53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
from sklearn.base import BaseEstimator
|
|
from sklearn.feature_extraction.text import TfidfVectorizer
|
|
from sklearn.pipeline import Pipeline
|
|
from sklearn.svm import SVC
|
|
|
|
from research.traditional_model import TraditionalModel
|
|
|
|
|
|
class SVMModel(TraditionalModel):
|
|
"""Support Vector Machine with character n-grams and RBF kernel"""
|
|
|
|
def build_model(self) -> BaseEstimator:
|
|
params = self.config.model_params
|
|
# TF-IDF downweights very common patterns; char n-grams (2,4) are effective
|
|
# for distinguishing name morphology under RBF kernels.
|
|
vectorizer = TfidfVectorizer(
|
|
analyzer="char",
|
|
ngram_range=params.get("ngram_range", (2, 4)),
|
|
max_features=params.get("max_features", 5000),
|
|
)
|
|
|
|
# RBF kernel captures non-linear interactions between n-grams; probability=True
|
|
# adds calibration at some cost. Larger cache helps speed kernel computations.
|
|
classifier = SVC(
|
|
kernel=params.get("kernel", "rbf"),
|
|
C=params.get("C", 1.0),
|
|
gamma=params.get("gamma", "scale"),
|
|
probability=True, # Enable probability prediction
|
|
class_weight=params.get("class_weight", None),
|
|
cache_size=params.get("cache_size", 1000),
|
|
random_state=self.config.random_seed,
|
|
verbose=2,
|
|
)
|
|
|
|
return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
|
|
|
|
def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
|
|
text_features = []
|
|
|
|
for feature_type in self.config.features:
|
|
if feature_type.value in X.columns:
|
|
text_features.append(X[feature_type.value].astype(str))
|
|
|
|
if len(text_features) == 1:
|
|
return text_features[0].values
|
|
else:
|
|
combined = text_features[0].astype(str)
|
|
for feature in text_features[1:]:
|
|
combined = combined + " " + feature.astype(str)
|
|
return combined.values
|