Transformers should implement fit() and transform()
0
I'm not seeing why this block of Pipeline code from sklearn won't work...cna anyone else figure out why I'm getting the error:
TypeError: All intermediate steps should be transformers and implement fit and transform.
pipeline = Pipeline([
('features', FeatureUnion([
('plots', Pipeline([
('selector', movies_encoded['Plot']),
('count_vector', CountVectorizer(tokenizer=nltk.word_tokenize)),
('tfidf', TfidfTransformer())
])),
('genres', Pipeline([
('selector', movies_encoded['Rating_Encoded']),
('labeler', LabelEncoder())
]))
])),
('neural_network', MLPClassifier(alpha=0.01, hidden_layer_sizes=(100, 100, ), early_stopping=False, verbose=True))
])
All of the estimators DO have either transform() or fit_transform() methods. Argh. Thanks!
scikit-learn
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
add a comment |
0
I'm not seeing why this block of Pipeline code from sklearn won't work...cna anyone else figure out why I'm getting the error:
TypeError: All intermediate steps should be transformers and implement fit and transform.
pipeline = Pipeline([
('features', FeatureUnion([
('plots', Pipeline([
('selector', movies_encoded['Plot']),
('count_vector', CountVectorizer(tokenizer=nltk.word_tokenize)),
('tfidf', TfidfTransformer())
])),
('genres', Pipeline([
('selector', movies_encoded['Rating_Encoded']),
('labeler', LabelEncoder())
]))
])),
('neural_network', MLPClassifier(alpha=0.01, hidden_layer_sizes=(100, 100, ), early_stopping=False, verbose=True))
])
All of the estimators DO have either transform() or fit_transform() methods. Argh. Thanks!
scikit-learn
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
The error is inmovies_encoded. You cannot simply put data columns inside thePipeline. You will need to pass the data only infit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…
– Vivek Kumar
Nov 19 '18 at 6:58
add a comment |
0
0
0
I'm not seeing why this block of Pipeline code from sklearn won't work...cna anyone else figure out why I'm getting the error:
TypeError: All intermediate steps should be transformers and implement fit and transform.
pipeline = Pipeline([
('features', FeatureUnion([
('plots', Pipeline([
('selector', movies_encoded['Plot']),
('count_vector', CountVectorizer(tokenizer=nltk.word_tokenize)),
('tfidf', TfidfTransformer())
])),
('genres', Pipeline([
('selector', movies_encoded['Rating_Encoded']),
('labeler', LabelEncoder())
]))
])),
('neural_network', MLPClassifier(alpha=0.01, hidden_layer_sizes=(100, 100, ), early_stopping=False, verbose=True))
])
All of the estimators DO have either transform() or fit_transform() methods. Argh. Thanks!
scikit-learn
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
I'm not seeing why this block of Pipeline code from sklearn won't work...cna anyone else figure out why I'm getting the error:
TypeError: All intermediate steps should be transformers and implement fit and transform.
pipeline = Pipeline([
('features', FeatureUnion([
('plots', Pipeline([
('selector', movies_encoded['Plot']),
('count_vector', CountVectorizer(tokenizer=nltk.word_tokenize)),
('tfidf', TfidfTransformer())
])),
('genres', Pipeline([
('selector', movies_encoded['Rating_Encoded']),
('labeler', LabelEncoder())
]))
])),
('neural_network', MLPClassifier(alpha=0.01, hidden_layer_sizes=(100, 100, ), early_stopping=False, verbose=True))
])
All of the estimators DO have either transform() or fit_transform() methods. Argh. Thanks!
scikit-learn
scikit-learn
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
asked Nov 18 '18 at 2:36
user2302078user2302078
72110
72110
do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
The error is inmovies_encoded. You cannot simply put data columns inside thePipeline. You will need to pass the data only infit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…
– Vivek Kumar
Nov 19 '18 at 6:58
add a comment |
do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
The error is inmovies_encoded. You cannot simply put data columns inside thePipeline. You will need to pass the data only infit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…
– Vivek Kumar
Nov 19 '18 at 6:58
do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
The error is in
movies_encoded. You cannot simply put data columns inside the Pipeline. You will need to pass the data only in fit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…– Vivek Kumar
Nov 19 '18 at 6:58
The error is in
movies_encoded. You cannot simply put data columns inside the Pipeline. You will need to pass the data only in fit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…– Vivek Kumar
Nov 19 '18 at 6:58
add a comment |
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do you put a pipeline inside a pipeline ? can you add some data and the full code ?
– seralouk
Nov 18 '18 at 21:13
Thanks! The data would come from the 4 variables for train_test_split(), and bellow the code block above I'd call pipeline.fit(X_train, y_train). But oddly, it's not working, as it's saying the fit() and transform() aren't applicable on my estimators, which they clearly are.
– user2302078
Nov 18 '18 at 22:56
The error is in
movies_encoded. You cannot simply put data columns inside thePipeline. You will need to pass the data only infit(). Check this example on how to select a single column from data: scikit-learn.org/stable/auto_examples/preprocessing/…– Vivek Kumar
Nov 19 '18 at 6:58