using_autoencoder_output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_real_output = torch.load(\"real_output_encoder.pkl\")\n",
    "ae_synthetic_output = torch.load(\"synthetic_output_encoder.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_tensor(data):\n",
    "    data = [i.detach().numpy() for i in data]\n",
    "    data = np.concatenate(data, axis=0)\n",
    "    final=[]\n",
    "    for i in data:\n",
    "        final.append(i.ravel())\n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_real_output = convert_tensor(ae_real_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae_synthetic_output = convert_tensor(ae_synthetic_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_real = pca.fit_transform(ae_real_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_synthetic = pca.fit_transform(ae_synthetic_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_scatter(data):\n",
    "    x = data[:,0]\n",
    "    y = data[:,1]\n",
    "    axes = plt.gca()\n",
    "    axes.set_xlim([-0.2,0.3])\n",
    "    axes.set_ylim([-0.2,0.3])\n",
    "    plt.scatter(x,y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(reduced_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scatter(reduced_synthetic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}