From b85ee9d64a536937912544c7bbd5b98b635b7e8d Mon Sep 17 00:00:00 2001
From: Christian C <cc@localhost>
Date: Mon, 11 Nov 2024 12:29:32 -0800
Subject: Initial commit

---
 code/sunlab/common/scaler/quantile_scaler.py | 52 ++++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 code/sunlab/common/scaler/quantile_scaler.py

(limited to 'code/sunlab/common/scaler/quantile_scaler.py')

diff --git a/code/sunlab/common/scaler/quantile_scaler.py b/code/sunlab/common/scaler/quantile_scaler.py
new file mode 100644
index 0000000..a0f53fd
--- /dev/null
+++ b/code/sunlab/common/scaler/quantile_scaler.py
@@ -0,0 +1,52 @@
+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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