71 lines
2.8 KiB
Python
71 lines
2.8 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, LSTM, 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 LSTMModel(NeuralNetworkModel):
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"""LSTM model for sequence learning"""
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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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# Mask padding tokens; required for LSTM to ignore padded timesteps.
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Embedding(
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input_dim=vocab_size,
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output_dim=params.get("embedding_dim", 64),
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input_length=max_len,
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mask_zero=True,
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),
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# Stacked bidirectional LSTMs: first returns sequences to feed the next.
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# Dropout/recurrent_dropout mitigate overfitting on short sequences.
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Bidirectional(
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LSTM(
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params.get("lstm_units", 32),
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return_sequences=True,
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dropout=params.get("dropout", 0.2),
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recurrent_dropout=params.get("recurrent_dropout", 0.0),
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)
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),
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# Second LSTM condenses sequence to a fixed vector for classification.
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Bidirectional(
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LSTM(
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params.get("lstm_units", 32),
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dropout=params.get("dropout", 0.2),
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recurrent_dropout=params.get("recurrent_dropout", 0.0),
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)
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),
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# Compact dense head with dropout; sufficient capacity for name signals.
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Dense(64, activation="relu"),
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Dropout(params.get("dropout", 0.5)),
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# Two-way softmax for binary classification.
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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 = self._collect_text_corpus(X)
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# Initialize tokenizer if needed
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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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# Convert to sequences
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sequences = self.tokenizer.texts_to_sequences(text_data)
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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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