57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
from typing import Any
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import numpy as np
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import pandas as pd
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from tensorflow.keras.layers import Embedding, Bidirectional, GRU, Dense, Dropout
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from tensorflow.keras.preprocessing.text import Tokenizer
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from research.neural_network_model import NeuralNetworkModel
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class BiGRUModel(NeuralNetworkModel):
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"""Bidirectional GRU model for name classification"""
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def build_model_with_vocab(self, vocab_size: int, max_len: int = 6, **kwargs) -> Any:
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params = kwargs
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model = Sequential(
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[
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Embedding(input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)),
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Bidirectional(
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GRU(
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params.get("gru_units", 32),
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return_sequences=True,
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dropout=params.get("dropout", 0.2),
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)
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),
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Bidirectional(GRU(params.get("gru_units", 32), dropout=params.get("dropout", 0.2))),
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Dense(64, activation="relu"),
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Dropout(params.get("dropout", 0.5)),
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Dense(2, activation="softmax"),
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]
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)
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model.compile(
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loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]
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)
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return model
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def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
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text_data = []
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for feature_type in self.config.features:
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if feature_type.value in X.columns:
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text_data.extend(X[feature_type.value].astype(str).tolist())
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if not text_data:
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raise ValueError("No text data found in the provided DataFrame.")
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if self.tokenizer is None:
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self.tokenizer = Tokenizer(char_level=False, lower=True, oov_token="<OOV>")
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self.tokenizer.fit_on_texts(text_data)
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sequences = self.tokenizer.texts_to_sequences(text_data[: len(X)])
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max_len = self.config.model_params.get("max_len", 6)
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return pad_sequences(sequences, maxlen=max_len, padding="post")
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