test_wgan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test WGAN\n",
    "Adapted from https://colab.research.google.com/github/timsainb/tensorflow2-generative-models/blob/master/3.0-WGAN-GP-fashion-mnist.ipynb\n",
    "\n",
    "4/23/2020\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make sure we have tqdm\n",
    "# import sys\n",
    "# !conda install --yes --prefix {sys.prefix} tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# from tqdm.autonotebook import tqdm\n",
    "from IPython import display\n",
    "%matplotlib inline\n",
    "\n",
    "from DataWGAN import DataWGAN\n",
    "from DataLoader import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.1.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_distribution(X, y, title=''):\n",
    "    \"\"\"\n",
    "    Show strip plots for 1D data of multiple classes to allow\n",
    "    comparison of distributions.\n",
    "    \"\"\"\n",
    "    if len(X.shape) > 1 and not(len(X.shape) == 2 and X.shape[1]==1):\n",
    "        print('** Just 1D data allowed')\n",
    "        return -1\n",
    "    \n",
    "    X = np.reshape(X, (-1, 1))\n",
    "    y = np.reshape(y, (-1, 1))    \n",
    "    D = np.hstack((X, y))\n",
    "\n",
    "    classes = np.unique(y)\n",
    "    C = len(classes)\n",
    "        \n",
    "    plt.figure(figsize=(10, 1))\n",
    "    fmt = ['bo', 'rv', 'gs', 'm.']\n",
    "    \n",
    "    for i, c in enumerate(classes):\n",
    "        mask = D[:, 1] == c\n",
    "        D_c = D[mask, 0]\n",
    "        plt.plot( D_c, (C-i-1)*np.ones(D_c.shape[0]), fmt[i], alpha=0.2, label='class ' + str(int(c)))\n",
    "        plt.plot([-0.02, 1.02], [i, i], 'k-', alpha=0.3)\n",
    "\n",
    "    plt.legend(loc='center right')\n",
    "    plt.ylim(-1, C)\n",
    "    plt.xlim(-0.1, 1.22)\n",
    "    plt.yticks(np.arange(C), classes)\n",
    "    plt.axis('off')\n",
    "    plt.title(title)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>education</th>\n",
       "      <th>education-num</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>race</th>\n",
       "      <th>sex</th>\n",
       "      <th>capital-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours-per-week</th>\n",
       "      <th>native-country</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age          workclass   education  education-num       marital-status  \\\n",
       "0   39          State-gov   Bachelors             13        Never-married   \n",
       "1   50   Self-emp-not-inc   Bachelors             13   Married-civ-spouse   \n",
       "2   38            Private     HS-grad              9             Divorced   \n",
       "3   53            Private        11th              7   Married-civ-spouse   \n",
       "4   28            Private   Bachelors             13   Married-civ-spouse   \n",
       "\n",
       "           occupation    relationship    race      sex  capital-gain  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male          2174   \n",
       "1     Exec-managerial         Husband   White     Male             0   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male             0   \n",
       "3   Handlers-cleaners         Husband   Black     Male             0   \n",
       "4      Prof-specialty            Wife   Black   Female             0   \n",
       "\n",
       "   capital-loss  hours-per-week  native-country   label  \n",
       "0             0              40   United-States   <=50K  \n",
       "1             0              13   United-States   <=50K  \n",
       "2             0              40   United-States   <=50K  \n",
       "3             0              40   United-States   <=50K  \n",
       "4             0              40            Cuba   <=50K  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = DataLoader(\"adult.data\")\n",
    "data.df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded data with shape (32561, 90)\n"
     ]
    }
   ],
   "source": [
    "numeric_data, labels, protected = data.get_numeric_data()\n",
    "\n",
    "numeric_data = numeric_data.astype('float32')\n",
    "labels = labels.astype('float32')\n",
    "protected = protected.astype('float32')\n",
    "\n",
    "print('Loaded data with shape', numeric_data.shape)\n",
    "\n",
    "data_train, data_test, target_train, target_test = train_test_split(numeric_data, labels, test_size=0.2, random_state=42)\n",
    "\n",
    "TRAIN_BUF=data_train.shape[0]\n",
    "BATCH_SIZE=128\n",
    "TEST_BUF=data_test.shape[0]\n",
    "DIMS = data_train.shape[1:]\n",
    "N_TRAIN_BATCHES = TRAIN_BUF // BATCH_SIZE\n",
    "N_TEST_BATCHES = TEST_BUF // BATCH_SIZE\n",
    "N_Z = 64\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R6R4HHiIiItI9DjxERESke/8PrPTmF/n8CKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x72 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show distribution of values for a few columns of data\n",
    "for i in range(6):\n",
    "    show_distribution(numeric_data[:, i], labels, title='Col ' + str(i+1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exampled data for plotting results\n",
    "def show_examples(model, nex=6):\n",
    "    samples = model.generate(tf.random.normal(shape=(BATCH_SIZE, N_Z)))\n",
    "\n",
    "    # Show distribution of values for a few columns of data\n",
    "    for i in range(nex):\n",
    "        # Don't have labels\n",
    "        x = samples[:, i]\n",
    "        y = np.zeros_like(x)\n",
    "        show_distribution(x, y, title='Col ' + str(i+1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a pandas dataframe to save the loss information to\n",
    "losses = pd.DataFrame(columns = ['disc_loss', 'gen_loss'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DataWGAN(DIMS, N_Z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 199 | disc_loss: -0.3441412150859833 | gen_loss: 0.5221619009971619\n"
     ]
    },
    {
     "data": {
      "image/png": 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   "source": [
    "n_epochs = 200\n",
    "for epoch in range(n_epochs):\n",
    "    # train\n",
    "    loss = []\n",
    "    train_idx = np.random.permutation(TRAIN_BUF)\n",
    "    for batch in range(N_TRAIN_BATCHES):\n",
    "        curr_idx = train_idx[batch*BATCH_SIZE:(batch + 1)*BATCH_SIZE]\n",
    "        train_x = data_train[curr_idx]\n",
    "        train_y = target_train[curr_idx]\n",
    "        model.train(train_x)\n",
    "        loss.append(model.compute_loss(train_x))\n",
    "        # print('\\n**** {:03d}/{:03d}: {:03d}/{:03d}'.format(epoch+1, N_TRAIN_BATCHES, batch+1, N_TRAIN_BATCHES))\n",
    "\n",
    "    losses.loc[len(losses)] = np.mean(loss, axis=0)\n",
    "\n",
    "    # plot results\n",
    "    display.clear_output()\n",
    "    print(\n",
    "        \"Epoch: {} | disc_loss: {} | gen_loss: {}\".format(\n",
    "            epoch, losses.disc_loss.values[-1], losses.gen_loss.values[-1]\n",
    "        )\n",
    "    )\n",
    "    plt.figure()\n",
    "    plt.plot(losses.disc_loss, label='Discriminator')\n",
    "    plt.plot(losses.gen_loss, label='Generator')\n",
    "    plt.legend()\n",
    "    plt.title('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.show()\n",
    "\n",
    "    show_examples(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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