{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><img src=\"img/logo_hse_black.jpg\"></center>\n",
    "\n",
    "<h1><center>Методы машинного обучения</center></h1>\n",
    "<h2><center>Семинар: методы сжатия признаков</center></h2>\n",
    "<h2><center>Введение в NLP</center></h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (12,8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Методы понижения размерности"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df_wine = pd.read_csv('data/winequality-red.csv', sep=';')\n",
    "\n",
    "# Make classification target feature\n",
    "df_wine.loc[:, 'quality_cat'] = (df_wine.quality > 5).astype(int)\n",
    "df_wine = df_wine.drop('quality', axis=1)\n",
    "\n",
    "# Get descriptive and target features\n",
    "X = df_wine.iloc[:, :-1].values\n",
    "y = df_wine.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Попробуем получить PCA разными способами"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA через sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 11)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=5, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=5, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1599, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 11)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-13.22490501,  -2.02389981,  -1.12682053,  -0.39923318,\n",
       "         -1.08538902],\n",
       "       [ 22.03772361,   4.40832155,  -0.31037799,  -0.19993774,\n",
       "         -0.55710939],\n",
       "       [  7.16267333,  -2.50146086,  -0.5818683 ,  -0.21906928,\n",
       "         -0.55293355],\n",
       "       [ 13.43006283,  -1.95112215,   2.63403954,  -1.5740594 ,\n",
       "         -0.06380573],\n",
       "       [-13.22490501,  -2.02389981,  -1.12682053,  -0.39923318,\n",
       "         -1.08538902]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.31963727,  0.52782051,  0.27097561,  2.5388055 ,  0.08746654,\n",
       "       15.87492183, 46.46779237,  0.99674668,  3.3111132 ,  0.65814884,\n",
       "       10.42298311])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.31963727,  0.52782051,  0.27097561,  2.5388055 ,  0.08746654,\n",
       "       15.87492183, 46.46779237,  0.99674668,  3.3111132 ,  0.65814884,\n",
       "       10.42298311])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.mean_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = X - X.mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_ = X_.dot(pca.components_.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-13.22490501,  -2.02389981,  -1.12682053,  -0.39923318,\n",
       "         -1.08538902],\n",
       "       [ 22.03772361,   4.40832155,  -0.31037799,  -0.19993774,\n",
       "         -0.55710939],\n",
       "       [  7.16267333,  -2.50146086,  -0.5818683 ,  -0.21906928,\n",
       "         -0.55293355],\n",
       "       [ 13.43006283,  -1.95112215,   2.63403954,  -1.5740594 ,\n",
       "         -0.06380573],\n",
       "       [-13.22490501,  -2.02389981,  -1.12682053,  -0.39923318,\n",
       "         -1.08538902]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# А теперь еще и шкалируем признаки\n",
    "\n",
    "X = (X - X.mean(axis=0))/X.std(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=5, random_state=None,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28173931, 0.1750827 , 0.1409585 , 0.11029387, 0.08720837])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA через ковариационную матрицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import eig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = X.T.dot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "lambd, W = eig(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4955.51277263, 3079.52959367, 2479.31903855, 1939.95881639,\n",
       "       1533.90802208,   95.23374076,  289.95173334,  551.08274479,\n",
       "        676.30776275,  933.48215949, 1054.71361553])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lambd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.48931422, -0.23858436,  0.46363166,  0.14610715,  0.21224658,\n",
       "        -0.03615752,  0.02357485,  0.39535301, -0.43851962,  0.24292133,\n",
       "        -0.11323206],\n",
       "       [-0.11050274,  0.27493048, -0.15179136,  0.27208024,  0.14805156,\n",
       "         0.51356681,  0.56948696,  0.23357549,  0.00671079, -0.03755392,\n",
       "        -0.38618096]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W.T[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "W = W.T[:5, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.48931422, -0.23858436,  0.46363166,  0.14610715,  0.21224658,\n",
       "        -0.03615752,  0.02357485,  0.39535301, -0.43851962,  0.24292133,\n",
       "        -0.11323206],\n",
       "       [-0.11050274,  0.27493048, -0.15179136,  0.27208024,  0.14805156,\n",
       "         0.51356681,  0.56948696,  0.23357549,  0.00671079, -0.03755392,\n",
       "        -0.38618096]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.components_[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = pca.transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z_ = X.dot(W.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.61952988,  0.45095009, -1.77445415,  0.04374031,  0.06701448],\n",
       "       [-0.79916993,  1.85655306, -0.91169017,  0.54806597, -0.01839156],\n",
       "       [-0.74847909,  0.88203886, -1.17139423,  0.41102067, -0.04353101]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.61952988,  0.45095009, -1.77445415,  0.04374031,  0.06701448],\n",
       "       [-0.79916993,  1.85655306, -0.91169017,  0.54806597, -0.01839156],\n",
       "       [-0.74847909,  0.88203886, -1.17139423,  0.41102067, -0.04353101]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28173931, 0.1750827 , 0.1409585 , 0.11029387, 0.08720837])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28173931, 0.1750827 , 0.1409585 , 0.11029387, 0.08720837,\n",
       "       0.00541439, 0.01648483, 0.0313311 , 0.03845061, 0.05307193,\n",
       "       0.05996439])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exp_var = lambd/lambd.sum()\n",
    "exp_var"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка качества при разных количествах компонент\n",
    "\n",
    "Реализуйте 2 пайплайна:\n",
    "    * StandartScaler + LogisticRegression\n",
    "    * StandartScaler + PCA + LogisticRegression\n",
    "\n",
    "Оцените качество пайплайна с PCA при разных количествах компонент и сравните его с первым"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = Pipeline([\n",
    "    ('scaler', StandardScaler()),\n",
    "    ('clf', LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = StratifiedKFold(n_splits=5, random_state=123, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8171715339500574"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_model1 = cross_val_score(model1, X, y, cv=cv, scoring='roc_auc').mean()\n",
    "score_model1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import validation_curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "k_range = range(1, 12)\n",
    "scores = []\n",
    "\n",
    "for k in k_range:\n",
    "    model2 = Pipeline([\n",
    "        ('scaler', StandardScaler()),\n",
    "        ('pca', PCA(n_components=k)),\n",
    "        ('clf', LogisticRegression())\n",
    "    ])\n",
    "\n",
    "    scores.append(cross_val_score(model2, X, y, cv=cv, scoring='roc_auc').mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1a1e4f2898>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(k_range, scores)\n",
    "plt.hlines(score_model1, 1,11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Singular Value Decomposition\n",
    "\n",
    "Для любой матрицы $A$ размера $n \\times m$ и ранга $r$ можно найти разложение вида:\n",
    "$$ A = U \\Sigma V^\\top ,$$\n",
    "где \n",
    "* $U$ - унитарная матрица, состоящая из собственных векторов $AA^\\top$\n",
    "* $V$ - унитарная матрица, состоящая из собственных векторов $A^\\top A$\n",
    "* $\\Sigma$ - диагональная матрица с сингулярными числами $s_i = \\sqrt{\\lambda_i}$\n",
    "\n",
    "<center><img src='img/svd.jpg' width=600></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVD via PCA\n",
    "Матрицы $U$ и $V$ ортогональны и могут быть использованы для перехода к ортогональному базису:\n",
    "$$ AV = U\\Sigma $$\n",
    "\n",
    "<center><img src='img/pca_svd.png' width=600></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import svd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Рационы питания в странах"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Загрузите набор данных о пищевом рационе в разных странах мира `diet.csv`\n",
    "* Примените на данных PCA с 2 компонентами\n",
    "* Изобразите объекты в сжатом пространстве"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/diet.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries</th>\n",
       "      <th>Energy (kcal/day)</th>\n",
       "      <th>Protein (g/day)</th>\n",
       "      <th>Fats (g/day)</th>\n",
       "      <th>Carbohydrates (g/day)</th>\n",
       "      <th>Animal Products + (kcal/day)</th>\n",
       "      <th>Animal Fats (kcal/day)</th>\n",
       "      <th>Bovine Meat (kcal/day)</th>\n",
       "      <th>Butter, Ghee (kcal/day)</th>\n",
       "      <th>Cheese (kcal/day)</th>\n",
       "      <th>...</th>\n",
       "      <th>Starchy Roots (kcal/day)</th>\n",
       "      <th>Sugar &amp; Sweeteners (kcal/day)</th>\n",
       "      <th>Sugar (Raw Equivalent) (kcal/day)</th>\n",
       "      <th>Sugar, Raw Equivalent (kcal/day)</th>\n",
       "      <th>Sugar, Refined Equiv (kcal/day)</th>\n",
       "      <th>Vegetable Oils (kcal/day)</th>\n",
       "      <th>Vegetables (kcal/day)</th>\n",
       "      <th>Wheat (kcal/day)</th>\n",
       "      <th>Wine (kcal/day)</th>\n",
       "      <th>Unnamed: 50</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>2860.0</td>\n",
       "      <td>96</td>\n",
       "      <td>86</td>\n",
       "      <td>425.50</td>\n",
       "      <td>813.0</td>\n",
       "      <td>49</td>\n",
       "      <td>62</td>\n",
       "      <td>11</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>57</td>\n",
       "      <td>193</td>\n",
       "      <td>187</td>\n",
       "      <td>191</td>\n",
       "      <td>187</td>\n",
       "      <td>174</td>\n",
       "      <td>94</td>\n",
       "      <td>1166</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>94</td>\n",
       "      <td>100</td>\n",
       "      <td>426.00</td>\n",
       "      <td>823.0</td>\n",
       "      <td>72</td>\n",
       "      <td>342</td>\n",
       "      <td>28</td>\n",
       "      <td>90</td>\n",
       "      <td>...</td>\n",
       "      <td>100</td>\n",
       "      <td>406</td>\n",
       "      <td>337</td>\n",
       "      <td>405</td>\n",
       "      <td>337</td>\n",
       "      <td>311</td>\n",
       "      <td>51</td>\n",
       "      <td>914</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Australia</td>\n",
       "      <td>3120.0</td>\n",
       "      <td>107</td>\n",
       "      <td>134</td>\n",
       "      <td>371.50</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>124</td>\n",
       "      <td>142</td>\n",
       "      <td>62</td>\n",
       "      <td>107</td>\n",
       "      <td>...</td>\n",
       "      <td>87</td>\n",
       "      <td>423</td>\n",
       "      <td>407</td>\n",
       "      <td>415</td>\n",
       "      <td>407</td>\n",
       "      <td>435</td>\n",
       "      <td>67</td>\n",
       "      <td>559</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Austria</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>111</td>\n",
       "      <td>162</td>\n",
       "      <td>459.50</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>320</td>\n",
       "      <td>59</td>\n",
       "      <td>102</td>\n",
       "      <td>193</td>\n",
       "      <td>...</td>\n",
       "      <td>113</td>\n",
       "      <td>437</td>\n",
       "      <td>404</td>\n",
       "      <td>424</td>\n",
       "      <td>404</td>\n",
       "      <td>442</td>\n",
       "      <td>61</td>\n",
       "      <td>617</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>48</td>\n",
       "      <td>25</td>\n",
       "      <td>445.75</td>\n",
       "      <td>65.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>59</td>\n",
       "      <td>29</td>\n",
       "      <td>131</td>\n",
       "      <td>10</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Countries  Energy (kcal/day)  Protein (g/day)  Fats (g/day)  \\\n",
       "0     Albania             2860.0               96            86   \n",
       "1   Argentina             2980.0               94           100   \n",
       "2   Australia             3120.0              107           134   \n",
       "3     Austria             3740.0              111           162   \n",
       "4  Bangladesh             2200.0               48            25   \n",
       "\n",
       "   Carbohydrates (g/day)  Animal Products + (kcal/day)  \\\n",
       "0                 425.50                         813.0   \n",
       "1                 426.00                         823.0   \n",
       "2                 371.50                        1033.0   \n",
       "3                 459.50                        1219.0   \n",
       "4                 445.75                          65.0   \n",
       "\n",
       "   Animal Fats (kcal/day)  Bovine Meat (kcal/day)  Butter, Ghee (kcal/day)  \\\n",
       "0                      49                      62                       11   \n",
       "1                      72                     342                       28   \n",
       "2                     124                     142                       62   \n",
       "3                     320                      59                      102   \n",
       "4                       5                       5                        3   \n",
       "\n",
       "   Cheese (kcal/day)     ...       Starchy Roots (kcal/day)  \\\n",
       "0                 50     ...                             57   \n",
       "1                 90     ...                            100   \n",
       "2                107     ...                             87   \n",
       "3                193     ...                            113   \n",
       "4                  0     ...                             42   \n",
       "\n",
       "   Sugar & Sweeteners (kcal/day)  Sugar (Raw Equivalent) (kcal/day)  \\\n",
       "0                            193                                187   \n",
       "1                            406                                337   \n",
       "2                            423                                407   \n",
       "3                            437                                404   \n",
       "4                             59                                 29   \n",
       "\n",
       "   Sugar, Raw Equivalent (kcal/day)  Sugar, Refined Equiv (kcal/day)  \\\n",
       "0                               191                              187   \n",
       "1                               405                              337   \n",
       "2                               415                              407   \n",
       "3                               424                              404   \n",
       "4                                59                               29   \n",
       "\n",
       "   Vegetable Oils (kcal/day)  Vegetables (kcal/day)  Wheat (kcal/day)  \\\n",
       "0                        174                     94              1166   \n",
       "1                        311                     51               914   \n",
       "2                        435                     67               559   \n",
       "3                        442                     61               617   \n",
       "4                        131                     10               180   \n",
       "\n",
       "   Wine (kcal/day)  Unnamed: 50  \n",
       "0                6          NaN  \n",
       "1               59          NaN  \n",
       "2               39          NaN  \n",
       "3               55          NaN  \n",
       "4                0          NaN  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_ = (X - X.mean(axis=0))/X.std(axis=0)\n",
    "pca = PCA(n_components=2, random_state=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pca.fit(X_)\n",
    "# Z = pca.transform(X_)\n",
    "Z = pca.fit_transform(X_) # Сразу fit на X а потом его transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a1e978780>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Z[:,0], Z[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = Z[:, 1] > 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "strange = df.loc[idx].values[0,1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_names = df.columns[1:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f0a1b00>"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:, 'Alcoholic Beverages (kcal/day)'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Energy (kcal/day)', 'Protein (g/day)', 'Fats (g/day)',\n",
       "       'Carbohydrates (g/day)', 'Animal Products + (kcal/day)',\n",
       "       'Animal Fats (kcal/day)', 'Bovine Meat (kcal/day)',\n",
       "       'Butter, Ghee (kcal/day)', 'Cheese (kcal/day)', 'Eggs (kcal/day)',\n",
       "       'Fats, Animals, Raw (kcal/day)', 'Fish, Seafood (kcal/day)',\n",
       "       'Freshwater Fish (kcal/day)', 'Honey (kcal/day)',\n",
       "       'Meat (kcal/day)', 'Milk - Excluding Butter (kcal/day)',\n",
       "       'Milk, Whole (kcal/day)', 'Mutton & Goat Meat (kcal/day)',\n",
       "       'Offals, Edible (kcal/day)', 'Pelagic Fish (kcal/day)',\n",
       "       'Pigmeat (kcal/day)', 'Poultry Meat (kcal/day)',\n",
       "       'Vegetal Products + (kcal/day)', 'Alcoholic Beverages (kcal/day)',\n",
       "       'Apples (kcal/day)', 'Bananas (kcal/day)', 'Beans (kcal/day)',\n",
       "       'Cereals - Excluding Beer (kcal/day)', 'Coconut Oil (kcal/day)',\n",
       "       'Coffee (kcal/day)', 'Fruits - Excluding Wine (kcal/day)',\n",
       "       'Nuts (kcal/day)', 'Olive Oil (kcal/day)', 'Palm Oil (kcal/day)',\n",
       "       'Potatoes (kcal/day)', 'Pulses (kcal/day)',\n",
       "       'Rice (Milled Equivalent) (kcal/day)',\n",
       "       'Rice (Paddy Equivalent) (kcal/day)',\n",
       "       'Roots & Tuber Dry Equiv (kcal/day)', 'Soyabean Oil (kcal/day)',\n",
       "       'Starchy Roots (kcal/day)', 'Sugar & Sweeteners (kcal/day)',\n",
       "       'Sugar (Raw Equivalent) (kcal/day)',\n",
       "       'Sugar, Raw Equivalent (kcal/day)',\n",
       "       'Sugar, Refined Equiv (kcal/day)', 'Vegetable Oils (kcal/day)',\n",
       "       'Vegetables (kcal/day)', 'Wheat (kcal/day)', 'Wine (kcal/day)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = X - strange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Protein (g/day)                        -2973.47\n",
       "Alcoholic Beverages (kcal/day)         -2685.57\n",
       "Coconut Oil (kcal/day)                 -1799.71\n",
       "Wine (kcal/day)                        -1435.55\n",
       "Nuts (kcal/day)                         -208.36\n",
       "Animal Fats (kcal/day)                 -199.267\n",
       "Roots & Tuber Dry Equiv (kcal/day)     -184.116\n",
       "Vegetables (kcal/day)                  -138.791\n",
       "Rice (Paddy Equivalent) (kcal/day)     -77.0349\n",
       "Pulses (kcal/day)                      -44.0116\n",
       "Mutton & Goat Meat (kcal/day)          -35.9651\n",
       "Offals, Edible (kcal/day)               -31.907\n",
       "Bananas (kcal/day)                     -19.3605\n",
       "Olive Oil (kcal/day)                   -18.5581\n",
       "Soyabean Oil (kcal/day)                -9.47674\n",
       "Freshwater Fish (kcal/day)             -6.88372\n",
       "Honey (kcal/day)                      -0.337209\n",
       "Cheese (kcal/day)                        2.7907\n",
       "Coffee (kcal/day)                       2.82558\n",
       "Butter, Ghee (kcal/day)                     3.5\n",
       "Bovine Meat (kcal/day)                  6.32558\n",
       "Fats (g/day)                            7.17442\n",
       "Pelagic Fish (kcal/day)                 7.80233\n",
       "Starchy Roots (kcal/day)                9.60465\n",
       "Apples (kcal/day)                       12.0581\n",
       "Beans (kcal/day)                        13.5814\n",
       "Eggs (kcal/day)                         15.8837\n",
       "Fats, Animals, Raw (kcal/day)           25.5698\n",
       "Milk, Whole (kcal/day)                  30.6163\n",
       "Fish, Seafood (kcal/day)                33.0233\n",
       "Vegetable Oils (kcal/day)               40.6744\n",
       "Palm Oil (kcal/day)                     44.6512\n",
       "Sugar (Raw Equivalent) (kcal/day)       45.1977\n",
       "Animal Products + (kcal/day)             48.218\n",
       "Sugar, Refined Equiv (kcal/day)         49.1512\n",
       "Poultry Meat (kcal/day)                 62.5581\n",
       "Sugar, Raw Equivalent (kcal/day)         72.314\n",
       "Milk - Excluding Butter (kcal/day)      73.9884\n",
       "Potatoes (kcal/day)                     78.0465\n",
       "Pigmeat (kcal/day)                       88.593\n",
       "Fruits - Excluding Wine (kcal/day)      103.605\n",
       "Rice (Milled Equivalent) (kcal/day)     215.523\n",
       "Sugar & Sweeteners (kcal/day)           233.547\n",
       "Meat (kcal/day)                         237.128\n",
       "Carbohydrates (g/day)                   372.802\n",
       "Wheat (kcal/day)                        489.163\n",
       "Cereals - Excluding Beer (kcal/day)     1085.94\n",
       "Vegetal Products + (kcal/day)           2231.48\n",
       "Energy (kcal/day)                       2501.66\n",
       "dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(index=field_names, data=d.mean(axis=0)).sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Скорее всего вы обнаружите некоторые выбросы, с этим ничего не поделать - PCA чувствителен к выбросам\n",
    "* Удалите объекты-выборосы и повторите процедуру"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA для текстов\n",
    "#### Он же Latent Semantic Index или Latent Semantic Allocation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Когда мы работаем с текстами, бы обычно переходим к модели мешка слов\n",
    "* Что если мы ходим применить для текстов PCA?\n",
    "* Центрировать данные мы не можем, так как тогда наша огромная матрица с признаками будет плотной, а значит будет очень много весить\n",
    "* Давайте не будем центрировать! =D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import TruncatedSVD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('data/sentiment/imdb_labelled.txt', sep='\\t', header=None, names=['text', 'label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A very, very, very slow-moving, aimless movie ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not sure who was more lost - the flat characte...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Attempting artiness with black &amp; white and cle...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Very little music or anything to speak of.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The best scene in the movie was when Gerardo i...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0  A very, very, very slow-moving, aimless movie ...      0\n",
       "1  Not sure who was more lost - the flat characte...      0\n",
       "2  Attempting artiness with black & white and cle...      0\n",
       "3       Very little music or anything to speak of.        0\n",
       "4  The best scene in the movie was when Gerardo i...      1"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import RandomizedSearchCV, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = df.text.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(748,)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<748x3047 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 11363 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(748, 3047)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcaish = TruncatedSVD(n_components=3, random_state=123)\n",
    "pca = PCA(n_components=3, random_state=123)\n",
    "Z = pcaish.fit_transform(X)\n",
    "# Z = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.27923403,  0.00931181, -0.19921826],\n",
       "       [ 2.00479625, -0.22796059, -0.33735339],\n",
       "       [ 3.35993389, -1.24344591,  1.14540515],\n",
       "       ...,\n",
       "       [ 0.56845862,  0.75834051,  0.25123094],\n",
       "       [ 0.04048141, -0.1155508 , -0.02562375],\n",
       "       [ 1.15704622, -0.18584428,  0.11468745]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Pipeline([\n",
    "    ('vect', CountVectorizer(max_df=0.95, stop_words='english', ngram_range=(1,1))),\n",
    "    ('pca', TruncatedSVD(n_components=3, random_state=123)),\n",
    "    ('clf', LogisticRegression())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = df.label.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/andrey.shestakov/anaconda3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('vect', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=0.95, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words='english',\n",
       "       ...penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(text, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T-SNE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание\n",
    "Сожмите признаковое пространство данных с цифрами с помощью tsne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = data['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs = data['images']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(imgs[1], cmap=plt.cm.gray_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 64)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(perplexity=5,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSNE(angle=0.5, early_exaggeration=12.0, init='random', learning_rate=200.0,\n",
       "   method='barnes_hut', metric='euclidean', min_grad_norm=1e-07,\n",
       "   n_components=2, n_iter=1000, n_iter_without_progress=300, perplexity=5,\n",
       "   random_state=None, verbose=0)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = tsne.embedding_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a210440f0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Z[:, 0], Z[:, 1], c=labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b04bf98>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Z[:, 0], Z[:, 1], c=labels)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "142px",
    "width": "252px"
   },
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": "block",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}