refactoring: add initial pipeline configuration and model classes

research/models/cnn_model.py

@@ -0,0 +1,75 @@
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from tensorflow.keras.layers import (
+    Embedding,
+    Conv1D,
+    MaxPooling1D,
+    GlobalMaxPooling1D,
+    Dense,
+    Dropout,
+)
+from tensorflow.keras.models import Sequential
+
+from research.neural_network_model import NeuralNetworkModel
+
+
+class CNNModel(NeuralNetworkModel):
+    """1D Convolutional Neural Network for character patterns"""
+
+    def build_model_with_vocab(self, vocab_size: int, max_len: int = 20, **kwargs) -> Any:
+        """Build CNN model with known vocabulary size"""
+
+        params = kwargs
+        model = Sequential(
+            [
+                Embedding(input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)),
+                Conv1D(
+                    filters=params.get("filters", 64),
+                    kernel_size=params.get("kernel_size", 3),
+                    activation="relu",
+                ),
+                MaxPooling1D(pool_size=2),
+                Conv1D(
+                    filters=params.get("filters", 64),
+                    kernel_size=params.get("kernel_size", 3),
+                    activation="relu",
+                ),
+                GlobalMaxPooling1D(),
+                Dense(64, activation="relu"),
+                Dropout(params.get("dropout", 0.5)),
+                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:
+        """Prepare sequences for CNN using extracted features"""
+        # X here contains the features already extracted by FeatureExtractor
+        from tensorflow.keras.preprocessing.text import Tokenizer
+        from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+        # Get text data from extracted features - use character level for CNN
+        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:
+            # Fallback - should not happen if FeatureExtractor is properly configured
+            text_data = [""] * len(X)
+
+        # Initialize character-level tokenizer
+        if self.tokenizer is None:
+            self.tokenizer = Tokenizer(char_level=True, lower=True, oov_token="<OOV>")
+            self.tokenizer.fit_on_texts(text_data)
+
+        sequences = self.tokenizer.texts_to_sequences(text_data[: len(X)])
+        max_len = self.config.model_params.get("max_len", 20)  # Longer for character level
+
+        return pad_sequences(sequences, maxlen=max_len, padding="post")