Is there a way to obtain loss value at each iteration while training a logistic regression?
Python sklearn show loss values during training has an working example for SGDRegressor however not working for logistic regression.
1 Answers
I think you should change the parameter of the scaler, from **kwargs to **params.
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
clf = LogisticRegression(**params)
clf.fit(X_tr, y_tr)
sys.stdout = old_stdout
loss_history = mystdout.getvalue()
loss_list = []
for line in loss_history.split('\n'):
if(len(line.split("loss: ")) == 1):
continue
loss_list.append(float(line.split("loss: ")[-1]))
plt.figure()
plt.plot(np.arange(len(loss_list)), loss_list)
plt.savefig("warmstart_plots/pure_LogRes:"+str(params)+".png")
plt.xlabel("Time in epochs")
plt.ylabel("Loss")
plt.close()
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