Help on error " ValueError: For numerical factors, num_columns must be an int "

[Robert]

On Wednesday, December 16, 2015 at 8:57:30 PM UTC-5, Josef Pktd wrote:
> On Wednesday, December 16, 2015 at 9:50:35 AM UTC-5, Robert wrote:
> > On Wednesday, December 16, 2015 at 6:34:21 AM UTC-5, Mark Lawrence wrote:
> > > On 16/12/2015 10:44, Robert wrote:
> > > > Hi,
> > > >
> > > > When I run the following code, there is an error:
> > > >
> > > > ValueError: For numerical factors, num_columns must be an int
> > > >
> > > >
> > > > ================
> > > > import numpy as np
> > > > import pandas as pd
> > > > from patsy import dmatrices
> > > > from sklearn.linear_model import LogisticRegression
> > > >
> > > > X = [0.5,0.75,1.0,1.25,1.5,1.75,1.75,2.0,2.25,2.5,2.75,3.0,3.25,
> > > > 3.5,4.0,4.25,4.5,4.75,5.0,5.5]
> > > > y = [0,0,0,0,0,0,1,0,1,0,1,0,1,0,1,1,1,1,1,1]
> > > >
> > > > zipped = list(zip(X,y))
> > > > df = pd.DataFrame(zipped,columns = ['study_hrs','p_or_f'])
> > > >
> > > > y, X = dmatrices('p_or_f ~ study_hrs', df, return_type="dataframe")
> > > > =======================
> > > >
> > > > I have check 'df' is this type:
> > > > =============
> > > > type(df)
> > > > Out[25]: pandas.core.frame.DataFrame
> > > > =============
> > > >
> > > > I cannot figure out where the problem is. Can you help me?
> > > > Thanks.
> > > >
> > > > Error message:
> > > > ..........
> > > >
> > > >
> > > > ---------------------------------------------------------------------------
> > > > ValueError                                Traceback (most recent call last)
> > > > C:\Users\rj\pyprj\stackoverflow_logisticregression0.py in <module>()
> > > >       17 df = pd.DataFrame(zipped,columns = ['study_hrs','p_or_f'])
> > > >       18
> > > > ---> 19 y, X = dmatrices('p_or_f ~ study_hrs', df, return_type="dataframe")
> > > >       20
> > > >       21 y = np.ravel(y)
> > > >
> > > > C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\patsy\highlevel.pyc in dmatrices(formula_like, data, eval_env, NA_action, return_type)
> > > >      295     eval_env = EvalEnvironment.capture(eval_env, reference=1)
> > > >      296     (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
> > > > --> 297                                       NA_action, return_type)
> > > >      298     if lhs.shape[1] == 0:
> > > >      299         raise PatsyError("model is missing required outcome variables")
> > > >
> > > > C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\patsy\highlevel.pyc in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
> > > >      150         return iter([data])
> > > >      151     design_infos = _try_incr_builders(formula_like, data_iter_maker, eval_env,
> > > > --> 152                                       NA_action)
> > > >      153     if design_infos is not None:
> > > >      154         return build_design_matrices(design_infos, data,
> > > >
> > > > C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\patsy\highlevel.pyc in _try_incr_builders(formula_like, data_iter_maker, eval_env, NA_action)
> > > >       55                                       data_iter_maker,
> > > >       56                                       eval_env,
> > > > ---> 57                                       NA_action)
> > > >       58     else:
> > > >       59         return None
> > > >
> > > > C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\patsy\build.pyc in design_matrix_builders(termlists, data_iter_maker, eval_env, NA_action)
> > > >      704                             factor_states[factor],
> > > >      705                             num_columns=num_column_counts[factor],
> > > > --> 706                             categories=None)
> > > >      707         else:
> > > >      708             assert factor in cat_levels_contrasts
> > > >
> > > > C:\Users\rj\AppData\Local\Enthought\Canopy\User\lib\site-packages\patsy\design_info.pyc in __init__(self, factor, type, state, num_columns, categories)
> > > >       86         if self.type == "numerical":
> > > >       87             if not isinstance(num_columns, int):
> > > > ---> 88                 raise ValueError("For numerical factors, num_columns "
> > > >       89                                  "must be an int")
> > > >       90             if categories is not None:
> > > >
> > > > ValueError: For numerical factors, num_columns must be an int
> > > >
> > > 
> > > Slap the ValueError into a search engine and the first hit is 
> > > https://groups.google.com/forum/#!topic/pystatsmodels/KcSzNqDxv-Q
> 
> This was fixed in patsy 0.4.1 as discussed in this statsmodels thread.
> You need to upgrade patsy from 0.4.0.
> 
> AFAIR, the type checking was too strict and broke with recent numpy versions.
> 
> Josef
> 
> 
> > > 
> > > -- 
> > > My fellow Pythonistas, ask not what our language can do for you, ask
> > > what you can do for our language.
> > > 
> > > Mark Lawrence
> > 
> > Hi,
> > I don't see a solution to my problem. I find the following demo code from 
> > 
> > https://patsy.readthedocs.org/en/v0.1.0/API-reference.html#patsy.dmatrix
> > 
> > It doesn't work either on the Canopy. Does it work on your computer?
> > Thanks,
> > 
> > /////////////
> > demo_data("a", "x", nlevels=3)
> > Out[134]: 
> > {'a': ['a1', 'a2', 'a3', 'a1', 'a2', 'a3'],
> >  'x': array([ 1.76405235,  0.40015721,  0.97873798,  2.2408932 ,  1.86755799,
> >         -0.97727788])}
> > 
> > mat = dmatrix("a + x", demo_data("a", "x", nlevels=3))

Thanks. It is right.