 Chemicals Segregation.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dfx = pd.read_csv(\"./Logistic_X_Train.csv\")\n",
    "dfy = pd.read_csv(\"./Logistic_Y_Train.csv\")\n",
    "\n",
    "dfXT = pd.read_csv(\"./Logistic_X_Test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3000, 3)\n",
      "(3000, 1)\n",
      "(1000, 3)\n"
     ]
    }
   ],
   "source": [
    "X_train = dfx.values\n",
    "Y_train = dfy.values\n",
    "\n",
    "X_test = dfXT.values\n",
    "\n",
    "print(X_train.shape)\n",
    "print(Y_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.scatter?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def sigmoid(z):\n",
    "    return 1/(1+np.exp(-z))\n",
    "\n",
    "\n",
    "def hypothesis(x,theta):\n",
    "    return sigmoid(np.dot(x,theta))\n",
    "\n",
    "def error(Y,X,theta):\n",
    "    m = X.shape[0]\n",
    "    total_error = 0.0\n",
    "    \n",
    "    for i in range(m):\n",
    "        y_hat = hypothesis(X[i],theta)\n",
    "        total_error += (Y[i]*np.log2(y_hat)+(1-Y[i])*np.log2(1-y_hat)) \n",
    "        \n",
    "    return total_error/m #Average Loss\n",
    "\n",
    "def get_grad(Y,X,theta):\n",
    "    \n",
    "    grad = np.zeros(theta.shape)\n",
    "    m = X.shape[0]\n",
    "    for i in range(m):\n",
    "        y_hat = hypothesis(X[i],theta)\n",
    "        x = X[i].reshape((-1,1))\n",
    "        grad += (Y[i] - y_hat)*x\n",
    "        \n",
    "        \n",
    "    return grad/m #Average Gradient\n",
    "\n",
    "\n",
    "def grad_descent(X,Y,learning_rate=0.05):\n",
    "    theta = np.zeros((X.shape[1],1))\n",
    "    \n",
    "    error_list = []\n",
    "    \n",
    "    for i in range(1000):\n",
    "        theta = theta + learning_rate*get_grad(Y,X,theta)\n",
    "        e = error(Y,X,theta)\n",
    "        error_list.append(e)\n",
    "        \n",
    "    return theta,error_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "theta,error_list = grad_descent(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "error_lis = np.array(error_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_lis.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 1)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(error_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(Xtest,theta):\n",
    "    prob = hypothesis(Xtest,theta)\n",
    "    if prob<=0.5:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Pred = np.array([predict(X_test[i],theta) for i in range(X_test.shape[0])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "outcome = pd.DataFrame({\"label\":Y_Pred})\n",
    "outcome.to_csv(\"./submissionChemical2.csv\", index= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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