# Automating Logistic Regression

I have $$3$$ datasets I'd like to run a logistic regression on split into

1. $$X_1, y_1$$

2. $$X_2, y_2$$

3. $$X_3, y_3$$

How can I run a loop so that I can run an automated logistic regression using the X_train, X_test split function from SkLearn and in doing so printing 3 separate accuracy results for each dataset?

To run on one data set of X, y is as follows:

X_train, X_test, y_train, y_test = train_test_split(X, y,
test_size=0.25, shuffle=False)

logreg = LogisticRegression()
logreg.fit(X_train,y_train)
y_pred = logreg.predict(X_test)

print('Accuracy:',metrics.accuracy_score(y_test, y_pred))

• you could make a list of your files and loop over the files to make a regression for each... Jun 21, 2019 at 18:35

Having $$(X1,y1), (X2,y2), (X2,y2)$$ saved in a dictionary:

dictionary = {X1:y1, X2:y2, X3:y3}

accuracies = []
for k,v in dictionary.items():
X_train, X_test, y_train, y_test = train_test_split(k, v,
test_size=0.25, shuffle=False)
logreg = LogisticRegression()
logreg.fit(X_train,y_train)
y_pred = logreg.predict(X_test)
accuracies.append(metrics.accuracy_score(y_test, y_pred))


But it's not really a good way especially when the datasets are too big.

So:

accuracies = []
for i in range(1,4):
X = 'X{}'.format(i)
y = 'y{}'.format(i)
X_train, X_test, y_train, y_test = train_test_split(vars()[X], vars()[y],
test_size=0.25, shuffle=False)
logreg = LogisticRegression()
logreg.fit(X_train,y_train)
y_pred = logreg.predict(X_test)
accuracies.append(metrics.accuracy_score(y_test, y_pred))


Note: in order to access the varibale names which are different in a small part, like here for X1,X2,..., we can use the mentioned method:

import numpy as np
X1= np.arange(1,10)
y1=[i**2 for i in X1]
X2= np.arange(-5,5)
y2=[i**2 for i in X2]
for i in range(1,3):
X = 'X{}'.format(i)
y = 'y{}'.format(i)
print('X_{}'.format(i) , vars()[X])
print('y_{}'.format(i) , vars()[y])


Output:

X_1 [1 2 3 4 5 6 7 8 9]
y_1 [1, 4, 9, 16, 25, 36, 49, 64, 81]
X_2 [-5 -4 -3 -2 -1  0  1  2  3  4]
y_2 [25, 16, 9, 4, 1, 0, 1, 4, 9, 16]

• Thanks, but when I run I get the following error: TypeError: unhashable type: 'numpy.ndarray' Jun 26, 2019 at 19:09
• @Novice Python charmer My fault, the first approach of using arrays as dictionary keys might have risen the problem. Have you tried the second approach? Jun 26, 2019 at 23:55