Created
July 6, 2020 16:25
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| import sys | |
| import os | |
| from sklearn.metrics import precision_recall_curve, auc | |
| import pickle | |
| import json | |
| # read command line parameters | |
| if len(sys.argv) != 5: | |
| sys.stderr.write('Arguments error. Usage:\n') | |
| sys.stderr.write( | |
| '\tpython3 evaluate.py model-filename features-dir-path scores-filename\ | |
| plots-filename\n' | |
| ) | |
| sys.exit(1) | |
| model_filename = sys.argv[1] | |
| features_path = sys.argv[2] | |
| test_features_file = os.path.join(os.path.join(features_path, 'test.pkl')) | |
| scores_file = sys.argv[3] | |
| plots_file = sys.argv[4] | |
| # load features | |
| with open(test_features_file, 'rb') as f: | |
| test_features = pickle.load(f) | |
| X_test = test_features.iloc[:,:-1] | |
| y_test = test_features.iloc[:,-1] | |
| # load model | |
| with open(model_filename, 'rb') as f: | |
| model = pickle.load(f) | |
| # make predictions | |
| predictions_by_class = model.predict_proba(X_test) | |
| predictions = predictions_by_class[:,-1] | |
| # generate scores | |
| precision, recall, thresholds = precision_recall_curve(y_test, predictions) | |
| auc = auc(recall, precision) | |
| # save scores | |
| with open(scores_file, 'w') as f: | |
| json.dump({'auc': auc}, f) | |
| # save plots | |
| with open(plots_file, 'w') as f: | |
| proc_dict = {'proc': [{ | |
| 'precision': p, | |
| 'recall': r, | |
| 'threshold': t | |
| } for p, r, t in zip(precision, recall, thresholds) | |
| ]} | |
| json.dump(proc_dict, f) |
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