Patsy Contrast Coding does not work

[astoermann]

Code:

cm_a = ContrastMatrix([[-.5], [.5]], ["[ThreatCondition]"])
cm_b = ContrastMatrix(
[
[2/3, 0], [-1/3, -.5], [-1/3, .5]
],
["[Condition:CvI]", "[Condition:URx]"])

fg_contrast_threatbycondition = fitgrid.lm(
epochs_fg_stmath,
LHS =['MiPa'],
RHS='C(ThreatCondition, cm_a) * C(Condition, cm_b)',
)

ERROR MESSAGE:

---

NameError Traceback (most recent call last)
~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/compat.py in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
35 try:
---> 36 return f(*args, **kwargs)
37 except Exception as e:

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/eval.py in eval(self, expr, source_name, inner_namespace)
165 return eval(code, {}, VarLookupDict([inner_namespace]
--> 166 + self._namespaces))
167

in

NameError: name 'cm_b' is not defined

The above exception was the direct cause of the following exception:

PatsyError Traceback (most recent call last)
in
2 epochs_fg_stmath,
3 LHS =['MiPa'],
----> 4 RHS='C(ThreatCondition, cm_a) * C(Condition, cm_b)',
5 )

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/fitgrid/models.py in lm(epochs, LHS, RHS, parallel, n_cores, eval_env)
162 channels=LHS,
163 parallel=parallel,
--> 164 n_cores=n_cores,
165 )
166

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/fitgrid/models.py in _run_model(epochs, function, channels, parallel, n_cores)
114 results = map(processor, groups)
115
--> 116 grid = pd.concat(results, axis=1).T
117 grid.index.name = epochs.time
118 return grid # dataframe, not FitGrid

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/pandas/core/reshape/concat.py in concat(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)
253 verify_integrity=verify_integrity,
254 copy=copy,
--> 255 sort=sort,
256 )
257

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/pandas/core/reshape/concat.py in init(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)
299 objs = [objs[k] for k in keys]
300 else:
--> 301 objs = list(objs)
302
303 if len(objs) == 0:

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/fitgrid/models.py in process_key_and_group(key_and_group, function, channels)
43 def process_key_and_group(key_and_group, function, channels):
44 key, group = key_and_group
---> 45 results = {channel: function(group, channel) for channel in channels}
46 return pd.Series(results, name=key)
47

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/fitgrid/models.py in (.0)
43 def process_key_and_group(key_and_group, function, channels):
44 key, group = key_and_group
---> 45 results = {channel: function(group, channel) for channel in channels}
46 return pd.Series(results, name=key)
47

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/fitgrid/models.py in lm_single(data, channel, RHS, eval_env)
121 def lm_single(data, channel, RHS, eval_env):
122 formula = channel + ' ~ ' + RHS
--> 123 return ols(formula, data, eval_env=eval_env).fit()
124
125

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/statsmodels/base/model.py in from_formula(cls, formula, data, subset, drop_cols, *args, **kwargs)
167
168 tmp = handle_formula_data(data, None, formula, depth=eval_env,
--> 169 missing=missing)
170 ((endog, exog), missing_idx, design_info) = tmp
171 max_endog = cls._formula_max_endog

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/statsmodels/formula/formulatools.py in handle_formula_data(Y, X, formula, depth, missing)
63 if data_util._is_using_pandas(Y, None):
64 result = dmatrices(formula, Y, depth, return_type='dataframe',
---> 65 NA_action=na_action)
66 else:
67 result = dmatrices(formula, Y, depth, return_type='dataframe',

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/highlevel.py in dmatrices(formula_like, data, eval_env, NA_action, return_type)
308 eval_env = EvalEnvironment.capture(eval_env, reference=1)
309 (lhs, rhs) = _do_highlevel_design(formula_like, data, eval_env,
--> 310 NA_action, return_type)
311 if lhs.shape[1] == 0:
312 raise PatsyError("model is missing required outcome variables")

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/highlevel.py in _do_highlevel_design(formula_like, data, eval_env, NA_action, return_type)
163 return iter([data])
164 design_infos = _try_incr_builders(formula_like, data_iter_maker, eval_env,
--> 165 NA_action)
166 if design_infos is not None:
167 return build_design_matrices(design_infos, data,

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/highlevel.py in _try_incr_builders(formula_like, data_iter_maker, eval_env, NA_action)
68 data_iter_maker,
69 eval_env,
---> 70 NA_action)
71 else:
72 return None

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/build.py in design_matrix_builders(termlists, data_iter_maker, eval_env, NA_action)
694 factor_states,
695 data_iter_maker,
--> 696 NA_action)
697 # Now we need the factor infos, which encapsulate the knowledge of
698 # how to turn any given factor into a chunk of data:

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/build.py in _examine_factor_types(factors, factor_states, data_iter_maker, NA_action)
441 for data in data_iter_maker():
442 for factor in list(examine_needed):
--> 443 value = factor.eval(factor_states[factor], data)
444 if factor in cat_sniffers or guess_categorical(value):
445 if factor not in cat_sniffers:

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/eval.py in eval(self, memorize_state, data)
564 return self._eval(memorize_state["eval_code"],
565 memorize_state,
--> 566 data)
567
568 getstate = no_pickling

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/eval.py in _eval(self, code, memorize_state, data)
549 memorize_state["eval_env"].eval,
550 code,
--> 551 inner_namespace=inner_namespace)
552
553 def memorize_chunk(self, state, which_pass, data):

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/compat.py in call_and_wrap_exc(msg, origin, f, *args, **kwargs)
41 origin)
42 # Use 'exec' to hide this syntax from the Python 2 parser:
---> 43 exec("raise new_exc from e")
44 else:
45 # In python 2, we just let the original exception escape -- better

~/.conda/envs/mkconda_0.0.10/lib/python3.6/site-packages/patsy/compat.py in

PatsyError: Error evaluating factor: NameError: name 'cm_b' is not defined
MiPa ~ C(ThreatCondition, cm_a) * C(Condition, cm_b)
^^^^^^^^^^^^^^^^^^

[astoermann]

Imported packages:
from patsy import dmatrix, ContrastMatrix, balanced