feat(gan): Changes to example

example.ipynb

@@ -22,11 +22,14 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import sklearn.cluster as cluster\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "from models.gan import model\n",
     "importlib.reload(model)\n",
     "\n",
     "from models.gan.model import GAN\n",
+    "from preprocessing.credit_fraud import *\n",
+    "\n",
     "model = GAN"
    ],
    "metadata": {
@@ -35,8 +38,32 @@
      "name": "#%%\n"
     }
    },
-   "execution_count": 1,
-   "outputs": []
+   "execution_count": 13,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Bad key \"text.kerning_factor\" on line 4 in\n",
+      "/home/fabiana/anaconda3/lib/python3.7/site-packages/matplotlib/mpl-data/stylelib/_classic_test_patch.mplstyle.\n",
+      "You probably need to get an updated matplotlibrc file from\n",
+      "https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
+      "or from the matplotlib source distribution\n"
+     ]
+    },
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'preprocessing.dataset'",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mModuleNotFoundError\u001B[0m                       Traceback (most recent call last)",
+      "\u001B[0;32m<ipython-input-13-5f27d42f34a9>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m     13\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     14\u001B[0m \u001B[0;32mfrom\u001B[0m \u001B[0mmodels\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgan\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmodel\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0mGAN\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 15\u001B[0;31m \u001B[0;32mfrom\u001B[0m \u001B[0mpreprocessing\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdataset\u001B[0m \u001B[0;32mimport\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0m",
+      "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'preprocessing.dataset'"
+     ]
+    }
+   ]
   },
   {
    "cell_type": "code",
@@ -83,6 +110,10 @@
     }
    ],
    "source": [
+    "#Before training the GAN do not forget to apply the required data transformations\n",
+    "#Such as Log transformation to some of the variables and Normalization such as MinMax.\n",
+    "\n",
+    "\n",
     "#For the purpose of this example we will only synthesize the minority class\n",
     "train_data = data.loc[ data['Class']==1 ].copy()\n",
     "\n",
@@ -115,7 +146,7 @@
     "batch_size = 128\n",
     "\n",
     "log_step = 100\n",
-    "epochs = 1000\n",
+    "epochs = 5000+1\n",
     "learning_rate = 5e-4\n",
     "models_dir = './cache'\n",
     "\n",
@@ -1217,7 +1248,71 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [],
+   "source": [
+    "#Training results visualization\n",
+    "#Adapt this code\n",
+    "model_steps = [ 0, 200, 500, 1000, 5000]\n",
+    "rows = len(model_steps)\n",
+    "columns = 5\n",
+    "\n",
+    "axarr = [[]]*len(model_steps)\n",
+    "\n",
+    "fig = plt.figure(figsize=(14,rows*3))\n",
+    "\n",
+    "for model_step_ix, model_step in enumerate( model_steps ):\n",
+    "    print(model_step)\n",
+    "        \n",
+    "    axarr[model_step_ix] = plt.subplot(rows, columns, model_step_ix*columns + 1)\n",
+    "    \n",
+    "    for group, color, marker, label in zip(real_samples.groupby('Class_1'), colors, markers, class_labels ):\n",
+    "        plt.scatter( group[1][[col1]], group[1][[col2]], \n",
+    "                         label=label, marker=marker, edgecolors=color, facecolors='none' )\n",
+    "    \n",
+    "    plt.title('Actual Fraud Data')\n",
+    "    plt.ylabel(col2) # Only add y label to left plot\n",
+    "    plt.xlabel(col1)\n",
+    "    xlims, ylims = axarr[model_step_ix].get_xlim(), axarr[model_step_ix].get_ylim()\n",
+    "        \n",
+    "    if model_step_ix == 0: \n",
+    "        legend = plt.legend()\n",
+    "        legend.get_frame().set_facecolor('white')\n",
+    "    \n",
+    "    for i, model_name in enumerate( model_names[:] ):\n",
+    "        \n",
+    "        [ model_name, with_class, type0, generator_model ] = models[model_name]\n",
+    "        \n",
+    "        generator_model.load_weights( base_dir + model_name + '_generator_model_weights_step_'+str(model_step)+'.h5')\n",
+    "\n",
+    "        ax = plt.subplot(rows, columns, model_step_ix*columns + 1 + (i+1) )\n",
+    "        \n",
+    "        if with_class:\n",
+    "            g_z = generator_model.predict([z, labels])\n",
+    "            gen_samples = pd.DataFrame(g_z, columns=data_cols+label_cols)\n",
+    "            for group, color, marker, label in zip( gen_samples.groupby('Class_1'), colors, markers, class_labels ):\n",
+    "                plt.scatter( group[1][[col1]], group[1][[col2]], \n",
+    "                                 label=label, marker=marker, edgecolors=color, facecolors='none' )\n",
+    "        else:\n",
+    "            g_z = generator_model.predict(z)\n",
+    "            gen_samples = pd.DataFrame(g_z, columns=data_cols)\n",
+    "            gen_samples.to_csv('Generated_sample.csv')\n",
+    "            plt.scatter( gen_samples[[col1]], gen_samples[[col2]], \n",
+    "                             label=class_labels[0], marker=markers[0], edgecolors=colors[0], facecolors='none' )\n",
+    "        plt.title(model_name)   \n",
+    "        plt.xlabel(data_cols[0])\n",
+    "        ax.set_xlim(xlims), ax.set_ylim(ylims)\n",
+    "\n",
+    "\n",
+    "plt.suptitle('Comparison of GAN outputs', size=16, fontweight='bold')\n",
+    "plt.tight_layout(rect=[0.075,0,1,0.95])\n",
+    "\n",
+    "# Adding text labels for traning steps\n",
+    "vpositions = np.array([ i._position.bounds[1] for i in axarr ])\n",
+    "vpositions += ((vpositions[0] - vpositions[1]) * 0.35 )\n",
+    "for model_step_ix, model_step in enumerate( model_steps ):\n",
+    "    fig.text( 0.05, vpositions[model_step_ix], 'training\\nstep\\n'+str(model_step), ha='center', va='center', size=12)\n",
+    "\n",
+    "plt.savefig('Comparison_of_GAN_outputs.png')"
+   ],
    "metadata": {
     "collapsed": false,
     "pycharm": {

preprocessing/credit_fraud.py

@@ -0,0 +1 @@
+#Include here the needed transformations

requirements.txt

@@ -1,5 +1,6 @@
 pandas==1.0.3
 numpy==1.17.4
 scikit-learn==0.22.2
+matplotlib
 
 tensorflow==2.1.0