def fit(self, mct=None):
"""
Fit the model; save and report results. This uses the ChoiceModels estimation
engine (originally from UrbanSim MNL).
The `fit()` method can be run as many times as desired. Results will not be saved
with Orca or ModelManager until the `register()` method is run.
After sampling alternatives for each chooser, the merged choice table is saved to
the class object for diagnostic use (`mergedchoicetable` with type
choicemodels.tools.MergedChoiceTable).
Parameters
----------
mct : choicemodels.tools.MergedChoiceTable
This parameter is a temporary backdoor allowing us to pass in a more
complicated choice table than can be generated within the template, for
example including sampling weights or interaction terms.
Returns
-------
None
"""
check_choicemodels_version()
from choicemodels import MultinomialLogit
from choicemodels.tools import MergedChoiceTable
if (mct is not None):
df_from_mct = mct.to_frame()
idx_names = df_from_mct.index.names
df_from_mct = df_from_mct.reset_index()
df_from_mct = apply_filter_query(
df_from_mct, self.chooser_filters).set_index(idx_names)
mct = MergedChoiceTable.from_df(df_from_mct)
else:
observations = get_data(tables=self.choosers,
filters=self.chooser_filters,
model_expression=self.model_expression,
extra_columns=self.choice_column)
if (self.chooser_sample_size is not None):
observations = observations.sample(self.chooser_sample_size)
alternatives = get_data(tables=self.alternatives,
filters=self.alt_filters,
model_expression=self.model_expression)
mct = MergedChoiceTable(observations=observations,
alternatives=alternatives,
chosen_alternatives=self.choice_column,
sample_size=self.alt_sample_size)
model = MultinomialLogit(data=mct,
model_expression=self.model_expression)
results = model.fit()
self.name = self._generate_name()
self.summary_table = str(results)
print(self.summary_table)
coefs = results.get_raw_results()['fit_parameters']['Coefficient']
self.fitted_parameters = coefs.tolist()
self.model = results
# Save merged choice table to the class object for diagnostics
self.mergedchoicetable = mct
def perform_mct_intx_ops(self, mct, nan_handling='zero'):
"""
Method to dynamically update a MergedChoiceTable object according to
a pre-defined set of operations specified in the model .yaml config.
Operations are performed sequentially as follows: 1) Pandas merges
with other Orca tables; 2) Pandas group-by aggregations; 3) rename
existing columns; 4) create new columns via Pandas `eval()`.
Parameters
----------
mct : choicemodels.tools.MergedChoiceTable
nan_handling : str
Either 'zero' or 'drop', where the former will replace all NaN's
and None's with 0 integers and the latter will drop all rows with
any NaN or Null values.
Returns
-------
MergedChoiceTable
"""
intx_ops = self.mct_intx_ops
mct_df = mct.to_frame()
og_mct_index = mct_df.index.names
mct_df.reset_index(inplace=True)
mct_df.index.name = 'mct_index'
# merges
intx_df = mct_df.copy()
for merge_args in intx_ops.get('successive_merges', []):
# make sure mct index is preserved during merge
left_cols = merge_args.get('mct_cols', intx_df.columns)
left_idx = merge_args.get('left_index', False)
if intx_df.index.name == mct_df.index.name:
if not left_idx:
intx_df.reset_index(inplace=True)
if mct_df.index.name not in left_cols:
left_cols += [mct_df.index.name]
elif mct_df.index.name in intx_df.columns:
if mct_df.index.name not in left_cols:
left_cols += [mct_df.index.name]
else:
raise KeyError(
'Column {0} must be preserved in intx ops!'.format(
mct_df.index.name))
left = intx_df[left_cols]
right = get_data(merge_args['right_table'],
extra_columns=merge_args.get('right_cols', None))
intx_df = pd.merge(left,
right,
how=merge_args.get('how', 'inner'),
on=merge_args.get('on_cols', None),
left_on=merge_args.get('left_on', None),
right_on=merge_args.get('right_on', None),
left_index=left_idx,
right_index=merge_args.get(
'right_index', False),
suffixes=merge_args.get('suffixes',
('_x', '_y')))
# aggs
aggs = intx_ops.get('aggregations', False)
if aggs:
intx_df = intx_df.groupby('mct_index').agg(aggs)
# rename cols
if intx_ops.get('rename_cols', False):
intx_df = intx_df.rename(columns=intx_ops['rename_cols'])
# update mct
mct_df = pd.merge(mct_df, intx_df, on='mct_index')
# create new cols from expressions
for eval_op in intx_ops.get('sequential_eval_ops', []):
new_col = eval_op['name']
expr = eval_op['expr']
engine = eval_op.get('engine', 'numexpr')
mct_df[new_col] = mct_df.eval(expr, engine=engine)
# restore original mct index
mct_df.set_index(og_mct_index, inplace=True)
# handle NaNs and Nones
if mct_df.isna().values.any():
if nan_handling == 'zero':
print("Replacing MCT None's and NaN's with 0")
mct_df = mct_df.fillna(0)
elif nan_handling == 'drop':
print("Dropping rows with None's/NaN's from MCT")
mct_df = mct_df.dropna(axis=0)
return MergedChoiceTable.from_df(mct_df)