I am getting an error when trying to use statsmodels .predict to predict my test values.
Code:
X_train, X_test, y_train, y_test = train_test_split(X_new_np, y, test_size=0.2, random_state=42)
logit = sm.Logit(y_train, X_train)
reg = logit.fit_regularized(start_params=None, method='l1_cvxopt_cp', maxiter= 1000, full_output=1, disp=1, callback=None, alpha=.01, trim_mode='auto', auto_trim_tol=0.01, size_trim_tol=0.0001, qc_tol=0.03)
reg.summary()
y_pred_test = logit.predict(X_test)
Error:
ValueError: shapes (1000,61) and (251,61) not aligned: 61 (dim 1) != 251 (dim 0)
You simply don't predict from the right object. reg
is the one that was fitted, you should then use reg.predict
. The following code runs without error (I used your fit_regularized parameters).
from sklearn.model_selection import train_test_split
import numpy as np
from statsmodels.api import Logit
x = np.random.randn(100,50)
y = np.random.randint(0,2,100).astype(bool)
print(x.shape, y.shape)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=.2)
logit = Logit(y_train, X_train)
reg = logit.fit_regularized(start_params=None, method='l1_cvxopt_cp',
maxiter= 1000, full_output=1, disp=1, callback=None,
alpha=.01, trim_mode='auto', auto_trim_tol=0.01,
size_trim_tol=0.0001, qc_tol=0.03)
print(reg.summary())
y_pred_test = reg.predict(X_test)