mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-19 17:17:04 -05:00
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
# Modified from https://www.geeksforgeeks.org/multiclass-classification-using-scikit-learn/
|
|
|
|
import argparse
|
|
|
|
# importing necessary libraries
|
|
import numpy as np
|
|
|
|
from sklearn import datasets
|
|
from sklearn.metrics import confusion_matrix
|
|
from sklearn.model_selection import train_test_split
|
|
|
|
import joblib
|
|
|
|
from azureml.core.run import Run
|
|
run = Run.get_context()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument('--kernel', type=str, default='linear',
|
|
help='Kernel type to be used in the algorithm')
|
|
parser.add_argument('--penalty', type=float, default=1.0,
|
|
help='Penalty parameter of the error term')
|
|
|
|
args = parser.parse_args()
|
|
run.log('Kernel type', np.str(args.kernel))
|
|
run.log('Penalty', np.float(args.penalty))
|
|
|
|
# loading the iris dataset
|
|
iris = datasets.load_iris()
|
|
|
|
# X -> features, y -> label
|
|
X = iris.data
|
|
y = iris.target
|
|
|
|
# dividing X, y into train and test data
|
|
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
|
|
|
|
# training a linear SVM classifier
|
|
from sklearn.svm import SVC
|
|
svm_model_linear = SVC(kernel=args.kernel, C=args.penalty).fit(X_train, y_train)
|
|
svm_predictions = svm_model_linear.predict(X_test)
|
|
|
|
# model accuracy for X_test
|
|
accuracy = svm_model_linear.score(X_test, y_test)
|
|
print('Accuracy of SVM classifier on test set: {:.2f}'.format(accuracy))
|
|
run.log('Accuracy', np.float(accuracy))
|
|
# creating a confusion matrix
|
|
cm = confusion_matrix(y_test, svm_predictions)
|
|
print(cm)
|
|
|
|
# save model
|
|
joblib.dump(svm_model_linear, 'model.joblib')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|