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from sklearn.linear_model import LogisticRegression
import joblib
from commons.Configs import configs
from commons.File import file
class Model:
def __init__(self, debug=False):
self.debug = debug
def train(self, x, y):
return LogisticRegression(solver='lbfgs', random_state=42).fit(x, y)
def save(self, clf):
# save model
joblib.dump(clf, configs.generatedModelPath)
print("Model saved to: ", configs.generatedModelPath)
def load(self):
if not file.exists(configs.generatedModelPath):
print("Model not found at: ", configs.generatedModelPath)
exit(1)
return joblib.load(configs.generatedModelPath)
model = Model()
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