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Merge 9c49885 into ce019b6
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@jiffyclub
jiffyclub committed Apr 29, 2015
2 parents ce019b6 + 9c49885 commit 6637838
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Showing 4 changed files with 221 additions and 37 deletions.
115 changes: 102 additions & 13 deletions urbansim/models/dcm.py
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Expand Up @@ -94,6 +94,41 @@ class DiscreteChoiceModel(object):
"""
__metaclass__ = abc.ABCMeta

@staticmethod
def _check_prob_choice_mode_compat(probability_mode, choice_mode):
"""
Check that the probability and choice modes are compatibly with
each other. Currently 'single_chooser' must be paired with
'aggregate' and 'full_product' must be paired with 'individual'.
"""
if (probability_mode == 'full_product' and
choice_mode == 'aggregate'):
raise ValueError(
"'full_product' probability mode is not compatible with "
"'aggregate' choice mode")

if (probability_mode == 'single_chooser' and
choice_mode == 'individual'):
raise ValueError(
"'single_chooser' probability mode is not compatible with "
"'individual' choice mode")

@staticmethod
def _check_prob_mode_interaction_compat(
probability_mode, interaction_predict_filters):
"""
The 'full_product' probability mode is currently incompatible with
post-interaction prediction filters, so make sure we don't have
both of those.
"""
if (interaction_predict_filters is not None and
probability_mode == 'full_product'):
raise ValueError(
"interaction filters may not be used in "
"'full_product' mode")

@abc.abstractmethod
def apply_fit_filters(self, choosers, alternatives):
choosers = util.apply_filter_query(choosers, self.choosers_fit_filters)
Expand Down Expand Up @@ -165,6 +200,9 @@ class MNLDiscreteChoiceModel(DiscreteChoiceModel):
In "single chooser" mode one agent is chosen for calculating
probabilities across all alternatives. In "full product" mode
probabilities are calculated for every chooser across all alternatives.
Currently "single chooser" mode must be used with a `choice_mode`
of 'aggregate' and "full product" mode must be used with a
`choice_mode` of 'individual'.
choice_mode : str, optional
Specify the method to use for making choices among alternatives.
Available string options are 'individual' and 'aggregate'.
Expand All @@ -173,6 +211,9 @@ class MNLDiscreteChoiceModel(DiscreteChoiceModel):
Aggregate mode implies that an alternative chosen by one agent
is unavailable to other agents and that the same probabilities
can be used for all choosers.
Currently "individual" mode must be used with a `probability_mode`
of 'full_product' and "aggregate" mode must be used with a
`probability_mode` of 'single_chooser'.
choosers_fit_filters : list of str, optional
Filters applied to choosers table before fitting the model.
choosers_predict_filters : list of str, optional
Expand All @@ -186,8 +227,13 @@ class MNLDiscreteChoiceModel(DiscreteChoiceModel):
interaction_predict_filters : list of str, optional
Filters applied to the merged choosers/alternatives table
before predicting agent choices.
estimation_sample_size : int, optional, whether to sample choosers
during estimation (needs to be applied after choosers_fit_filters)
estimation_sample_size : int, optional
Whether to sample choosers during estimation
(needs to be applied after choosers_fit_filters).
prediction_sample_size : int, optional
Whether (and how much) to sample alternatives during prediction.
Note that this can lead to multiple choosers picking the same
alternative.
choice_column : optional
Name of the column in the `alternatives` table that choosers
should choose. e.g. the 'building_id' column. If not provided
Expand All @@ -204,7 +250,12 @@ def __init__(
alts_fit_filters=None, alts_predict_filters=None,
interaction_predict_filters=None,
estimation_sample_size=None,
prediction_sample_size=None,
choice_column=None, name=None):
self._check_prob_choice_mode_compat(probability_mode, choice_mode)
self._check_prob_mode_interaction_compat(
probability_mode, interaction_predict_filters)

self.model_expression = model_expression
self.sample_size = sample_size
self.probability_mode = probability_mode
Expand All @@ -215,6 +266,7 @@ def __init__(
self.alts_predict_filters = alts_predict_filters
self.interaction_predict_filters = interaction_predict_filters
self.estimation_sample_size = estimation_sample_size
self.prediction_sample_size = prediction_sample_size
self.choice_column = choice_column
self.name = name if name is not None else 'MNLDiscreteChoiceModel'
self.sim_pdf = None
Expand Down Expand Up @@ -254,6 +306,7 @@ def from_yaml(cls, yaml_str=None, str_or_buffer=None):
interaction_predict_filters=cfg.get(
'interaction_predict_filters', None),
estimation_sample_size=cfg.get('estimation_sample_size', None),
prediction_sample_size=cfg.get('prediction_sample_size', None),
choice_column=cfg.get('choice_column', None),
name=cfg.get('name', None)
)
Expand Down Expand Up @@ -449,12 +502,17 @@ def probabilities(self, choosers, alternatives, filter_tables=True):
choosers, alternatives = self.apply_predict_filters(
choosers, alternatives)

if self.prediction_sample_size is not None:
sample_size = self.prediction_sample_size
else:
sample_size = len(alternatives)

if self.probability_mode == 'single_chooser':
_, merged, _ = interaction.mnl_interaction_dataset(
choosers.head(1), alternatives, len(alternatives))
choosers.head(1), alternatives, sample_size)
elif self.probability_mode == 'full_product':
_, merged, _ = interaction.mnl_interaction_dataset(
choosers, alternatives, len(alternatives))
choosers, alternatives, sample_size)
else:
raise ValueError(
'Unrecognized probability_mode option: {}'.format(
Expand All @@ -478,10 +536,15 @@ def probabilities(self, choosers, alternatives, filter_tables=True):

# probabilities are returned from mnl_simulate as a 2d array
# with choosers along rows and alternatives along columns
if self.probability_mode == 'single_chooser':
numalts = len(merged)
else:
numalts = sample_size

probabilities = mnl.mnl_simulate(
model_design.as_matrix(),
coeffs,
numalts=len(merged), returnprobs=True)
numalts=numalts, returnprobs=True)

# want to turn probabilities into a Series with a MultiIndex
# of chooser IDs and alternative IDs.
Expand Down Expand Up @@ -610,6 +673,7 @@ def to_dict(self):
'alts_predict_filters': self.alts_predict_filters,
'interaction_predict_filters': self.interaction_predict_filters,
'estimation_sample_size': self.estimation_sample_size,
'prediction_sample_size': self.prediction_sample_size,
'choice_column': self.choice_column,
'fitted': self.fitted,
'log_likelihoods': self.log_likelihoods,
Expand Down Expand Up @@ -816,7 +880,7 @@ def add_model_from_params(
choosers_fit_filters=None, choosers_predict_filters=None,
alts_fit_filters=None, alts_predict_filters=None,
interaction_predict_filters=None, estimation_sample_size=None,
choice_column=None):
prediction_sample_size=None, choice_column=None):
"""
Add a model by passing parameters through to MNLDiscreteChoiceModel.
Expand Down Expand Up @@ -857,8 +921,13 @@ def add_model_from_params(
interaction_predict_filters : list of str, optional
Filters applied to the merged choosers/alternatives table
before predicting agent choices.
estimation_sample_size : int, optional, whether to sample choosers
during estimation (needs to be applied after choosers_fit_filters)
estimation_sample_size : int, optional
Whether to sample choosers during estimation
(needs to be applied after choosers_fit_filters)
prediction_sample_size : int, optional
Whether (and how much) to sample alternatives during prediction.
Note that this can lead to multiple choosers picking the same
alternative.
choice_column : optional
Name of the column in the `alternatives` table that choosers
should choose. e.g. the 'building_id' column. If not provided
Expand All @@ -872,7 +941,7 @@ def add_model_from_params(
choosers_fit_filters, choosers_predict_filters,
alts_fit_filters, alts_predict_filters,
interaction_predict_filters, estimation_sample_size,
choice_column, name)
prediction_sample_size, choice_column, name)

def _iter_groups(self, data):
"""
Expand Down Expand Up @@ -1160,14 +1229,20 @@ class SegmentedMNLDiscreteChoiceModel(DiscreteChoiceModel):
In "single chooser" mode one agent is chosen for calculating
probabilities across all alternatives. In "full product" mode
probabilities are calculated for every chooser across all alternatives.
choice_mode : str or callable, optional
Currently "single chooser" mode must be used with a `choice_mode`
of 'aggregate' and "full product" mode must be used with a
`choice_mode` of 'individual'.
choice_mode : str, optional
Specify the method to use for making choices among alternatives.
Available string options are 'individual' and 'aggregate'.
In "individual" mode choices will be made separately for each chooser.
In "aggregate" mode choices are made for all choosers at once.
Aggregate mode implies that an alternative chosen by one agent
is unavailable to other agents and that the same probabilities
can be used for all choosers.
Currently "individual" mode must be used with a `probability_mode`
of 'full_product' and "aggregate" mode must be used with a
`probability_mode` of 'single_chooser'.
choosers_fit_filters : list of str, optional
Filters applied to choosers table before fitting the model.
choosers_predict_filters : list of str, optional
Expand All @@ -1181,8 +1256,13 @@ class SegmentedMNLDiscreteChoiceModel(DiscreteChoiceModel):
interaction_predict_filters : list of str, optional
Filters applied to the merged choosers/alternatives table
before predicting agent choices.
estimation_sample_size : int, optional, whether to sample choosers
during estimation (needs to be applied after choosers_fit_filters)
estimation_sample_size : int, optional
Whether to sample choosers during estimation
(needs to be applied after choosers_fit_filters)
prediction_sample_size : int, optional
Whether (and how much) to sample alternatives during prediction.
Note that this can lead to multiple choosers picking the same
alternative.
choice_column : optional
Name of the column in the `alternatives` table that choosers
should choose. e.g. the 'building_id' column. If not provided
Expand All @@ -1205,9 +1285,13 @@ def __init__(
choosers_fit_filters=None, choosers_predict_filters=None,
alts_fit_filters=None, alts_predict_filters=None,
interaction_predict_filters=None,
estimation_sample_size=None,
estimation_sample_size=None, prediction_sample_size=None,
choice_column=None, default_model_expr=None, remove_alts=False,
name=None):
self._check_prob_choice_mode_compat(probability_mode, choice_mode)
self._check_prob_mode_interaction_compat(
probability_mode, interaction_predict_filters)

self.segmentation_col = segmentation_col
self.sample_size = sample_size
self.probability_mode = probability_mode
Expand All @@ -1218,6 +1302,7 @@ def __init__(
self.alts_predict_filters = alts_predict_filters
self.interaction_predict_filters = interaction_predict_filters
self.estimation_sample_size = estimation_sample_size
self.prediction_sample_size = prediction_sample_size
self.choice_column = choice_column
self.default_model_expr = default_model_expr
self.remove_alts = remove_alts
Expand Down Expand Up @@ -1259,6 +1344,7 @@ def from_yaml(cls, yaml_str=None, str_or_buffer=None):
cfg['alts_predict_filters'],
cfg['interaction_predict_filters'],
cfg['estimation_sample_size'],
cfg['prediction_sample_size'],
cfg['choice_column'],
default_model_expr,
cfg['remove_alts'],
Expand All @@ -1280,6 +1366,7 @@ def from_yaml(cls, yaml_str=None, str_or_buffer=None):
m['interaction_predict_filters'] = \
cfg['interaction_predict_filters']
m['estimation_sample_size'] = cfg['estimation_sample_size']
m['prediction_sample_size'] = cfg['prediction_sample_size']
m['choice_column'] = cfg['choice_column']

model = MNLDiscreteChoiceModel.from_yaml(
Expand Down Expand Up @@ -1557,6 +1644,7 @@ def _process_model_dict(self, d):
del d['alts_predict_filters']
del d['interaction_predict_filters']
del d['estimation_sample_size']
del d['prediction_sample_size']
del d['choice_column']

if d['model_expression'] == self.default_model_expr:
Expand Down Expand Up @@ -1585,6 +1673,7 @@ def to_dict(self):
'alts_predict_filters': self.alts_predict_filters,
'interaction_predict_filters': self.interaction_predict_filters,
'estimation_sample_size': self.estimation_sample_size,
'prediction_sample_size': self.prediction_sample_size,
'choice_column': self.choice_column,
'default_config': {
'model_expression': self.default_model_expr,
Expand Down

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