notebook.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# No longer needed\n",
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize,\n",
    "                          cmap=plt.cm.Blues):\n",
    "\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "    \n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nG, nS, nQ= 30000, 15000, 15000\n",
    "x_tr1= pd.concat([pd.read_csv('{}[0-15].csv'.format(i))[:j] for i,j in {'GALAXY':nG,'STAR':nS,'QSO':nQ}.items()])\n",
    "x_tr= x_tr1.sample(frac=1).reset_index(drop=True)\n",
    "\n",
    "frames1= [pd.read_csv('{}[0-15].csv'.format(i))[j:] for i,j in {'GALAXY':nG,'STAR':nS,'QSO':nQ}.items()]\n",
    "frames2= [pd.read_csv('{}[15-24].csv'.format(i)) for i in ['GALAXY','STAR','QSO']]\n",
    "    \n",
    "x_te1= pd.concat([frames2[0],frames2[1],frames2[2],frames1[0],frames1[1],frames1[2]])\n",
    "x_te= x_te1.sample(frac=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tr= []\n",
    "for i in range(len(x_tr)): \n",
    "    c= x_tr['class'][i] \n",
    "    y_tr.append([c=='STAR',c=='GALAXY',c=='QSO']) \n",
    "y_tr= np.array(y_tr).astype(int) \n",
    " \n",
    "x_tr= np.array(x_tr.drop(columns=['class'])) \n",
    " \n",
    "y_te= [] \n",
    "for i in range(len(x_te)): \n",
    "    c= x_te['class'][i] \n",
    "    y_te.append([c=='STAR',c=='GALAXY',c=='QSO']) \n",
    "y_te= np.array(y_te).astype(int) \n",
    " \n",
    "x_te= np.array(x_te.drop(columns=['class']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(64, activation='relu', input_shape=(x_tr.shape[1],)))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(0.4))\n",
    "model.add(Dense(3, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(x_tr,y_tr,epochs=20,batch_size=300,verbose=1,shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.evaluate(x_te,y_te)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(x_te)\n",
    "y_true = y_te.argmax(axis=1)\n",
    "y_pred = y_pred.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = pd.DataFrame(confusion_matrix(y_true, y_pred, normalize='pred'),\n",
    "                      index=['STAR','GALAXY','QSO'], columns=['STAR','GALAXY','QSO']))\n",
    "sns.heatmap(cm, annot=True, cmap=plt.cm.Blues)\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  }
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