diff --git a/3217-classification-lr-example2.py b/3217-classification-lr-example2.py deleted file mode 100644 index 8896f7e5c4680a3dfe44e9a69d2b1f8e6dd66659..0000000000000000000000000000000000000000 --- a/3217-classification-lr-example2.py +++ /dev/null @@ -1,44 +0,0 @@ -from sklearn import datasets, neighbors, linear_model -from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score -import matplotlib.pyplot as plt - -X_digits, y_digits = datasets.load_digits(return_X_y=True) -X_digits = X_digits / X_digits.max() - -n_samples = len(X_digits) - -ratio = 0.9 -print (n_samples) -#train data -X_train = X_digits[: int(ratio * n_samples)] -y_train = y_digits[: int(ratio * n_samples)] -print (X_train.shape) - - -#test data - -X_test = X_digits[int(ratio * n_samples) :] -y_test = y_digits[int(ratio * n_samples) :] -print (X_test.shape) - -logistic = linear_model.LogisticRegression(max_iter=1000) - -print( - "LogisticRegression score: %f" - % logistic.fit(X_train, y_train).score(X_test, y_test)) - -#Get results on actual test labels and predicted labels - -predictions = logistic.predict(X_test) - -#print (predictions) -#print (y_test) -#get f1 score - -print (f1_score(y_test, predictions, average='macro')) - -#get confusion matrix -cm = confusion_matrix(y_test, predictions, labels=logistic.classes_) -disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=logistic.classes_) -disp.plot() -plt.show()