97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
import numpy as np
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import pandas as pd
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import xgboost as xgb
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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 XGBoostModel(TraditionalModel):
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"""XGBoost with engineered features and character embeddings"""
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def __init__(self, config):
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super().__init__(config)
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# Store vectorizers and encoders to ensure consistent feature space
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self.vectorizers = {}
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self.label_encoders = {}
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def build_model(self) -> BaseEstimator:
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params = self.config.model_params
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return xgb.XGBClassifier(
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n_estimators=params.get("n_estimators", 100),
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max_depth=params.get("max_depth", 6),
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learning_rate=params.get("learning_rate", 0.1),
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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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eval_metric="logloss",
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verbosity=2
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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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# Numerical features
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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-level features for names
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feature_key = f"vectorizer_{feature_type.value}"
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if feature_key not in self.vectorizers:
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# First time - create and fit vectorizer
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self.vectorizers[feature_key] = CountVectorizer(
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analyzer="char", ngram_range=(2, 3), max_features=100
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)
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char_features = self.vectorizers[feature_key].fit_transform(
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column.fillna("").astype(str)
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).toarray()
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else:
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# Subsequent times - use existing vectorizer
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char_features = self.vectorizers[feature_key].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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# Categorical features
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feature_key = f"encoder_{feature_type.value}"
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if feature_key not in self.label_encoders:
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# First time - create and fit encoder
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self.label_encoders[feature_key] = LabelEncoder()
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encoded = self.label_encoders[feature_key].fit_transform(
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column.fillna("unknown").astype(str)
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)
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else:
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# Subsequent times - use existing encoder
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# Handle unseen labels by mapping them to a default value
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column_clean = column.fillna("unknown").astype(str)
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# Get the classes the encoder was trained on
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known_classes = set(self.label_encoders[feature_key].classes_)
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# Map unseen values to "unknown" if it exists, otherwise to the first class
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if "unknown" in known_classes:
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default_class = "unknown"
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else:
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default_class = self.label_encoders[feature_key].classes_[0]
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# Replace unseen values with default
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column_mapped = column_clean.apply(
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lambda x: x if x in known_classes else default_class
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)
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encoded = self.label_encoders[feature_key].transform(column_mapped)
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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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