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