implementation_of_perceptron.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import joblib\n",
    "import pandas as pd\n",
    "from matplotlib.colors import ListedColormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Perceptron:\n",
    "    def __init__(self, eta: float = None, epochs: int = None):\n",
    "        self.weights = np.random.randn(3) * 1e-4\n",
    "        training = (eta is not None) and (epochs is not None)\n",
    "        if training:\n",
    "            print(f\"initial weights before training: \\n{self.weights}\")\n",
    "            self.eta = eta\n",
    "            self.epochs = epochs\n",
    "    \n",
    "    def _z_outcome(self, inputs, weights):\n",
    "        return np.dot(inputs, weights)\n",
    "\n",
    "    def activation_function(self,z):\n",
    "        return np.where(z > 0, 1, 0)\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        X_with_bias = np.c_[self.X, -np.ones((len(self.X),1))]  # doing a concatenation operation\n",
    "        print(\"X with bias: \\n{X_with_bias}\")\n",
    "\n",
    "        for epoch in range(self.epochs):\n",
    "            print(\"--\"*10)\n",
    "            print(f\"for epoch >> {epoch}\")\n",
    "            print(\"--\"*10)\n",
    "            \n",
    "            z = self._z_outcome(X_with_bias, self.weights)\n",
    "            y_hat = self.activation_function(z)\n",
    "            print(f\"predicted value after forward pass: \\n{y_hat}\")\n",
    "            \n",
    "            self.error = self.y - y_hat\n",
    "            print(f\"error: \\n{self.error}\")\n",
    "            \n",
    "            self.weights = self.weights + self.eta * np.dot(X_with_bias.T, self.error)\n",
    "            print(f\"updated weights after epoch: {epoch + 1}/{self.epochs}: \\n{self.weights}\")\n",
    "            print(\"--\"*10)\n",
    "\n",
    "    def predict(self, X):\n",
    "        X_with_bias = np.c_[X, -np.ones((len(X),1))]\n",
    "        z = self._z_outcome(X_with_bias, self.weights)\n",
    "        return self.activation_function(z)\n",
    "\n",
    "\n",
    "    def total_loss(self):\n",
    "        total_loss = np.sum(self.error)\n",
    "        print(f\"\\ntotal loss: {total_loss}\\n\")\n",
    "        return total_loss\n",
    "\n",
    "    def _create_dir_return_path(self, model_dir, filename):\n",
    "        os.makedirs(model_dir, exist_ok=True)\n",
    "        return os.path.join(model_dir, filename)\n",
    "\n",
    "\n",
    "    def save(self, filename, model_dir = None):\n",
    "        if model_dir is not None:\n",
    "            model_file_path = self._create_dir_return_path(model_dir, filename)\n",
    "            joblib.dump(self, model_file_path)\n",
    "        else:\n",
    "            model_file_path = self._create_dir_return_path(\"model\", filename)\n",
    "            joblib.dump(self, model_file_path)\n",
    "\n",
    "    def load(self, filepath):\n",
    "        return joblib.load(filepath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data(df, target_column = \"y\"):\n",
    "    X = df.drop(\"y\", axis = 1)\n",
    "    y = df[\"y\"]\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x1  x2  y\n",
      "0   0   0  0\n",
      "1   0   1  0\n",
      "2   1   0  0\n",
      "3   1   1  1\n"
     ]
    }
   ],
   "source": [
    "AND = {\n",
    "    'x1' : [0,0,1,1],\n",
    "    'x2' : [0,1,0,1],\n",
    "    'y'  : [0,0,0,1]\n",
    "}\n",
    "\n",
    "df_AND = pd.DataFrame(AND)\n",
    "print(df_AND)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial weights before training: \n",
      "[ 6.10721643e-05  1.95827790e-04 -2.29164768e-04]\n",
      "X with bias: \n",
      "{X_with_bias}\n",
      "--------------------\n",
      "for epoch >> 0\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1   -1\n",
      "2   -1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 1/10: \n",
      "[-0.09993893 -0.09980417  0.29977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 1\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 2/10: \n",
      "[6.10721643e-05 1.95827790e-04 1.99770835e-01]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 2\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 3/10: \n",
      "[0.10006107 0.10019583 0.09977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 3\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1   -1\n",
      "2   -1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 4/10: \n",
      "[6.10721643e-05 1.95827790e-04 2.99770835e-01]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 4\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 5/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 5\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 6/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 6\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 7/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 7\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 8/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 8\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 9/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 9\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 10/10: \n",
      "[0.10006107 0.10019583 0.19977084]\n",
      "--------------------\n",
      "\n",
      "total loss: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y  = prepare_data(df_AND)\n",
    "\n",
    "ETA = 0.1\n",
    "EPOCHS = 10\n",
    "\n",
    "model_and = Perceptron(eta = ETA, epochs = EPOCHS)\n",
    "model_and.fit(X,y)\n",
    "\n",
    "_ = model_and.total_loss()  # _ denotes that it is a dummy value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_and.save(filename=\"and.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload_model_and = Perceptron().load(filepath= r'C:\\Users\\USER\\Desktop\\Project\\Learn-Implement-Predict\\model\\and.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reload_model_and.predict(X = [[1,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x1  x2  y\n",
      "0   0   0  0\n",
      "1   0   1  1\n",
      "2   1   0  1\n",
      "3   1   1  1\n"
     ]
    }
   ],
   "source": [
    "OR = {\n",
    "    'x1' : [0,0,1,1],\n",
    "    'x2' : [0,1,0,1],\n",
    "    'y'  : [0,1,1,1]\n",
    "}\n",
    "\n",
    "df_OR = pd.DataFrame(OR)\n",
    "print(df_OR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial weights before training: \n",
      "[ 8.17407602e-05 -1.32002297e-04 -2.17591682e-05]\n",
      "X with bias: \n",
      "{X_with_bias}\n",
      "--------------------\n",
      "for epoch >> 0\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 0 1 0]\n",
      "error: \n",
      "0   -1\n",
      "1    1\n",
      "2    0\n",
      "3    1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 1/10: \n",
      "[ 0.10008174  0.199868   -0.10002176]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 1\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 2/10: \n",
      "[ 1.00081741e-01  1.99867998e-01 -2.17591682e-05]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 2\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 3/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 3\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 4/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 4\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 5/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 5\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 6/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 6\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 7/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 7\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 8/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 8\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 9/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 9\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 1 1 1]\n",
      "error: \n",
      "0    0\n",
      "1    0\n",
      "2    0\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 10/10: \n",
      "[0.10008174 0.199868   0.09997824]\n",
      "--------------------\n",
      "\n",
      "total loss: 0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y  = prepare_data(df_OR)\n",
    "\n",
    "ETA = 0.1\n",
    "EPOCHS = 10\n",
    "\n",
    "model_or = Perceptron(eta = ETA, epochs = EPOCHS)\n",
    "model_or.fit(X,y)\n",
    "\n",
    "_ = model_or.total_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_or.save(filename = \"or.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_or.predict(X = [[0,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "reload_model_or = Perceptron().load(filepath= r'C:\\Users\\USER\\Desktop\\Project\\Learn-Implement-Predict\\model\\or.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_or.predict(X = [[0,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   x1  x2  y\n",
      "0   0   0  0\n",
      "1   0   1  1\n",
      "2   1   0  1\n",
      "3   1   1  0\n"
     ]
    }
   ],
   "source": [
    "XOR = {\n",
    "    'x1' : [0,0,1,1],\n",
    "    'x2' : [0,1,0,1],\n",
    "    'y'  : [0,1,1,0]\n",
    "}\n",
    "\n",
    "df_XOR = pd.DataFrame(XOR)\n",
    "print(df_XOR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "initial weights before training: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "X with bias: \n",
      "{X_with_bias}\n",
      "--------------------\n",
      "for epoch >> 0\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3   -1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 1/10: \n",
      "[-0.09997974 -0.09991698  0.19993759]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 1\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 2/10: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 2\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3   -1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 3/10: \n",
      "[-0.09997974 -0.09991698  0.19993759]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 3\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 4/10: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 4\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3   -1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 5/10: \n",
      "[-0.09997974 -0.09991698  0.19993759]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 5\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 6/10: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 6\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3   -1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 7/10: \n",
      "[-0.09997974 -0.09991698  0.19993759]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 7\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 8/10: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 8\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[1 1 1 1]\n",
      "error: \n",
      "0   -1\n",
      "1    0\n",
      "2    0\n",
      "3   -1\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 9/10: \n",
      "[-0.09997974 -0.09991698  0.19993759]\n",
      "--------------------\n",
      "--------------------\n",
      "for epoch >> 9\n",
      "--------------------\n",
      "predicted value after forward pass: \n",
      "[0 0 0 0]\n",
      "error: \n",
      "0    0\n",
      "1    1\n",
      "2    1\n",
      "3    0\n",
      "Name: y, dtype: int64\n",
      "updated weights after epoch: 10/10: \n",
      "[ 2.02633550e-05  8.30239831e-05 -6.24136183e-05]\n",
      "--------------------\n",
      "\n",
      "total loss: 2\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X, y  = prepare_data(df_XOR)\n",
    "\n",
    "ETA = 0.1\n",
    "EPOCHS = 10\n",
    "\n",
    "model_xor = Perceptron(eta = ETA, epochs = EPOCHS)\n",
    "model_xor.fit(X,y)\n",
    "\n",
    "_ = model_xor.total_loss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A single perceptron is a linear classifier. It can't classify non-linear data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_plot(df, model, filename = \"plot.png\", plot_dir = \"plots\"):\n",
    "\n",
    "    def _create_base_plot(df):\n",
    "        df.plot(kind = \"scatter\", x = \"x1\", y = \"x2\", c = \"y\", s = 100, cmap = \"coolwarm\")\n",
    "        plt.axhline(y = 0, color = \"black\", linestyle = \"--\", linewidth = 1)\n",
    "        plt.axvline(x = 0, color = \"black\", linestyle = \"--\", linewidth = 1)\n",
    "\n",
    "        figure = plt.gcf()\n",
    "        figure.set_size_inches(10, 8)\n",
    "\n",
    "    def _plot_decision_regressions(X, y, classifier, resolution = 0.02):\n",
    "        colors = (\"cyan\",  \"lightgreen\")\n",
    "        cmap = ListedColorMap(colors)\n",
    "        X = X.values\n",
    "        x1 = X[:, 0]\n",
    "        x2 = X[:, 1]\n",
    "\n",
    "        x1_min, x1_max = x1.min() - 1, x1.max() + 1\n",
    "        x2_min, x2_max = x2.min() - 1, x2.max() + 1\n",
    "\n",
    "        xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),\n",
    "                            np.arange(x2_min, x2_max, resolution)\n",
    "                            ) \n",
    "\n",
    "        y_hat = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)  # ravel() flattens the array\n",
    "        y_hat = y_hat.reshape(xx1.shape)\n",
    "        plt.contourf(xx1, xx2, y_hat, alpha = 0.2, cmap = cmap)\n",
    "        plt.xlim(xx1.min(), xx1.max())\n",
    "        plt.ylim(xx2.min(), xx2.max())\n",
    "\n",
    "        plt.plot()\n",
    "\n",
    "    X, y = prepare_data(df)\n",
    "\n",
    "    _create_base_plot(df)\n",
    "    _plot_decision_regressions(X, y, model)\n",
    "\n",
    "    os.makedirs(plot_dir, exist_ok=True)\n",
    "    plot_path = os.path.join(plot_dir, filename)\n",
    "    plt.savefig(plot_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_plot(df_OR, model_or, filename = \"or.png\")"
   ]
  }
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