From 604342288706a9eed2d888525ab8f82ff3371163 Mon Sep 17 00:00:00 2001
From: Aanish Pradhan <aanish.pradhan@gmail.com>
Date: Sun, 28 Apr 2024 10:58:02 -0400
Subject: [PATCH] Added code for generating contour plots in each epoch

---
 src/contour.ipynb | 568 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 568 insertions(+)
 create mode 100644 src/contour.ipynb

diff --git a/src/contour.ipynb b/src/contour.ipynb
new file mode 100644
index 0000000..8f0671b
--- /dev/null
+++ b/src/contour.ipynb
@@ -0,0 +1,568 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# IMPORT PACKAGES\n",
+    "import matplotlib.pyplot as plt # Data visualization\n",
+    "import numpy as np # Linear algebra\n",
+    "import os # OS interfacing\n",
+    "import pandas as pd # Dataframe manipulation\n",
+    "import shutil # File operations\n",
+    "import sklearn.datasets as datasets # Datasets\n",
+    "import torch # Tensors\n",
+    "import torch.optim as optimizers # Neural network optimizers\n",
+    "import torch.nn as nn # Neural network layers\n",
+    "import torch.nn.functional as functions # Layer functions (e.g., ReLU, Softmax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DATA INESTION\n",
+    "rawData = pd.read_csv(\"../assets/Datasets/moons.csv\", sep = ',')\n",
+    "\n",
+    "# DATA WRANGLING\n",
+    "X = torch.tensor(rawData[['x', 'y']].to_numpy()).float()\n",
+    "y = torch.tensor(rawData['cluster'].to_numpy())\n",
+    "data = torch.utils.data.DataLoader(torch.utils.data.TensorDataset(X, y), \n",
+    "\tshuffle = True)\n",
+    "del(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuralNetwork(nn.Module):\n",
+    "\tdef __init__(self):\n",
+    "\t\tsuper(NeuralNetwork, self).__init__()\n",
+    "\t\tself.fc1 = nn.Linear(2, 100) # Entry to Output\n",
+    "\t\t#self.fc2 = nn.Linear(10, 10)\n",
+    "\t\t#self.fc3 = nn.Linear(10, 10)\n",
+    "\t\t#self.fc4 = nn.Linear(10, 10)\n",
+    "\t\t#self.fc5 = nn.Linear(10, 10)\n",
+    "\t\tself.fc6 = nn.Linear(100, 2)\n",
+    "\n",
+    "\tdef forwardPropagation(self, x):\n",
+    "\t\tx = functions.sigmoid(self.fc1(x))\n",
+    "\t\t#x = functions.sigmoid(self.fc2(x))\n",
+    "\t\t#x = functions.sigmoid(self.fc3(x))\n",
+    "\t\t#x = functions.sigmoid(self.fc4(x))\n",
+    "\t\t#x = functions.sigmoid(self.fc5(x))\n",
+    "\t\tx = self.fc6(x)\n",
+    "\t\treturn x\n",
+    "\t\n",
+    "\tdef train(self, trainingData: torch.utils.data.dataloader.DataLoader, \n",
+    "\t\tnumberOfEpochs: int, costFunction: torch.nn.modules.loss, \n",
+    "\t\toptimizer: torch.optim.Optimizer, learningRate: float):\n",
+    "\n",
+    "\t\toptimizer = optimizer(self.parameters(), learningRate)\n",
+    "\t\tcosts = np.zeros(numberOfEpochs) # Store overall cost at each epoch\n",
+    "\n",
+    "\t\tif os.path.exists(\"contourfigs/\") and os.path.isdir(\"contourfigs/\"):\n",
+    "\t\t\tshutil.rmtree(\"contourfigs\")\n",
+    "\t\t\n",
+    "\t\tos.mkdir(\"contourfigs/\")\n",
+    "\n",
+    "\t\tfor epoch in range(numberOfEpochs):\n",
+    "\n",
+    "\t\t\t# Generate decision boundary matrices\n",
+    "\t\t\twith torch.no_grad():\n",
+    "\t\t\t\t# Generate grid domain\n",
+    "\t\t\t\tx = np.linspace(rawData['x'].min() - 1, rawData['x'].max(), num = 100)\n",
+    "\t\t\t\ty = np.linspace(rawData['y'].min() - 1, rawData['y'].max(), num = 100)\n",
+    "\t\t\t\tx, y = np.meshgrid(x, y)\n",
+    "\t\t\t\tinputSpace = torch.tensor(np.vstack([x.flatten(), y.flatten()]).T)\n",
+    "\n",
+    "\t\t\t\t# Compute predicted class probabilities\n",
+    "\t\t\t\toutputSpace = self.predict(inputSpace.float()).numpy()\n",
+    "\t\t\t\toutputSpace = np.max(outputSpace, axis = 1).reshape(x.shape[0], x.shape[0])\n",
+    "\n",
+    "\t\t\t\t# Created melted data frame\n",
+    "\t\t\t\t# currentEpoch = np.ones((x.shape[0] * y.shape[0], 1)) * (epoch + 1)\n",
+    "\t\t\t\t# decisionBoundary = np.hstack([x.reshape(-1, 1), y.reshape(-1, 1), outputSpace, currentEpoch])\n",
+    "\t\t\t\t# decisionBoundaries.append(decisionBoundary)\n",
+    "\t\n",
+    "\t\t\t\tplt.contourf(x, y, outputSpace, cmap = \"RdBu\")\n",
+    "\t\t\t\tplt.scatter(rawData['x'], rawData['y'], c = rawData['cluster'], cmap = \"RdBu\")\n",
+    "\t\t\t\tplt.savefig(\"contourfigs/\" + str(epoch) + \".png\", dpi = 300)\n",
+    "\t\t\t\t#plt.show()\n",
+    "\n",
+    "\t\t\toptimizer.zero_grad() # Remove gradients from previous backprop\n",
+    "\t\t\tcost = 0.0 # Not sure if this is needed because of zero_grad()\n",
+    "\t\t\tfor observation in trainingData:\n",
+    "\t\t\t\tX, label = observation\n",
+    "\t\t\t\tprediction = self.forwardPropagation(X.view(-1, 2))\n",
+    "\t\t\t\tloss = costFunction(prediction, label)\n",
+    "\t\t\t\tcost += loss\n",
+    "\n",
+    "\t\t\tcosts[epoch] = cost\n",
+    "\t\t\tprint(\"Cost: \", costs[epoch])\n",
+    "\t\t\tcost.backward()\n",
+    "\t\t\toptimizer.step()\n",
+    "\n",
+    "\tdef predict(self, x: torch.tensor):\n",
+    "\t\twith torch.no_grad():\n",
+    "\t\t\treturn(self.forwardPropagation(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
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+      "Cost:  2.9557652473449707\n",
+      "Cost:  2.9263916015625\n",
+      "Cost:  2.897573471069336\n",
+      "Cost:  2.86930513381958\n",
+      "Cost:  2.8415722846984863\n",
+      "Cost:  2.8143582344055176\n",
+      "Cost:  2.7876529693603516\n",
+      "Cost:  2.761441469192505\n",
+      "Cost:  2.7357141971588135\n",
+      "Cost:  2.7104508876800537\n",
+      "Cost:  2.685649871826172\n",
+      "Cost:  2.6612939834594727\n",
+      "Cost:  2.6373722553253174\n",
+      "Cost:  2.613874912261963\n",
+      "Cost:  2.5907881259918213\n",
+      "Cost:  2.568101644515991\n",
+      "Cost:  2.5458030700683594\n",
+      "Cost:  2.523887872695923\n",
+      "Cost:  2.502337694168091\n",
+      "Cost:  2.481149673461914\n",
+      "Cost:  2.460310220718384\n",
+      "Cost:  2.439811944961548\n",
+      "Cost:  2.4196431636810303\n",
+      "Cost:  2.3997998237609863\n",
+      "Cost:  2.38027024269104\n",
+      "Cost:  2.361044406890869\n",
+      "Cost:  2.342115879058838\n",
+      "Cost:  2.3234822750091553\n",
+      "Cost:  2.305124044418335\n",
+      "Cost:  2.287046432495117\n",
+      "Cost:  2.269237518310547\n",
+      "Cost:  2.2516868114471436\n",
+      "Cost:  2.2343931198120117\n",
+      "Cost:  2.21734619140625\n",
+      "Cost:  2.2005465030670166\n",
+      "Cost:  2.1839780807495117\n",
+      "Cost:  2.1676435470581055\n",
+      "Cost:  2.1515321731567383\n",
+      "Cost:  2.1356449127197266\n",
+      "Cost:  2.119969367980957\n",
+      "Cost:  2.1045053005218506\n",
+      "Cost:  2.089246988296509\n",
+      "Cost:  2.0741865634918213\n",
+      "Cost:  2.059324264526367\n",
+      "Cost:  2.0446557998657227\n",
+      "Cost:  2.0301718711853027\n",
+      "Cost:  2.015872001647949\n",
+      "Cost:  2.0017528533935547\n",
+      "Cost:  1.9878088235855103\n",
+      "Cost:  1.9740378856658936\n",
+      "Cost:  1.960433840751648\n",
+      "Cost:  1.946998119354248\n",
+      "Cost:  1.9337214231491089\n",
+      "Cost:  1.9206030368804932\n",
+      "Cost:  1.9076440334320068\n",
+      "Cost:  1.8948347568511963\n",
+      "Cost:  1.8821758031845093\n",
+      "Cost:  1.8696630001068115\n",
+      "Cost:  1.8572969436645508\n",
+      "Cost:  1.8450708389282227\n",
+      "Cost:  1.8329830169677734\n",
+      "Cost:  1.8210309743881226\n",
+      "Cost:  1.8092148303985596\n",
+      "Cost:  1.7975294589996338\n",
+      "Cost:  1.785971760749817\n",
+      "Cost:  1.7745416164398193\n",
+      "Cost:  1.7632384300231934\n",
+      "Cost:  1.7520579099655151\n",
+      "Cost:  1.7409944534301758\n",
+      "Cost:  1.730053186416626\n",
+      "Cost:  1.7192271947860718\n",
+      "Cost:  1.7085163593292236\n",
+      "Cost:  1.6979193687438965\n",
+      "Cost:  1.6874330043792725\n",
+      "Cost:  1.6770539283752441\n",
+      "Cost:  1.6667841672897339\n",
+      "Cost:  1.656619668006897\n",
+      "Cost:  1.646559715270996\n",
+      "Cost:  1.6366010904312134\n",
+      "Cost:  1.6267446279525757\n",
+      "Cost:  1.6169883012771606\n",
+      "Cost:  1.6073280572891235\n",
+      "Cost:  1.597764253616333\n",
+      "Cost:  1.5882989168167114\n",
+      "Cost:  1.5789240598678589\n",
+      "Cost:  1.5696417093276978\n",
+      "Cost:  1.5604512691497803\n",
+      "Cost:  1.5513503551483154\n",
+      "Cost:  1.5423380136489868\n",
+      "Cost:  1.533411979675293\n",
+      "Cost:  1.5245721340179443\n",
+      "Cost:  1.515816569328308\n",
+      "Cost:  1.5071443319320679\n",
+      "Cost:  1.498555064201355\n",
+      "Cost:  1.4900486469268799\n",
+      "Cost:  1.4816186428070068\n",
+      "Cost:  1.4732693433761597\n",
+      "Cost:  1.464996099472046\n",
+      "Cost:  1.4568030834197998\n",
+      "Cost:  1.4486826658248901\n",
+      "Cost:  1.440637469291687\n",
+      "Cost:  1.4326674938201904\n",
+      "Cost:  1.4247702360153198\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "network = NeuralNetwork()\n",
+    "network.zero_grad()\n",
+    "network.train(data, 400, nn.CrossEntropyLoss(), optimizers.Adam, 0.1)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}