 10, 'learning_rate': 0.0001, 'subsample': 1.0}, MRR top 5 is: 0.6096, Mean acc at 5: 0.2421, Mean acc at 1: 0.5263\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 0.5}, MRR top 5 is: 0.7627, Mean acc at 5: 0.3263, Mean acc at 1: 0.6842\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.1, 'subsample': 1.0}, MRR top 5 is: 0.6829, Mean acc at 5: 0.3053, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 0.5}, MRR top 5 is: 0.6610, Mean acc at 5: 0.2737, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.01, 'subsample': 1.0}, MRR top 5 is: 0.6864, Mean acc at 5: 0.2895, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 0.5}, MRR top 5 is: 0.7193, Mean acc at 5: 0.2842, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.001, 'subsample': 1.0}, MRR top 5 is: 0.6675, Mean acc at 5: 0.2737, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 0.5}, MRR top 5 is: 0.6996, Mean acc at 5: 0.3053, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 50, 'learning_rate': 0.0001, 'subsample': 1.0}, MRR top 5 is: 0.6382, Mean acc at 5: 0.2316, Mean acc at 1: 0.5526\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 0.5}, MRR top 5 is: 0.6640, Mean acc at 5: 0.2684, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.1, 'subsample': 1.0}, MRR top 5 is: 0.6982, Mean acc at 5: 0.2737, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 0.5}, MRR top 5 is: 0.7114, Mean acc at 5: 0.3211, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.01, 'subsample': 1.0}, MRR top 5 is: 0.7136, Mean acc at 5: 0.2947, Mean acc at 1: 0.6053\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 0.5}, MRR top 5 is: 0.7083, Mean acc at 5: 0.3000, Mean acc at 1: 0.6316\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.001, 'subsample': 1.0}, MRR top 5 is: 0.6895, Mean acc at 5: 0.2421, Mean acc at 1: 0.5789\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 0.5}, MRR top 5 is: 0.7232, Mean acc at 5: 0.3053, Mean acc at 1: 0.6579\n",
      "Params: {'n_estimators': 100, 'learning_rate': 0.0001, 'subsample': 1.0}, MRR top 5 is: 0.6294, Mean acc at 5: 0.2316, Mean acc at 1: 0.5526\n",
      "Starting with estimator: SVC\n",
      "Params: {'C': 0.001, 'gamma': 0.0001, 'probability': True}, MRR top 5 is: 0.0421, Mean acc at 5: 0.0421, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.001, 'gamma': 0.001, 'probability': True}, MRR top 5 is: 0.0368, Mean acc at 5: 0.0368, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.001, 'gamma': 0.01, 'probability': True}, MRR top 5 is: 0.0368, Mean acc at 5: 0.0368, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.001, 'gamma': 0.1, 'probability': True}, MRR top 5 is: 0.0500, Mean acc at 5: 0.0474, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.001, 'gamma': 1.0, 'probability': True}, MRR top 5 is: 0.0579, Mean acc at 5: 0.0526, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.01, 'gamma': 0.0001, 'probability': True}, MRR top 5 is: 0.0316, Mean acc at 5: 0.0316, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.01, 'gamma': 0.001, 'probability': True}, MRR top 5 is: 0.0421, Mean acc at 5: 0.0421, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.01, 'gamma': 0.01, 'probability': True}, MRR top 5 is: 0.0368, Mean acc at 5: 0.0368, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.01, 'gamma': 0.1, 'probability': True}, MRR top 5 is: 0.0539, Mean acc at 5: 0.0526, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.01, 'gamma': 1.0, 'probability': True}, MRR top 5 is: 0.0873, Mean acc at 5: 0.0737, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.1, 'gamma': 0.0001, 'probability': True}, MRR top 5 is: 0.0421, Mean acc at 5: 0.0421, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.1, 'gamma': 0.001, 'probability': True}, MRR top 5 is: 0.0263, Mean acc at 5: 0.0263, Mean acc at 1: 0.0000\n",
      "Params: {'C': 0.1, 'gamma': 0.01, 'probability': True}, MRR top 5 is: 0.0487, Mean acc at 5: 0.0474, Mean acc at 1: 0.0000\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-139-7b4a7b23a66d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"log_cls_mrr.txt\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mscaler\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;31m# poly_fit=PolynomialFeatures(degree=4, interaction_only=True),\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n",
      "\u001b[0;32m<ipython-input-135-c454454b2da4>\u001b[0m in \u001b[0;36mcls_pipeline\u001b[0;34m(data_frame, estimators, params, filename, poly_fit, scaler)\u001b[0m\n\u001b[1;32m     67\u001b[0m                     \u001b[0mshuffle_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m                     \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshuffle_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     70\u001b[0m                     \u001b[0mpredicted_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     71\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    210\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0mseed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'i'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msolver_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0;31m# see comment on the other call to np.iinfo in this file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36m_dense_fit\u001b[0;34m(self, X, y, sample_weight, solver_type, kernel, random_seed)\u001b[0m\n\u001b[1;32m    269\u001b[0m                 \u001b[0mcache_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcache_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcoef0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m                 \u001b[0mgamma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gamma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepsilon\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m                 max_iter=self.max_iter, random_seed=random_seed)\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_warn_from_fit_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "best_estimator = cls_pipeline(\n",
    "    data_frame=cls_data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_cls_mrr.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    # poly_fit=PolynomialFeatures(degree=4, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_estimator = cls_pipeline(\n",
    "    data_frame=cls_data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_cls_poly_2.txt\",\n",
    "    scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting with estimator: LogisticRegression\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.001, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2079, Mean acc at 5: 0.2211, Mean acc at 1: 0.5263\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.001, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2079, Mean acc at 5: 0.2421, Mean acc at 1: 0.5526\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.01, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2474, Mean acc at 5: 0.2526, Mean acc at 1: 0.5789\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.01, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2395, Mean acc at 5: 0.2579, Mean acc at 1: 0.5789\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'newton-cg', 'C': 0.1, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2105, Mean acc at 5: 0.2316, Mean acc at 1: 0.5789\n",
      "Params: {'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 0.1, 'n_jobs': -1, 'max_iter': 2000}, accuracy over all classes is: 0.2079, Mean acc at 5: 0.2316, Mean acc at 1: 0.6053\n"
     ]
    }
   ],
   "source": [
    "best_estimator = cls_pipeline(\n",
    "    data_frame=cls_data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_cls_poly_2_all.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=2, interaction_only=False),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_estimator = cls_pipeline(\n",
    "    data_frame=cls_data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_cls_poly_3.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=3, interaction_only=True),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "best_estimator = cls_pipeline(\n",
    "    data_frame=cls_data_frame,\n",
    "    estimators=estimators,\n",
    "    params=params,\n",
    "    filename=\"log_cls_poly_3_all.txt\",\n",
    "    # scaler=MinMaxScaler(),\n",
    "    poly_fit=PolynomialFeatures(degree=3, interaction_only=False),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = os.listdir('.')\n",
    "result_list = []\n",
    "for name in results:\n",
    "    if 'log_cls' in name:\n",
    "        added_name = name.split(\".txt\")[0].split(\"log_cls\")[1]\n",
    "        estimator = None\n",
    "        with open(name) as f:\n",
    "            lines = f.readlines()\n",
    "        for line in lines:\n",
    "            if \"{\" not in line:\n",
    "                estimator = line.split(\" \")[-1].strip()\n",
    "                continue\n",
    "            params = line.split(\"{\")[1].split(\"}\")[0].strip().replace(\"'\", \"\")\n",
    "            #mse = float(line.split(\"MSE over all splits is: \")[1].split(\",\")[0].strip())\n",
    "            acc_top_1 = float(line.split(\"Mean acc at 1: \")[1].strip())\n",
    "            acc_top_5 = float(line.split(\"Mean acc at 5: \")[1].split(\",\")[0].strip())\n",
    "            result_list.append((added_name,estimator, params, acc_top_5, acc_top_1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_results = sorted(result_list, key=lambda item: -item[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 1.0, n_jobs: -1, max_iter: 2000', 0.3421, 0.7105)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 1.0, n_jobs: -1, max_iter: 2000', 0.3421, 0.7105)\n",
      "('', 'SVC', 'C: 100, gamma: 0.1, probability: True', 0.3316, 0.7105)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 1.0, n_jobs: -1, max_iter: 2000', 0.3421, 0.7105)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 1.0, n_jobs: -1, max_iter: 2000', 0.3421, 0.7105)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 3, n_jobs: -1', 0.3211, 0.7105)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 0.5', 0.3263, 0.6842)\n",
      "('', 'SVC', 'C: 10, gamma: 0.1, probability: True', 0.3211, 0.6842)\n",
      "('', 'SVC', 'C: 100, gamma: 0.01, probability: True', 0.3211, 0.6842)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 0.5', 0.3263, 0.6842)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 2, n_jobs: -1', 0.2632, 0.6842)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 2, n_jobs: -1', 0.2737, 0.6579)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 3, n_jobs: -1', 0.2632, 0.6579)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 2, n_jobs: -1', 0.2842, 0.6579)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 3, n_jobs: -1', 0.2474, 0.6579)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2895, 0.6579)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2895, 0.6579)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 0.5', 0.3105, 0.6579)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 0.5', 0.3158, 0.6579)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 0.5', 0.3158, 0.6579)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2895, 0.6579)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2895, 0.6579)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 2, n_jobs: -1', 0.2895, 0.6579)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 0.5', 0.2316, 0.6579)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 0.5', 0.2842, 0.6579)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 1.0', 0.2737, 0.6579)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 0.5', 0.3211, 0.6579)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 0.5', 0.3053, 0.6579)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 3, n_jobs: -1', 0.2684, 0.6579)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 3, n_jobs: -1', 0.2632, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.1, n_jobs: -1, max_iter: 2000', 0.2789, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.1, n_jobs: -1, max_iter: 2000', 0.2789, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 1.0, n_jobs: -1, max_iter: 2000', 0.2421, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 1.0, n_jobs: -1, max_iter: 2000', 0.2421, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2263, 0.6579)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 10.0, n_jobs: -1, max_iter: 2000', 0.2263, 0.6579)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.1, n_jobs: -1, max_iter: 2000', 0.3368, 0.6316)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.1, n_jobs: -1, max_iter: 2000', 0.3368, 0.6316)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 100.0, n_jobs: -1, max_iter: 2000', 0.3105, 0.6316)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 100.0, n_jobs: -1, max_iter: 2000', 0.3105, 0.6316)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 2, n_jobs: -1', 0.2579, 0.6316)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 3, n_jobs: -1', 0.3, 0.6316)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 2, n_jobs: -1', 0.2895, 0.6316)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 3, n_jobs: -1', 0.2789, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 0.5', 0.2684, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 0.5', 0.2842, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 0.5', 0.3053, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 0.5', 0.3526, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 1.0', 0.2895, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 0.5', 0.2789, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 0.5', 0.3211, 0.6316)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 0.5', 0.2789, 0.6316)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.1, n_jobs: -1, max_iter: 2000', 0.3368, 0.6316)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.1, n_jobs: -1, max_iter: 2000', 0.3368, 0.6316)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 100.0, n_jobs: -1, max_iter: 2000', 0.3105, 0.6316)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 100.0, n_jobs: -1, max_iter: 2000', 0.3105, 0.6316)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 3, n_jobs: -1', 0.2632, 0.6316)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 0.5', 0.2789, 0.6316)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 1.0', 0.3053, 0.6316)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 0.5', 0.3053, 0.6316)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 0.5', 0.3, 0.6316)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 3, n_jobs: -1', 0.2684, 0.6316)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.001, n_jobs: -1, max_iter: 2000', 0.2737, 0.6316)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.001, n_jobs: -1, max_iter: 2000', 0.2737, 0.6316)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.01, n_jobs: -1, max_iter: 2000', 0.2789, 0.6316)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.01, n_jobs: -1, max_iter: 2000', 0.2789, 0.6316)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 1.0', 0.2, 0.6316)\n",
      "('_poly_2', 'SVC', 'C: 100, gamma: 0.01, probability: True', 0.2368, 0.6316)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 3, n_jobs: -1', 0.2842, 0.6053)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.1, n_jobs: -1, max_iter: 2000', 0.2316, 0.6053)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.001, n_jobs: -1, max_iter: 2000', 0.3053, 0.6053)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.001, n_jobs: -1, max_iter: 2000', 0.3053, 0.6053)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.01, n_jobs: -1, max_iter: 2000', 0.3105, 0.6053)\n",
      "('', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.01, n_jobs: -1, max_iter: 2000', 0.3105, 0.6053)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 3, n_jobs: -1', 0.2263, 0.6053)\n",
      "('', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 2, n_jobs: -1', 0.3053, 0.6053)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 1.0', 0.2579, 0.6053)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 1.0', 0.3, 0.6053)\n",
      "('', 'SVC', 'C: 1.0, gamma: 1.0, probability: True', 0.3053, 0.6053)\n",
      "('', 'SVC', 'C: 10, gamma: 1.0, probability: True', 0.3053, 0.6053)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.001, n_jobs: -1, max_iter: 2000', 0.3053, 0.6053)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.001, n_jobs: -1, max_iter: 2000', 0.3053, 0.6053)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.01, n_jobs: -1, max_iter: 2000', 0.3105, 0.6053)\n",
      "('_mrr', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.01, n_jobs: -1, max_iter: 2000', 0.3105, 0.6053)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 2, n_jobs: -1', 0.2368, 0.6053)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 2, n_jobs: -1', 0.2474, 0.6053)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 1.0', 0.2368, 0.6053)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 1.0', 0.2947, 0.6053)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 2, n_jobs: -1', 0.2632, 0.6053)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 3, n_jobs: -1', 0.2632, 0.6053)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 2, n_jobs: -1', 0.2421, 0.6053)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 200, min_samples_split: 3, n_jobs: -1', 0.2368, 0.6053)\n",
      "('_poly_2', 'SVC', 'C: 10, gamma: 0.01, probability: True', 0.2632, 0.6053)\n",
      "('_poly_2', 'SVC', 'C: 100, gamma: 0.001, probability: True', 0.2263, 0.6053)\n",
      "('_poly_3', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 2, n_jobs: -1', 0.2263, 0.5789)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.01, n_jobs: -1, max_iter: 2000', 0.2526, 0.5789)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.01, n_jobs: -1, max_iter: 2000', 0.2579, 0.5789)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.1, n_jobs: -1, max_iter: 2000', 0.2316, 0.5789)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 1.0', 0.2158, 0.5789)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 1.0', 0.2474, 0.5789)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 1.0', 0.2526, 0.5789)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 1.0', 0.2895, 0.5789)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 1.0', 0.2526, 0.5789)\n",
      "('', 'SVC', 'C: 100, gamma: 1.0, probability: True', 0.2263, 0.5789)\n",
      "('_mrr', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 3, n_jobs: -1', 0.2211, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 1.0', 0.2, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 1.0', 0.2316, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 0.5', 0.2632, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 0.5', 0.2737, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 1.0', 0.2895, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 1.0', 0.2737, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 0.5', 0.2684, 0.5789)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 1.0', 0.2421, 0.5789)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 100, min_samples_split: 2, n_jobs: -1', 0.2316, 0.5789)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 0.5', 0.2684, 0.5789)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 0.5', 0.2684, 0.5789)\n",
      "('_poly_2', 'SVC', 'C: 10, gamma: 0.1, probability: True', 0.2368, 0.5789)\n",
      "('_poly_2', 'SVC', 'C: 100, gamma: 0.1, probability: True', 0.2158, 0.5789)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 0.001, n_jobs: -1, max_iter: 2000', 0.2421, 0.5526)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 1.0', 0.2421, 0.5526)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 1.0', 0.2421, 0.5526)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 1.0', 0.2211, 0.5526)\n",
      "('', 'SVC', 'C: 1.0, gamma: 0.1, probability: True', 0.3053, 0.5526)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 0.5', 0.3211, 0.5526)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 1.0', 0.2316, 0.5526)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 1.0', 0.2316, 0.5526)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 1.0', 0.1947, 0.5526)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 0.5', 0.2421, 0.5526)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 0.5', 0.2526, 0.5526)\n",
      "('_poly_2', 'SVC', 'C: 1.0, gamma: 0.1, probability: True', 0.2421, 0.5526)\n",
      "('_poly_2_all', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 0.001, n_jobs: -1, max_iter: 2000', 0.2211, 0.5263)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 1.0', 0.2211, 0.5263)\n",
      "('', 'SVC', 'C: 0.1, gamma: 1.0, probability: True', 0.2947, 0.5263)\n",
      "('', 'SVC', 'C: 10, gamma: 0.01, probability: True', 0.2842, 0.5263)\n",
      "('', 'SVC', 'C: 100, gamma: 0.001, probability: True', 0.2737, 0.5263)\n",
      "('_mrr', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 1.0', 0.2421, 0.5263)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 0.5', 0.2211, 0.5263)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.1, subsample: 0.5', 0.1895, 0.5263)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 1.0', 0.2316, 0.5263)\n",
      "('', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 0.5', 0.2263, 0.5)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: newton-cg, C: 100.0, n_jobs: -1, max_iter: 2000', 0.1579, 0.5)\n",
      "('_poly_2', 'LogisticRegression', 'multi_class: multinomial, solver: lbfgs, C: 100.0, n_jobs: -1, max_iter: 2000', 0.1579, 0.5)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 2, n_jobs: -1', 0.1895, 0.5)\n",
      "('_poly_2', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 3, n_jobs: -1', 0.1737, 0.5)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.1, subsample: 1.0', 0.1632, 0.5)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.01, subsample: 0.5', 0.2158, 0.5)\n",
      "('_poly_2', 'SVC', 'C: 100, gamma: 0.0001, probability: True', 0.1947, 0.5)\n",
      "('_poly_3_all', 'RandomForestClassifier', 'n_estimators: 10, min_samples_split: 2, n_jobs: -1', 0.1947, 0.4737)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 1.0', 0.1632, 0.4737)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 0.5', 0.2211, 0.4737)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 0.5', 0.2053, 0.4737)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 0.5', 0.2158, 0.4737)\n",
      "('_poly_2', 'SVC', 'C: 10, gamma: 0.001, probability: True', 0.2158, 0.4737)\n",
      "('_poly_2', 'SVC', 'C: 0.1, gamma: 0.1, probability: True', 0.1842, 0.4474)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.01, subsample: 0.5', 0.1895, 0.4211)\n",
      "('_poly_2', 'SVC', 'C: 1.0, gamma: 0.01, probability: True', 0.1895, 0.4211)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.01, subsample: 1.0', 0.1842, 0.3947)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.001, subsample: 1.0', 0.1842, 0.3947)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.1, subsample: 0.5', 0.1737, 0.3421)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.001, subsample: 1.0', 0.1579, 0.3421)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.001, subsample: 1.0', 0.1105, 0.3158)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 10, learning_rate: 0.0001, subsample: 1.0', 0.1474, 0.3158)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 50, learning_rate: 0.0001, subsample: 1.0', 0.1421, 0.3158)\n",
      "('_poly_2', 'GradientBoostingClassifier', 'n_estimators: 100, learning_rate: 0.0001, subsample: 1.0', 0.1421, 0.3158)\n",
      "('_poly_2', 'SVC', 'C: 10, gamma: 1.0, probability: True', 0.1158, 0.2368)\n",
      "('_poly_2', 'SVC', 'C: 100, gamma: 1.0, probability: True', 0.1, 0.2368)\n",
      "('_poly_2', 'SVC', 'C: 1.0, gamma: 1.0, probability: True', 0.1158, 0.2105)\n",
      "('', 'SVC', 'C: 1.0, gamma: 0.01, probability: True', 0.1053, 0.1316)\n",
      "('_poly_2', 'SVC', 'C: 0.1, gamma: 1.0, probability: True', 0.0947, 0.1053)\n",
      "('', 'SVC', 'C: 10, gamma: 0.001, probability: True', 0.0895, 0.0789)\n",
      "('_poly_2', 'SVC', 'C: 0.001, gamma: 0.0001, probability: True', 0.0421, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 0.001, gamma: 0.001, probability: True', 0.0368, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 0.01, gamma: 0.0001, probability: True', 0.0316, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 0.01, gamma: 0.001, probability: True', 0.0368, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 0.1, gamma: 0.0001, probability: True', 0.0316, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 0.1, gamma: 0.001, probability: True', 0.0421, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 1.0, gamma: 0.0001, probability: True', 0.0316, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 1.0, gamma: 0.001, probability: True', 0.0421, 0.0263)\n",
      "('_poly_2', 'SVC', 'C: 10, gamma: 0.0001, probability: True', 0.0474, 0.0263)\n",
      "('_poly_3', 'SVC', 'C: 0.001, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.001, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.001, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.001, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.001, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.01, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.01, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.01, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.01, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.01, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.1, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.1, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.1, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.1, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 0.1, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 1.0, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 1.0, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 1.0, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 1.0, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 1.0, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 10, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 10, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 10, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 10, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 10, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 100, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 100, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 100, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 100, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3', 'SVC', 'C: 100, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('', 'SVC', 'C: 0.001, gamma: 0.0001, probability: True', 0.0316, 0.0)\n",
      "('', 'SVC', 'C: 0.001, gamma: 0.001, probability: True', 0.0421, 0.0)\n",
      "('', 'SVC', 'C: 0.001, gamma: 0.01, probability: True', 0.0263, 0.0)\n",
      "('', 'SVC', 'C: 0.001, gamma: 0.1, probability: True', 0.0474, 0.0)\n",
      "('', 'SVC', 'C: 0.001, gamma: 1.0, probability: True', 0.0421, 0.0)\n",
      "('', 'SVC', 'C: 0.01, gamma: 0.0001, probability: True', 0.0316, 0.0)\n",
      "('', 'SVC', 'C: 0.01, gamma: 0.001, probability: True', 0.0316, 0.0)\n",
      "('', 'SVC', 'C: 0.01, gamma: 0.01, probability: True', 0.0368, 0.0)\n",
      "('', 'SVC', 'C: 0.01, gamma: 0.1, probability: True', 0.0526, 0.0)\n",
      "('', 'SVC', 'C: 0.01, gamma: 1.0, probability: True', 0.0684, 0.0)\n",
      "('', 'SVC', 'C: 0.1, gamma: 0.0001, probability: True', 0.0421, 0.0)\n",
      "('', 'SVC', 'C: 0.1, gamma: 0.001, probability: True', 0.0368, 0.0)\n",
      "('', 'SVC', 'C: 0.1, gamma: 0.01, probability: True', 0.0474, 0.0)\n",
      "('', 'SVC', 'C: 0.1, gamma: 0.1, probability: True', 0.0474, 0.0)\n",
      "('', 'SVC', 'C: 1.0, gamma: 0.0001, probability: True', 0.0368, 0.0)\n",
      "('', 'SVC', 'C: 1.0, gamma: 0.001, probability: True', 0.0316, 0.0)\n",
      "('', 'SVC', 'C: 10, gamma: 0.0001, probability: True', 0.0368, 0.0)\n",
      "('', 'SVC', 'C: 100, gamma: 0.0001, probability: True', 0.0368, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.001, gamma: 0.0001, probability: True', 0.0421, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.001, gamma: 0.001, probability: True', 0.0368, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.001, gamma: 0.01, probability: True', 0.0368, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.001, gamma: 0.1, probability: True', 0.0474, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.001, gamma: 1.0, probability: True', 0.0526, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.01, gamma: 0.0001, probability: True', 0.0316, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.01, gamma: 0.001, probability: True', 0.0421, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.01, gamma: 0.01, probability: True', 0.0368, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.01, gamma: 0.1, probability: True', 0.0526, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.01, gamma: 1.0, probability: True', 0.0737, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.1, gamma: 0.0001, probability: True', 0.0421, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.1, gamma: 0.001, probability: True', 0.0263, 0.0)\n",
      "('_mrr', 'SVC', 'C: 0.1, gamma: 0.01, probability: True', 0.0474, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.001, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.001, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.001, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.001, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.001, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.01, gamma: 0.0001, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.01, gamma: 0.001, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.01, gamma: 0.01, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.01, gamma: 0.1, probability: True', 0.0, 0.0)\n",
      "('_poly_3_all', 'SVC', 'C: 0.01, gamma: 1.0, probability: True', 0.0, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.001, gamma: 0.01, probability: True', 0.0316, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.001, gamma: 0.1, probability: True', 0.0158, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.001, gamma: 1.0, probability: True', 0.0421, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.01, gamma: 0.01, probability: True', 0.0263, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.01, gamma: 0.1, probability: True', 0.0421, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.01, gamma: 1.0, probability: True', 0.0368, 0.0)\n",
      "('_poly_2', 'SVC', 'C: 0.1, gamma: 0.01, probability: True', 0.0421, 0.0)\n"
     ]
    }
   ],
   "source": [
    "for item in sorted_results:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "season = '2017-18'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "        'stl_per_g', 'blk_per_g', 'ws', 'per', 'win_pct']\n",
    "target_columns = ['rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = cls_data_frame.loc[data_frame.season != season]\n",
    "validation_data = cls_data_frame.loc[data_frame.season == season]\n",
    "#validation_data = validation_data.sort_values(by='award_share', ascending=False)\n",
    "\n",
    "# Get train data\n",
    "train_x = train_data[train_columns].to_numpy()\n",
    "train_y = train_data[target_columns].to_numpy()\n",
    "train_y = train_y.reshape(train_y.shape[0], )\n",
    "\n",
    "# Validate over one season only\n",
    "val_x = validation_data[train_columns].to_numpy()\n",
    "val_y = validation_data[target_columns].to_numpy()\n",
    "val_y = val_y.reshape(val_y.shape[0], )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'multi_class': 'multinomial', 'solver': 'lbfgs', 'C': 100.0, 'n_jobs': -1, 'max_iter': 2000\n",
    "model = LogisticRegression(multi_class='multinomial', solver='newton-cg', C=1.0, max_iter=2000, n_jobs=-1)\n",
    "#model = SVC(probability=True, C=10, gamma=0.001)\n",
    "#model = RandomForestClassifier(n_estimators=100, min_samples_split=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=2000, multi_class='multinomial',\n",
       "          n_jobs=-1, penalty='l2', random_state=None, solver='newton-cg',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.53549216, 0.18391131, 0.12019933, 0.07713274, 0.03742477,\n",
       "        0.03764238, 0.00458416, 0.00154636, 0.00134901, 0.00071779],\n",
       "       [0.37499232, 0.23795557, 0.15453131, 0.12226833, 0.03858387,\n",
       "        0.0438    , 0.01046086, 0.00651576, 0.00774268, 0.00314928],\n",
       "       [0.06790764, 0.13855452, 0.26355533, 0.19615421, 0.13753144,\n",
       "        0.05911776, 0.09961507, 0.00721826, 0.01225656, 0.01808922],\n",
       "       [0.08372429, 0.1093943 , 0.10665979, 0.15958171, 0.1968173 ,\n",
       "        0.17441712, 0.04303401, 0.05280967, 0.04272324, 0.03083858],\n",
       "       [0.11634017, 0.1506929 , 0.1086021 , 0.18256616, 0.03043562,\n",
       "        0.10391622, 0.04209724, 0.10785371, 0.12834714, 0.02914874],\n",
       "       [0.04991504, 0.11357897, 0.20421493, 0.20989684, 0.09787445,\n",
       "        0.10633822, 0.09653779, 0.0278316 , 0.04783076, 0.0459814 ],\n",
       "       [0.04751587, 0.0743863 , 0.11604058, 0.24447418, 0.2116348 ,\n",
       "        0.09911273, 0.0685396 , 0.05128707, 0.04086193, 0.04614696],\n",
       "       [0.00448738, 0.01157287, 0.0196373 , 0.03945433, 0.10272655,\n",
       "        0.09059248, 0.08940143, 0.2631368 , 0.20177603, 0.17721482],\n",
       "       [0.0104851 , 0.02864071, 0.05540753, 0.16477258, 0.09190873,\n",
       "        0.09501565, 0.1519508 , 0.07465743, 0.06821631, 0.25894514],\n",
       "       [0.00098333, 0.00955497, 0.05467786, 0.0405921 , 0.07124598,\n",
       "        0.19377307, 0.16121439, 0.08128789, 0.12678487, 0.25988553]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_proba(val_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. is index: 1\n",
      "2. is index: 2\n",
      "3. is index: 3\n",
      "4. is index: 7\n",
      "5. is index: 4\n",
      "6. is index: 10\n",
      "7. is index: 9\n",
      "8. is index: 8\n",
      "9. is index: 5\n",
      "10. is index: 6\n"
     ]
    }
   ],
   "source": [
    "preds = model.predict_proba(val_x)\n",
    "visited = []\n",
    "for i in range(len(preds)):\n",
    "    rank_i_values = preds[:,i]\n",
    "    for top_rank in np.argsort(preds[:,i])[::-1]:\n",
    "        if top_rank in visited:\n",
    "            continue\n",
    "        print(f\"{i+1}. is index: {top_rank+1}\")\n",
    "        visited.append(top_rank)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "        'stl_per_g', 'blk_per_g', 'ws', 'per', 'usg_pct', 'ws_per_48']\n",
    "train_regression_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'win_pct']\n",
    "train_cls_columns = ['ts_pct', 'bpm', 'per', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'usg_pct', 'win_pct']\n",
    "target_reg_columns = ['award_share']\n",
    "target_cls_columns = ['rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_reg_columns = [\n",
    "    'TS_pct', 'BPM', 'MP_x', 'PTS', 'TRB', 'AST', 'STL', 'BLK', 'WS', 'win_pct',\n",
    "]\n",
    "test_cls_columns = [\n",
    "        'TS_pct', 'BPM','PER', 'PTS', 'TRB', 'AST', 'STL', 'BLK', 'WS', 'PER', 'USG_pct',\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "per_g_df = pd.read_csv('player_per_g.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "advanced_df = pd.read_csv('advanced.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = pd.merge(per_g_df, advanced_df, on=['PlayerId', 'Player'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_merged = merged.sort_values(by='WS', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_players = sorted_merged[:40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-77-28be1bee83e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mplayer_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_players\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPlayerId\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mfull_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf'{base_url}/{player_id[0]}/{player_id}.html'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0msoup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(url, params, **kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'allow_redirects'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'get'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/api.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(method, url, **kwargs)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;31m# cases, and look like a memory leak in others.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0msessions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m    531\u001b[0m         }\n\u001b[1;32m    532\u001b[0m         \u001b[0msend_kwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msettings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 533\u001b[0;31m         \u001b[0mresp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0msend_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    535\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mresp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/sessions.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m    684\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    685\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 686\u001b[0;31m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    687\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/models.py\u001b[0m in \u001b[0;36mcontent\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    826\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 828\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_content\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCONTENT_CHUNK_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34mb''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    829\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    830\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_content_consumed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/requests/models.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m()\u001b[0m\n\u001b[1;32m    748\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'stream'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    749\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 750\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchunk_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    751\u001b[0m                         \u001b[0;32myield\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    752\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mProtocolError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mstream\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    488\u001b[0m         \"\"\"\n\u001b[1;32m    489\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunked\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msupports_chunked_reads\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_chunked\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecode_content\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecode_content\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    491\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36mread_chunked\u001b[0;34m(self, amt, decode_content)\u001b[0m\n\u001b[1;32m    667\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    668\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m                 \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_handle_chunk\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    670\u001b[0m                 decoded = self._decode(chunk, decode_content=decode_content,\n\u001b[1;32m    671\u001b[0m                                        flush_decoder=False)\n",
      "\u001b[0;32m~/.local/lib/python3.6/site-packages/urllib3/response.py\u001b[0m in \u001b[0;36m_handle_chunk\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    613\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 615\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_safe_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    616\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchunk_left\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mamt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    617\u001b[0m             \u001b[0mreturned_chunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_safe_read\u001b[0;34m(self, amt)\u001b[0m\n\u001b[1;32m    610\u001b[0m         \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    611\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mamt\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 612\u001b[0;31m             \u001b[0mchunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMAXAMOUNT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    613\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mchunk\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    614\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mIncompleteRead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mb''\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mamt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m   1010\u001b[0m                   \u001b[0;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1011\u001b[0m                   self.__class__)\n\u001b[0;32m-> 1012\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1013\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1014\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Read on closed or unwrapped SSL socket.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    873\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 874\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    875\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mSSLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    876\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSSL_ERROR_EOF\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuppress_ragged_eofs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m    629\u001b[0m         \"\"\"\n\u001b[1;32m    630\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 631\u001b[0;31m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    632\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    633\u001b[0m             \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "base_url = 'https://www.basketball-reference.com/players'\n",
    "win_percentages = []\n",
    "for i in range(len(top_players)):\n",
    "    player_id = top_players.iloc[i].PlayerId[1:]\n",
    "    full_url = f'{base_url}/{player_id[0]}/{player_id}.html'\n",
    "    response = requests.get(full_url)\n",
    "    soup = BeautifulSoup(response.text)\n",
    "    time.sleep(0.5)\n",
    "    string = f\"Player: {player_id} \"\n",
    "    for p in soup.findAll('p'):\n",
    "        if \"Team\" in p.text:\n",
    "            full_url = 'https://www.basketball-reference.com' + str(p.find('a')['href'])\n",
    "            soup = BeautifulSoup(requests.get(full_url).text)\n",
    "            time.sleep(0.5)\n",
    "            for item in soup.findAll(\"p\"):\n",
    "                if \"Record\" in item.text:\n",
    "                    record = re.findall(\"\\d+\\-\\d+\", item.text)[0]\n",
    "                    splitted = record.split(\"-\")\n",
    "                    win_pct = float(splitted[0]) / (float(splitted[1]) + float(splitted[0]))\n",
    "                    win_percentages.append(win_pct)\n",
    "                    string += \"found\"\n",
    "                    break\n",
    "            break\n",
    "    print(f\"{string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/daniel/.local/lib/python3.6/site-packages/pandas/core/indexing.py:362: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/home/daniel/.local/lib/python3.6/site-packages/pandas/core/indexing.py:543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "top_players.loc[:, 'win_pct'] = win_percentages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load test data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.read_csv(\"mvp_votings.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_players = pd.read_csv('top_players.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_data_frame = pd.read_csv('cls_data_frame.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
       "       'season', 'player', 'win_pct', 'votes_first', 'points_won',\n",
       "       'points_max', 'award_share', 'g', 'mp_per_g', 'pts_per_g', 'trb_per_g',\n",
       "       'ast_per_g', 'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct',\n",
       "       'ws', 'ws_per_48'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_frame.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "        'stl_per_g', 'blk_per_g', 'ws', 'per', 'usg_pct', 'ws_per_48']\n",
    "train_regression_columns = ['ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'win_pct']\n",
    "train_cls_columns = ['ts_pct', 'bpm', 'per', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'usg_pct', 'win_pct']\n",
    "target_reg_columns = ['award_share']\n",
    "target_cls_columns = ['rank']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_reg_columns = [\n",
    "'ts_pct', 'bpm', 'mp_per_g', 'pts_per_g', 'trb_per_g', 'ast_per_g',\n",
    "            'stl_per_g', 'blk_per_g', 'ws', 'win_pct'\n",
    "]\n",
    "test_cls_columns = [\n",
    "        'TS_pct', 'BPM','PER', 'PTS', 'TRB', 'AST', 'STL', 'BLK', 'WS', 'PER', 'USG_pct',\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_columns = top_players[\n",
    "    [\n",
    "        'Player', 'PlayerId', 'Pos_x', 'Age_x', 'Tm_x', 'G_x','MP_x', 'FGA', 'FG_pct', '3PA', '3P_pct', \n",
    "        'FTA', 'FT_pct', 'TRB', 'AST', 'STL', 'BLK', 'PTS', 'PER', 'TS_pct', 'USG_pct', 'WS', 'WS_48',\n",
    "        'BPM', 'win_pct'\n",
    "    ]\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_columns.columns = [\n",
    "    'player', 'PlayerId', 'Pos_x', 'Age_x', 'Tm_x', 'g','mp_per_g', 'fga', 'fg_pct', 'fg3a', 'fg3_pct', \n",
    "    'fta', 'ft_pct', 'trb_per_g', 'ast_per_g', 'stl_per_g', 'blk_per_g',\n",
    "    'pts_per_g', 'per', 'ts_pct', 'usg_pct', 'ws', 'ws_per_48',\n",
    "    'bpm', 'win_pct'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_columns = filtered_columns.iloc[:, [0, *list(range(5, len(filtered_columns.iloc[0])))]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"test_data = filtered_columns[\\n    [ 'fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\\n       'player', 'win_pct', 'g', 'mp_per_g', 'pts_per_g', 'trb_per_g',\\n       'ast_per_g', 'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct',\\n       'ws', 'ws_per_48']\\n]\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"test_data = filtered_columns[\n",
    "    [ 'fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
    "       'player', 'win_pct', 'g', 'mp_per_g', 'pts_per_g', 'trb_per_g',\n",
    "       'ast_per_g', 'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct',\n",
    "       'ws', 'ws_per_48']\n",
    "]\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'fga', 'fg3a', 'fta', 'per', 'ts_pct', 'usg_pct', 'bpm',\n",
       "       'player', 'win_pct', 'g', 'mp_per_g', 'pts_per_g', 'trb_per_g',\n",
       "       'ast_per_g', 'stl_per_g', 'blk_per_g', 'fg_pct', 'fg3_pct', 'ft_pct',\n",
       "       'ws', 'ws_per_48'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test_data.to_csv('test_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures, normalize, StandardScaler, MinMaxScaler\n",
    "from sklearn.linear_model import SGDRegressor, LinearRegression, Ridge, Lasso, ElasticNet\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, BaggingRegressor, AdaBoostRegressor\n",
    "from sklearn.utils import shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions_for_regression_model(model, poly_fit, train_data, test_data, scaler=None, should_print=True):\n",
    "    train_x = train_data[train_regression_columns].to_numpy()\n",
    "    train_y = train_data[target_reg_columns].to_numpy()\n",
    "\n",
    "    train_x, train_y = shuffle(train_x, train_y)\n",
    "    train_y = train_y.reshape(train_y.shape[0], )\n",
    "    \n",
    "    test_x = test_data[test_reg_columns].to_numpy()\n",
    "    test_x = np.nan_to_num(test_x)\n",
    "    \n",
    "    if poly_fit is not None:\n",
    "        train_x = poly_fit.fit_transform(train_x)\n",
    "        test_x = poly_fit.fit_transform(test_x)\n",
    "    \n",
    "    if scaler is not None:\n",
    "        train_x = scaler.fit_transform(train_x)\n",
    "        test_x = scaler.fit_transform(test_x)\n",
    "    \n",
    "    model.fit(train_x, train_y)\n",
    "    predict_y = model.predict(test_x)\n",
    "    sorted_indices = np.argsort(predict_y)[::-1]\n",
    "    predictions = predict_y[sorted_indices]\n",
    "    \n",
    "    formatted_preds = []\n",
    "    if should_print:\n",
    "        print(f\"Predictions:\")\n",
    "    for i in range(10):\n",
    "        if predictions[i] < 0:\n",
    "            break\n",
    "        if should_print:\n",
    "            print(f\"{i+1}. {top_players.iloc[sorted_indices[i]].Player}: {predictions[i]}\")\n",
    "        formatted_preds.append((top_players.iloc[sorted_indices[i]].Player, predictions[i]))\n",
    "    return formatted_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.7503165092408556\n",
      "2. Giannis Antetokounmpo: 0.7128233623607908\n",
      "3. Joel Embiid: 0.2981501420139886\n",
      "4. Kawhi Leonard: 0.26369955851669674\n",
      "5. Paul George: 0.17653668514623136\n",
      "6. Stephen Curry: 0.17401853065740572\n",
      "7. Nikola Jokic: 0.1725925733183824\n",
      "8. Russell Westbrook: 0.1306408948130648\n",
      "9. Rudy Gobert: 0.1182442690242957\n",
      "10. Damian Lillard: 0.10123934904712831\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('James Harden', 0.7503165092408556),\n",
       " ('Giannis Antetokounmpo', 0.7128233623607908),\n",
       " ('Joel Embiid', 0.2981501420139886),\n",
       " ('Kawhi Leonard', 0.26369955851669674),\n",
       " ('Paul George', 0.17653668514623136),\n",
       " ('Stephen Curry', 0.17401853065740572),\n",
       " ('Nikola Jokic', 0.1725925733183824),\n",
       " ('Russell Westbrook', 0.1306408948130648),\n",
       " ('Rudy Gobert', 0.1182442690242957),\n",
       " ('Damian Lillard', 0.10123934904712831)]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = GradientBoostingRegressor(n_estimators=50, learning_rate=0.1, subsample=1.0),\n",
    "    poly_fit = PolynomialFeatures(degree=2, interaction_only=False),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.7503165092408556\n",
      "2. Giannis Antetokounmpo: 0.7128233623607908\n",
      "3. Joel Embiid: 0.29815014201398854\n",
      "4. Kawhi Leonard: 0.26369955851669674\n",
      "5. Paul George: 0.17653668514623141\n",
      "6. Stephen Curry: 0.1740185306574057\n",
      "7. Nikola Jokic: 0.1725925733183824\n",
      "8. Russell Westbrook: 0.13064089481306476\n",
      "9. Rudy Gobert: 0.11824426902429568\n",
      "10. Damian Lillard: 0.10123934904712836\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = GradientBoostingRegressor(n_estimators=50, learning_rate=0.1, subsample=1.0),\n",
    "    poly_fit = PolynomialFeatures(degree=2, interaction_only=True),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.6488600000000002\n",
      "2. Giannis Antetokounmpo: 0.63826\n",
      "3. Kawhi Leonard: 0.24178\n",
      "4. Stephen Curry: 0.19312000000000004\n",
      "5. Russell Westbrook: 0.18977999999999992\n",
      "6. Nikola Jokic: 0.17467999999999992\n",
      "7. Paul George: 0.14970000000000003\n",
      "8. Joel Embiid: 0.13787999999999997\n",
      "9. Damian Lillard: 0.1128\n",
      "10. Kevin Durant: 0.11047999999999997\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = RandomForestRegressor(n_estimators=50),\n",
    "    poly_fit = PolynomialFeatures(degree=3, interaction_only=True),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. Giannis Antetokounmpo: 0.6310100000000001\n",
      "2. James Harden: 0.60273\n",
      "3. Kawhi Leonard: 0.23797000000000007\n",
      "4. Paul George: 0.2205600000000001\n",
      "5. Stephen Curry: 0.19235\n",
      "6. Nikola Jokic: 0.18689999999999998\n",
      "7. Joel Embiid: 0.1748199999999999\n",
      "8. Rudy Gobert: 0.14081000000000002\n",
      "9. Russell Westbrook: 0.13942999999999992\n",
      "10. Kevin Durant: 0.12348999999999992\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = RandomForestRegressor(n_estimators=100),\n",
    "    poly_fit = PolynomialFeatures(degree=2, interaction_only=True),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.7892392728829574\n",
      "2. Giannis Antetokounmpo: 0.6896219134702817\n",
      "3. Nikola Jokic: 0.31948060540152395\n",
      "4. Damian Lillard: 0.21741540752261773\n",
      "5. Paul George: 0.20085552624883912\n",
      "6. Kevin Durant: 0.1969068609607913\n",
      "7. Kawhi Leonard: 0.17613448218194344\n",
      "8. Stephen Curry: 0.17381575200219318\n",
      "9. Joel Embiid: 0.061180686466423884\n",
      "10. Kyrie Irving: 0.05430756068296955\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = Ridge(alpha=10.0),\n",
    "    poly_fit = PolynomialFeatures(degree=2, interaction_only=True),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.7909472985550989\n",
      "2. Giannis Antetokounmpo: 0.6264787281077004\n",
      "3. Dwight Powell: 0.44021169226188306\n",
      "4. DeAndre Jordan: 0.3735670002124145\n",
      "5. Nikola Jokic: 0.3127727855190645\n",
      "6. Damian Lillard: 0.20571356093716342\n",
      "7. D.J. Augustin: 0.1777310516280819\n",
      "8. Paul George: 0.17198543484372708\n",
      "9. Kevin Durant: 0.17101546432015446\n",
      "10. Stephen Curry: 0.14732917585002814\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = LinearRegression(normalize=True),\n",
    "    poly_fit = PolynomialFeatures(degree=2, interaction_only=True),\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 0.7563476230848476\n",
      "2. Giannis Antetokounmpo: 0.6746083507874783\n",
      "3. Nikola Jokic: 0.21871061822342422\n",
      "4. Paul George: 0.21036571339862856\n",
      "5. Rudy Gobert: 0.20936196190336712\n",
      "6. Damian Lillard: 0.20200699254779234\n",
      "7. Kevin Durant: 0.19205147330531072\n",
      "8. Kawhi Leonard: 0.13294650085702922\n",
      "9. Stephen Curry: 0.13207131804327157\n",
      "10. Clint Capela: 0.032160546528826855\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = SVR(C=100, gamma=0.001),\n",
    "    poly_fit = None,\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. James Harden: 1.154675740170516\n",
      "2. Giannis Antetokounmpo: 1.0386462648887385\n",
      "3. Nikola Jokic: 0.4901939268491997\n",
      "4. Rudy Gobert: 0.4686246990682559\n",
      "5. Damian Lillard: 0.36504592734324426\n",
      "6. Kevin Durant: 0.3463866404815956\n",
      "7. Paul George: 0.31713392580389943\n",
      "8. Joel Embiid: 0.2957015900849296\n",
      "9. Russell Westbrook: 0.2782817213866746\n",
      "10. Stephen Curry: 0.2077814108315561\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = SVR(C=100, gamma=0.01),\n",
    "    poly_fit = None,\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data,\n",
    "    scaler=MinMaxScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predictions:\n",
      "1. Giannis Antetokounmpo: 0.2954057689671746\n",
      "2. James Harden: 0.2914771242253993\n",
      "3. Rudy Gobert: 0.16625874725946113\n",
      "4. Nikola Jokic: 0.16533157783545852\n",
      "5. Stephen Curry: 0.144578519811052\n",
      "6. Damian Lillard: 0.1434476414419158\n",
      "7. Paul George: 0.13872571505515233\n",
      "8. Kawhi Leonard: 0.1302470085313449\n",
      "9. Kevin Durant: 0.12892009493992332\n",
      "10. Karl-Anthony Towns: 0.1286063133521597\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_regression_model(\n",
    "    model = GradientBoostingRegressor(n_estimators=50, learning_rate=0.01 ,subsample=0.5),\n",
    "    poly_fit = None,\n",
    "    train_data=data_frame,\n",
    "    test_data=test_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Large scale\" regression prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I will load data from results file and run predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_dict = {\n",
    "    'normal': None,\n",
    "    'poly_2': PolynomialFeatures(degree=2, interaction_only=True),\n",
    "    'poly_2_all': PolynomialFeatures(degree=2, interaction_only=False),\n",
    "    'poly_3': PolynomialFeatures(degree=3, interaction_only=True),\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "estimators = {\n",
    "    'SVR': SVR,\n",
    "    'RandomForestRegressor': RandomForestRegressor,\n",
    "    'Ridge': Ridge,\n",
    "    'GradientBoostingRegressor': GradientBoostingRegressor,\n",
    "    'LinearRegression': LinearRegression,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_results(file_name, top_k=20):\n",
    "\n",
    "    with open(file_name) as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    predictions_counter = defaultdict(list)\n",
    "    score_counter = defaultdict(list)\n",
    "\n",
    "    for line in lines[:top_k]:\n",
    "        splitted = line.split(\"|\")\n",
    "        poly = splitted[0]\n",
    "        estimator = splitted[1]\n",
    "        if estimator == \"Lasso\":\n",
    "            continue\n",
    "        params = splitted[2]\n",
    "        param_dict = {}\n",
    "        polynomial = None\n",
    "        for key in poly_dict:\n",
    "            if poly in key:\n",
    "                polynomial = poly_dict[key]\n",
    "                break\n",
    "        scaler = None\n",
    "        if \"scaler\" in poly:\n",
    "            scaler = MinMaxScaler()\n",
    "        for param_value in params.split(\",\"):\n",
    "            param_value = param_value.strip()\n",
    "            param_value_split = param_value.split(\":\")\n",
    "            key = param_value_split[0].strip()\n",
    "            value = param_value_split[1].strip()\n",
    "            if key == 'normalize':\n",
    "                value = bool(value)\n",
    "            elif key == \"kernel\":\n",
    "                value = str(value)\n",
    "            elif key == \"n_estimators\" or key == 'n_jobs' or key == 'min_samples_split':\n",
    "                value = int(value)\n",
    "            else:\n",
    "                value = float(value)\n",
    "            param_dict[key] = value\n",
    "\n",
    "        predictions = get_predictions_for_regression_model(\n",
    "            model = estimators[estimator](**param_dict),\n",
    "            poly_fit = polynomial,\n",
    "            train_data=data_frame,\n",
    "            test_data=test_data,\n",
    "            scaler=scaler,\n",
    "            should_print=False\n",
    "        )\n",
    "\n",
    "        for index, item in enumerate(predictions):\n",
    "            name, score = item\n",
    "            predictions_counter[name].append((index+1))\n",
    "            score_counter[name].append(score)\n",
    "    \n",
    "    return predictions_counter, score_counter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_counter, score_counter = get_results(\"reg_results_mse_sorted.txt\", top_k=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_rank = {}\n",
    "for key in predictions_counter:\n",
    "    if len(predictions_counter[key]) < 10:\n",
    "        continue\n",
    "    avg_rank[key] = np.mean(predictions_counter[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_items = sorted(avg_rank.items(), key=lambda kv: -kv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.barh([item[0] for item in sorted_items], [item[1] for item in sorted_items])\n",
    "ax.set_title(\"Average rank of players by top 50 models based on MSE\", size=18)\n",
    "_ = ax.set_xlabel(\"Rank\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_rank = {}\n",
    "for key in score_counter:\n",
    "    if len(score_counter[key]) < 10:\n",
    "        continue\n",
    "    avg_rank[key] = np.mean(score_counter[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_items = sorted(avg_rank.items(), key=lambda kv: -kv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [item[0].split(\" \")[0][0] + \". \" + item[0].split(\" \")[1] for item in sorted_items]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.bar([item[0] for item in sorted_items], [item[1] for item in sorted_items])\n",
    "ax.set_title(\"Average score of players by top 50 models based on MSE\", size=18)\n",
    "ax.set_ylabel(\"Score\", size=14)\n",
    "_ = ax.set_xticklabels(labels=[item[0] for item in sorted_items], rotation=45, ha='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 1 accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check top models in predicting mvp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_counter, score_counter = get_results(\"reg_results_sorted_top_1.txt\", top_k=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_rank = {}\n",
    "for key in predictions_counter:\n",
    "    if len(predictions_counter[key]) < 10:\n",
    "        continue\n",
    "    avg_rank[key] = np.mean(predictions_counter[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_items = sorted(avg_rank.items(), key=lambda kv: -kv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.barh([item[0] for item in sorted_items], [item[1] for item in sorted_items])\n",
    "ax.set_title(\"Average rank of players by top 50 models based on top 1 accuracy\", size=18)\n",
    "_ = ax.set_xlabel(\"Rank\", size=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [],
   "source": [
    "avg_rank = {}\n",
    "for key in score_counter:\n",
    "    if len(score_counter[key]) < 10:\n",
    "        continue\n",
    "    avg_rank[key] = np.mean(score_counter[key])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_items = sorted(avg_rank.items(), key=lambda kv: -kv[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [],
   "source": [
    "names = [item[0].split(\" \")[0][0] + \". \" + item[0].split(\" \")[1] for item in sorted_items]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12))\n",
    "\n",
    "ax.bar([item[0] for item in sorted_items], [item[1] for item in sorted_items])\n",
    "ax.set_title(\"Average score of players by top 50 models based on top 1 accuracy\", size=18)\n",
    "ax.set_ylabel(\"Score\", size=14)\n",
    "_ = ax.set_xticklabels(labels=[item[0] for item in sorted_items], rotation=45, ha='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_predictions_for_cls_model(model, poly_fit, train_data, test_data, scaler=None):\n",
    "    train_x = train_data[train_cls_columns].to_numpy()\n",
    "    train_y = train_data[target_cls_columns].to_numpy()\n",
    "\n",
    "    train_x, train_y = shuffle(train_x, train_y)\n",
    "    train_y = train_y.reshape(train_y.shape[0], )\n",
    "    \n",
    "    test_x = test_data[test_cls_columns].to_numpy()\n",
    "    test_x = np.nan_to_num(test_x)\n",
    "    \n",
    "    if poly_fit is not None:\n",
    "        train_x = poly_fit.fit_transform(train_x)\n",
    "        test_x = poly_fit.fit_transform(test_x)\n",
    "    \n",
    "    if scaler is not None:\n",
    "        train_x = scaler.fit_transform(train_x)\n",
    "        test_x = scaler.fit_transform(test_x)\n",
    "    \n",
    "    model.fit(train_x, train_y)\n",
    "\n",
    "    preds = model.predict_proba(test_x)\n",
    "    sorted_indices = np.argsort([float(item) for item in preds[:,0]])[::-1]\n",
    "    for i, index in enumerate(sorted_indices[:10]):\n",
    "        print(f\"{i+1}. is index: {test_data.iloc[index].Player}, {preds[index,0]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. is index: James Harden, 0.8872124899681998\n",
      "2. is index: Giannis Antetokounmpo, 0.5915625383132933\n",
      "3. is index: Joel Embiid, 0.13256664560432202\n",
      "4. is index: Nikola Jokic, 0.10364580312770617\n",
      "5. is index: Damian Lillard, 0.09938941629038436\n",
      "6. is index: Paul George, 0.09715353483498966\n",
      "7. is index: Russell Westbrook, 0.07622540795424594\n",
      "8. is index: Kevin Durant, 0.06844237054322204\n",
      "9. is index: Karl-Anthony Towns, 0.06418638294669467\n",
      "10. is index: Kawhi Leonard, 0.03927932704101305\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_cls_model(\n",
    "    model = SVC(C=100, gamma=0.1, probability=True),\n",
    "    poly_fit=None,\n",
    "    train_data=cls_data_frame,\n",
    "    test_data=top_players,\n",
    "    scaler=MinMaxScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. is index: James Harden, 0.7936514473019313\n",
      "2. is index: Giannis Antetokounmpo, 0.5276188287021737\n",
      "3. is index: Nikola Jokic, 0.18976004603386226\n",
      "4. is index: Damian Lillard, 0.12026660153111093\n",
      "5. is index: Karl-Anthony Towns, 0.09493431476682627\n",
      "6. is index: Paul George, 0.0948561110744394\n",
      "7. is index: Kevin Durant, 0.09068631757321668\n",
      "8. is index: Joel Embiid, 0.08676239854234064\n",
      "9. is index: Stephen Curry, 0.07744223868443946\n",
      "10. is index: Nikola Vucevic, 0.06555454010136422\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_cls_model(\n",
    "    model = LogisticRegression(multi_class='multinomial', solver='newton-cg', C=1.0),\n",
    "    poly_fit=None,\n",
    "    train_data=cls_data_frame,\n",
    "    test_data=top_players,\n",
    "    scaler=MinMaxScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1. is index: James Harden, 0.7949999999999998\n",
      "2. is index: Giannis Antetokounmpo, 0.5883333333333333\n",
      "3. is index: Nikola Jokic, 0.231\n",
      "4. is index: Joel Embiid, 0.11\n",
      "5. is index: Rudy Gobert, 0.09833333333333333\n",
      "6. is index: Paul George, 0.085\n",
      "7. is index: Karl-Anthony Towns, 0.076\n",
      "8. is index: Kevin Durant, 0.05333333333333333\n",
      "9. is index: Kawhi Leonard, 0.05333333333333333\n",
      "10. is index: Damian Lillard, 0.04666666666666666\n"
     ]
    }
   ],
   "source": [
    "get_predictions_for_cls_model(\n",
    "    model = RandomForestClassifier(n_estimators=100, min_samples_split=3),\n",
    "    poly_fit=None,\n",
    "    train_data=cls_data_frame,\n",
    "    test_data=top_players,\n",
    "    scaler=MinMaxScaler()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}