influencers.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from sklearn.metrics import roc_curve\n",
    "import callback"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load Training Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"./train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features\n",
    "X = train_data.iloc[:,1:].as_matrix()\n",
    "m,_ = X.shape\n",
    "print(\"X: \", X.shape)\n",
    "\n",
    "# Labels\n",
    "Y = np.reshape(train_data[\"Choice\"].as_matrix(), (-1, 1))\n",
    "print(\"Y: \",Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data into training and validation set\n",
    "80% of the data will be the training set and rest is going to be our validation set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_n = int(.80 * m)   \n",
    "\n",
    "train_x = X[:train_n, :]\n",
    "train_y = Y[:train_n, :]\n",
    "\n",
    "val_x = X[train_n:, :]\n",
    "val_y = Y[train_n:, :]\n",
    "\n",
    "print(\"Training set size: \", train_x.shape)\n",
    "print(\"Validation set size: \", val_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rec_model():\n",
    "    model = Sequential()\n",
    "    \n",
    "    model.add(Dense(50, activation='relu', input_dim=22, kernel_initializer='uniform'))\n",
    "    model.add(Dropout(0.5))\n",
    "    model.add(Dense(10, kernel_initializer='uniform'))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(Dense(1, activation='sigmoid', kernel_initializer='uniform'))\n",
    "    \n",
    "    model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "roc_curve_area = callback.roc_curve_area()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = rec_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = rec_model()\n",
    "model.fit(train_x, train_y,\n",
    "          epochs=20,\n",
    "          batch_size=64,\n",
    "          validation_data=(val_x, val_y),\n",
    "          callbacks=[roc_curve_area])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_csv(\"./test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = test_data.as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission = pd.read_csv('./sample_predictions.csv')\n",
    "sample_submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission[\"Choice\"] = y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission.to_csv('./submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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