backward_search_analysis_confusion_matrix.py

"""This code retain columns of index 2 and 3 then performs classification using DTC and RFC to find confusion matrix."""

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.utils import shuffle
from datetime import datetime

# Record start time
start_time = datetime.now()

# Load training data, extract the machine failed labels (last column), and delete the column from the original data
train_data = np.loadtxt("dataset\\ai4i2020_train_data.csv", delimiter=",")
train_data = shuffle(train_data, random_state=42)  # Randomize the data
train_data_labels = train_data[:, 6]
train_data = np.delete(train_data, 6, axis=1)

# Load testing data, extract the machine failed labels (last column), and delete the column from the original data
test_data = np.loadtxt("dataset\\ai4i2020_test_data.csv", delimiter=",")
test_data_labels = test_data[:, 6]
test_data = np.delete(test_data, 6, axis=1)

# Calculate number of features
attributes = np.shape(train_data)[1]  # 6

# Variable to store confusion matrix errors
# Class A = Machine fails = 1
# Class B = Machine does NOT fail = 0
dtc_confusion_matrix = np.empty(shape=(2, 2))
dtc_confusion_matrix_percentage = np.empty(shape=(2, 2))
rfc_confusion_matrix = np.empty(shape=(2, 2))
rfc_confusion_matrix_percentage = np.empty(shape=(2, 2))

# Create instances of classifiers
clf_dtc = DecisionTreeClassifier()
clf_rfc = RandomForestClassifier()

# --- Decision Tree Classification and Random Forest Classification using backward search begins ---
# Remove the dimensions from the data and retain columns of index 2 and 3 only
train_data_reduced = np.delete(train_data, obj=[0, 1, 3, 5], axis=1)
test_data_reduced = np.delete(test_data, obj=[0, 1, 3, 5], axis=1)

# Decision Tree Classification and Random Forest Classification on the reduced data
dtc_train_start_time = datetime.now()
clf_dtc.fit(train_data_reduced, train_data_labels)  # Training of the model
dtc_train_time = datetime.now() - dtc_train_start_time
dtc_test_start_time = datetime.now()
dtc_prediction = clf_dtc.predict(test_data_reduced)  # Testing of the model
dtc_test_time = datetime.now() - dtc_test_start_time

rfc_train_start_time = datetime.now()
clf_rfc.fit(train_data_reduced, train_data_labels)  # Training of the model
rfc_train_time = datetime.now() - rfc_train_start_time
rfc_test_start_time = datetime.now()
rfc_prediction = clf_rfc.predict(test_data_reduced)  # Testing of the model
rfc_test_time = datetime.now() - rfc_test_start_time

for i in range (2):
    for j in range(2):
        dtc_confusion_matrix[i][j] = sum(np.logical_and(dtc_prediction == i, test_data_labels == j))
        rfc_confusion_matrix[i][j] = sum(np.logical_and(rfc_prediction == i, test_data_labels == j))

        if j == 0:
            dtc_confusion_matrix_percentage[i][j] = (dtc_confusion_matrix[i][j] / 2428)*100
            rfc_confusion_matrix_percentage[i][j] = (rfc_confusion_matrix[i][j] / 2428) * 100

        else:
            dtc_confusion_matrix_percentage[i][j] = (dtc_confusion_matrix[i][j] / 72) * 100
            rfc_confusion_matrix_percentage[i][j] = (rfc_confusion_matrix[i][j] / 72) * 100

# Print confusion matrices and accuracies
"""
Class B True -ve    Class A True +ve
Class B False -ve   Class A False +ve
"""

print("Decision Tree Classification Confusion Matrix:")
print(dtc_confusion_matrix)

print("\nDecision Tree Classification Confusion Matrix in percentages:")
print(dtc_confusion_matrix_percentage)

print("\nRandom Forest Classification Confusion Matrix:")
print(rfc_confusion_matrix)

print("\nRandom Forest Classification Confusion Matrix in percentages:")
print(rfc_confusion_matrix_percentage)

print(f"\nDecision Tree Classification Accuracy: "
      f"{((sum(dtc_prediction == test_data_labels))/len(test_data_labels))*100}%")
print(f"Random Forest Classification Accuracy: "
      f"{((sum(rfc_prediction == test_data_labels))/len(test_data_labels))*100}%")

# Record end time and show total run time
print(f"\nProgram completed in {(datetime.now() - start_time).seconds}."
      f"{(datetime.now() - start_time).microseconds} seconds.")

print(f"\nDecision Tree Classification training time: {dtc_train_time.seconds}.{dtc_train_time.microseconds} seconds")
print(f"Decision Tree Classification testing time: {dtc_test_time.seconds}.{dtc_test_time.microseconds} seconds")

print(f"\nRandom Forest Classification training time: {rfc_train_time.seconds}.{rfc_train_time.microseconds} seconds")
print(f"Random Forest Classification testing time: {rfc_test_time.seconds}.{rfc_test_time.microseconds} seconds")