Galaxy classification (KNeighbors) + Validation Curve.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Headers and libraries that we're gonna use\n",
    "\n",
    "%matplotlib inline\n",
    "#print(__doc__)\n",
    "\n",
    "import numpy as np # We need NumPy in order to work with arrays which we'll use to order our data\n",
    "import matplotlib.pyplot as plt # Matplotlib will be used for all the plots that we will make\n",
    "from sklearn.utils.validation import check_random_state #to put a global seed for random number generator\n",
    "from tutorial import plot_clf\n",
    "\n",
    "#Regressor algorithms\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "#Classification\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.gaussian_process import GaussianProcessClassifier\n",
    "from sklearn.metrics import zero_one_loss\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9741\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "photo_names = []\n",
    "photos = []\n",
    "tags = []\n",
    "\n",
    "with open('training_solutions_rev1.csv', 'r') as csvfile:\n",
    "    reader = csv.reader(csvfile)\n",
    "    i = 0\n",
    "    restrictor = 10000\n",
    "    for row in reader:\n",
    "        if (restrictor > 0):\n",
    "            if i >1:\n",
    "                a = float(row[1])\n",
    "                b = float(row[2])\n",
    "                c = float(row[3])\n",
    "                suma = a+b+c\n",
    "                \n",
    "                a = float(a)/float(suma)\n",
    "                b = float(b)/float(suma)\n",
    "                c = float(c)/float(suma)\n",
    "                \n",
    "                maxim = np.max([a, b, c])\n",
    "                \n",
    "                if (maxim >= 0.5):\n",
    "                    if (maxim == a):\n",
    "                        photo_names.append(row[0])\n",
    "                        tag = \"smooth\"\n",
    "                        tags.append([tag, row[0]])\n",
    "                    elif (maxim == b):\n",
    "                        photo_names.append(row[0])\n",
    "                        tag = \"features\"\n",
    "                        tags.append([tag, row[0]])\n",
    "                    else:\n",
    "                        tag = \"point\"\n",
    "                \n",
    "            i += 1\n",
    "            restrictor -= 1\n",
    "        \n",
    "\n",
    "print(len(photo_names))\n",
    "       \n",
    "import scipy.misc as scp_misc\n",
    "restrictor = 10000\n",
    "for photo in photo_names:\n",
    "    if (restrictor > 0):\n",
    "        img = scp_misc.imread(\"images_training_rev1/\"+photo+\".jpg\", mode=\"L\")\n",
    "        img = scp_misc.imresize(img, 25)\n",
    "        photos.append(img)\n",
    "        restrictor -= 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = 0\n",
    "while (a < len(photos)):\n",
    "    photos[a] = np.reshape(photos[a], -1)\n",
    "    a += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's divide the samples into training and test samples. *Comment*: maybe we should massage the y sample.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = photos\n",
    "y = tags\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)\n",
    "\n",
    "clf = KNeighborsClassifier()\n",
    "\n",
    "y_test_id = []\n",
    "y_test_v = []\n",
    "y_train_id = []\n",
    "y_train_v = []\n",
    " \n",
    "for value in range(len(y_test)):\n",
    "    y_test_id.append(y_test[value][1])\n",
    "    y_test_v.append(y_test[value][0])\n",
    "for value in range(len(y_train)):\n",
    "    y_train_id.append(y_train[value][1])\n",
    "    y_train_v.append(y_train[value][0])\n",
    "\n",
    "y_test_predict = dict()\n",
    "clf.fit(X_train, y_train_v)\n",
    "y_test_predict = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9741\n"
     ]
    }
   ],
   "source": [
    "y2 = []\n",
    "print len(y)\n",
    "for value in range(len(y)):\n",
    "    y2.append(y[value][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
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      "(['smooth', '164588'], 'features')\n",
      "(['smooth', '107461'], 'smooth')\n",
      "(['smooth', '122068'], 'features')\n",
      "(['features', '117100'], 'features')\n",
      "(['features', '157222'], 'features')\n",
      "(['smooth', '220833'], 'features')\n",
      "(['smooth', '239928'], 'smooth')\n",
      "(['smooth', '240889'], 'smooth')\n",
      "(['smooth', '170056'], 'smooth')\n",
      "(['features', '160161'], 'features')\n",
      "(['features', '153443'], 'features')\n",
      "(['smooth', '111274'], 'smooth')\n",
      "(['smooth', '191276'], 'smooth')\n",
      "(['features', '212749'], 'features')\n",
      "(['smooth', '224067'], 'features')\n",
      "(['features', '133824'], 'features')\n",
      "(['smooth', '141282'], 'smooth')\n",
      "(['features', '100571'], 'smooth')\n",
      "(['features', '115299'], 'features')\n",
      "(['features', '210995'], 'features')\n",
      "(['smooth', '193234'], 'smooth')\n",
      "(['smooth', '118074'], 'smooth')\n",
      "(['smooth', '120223'], 'features')\n",
      "(['features', '133355'], 'smooth')\n",
      "(['features', '230559'], 'features')\n",
      "(['features', '204467'], 'smooth')\n",
      "(['features', '193854'], 'smooth')\n",
      "(['features', '168724'], 'smooth')\n",
      "(['features', '123770'], 'smooth')\n",
      "(['smooth', '117627'], 'smooth')\n",
      "(['features', '135336'], 'features')\n",
      "(['smooth', '125576'], 'smooth')\n",
      "(['features', '110389'], 'features')\n",
      "(['smooth', '195336'], 'smooth')\n",
      "(['features', '209401'], 'features')\n",
      "(['features', '183087'], 'features')\n",
      "(['smooth', '243369'], 'features')\n",
      "(['features', '110596'], 'features')\n",
      "(['smooth', '181229'], 'smooth')\n",
      "(['features', '160851'], 'features')\n",
      "(['features', '200831'], 'features')\n",
      "(['features', '196781'], 'features')\n",
      "(['smooth', '158387'], 'smooth')\n",
      "(['smooth', '176783'], 'smooth')\n",
      "(['smooth', '145922'], 'smooth')\n",
      "(['smooth', '206258'], 'smooth')\n",
      "(['smooth', '179122'], 'features')\n",
      "(['smooth', '226783'], 'features')\n",
      "(['features', '221778'], 'features')\n",
      "(['smooth', '217169'], 'smooth')\n",
      "(['features', '176405'], 'features')\n",
      "(['features', '172616'], 'features')\n",
      "(['smooth', '226099'], 'features')\n",
      "(['features', '176605'], 'features')\n",
      "(['features', '194835'], 'features')\n",
      "(['smooth', '131054'], 'smooth')\n",
      "(['smooth', '113965'], 'smooth')\n",
      "(['smooth', '209275'], 'smooth')\n",
      "(['smooth', '114587'], 'features')\n",
      "(['features', '162752'], 'smooth')\n",
      "(['smooth', '126475'], 'smooth')\n",
      "(['smooth', '147081'], 'smooth')\n",
      "(['features', '147133'], 'features')\n",
      "(['smooth', '243426'], 'smooth')\n",
      "(['features', '139432'], 'features')\n",
      "(['features', '118777'], 'features')\n",
      "(['smooth', '186171'], 'smooth')\n",
      "(['features', '219618'], 'features')\n",
      "(['smooth', '189726'], 'smooth')\n",
      "(['features', '143341'], 'features')\n",
      "(['smooth', '123387'], 'smooth')\n",
      "(['smooth', '233948'], 'features')\n",
      "(['features', '238372'], 'features')\n",
      "(['features', '186540'], 'features')\n",
      "(['smooth', '150683'], 'features')\n",
      "(['smooth', '145198'], 'features')\n",
      "(['smooth', '220162'], 'smooth')\n",
      "(['smooth', '157525'], 'smooth')\n",
      "(['smooth', '132280'], 'smooth')\n",
      "(['smooth', '100367'], 'features')\n",
      "(['smooth', '139303'], 'smooth')\n",
      "(['features', '222931'], 'smooth')\n",
      "(['smooth', '123582'], 'features')\n",
      "(['smooth', '215444'], 'smooth')\n",
      "(['features', '193524'], 'features')\n",
      "(['features', '129939'], 'features')\n",
      "(['smooth', '214991'], 'smooth')\n",
      "(['smooth', '113444'], 'smooth')\n",
      "(['smooth', '148072'], 'smooth')\n",
      "(['smooth', '199960'], 'smooth')\n",
      "(['smooth', '207798'], 'features')\n",
      "(['smooth', '183772'], 'features')\n",
      "(['smooth', '185050'], 'features')\n",
      "(['smooth', '184232'], 'features')\n",
      "(['smooth', '220966'], 'smooth')\n",
      "(['features', '158950'], 'features')\n",
      "(['smooth', '177517'], 'features')\n",
      "(['features', '120042'], 'features')\n",
      "(['smooth', '128978'], 'smooth')\n",
      "(['features', '193111'], 'features')\n",
      "(['smooth', '142257'], 'smooth')\n"
     ]
    }
   ],
   "source": [
    "for hoho in range(len(y_test_id)):\n",
    "    print([y_test_predict[hoho], y_test_id[hoho]], y_test_v[hoho])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.681034482759\n"
     ]
    }
   ],
   "source": [
    "LEN = len(y_test_id)\n",
    "SCORE = 0.0\n",
    "for elem in range(LEN):\n",
    "    if y_test_predict[elem] == y_test_v[elem]:\n",
    "        SCORE = SCORE + 1\n",
    "SCORE = float(SCORE)\n",
    "print (SCORE/LEN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.4  0.4  0.2  0.8  0.8  0.   1.   0.6  0.6  0. ]\n",
      "['features' 'features' 'features' 'smooth' 'smooth' 'features' 'smooth'\n",
      " 'features' 'smooth' 'features']\n",
      "[ 0.4  0.4  0.2 ...,  0.2  0.   0.2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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OHAJOn7V+Wtf2WPsAUFW7gF096gEgyVRVTfY9zkrimJ/4WhsvOObF1Geq5zZgXZIzkhwH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\njMEvSY35P83NLeWbNt4NAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f46ac4dc510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "d = clf.predict_proba(X_train)[:, 1]\n",
    "\n",
    "y_train_v = np.array(y_train_v)\n",
    "\n",
    "print(d[0:10])\n",
    "print(y_train_v[0:10])\n",
    "print(d[y_train_v == \"features\"])\n",
    "\n",
    "plt.hist(d[y_train_v == \"features\"], bins=50, normed=True, color=\"b\", alpha=0.5)\n",
    "plt.hist(d[y_train_v == \"smooth\"], bins=50, normed=True, color=\"r\", alpha=0.5)\n",
    "#plt.hist(d[y_train_v == \"point\"], bins=50, normed=True, color=\"y\", alpha=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6c75da93d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g/Pls7Ivq1d1777GzLlwIXHutEQRLTuaDCw83RlLl5WxQy5YB//oXO3hdHYWp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\nnES4wXAcx3ES8f+bKyfPYJtihwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6c761573d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import validation_curve\n",
    "\n",
    "# Evaluate parameter range in CV\n",
    "param_range = range(2, 200)\n",
    "param_name = \"max_leaf_nodes\"\n",
    "\n",
    "train_scores, test_scores = validation_curve(\n",
    "    RandomForestClassifier(), X, y2, \n",
    "    param_name=param_name, \n",
    "    param_range=param_range, cv=5, n_jobs=-1)\n",
    "\n",
    "train_scores_mean = np.mean(train_scores, axis=1)\n",
    "train_scores_std = np.std(train_scores, axis=1)\n",
    "test_scores_mean = np.mean(test_scores, axis=1)\n",
    "test_scores_std = np.std(test_scores, axis=1)\n",
    "\n",
    "# Plot parameter VS estimated error\n",
    "plt.xlabel(param_name)\n",
    "plt.ylabel(\"score\")\n",
    "plt.xlim(min(param_range), max(param_range))\n",
    "plt.plot(param_range, 1. - train_scores_mean, color=\"red\", label=\"Training error\")\n",
    "plt.fill_between(param_range, \n",
    "                 1. - train_scores_mean + train_scores_std,\n",
    "                 1. - train_scores_mean - train_scores_std,\n",
    "                 alpha=0.2, color=\"red\")\n",
    "plt.plot(param_range, 1. - test_scores_mean, color=\"blue\", label=\"CV error\")\n",
    "plt.fill_between(param_range, \n",
    "                 1. - test_scores_mean + test_scores_std,\n",
    "                 1. - test_scores_mean - test_scores_std, \n",
    "                 alpha=0.2, color=\"blue\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}