87 lines
2.8 KiB
Python
87 lines
2.8 KiB
Python
import keras.backend as K
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import dataset
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from . import pauls_networks
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from . import renes_networks
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def get_models_by_params(params: dict):
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# decomposing param section
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# mainly embedding model
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network_type = params.get("type")
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vocab_size = len(dataset.get_character_dict()) + 1
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embedding_size = params.get("embedding_size")
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input_length = params.get("input_length")
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filter_embedding = params.get("filter_embedding")
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kernel_embedding = params.get("kernel_embedding")
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hidden_embedding = params.get("hidden_embedding")
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dropout = params.get("dropout")
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# mainly prediction model
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flow_features = params.get("flow_features")
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domain_features = params.get("domain_features")
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window_size = params.get("window_size")
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domain_length = params.get("domain_length")
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filter_main = params.get("filter_main")
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kernel_main = params.get("kernels_main")
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dense_dim = params.get("dense_main")
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# create models
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networks = renes_networks if network_type == "rene" else pauls_networks
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embedding_model = networks.get_embedding(vocab_size, embedding_size, input_length,
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filter_embedding, kernel_embedding, hidden_embedding, drop_out=dropout)
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predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, embedding_model)
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new_model = networks.get_new_model(dropout, flow_features, domain_features, window_size, domain_length,
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filter_main, kernel_main, dense_dim, embedding_model)
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return embedding_model, predict_model, new_model
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def get_metrics():
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return dict([
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("precision", precision),
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("recall", recall),
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("f1_score", f1_score),
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])
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def get_metric_functions():
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return [precision, recall, f1_score]
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def precision(y_true, y_pred):
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# Count positive samples.
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true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
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predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
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return true_positives / (predicted_positives + K.epsilon())
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def recall(y_true, y_pred):
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# Count positive samples.
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true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
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possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
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return true_positives / (possible_positives + K.epsilon())
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def f1_score(y_true, y_pred):
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return f_score(1)(y_true, y_pred)
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def f05_score(y_true, y_pred):
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return f_score(0.5)(y_true, y_pred)
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def f_score(beta):
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def _f(y_true, y_pred):
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p = precision(y_true, y_pred)
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r = recall(y_true, y_pred)
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bb = beta ** 2
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fbeta_score = (1 + bb) * (p * r) / (bb * p + r + K.epsilon())
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return fbeta_score
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return _f
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