refactoring: uv

src/ners/research/models/cnn_model.py

@@ -0,0 +1,86 @@
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from tensorflow.keras.layers import (
+    Embedding,
+    Conv1D,
+    MaxPooling1D,
+    GlobalMaxPooling1D,
+    Dense,
+    Dropout,
+    SpatialDropout1D,
+)
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+from ners.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, **kwargs) -> Any:
+        """Build CNN model with known vocabulary size"""
+
+        params = kwargs
+        model = Sequential(
+            [
+                # Learn char/subword embeddings; spatial dropout regularizes across channels
+                # to make the model robust to noisy characters and transliteration.
+                Embedding(
+                    input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)
+                ),
+                SpatialDropout1D(rate=params.get("embedding_dropout", 0.1)),
+                # Small kernels capture short n-gram like patterns; padding='same' keeps
+                # sequence length stable for simpler pooling behavior.
+                Conv1D(
+                    filters=params.get("filters", 64),
+                    kernel_size=params.get("kernel_size", 3),
+                    activation="relu",
+                    padding="same",
+                ),
+                # Downsample to gain some position invariance and reduce computation.
+                MaxPooling1D(pool_size=2),
+                # Second conv layer to compose higher-level motifs (e.g., suffix+vowel).
+                Conv1D(
+                    filters=params.get("filters", 64),
+                    kernel_size=params.get("kernel_size", 3),
+                    activation="relu",
+                    padding="same",
+                ),
+                # Global max pooling picks strongest motif evidence anywhere in the name.
+                GlobalMaxPooling1D(),
+                # Compact dense head with dropout to control overfitting.
+                Dense(64, activation="relu"),
+                Dropout(params.get("dropout", 0.5)),
+                # Two-way softmax for binary classification.
+                Dense(2, activation="softmax", dtype="float32"),
+            ]
+        )
+
+        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
+        # Get text data from extracted features - use character level for CNN
+        text_data = self._collect_text_corpus(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)
+        max_len = self.config.model_params.get(
+            "max_len", 20
+        )  # Longer for character level
+
+        return pad_sequences(sequences, maxlen=max_len, padding="post")