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72e2b6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import pickle
from pathlib import Path
import numpy as np
import scipy.sparse as sp
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
class LogisticRegressionModel:
def __init__(self, C: float = 1.0, max_iter: int = 1000, random_state: int = 42):
self.model = LogisticRegression(
C=C,
max_iter=max_iter,
random_state=random_state,
n_jobs=-1,
)
def fit(self, X: sp.csc_matrix, y: np.ndarray) -> None:
self.model.fit(X, y)
def predict(self, X: sp.csr_matrix) -> np.ndarray:
return self.model.predict(X)
def predict_proba(self, X: sp.csr_matrix) -> np.ndarray:
return self.model.predict_proba(X)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "wb") as f:
pickle.dump(self.model, f)
def load(self, load_path: str) -> None:
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Model not found: {path}")
with open(path, "rb") as f:
self.model = pickle.load(f)
class SVMModel:
def __init__(self, C: float = 1.0, max_iter: int = 1000, random_state: int = 42):
self.model = LinearSVC(
C=C,
max_iter=max_iter,
random_state=random_state,
)
def fit(self, X: sp.csr_matrix, y: np.ndarray) -> None:
self.model.fit(X, y)
def predict(self, X: sp.csr_matrix) -> np.ndarray:
return self.model.predict(X)
def save(self, save_path: str) -> None:
path = Path(save_path)
path.parent.mkdir(parents=True, exist_ok=True)
with open(path, "wb") as f:
pickle.dump(self.model, f)
def load(self, load_path: str) -> None:
path = Path(load_path)
if not path.exists():
raise FileNotFoundError(f"Model not found: {path}")
with open(path, "rb") as f:
self.model = pickle.load(f)
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