问题
I want to apply the scaling sklearn.preprocessing.scale module that scikit-learn offers for centering a dataset that I will use to train an svm classifier.
How can I then store the standardization parameters so that I can also apply them to the data that I want to classify?
I know I can use the standarScaler but can I somehow serialize it to a file so that I wont have to fit it to my data every time I want to run the classifier?
回答1:
I think that the best way is to pickle it post fit, as this is the most generic option. Perhaps you'll later create a pipeline composed of both a feature extractor and scaler. By pickling a (possibly compound) stage, you're making things more generic. The sklearn documentation on model persistence discusses how to do this.
Having said that, you can query sklearn.preprocessing.StandardScaler for the fit parameters:
scale_ : ndarray, shape (n_features,) Per feature relative scaling of the data. New in version 0.17: scale_ is recommended instead of deprecated std_. mean_ : array of floats with shape [n_features] The mean value for each feature in the training set.
The following short snippet illustrates this:
from sklearn import preprocessing
import numpy as np
s = preprocessing.StandardScaler()
s.fit(np.array([[1., 2, 3, 4]]).T)
>>> s.mean_, s.scale_
(array([ 2.5]), array([ 1.11803399]))
回答2:
Pickling is usually a bad idea, at least in production, so I am using another approach:
# scaler is fitted instance of MinMaxScaler
scaler_data_ = np.array([scaler.data_min_, scaler.data_max_])
np.save("my_scaler.npy", scaler_data_)
#some not scaled X
Xreal = np.array([1.9261148646249848, 0.7327923702472628, 118, 1083])
scaler_data_ = np.load("my_scaler.npy")
Xmin, Xmax = scaler_data_[0], scaler_data_[1]
Xscaled = (Xreal - Xmin) / (Xmax-Xmin)
Xscaled
# -> array([0.63062502, 0.35320565, 0.15144766, 0.69116555])
回答3:
Scale with standard scaler
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(data)
scaled_data = scaler.transform(data)
save mean_ and var_ for later use
means = scaler.mean_
vars = scaler.var_
(you can print and copy paste means and vars or save to disk with np.save....)
Later use of saved parameters
def scale_data(array,means=means,stds=vars **0.5):
return (array-means)/stds
scale_new_data = scale_data(new_data)
来源：https://stackoverflow.com/questions/35944783/how-to-store-scaling-parameters-for-later-use