I have X as a sparse matrix and y as a pandas Series.
I then proceed with the following code:
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)
estim = HyperoptEstimator(classifier=any_sparse_classifier('clf'),
preprocessing=[],
algo=tpe.suggest,
max_evals=100,
trial_timeout=120)
estim.fit(X_train, y_train)
I got the following error:
Scikit-learn - ValueError: Input contains NaN, infinity or a value too large for dtype('float64')
After that, I updated all my conda packages, and re-installed hyperopt sklearn. Now I get the following error:
KeyError: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'
Note that same thing happens when using passive_aggressive as well. Also note that when I run sklearn's PassiveAgressiveClassifier with the same train data, it works fine.
I have tested both my sparse matrix as well as my target values (y) for NaN, infinity or too large numbers. No such entries exist.
It's interesting to note that running the following code:
estim.fit(X,y)
, which contains all the data, runs normally without any problems.
So I checked X_train and y_train for NaN, infinity or too large numbers (in case something is wrong with sklearn's train_test_split), but again, everything seems fine.
I have X as a sparse matrix and y as a pandas Series.
I then proceed with the following code:
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=42)
estim = HyperoptEstimator(classifier=any_sparse_classifier('clf'),
preprocessing=[],
algo=tpe.suggest,
max_evals=100,
trial_timeout=120)
estim.fit(X_train, y_train)
I got the following error:
Scikit-learn - ValueError: Input contains NaN, infinity or a value too large for dtype('float64')
After that, I updated all my conda packages, and re-installed hyperopt sklearn. Now I get the following error:
KeyError: 'Passing list-likes to .loc or [] with any missing labels is no longer supported, see https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike'
Note that same thing happens when using passive_aggressive as well. Also note that when I run sklearn's PassiveAgressiveClassifier with the same train data, it works fine.
I have tested both my sparse matrix as well as my target values (y) for NaN, infinity or too large numbers. No such entries exist.
It's interesting to note that running the following code:
estim.fit(X,y)
, which contains all the data, runs normally without any problems.
So I checked X_train and y_train for NaN, infinity or too large numbers (in case something is wrong with sklearn's train_test_split), but again, everything seems fine.