bridgescaler 0.1b2

Tool to automagically save scikit-learn scaler properties to a portable, readable format.

bridgescaler

Bridge your scikit-learn scaler parameters between Python sessions and users. Bridgescaler allows you to save the properties of a scikit-learn scaler object to a json file, and then repopulate a new scaler object with the same properties.

Dependencies

• scikit-learn
• numpy
• pandas

Installation

git clone https://github.com/NCAR/bridgescaler.git
cd bridgescaler
pip install .

Usage

bridgescaler supports all the common scikit-learn scaler classes:

• StandardScaler
• RobustScaler
• MinMaxScaler
• MaxAbsScaler
• QuantileTransformer
• PowerTransformer
• SplineTransformer

First, create some synthetic data to transform.

import numpy as np
import pandas as pd

# specify distribution parameters for each variable
locs = np.array([0, 5, -2, 350.5], dtype=np.float32)
scales = np.array([1.0, 10, 0.1, 5000.0])
names = ["A", "B", "C", "D"]
num_examples = 205
x_data_dict = {}
for l in range(locs.shape[0]):
    # sample from random normal with different parameters
    x_data_dict[names[l]] = np.random.normal(loc=locs[l], scale=scales[l], size=num_examples)
x_data = pd.DataFrame(x_data_dict)

Now, let's fit and transform the data with StandardScaler.

from sklearn.preprocessing import StandardScaler
from bridgescaler import save_scaler, load_scaler
scaler = StandardScaler()
scaler.fit_transform(x_data)
filename = "x_standard_scaler.json"
# save to json file
save_scaler(scaler, filename)

# create new StandardScaler from json file information.
new_scaler = load_scaler(filename) # new_scaler is a StandardScaler object