81 lines
3.2 KiB
Python
81 lines
3.2 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 (
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Embedding,
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Conv1D,
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MaxPooling1D,
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GlobalMaxPooling1D,
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Dense,
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Dropout,
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SpatialDropout1D,
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)
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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from research.neural_network_model import NeuralNetworkModel
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class CNNModel(NeuralNetworkModel):
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"""1D Convolutional Neural Network for character patterns"""
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def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
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"""Build CNN model with known vocabulary size"""
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params = kwargs
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model = Sequential(
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[
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# Learn char/subword embeddings; spatial dropout regularizes across channels
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# to make the model robust to noisy characters and transliteration.
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Embedding(input_dim=vocab_size, output_dim=params.get("embedding_dim", 64)),
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SpatialDropout1D(rate=params.get("embedding_dropout", 0.1)),
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# Small kernels capture short n-gram like patterns; padding='same' keeps
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# sequence length stable for simpler pooling behavior.
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Conv1D(
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filters=params.get("filters", 64),
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kernel_size=params.get("kernel_size", 3),
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activation="relu",
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padding="same",
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),
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# Downsample to gain some position invariance and reduce computation.
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MaxPooling1D(pool_size=2),
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# Second conv layer to compose higher-level motifs (e.g., suffix+vowel).
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Conv1D(
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filters=params.get("filters", 64),
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kernel_size=params.get("kernel_size", 3),
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activation="relu",
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padding="same",
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),
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# Global max pooling picks strongest motif evidence anywhere in the name.
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GlobalMaxPooling1D(),
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# Compact dense head with dropout to control overfitting.
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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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"""Prepare sequences for CNN using extracted features"""
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# X here contains the features already extracted by FeatureExtractor
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# Get text data from extracted features - use character level for CNN
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text_data = self._collect_text_corpus(X)
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# Initialize character-level tokenizer
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if self.tokenizer is None:
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self.tokenizer = Tokenizer(char_level=True, lower=True, oov_token="<OOV>")
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self.tokenizer.fit_on_texts(text_data)
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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", 20) # Longer for character level
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return pad_sequences(sequences, maxlen=max_len, padding="post")
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