I'm trying to tune the hyperparameters of MLP classifier using GridSearchCV but facing the following issue:
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan.
Details:
ValueError: learning rate 0.01 is not supported.
FitFailedWarning)
/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan.
Details:
ValueError: learning rate 0.02 is not supported
........
Code:
clf = MLPClassifier()
params= {
'hidden_layer_sizes': hidden_layers_generator(X,np.arange(1,17,1)),
'solver': ['sgd'],
'momentum': np.arange(0.1,1.1,0.1),
'learning_rate': np.arange(0.01,1.01,0.01),
'max_iter': np.arange(100,2100,100)}
grid = GridSearchCV(clf, params, cv=10, scoring='accuracy')
grid.fit(X, y)
grid_mean_scores = grid.cv_results_['mean_test_score']
pd.DataFrame(grid.cv_results_)[['mean_test_score', 'std_test_score', 'params']]
The code of hidden_layers_generator is as follows:
from itertools import combinations_with_replacement
def hidden_layers_generator(df,hidden_layers):
hd_sizes = []
for l in range(1, len(hidden_layers)):
comb = combinations_with_replacement(np.arange(1,len(df.columns),10), l)
hd_sizes.append(list(comb))
return hd_sizes
Here's a small snippet of X and y dataframes:
X.head()
sl sw pl pw
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
y.head()
0 0
1 1
2 1
3 0
4 0
If you look at the documentation of MLPClassifier, you will see that learning_rate parameter is not what you think but instead, it is a kind of scheduler. What you want is learning_rate_init parameter. So change this line in the configuration:
'learning_rate': np.arange(0.01,1.01,0.01),
to
'learning_rate_init': np.arange(0.01,1.01,0.01),