Load and predict new data sklearn

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I trained a Logistic model, cross-validated and saved it to file using joblib module. Now I want to load this model and predict new data with it. Is this the correct way to do this? Especially the standardization. Should I use scaler.fit() on my new data too? In the tutorials I followed, scaler.fit was only used on the training set, so I'm a bit lost here.

Here is my code:

#Loading the saved model with joblib
model = joblib.load('model.pkl')

# New data to predict
pr = pd.read_csv('set_to_predict.csv')
pred_cols = list(pr.columns.values)[:-1]

# Standardize new data
scaler = StandardScaler()
X_pred = scaler.fit(pr[pred_cols]).transform(pr[pred_cols])

pred = pd.Series(model.predict(X_pred))
print pred
joblib logistic-regression scikit-learn machine-learning python
1 Answers

No, it's incorrect. All the data preparation steps should be fit using train data. Otherwise, you risk applying the wrong transformations, because means and variances that StandardScaler estimates do probably differ between train and test data.

The easiest way to train, save, load and apply all the steps simultaneously is to use Pipelines:

At training:

# prepare the pipeline
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib

pipe = make_pipeline(StandardScaler(), LogisticRegression)
pipe.fit(X_train, y_train)
joblib.dump(pipe, 'model.pkl')

At prediction:

#Loading the saved model with joblib
pipe = joblib.load('model.pkl')

# New data to predict
pr = pd.read_csv('set_to_predict.csv')
pred_cols = list(pr.columns.values)[:-1]

# apply the whole pipeline to data
pred = pd.Series(pipe.predict(pr[pred_cols]))
print pred
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