ProjectStage1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4db56675",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\andy\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score for the training set is 100.0 %\n",
      "The accuracy score for the testing set is 88.88888888888889 %\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<function sklearn.metrics._classification.accuracy_score(y_true, y_pred, *, normalize=True, sample_weight=None)>,\n",
       " <function sklearn.metrics._classification.confusion_matrix(y_true, y_pred, *, labels=None, sample_weight=None, normalize=None)>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#all the imports for project stage 1\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from numpy import asarray\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "import os, os.path\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "\n",
    "\n",
    "folderTraining=    # folder pathway for the training data (add the folder pathway for the dataset)\n",
    "folderTesting=     # folder pathway for the testing data  (add the folder pathway for the dataset)\n",
    "width = 5                                                         # desired width of use for each image\n",
    "length = 10                                                        # desired length of use for each image\n",
    "model = training(folderTraining,width,length)                      # call to the training function and returns Logistic regression model with the inputted training data set\n",
    "testing(folderTesting,model,width,length)                          # call to the testing fucntions which returns the confustion matrix for the testing data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bccb6dc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def training(path,width,length):                                                                                          #method heading training, takes in the parameters of folder pathway, width and length of the image\n",
    "    imgData_train, class_label_train = getData(path,width,length)                                                         #Recieve Image Data and class label as matrix of the training dataset from getData()\n",
    "    class_label_train = np.ravel(class_label_train)                                                                       #ravels the class_label_train input as a 1-D array, containing the elements of the input\n",
    "    model = LogisticRegression()                                                                                          #Creating a Logistic Regression model\n",
    "    model.fit(imgData_train,class_label_train)                                                                            #Fitting the model to the training dataset\n",
    "    class_label_pred = model.predict(imgData_train)                                                                       #List of predicted values from the input from training data\n",
    "    print(f'The accuracy score for the training set is {accuracy_score(class_label_train, class_label_pred)*100} %')      #Calculate the accuracy score from the given training data set\n",
    "    conf_matrix = confusion_matrix(class_label_train, class_label_pred)                                                   #calculate the confustion matrix from the train class labels to the predicted class labels\n",
    "    disp1 = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=model.classes_)                            #display the confustion matrix as an formatted image with true label vertically and the predicated label horizontally\n",
    "    disp1.plot()                                                                                                          #plot the confusion matrix\n",
    "    disp1.ax_.set_title('Training Data Confusion Matrix')                                                                 # set the title for the confusion matrix\n",
    "    return model                                                                                                          #return the model of the Logistic Regression model trained with the training dataset\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "38e1de56",
   "metadata": {},
   "outputs": [],
   "source": [
    "def testing(path, model, width , length):                                                           #method heading testing, takes in the parameters of folder pathway, width and length of the image, and the LogisticRegression model from the training dataset\n",
    "        imgData_test, class_label_test = getData(path,width,length)                                 #Recieve Image Data and class label as matrix of the testing dataset from getData()\n",
    "        class_label_pred = model.predict(imgData_test)                                              #List of predicted values from the input from testing data\n",
    "        acc_score = accuracy_score(class_label_test,class_label_pred) * 100                         #Calculate the accuracy score from the given testing data set\n",
    "        print(f'The accuracy score for the testing set is {acc_score} %')                           #print the accuracy score\n",
    "        conf_matrix = confusion_matrix(class_label_test,class_label_pred)                           #calculate the confustion matrix from the test class labels to the predicted class labels\n",
    "        disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix,display_labels=model.classes_)   #display the confustion matrix as an formatted image with true label vertically and the predicated label horizontally\n",
    "        disp.plot()                                                                                 #plot the confusion matrix\n",
    "        disp.ax_.set_title('Testing Data Confusion Matrix')                                         # set the title for the confusion matrix                                                                           \n",
    "        return accuracy_score, confusion_matrix                                                     # return the value for the accuracy score and the confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbfa8229",
   "metadata": {},
   "outputs": [],
   "source": [
    "                                                    \n",
    "def getData(dataFolder,width, length):                #method heading getData, takes in the parameters of folder pathway, width and length of the image\n",
    "    data_path= os.listdir(dataFolder)                 #list of the file names in the folder of interest such as training and testing                       \n",
    "    imgData= np.empty((len(data_path),width*length))  #creates an empty array of features for the image Data\n",
    "    class_label = np.empty((len(data_path),1))        #creates an empty array of labels for the image Data                \n",
    "    for i in range (len(data_path)):                  #a loop that iterates through the items in the folder of interest and appends a reshaped data array to the image data, which will be used as the X_train, X_test              \n",
    "        pathway = dataFolder + '/' + data_path[i]     #creates a string for the pathway for each image    \n",
    "        image = Image.open(pathway).convert('L')      #Uses the library Image to instantiate the image and converts it to greyscale\n",
    "        image= image.resize((width,length))           # resizes the image to the width and length of users choice          \n",
    "        image = asarray(image)                        #Converts the image to an array of data values           \n",
    "        imgData[i,:] = image.reshape(1,-1)            # Converts and reshapes features of the image data to 1D array    \n",
    "        \n",
    "        \n",
    "        #Since labels are in string value, we need to change it to a int value to name the labels\n",
    "        # 0  means Circle\n",
    "        # 1 means Rectangle\n",
    "        # 2 means Square\n",
    "        if 'cir' in data_path[i]:\n",
    "              class_label[i,0] = 0\n",
    "        if 'rec' in data_path[i]:\n",
    "              class_label[i,0] = 1\n",
    "        if 'squ' in data_path[i]:\n",
    "              class_label[i,0] = 2\n",
    "    return imgData,class_label                         #returns value for the reshaped image data and the integer converted class label describing each lego brick"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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