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pythonnumpyscikit-learn
NickBraunagel
NickBraunagel

Reputation: 1599

How to Retrieve Original Variables After Scikit Model Run w/OneHotEncoding

I have successfully ran a logistic regression model from the scikit-learn SGDClassifier package but cannot easily interpret the model's coefficients (accessed via SGDClassifier.coef_) because the input data was transformed via scikit-learn's OneHotEncoder.

My original input data X is of shape (12000,11):

X = np.array([[1,4,3...9,4,1],
              [5,9,2...3,1,4],
              ...
              [7,8,1...6,7,8]
              ])

I then applied one hot encoding:

from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder()
X_OHE = enc.fit_transform(X).toarray()

which produces an array of shape (12000, 696):

X_OHE = np.array([[1,0,1...0,0,1],
                 [0,0,0...0,1,0],
                  ...
                 [1,0,1...0,0,1]
                 ])

I then access the model's coefficients with SGDClassifier.coef_ which produces an array of shape (1,696):

coefs = np.array([[-1.233e+00,0.9123e+00,-2.431e+00...-0.238e+01,-1.33e+00,0.001e-01]])

How do I map the coefficient values back to the original values in X, so I can say something like, "if variable foo has a value of bar, the target variable increases/decreases by bar_coeff"?

Let me know if you need more info on the data or the model parameters. Thank you.

I found one unanswered question about this on SO: How to retrieve coefficient names after label encoding and one hot encoding on scikit-learn?

Upvotes: 7

Views: 1605

Answers (1)

NickBraunagel
NickBraunagel

Reputation: 1599

After reviewing this user's detailed explanation of OneHotEncoder here, I was able to create a (somewhat hack-y) approach to relating model coefficients back to the original data set.

Assuming you've correctly setup your OneHotEncoder:

from sklearn.preprocessing import OneHotEncoder
from scipy import sparse

enc = OneHotEncoder()
X_OHE = enc.fit_transform(X)   # X and X_OHE as described in question

And you have successfully ran a GLM model, say:

from sklearn import linear_model

clf = linear_model.SGDClassifier()
clf.fit(X_train, y_train)

Which has coefficients clf.coef_:

print clf.coef_
# np.array([[-1.233e+00,0.9123e+00,-2.431e+00...-0.238e+01,-1.33e+00,0.001e-01]])

You can use the below approach to trace the encoded 1's and 0's in X_OHE back to the original values in X. I'd recommend reading the mentioned detailed explanation on OneHotEncoding (link at top), else the below will seem like gibberish. But in a nutshell, the below iterates over each feature in X_OHE and uses the feature_indices parameter internal to enc to make the translation.

import pandas as pd
import numpy as np
results = []

for i in range(enc.active_features_.shape[0]):
    f = enc.active_features_[i]

    index_range = np.extract(enc.feature_indices_ <= f, enc.feature_indices_)
    s = len(index_range) - 1
    f_index = index_range[-1]
    f_label_decoded = f - f_index

    results.append({
            'label_decoded_value': f_label_decoded,
            'coefficient': clf.coef_[0][i]
        })

R = pd.DataFrame.from_records(results)

Where R looks like this (I original encoded the names of company departments):

coefficient label_decoded_value
3.929413    DepartmentFoo1
3.718078    DepartmentFoo2
3.101869    DepartmentFoo3
2.892845    DepartmentFoo4
...

So, now you can say, "The target variables increases by 3.929413 when an employee is in department 'Foo1'.

Upvotes: 1

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