Notebooks/PMF_SU_2018_Vjezbe4_00_Smanjivanje_dimenzionalnosti.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Strojno učenje - PMF\n",
    "\n",
    "Akademska godina 2017./2018.\n",
    "[https://web.math.pmf.unizg.hr/nastava/su/](https://web.math.pmf.unizg.hr/nastava/su/)\n",
    "\n",
    "Matija Piškorec\n",
    "\n",
    "kernel: Python 3.6\n",
    "\n",
    "# 4. Smanjivanje dimenzionalnosti\n",
    "\n",
    "Glavni cilj smanjivanja dimenzionalnosti je projicirati naše podatke u prostor niže dimenzionalnosti ali tako da se održi što više korisne informacije (prema nekom kriteriju) o originalnim podacima. Projiciranje podataka u drugi prostor se ponekad naziva i ekstrakcijom ili generiranjem značajki (engl. feature extraction) zato što efektivno stvaramo nove atribute.\n",
    "\n",
    "Primjerice, analiza glavnih komponenti (engl. principal component analysis, PCA) je linearna projekcija podataka gdje su nove značajke (komponente) poredane po količini varijance koju objašnjavaju u originalnim podacima. U tom slučaju možemo smanjiti dimenzionalnost podataka tako da samo zadržimo prvih nekoliko komponenti i odbacimo ostale. Tehnički, ovo je jako slično odabiru značajki, s time da ovdje odabiremo značajke u transformiranom prostoru.\n",
    "\n",
    "Većina sadržaja ove vježbe preuzeta je s jedne od vježbi za [First Int'l Summer School on Data Science](https://sites.google.com/site/ssdatascience2016/lecture-notes) održanoj u Splitu u rujnu 2016., koje su dostupne na [Github repozitoriju](https://github.com/SSDS2016/Day-3). Od ostalih materijala, službena scikit-learn [korisnička dokumentacija](http://scikit-learn.org/stable/user_guide.html) ima nekoliko primjera s PCA i drugim metodama smanjivanja dimenzionalnosti [[1]](#scikit-decomposition), a postoje i korisni primjeri na Githubu [[2]](#islr-ch10),[[3]](pyml-ch5),[[4]](#sklearn_pycon2015)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Preduvjeti\n",
    "--------------\n",
    "\n",
    "U ovoj vježbi koristit ćemo \"Wisconsin Breast Cancer\" dataset koji sadrži 569 primjera i 30 atributa, dok je ciljna varijabla ili 1 (maligni tumor) ili 0 (benigni tumor). Dataset je dostupan lokalno, inače se može dohvatiti preko scikit-learn funkcije [`sklearn.datasets.load_breast_cancer`](http://scikit-learn.org/dev/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn.datasets.load_breast_cancer) (verzija 0.17.1).\n",
    "\n",
    "Za ostale datasetove dostupne unutar scikit-learna pogledajte službenu dokumentaciju na [`sklearn.datasets`](http://scikit-learn.org/dev/modules/classes.html#module-sklearn.datasets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
       "3        11.42         20.38           77.58      386.1          0.14250   \n",
       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension   ...    worst texture  worst perimeter  worst area  \\\n",
       "0                 0.07871   ...            17.33           184.60      2019.0   \n",
       "1                 0.05667   ...            23.41           158.80      1956.0   \n",
       "2                 0.05999   ...            25.53           152.50      1709.0   \n",
       "3                 0.09744   ...            26.50            98.87       567.7   \n",
       "4                 0.05883   ...            16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  target  \n",
       "0          0.4601                  0.11890     0.0  \n",
       "1          0.2750                  0.08902     0.0  \n",
       "2          0.3613                  0.08758     0.0  \n",
       "3          0.6638                  0.17300     0.0  \n",
       "4          0.2364                  0.07678     0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer = pd.read_csv('../assets/datasets/csv/breast_cancer.csv',index_col=0)\n",
    "breast_cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      "mean radius                569 non-null float64\n",
      "mean texture               569 non-null float64\n",
      "mean perimeter             569 non-null float64\n",
      "mean area                  569 non-null float64\n",
      "mean smoothness            569 non-null float64\n",
      "mean compactness           569 non-null float64\n",
      "mean concavity             569 non-null float64\n",
      "mean concave points        569 non-null float64\n",
      "mean symmetry              569 non-null float64\n",
      "mean fractal dimension     569 non-null float64\n",
      "radius error               569 non-null float64\n",
      "texture error              569 non-null float64\n",
      "perimeter error            569 non-null float64\n",
      "area error                 569 non-null float64\n",
      "smoothness error           569 non-null float64\n",
      "compactness error          569 non-null float64\n",
      "concavity error            569 non-null float64\n",
      "concave points error       569 non-null float64\n",
      "symmetry error             569 non-null float64\n",
      "fractal dimension error    569 non-null float64\n",
      "worst radius               569 non-null float64\n",
      "worst texture              569 non-null float64\n",
      "worst perimeter            569 non-null float64\n",
      "worst area                 569 non-null float64\n",
      "worst smoothness           569 non-null float64\n",
      "worst compactness          569 non-null float64\n",
      "worst concavity            569 non-null float64\n",
      "worst concave points       569 non-null float64\n",
      "worst symmetry             569 non-null float64\n",
      "worst fractal dimension    569 non-null float64\n",
      "target                     569 non-null float64\n",
      "dtypes: float64(31)\n",
      "memory usage: 142.2 KB\n"
     ]
    }
   ],
   "source": [
    "breast_cancer.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Iako nije nužno potrebno u našem slučaju, slijedit ćemo standardnu praksu strojnog učenja i podijeliti naš dataset u skup za treniranje i skup za testiranje s funkcijom [`sklearn.cross_validation.train_test_split`](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html), te provesti svu analizu samo na skupu za treniranje."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Broj primjera za treniranje: 455\n",
      "Broj primjera za testiranje: 114\n"
     ]
    }
   ],
   "source": [
    "breast_cancer_train, breast_cancer_test = train_test_split(breast_cancer, test_size=0.20, random_state=42)\n",
    "print('Broj primjera za treniranje: ' + str(len(breast_cancer_train)))\n",
    "print('Broj primjera za testiranje: ' + str(len(breast_cancer_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.1. Eksploratorna analiza breast cancer dataseta\n",
    "----------------------------------------------------------\n",
    "\n",
    "Prije nego transformiramo naš dataset u novi prostor značajki, analizirajmo ga prvo uz pomoć pojedinačnih histograma za svaku od značajki i za svaki ishod ciljne varijable. Za sličan primjer pogledajte poglavlje 3 iz knjige \"Introduction to Machine Learning with Python\" [[5]](#intro-ml-py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAskAAAWYCAYAAABEQFsxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9911f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "breast_cancer = pd.read_csv('../assets/datasets/csv/breast_cancer.csv',index_col=0)\n",
    "\n",
    "malignant = breast_cancer.loc[ breast_cancer['target'] == 0, : ]\n",
    "benign = breast_cancer.loc[ breast_cancer['target'] == 1, : ]\n",
    "\n",
    "fig, axes = plt.subplots(15, 2, figsize=(10, 20))\n",
    "ax = axes.ravel() # axes are 2-dimensional so we unfold them\n",
    "for i in range(30):\n",
    "    _, bins = np.histogram(breast_cancer.iloc[:,i], bins=50)\n",
    "    ax[i].hist(malignant.iloc[:,i], bins=bins, color='r', alpha=.5, label='malignant')\n",
    "    ax[i].hist(benign.iloc[:,i], bins=bins, color='b', alpha=.5, label='benign')\n",
    "    ax[i].set_title(breast_cancer.columns.values[i])\n",
    "    ax[i].set_yticks(()) # remove ticks on y-axis\n",
    "    ax[i].legend(loc='upper right')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idealno, želimo da se naše dvije klase (maligna i benigna) u potpunosti razdvoje uz pomoć barem jedne značajke. U tom slučaju naš prediktivni model bi bio jako jednostavan, pošto bi samo morali provjeriti je li vrijednost te značajke iznad ili ispod određene granične vrijednosti. Nažalost, ovdje to nije slučaj."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vježba 4.1.** Gledajući histograme izračunate ranije, koje značajke se čine korelirane? Potvrdite to računanjem potpune matrice korelacije uz pomoć [`pandas.DataFrame.corr`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.corr.html) nakon čega je prikažite s [`matplotlib.pyplot.imshow`](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmkAAAJBCAYAAAAdjiKJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/H+Hlxlp4SMC5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd2f8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm as cm\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "X_corr = breast_cancer.drop('target',axis=1).corr()\n",
    "cmap = cm.get_cmap('RdBu', 30)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "iax = ax.imshow(X_corr, interpolation=\"nearest\", cmap=cmap)\n",
    "ax.grid(False)\n",
    "ax.set(title='Korelacija znacajki')\n",
    "ax.set_xticks(range(X_corr.shape[1]))\n",
    "ax.set_xticklabels(breast_cancer.drop('target',axis=1).columns.values, rotation=90)\n",
    "ax.set_yticks(range(X_corr.shape[1]))\n",
    "ax.set_yticklabels(breast_cancer.drop('target',axis=1).columns.values)\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "cbar = plt.colorbar(iax,cax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.2. Analiza glavnih komponenti (PCA)\n",
    "-----------------------------\n",
    "\n",
    "Iz eksploratorne analize vidimo da su brojne značajke korelirane. Iskoristit ćemo analizu glavnih komponenti (engl. principal component analysis, PCA) da projiciramo naše podatke u novi prostor značajki (komponenti) gdje su one ortogonalne i poredane po količini varijance koju objašnjavaju u originalnim podacima. Za računanje PCA komponenti koristit ćemo [`sklearn.decomposition.PCA`](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA), no prije toga ćemo standardizirati sve značajke s funkcijom [`sklearn.preprocessing.StandardScaler`](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler) koja oduzima srednju vrijednost i standardizira sve značajke tako da imaju jediničnu varijancu.\n",
    "\n",
    "Za više detalja pogledajte službenu dokumentaciju [\"2.5.1. Principal component analysis (PCA)\"](http://scikit-learn.org/stable/modules/decomposition.html#principal-component-analysis-pca)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimenzije originalnih podataka: (569, 30)\n",
      "Dimenzije projiciranih podataka: (569, 30)\n"
     ]
    }
   ],
   "source": [
    "breast_cancer = pd.read_csv('../assets/datasets/csv/breast_cancer.csv',index_col=0)\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = breast_cancer.drop('target',axis=1)\n",
    "scaler.fit(X)\n",
    "X_scaled = scaler.transform(X)\n",
    "\n",
    "pca = PCA() # use option n_components=2 to calculate just first two principal components\n",
    "pca.fit(X_scaled) # fit PCA model to breast cancer data\n",
    "X_pca = pca.transform(X_scaled) # transform data onto the principal components\n",
    "print(\"Dimenzije originalnih podataka: %s\" % str(X_scaled.shape))\n",
    "print(\"Dimenzije projiciranih podataka: %s\" % str(X_pca.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vidimo da projicirani podaci i dalje imaju dimenzije originalnih podataka. Ali transformirane značajke, ili *glavne komponente* (engl. principal components) su sada poredane po količini varijance koju objašnjavaju u originalnim podacima."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "      <th>PC5</th>\n",
       "      <th>PC6</th>\n",
       "      <th>PC7</th>\n",
       "      <th>PC8</th>\n",
       "      <th>PC9</th>\n",
       "      <th>PC10</th>\n",
       "      <th>...</th>\n",
       "      <th>PC21</th>\n",
       "      <th>PC22</th>\n",
       "      <th>PC23</th>\n",
       "      <th>PC24</th>\n",
       "      <th>PC25</th>\n",
       "      <th>PC26</th>\n",
       "      <th>PC27</th>\n",
       "      <th>PC28</th>\n",
       "      <th>PC29</th>\n",
       "      <th>PC30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.192837</td>\n",
       "      <td>1.948583</td>\n",
       "      <td>-1.123166</td>\n",
       "      <td>3.633731</td>\n",
       "      <td>-1.195110</td>\n",
       "      <td>1.411424</td>\n",
       "      <td>2.159370</td>\n",
       "      <td>-0.398407</td>\n",
       "      <td>-0.157118</td>\n",
       "      <td>-0.877402</td>\n",
       "      <td>...</td>\n",
       "      <td>0.096515</td>\n",
       "      <td>0.068850</td>\n",
       "      <td>0.084519</td>\n",
       "      <td>-0.175256</td>\n",
       "      <td>-0.151020</td>\n",
       "      <td>-0.201503</td>\n",
       "      <td>-0.252585</td>\n",
       "      <td>-0.033914</td>\n",
       "      <td>0.045648</td>\n",
       "      <td>-0.047169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.387802</td>\n",
       "      <td>-3.768172</td>\n",
       "      <td>-0.529293</td>\n",
       "      <td>1.118264</td>\n",
       "      <td>0.621775</td>\n",
       "      <td>0.028656</td>\n",
       "      <td>0.013358</td>\n",
       "      <td>0.240988</td>\n",
       "      <td>-0.711905</td>\n",
       "      <td>1.106995</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.077327</td>\n",
       "      <td>-0.094578</td>\n",
       "      <td>-0.217718</td>\n",
       "      <td>0.011290</td>\n",
       "      <td>-0.170510</td>\n",
       "      <td>-0.041129</td>\n",
       "      <td>0.181270</td>\n",
       "      <td>0.032624</td>\n",
       "      <td>-0.005687</td>\n",
       "      <td>-0.001868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.733896</td>\n",
       "      <td>-1.075174</td>\n",
       "      <td>-0.551748</td>\n",
       "      <td>0.912083</td>\n",
       "      <td>-0.177086</td>\n",
       "      <td>0.541452</td>\n",
       "      <td>-0.668166</td>\n",
       "      <td>0.097374</td>\n",
       "      <td>0.024066</td>\n",
       "      <td>0.454275</td>\n",
       "      <td>...</td>\n",
       "      <td>0.311067</td>\n",
       "      <td>-0.060309</td>\n",
       "      <td>-0.074291</td>\n",
       "      <td>0.102762</td>\n",
       "      <td>0.171158</td>\n",
       "      <td>0.004735</td>\n",
       "      <td>0.049569</td>\n",
       "      <td>0.047026</td>\n",
       "      <td>0.003146</td>\n",
       "      <td>0.000751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.122953</td>\n",
       "      <td>10.275589</td>\n",
       "      <td>-3.232790</td>\n",
       "      <td>0.152547</td>\n",
       "      <td>-2.960878</td>\n",
       "      <td>3.053422</td>\n",
       "      <td>1.429911</td>\n",
       "      <td>1.059565</td>\n",
       "      <td>-1.405440</td>\n",
       "      <td>-1.116975</td>\n",
       "      <td>...</td>\n",
       "      <td>0.434193</td>\n",
       "      <td>-0.203266</td>\n",
       "      <td>-0.124105</td>\n",
       "      <td>0.153430</td>\n",
       "      <td>0.077496</td>\n",
       "      <td>-0.275225</td>\n",
       "      <td>0.183462</td>\n",
       "      <td>0.042484</td>\n",
       "      <td>-0.069295</td>\n",
       "      <td>-0.019937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.935302</td>\n",
       "      <td>-1.948072</td>\n",
       "      <td>1.389767</td>\n",
       "      <td>2.940639</td>\n",
       "      <td>0.546747</td>\n",
       "      <td>-1.226495</td>\n",
       "      <td>-0.936213</td>\n",
       "      <td>0.636376</td>\n",
       "      <td>-0.263805</td>\n",
       "      <td>0.377704</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.116545</td>\n",
       "      <td>-0.017650</td>\n",
       "      <td>0.139454</td>\n",
       "      <td>-0.005332</td>\n",
       "      <td>0.003062</td>\n",
       "      <td>0.039254</td>\n",
       "      <td>0.032168</td>\n",
       "      <td>-0.034786</td>\n",
       "      <td>0.005038</td>\n",
       "      <td>0.021214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        PC1        PC2       PC3       PC4       PC5       PC6       PC7  \\\n",
       "0  9.192837   1.948583 -1.123166  3.633731 -1.195110  1.411424  2.159370   \n",
       "1  2.387802  -3.768172 -0.529293  1.118264  0.621775  0.028656  0.013358   \n",
       "2  5.733896  -1.075174 -0.551748  0.912083 -0.177086  0.541452 -0.668166   \n",
       "3  7.122953  10.275589 -3.232790  0.152547 -2.960878  3.053422  1.429911   \n",
       "4  3.935302  -1.948072  1.389767  2.940639  0.546747 -1.226495 -0.936213   \n",
       "\n",
       "        PC8       PC9      PC10    ...         PC21      PC22      PC23  \\\n",
       "0 -0.398407 -0.157118 -0.877402    ...     0.096515  0.068850  0.084519   \n",
       "1  0.240988 -0.711905  1.106995    ...    -0.077327 -0.094578 -0.217718   \n",
       "2  0.097374  0.024066  0.454275    ...     0.311067 -0.060309 -0.074291   \n",
       "3  1.059565 -1.405440 -1.116975    ...     0.434193 -0.203266 -0.124105   \n",
       "4  0.636376 -0.263805  0.377704    ...    -0.116545 -0.017650  0.139454   \n",
       "\n",
       "       PC24      PC25      PC26      PC27      PC28      PC29      PC30  \n",
       "0 -0.175256 -0.151020 -0.201503 -0.252585 -0.033914  0.045648 -0.047169  \n",
       "1  0.011290 -0.170510 -0.041129  0.181270  0.032624 -0.005687 -0.001868  \n",
       "2  0.102762  0.171158  0.004735  0.049569  0.047026  0.003146  0.000751  \n",
       "3  0.153430  0.077496 -0.275225  0.183462  0.042484 -0.069295 -0.019937  \n",
       "4 -0.005332  0.003062  0.039254  0.032168 -0.034786  0.005038  0.021214  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_cancer_pca = pd.DataFrame(X_pca, columns=['PC'+str(i) for i in range(1,X_pca.shape[1]+1)])\n",
    "breast_cancer_pca.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vježba 4.2.** Izračunajte i prikažite poptunu matricu korelacije za glavne komponente u projiciranom datasetu. Postoji li neka razlika u usporedbi s korelacijskom matricom originalnog dataseta?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAHkCAYAAABBrjeJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmQolSdI8cUAgSZIcEEiSJAcEkiQJ+P8B15GQXPOpsGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb917eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm as cm\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "\n",
    "X_corr = breast_cancer_pca.corr()\n",
    "cmap = cm.get_cmap('RdBu', 30)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8, 8))\n",
    "iax = ax.imshow(X_corr, interpolation=\"nearest\", cmap=cmap)\n",
    "plt.grid(False)\n",
    "ax.set(title='Korelacija znacajki')\n",
    "ax.set_xticks(range(X_corr.shape[1]))\n",
    "ax.set_xticklabels(breast_cancer_pca.columns.values[:-1], rotation=90)\n",
    "ax.set_yticks(range(X_corr.shape[1]))\n",
    "ax.set_yticklabels(breast_cancer_pca.columns.values[:-1])\n",
    "divider = make_axes_locatable(ax)\n",
    "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
    "cbar = plt.colorbar(iax,cax)\n",
    "\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Možemo prikazati varijancu objašnjenu sa svakom od glavnih komponenti, kao i kumulativnu varijancu objašnjenu uključivanjem svake naredne glavne komponente u našem projiciranom datasetu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb0AAAFACAYAAAAoIqKDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf321a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "explained_variance_ratio = pca.explained_variance_ratio_\n",
    "explained_variance_ratio_cummulative = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "plt.plot(range(1,X_pca.shape[1]+1), explained_variance_ratio, '-o', label='individualne komponente', c='b')\n",
    "plt.plot(range(1,X_pca.shape[1]+1), explained_variance_ratio_cummulative, '-s', label='kumulativno', c='r')\n",
    "\n",
    "plt.ylabel('udio objasnjene varijance')\n",
    "plt.xlabel('glavna komponenta')\n",
    "plt.xlim(0.75,X_pca.shape[1]+1.25)\n",
    "plt.ylim(0,1.05)\n",
    "plt.xticks(range(1,X_pca.shape[1]+1))\n",
    "plt.legend(loc='center right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primjećujemo da prve dvije komponente zajedno objašnjavaju više od 60% varijance u podacima, a prvih deset više od 95%. S obzirom da nam originalni podaci imaju 30 značajki ovo je značajna redukcija. Pogledajmo kako naši podaci izgledaju kada se projiciraju na prve dvije glavne komponente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUwAAAFACAYAAADXrEUkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf8699e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5))\n",
    "\n",
    "plt.scatter(breast_cancer_pca.loc[breast_cancer.loc[:,'target']==0,'PC1'], \n",
    "            breast_cancer_pca.loc[breast_cancer.loc[:,'target']==0,'PC2'], \n",
    "            color='red', marker='.', label='malignant')\n",
    "plt.scatter(breast_cancer_pca.loc[breast_cancer.loc[:,'target']==1,'PC1'], \n",
    "            breast_cancer_pca.loc[breast_cancer.loc[:,'target']==1,'PC2'], \n",
    "            color='gray', marker='.', label='benign')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"prva glavna komponenta\")\n",
    "plt.ylabel(\"druga glavna komponenta\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Vježba 4.3.** Konstruirajte dva dataseta - jedan koji sadrži samo dva jako korelirana atributa i jedan koji koristi samo dva manje korelirana atributa (iskoristite korelacijsku matricu izračunatu ranije kao pomoć). Izračunajte PCA za oba dataseta i usporedite varijance objašnjene svakom od komponenti. Koji od navedena dva dataseta ima više varijance objašnjene s prvom glavnom komponentom i zašto?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Korelacija izmedju \"mean radius\" i \"mean perimeter\" = 0.998\n",
      "Korelacija izmedju \"mean radius\" i \"worst concavity\" = 0.527\n"
     ]
    }
   ],
   "source": [
    "breast_cancer_correlated = breast_cancer[['mean radius','mean perimeter']]\n",
    "breast_cancer_uncorrelated = breast_cancer[['mean radius','worst concavity']]\n",
    "\n",
    "R1 = breast_cancer_correlated.corr().loc['mean radius','mean perimeter']\n",
    "R2 = breast_cancer_uncorrelated.corr().loc['mean radius','worst concavity']\n",
    "\n",
    "print('Korelacija izmedju \"mean radius\" i \"mean perimeter\" = ' + '{0:.3f}'.format(R1))\n",
    "print('Korelacija izmedju \"mean radius\" i \"worst concavity\" = ' + '{0:.3f}'.format(R2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objasnjena varijanca (korelirani atributi), PC1 = 0.9989276407469055, PC2 = 0.0010723592530945717\n",
      "Objasnjena varijanca (nekorelirani atributi), PC1 = 0.7634557309192727, PC2 = 0.23654426908072745\n"
     ]
    }
   ],
   "source": [
    "X_scaled_correlated = StandardScaler().fit(breast_cancer_correlated).transform(breast_cancer_correlated)\n",
    "variance_correlated = PCA().fit(X_scaled_correlated).explained_variance_ratio_\n",
    "\n",
    "X_scaled_uncorrelated = StandardScaler().fit(breast_cancer_uncorrelated).transform(breast_cancer_uncorrelated)\n",
    "variance_uncorrelated = PCA().fit(X_scaled_uncorrelated).explained_variance_ratio_\n",
    "\n",
    "print('Objasnjena varijanca (korelirani atributi), PC1 = ' + str(variance_correlated[0]) + ', PC2 = ' + str(variance_correlated[1]))\n",
    "print('Objasnjena varijanca (nekorelirani atributi), PC1 = ' + str(variance_uncorrelated[0]) + ', PC2 = ' + str(variance_uncorrelated[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kod koreliranih atributa veći udio varijance je objašnjen s prvom glavnom komponentnom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.3. Prediktivno modeliranje nakon PCA projekcije\n",
    "----------------------------------------------\n",
    "\n",
    "Iako PCA analiza može biti korisna i u drugim primjenama (na primjer, u grupiranju podataka) ovdje nas najviše zanima može li nam pomoći u predikciji. Pošto PCA efektivno dekorelira značajke, trebala bi pomoći kod metoda gdje je prisutnost velikog broja koreliranih značajki problematično. Također, predikcija na smanjenom datasetu je računalno efikasnija. U sljedećem primjeru ćemo kao tehniku klasifikacije koristiti logističku regresiju implementiranu u [`sklearn.linear_model.LogisticRegression`](http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html). Točnost naših predikcija ćemo evaluirati s unakrsnom validacijom (engl. cross-validation) implementiranom u [`sklearn.grid_search.GridSearchCV`](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html#sklearn.grid_search.GridSearchCV) za različiti broj glavnih komponenti. Na kraju ćemo najbolji model evaluirati na zasebnom test skupu.\n",
    "\n",
    "Za pojednostavljenje predikcijskog workflowa koristit ćemo [`sklearn.pipeline.Pipeline`](http://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html). Osim što poboljšavaju čitljivost programskog koda, pipelineovi osiguravaju izvršavanje svih koraka za pretprocesiranje i na testnom skupu. Za sličan primjer pogledajte [\"Pipelining: chaining a PCA and a logistic regression\"](http://scikit-learn.org/stable/auto_examples/plot_digits_pipe.html), \n",
    "\n",
    "Napomena: Pošto je broj malignih i benignih primjera u našem skupu podataka otprilike podjednak (provjerite s  `breast_cancer['target'].value_counts()`) možemo korisiti točnost ([`sklearn.metrics.accuracy_score`](http://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html#sklearn.metrics.accuracy_score)) kao evaluacijsku mjeru za naš klasifikator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "breast_cancer = pd.read_csv('../assets/datasets/csv/breast_cancer.csv',index_col=0)\n",
    "breast_cancer_train, breast_cancer_test = train_test_split(breast_cancer, test_size=0.30, random_state=39)\n",
    "\n",
    "X_train = breast_cancer_train.drop('target',axis=1)\n",
    "y_train = breast_cancer_train['target']\n",
    "\n",
    "X_test = breast_cancer_test.drop('target',axis=1)\n",
    "y_test = breast_cancer_test['target']\n",
    "\n",
    "pipe = Pipeline(steps=[('scaler', StandardScaler()),\n",
    "                       ('pca', PCA()),\n",
    "                       ('logistic',LogisticRegression())])\n",
    "\n",
    "components_to_evaluate = 10\n",
    "\n",
    "estimator = GridSearchCV(pipe,\n",
    "                         dict(pca__n_components=range(1,components_to_evaluate)),\n",
    "                         cv=10,\n",
    "                         scoring='accuracy')\n",
    "\n",
    "model = estimator.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb0AAAFNCAYAAACUvLFdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMsOhZ2ZmmfH/AaqMsftf28xPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc7a7b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "\n",
    "results_selected = zip(model.cv_results_['params'],model.cv_results_['mean_test_score'],model.cv_results_['std_test_score'])\n",
    "for params, mean_scores, std_scores in results_selected:\n",
    "    plt.errorbar(params['pca__n_components'], mean_scores, yerr=std_scores, fmt='ok')\n",
    "\n",
    "plt.hlines(accuracy_score(y_test, model.predict(X_test)), 0, components_to_evaluate, \n",
    "           color='red', linestyle='--', \n",
    "           label='tocnost na test setu (' + str(model.best_params_['pca__n_components']) + ' komponenti)')\n",
    "\n",
    "plt.ylabel('tocnost')\n",
    "plt.xlabel('broj glavnih komponenti')\n",
    "plt.title('Unakrsna validacija s PCA i logistickom regresijom')\n",
    "plt.xlim(0.5,components_to_evaluate-0.5)\n",
    "plt.ylim(0.6,1.05)\n",
    "plt.xticks(range(1,components_to_evaluate))\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Literatura\n",
    "-----------\n",
    "\n",
    "<a name=\"scikit-decomposition\">[1]</a> *\"scikit-learn documentation - 2.5. Decomposing signals in components\"*, [link](http://scikit-learn.org/stable/modules/decomposition.html)\n",
    "\n",
    "<a name=\"islr-ch10\">[2]</a> *\"ISLR-Python - Chapter 10\"*, [link](https://github.com/JWarmenhoven/ISLR-python/blob/master/Notebooks/Chapter%2010.ipynb)\n",
    "\n",
    "<a name=\"pyml-ch5\">[3]</a> *\"Python Machine Learning Code examples - Chapter 5\"*, [link](https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)\n",
    "\n",
    "<a name=\"sklearn_pycon2015\">[4]</a> *\"PyCon 2015 Scikit-learn Tutorial - 04.1-Dimensionality-PCA\"*, [link](https://github.com/jakevdp/sklearn_pycon2015/blob/master/notebooks/04.1-Dimensionality-PCA.ipynb)\n",
    "\n",
    "<a name=\"intro-ml-py\">[5]</a> *\"Introduction to Machine Learning with Python - Chapter 3 Unsupervised Learning and Preprocessing\"* \n",
    "\n",
    "<a name=\"pymf\">[6]</a> *\"PyMF - Python Matrix Factorization Module\"*, [link](https://github.com/cthurau/pymf)\n",
    "\n",
    "<a name=\"pyml-ch4\">[7]</a> *\"Python Machine Learning Code examples - Chapter 4\"*, [link](https://github.com/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}