98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
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.ensemble import VotingClassifier, RandomForestClassifier
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from sklearn.feature_extraction.text import CountVectorizer
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import Pipeline
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from research.experiment import ExperimentConfig
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from research.traditional_model import TraditionalModel
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class EnsembleModel(TraditionalModel):
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"""Ensemble model combining multiple base models"""
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@property
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def architecture(self) -> str:
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"""Return the architecture type"""
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return "ensemble"
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def __init__(self, config: ExperimentConfig):
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super().__init__(config)
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self.base_models = []
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self.model_weights = None
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def build_model(self) -> BaseEstimator:
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params = self.config.model_params
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base_model_types = params.get(
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"base_models", ["logistic_regression", "random_forest", "naive_bayes"]
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)
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# Create base models with simplified configs
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estimators = []
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for model_type in base_model_types:
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if model_type == "logistic_regression":
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model = Pipeline(
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[
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(
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"vectorizer",
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CountVectorizer(analyzer="char", ngram_range=(2, 4), max_features=5000),
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),
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(
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"classifier",
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LogisticRegression(max_iter=1000, random_state=self.config.random_seed),
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),
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]
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)
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estimators.append((f"logistic_regression", model))
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elif model_type == "random_forest":
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model = Pipeline(
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[
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(
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"vectorizer",
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TfidfVectorizer(analyzer="char", ngram_range=(2, 3), max_features=3000),
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),
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(
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"classifier",
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RandomForestClassifier(
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n_estimators=50, random_state=self.config.random_seed
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),
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),
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]
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)
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estimators.append((f"rf", model))
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elif model_type == "naive_bayes":
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model = Pipeline(
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[
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(
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"vectorizer",
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CountVectorizer(analyzer="char", ngram_range=(1, 3), max_features=4000),
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),
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("classifier", MultinomialNB()),
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]
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)
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estimators.append((f"nb", model))
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voting_type = params.get("voting", "soft") # 'hard' or 'soft'
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return VotingClassifier(estimators=estimators, voting=voting_type)
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def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
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text_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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text_features.append(X[feature_type.value].astype(str))
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if len(text_features) == 1:
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return text_features[0].values
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else:
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combined = text_features[0].astype(str)
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for feature in text_features[1:]:
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combined = combined + " " + feature.astype(str)
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return combined.values
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