52 lines
2.0 KiB
Python
52 lines
2.0 KiB
Python
import lightgbm as lgb
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import numpy as np
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import pandas as pd
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from sklearn.base import BaseEstimator
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.preprocessing import LabelEncoder
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from research.traditional_model import TraditionalModel
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class LightGBMModel(TraditionalModel):
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"""LightGBM with engineered features"""
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def build_model(self) -> BaseEstimator:
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params = self.config.model_params
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return lgb.LGBMClassifier(
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n_estimators=params.get("n_estimators", 100),
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max_depth=params.get("max_depth", -1),
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learning_rate=params.get("learning_rate", 0.1),
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num_leaves=params.get("num_leaves", 31),
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subsample=params.get("subsample", 0.8),
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colsample_bytree=params.get("colsample_bytree", 0.8),
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random_state=self.config.random_seed,
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verbose=-1,
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)
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def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
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features = []
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for feature_type in self.config.features:
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if feature_type.value in X.columns:
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column = X[feature_type.value]
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if feature_type.value in ["name_length", "word_count"]:
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features.append(column.fillna(0).values.reshape(-1, 1))
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elif feature_type.value in ["full_name", "native_name", "surname"]:
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# Character n-grams for text features
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vectorizer = CountVectorizer(
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analyzer="char", ngram_range=(2, 3), max_features=50
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)
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char_features = vectorizer.fit_transform(
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column.fillna("").astype(str)
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).toarray()
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features.append(char_features)
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else:
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le = LabelEncoder()
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encoded = le.fit_transform(column.fillna("unknown").astype(str))
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features.append(encoded.reshape(-1, 1))
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return np.hstack(features) if features else np.array([]).reshape(len(X), 0)
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