ProblemSolving-FrameWork-ML/save-load_Model.py at main · MvMukesh/ProblemSolving-FrameWork-ML · GitHub

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'''

Save and Load ML modules

A Script, to save model in specific file and load it later in order to make predictions.

Serialize ML algorithms and save serialized format to a file.

Load file to deserialize models and use it to make new pridictions.

- Pickle

- Joblib

'''

from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

seed = 7

filename = 'pima-indians-diabetes.data.csv'

names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class'] dataframe = read_csv(filename, names=names)

array = dataframe.values

X = array[:,0:8]

Y = array[:,8]

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.33, random_state = seed)

# fit the model

model = LogisticRegressino()

model.fit(X_train, Y_train)

''' 1. Pickle '''

from pickle import dump

from pickle import load

# Save the model to disk

filename = 'finalized_model.sav'

dump(model, open(filename, 'wb'))

# some time later...

# load the model from disk

loaded_model = load(open(filename, 'rb'))

result = loaded_model.score(X_test, Y_test)

print(result)

''' 2. Joblib '''

# Useful for some machine learning algorithms that require a lot of parameters or store entire dataset

from sklearn.externals.joblib import dump

from sklearn.externals.joblib import load

# save the model to disk

filename = 'finalized_model.sav'

dump(model, filenmae)

# some time later

# load the model from disk

loaded_model = load(filename)

result = loaded_model.score(X_test,Y_test)

print(result)

ProblemSolving-FrameWork-ML/save-load_Model.py at main · MvMukesh/ProblemSolving-FrameWork-ML · GitHub

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