I am doing topic modeling using sklearn. While trying to get the log-likelihood from Grid Search output, I am getting the below error:
AttributeError: 'str' object has no attribute 'parameters'
I think I understand the issue which is: 'parameters' is used in the older version and I am using the new version (0.22) of sklearn and that is giving error. I also search for the term which is used in the new version but couldn't find it. Below is the code:
# Get Log Likelyhoods from Grid Search Output
n_components = [10, 15, 20, 25, 30]
log_likelyhoods_5 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.5]
log_likelyhoods_7 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.7]
log_likelyhoods_9 = [round(gscore.mean_validation_score) for gscore in model.cv_results_ if gscore.parameters['learning_decay']==0.9]
# Show graph
plt.figure(figsize=(12, 8))
plt.plot(n_components, log_likelyhoods_5, label='0.5')
plt.plot(n_components, log_likelyhoods_7, label='0.7')
plt.plot(n_components, log_likelyhoods_9, label='0.9')
plt.title("Choosing Optimal LDA Model")
plt.xlabel("Num Topics")
plt.ylabel("Log Likelyhood Scores")
plt.legend(title='Learning decay', loc='best')
plt.show()
Thanks in advance!
There is key 'params' which is used to store a list of parameter settings dicts for all the parameter candidates. You can see the GridSearchCv doc here from sklearn documentation.
In your code, gscore is a string key value of cv_results_.
Output of cv_results_ is a dictionary of string key like 'params','split0_test_score' etc(you can refer the doc) and their value as list or array etc.
So, you need to make following change to your code :
log_likelyhoods_5 = [round(model.cv_results_['mean_test_score'][index]) for index, gscore in enumerate(model.cv_results_['params']) if gscore['learning_decay']==0.5]