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classify.py
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classify.py
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from sklearn import svm, metrics, tree,neural_network
import numpy as np
import csv
my_data = np.loadtxt('edited_data/dataset_classification_edited.csv',delimiter=',', dtype='str')
prediction_data = np.loadtxt('edited_data/classification_unlabeled_edited.csv', delimiter=',', dtype='str')
# print(my_data)
training_data = my_data[:, 0:6]
validation_data = my_data[:, 6]
# print(training_data)
classifier = tree.DecisionTreeClassifier(max_depth=10)
classifier.fit(training_data, validation_data)
unknown = classifier.predict(prediction_data)
init_file_data = np.loadtxt('initial_data/test_classification_unlabeled.csv', delimiter=',', dtype='str')
with open('results/predictions_classification_whole_dataset.csv', "w", newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
first_row = np.append(init_file_data[0], ['class'])
writer.writerow(first_row)
i = 0
for row in init_file_data[1:, :]:
row = np.append(row, [unknown[i]])
writer.writerow(row)
i += 1
with open('results/predictions_classification_with_id.csv', "w", newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
i = 0
for row in init_file_data[1:, 0]:
row = np.append(row, [unknown[i]])
writer.writerow(row)
i += 1
with open('results/predictions_classification.csv', "w", newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
for row in unknown:
writer.writerow([row])
# only for visualization
device_classes = np.loadtxt('edited_data/classes.csv', delimiter=',', dtype='str')
device_classes = device_classes[:, 0]
with open('results/predictions_classification_visualization.csv', "w", newline='') as csv_file:
writer = csv.writer(csv_file, delimiter=',')
i = 0
for row in prediction_data:
device_class = unknown[i]
value = np.where(device_classes == device_class)
device_class = value[0][0] + 1
row = np.append(row, [device_class])
writer.writerow(row)
i += 1