{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Changes in the current versions:\n", "- a new concept in the training method is introduced called neighbourhood epochs\n", "- in the last versio we were training the GAN with random minority neighbourhood batches\n", "- however if the minority class were too big, there is a chance that not all minority points would have been used for the training\n", "- thus, we now use all minority neighbourhoods for training in one epoch and call it a neighbourhood epoch\n", "- we delete the previous NMB function and use the function NMB_guided to generate index wise neighbourhoods instead" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import math\n", "import random\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import random\n", "from scipy import ndarray\n", "from sklearn.neighbors import NearestNeighbors\n", "from sklearn.decomposition import PCA\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import f1_score\n", "from collections import Counter\n", "from imblearn.datasets import fetch_datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import keras\n", "from keras.layers import Dense, Dropout, Input\n", "from keras.models import Model,Sequential\n", "from tqdm import tqdm\n", "from keras.layers.advanced_activations import LeakyReLU\n", "from keras.optimizers import Adam\n", "from keras.optimizers import RMSprop\n", "from keras import losses\n", "from keras import backend as K\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.ensemble import GradientBoostingClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = fetch_datasets()['yeast_me2']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating label and feature matrices" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1484,)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels=data.target ## labels of the data\n", "labels.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1484, 8)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features=data.data ## features of the data\n", "features.shape" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "n_feat=len(features[1]) ## number of features, this is later used in the model architecture also" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dividing data into training and testing datasets" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_1=np.where(labels == 1)[0] ## minority indexes\n", "label_1=list(label_1)\n", "len(label_1)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "features_1=features[label_1] ## minority points" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "## dividing minority points into training and test sets\n", "features_1_trn=features_1[list(range(0,math.ceil(len(features_1)*2/3)))]\n", "features_1_tst=features_1[list(range(math.ceil(len(features_1)*2/3),len(features_1)))]\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_1_trn)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_1_tst)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1433" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label_0=np.where(labels == -1)[0] ## majority indexes\n", "label_0=list(label_0)\n", "len(label_0)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "features_0=features[label_0] ## majority points\n", "## division of majority into train and test sets\n", "features_0_trn=features_0[list(range(0,math.ceil(len(features_0)*2/3)))]\n", "features_0_tst=features_0[list(range(math.ceil(len(features_0)*2/3), len(features_0)))]\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "956" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_0_trn)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "477" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_0_tst)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "training_data=np.concatenate((features_1_trn,features_0_trn)) ## concatenating majority and minority for training data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "test_data=np.concatenate((features_1_tst,features_0_tst)) ## concatenating majority and minority for test data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_1_trn)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "training_labels=np.concatenate((np.zeros(len(features_1_trn))+1, np.zeros(len(features_0_trn)))) ## generating traing labels" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "test_labels=np.concatenate((np.zeros(len(features_1_tst))+1, np.zeros(len(features_0_tst)))) ## generating test labels" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(990,)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "training_labels.shape" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "34" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(features_1_trn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Until now we have obtained the data. We divided it into training and test sets. we separated obtained seperate variables for the majority and miority classes and their labels for both sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Convex space learner" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some Notes:\n", "- neb is the variable for oversampling neighbourhood size\n", "- gen is the variable for number of convex combinations of minority samples to be generated from a chosen neighbourhood \n", "- the current model architecture requires neb to be equal to gen\n", "- This is an affordable constraint as once the network it trained we can generate as many samples we want by using the network multiple times" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "neb=gen=5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are generating the 'labels' array now, an array of size 2xgen. This array will later be used as batch labels to train the discriminator (See Figure)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "labels=[]\n", "for i in range(2*gen):\n", " if i minority class data\n", " ## data_maj -> majority class data\n", " ## neb -> oversampling neighbourhood\n", " ## gen -> convex combinations generated from each neighbourhood\n", " \n", " from sklearn.neighbors import NearestNeighbors\n", " from sklearn.utils import shuffle\n", " neigh = NearestNeighbors(neb)\n", " n_feat=data_min.shape[1]\n", " neigh.fit(data_maj)\n", " bmbi=[]\n", " for i in range(len(data_min)):\n", " indices=neigh.kneighbors([data_min[i]],neb,return_distance=False)\n", " bmbi.append(indices)\n", " bmbi=np.unique(np.array(bmbi).flatten())\n", " bmbi=shuffle(bmbi)\n", " bmb=features_0_trn[np.random.randint(len(features_0_trn),size=gen)]\n", " bmb=tf.convert_to_tensor(bmb)\n", " return bmb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of the function BMB generating a borderline majority neighbourhood of size 5. The majority class is first analyzed to find the borderline samples. This is the subset of majority class samples that are in the neb-earest neighbour set of at least one minority class sample. We call this function everytime we want to input such a batch as a part of the discriminator input. The other part of the discriminator input is to be generated by the generator. (See Figure)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "BMB(features_1_trn,features_0_trn,5,gen)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def NMB_guided(data_min, neb, index):\n", " ## generate a minority neighbourhood batch for a particular minority sample\n", " ## we need this for minority data generation\n", " ## we will generate synthetic samples for each training data neighbourhood\n", " ## index -> index of the minority sample in a training data whose neighbourhood we want to obtain\n", " ## data_min -> minority class data\n", " ## neb -> oversampling neighbourhood\n", " \n", " from sklearn.neighbors import NearestNeighbors\n", " from sklearn.utils import shuffle\n", " neigh = NearestNeighbors(neb)\n", " neigh.fit(data_min)\n", " ind=index\n", " nmbi=neigh.kneighbors([data_min[ind]],neb,return_distance=False)\n", " nmbi=shuffle(nmbi)\n", " nmb=features_1_trn[nmbi]\n", " nmb=tf.convert_to_tensor(nmb[0])\n", " return (nmb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An example of the function NMB generating a minority neighbourhood of size 5. We call this function everytime we want to input such a batch to the generator network" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NMB_guided(features_1_trn, neb, 0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Network architecture" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/jpeg": 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mkT1Ul1LZ7EIc9s1b8KfG+88XfFHS/D9ppsVnZT2zm8iuY3+0W9wgcsgbcFI+Vf4c89jwGtXbqJ6K57LVXVNRt9H0i71K9Yrb2cLzykDJCqCTj8BVqvMvjZf+MrXwrcw+F9Js73SprG4GqzzuA9um0coDIuTtLHo3QVE21F2Lgk5JM5jQPix8TvGt19u8J+DrA6F9oERmuGJdRnk7jIoYgHJ2qfSvdK+e/gFqfxATRdIsrPQ7CTwi1xL5uoM485eWJwPNB+9x9z/Guy8Z/GC5sPFTeE/Aehv4h11B++wSIrc8cNjrjIzyoGRznIFuySX/AA/QhXbb/wCGPUqK8Sn+K/xH8HeXefEjwPBFpLuFe502QEw5PU4kcfQErn1r1yy8QaZf+HI9etbuNtMkg+0C4PChAMkn0xg5HbFLS1x63saNFeKJ8X/G/jO4um+FngyK9sLaXy/tuoyhVk+gLoAfbcTgjOM0ab8afE+heLbLQfil4Xi0pr5wkV3asdi5IAPLMGGSMkNx6ULVpdweib7HqXizxTp/gzwzda5rHm/ZbYDKwpuZiSAoA9yQOcCp/Dusp4h8N6frEMTQx31uk6xsclQwyAT6815F+0he+K08MTWlnplrJ4YeOFru9Zh5sc3m8KBvBxwn8B6nn01/gtqfjiXwxaw+ItGsbTQoNLjOm3ULgyT4A27gJGIyvP3VpRldSb6f5f1Ycla3n/X/AA56xRXnPwa+IWq/ETQdSvtat7OCW1u/IQWiOqldoOTuZueaufF7xvqXgDwQNY0aC1nuDdJDtukZk2sGzwrKc8etEmoq78vxBJt2Xn+B3VFcR4u8aajoHwcfxZZw2r362lvOI5UYxbpCgYYDA4+Y459K4XT/AItfEfxZpVvfeBvBtrfW8ESC+uZyUWSbaC6xKZFOAcjqxpvSTj2EtYqXRnuNFcP40+Jlr4C8K2N5rloZNYvY1EOmW75Ly4G4bscKCcbsenBrjB4++NLQ/wBqL8PLL+zceZ5Bc/adnXG3zN27H/TPPtQ2k2uwWuk+57XRXH/Dr4j6Z8RNIlns4ns761YJeWMpy8LH37qcHBwOh4Fcn4h+LfiG+8X33hj4Y+Gk1q8087bq7uZNsKMDgrjco4PGSwyQcDjNDdnYFqrnrlFeJf8AC5PGfg/VLSD4q+EItPs7uQIt7YPlY/r87hvUjcDjJwa9sR1kRXQhlYZBHQinuroWzsLRRRQAUUUUAFFFFABRRRQB5XRRRXuHnhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFOjjeWQJEjO7HAVRkmmgEnA5NehaTptr4a0dr29A8/ZmR8ZK5/hH+eTWVWqqa8zSEHN2ObtfB2rXCbmSOAHoJX5P4DNPm8E6rFGWQ28xH8KOcn8wKL7xnqVxMfsjLaxZ4CqGJHuT/SmWnjDVbeYNPKtzH3R1A/IgVj/tL109C/3S01MW4t5rWZobmJopF6qwwajr0bULS18UaCtxbgebtLRMeqt3U/yrzkgqxDDBBwQe1aUavtE09GiakOXVbMKKKK3MgooooAKKKKACiiigAooooAKKKKACiiigArQvdD1HTrcT3lv5cZYKG3qefwNZ4613/jb/AJF6P/rsv8jWNWo4Sil1NacFJO/Q4CiiitjIKKKKACnOjxuVkVkYdQwwa6PwRbwz6tM00auY49ybhnacjmqHij/kZbz/AHh/6CKy9pep7PyL5fd5jJooorUgKKKKACiiigC1YaZd6nK0djF5rINzDcFwPxNR3drNZXT290myVMblyDjjPUV0fgP/AJCdz/1x/qKzfFX/ACM959V/9BFYKo3WcOljTlXJzeZkUUUVuZhRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAelaV/yB7P8A64J/6CKtVV0r/kD2f/XBP/QRVqvEl8TO9bBRRRSGFFFFABRRRQAUVBe3aWGn3F3MGaO3iaVgo5IUZOPfivMZv2ifBMHh+21R11EtdOypZLChnAU4LMN+0DPTLZPPFTzK9h2Z6rRWFoHjHSPEPg+PxNbSvbaY6M5kvF8rywpIYtnjAIPOcVwGoftK+BLG+NvANUv0Bx59tbKE/wDH2Vv0pt2dmC1V0euUVgeEvG+geN9Oa88OX6XSxkCWMgrJET2ZTyPr0OODT/FfjPQvBWli/wDEd+lrEx2xpgs8p9FUcn+Q70S93cFrsbhOATXKeA/iJpPxDs7250W3vYEsphDILtEUliM8bWbiuW0T9ojwNrmprYNJfaa0h2xzX0KrGxPAG5WbH1bArn/2Xznw/wCI8f8AQQX/ANAojq32t+oPSPnf9H/ke6UVyfhL4i6T4y1zWtK0u3vYp9Fm8m4a4RFVzuZfkKsSRlD1A7VqeLPE9n4O8L3mvanFPLa2gUyJbqGc7mCjAJA6sO9F0lfoCTb5epsUVmeHNetfE/hux1qwjmjtr2ISxpOAHAPqASM/ia4nxT8evBHhbUJLGS5udSuYm2Spp8QkEZ7gszKpP0JxQ3yuzBe8ro9Jori/BHxY8K+Ppmt9Fu5Ir1V3mzu4/Ll2+o5Ib8CcVu+JfFWi+ENJbUvEV/HZWwbapYEs7f3VUZLH2AoeiuwWrsjXoryGD9pjwLNfC3ki1aCItj7TJaqYx74Vy2P+A16npeq2Ot6ZDqGk3UV3aTrujmibKsP89u1Naq4tnYt0UVm6/wCIdK8L6RLqmvXsdlaRdZJO57AAcsfYc0m0tWNJvY0q5Tw38RNJ8U+LNa8Pafb3sd3ozslw86II2Icr8hDEnkdwK5C0/aT8B3WqC0kOpW0RbaLua2HlfX5WLY/4DWD8FriG7+NvxBuLWVJoZZneORGyrqZmIII6iiOs0uln+AS0g31uvxPTbP4iaTe/Em88ExW96NStIfOklZE8kjarcHduzhx/D611dcLYeKPCdx8Yr/w/a6H5XiSC33z6l9kiHmJtQ480HeeGUYI7e1XNZ+JWhaB45tfC+qC4hubi1a7+1MEFvFGquxLsWBHEZ7HtQn7qG07u3Q66ivJJv2lPAcWqfZF/tOWHdt+2JajyvrgsHx/wGuk8TfF/wd4W0ez1C71L7Ut9EJrWGzXzJJk/vAcADt8xHII6ii6tcVnex29FcF4I+MvhPx7qB0/Spbm1v8Fktb2MI8gHUqVLKfpnPtXe0xBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABVe+vEsbVpn5PRV9TVisPxNu8q3x93c2frxj+tXTipSSZFSTjFtGPdahc3jkyyHHZFOAPwqKK4mgbdDKyH2NR0V6PKkrHn3d7nVaPqn25DHNgTIMnH8Q9a0643TJ3trwTRoZNiMSo7jFdXZ3kV7biWI/7y91PpXDWp8rutjtpT5lZ7k9FFFYmwyWGOeIxzIHRuoNZ1rp1xYXw+zTZtG5ZH5K/T/GtSiqUmlYTim7hRVXUIrmW3H2OXy5FO7/e9qrWOrCST7Ner5FwOOeAxoUW1dCcknZmnRRQSAMk4A7mpKCisu51keZ5Gnxm4m9QMqP8AGtC3Mxt0+0hRLj5gp4zVOLSuyVJN2RmT3t7dzvbafCYwp2vK/armn2Jso33TPK8hy5Y8Zq3RTctLIFHW7MjXNMa7UT24zKgwV/vD/GuZIKsQwII6g13tRyW0ExzNDHIf9pQa1p1nFWZlUoqTujiYYJbiQRwoXY9gK6/TLEWFmI85cnc596sxxRxLiJFQeijFOqalVz0Kp0lDUK5T4l+Dz468A3+iRSLFcyBZLd3+6JFORn2PT8a6uiueUVJWZvFuLujzrwd4k1DRfAen6LqPhnWRrmm2q2otEsnaKZkXapW4x5QUgA5LjHNYC/CzV7fwn4j8QX7R3vjfVCt2nlnKwNHIsqQoT15RRn2A6DJ9koqpXk3LqJaWXQ5LwBrfiXxJY3Oq+JtGk0JJGWO106XmRQoO6RsqGG4nABHAUeuT1tFFDEjgPjPJPL8ObzTbDTtQ1C8vHi8mGyspZ/uSozbiikLwO5Ge2a7XTb2PUdNhuoY7iJJFyEuYHhkXtgo4DD8RVqihaKw30CiiigQUUUUAFFFFABRRRQAUUUUAFUbbW9KvLy6tLPU7Oe5s/wDj5hiuEZ4P99Qcr0PWr1fMOk+FZ/G3x88Y6FLf3Fpo73L3Gopbtta4VHwkeewJfP4euMTduaiuz/AqyUXJ+X4n0PZ+LfDmo332LT9f0u6u84+zwXsbyf8AfIOa168I+KXwQ8J6N4Avta8LWk2m3+mILgMtzI4kAIyDvY4OMkEY5rpfD3xCurT9nGHxdqJ+0XttZOm6Q582VXMSFvXJC5/GjmSi2+n9foHK20l1PQ9U13SNDjWTWtUstORzhWu7hIg30LEVJpur6brNt9o0jULW/gzjzbWZZVz9VJFeFfDL4TWfj3R/+E0+JEtzq95qjM8ULzsiqgJAJKkHscAEADHHpW+I/gP/AIU9NZ+OPhzPPZQxTrDd2Lys8bKemSTkqSMEEnkgjGKbfK/f/wCGErS+H/hz37U9X03RbT7VrOoWun2+4J511MsSbj0G5iBmq914m0KyvYbO91rTre6nAaKCW7RHkB6FVJyc+1eTfHrVYtd+A+m6rbAiK+uLa4QHqAyMcfrVnwb8EfDGr+EtP1jxdb3Gp61qEUd3Pcy3MgKlgGCBQQuAMDkGhX5pJ9HYHa0WuquerPrWlx6vHpMmpWa6jInmJZtOomdefmCZ3EcHnHY1WfxX4dj1T+zZNe0xb/dt+yteRiXPpsznP4V4P8U7DUNT/aS0XStFvZNPmvdOjtjcQ8NFExlEhX0OzdXReN/gJ4L074dapdaRZ3EGo2Fm9wl21y7tIUUsQyk7ecdgPap5rR5+mv4FKN5cnXT8T2O/1Kx0qza71S8t7K2T701xKsaL9WYgVHpmtaXrdu0+jalZ6hCp2tJaTrKoPplSa8I+E/ghPil4Wg1r4iXd1q1rZZsdPs2naNERAMuxUgliTjOei854xWuvD1p8Kf2jPDlv4Wkmt9O1hVSa2eRnADsyFcnkjIVhnJBq7Wmovrt+hF/ccl03/U+jaxLnxt4VsrprW88TaPb3CnDRS38SuD6YLZrk/jFoXjHxTpNhonhHEVldTY1O4E6oyx8DGCQSvJJA5OAO9VY/2ffhza6SIrrTZZJEjw95JeyqxOPvEBgg/LFTd6sqy2PToZoriFJreRJYnGVdGDKw9QR1rw7Tv+TydU/68B/6Ijqh8CJv7B+Knijwhp2pnUNHhR5rdt4dSUdVDAjjJD4JHXHtV/Tv+TydU/68B/6IjpqzlCS2ab/ANVGcX0t+aO68T+F/Cep/EjQNY1rXPset2W37DZfa4o/tGHJH7thubkkfKRXS6p4p8P6Hcrb63rum6dO6b1iu7yOJmXJGQGIOMg8+1eQfE/8A5OR8A/8AbP8A9GtXpXin4Z+EfGmpRX/ibSfttzFEIUf7TLHhAScYRwOrGlG7p3XdhKyqNPsix/wsPwV/0N+g/wDgzh/+KrR0nxFouveb/YesWGpeTjzfsd0k3l5zjO0nGcHr6V4F8WPCHw58H29vovhvwuLzxRqZCWkC3tw5iBOA5XzOTngA9T14Br0f4YeAbX4U+Brq51CQPqE0X2nUZVOVUIpIRfZQTz3JPsAKSs5PZA07pLdne6hqdhpNqbnVb22srcHBluZVjQfixAqHS9f0fXFdtE1ax1EJ982lyku367ScV84eEl8N/FTXNQ8U/FfxNZwRrOYrLSJ9SS3CR4z0LBgoyAMYyQSSat+PvDPw/wDDWlDxL8LPFVhp2taayyJbWmsLOZ1yAQAzs2cHOOQQCCOaL2V5f8MFru0T6UornvAXiX/hMPAela4yqst3BmVV6CRSVfHtuBroauScXZkJ3VwooopDCiiigAooooAKKKKACiiigAooooAKKKKACiiigDzXVf8AkMXn/Xd//QjVWrWq/wDIYvP+u7/+hGqte1H4UcD3CiiiqEFFFFABRRRQB3/g+9jvtDayl5aHKFT3Q9P6iuM1XTZtKv3t5gcA5RscOvY03TtQn0u8W5tWw68EHow9DXaxeIdE1q2EWpKkbd0nHAPqG7fXg1xyUqNRzirpnRFxnHlbs0cBXQ+D9Ke81Rbt1PkW5zkjhm7D+tbY0vwkrb/OtTjnBu8/+zU3UfFthYWv2fRkWRwMLtXbGn+P4Up1pVFy04u7BU1F3kzL8bS239oxQW8UayIu6V1UAknoCfp/Ot+6Q654LH2f5pHhVgB3ZcZH5givPZpZJ5nlmYvI5LMx7mtfQPEcujMYnUzWzHJTPKn1H+FOdCSpxUd0CqLnbezMUgqxDAgg4IPat7wfYyXOuRzhT5VvlmbHGcYA/wA+lbc2q+FNQYTXaKJTyd0LBvxKjmmXXi/T7C18jRLcMcfKdmxF98dTRKrUnHlUHdgoRTu5aFDx1crJqkECnJhjy3sSen5AfnW1cf8AJPh/16J/SuBnnkuZ3mncvI5yzHua66bXdNfwcLFbnNz9nVNnlt1GOM4xU1KTjCEVrZlQmnUcmcdXaeAf9Ve/7yf1ri66fwjq9jpcd0L6fyjIV2/IzZxn0BrfEJuk0jGm7TTZi6z/AMhy9/67v/M1v+Bb/ZdT2LniQeYn1HX9P5VzmpzJcardTQtujklZlOMZBNJp921hqEF0nWJwxA7juPypyhz0uV9huVqnMu53mi6Mum6vqVywCpu2xH0Ujcf5gfhXD6tenUdVuLo9Hf5fZRwP0rrtc8U2E2jTQ6fcGSaUbMbGGAepyR6VwtY4aMm3Oe+xpVlG3LH1O88DXKyaTNbZ+eKTOM9iP8Qa5nUbrWNO1CW2mv7wFGO0mZvmHY9aqaZqU+lXq3NseRwynow9DXX/APCS6DqsAGqwbGA6SRlseuCOf5UpRlTqOajdMUZKUOVuzRy9pfaxe3cdvBf3bSSNgfv249+tafiPS73TbFDdaxNdrI+0ROW575wSelaseveG9JjLabFukIx+7iIY/Vm7VymsavPrN550+FVRiOMHhB/j71Ueec01GyQPljF3d2O0TSZNY1FYFysa/NK/91f8a7TU9etdBuLSxijGwYEgX/lmnQfj3/D3rN0rWNF0PRWSC48+6K7nAjYb29MkdBXI3VzLeXUlxO26SRtzGk4OtU95e6vxEmqcbrdnX+L9GW4hGrWQDYUGXb/EvZv89q4uut8NeJbe2sWstWk2xoP3bFS2QeqnANc9qqWSahJ/Zk3m2zfMvykbfbkVdHng3Tl02YVLSXOtynW54QuVtvEUQc4EymPPueR+orDpVZkYMhKspyCDyDW8o80XHuYp2dzqPHNnImoxXYUmKRAhPowzx+VcsBk4HJrs9P8AGFrc2gttchzxhn2blf3I9ami1PwnYubi1RDKORthYkH23DArkhOdKPI4t27HRKMZvmTM6+8L21h4d+23E0yXIjBMZIxuPbpW5bL/AGz4HWGI5cweXjP8S/8A1xXKa/4hl1qRUVTFbRnKoTyT6mmaFr8+izEBfNt3Pzxk459R70SpVZ07ve9wU4QnpsZTo0bskilWU4KkYINa/hewkvddgZVJjgYSO2OBjp+tdDNrHhbU8S30YWQ9d8TBvxK9fzps/izStNtTDo1uHPUbU2ID6nuf8805Vqko8qg7k+zje/NoVfHlyrXNpbqcsis7D0zjH8jW5qD3GpeFRPpksiTNGsi+UxDHHVePxrzu6upr26e4uXLySHLGtjw/4ll0fMMyma1Y52g8ofUf4UpUJKnFR3RSqpzbezM46xqY66jd/wDf9v8AGtbQodY1uaQLql3DFGOZDIzDPYdRWvNqnhO/YTXSIJDyd0LBs++0c0y78XafY2nkaJAGOPlOzYi++OpNEpykrRhZgopO7loc3r1vNaao1vc3r3skajLuSSO+OSf8muv03/kQD/17S/8As1cDLK88zyzMXdyWZj3NdbY67p0Pg82Ulxi48l12bG6nOOcY71VeEvZxW7uhUpL2l+hx9eheK/8AkU/xjrz2ux8Qa9pt94e+zWtzvmyny+Ww6deSMU68W5wsupNJpKV+xz2gf8jBZf8AXZa6Dx9/rLD6Sf8Astc3pE8drrFrPO22OOQMzYJwPwrY8X6tZao9obGbzfLD7vkZcZxjqB6U5xbrxdu4otckl6G74bZdR8IG1DfMFeFvbOcfoRXAzQyW87wzKUkQ7WU9jV/Rdan0W6MkQ3xvxJGTgMP6Gunl1vwzqqiTUIwsmMfvIm3fmtR79Go2ldMtNTgot2aOa8P6P/bOpeTIXWFFLSMnUen6/wBatXmm2+leK7O1tZXkCyRlt+MglunHtitmfxXpWmWhh0SAOx6YTYgPqc8n/PNcnb3jPrUN5eSEnz1kkcjP8WTVxdScnJqy7EyUYxtuzrPHv/HnZ/8AXRv5VZ8Hzrc+HDbqxV4mZDg8jPIP6/pWR4u1mw1S2tksZ/NZHJYbGXAx7isTSNWuNHvBPb/Mp4eMnhx/nvWMaMp4flejNJVEqikiS7vtYsruS3uL+8WSNsEGduffrT9PutY1K+itYNQvNznk+cx2juetdK3iHw7q0I/tOLY4GMSREkfRl/8ArUL4i8PaTA39lw73IxiOMqT9WbnH51XtJWt7PX8BcqbupaGN4l06702CFbvVpbwSMdschbjA64JPr+tb/gz/AJFtv+ur/wAhXF6pqk+rXrXFyQOMKg6KPSuk8M67p2n6I1veXHlymRjt2MeCB6ClVhP2FnqxRlH2qa2OProfBP8AyMB/64t/MVz1bPha/ttO1gz3snlx+Uy7tpPOR6V1Vk3TaRlD4kT+Nf8AkYT/ANcl/rW/4P8A+RYk/wCuj/yFcx4ov7bUdZM9nJ5kflqu7aRz+NaHhXxHbaZbvaX25Y2fekgGQOOQQOe1cs4SeHUUtTbmSrXOXorpvEl1oVzYr/ZPlC483LbISmRg55IHtXM1105uau1YxlHldr3CiiirICiiigAooooA7/wz/wAi7a/8D/8AQzWrWV4Z/wCRdtf+B/8AoZrVrxqnxv1O6PwoKKKKgoKtwf6laqVbg/1K1Mtio7klFFFZlhRRRQBFc/6sfWqtWrn/AFY+tVa0jsRLcKKKKok4DxN/yMV1/wAA/wDQBWVWr4m/5GK6/wCAf+gCsqvZp/AvQ4ZfEwoooqyQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA6nwH/yE7n/rj/UV0Fje48U6lYueGCSoP+AgH+lc/wCA/wDkJ3P/AFx/qKbqd7/Z/j43BOFVkD/7pUA/oa8+pDnrSj5HTTly07+Zj65Zf2frVzABhQ+5P908iotMszf6nb2o/wCWjgH2Hf8ATNdN47svmtr5B1HlOf1H9ai8DWW+7nvXHyxLsUn1PX9P51tGt+45yZw/ecvc3rq8A8Uadp8fCojyMB/ukD+tc546/wCQ1D/1wH/oRpdIvf7Q8eG5zlXL7f8AdCkD9BSeOv8AkNQ/9cB/6Eaxpw5KsV5f5mkpc0Jev+Ra8M6Fp2oaI1xeW/mSiRhu3sOAB6GuXsIkm1K2ilG5HlVWGcZBIzXceDP+Rbb/AK6v/IVxOl/8hiz/AOu6f+hCtqcm5zTZlJLki/U7G/8ABtnLdQfZB9lt1DGdt5YnpgDJOO/NRx2HhASC382N5Om4ztz+IOKm8cXUkOkxQxkqJpMPjuAM4/PFcFWVGM6sLuTNKjjCWiOo8ReFY9PtWvdPZjEp+eNjnaD3B9K5qGGS4mSKFC8jnaqjua7+0drjwCTKcn7K65+mQP5VheB7VZtXlncZ8mP5fYnjP5Zq6dWUYS5teUmcU+VrS5o2nhCwsbXz9buAT/EN+xF9s9TT4tL8KagxgtHUSngbZmDfhuPNY3jG+kuNbe2LHyrcAKvbJGSf1/SufBIIIOCOhFEKdSpHnlJq4SlGD5Ujb1/w3NoxEsbGa1Y4D45U+h/xpPCun22pas0N7F5kYiLBdxHOR6H3rq9PkOueDiLr5naNkZj3I6H68A1zvgf/AJDz/wDXBv5ikqk3TmpboHGPNFrZmrJ4a0bT557nU3WO2ZsQxGQgAY+uSc5rJ1y10OP7IdIfc0r4ZUkLAL755B5/nR42uGl1wQ5+WGMAD0J5P9Kwbf8A4+ov98fzqqMJSSnKTFWko3ikdT4s0TT9M02KWxt/KdpgpO9jkYJ7n2q74S/sbEX2Uf8AEx8k+b9/pkZ68enSneO/+QNB/wBfA/8AQWrG8D/8h5/+uDfzFYxvPDttsuVo1VZGt4iPh77RdfbR/wATDy+P9Z12/L049Kg0nQtL1fw35sVvtuwjIX8xuHHQ4zj0P41keL/+RmuP91P/AEEVe8D3/lX8tk5+WZdyD/aH/wBb+VVySVDmi3fRjlJe1s0cuylWKsMEHBB7VreGdLTVdXEc6boI1LyDJGewGR7mneKrH7Dr0u0YSf8Aer+PX9c10PhOBNM8Oz6jOMeZlz/ur0/XNbVKv7nmXUyjT/ecpzvie3sbPVvsunQiNY1G/wCZmyx57k9sVj1JcTvdXUs8py8jFm+pNR1tCLjFJkTacm0FFFFWQFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHa+Df+QPL/ANdz/wCgrW/WB4N/5A8v/Xc/+grW/XkVv4jO2n8KCiiisizyPxX41+HvgH4hzXkGk3GpeLrqPy5xp6mRwGwQGy20EgDoC2PY1ka/8UvHHiHw3qlppfwyv7S1ktJRLd6i7IqxFTubDKoJxk4DH8a519Qvfg18bde17xLod1f6XqryNBfwJuKq77xtY8Z/hKkg8Z6del1j4rax8StAu9F+GvhbU2+2RNDPqN8ixRwIRhsFSQWIJxzn0BrF+9S+W3byNfhqfPfv5lv9m5ifhDeAnIW/mA9vkSqH7MztF4E8QyIMst+zAe4iWrP7NVzG/wALtUtQ376C/kMiHqoMa4P6H8jUX7Lwz4N1wHp/aX/tNa2mrykl/Kv0Mo6QV/5n+pB+zDbx3Ol+I9ZuBv1C5vgksrcsRt3/AKsxJr1/xdp9tqvg3V7G+VWgms5VfcOnykg/gefwrxCCPX/gH441W5i0a51bwfqknm77VSTb8kjPZSu4jnAYY544s+JfjRffEXQ7jw58NPDerT3V+hgnuZ4lUQo3B+6zAZGRuYgCs5+/T5Y9rW7Gkfdqcz73uR/CC8nuP2afFkMzFo7ZL2OLJ6KbcMR+bE/jXT/s12kMHwnE8aASXF9K0jd2Iwo/QVd0zwV/wgX7PusaPK6yXf8AZl3NdOn3TK0TZA9gAB+FQfs4/wDJH7b/AK+5/wD0Ktb/ALyX+FfmZv8Ahw/xP8jn/wBpuNfsPhWYL+9W/dVfuAQpP8h+VM/aGY33iTwJo1yT9hu7wmZezEvGv8mb86m/ab/5BXhj/sIt/wCgiun+NPgG88a+Era40P8A5DGkyfaLVQcGQYG5Aex4BHuvvWSdld7KX6R/4ct6u3eP6s9JjRYo1jjUIigKqgYAA7V4ZdafbWP7Yli9qiobuwaeUL/fMUik/UhQal0z9oyO0s4tN8TeFdZj8Rooje2ggGJXx1wxDLk9tpx71y/hC71/Uf2o7LUPFdn9gvry2eZbMnJt4jCwRD6HA59yeB0q1rUTXnr8n/XyIelNr0/NH0xXN/Eb/kmPiX/sF3H/AKLNdJXN/Eb/AJJj4l/7Bdx/6LNRU+CXoaUv4kfU5H9nxinwUsmAyVmuCB/20NeQfCXVfiMl3r+q+BfD+m6xNeXIN7cXsiq6MSzYGZU4O4noa9h/Z3/5Izp//XxP/wCjDXDG08RfAf4g6nqVlpFxq/hHVX8yT7MuTAMkjOPusu4gZwGB6g9LlpNN9v8AL8yI6waXf/M1NZ1D4669od7pWoeBdCa2vYWhl2zpnawxkZuevpXQ/C7wze+Gvg7faL8SbSOztIpJjJHNcIyC3IDHLIxAGS/Gc1z+qftCy+ItPk074a+HNXutYmGxJJrdGEGf49ql84/2sDueOK6fXvC/jLxF8Br3SNdu47vxLcxCV1jVEUkOHEXy4XOFxnpk+lS9IyaXSxSs5RTfU5rR/jJ4d0XT20T4XeCNa1e0tXbattE2wliSTn535yfvAH2rg/i94p8WeJZPD114j8Jt4ctI7lja+dIWldvl3ZyAQBgfwiui+H/xr0zwB4TtfCviXwzq1pqNiWQpbW65lJYncyuykNzz19faue+MeveKPFlvpXiPVPD1xonh61uvJtI7k4mldvmLsvGBhOOMehNVpzxd7q61Er2atbRnr/7QXPwU1P8A662//o1a6L4d/wDJIvD/AP2CYf8A0WKx/jLp114l+C+ox6JE17I6Q3EaQjcZEV1YlQOvy5NY3wR+JemeJNFsvCC2V1b6jpenKsxlCmNwhVDg5znkHBApJNqpDr/wBX0hLp/wTL/ZdI/4Q/XRnkalnH/bNa1P2lv+STr/ANhCH+T1wnhXxFf/AAB8Sa1pPirRL6fRryfzLa8towd2MhSpJCnKkZGQQRUHxf8AGWs/EbwSNQ07QrrTPDGnzpKbm/GyS7lb5FCKMjA3N0JHuOlRUanBW8v0NI+7Ud/P8bno/wASv+TYZP8AsG2X/oUVdB8GbaK1+Dvh1YVCh7bzG92ZiSfzNc/8Sv8Ak2GT/sG2X/oUVdL8If8AkkPhv/ryX+ZreXx1PVfqYQ/hU/n/AO2nj/xUu9euP2lNJi8PWFvqeoWVrG1laXbARMwV3JOWUZHJ+8OVH0rrP+Em+P8A/wBCPoP/AH/T/wCSaf8AGbwRr7eI9K8feCImuNV0oBZrZF3NIikkEL1bqylRyQeKrR/tM6RHbC3v/DGtQ6yBtazVEK7/AE3Fg3X/AGM+1Ywso2b1u/xZtK7lddkRfC7wn4+0/wCL2p+JfFWgwaVbapbyfaFtbiJo/MJUjCiRm5IJzzyTVPVvCHxF+GnjrWfEPw9tYdZ0zVpWnntXUOyksWwUyrHBZsFCeDzXafC3UviF4ivtQ1rxnH/ZmkzEiw0x7dUkTnqTgPgDj5uSTngYrj38aeL/AISeONXHjW21XXvDl5Jvtb5CZBAuTtC5+UHBwVJXkZ+renKttP6TJWvM99f6aGx/HrTri6g0r4qeB5bBldXDT2/mqj9N/lSKGUDJ5G49a92tJ7e5soJ7F45LaSNXheMgqyEZBGO2K+ePHXxEb40aFH4X8C+FdSupJbhHku7uJVW32nqCpYLnoSSOMjnNe7+FdGbw74R0rR3l857G0jgaT+8VUAn6Zq46xbff7yZaNJf8Ma1FFFABRRRQAUUUUAFFFFAHldFFFe4eeFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQBp+HLdbrxDaRuMqH3n/gIz/Sui8eXLLBaWqnh2Lt+HA/maw/CbhPE1tn+LcP/AB01q+PlP2iybsUYfqK46muIgn/W50Q0pSZyNFFFdhzly21e/s7Vre1uniiZtxC8HP16iqyrJcThV3SSyNgdyxJplbnhCBZ/EURYZESs4HvjH9aiVoJysUruyN2z8NaZo9iLvW2WRwMtvPyKfQD+KkTVvCk8phNnDGDwHa2AB/Ecis/x1cu+pw22f3ccW/HqST/hXL1y06Tqx55t6m8pKD5Yo63X/CkMNo19pJzEo3PFu3Db6qaxvDcMc/iG1jnjWSNi2VdQQflPY11Xgq4a50SWCY7likKrn+6RnH8653QYhB4zihXpHLIo/AMKITklOnJ3shSSajNdTpNS0DSIrw39+Ire0jjCiJF2BmyeTjr24HpUMGqeFbiYQC0gjycK724AP49vxqn49mf7RaQ5+QKz49TnFcjU0aTqU1KUmVUnySskdT4q8OQafCt7YArEW2yR5yFz0I9qn8GpY31rPbXVpbyyxncGeJSxU+/Xg/zrR1smTwLufkmGIk++VrkvDl//AGfrkEjHEbny3+h/+vg/hThzVKMot6oUrRlGXcq6nZnT9TuLVv8Alm5A9x2/TFTaFYf2jrVvAwym7dJ/ujk/4fjW546sdlzBfIOJB5b/AFHI/T+VWPA1kI4LnUJBjP7tCewHJP8AL8q09t+45+v6kun+85Sp40FnbS29pZ2sEL48yRo4wp9AMj8f0p/h/wAKxXFot9qpIiYbkiztyvqTWHe3B1jxA0hJ2zzBV9lzgfpXYeM52tNASCH5VlkEZxx8oBOP0FZy54QjTT1Zfuym5PZFd9W8KQSCFbSGQDgutsGA/E8mnXfhvS9ZsTdaKyRueVKH5GPoR2rha6bwPdPHq0lvn93LGWK+46H+dOdF0488JO6JjUUnZrQ5uSN4ZmilUq6MVZT2IrvfG3/IvR/9dl/ka57xjAsPiJ2TjzUVz9en9K6Hxt/yL0f/AF2X+RpVJc7pS/roXCPK5ryMbwVaW93d3QureKYKgIEiBsc+9ZniOKODxDdxwxrGisMKi4A+Udq2fAX/AB+3f/XNf51k+KP+RlvP94f+giri39Za8v8AIy/5dfMpaaqyaraI6hlaZAVIyCNwrp/Gtla2lpam1toYSzsCY4wuePauZ0v/AJDFn/13T/0IV1vj3/jzs/8Aro38qdVv20B0/gkWPCmoafcxrb2lp5VzFAvmy+Wo34wDyOTz61U8RatpCSXto+n7rzbt8/yU+8Rwd2c1U8B/8hO5/wCuP9RWZ4o/5GW8/wB4f+gislTi8Q15XKU37O/mO8P6A+tXDF2MdvH99x1J9B710lzJ4Y0Q/Z5LaKaVfvL5fmMPqT/LNXPDVs0PhWIQbVllVnBPTcc4J/SsBvA2ouxZ7u3ZmOSSW5P5VMqkZ1GpyskOMXGCcVds00sfDviOFlslSCZRn92uxl/4D0NcdqenTaXfva3H3l5Vh0YdiK6fS/COoadqcF0LmDEbgsAW5XuOnpS+PYB5dncAfNlkJ9R1H9aqnUUaijGV0wlFyg3JWaKPhnwympRm8v8AItgcIgON578+las2p+FbSUW4tIJQvBdLcMB+J6/hmrOqMdN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"text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='CoSPOV.jpg')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "def conv_sample_gen():\n", " ## the generator network to generate synthetic samples from the convex space of arbitrary minority neighbourhoods\n", " \n", " min_neb_batch = keras.layers.Input(shape=(n_feat,)) ## takes minority batch as input\n", " x=tf.reshape(min_neb_batch, (1,neb,n_feat), name=None) ## reshaping the 2D tensor to 3D for using 1-D convolution, otherwise 1-D convolution won't work.\n", " x= keras.layers.Conv1D(n_feat, 3, activation='relu')(x) ## using 1-D convolution, feature dimension remains the same\n", " x= keras.layers.Flatten()(x) ## flatten after convolution\n", " x= keras.layers.Dense(neb*gen, activation='relu')(x) ## add dense layer to transform the vector to a convenient dimension\n", " x= keras.layers.Reshape((neb,gen))(x)## again, witching to 2-D tensor once we have the convenient shape\n", " s=K.sum(x,axis=1) ## row wise sum\n", " s_non_zero=tf.keras.layers.Lambda(lambda x: x+.0001)(s) ## adding a small constant to always ensure the row sums are non zero. if this is not done then during initialization the sum can be zero\n", " sinv=tf.math.reciprocal(s_non_zero) ## reprocals of the approximated row sum\n", " x=keras.layers.Multiply()([sinv,x]) ## At this step we ensure that row sum is 1 for every row in x. That means, each row is set of convex co-efficient\n", " aff=tf.transpose(x[0]) ## Now we transpose the matrix. So each column is now a set of convex coefficients\n", " synth=tf.matmul(aff,min_neb_batch) ## We now do matrix multiplication of the affine combinations with the original minority batch taken as input. This generates a convex transformation of the input minority batch\n", " model = Model(inputs=min_neb_batch, outputs=synth) ## finally we compile the generator with an arbitrary minortiy neighbourhood batch as input and a covex space transformation of the same number of samples as output\n", " opt = keras.optimizers.Adam(learning_rate=0.001)\n", " model.compile(loss='mean_squared_logarithmic_error', optimizer=opt)\n", " return model" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_1 (InputLayer) [(None, 8)] 0 \n", "__________________________________________________________________________________________________\n", "tf.reshape (TFOpLambda) (1, 5, 8) 0 input_1[0][0] \n", "__________________________________________________________________________________________________\n", "conv1d (Conv1D) (1, 3, 8) 200 tf.reshape[0][0] \n", "__________________________________________________________________________________________________\n", "flatten (Flatten) (1, 24) 0 conv1d[0][0] \n", "__________________________________________________________________________________________________\n", "dense (Dense) (1, 25) 625 flatten[0][0] \n", "__________________________________________________________________________________________________\n", "reshape (Reshape) (1, 5, 5) 0 dense[0][0] \n", "__________________________________________________________________________________________________\n", "tf.math.reduce_sum (TFOpLambda) (1, 5) 0 reshape[0][0] \n", "__________________________________________________________________________________________________\n", "lambda (Lambda) (1, 5) 0 tf.math.reduce_sum[0][0] \n", "__________________________________________________________________________________________________\n", "tf.math.reciprocal (TFOpLambda) (1, 5) 0 lambda[0][0] \n", "__________________________________________________________________________________________________\n", "multiply (Multiply) (1, 5, 5) 0 tf.math.reciprocal[0][0] \n", " reshape[0][0] \n", "__________________________________________________________________________________________________\n", "tf.__operators__.getitem (Slici (5, 5) 0 multiply[0][0] \n", "__________________________________________________________________________________________________\n", "tf.compat.v1.transpose (TFOpLam (5, 5) 0 tf.__operators__.getitem[0][0] \n", "__________________________________________________________________________________________________\n", "tf.linalg.matmul (TFOpLambda) (5, 8) 0 tf.compat.v1.transpose[0][0] \n", " input_1[0][0] \n", "==================================================================================================\n", "Total params: 825\n", "Trainable params: 825\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "##instanciate network and visualize architecture\n", "conv_sample_generator=conv_sample_gen()\n", "conv_sample_generator.summary()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "def maj_min_disc():\n", " ## the discriminator is trained intwo phase: \n", " ## first phase: while training GAN the discriminator learns to differentiate synthetic minority samples generated from convex minority data space against the borderline majority samples\n", " ## second phase: after the GAN generator learns to create synthetic samples, it can be used to generate synthetic samples to balance the dataset\n", " ## and then rettrain the discriminator with the balanced dataset\n", " \n", " samples=keras.layers.Input(shape=(n_feat,)) ## takes as input synthetic sample generated as input stacked upon a batch of borderline majority samples \n", " y= keras.layers.Dense(250, activation='relu')(samples) ## passed through two dense layers \n", " y= keras.layers.Dense(125, activation='relu')(y)\n", " output= keras.layers.Dense(2, activation='sigmoid')(y) ## two output nodes. outputs have to be one-hot coded (see labels variable before)\n", " model = Model(inputs=samples, outputs=output) ## compile model\n", " opt = keras.optimizers.Adam(learning_rate=0.0001)\n", " model.compile(loss='binary_crossentropy', optimizer=opt)\n", " return model" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_1\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) [(None, 8)] 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 250) 2250 \n", "_________________________________________________________________\n", "dense_2 (Dense) (None, 125) 31375 \n", "_________________________________________________________________\n", "dense_3 (Dense) (None, 2) 252 \n", "=================================================================\n", "Total params: 33,877\n", "Trainable params: 33,877\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "##instanciate network and visualize architecture\n", "maj_min_discriminator=maj_min_disc()\n", "maj_min_discriminator.summary()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def convGAN(conv_coeff_generator,maj_min_discriminator):\n", " ## for joining the generator and the discriminator\n", " ## conv_coeff_generator-> generator network instance\n", " ## maj_min_discriminator -> discriminator network instance\n", " \n", " maj_min_disc.trainable=False ## by default the discriminator trainability is switched off. \n", " ## Thus training the GAN means training the generator network as per previously trained discriminator network.\n", " batch_data = keras.layers.Input(shape=(n_feat,)) ## input receives a neighbourhood minority batch and a proximal majority batch concatenated\n", " min_batch = tf.keras.layers.Lambda(lambda x: x[:neb])(batch_data) ## extract minority batch\n", " maj_batch = tf.keras.layers.Lambda(lambda x: x[neb:])(batch_data) ## extract majority batch \n", " conv_samples=conv_sample_generator(min_batch) ## pass minority batch into generator to obtain convex space transformation (synthetic samples) of the minority neighbourhood input batch\n", " new_samples=tf.concat([conv_samples,maj_batch],axis=0) ## concatenate the synthetic samples with the majority samples \n", " output=maj_min_discriminator(new_samples) ## pass the concatenated vector into the discriminator to know its decisions\n", " ## note that, the discriminator will not be traied but will make decisions based on its previous training while using this function\n", " model = Model(inputs=batch_data, outputs=output)\n", " opt = keras.optimizers.Adam(learning_rate=0.0001)\n", " model.compile(loss='mse', optimizer=opt)\n", " return model" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"model_2\"\n", "__________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", "input_3 (InputLayer) [(None, 8)] 0 \n", "__________________________________________________________________________________________________\n", "lambda_1 (Lambda) (None, 8) 0 input_3[0][0] \n", "__________________________________________________________________________________________________\n", "model (Functional) (5, 8) 825 lambda_1[0][0] \n", "__________________________________________________________________________________________________\n", "lambda_2 (Lambda) (None, 8) 0 input_3[0][0] \n", "__________________________________________________________________________________________________\n", "tf.concat (TFOpLambda) (None, 8) 0 model[0][0] \n", " lambda_2[0][0] \n", "__________________________________________________________________________________________________\n", "model_1 (Functional) (None, 2) 33877 tf.concat[0][0] \n", "==================================================================================================\n", "Total params: 34,702\n", "Trainable params: 34,702\n", "Non-trainable params: 0\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "## instanciate network and visualize architecture\n", "cg=convGAN(conv_sample_generator,maj_min_discriminator)\n", "cg.summary()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 neighbourhood batches trained; running neighbourhood epoch 1\n", "20 neighbourhood batches trained; running neighbourhood epoch 1\n", "30 neighbourhood batches trained; running neighbourhood epoch 1\n", "Neighbourhood epoch 1 complete\n", "40 neighbourhood batches trained; running neighbourhood epoch 2\n", "50 neighbourhood batches trained; running neighbourhood epoch 2\n", "60 neighbourhood batches trained; running neighbourhood epoch 2\n", "Neighbourhood epoch 2 complete\n", "70 neighbourhood batches trained; running neighbourhood epoch 3\n", "80 neighbourhood batches trained; running neighbourhood epoch 3\n", "90 neighbourhood batches trained; running neighbourhood epoch 3\n", "Neighbourhood epoch 3 complete\n", "100 neighbourhood batches trained; running neighbourhood epoch 4\n", "110 neighbourhood batches trained; running neighbourhood epoch 4\n", "120 neighbourhood batches trained; running neighbourhood epoch 4\n", "130 neighbourhood batches trained; running neighbourhood epoch 4\n", "Neighbourhood epoch 4 complete\n", "140 neighbourhood batches trained; running neighbourhood epoch 5\n", "150 neighbourhood batches trained; running neighbourhood epoch 5\n", "160 neighbourhood batches trained; running neighbourhood epoch 5\n", "Neighbourhood epoch 5 complete\n", "170 neighbourhood batches trained; running neighbourhood epoch 6\n", "180 neighbourhood batches trained; running neighbourhood epoch 6\n", "190 neighbourhood batches trained; running neighbourhood epoch 6\n", "Neighbourhood epoch 6 complete\n", "200 neighbourhood batches trained; running neighbourhood epoch 7\n", "210 neighbourhood batches trained; running neighbourhood epoch 7\n", "220 neighbourhood batches trained; running neighbourhood epoch 7\n", "230 neighbourhood batches trained; running neighbourhood epoch 7\n", "Neighbourhood epoch 7 complete\n" ] } ], "source": [ "## this is the main training process where the GAn learns to generate appropriate samples from the convex space\n", "## this is the first training phase for the discriminator and the only training phase for the generator.\n", "step=1\n", "neb_epochs=7 ##number of time each minority batch is used for training\n", "loss_history=[] ## this is for stroring the loss for every run\n", "min_idx=0\n", "neb_epoch_count=1\n", "while step<(neb_epochs*len(features_1_trn)):\n", " \n", " \n", " min_batch=NMB_guided(features_1_trn, neb, min_idx) ## generate minority neighbourhood batch for every minority class sampls by index\n", " min_idx=min_idx+1 \n", " maj_batch=BMB(features_1_trn,features_0_trn,neb,gen) ## generate random proximal majority batch \n", " \n", " conv_samples=conv_sample_generator.predict(min_batch) ## generate synthetic samples from convex space of minority neighbourhood batch using generator\n", " concat_sample=tf.concat([conv_samples,maj_batch],axis=0) ## concatenate them with the majority batch\n", " \n", " maj_min_discriminator.trainable=True ## switch on discriminator training\n", " maj_min_discriminator.fit(x=concat_sample,y=labels,verbose=0) ## train the discriminator with the concatenated samples and the one-hot encoded labels \n", " maj_min_discriminator.trainable=False ## switch off the discriminator training again\n", " \n", " gan_loss_history=cg.fit(concat_sample,y=labels,verbose=0) ## use the GAN to make the generator learn on the decisions made by the previous discriminator training\n", " \n", " loss_history.append(gan_loss_history.history['loss']) ## store the loss for the step\n", " \n", " if step%10 == 0:\n", " print(str(step)+' neighbourhood batches trained; running neighbourhood epoch ' + str(neb_epoch_count))\n", " \n", " if min_idx==len(features_1_trn)-1:\n", " print(str('Neighbourhood epoch '+ str(neb_epoch_count) +' complete'))\n", " neb_epoch_count=neb_epoch_count+1\n", " min_idx=0\n", " \n", " \n", " step=step+1\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## plotting the loss history\n", "## we see the loss has a decreasing trend but fluctuates with incresing runs\n", "## this is due to the large variation in the inputs induced by the permutations of the input minority and majority batches\n", "## and also the diverse cinvex combinations generrated by the generatr network on these permuted batches\n", "## the permutations can be important while lerning from lesser amounts of data\n", "\n", "run_range=range(1,len(loss_history)+1)\n", "plt.rcParams[\"figure.figsize\"] = (16,10)\n", "plt.xticks(fontsize=20)\n", "plt.yticks(fontsize=20)\n", "plt.xlabel('runs',fontsize=25)\n", "plt.ylabel('loss', fontsize=25)\n", "plt.plot(run_range, loss_history)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "## plotting the PCA for the minority majority batches and the synthetic samples during the last training run\n", "## note that the synthetic samples are generated far from the majority class yet closer to minority\n", "samples_for_plot=np.concatenate([np.array(min_batch),conv_samples,np.array(maj_batch)])\n", "lab=np.concatenate([np.zeros(neb),np.zeros(neb)+1,np.zeros(neb)+2])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "## do PCA\n", "pca = PCA(n_components=2)\n", "pca.fit(samples_for_plot)\n", "data_pca = pca.transform(samples_for_plot)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## plot\n", "plt.rcParams[\"figure.figsize\"] = (12,12)\n", "\n", "colors=['r', 'b', 'g']\n", "plt.xticks(fontsize=20)\n", "plt.yticks(fontsize=20)\n", "plt.xlabel('PCA0',fontsize=25)\n", "plt.ylabel('PCA1', fontsize=25)\n", "classes = ['minority', 'synthetic minority', 'majority']\n", "\n", "scatter=plt.scatter(data_pca[:,0], data_pca[:,1], c=lab, cmap='Set1')\n", "plt.legend(handles=scatter.legend_elements()[0], labels=classes, fontsize=20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "## onehot encoding of the entire training labels\n", "training_labels_oh=[]\n", "for i in range(len(training_data)):\n", " if i required number of data points that can be generated from a neighbourhood\n", " ## data_min -> minority class data\n", " ## neb -> oversampling neighbourhood\n", " ## index -> index of the minority sample in a training data whose neighbourhood we want to obtain\n", " \n", " runs=int(synth_num/neb)+1\n", " synth_set=[]\n", " for run in range(runs):\n", " batch=NMB_guided(data_min, neb, index)\n", " synth_batch=conv_sample_generator.predict(batch)\n", " for i in range(len(synth_batch)):\n", " synth_set.append(synth_batch[i])\n", " synth_set=synth_set[:synth_num]\n", " synth_set=np.array(synth_set)\n", " return(synth_set)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "## roughly claculate the upper bound of the synthetic samples to be generated from each neighbourhood\n", "synth_num=((len(features_0_trn)-len(features_1_trn))//len(features_1_trn))+1" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "## generate synth_num synthetic samples from each minority neighbourhood\n", "synth_set=[]\n", "for i in range(len(features_1_trn)):\n", " synth_i=generate_data_for_min_point(features_1_trn,neb,i,synth_num)\n", " for k in range(len(synth_i)):\n", " synth_set.append(synth_i[k])\n", "synth_set=synth_set[:(len(features_0_trn)-len(features_1_trn))] ## extract the exact number of synthetic samples needed to exactly balance the two classes\n", "synth_set=np.array(synth_set)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "## generate the data and labels for the oversampled data to visualize and retrain the discriminator for second phase learning" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "ovs_min_class=np.concatenate((features_1_trn,synth_set),axis=0)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "ovs_training_dataset=np.concatenate((ovs_min_class,features_0_trn),axis=0)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "ovs_pca_labels=np.concatenate((np.zeros(len(features_1_trn)),np.zeros(len(synth_set))+1,np.zeros(len(features_0_trn))+2))" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "ovs_training_labels=np.concatenate((np.zeros(len(ovs_min_class))+1,np.zeros(len(features_0_trn))+0))" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "ovs_training_labels_oh=[]\n", "for i in range(len(ovs_training_dataset)):\n", " if i" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## plot PCA\n", "plt.rcParams[\"figure.figsize\"] = (12,12)\n", "\n", "colors=['r', 'b', 'g']\n", "plt.xticks(fontsize=20)\n", "plt.yticks(fontsize=20)\n", "plt.xlabel('PCA1',fontsize=25)\n", "plt.ylabel('PCA2', fontsize=25)\n", "classes = ['minority', 'synthetic minority', 'majority']\n", "\n", "scatter=plt.scatter(data_pca[:,0], data_pca[:,1], c=ovs_pca_labels, cmap='Set1')\n", "plt.legend(handles=scatter.legend_elements()[0], labels=classes, fontsize=20)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.4358\n", "Epoch 2/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.3846\n", "Epoch 3/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.3502\n", "Epoch 4/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.3282\n", "Epoch 5/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.3098\n", "Epoch 6/300\n", "96/96 [==============================] - ETA: 0s - loss: 0.302 - 1s 6ms/step - loss: 0.2981\n", "Epoch 7/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.2897\n", "Epoch 8/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.2818\n", "Epoch 9/300\n", "96/96 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[==============================] - 1s 7ms/step - loss: 0.1424\n", "Epoch 193/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1410\n", "Epoch 194/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1415\n", "Epoch 195/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1399\n", "Epoch 196/300\n", "96/96 [==============================] - 1s 8ms/step - loss: 0.1401\n", "Epoch 197/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1394\n", "Epoch 198/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1397\n", "Epoch 199/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1394\n", "Epoch 200/300\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "96/96 [==============================] - 1s 7ms/step - loss: 0.1376\n", "Epoch 201/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1404\n", "Epoch 202/300\n", "96/96 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213/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1331\n", "Epoch 214/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1330\n", "Epoch 215/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1333\n", "Epoch 216/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1333\n", "Epoch 217/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1331\n", "Epoch 218/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1342\n", "Epoch 219/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1322\n", "Epoch 220/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1316\n", "Epoch 221/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1307\n", "Epoch 222/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1299\n", "Epoch 223/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 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[==============================] - 1s 5ms/step - loss: 0.1268A: 0s - loss: \n", "Epoch 235/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1269\n", "Epoch 236/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1265\n", "Epoch 237/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1272\n", "Epoch 238/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1264\n", "Epoch 239/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1252\n", "Epoch 240/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1258\n", "Epoch 241/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1237A: 0s - loss: 0.123\n", "Epoch 242/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1252\n", "Epoch 243/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1274\n", "Epoch 244/300\n", "96/96 [==============================] - 1s 6ms/step 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"96/96 [==============================] - 0s 3ms/step - loss: 0.1193\n", "Epoch 256/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1216\n", "Epoch 257/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1194\n", "Epoch 258/300\n", "96/96 [==============================] - 0s 3ms/step - loss: 0.1181\n", "Epoch 259/300\n", "96/96 [==============================] - 0s 3ms/step - loss: 0.1177\n", "Epoch 260/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1188\n", "Epoch 261/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1203\n", "Epoch 262/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1171\n", "Epoch 263/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1183\n", "Epoch 264/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1174\n", "Epoch 265/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1160\n", "Epoch 266/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1172\n", "Epoch 267/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1146\n", "Epoch 268/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1153\n", "Epoch 269/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1143\n", "Epoch 270/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1163\n", "Epoch 271/300\n", "96/96 [==============================] - 0s 3ms/step - loss: 0.1142\n", "Epoch 272/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1170\n", "Epoch 273/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1142\n", "Epoch 274/300\n", "96/96 [==============================] - 1s 8ms/step - loss: 0.1134\n", "Epoch 275/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1138\n", "Epoch 276/300\n", "96/96 [==============================] - 1s 7ms/step - loss: 0.1126\n", "Epoch 277/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1135\n", "Epoch 278/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1123\n", "Epoch 279/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1128\n", "Epoch 280/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1140\n", "Epoch 281/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1110\n", "Epoch 282/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1117\n", "Epoch 283/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1114\n", "Epoch 284/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1104\n", "Epoch 285/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1103\n", "Epoch 286/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1104\n", "Epoch 287/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1121\n", "Epoch 288/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1106\n", "Epoch 289/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1095\n", "Epoch 290/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1103\n", "Epoch 291/300\n", "96/96 [==============================] - 1s 5ms/step - loss: 0.1111\n", "Epoch 292/300\n", "96/96 [==============================] - 0s 5ms/step - loss: 0.1116\n", "Epoch 293/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1113\n", "Epoch 294/300\n", "96/96 [==============================] - 0s 4ms/step - loss: 0.1087\n", "Epoch 295/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1072\n", "Epoch 296/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1069\n", "Epoch 297/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1097\n", "Epoch 298/300\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "96/96 [==============================] - 1s 6ms/step - loss: 0.1087\n", "Epoch 299/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1068\n", "Epoch 300/300\n", "96/96 [==============================] - 1s 6ms/step - loss: 0.1083\n" ] } ], "source": [ "## second phase training of the discriminator with balanced data\n", "history_second_learning=maj_min_discriminator.fit(x=ovs_training_dataset,y=ovs_training_labels_oh, batch_size=20, epochs=300) " ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## loss of the second phase learning smoothly decreses \n", "## this is because now the data is fixed and diverse convex combinations are no longer fed into the discriminator at every training step\n", "run_range=range(1,300+1)\n", "plt.rcParams[\"figure.figsize\"] = (16,10)\n", "plt.xticks(fontsize=20)\n", "plt.yticks(fontsize=20)\n", "plt.xlabel('runs',fontsize=25)\n", "plt.ylabel('loss', fontsize=25)\n", "plt.plot(run_range, history_second_learning.history['loss'])" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[454 23]\n", " [ 6 11]]\n", "0.4313725490196078\n" ] } ], "source": [ "## finally after second phase training the discriminator classifier has a more balanced performance\n", "## meaning better F1-Score\n", "## the recall decreases but the precision improves\n", "\n", "y_pred_2d=maj_min_discriminator.predict(tf.convert_to_tensor(test_data))\n", "y_pred=np.digitize(y_pred_2d[:,0], [.5])\n", "print(confusion_matrix(test_labels, y_pred))\n", "print(f1_score(test_labels, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What needs to be done next:\n", "- make the model reproducible, such that fixing a random seed produces fixed results\n", "- a better class based framework for the model\n", "- run the model in a 5-fold cross validation framework\n", "- benchmark the model on other datasets\n", "- more experimentations on the architecture (adding dropout layers)?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }