Cannot import module after successfully installing with pip

[qmeeus]

I'm not an expert, but I think that the package is not configured correctly.

To reproduce:

pip install bartpy
python
>>> import bartpy
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'bartpy'

Note that this works (hence my thought that the package configuration is the problem)

git clone https://github.com/JakeColtman/bartpy ~/bin/bartpy
python
>>> import sys
>>> sys.path.append("<home>/bin/bartpy")
>>> import bartpy

[JakeColtman]

Thanks for raising this issue and sorry for the slow reply, I took a break from working on this over Christmas and it kinda expanded out :)

I just recreated the issue and I'll have a look into configuring it properly.

[vinhdang-tsocial]

FYI, I have the exact same problem here.

[JakeColtman]

It was indeed a config issue - should work now :)

[JakeColtman]

Fair warning - it's still quite experimental, so not everything is guaranteed to work. I'd recommend https://cran.r-project.org/web/packages/bartMachine/bartMachine.pdf if you're doing anything serious

[benman1]

Hi Jake Thanks for creating this - it looks superb. I am trying to run the sklearn API example from the readme, and still failing. I first got a problem about missing samplers, which disappeared after reinstalling from github (had to add the #egg=bartpy). However, now I get TypeError: Categorical is not ordered for operation min you can use .as_ordered() to change the Categorical to an ordered one . I'll leave the full description here.

$ pip install git+https://github.com/JakeColtman/bartpy.git#egg=bartpy

from bartpy.sklearnmodel import SklearnModel
model = SklearnModel() # Use default parameters
model.fit(X, y) # Fit the model
predictions = model.predict() # Make predictions on the train set
out_of_sample_predictions = model.predict(X_test) # Make predictions on new data

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-16-e5c59b158acd> in <module>
      1 from bartpy.sklearnmodel import SklearnModel
      2 model = SklearnModel() # Use default parameters
----> 3 model.fit(X, y) # Fit the model
      4 predictions = model.predict() # Make predictions on the train set
      5 out_of_sample_predictions = model.predict(X_test) # Make predictions on new data

~/anaconda3/lib/python3.7/site-packages/bartpy/sklearnmodel.py in fit(self, X, y)
    138             self with trained parameter values
    139         """
--> 140         self.model = self._construct_model(X, y)
    141         self.extract = Parallel(n_jobs=self.n_jobs)(self.f_delayed_chains(X, y))
    142         self.combined_chains = self._combine_chains(self.extract)

~/anaconda3/lib/python3.7/site-packages/bartpy/sklearnmodel.py in _construct_model(self, X, y)
    164         if len(X) == 0 or X.shape[1] == 0:
    165             raise ValueError("Empty covariate matrix passed")
--> 166         self.data = self._convert_covariates_to_data(X, y)
    167         self.sigma = Sigma(self.sigma_a, self.sigma_b, self.data.y.normalizing_scale)
    168         self.model = Model(self.data,

~/anaconda3/lib/python3.7/site-packages/bartpy/sklearnmodel.py in _convert_covariates_to_data(X, y)
    159             X: pd.DataFrame = X
    160             X = X.values
--> 161         return Data(deepcopy(X), deepcopy(y), normalize=True)
    162 
    163     def _construct_model(self, X: np.ndarray, y: np.ndarray) -> Model:

~/anaconda3/lib/python3.7/site-packages/bartpy/data.py in __init__(self, X, y, normalize, cache, unique_columns)
     58     return Data(X, y, normalize=normalize)
     59 
---> 60 
     61 class CovariateMatrix(object):
     62 

~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py in stat_func(self, axis, skipna, level, numeric_only, **kwargs)
  10944                                       skipna=skipna)
  10945         return self._reduce(f, name, axis=axis, skipna=skipna,
> 10946                             numeric_only=numeric_only)
  10947 
  10948     return set_function_name(stat_func, name, cls)

~/anaconda3/lib/python3.7/site-packages/pandas/core/series.py in _reduce(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)
   3613         # dispatch to ExtensionArray interface
   3614         if isinstance(delegate, ExtensionArray):
-> 3615             return delegate._reduce(name, skipna=skipna, **kwds)
   3616         elif is_datetime64_dtype(delegate):
   3617             # use DatetimeIndex implementation to handle skipna correctly

~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in _reduce(self, name, axis, skipna, **kwargs)
   2179             msg = 'Categorical cannot perform the operation {op}'
   2180             raise TypeError(msg.format(op=name))
-> 2181         return func(**kwargs)
   2182 
   2183     def min(self, numeric_only=None, **kwargs):

~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in min(self, numeric_only, **kwargs)
   2196         min : the minimum of this `Categorical`
   2197         """
-> 2198         self.check_for_ordered('min')
   2199         if numeric_only:
   2200             good = self._codes != -1

~/anaconda3/lib/python3.7/site-packages/pandas/core/arrays/categorical.py in check_for_ordered(self, op)
   1517             raise TypeError("Categorical is not ordered for operation {op}\n"
   1518                             "you can use .as_ordered() to change the "
-> 1519                             "Categorical to an ordered one\n".format(op=op))
   1520 
   1521     def _values_for_argsort(self):

TypeError: Categorical is not ordered for operation min
you can use .as_ordered() to change the Categorical to an ordered one