def gen_test_report(clf, y_test, X_test, args, sub_str='_test_report_per_cls'):
#Writing test report to file
y_true, y_pred = y_test, clf.predict(X_test)
#ipdb.set_trace()
#Producing pandas ML confusion matrix and statistical summary
tmp_confusion_matrix = ConfusionMatrix(y_true, y_pred)
#tmp_stat_summary=tmp_confusion_matrix.stats()
tmp_confusion_matrix = tmp_confusion_matrix.to_dataframe()
tmp_confusion_matrix.to_csv(
os.path.join(args.output_model_dir,
args.f_nm_str + '_confusion_matrix'))
#Generation dictionary for analysis
#with open(os.path.join(args.output_model_dir,args.f_nm_str+'_descriptive_stat.pickle')) as fb:
# pickle.dump(tmp_stat_summary,fb)
file_nm_test_report = args.f_nm_str + sub_str
#Generating report on test data for analysis
test_report_raw = classification_report(y_true, y_pred, output_dict=True)
test_report_df = pd.DataFrame(test_report_raw).transpose()
#Writing best model to file directory for models
print(test_report_raw)
test_report_df.to_csv(
os.path.join(args.output_model_dir, file_nm_test_report))
for idx, row in df.iterrows():
img = image.load_img(row['img_path'], target_size=(size, size))
img = image.img_to_array(img)
img = preprocess_input(np.expand_dims(img, axis=0))
pred = m.predict(img)
res = classes[np.argmax(pred)]
pred = pred[0].tolist()
df.loc[idx, 'predictions'] = str([round(i, 3) for i in pred])
df.loc[idx, 'pred'] = res
df.to_csv(op_csv, index=False)
# Metrics
print(accuracy_score(df['labels'].tolist(), df['pred'].tolist()))
print(
classification_report(df['labels'].tolist(),
df['pred'].tolist(),
target_names=classes))
cm = ConfusionMatrix(df['labels'].tolist(), df['pred'].tolist()).to_dataframe()
# Save metrics
cm.to_csv(os.path.join(os.path.dirname(op_csv), 'confusion_matrix.csv'),
index=False)
fig = plt.figure()
plt.matshow(cm)
plt.title('Confusion Matrix')
plt.colorbar()
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.savefig(os.path.join(os.path.dirname(op_csv), 'confusion_matrix.jpg'))
