67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
from typing import Dict
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
from sklearn.base import BaseEstimator
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
from sklearn.preprocessing import LabelEncoder
|
|
|
|
from research.traditional_model import TraditionalModel
|
|
|
|
|
|
class RandomForestModel(TraditionalModel):
|
|
"""Random Forest with engineered features"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
# Persist encoders so categorical mappings stay consistent.
|
|
self.label_encoders: Dict[str, LabelEncoder] = {}
|
|
|
|
def build_model(self) -> BaseEstimator:
|
|
|
|
params = self.config.model_params
|
|
|
|
# Tree ensemble is robust to mixed numeric/categorical encodings; parallelize
|
|
# across trees for speed. Keep depth moderate for generalisation.
|
|
return RandomForestClassifier(
|
|
n_estimators=params.get("n_estimators", 100),
|
|
max_depth=params.get("max_depth", None),
|
|
random_state=self.config.random_seed,
|
|
verbose=2,
|
|
n_jobs=params.get("n_jobs", -1),
|
|
)
|
|
|
|
def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
|
|
features = []
|
|
|
|
for feature_type in self.config.features:
|
|
if feature_type.value in X.columns:
|
|
column = X[feature_type.value]
|
|
|
|
# Handle different feature types
|
|
if feature_type.value in ["name_length", "word_count"]:
|
|
# Numerical features
|
|
features.append(column.fillna(0).values.reshape(-1, 1))
|
|
else:
|
|
# Categorical features (encode them persistently)
|
|
feature_key = f"encoder_{feature_type.value}"
|
|
|
|
if feature_key not in self.label_encoders:
|
|
self.label_encoders[feature_key] = LabelEncoder()
|
|
encoded = self.label_encoders[feature_key].fit_transform(
|
|
column.fillna("unknown").astype(str)
|
|
)
|
|
else:
|
|
encoder = self.label_encoders[feature_key]
|
|
column_clean = column.fillna("unknown").astype(str)
|
|
known_classes = set(encoder.classes_)
|
|
default_class = "unknown" if "unknown" in known_classes else encoder.classes_[0]
|
|
column_mapped = column_clean.apply(
|
|
lambda value: value if value in known_classes else default_class
|
|
)
|
|
encoded = encoder.transform(column_mapped)
|
|
|
|
features.append(encoded.reshape(-1, 1))
|
|
|
|
return np.hstack(features) if features else np.array([]).reshape(len(X), 0)
|