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from .adversarial_scaler import AdversarialScaler
class QuantileScaler(AdversarialScaler):
"""# QuantileScaler
Scale the data based on the quantile distributions of each column"""
def __init__(self, base_directory):
"""# QuantileScaler initialization
- Initialize the base directory of the model where it will live
- Initialize the scaler model"""
super().__init__(base_directory)
from sklearn.preprocessing import QuantileTransformer as QS
self.scaler_base = QS()
self.scaler = None
def init(self, data):
"""# Scaler initialization
Initialize the scaler transformation with the data"""
self.scaler = self.scaler_base.fit(data)
return self
def load(self):
"""# Scaler loading
Load the data scaler model from a file"""
from pickle import load
with open(
f"{self.base_directory}/portable/quantile_scaler.pkl", "rb"
) as fhandle:
self.scaler = load(fhandle)
return self
def save(self):
"""# Scaler saving
Save the data scaler model"""
from pickle import dump
with open(
f"{self.base_directory}/portable/quantile_scaler.pkl", "wb"
) as fhandle:
dump(self.scaler, fhandle)
def __call__(self, *args, **kwargs):
"""# Scale the given data"""
return self.scaler.transform(*args, **kwargs)
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