refactoring: add initial pipeline configuration and model classes

research/models/lstm_model.py

@@ -0,0 +1,52 @@
+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")