mirror of
https://github.com/WallyS02/Song-Lyrics-Generator.git
synced 2024-10-03 11:26:10 +00:00
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
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import math
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import numpy as np
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from keras import Model
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from keras.callbacks import Callback
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from keras_nlp.src.metrics import EditDistance
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from numpy.random import choice
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class QualityEvaluatorCallback(Callback):
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def __init__(self, validation_X, validation_y):
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super().__init__()
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self.validation_X = validation_X
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self.validation_y = validation_y
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def on_epoch_end(self, epoch, logs=None):
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print(f"{epoch} Calculating quality metrics!")
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perplexity = logs.get("loss")**math.e
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print(f"Perplexity: {perplexity}")
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# true_positives = {}
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# false_positives = {}
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# false_negatives = {}
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#
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# v_set_size = self.validation_X.shape[0]
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# random_ids = np.random.randint(0, v_set_size, size=100)
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# validation_X_subset = self.validation_X[random_ids]
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# validation_y_subset = self.validation_y[random_ids]
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#
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# i = 0
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# for X, label in zip(validation_X_subset, validation_y_subset):
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# prediction = self.keras_model.predict(X, verbose=0)
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#
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# predicted_idx = np.argmax(prediction)
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# valid_idx = np.argmax(label)
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#
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# if predicted_idx not in true_positives:
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# true_positives[predicted_idx] = 0
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# if predicted_idx not in false_positives:
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# false_positives[predicted_idx] = 0
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# if predicted_idx not in false_negatives:
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# false_negatives[predicted_idx] = 0
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#
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# if valid_idx not in true_positives:
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# true_positives[valid_idx] = 0
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# if valid_idx not in false_positives:
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# false_positives[valid_idx] = 0
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# if valid_idx not in false_negatives:
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# false_negatives[valid_idx] = 0
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#
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# if predicted_idx == valid_idx:
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# true_positives[predicted_idx] += 1
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# else:
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# false_positives[predicted_idx] += 1
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# false_negatives[valid_idx] += 1
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#
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# precisions = []
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# for tp, fp in zip(true_positives.values(), false_positives.values()):
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# precisions.append(tp/(tp + fp + 10e-5))
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#
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# mean_precision = np.average(precisions)
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# print(f"Mean precision: {mean_precision}")
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