Saving Machine Learning Models Saving Machine Learning Models
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title author date
Saving Machine Learning Models
Damian Mingle
04/30/2018

Let’s take a look at two conventional ways to save models using scikit-learn

  1. a pickle string
  2. a pickled model as a file.

Preliminaries

from sklearn import datasets
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn.externals import joblib

Load Data

# Load example data (iris dataset)
iris = datasets.load_iris()

# Create a matrix of features and a vector target
features, target = iris.data, iris.target

Train Model

# Train an example model (logistic Regression)
clf = LogisticRegression(random_state=0)
clf.fit(features, target)
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False)

A Pickle String

# Save the trained model as a pickle string.
saved_model = pickle.dumps(clf)
# Display pickled model
saved_model
b'\x80\x03csklearn.linear_model.logistic\nLogisticRegression\nq\x00)\x81q\x01}q\x02(X\x06\x00\x00\x00solverq\x03X\t\x00\x00\x00liblinearq\x04X\n\x00\x00\x00intercept_q\x05cnumpy.core.multiarray\n_reconstruct\nq\x06cnumpy\nndarray\nq\x07K\x00\x85q\x08C\x01bq\t\x87q\nRq\x0b(K\x01K\x03\x85q\x0ccnumpy\ndtype\nq\rX\x02\x00\x00\x00f8q\x0eK\x00K\x01\x87q\x0fRq\x10(K\x03X\x01\x00\x00\x00<q\x11NNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq\x12b\x89C\x18\xce\x86D\x03\xb1\xff\xd0?\xcd\xcc=I\xe5]\xf1?\xa9\'\xad\x8dxo\xf3\xbfq\x13tq\x14bX\x0c\x00\x00\x00random_stateq\x15K\x00X\x07\x00\x00\x00penaltyq\x16X\x02\x00\x00\x00l2q\x17X\n\x00\x00\x00warm_startq\x18\x89X\x0c\x00\x00\x00class_weightq\x19NX\x11\x00\x00\x00intercept_scalingq\x1aK\x01X\x01\x00\x00\x00Cq\x1bG?\xf0\x00\x00\x00\x00\x00\x00X\x08\x00\x00\x00max_iterq\x1cKdX\r\x00\x00\x00fit_interceptq\x1d\x88X\x0b\x00\x00\x00multi_classq\x1eX\x03\x00\x00\x00ovrq\x1fX\x07\x00\x00\x00n_iter_q h\x06h\x07K\x00\x85q!h\t\x87q"Rq#(K\x01K\x01\x85q$h\rX\x02\x00\x00\x00i4q%K\x00K\x01\x87q&Rq\'(K\x03h\x11NNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq(b\x89C\x04\x07\x00\x00\x00q)tq*bX\x07\x00\x00\x00verboseq+K\x00X\x05\x00\x00\x00coef_q,h\x06h\x07K\x00\x85q-h\t\x87q.Rq/(K\x01K\x03K\x04\x86q0h\x10\x88C`\xa6\x1c\x904+\x8f\xda?\x8b7\xf6\x7f9\xaa\xda?.VL\xe5\x05R\xfb\xbf\xf3\xad\xd9^ya\xf7?\x95\x86\x10B\x03\x9d\xf9\xbf\x92\xa7x\xf5\\\x8c\xf8\xbf\x8b$8y\xdd\x18\x02\xc0\x8f\x8f\xee\xd9+|\xe2?X\x10\xf2\xcc\x8c\xc4\x03@\xda\xb0;l,w\xf0\xbf\xbb^\xe7W*+\xf6\xbf\xe2T`-lq\x04@q1tq2bX\x10\x00\x00\x00_sklearn_versionq3X\x06\x00\x00\x000.19.0q4X\x06\x00\x00\x00n_jobsq5K\x01X\x08\x00\x00\x00classes_q6h\x06h\x07K\x00\x85q7h\t\x87q8Rq9(K\x01K\x03\x85q:h\rX\x02\x00\x00\x00i4q;K\x00K\x01\x87q<Rq=(K\x03h\x11NNNJ\xff\xff\xff\xffJ\xff\xff\xff\xffK\x00tq>b\x89C\x0c\x00\x00\x00\x00\x01\x00\x00\x00\x02\x00\x00\x00q?tq@bX\x04\x00\x00\x00dualqA\x89X\x03\x00\x00\x00tolqBG?\x1a6\xe2\xeb\x1cC-ub.'
# Load the pickled model
clf_from_pickle = pickle.loads(saved_model)

# Load pickled model to make predictions
clf_from_pickle.predict(features)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])

A Pickled Model as a File

# Save the model as a pickle in a file
joblib.dump(clf, 'example_file.pkl')
['example_file.pkl']
# Load the model from a pickled file
clf_from_joblib = joblib.load('example_file.pkl')
# Load pickled model to make predictions
clf_from_joblib.predict(features)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
       2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
——————————
Original Source
Damian Mingle

Damian Mingle

Damian Mingle is an American businessman, investor, and data scientist. He is the Founder and Chief Data Scientist of LoveToThink.org, a way for skilled professionals to contribute their expertise and empower the world’s social changemakers. Formerly, Damian was the Chief Data Scientist at Intermedix (an R1 company) where he was responsible for leading a team of international data scientists to drive business value. As a leading authority on data science, Damian speaks nationally and internationally on patient safety, global health, and applied data science.

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