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i want to establish a pipe line to pubg data on kaggle to procces it but when i implement a pipe line this error get to me:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
/tmp/ipykernel_35/3879657662.py in <module>
8 ])
9
---> 10 pubg_num_tr = num_pipeline.fit_transform(pubg_num)
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
424 """
425 fit_params_steps = self._check_fit_params(**fit_params)
--> 426 Xt = self._fit(X, y, **fit_params_steps)
427
428 last_step = self._final_estimator
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params_steps)
353 message_clsname="Pipeline",
354 message=self._log_message(step_idx),
--> 355 **fit_params_steps[name],
356 )
357 # Replace the transformer of the step with the fitted
/opt/conda/lib/python3.7/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
347
348 def __call__(self, *args, **kwargs):
--> 349 return self.func(*args, **kwargs)
350
351 def call_and_shelve(self, *args, **kwargs):
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
891 with _print_elapsed_time(message_clsname, message):
892 if hasattr(transformer, "fit_transform"):
--> 893 res = transformer.fit_transform(X, y, **fit_params)
894 else:
895 res = transformer.fit(X, y, **fit_params).transform(X)
/opt/conda/lib/python3.7/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
845 if y is None:
846 # fit method of arity 1 (unsupervised transformation)
--> 847 return self.fit(X, **fit_params).transform(X)
848 else:
849 # fit method of arity 2 (supervised transformation)
/tmp/ipykernel_35/2077244363.py in transform(self, X)
13 total_distance = X[:, walkDistance_ix] + X[:, rideDistance_ix]+X[:, swimDistance_ix]
14 if self.add_total_distance_per_seconda:
---> 15 add_total_distance_per_seconda = X[:, total_distance] / X[:, matchDuration_ix]
16 return np.c_[X, walk_distance_per_seconda, total_distance,
17 add_total_distance_per_seconda]
IndexError: arrays used as indices must be of integer (or boolean) type
my pipeline code is:
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
num_pipeline = Pipeline([
('imputer', SimpleImputer(strategy="median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler())
])
pubg_num_tr = num_pipeline.fit_transform(pubg_num)
i implemented an attribute adder and it worked properly but when i turn on the pipline it fails, i need a solution without the need to converse a float to integers because it harms data.
I've got a dataset with multiple text columns and a target column. I'm trying to use a Cusom Class of Spacy to use Glove embeddings for my text column, and also trying to do it with a Pipeline. But I'm getting a ValueError. Following is my code:
data_features = df.copy()[["title", "description"]]
train_data, test_data, train_target, test_target = train_test_split(data_features, df['target'], test_size = 0.1)
I created this custom class to use glove embeddings. I got the code from this tutorial.
class SpacyVectorTransformer(BaseEstimator, TransformerMixin):
def __init__(self, nlp):
self.nlp = nlp
self.dim = 300
def fit(self, X, y):
return self
def transform(self, X):
return [self.nlp(text).vector for text in X]
Loading the nlp model:
nlp = spacy.load("en_core_web_sm")
This is the column transformer that I'm trying to use in my pipeline:
col_preprocessor = ColumnTransformer(
[
('title_glove', SpacyVectorTransformer(nlp), 'title'),
('description_glove', SpacyVectorTransformer(nlp), 'description'),
],
remainder='drop',
n_jobs=1
)
Here is my pipeline:
pipeline_glove = Pipeline([
('col_preprocessor', col_preprocessor),
('classifier', LogisticRegression())
])
When I run the fit method, I get the error that follows:
pipeline_glove.fit(train_data, train_target)
Error:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-219-8543ea744205> in <module>
----> 1 pipeline_glove.fit(train_data, train_target)
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
328 """
329 fit_params_steps = self._check_fit_params(**fit_params)
--> 330 Xt = self._fit(X, y, **fit_params_steps)
331 with _print_elapsed_time('Pipeline',
332 self._log_message(len(self.steps) - 1)):
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params_steps)
294 message_clsname='Pipeline',
295 message=self._log_message(step_idx),
--> 296 **fit_params_steps[name])
297 # Replace the transformer of the step with the fitted
298 # transformer. This is necessary when loading the transformer
/opt/conda/lib/python3.7/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
353
354 def __call__(self, *args, **kwargs):
--> 355 return self.func(*args, **kwargs)
356
357 def call_and_shelve(self, *args, **kwargs):
/opt/conda/lib/python3.7/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
738 with _print_elapsed_time(message_clsname, message):
739 if hasattr(transformer, 'fit_transform'):
--> 740 res = transformer.fit_transform(X, y, **fit_params)
741 else:
742 res = transformer.fit(X, y, **fit_params).transform(X)
/opt/conda/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py in fit_transform(self, X, y)
549
550 self._update_fitted_transformers(transformers)
--> 551 self._validate_output(Xs)
552
553 return self._hstack(list(Xs))
/opt/conda/lib/python3.7/site-packages/sklearn/compose/_column_transformer.py in _validate_output(self, result)
410 raise ValueError(
411 "The output of the '{0}' transformer should be 2D (scipy "
--> 412 "matrix, array, or pandas DataFrame).".format(name))
413
414 def _validate_features(self, n_features, feature_names):
ValueError: The output of the 'title_glove' transformer should be 2D (scipy matrix, array, or pandas DataFrame).
the error message tells you, what you need to fix.
ValueError: The output of the 'title_glove' transformer should be 2D
(scipy matrix, array, or pandas DataFrame).
But what you are returning with your current transformer (SpacyVectorTransformer) is a list. You can fix it, by turning the list into a pandas DataFrame for instance like this:
import pandas as pd
class SpacyVectorTransformer(BaseEstimator, TransformerMixin):
def __init__(self, nlp):
self.nlp = nlp
self.dim = 300
def fit(self, X, y):
return self
def transform(self, X):
return pd.DataFrame([self.nlp(text).vector for text in X])
Next time, please also provide a minimal, reproducible example. In your provided code, there are no imports as well as no DataFrame called "df".
Im trying to extract few text features(word_count.char_count...) & tf-idf from a twitter dataset for sentiment analysis. Using sklearn's featureUnion to combine them and give them to a classifier in a Pipeline.
Im getting the following error ValueError: blocks[0,:] has incompatible row dimensions. Got blocks[0,8].shape[0] == 7920, expected 1. Here is the code:
features_union = FeatureUnion(transformer_list = [('word_count', WordCalculator()),
('char_count', CharCalculator()),
('avg_word_len', AvdWordLengthCalculater()),
('stop_words_count', StopWordsCalculater()),
('spl_char_count', SplCharCalculater()),
('hash_tag_count', HashTagCalculator()),
('num_count',NumericsCalculator()),
('cap_letter_count',CapsCalculator()),
('tfidf_feature',Pipeline([('preprocessor', Preprocessor()),
('selector', ItemSelector('tweet')),
('count', CountVectorizer()),
('tfidf', TfidfTransformer())]))])
pipeline = Pipeline([('noise_remover', UrlRemover()),
('features', features_union),
('model', MultinomialNB())])
pipeline.fit(train, train['label'])```
Here is the complete error log
ValueError Traceback (most recent call last)
<ipython-input-33-bb532fc90bb0> in <module>
14 ('features', features_union),
15 ('model', MultinomialNB())])
---> 16 pipeline.fit(train, train['label'])
~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
348 This estimator
349 """
--> 350 Xt, fit_params = self._fit(X, y, **fit_params)
351 with _print_elapsed_time('Pipeline',
352 self._log_message(len(self.steps) - 1)):
~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
313 message_clsname='Pipeline',
314 message=self._log_message(step_idx),
--> 315 **fit_params_steps[name])
316 # Replace the transformer of the step with the fitted
317 # transformer. This is necessary when loading the transformer
~/opt/anaconda3/lib/python3.7/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
353
354 def __call__(self, *args, **kwargs):
--> 355 return self.func(*args, **kwargs)
356
357 def call_and_shelve(self, *args, **kwargs):
~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
726 with _print_elapsed_time(message_clsname, message):
727 if hasattr(transformer, 'fit_transform'):
--> 728 res = transformer.fit_transform(X, y, **fit_params)
729 else:
730 res = transformer.fit(X, y, **fit_params).transform(X)
~/opt/anaconda3/lib/python3.7/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
943
944 if any(sparse.issparse(f) for f in Xs):
--> 945 Xs = sparse.hstack(Xs).tocsr()
946 else:
947 Xs = np.hstack(Xs)
~/opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/construct.py in hstack(blocks, format, dtype)
463
464 """
--> 465 return bmat([blocks], format=format, dtype=dtype)
466
467
~/opt/anaconda3/lib/python3.7/site-packages/scipy/sparse/construct.py in bmat(blocks, format, dtype)
584 exp=brow_lengths[i],
585 got=A.shape[0]))
--> 586 raise ValueError(msg)
587
588 if bcol_lengths[j] == 0:
ValueError: blocks[0,:] has incompatible row dimensions. Got blocks[0,8].shape[0] == 7920, expected 1.
data set sample:
0 1 0 #fingerprint #Pregnancy Test https://google.com...
1 2 0 Finally a transparant silicon case ^^ Thanks t...
2 3 0 We love this! Would you go? #talk #makememorie...
3 4 0 I'm wired I know I'm George I was made that wa...
4 5 1 What amazing service! Apple won't even talk to...
dataset shape - (7920, 3)
Any immediate help on this would be grateful.
I'm trying to modify this example to use a Pandas dataframe instead of the test datasets. I am not able to do so, as ItemSelector does not seem to recognise the column name.
Please do note the columns of the dataframe df_resolved.columns returns:
Index(['u_category', ... ... 'resolution_time', 'rawtext'],
dtype='object')
So I obviously do have this in my dataframe.
However, when I try to run the solution, I get the error
"ValueError: no field of name u_category"
Also, I don't seem to be able to modify the code to support choosing multiple columns in the ItemSelector, so in this solution, I'd have to apply the transformers separately with each column.
My code is:
import numpy as np
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.datasets import fetch_20newsgroups
from sklearn.datasets.twenty_newsgroups import strip_newsgroup_footer
from sklearn.datasets.twenty_newsgroups import strip_newsgroup_quoting
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction import DictVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report
from sklearn.pipeline import FeatureUnion
from sklearn.pipeline import Pipeline
from sklearn.svm import SVC
class ItemSelector(BaseEstimator, TransformerMixin):
def __init__(self, key):
self.key = key
def fit(self, x, y=None):
return self
def transform(self, data_dict):
return data_dict[self.key]
class TextStats(BaseEstimator, TransformerMixin):
"""Extract features from each document for DictVectorizer"""
def fit(self, x, y=None):
return self
def transform(self, posts):
return [{'length': len(text),
'num_sentences': text.count('.')}
for text in posts]
class SubjectBodyExtractor(BaseEstimator, TransformerMixin):
"""Extract the subject & body from a usenet post in a single pass.
Takes a sequence of strings and produces a dict of sequences. Keys are
`subject` and `body`.
"""
def fit(self, x, y=None):
return self
def transform(self, posts):
features = np.recarray(shape=(len(posts),),
dtype=[('subject', object), ('body', object)])
for i, text in enumerate(posts):
headers, _, bod = text.partition('\n\n')
bod = strip_newsgroup_footer(bod)
bod = strip_newsgroup_quoting(bod)
features['body'][i] = bod
prefix = 'Subject:'
sub = ''
for line in headers.split('\n'):
if line.startswith(prefix):
sub = line[len(prefix):]
break
features['subject'][i] = sub
return features
pipeline = Pipeline([
# Extract the subject & body
('subjectbody', SubjectBodyExtractor()),
# Use FeatureUnion to combine the features from subject and body
('union', FeatureUnion(
transformer_list=[
# Pipeline for pulling features from the post's subject line
('rawtext', Pipeline([
('selector', ItemSelector(key='u_category')),
('labelenc', preprocessing.LabelEncoder()),
])),
# Pipeline for standard bag-of-words model for body
('features', Pipeline([
('selector', ItemSelector(key='rawtext')),
('tfidf', TfidfVectorizer(max_df=0.5, min_df=1,
stop_words='english',
token_pattern=u'(?ui)\\b\\w*[a-z]{2,}\\w*\\b')),
])),
],
# weight components in FeatureUnion
transformer_weights={
'rawtext': 1.0,
'features': 1.0,
},
)),
# Use a SVC classifier on the combined features
('linear_svc', LinearSVC(penalty="l2")),
])
# limit the list of categories to make running this example faster.
X_train, X_test, y_train, y_test = train_test_split(df_resolved.ix[:, (df_resolved.columns != 'assignment_group.name')], df_resolved['assignment_group.name'], test_size=0.2, random_state=42)
pipeline.fit(X_train, y_train)
print(pipeline.score(X_test, y_test))
How can I modify this code to work properly with my dataframe, and possibly support applying a transformer to multiple columns at once?
If I take the ItemSelector out, it seems to work. So this works:
ds = ItemSelector(key='u_category')
ds.fit(df_resolved)
labelenc = preprocessing.LabelEncoder()
labelenc_transformed = labelenc.fit_transform(ds.transform(df_resolved))
FULL STACK TRACE:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-93-a4ba29c137ec> in <module>()
136
137
--> 138 pipeline.fit(X_train, y_train)
139 #y = pipeline.predict(X_test)
140 #print(classification_report(y, test.target))
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
266 This estimator
267 """
--> 268 Xt, fit_params = self._fit(X, y, **fit_params)
269 if self._final_estimator is not None:
270 self._final_estimator.fit(Xt, y, **fit_params)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
232 pass
233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
235 else:
236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
732 delayed(_fit_transform_one)(trans, name, weight, X, y,
733 **fit_params)
--> 734 for name, trans, weight in self._iter())
735
736 if not result:
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, name, weight, X, y, **fit_params)
575 **fit_params):
576 if hasattr(transformer, 'fit_transform'):
--> 577 res = transformer.fit_transform(X, y, **fit_params)
578 else:
579 res = transformer.fit(X, y, **fit_params).transform(X)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
299 """
300 last_step = self._final_estimator
--> 301 Xt, fit_params = self._fit(X, y, **fit_params)
302 if hasattr(last_step, 'fit_transform'):
303 return last_step.fit_transform(Xt, y, **fit_params)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
232 pass
233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
235 else:
236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
495 else:
496 # fit method of arity 2 (supervised transformation)
--> 497 return self.fit(X, y, **fit_params).transform(X)
498
499
<ipython-input-93-a4ba29c137ec> in transform(self, data_dict)
55
56 def transform(self, data_dict):
---> 57 return data_dict[self.key]
58
59
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/numpy/core/records.py in __getitem__(self, indx)
497
498 def __getitem__(self, indx):
--> 499 obj = super(recarray, self).__getitem__(indx)
500
501 # copy behavior of getattr, except that here
ValueError: no field of name u_category
UPDATE:
Even if I use dataframes (NO train_test_split), the issue persists:
UPDATE 2:
OK so I removed the SubjectBodyExtractor, since I won't need that. Now the ValueError: no field of name u_category is gone, but I have a new error: TypeError: fit_transform() takes 2 positional arguments but 3 were given.
Stack trace:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-110-292294015e44> in <module>()
129
130
--> 131 pipeline.fit(X_train.ix[:, (X_test.columns != 'assignment_group.name')], X_test['assignment_group.name'])
132 #y = pipeline.predict(X_test)
133 #print(classification_report(y, test.target))
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
266 This estimator
267 """
--> 268 Xt, fit_params = self._fit(X, y, **fit_params)
269 if self._final_estimator is not None:
270 self._final_estimator.fit(Xt, y, **fit_params)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
232 pass
233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
235 else:
236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
732 delayed(_fit_transform_one)(trans, name, weight, X, y,
733 **fit_params)
--> 734 for name, trans, weight in self._iter())
735
736 if not result:
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/_parallel_backends.py in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/externals/joblib/parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, name, weight, X, y, **fit_params)
575 **fit_params):
576 if hasattr(transformer, 'fit_transform'):
--> 577 res = transformer.fit_transform(X, y, **fit_params)
578 else:
579 res = transformer.fit(X, y, **fit_params).transform(X)
/Users/csanadpoda/Documents/Jupyter/anaconda/lib/python3.6/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
301 Xt, fit_params = self._fit(X, y, **fit_params)
302 if hasattr(last_step, 'fit_transform'):
--> 303 return last_step.fit_transform(Xt, y, **fit_params)
304 elif last_step is None:
305 return Xt
TypeError: fit_transform() takes 2 positional arguments but 3 were given
Yes, thats because LabelEncoder only requires a single array y whereas FeatureUnion will try sending X and y both to it.
See this: https://github.com/scikit-learn/scikit-learn/issues/3956
You can use a simple workaround for this:
Define a custom labelEncoder like this:
class MyLabelEncoder(BaseEstimator, TransformerMixin):
def __init__(self):
self.le = LabelEncoder()
def fit(self, x, y=None):
return self.le.fit(x)
def transform(self, x, y=None):
return self.le.transform(x).reshape(-1,1)
def fit_transform(self, x, y=None):
self.fit(x)
return self.transform(x)
And in the pipeline, do this:
....
....
('selector', ItemSelector(key='u_category')),
('labelenc', MyLabelEncoder()),
Please note the reshape(-1,1) in the trasform() method. Thats because FeatureUnion only works with 2-d data. All the individual transformers inside the FeatureUnion should only return 2-d data.
you may need to add them in the features array like this , please try to add the two selectors in the features like this and show me the results
features = np.recarray(shape=(len(posts),),
dtype=[('u_category', object), ('rawtext', object)])
I keep getting an
IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices
while trying to fit my dataframe to the following pipeline. Train and Test are two dataframes with same columns. There are different columns but I only want to focus on three of them through the ItemSelector.
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn import preprocessing
from sklearn.pipeline import FeatureUnion
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import LinearSVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
class ItemSelector(BaseEstimator, TransformerMixin):
def __init__(self, column):
self.column = column
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.column]
def predictCases(train, test):
target_names = sorted(list(set(train['TARGET'].values)))
y_train = np.array([target_names.index(x) for x in train['TARGET'].values])
y_test = np.array([target_names.index(x) for x in test['TARGET'].values])
# train and predict
classifier = Pipeline([
('union', FeatureUnion([
('text', Pipeline([
('selector', ItemSelector(column='TEXT')),
('tfidf_vec', TfidfVectorizer())
])),
('feature1', Pipeline([
('selector', ItemSelector(column='CATEG_FEAT1')),
('lbe', LabelEncoder())
])),
('feature2', Pipeline([
('selector', ItemSelector(column='CATEG_FEAT2')),
('lbe', LabelEncoder())
]))
])),
('clf', OneVsRestClassifier(LinearSVC()))])
classifier.fit(train.values, y_train)
predicted = classifier.predict(test.values)
return(metrics.precision_recall_fscore_support(y_test, predicted))
Full Error:
IndexError Traceback (most recent call last)
<ipython-input-19-95d9d0c337f4> in <module>()
----> 1 tt = predictCases(train_resampled, validate)
<ipython-input-17-efc951f4192e> in predictCases(train, test)
24 ])),
25 ('clf', OneVsRestClassifier(LinearSVC()))])
---> 26 classifier.fit(train.values, y_train)
27 predicted = classifier.predict(test.values)
28 return(metrics.precision_recall_fscore_support(y_test, predicted))
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit(self, X, y, **fit_params)
266 This estimator
267 """
--> 268 Xt, fit_params = self._fit(X, y, **fit_params)
269 if self._final_estimator is not None:
270 self._final_estimator.fit(Xt, y, **fit_params)
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
232 pass
233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
235 else:
236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
732 delayed(_fit_transform_one)(trans, name, weight, X, y,
733 **fit_params)
--> 734 for name, trans, weight in self._iter())
735
736 if not result:
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit_transform_one(transformer, name, weight, X, y, **fit_params)
575 **fit_params):
576 if hasattr(transformer, 'fit_transform'):
--> 577 res = transformer.fit_transform(X, y, **fit_params)
578 else:
579 res = transformer.fit(X, y, **fit_params).transform(X)
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in fit_transform(self, X, y, **fit_params)
299 """
300 last_step = self._final_estimator
--> 301 Xt, fit_params = self._fit(X, y, **fit_params)
302 if hasattr(last_step, 'fit_transform'):
303 return last_step.fit_transform(Xt, y, **fit_params)
C:\\Anaconda3\lib\site-packages\sklearn\pipeline.py in _fit(self, X, y, **fit_params)
232 pass
233 elif hasattr(transform, "fit_transform"):
--> 234 Xt = transform.fit_transform(Xt, y, **fit_params_steps[name])
235 else:
236 Xt = transform.fit(Xt, y, **fit_params_steps[name]) \
C:\\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
495 else:
496 # fit method of arity 2 (supervised transformation)
--> 497 return self.fit(X, y, **fit_params).transform(X)
498
499
<ipython-input-2-fdc42fd9d831> in transform(self, X)
10
11 def transform(self, X):
---> 12 return X[self.column]
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
Edit:
If I use train instead of train.values in fit I get the following error:
TypeError: fit_transform() takes 2 positional arguments but 3 were given
You're passing test.values (i.e. a numpy array with raw DataFrame values) to classifier.predict and classifier.fit, while your transformer expects a DataFrame object.