Lucas Oskorep
25 lines
1.1 KiB
Python
25 lines
1.1 KiB
Python
import numpy as np
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import pandas as pd
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from sklearn.metrics import accuracy_score, confusion_matrix, log_loss
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def get_metrics(gen, model, save_predictions_file=None):
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model_output = model.predict(gen, verbose=True, workers=12)
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prediction_indices = np.argmax(model_output, axis=1)
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label_index = {v: k for k, v in gen.class_indices.items()}
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predictions = [label_index[p] for p in prediction_indices]
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reals = [label_index[p] for p in gen.classes]
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acc = accuracy_score(reals, predictions)
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ll = log_loss(gen.classes, model_output, labels=[l for l in label_index.keys()])
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conf_mat = confusion_matrix(reals, predictions, labels=[l for l in label_index.values()])
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# print(classification_report(reals, predictions, labels=[l for l in label_index.values()]))
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print("Testing accuracy score is ", acc)
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print("Confusion Matrix", conf_mat)
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if save_predictions_file:
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df = pd.DataFrame(columns=['fname', 'prediction', 'true_val'])
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df['fname'] = [x for x in gen.filenames]
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df['prediction'] = predictions
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df["true_val"] = reals
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df.to_csv(save_predictions_file, index=False)
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return acc, ll
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