Iris_data_study_classification.py

#Loading different subroutines for main program

#Import and Conversion, Normalization of Data
from Open_conversion_data import load_csv
from Open_conversion_data import str_column_to_float
from Open_conversion_data import str_column_to_int
from Open_conversion_data import dataset_minmax
from Open_conversion_data import Normalize_Dataset

#Splitting dataset in train, test or folds for cv
from Split_dataset import train_test_split
from Split_dataset import cross_validation_split

#KNN algorithm for classification
from My_KNN import getNeighbors
from My_KNN import getResponse

#Accuracy of the predictions
from Performance_assessment import getAccuracy
from Performance_assessment import confusion_matrix
from Performance_assessment import print_confusion_matrix
from Performance_assessment import recall_precision_calc

def main():

    # Load iris dataset
    filename = 'iris.csv'
    dataset = load_csv(filename)
    print('Loaded data file {0} with {1} rows and {2} columns'.format(filename, len(dataset), len(dataset[0])))
    print('First line of dataset: ', dataset[0])

    # convert string columns to float
    for i in range(4):
        str_column_to_float(dataset, i)
    # convert class column to int
    lookup = str_column_to_int(dataset, 4)
    print('First line of dataset with class defined by integer: ', dataset[0])
    print('')
    print('Dictionary of lookup classes: ', lookup)
    print('\n')
    
    # normalization of dataset
    minmax = dataset_minmax(dataset)
    Normalize_Dataset(dataset, minmax)

    # Splitting dataset between Training and Testing Set
    split = 0.6
    trainingSet, testSet = train_test_split(dataset, split)

    #generate predictions
    print('Algorithm solving:')
    predictions = []
    num_neighbors = 3
    for i in range(len(testSet)):
        neighbors = getNeighbors(trainingSet, testSet[i], num_neighbors, "Euclidean")
        classify = getResponse(neighbors)
        predictions.append(classify)
        print('> predicted=' + repr(classify) + ', actual=' + repr(testSet[i][-1]))

    #Accuracy Assessment
    accuracy = getAccuracy(testSet,predictions)
    print('Accuracy :' + repr(accuracy) + '%')
    unique, matrix = confusion_matrix(testSet, predictions)

    print('\n')
    print_confusion_matrix(unique, matrix)
    print('\n')

    #Calculate properties for recall and precision
    Recall, Precision, F1_score = recall_precision_calc(matrix)
    print('Recall:', Recall)
    print('Precision:', Precision)
    print('F1 score:', F1_score)

main()