[Python]Saving ML Models

# https://machinelearningmastery.com/save-load-machine-learning-models-python-scikit-learn/

# Save Model Using Pickle
import pandas
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
import pickle
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
X = array[:,0:8]
Y = array[:,8]
test_size = 0.33
seed = 7
X_train, X_test, Y_train, Y_test = model_selection.train_test_split(X, Y, test_size=test_size, random_state=seed)
# Fit the model on training set
model = LogisticRegression(max_iter=1000)
model.fit(X_train, Y_train)

# save the model to disk
#filename = 'finalized_model.sav'
#pickle.dump(model, open(filename, 'wb'))

# some time later...

# load the model from disk
#loaded_model = pickle.load(open(filename, 'rb'))
#result = loaded_model.score(X_test, Y_test)
#print(result)

 

 

from sklearn.linear_model import LogisticRegression

# X_train and y_train are your training data
model = LogisticRegression(max_iter=1000)
model.fit(X_train, Y_train)

import joblib

# Save the trained model to a file
joblib.dump(model, 'logistic_regression_model.pkl')

# Load the trained model from the file
loaded_model = joblib.load('logistic_regression_model.pkl')

# X_test is your test data
predictions = loaded_model.predict(X_test)

print(X_test)
print(predictions)

[[  1.     90.     62.    ...  27.2     0.58   24.   ]
 [  7.    181.     84.    ...  35.9     0.586  51.   ]
 [ 13.    152.     90.    ...  26.8     0.731  43.   ]
 ...
 [  4.    118.     70.    ...  44.5     0.904  26.   ]
 [  7.    152.     88.    ...  50.      0.337  36.   ]
 [  7.    168.     88.    ...  38.2     0.787  40.   ]]
[0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1.
 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 0. 0. 1. 0.
 1. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 1. 0. 1. 1.
 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 1. 0. 0.
 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1.
 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1.
 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.
 0. 1. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.
 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0.
 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1. 1. 0.
 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 1.]