Chapter 2: Shows Error when executing full_pipeline.fit_transform(housing) ValueError: operands could not be broadcast together with shapes (16512,) (3,8)

[navctns]

Error Description:

ValueError                                Traceback (most recent call last)
<ipython-input-58-c19e1bb11abe> in <module>
      8 ])
      9 #run full_pipeline
---> 10 housing_prepared=full_pipeline.fit_transform(housing)

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
    910             sum of n_components (output dimension) over transformers.
    911         """
--> 912         results = self._parallel_func(X, y, fit_params, _fit_transform_one)
    913         if not results:
    914             # All transformers are None

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in _parallel_func(self, X, y, fit_params, func)
    940             message=self._log_message(name, idx, len(transformers)),
    941             **fit_params) for idx, (name, transformer,
--> 942                                     weight) in enumerate(transformers, 1))
    943 
    944     def transform(self, X):

~/venv/local/lib/python3.5/site-packages/joblib/parallel.py in __call__(self, iterable)
    919             # remaining jobs.
    920             self._iterating = False
--> 921             if self.dispatch_one_batch(iterator):
    922                 self._iterating = self._original_iterator is not None
    923 

~/venv/local/lib/python3.5/site-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
    757                 return False
    758             else:
--> 759                 self._dispatch(tasks)
    760                 return True
    761 

~/venv/local/lib/python3.5/site-packages/joblib/parallel.py in _dispatch(self, batch)
    714         with self._lock:
    715             job_idx = len(self._jobs)
--> 716             job = self._backend.apply_async(batch, callback=cb)
    717             # A job can complete so quickly than its callback is
    718             # called before we get here, causing self._jobs to

~/venv/local/lib/python3.5/site-packages/joblib/_parallel_backends.py in apply_async(self, func, callback)
    180     def apply_async(self, func, callback=None):
    181         """Schedule a func to be run"""
--> 182         result = ImmediateResult(func)
    183         if callback:
    184             callback(result)

~/venv/local/lib/python3.5/site-packages/joblib/_parallel_backends.py in __init__(self, batch)
    547         # Don't delay the application, to avoid keeping the input
    548         # arguments in memory
--> 549         self.results = batch()
    550 
    551     def get(self):

~/venv/local/lib/python3.5/site-packages/joblib/parallel.py in __call__(self)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/venv/local/lib/python3.5/site-packages/joblib/parallel.py in <listcomp>(.0)
    223         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    224             return [func(*args, **kwargs)
--> 225                     for func, args, kwargs in self.items]
    226 
    227     def __len__(self):

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    714     with _print_elapsed_time(message_clsname, message):
    715         if hasattr(transformer, 'fit_transform'):
--> 716             res = transformer.fit_transform(X, y, **fit_params)
    717         else:
    718             res = transformer.fit(X, y, **fit_params).transform(X)

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in fit_transform(self, X, y, **fit_params)
    385         """
    386         last_step = self._final_estimator
--> 387         Xt, fit_params = self._fit(X, y, **fit_params)
    388         with _print_elapsed_time('Pipeline',
    389                                  self._log_message(len(self.steps) - 1)):

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
    315                 message_clsname='Pipeline',
    316                 message=self._log_message(step_idx),
--> 317                 **fit_params_steps[name])
    318             # Replace the transformer of the step with the fitted
    319             # transformer. This is necessary when loading the transformer

~/venv/local/lib/python3.5/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):

~/venv/local/lib/python3.5/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
    714     with _print_elapsed_time(message_clsname, message):
    715         if hasattr(transformer, 'fit_transform'):
--> 716             res = transformer.fit_transform(X, y, **fit_params)
    717         else:
    718             res = transformer.fit(X, y, **fit_params).transform(X)

~/venv/local/lib/python3.5/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
    551         if y is None:
    552             # fit method of arity 1 (unsupervised transformation)
--> 553             return self.fit(X, **fit_params).transform(X)
    554         else:
    555             # fit method of arity 2 (supervised transformation)

<ipython-input-38-b08c34d1bb78> in transform(self, X, y)
     20 
     21         if self.add_bedrooms_per_room:# if adding an additional attribute ,ie add_bedrooms_per_room=True
---> 22             bedrooms_per_room=X[:,bedrooms_ix]/X[:rooms_ix]
     23             return np.c_[X,rooms_per_household, population_per_household,bedrooms_per_room]
     24         else: #return the existing attributes

ValueError: operands could not be broadcast together with shapes (16512,) (3,8) 

[ageron]

Hi @navctns , This might be a duplicate of #347, could you please check out my answers in that issue? Hope this helps!

[gandharavkatyal1]

I resolved this error by updating the libaries to latest i had some mismtach in the version on pandas and scikit learn.