def eval_nl_logsums(choosers, spec, nest_spec, locals_d, trace_label=None):
"""
like eval_nl except return logsums instead of making choices
Returns
-------
logsums : pandas.Series
Index will be that of `choosers`, values will be nest logsum
based on spec column values
"""
trace_label = tracing.extend_trace_label(trace_label, 'nl_logsums')
check_for_variability = tracing.check_for_variability()
# logger.debug("running eval_nl_logsums")
t0 = tracing.print_elapsed_time()
# column names of expression_values match spec index values
expression_values = eval_variables(spec.index, choosers, locals_d)
t0 = tracing.print_elapsed_time("eval_variables", t0, debug=True)
if check_for_variability:
_check_for_variability(expression_values, trace_label)
t0 = tracing.print_elapsed_time("_check_for_variability",
t0,
debug=True)
# raw utilities of all the leaves
raw_utilities = compute_utilities(expression_values, spec)
t0 = tracing.print_elapsed_time("expression_values.dot", t0, debug=True)
# exponentiated utilities of leaves and nests
nested_exp_utilities = compute_nested_exp_utilities(
raw_utilities, nest_spec)
t0 = tracing.print_elapsed_time("compute_nested_exp_utilities",
t0,
debug=True)
logsums = np.log(nested_exp_utilities.root)
logsums = pd.Series(logsums, index=choosers.index)
t0 = tracing.print_elapsed_time("logsums", t0, debug=True)
if trace_label:
# add logsum to nested_exp_utilities for tracing
nested_exp_utilities['logsum'] = logsums
tracing.trace_df(choosers, '%s.choosers' % trace_label)
tracing.trace_df(raw_utilities,
'%s.raw_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(nested_exp_utilities,
'%s.nested_exp_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(logsums,
'%s.logsums' % trace_label,
column_labels=['alternative', 'logsum'])
return logsums
def eval_mnl_logsums(choosers, spec, locals_d, trace_label=None):
"""
like eval_nl except return logsums instead of making choices
Returns
-------
logsums : pandas.Series
Index will be that of `choosers`, values will be
logsum across spec column values
"""
trace_label = tracing.extend_trace_label(trace_label, 'mnl')
check_for_variability = tracing.check_for_variability()
print("running eval_mnl_logsums")
expression_values = eval_variables(spec.index, choosers, locals_d)
if check_for_variability:
_check_for_variability(expression_values, trace_label)
# utility values
utilities = compute_utilities(expression_values, spec)
# logsum is log of exponentiated utilities summed across
# columns of each chooser row
utils_arr = utilities.as_matrix().astype('float')
logsums = np.log(np.exp(utils_arr).sum(axis=1))
logsums = pd.Series(logsums, index=choosers.index)
if trace_label:
# add logsum to utilities for tracing
utilities['logsum'] = logsums
tracing.trace_df(choosers, '%s.choosers' % trace_label)
tracing.trace_df(utilities,
'%s.utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(logsums,
'%s.logsums' % trace_label,
column_labels=['alternative', 'logsum'])
tracing.trace_df(expression_values,
'%s.expression_values' % trace_label,
column_labels=['expression', None])
return logsums
def eval_interaction_utilities(spec, df, locals_d, trace_label, trace_rows):
"""
Compute the utilities for a single-alternative spec evaluated in the context of df
We could compute the utilities for interaction datasets just as we do for simple_simulate
specs with multiple alternative columns byt calling eval_variables and then computing the
utilities by matrix-multiplication of eval results with the utility coefficients in the
spec alternative columns.
But interaction simulate computes the utilities of each alternative in the context of a
separate row in interaction dataset df, and so there is only one alternative in spec.
This turns out to be quite a bit faster (in this special case) than the pandas dot function.
For efficiency, we combine eval_variables and multiplication of coefficients into a single step,
so we don't have to create a separate column for each partial utility. Instead, we simply
multiply the eval result by a single alternative coefficient and sum the partial utilities.
spec : dataframe
one row per spec expression and one col with utility coefficient
df : dataframe
cross join (cartesian product) of choosers with alternatives
combines columns of choosers and alternatives
len(df) == len(choosers) * len(alternatives)
index values (non-unique) are index values from alternatives df
interaction_utilities : dataframe
the utility of each alternative is sum of the partial utilities determined by the
various spec expressions and their corresponding coefficients
yielding a dataframe with len(interaction_df) rows and one utility column
having the same index as interaction_df (non-unique values from alternatives df)
Returns
-------
utilities : pandas.DataFrame
Will have the index of `df` and a single column of utilities
"""
assert (len(spec.columns) == 1)
# avoid altering caller's passed-in locals_d parameter (they may be looping)
locals_d = locals_d.copy() if locals_d is not None else {}
locals_d.update(locals())
def to_series(x):
if np.isscalar(x):
return pd.Series([x] * len(df), index=df.index)
return x
if trace_rows is not None and trace_rows.any():
# # convert to numpy array so we can slice ndarrays as well as series
# trace_rows = np.asanyarray(trace_rows)
assert type(trace_rows) == np.ndarray
trace_eval_results = []
else:
trace_eval_results = None
check_for_variability = tracing.check_for_variability()
# need to be able to identify which variables causes an error, which keeps
# this from being expressed more parsimoniously
utilities = pd.DataFrame({'utility': 0.0}, index=df.index)
no_variability = has_missing_vals = 0
for expr, coefficient in zip(spec.index, spec.iloc[:, 0]):
try:
if expr.startswith('@'):
v = to_series(eval(expr[1:], globals(), locals_d))
else:
v = df.eval(expr)
if check_for_variability and v.std() == 0:
logger.info("%s: no variability (%s) in: %s" %
(trace_label, v.iloc[0], expr))
no_variability += 1
# FIXME - how likely is this to happen? Not sure it is really a problem?
if check_for_variability and np.count_nonzero(
v.isnull().values) > 0:
logger.info("%s: missing values in: %s" % (trace_label, expr))
has_missing_vals += 1
utilities.utility += (v * coefficient).astype('float')
if trace_eval_results is not None:
trace_eval_results.append((expr, v[trace_rows]))
trace_eval_results.append(
('partial utility (coefficient = %s)' % coefficient,
v[trace_rows] * coefficient))
# trace_eval_results.append(('cumulative utility',
# utilities.utility[trace_rows]))
except Exception as err:
logger.exception("Variable evaluation failed for: %s" % str(expr))
raise err
if no_variability > 0:
logger.warn("%s: %s columns have no variability" %
(trace_label, no_variability))
if has_missing_vals > 0:
logger.warn("%s: %s columns have missing values" %
(trace_label, has_missing_vals))
if trace_eval_results is not None:
trace_eval_results.append(
('total utility', utilities.utility[trace_rows]))
trace_eval_results = pd.DataFrame.from_items(trace_eval_results)
trace_eval_results.index = df[trace_rows].index
# add df columns to trace_results
trace_eval_results = pd.concat([df[trace_rows], trace_eval_results],
axis=1)
return utilities, trace_eval_results
def eval_nl(choosers,
spec,
nest_spec,
locals_d=None,
trace_label=None,
trace_choice_name=None):
"""
Run a nested-logit simulation for when the model spec does not involve alternative
specific data, e.g. there are no interactions with alternative
properties and no need to sample from alternatives.
Parameters
----------
choosers : pandas.DataFrame
spec : pandas.DataFrame
A table of variable specifications and coefficient values.
Variable expressions should be in the table index and the table
should have a column for each alternative.
nest_spec:
dictionary specifying nesting structure and nesting coefficients
(from the model spec yaml file)
locals_d : Dict
This is a dictionary of local variables that will be the environment
for an evaluation of an expression that begins with @
trace_label: str
This is the label to be used for trace log file entries and dump file names
when household tracing enabled. No tracing occurs if label is empty or None.
trace_choice_name: str
This is the column label to be used in trace file csv dump of choices
Returns
-------
choices : pandas.Series
Index will be that of `choosers`, values will match the columns
of `spec`.
"""
trace_label = tracing.extend_trace_label(trace_label, 'nl')
check_for_variability = tracing.check_for_variability()
# column names of model_design match spec index values
model_design = eval_variables(spec.index, choosers, locals_d)
if check_for_variability:
_check_for_variability(model_design, trace_label)
# raw utilities of all the leaves
# matrix product of spec expression evals with utility coefficients of alternatives
# sums the partial utilities (represented by each spec row) of the alternatives
# resulting in a dataframe with one row per chooser and one column per alternative
# pandas dot matrix-multiply depends on column names of model_design matching spec index values
raw_utilities = model_design.dot(spec)
# exponentiated utilities of leaves and nests
nested_exp_utilities = compute_nested_exp_utilities(
raw_utilities, nest_spec)
# probabilities of alternatives relative to siblings sharing the same nest
nested_probabilities = compute_nested_probabilities(
nested_exp_utilities, nest_spec, trace_label=trace_label)
# global (flattened) leaf probabilities based on relative nest coefficients
base_probabilities = compute_base_probabilities(nested_probabilities,
nest_spec)
# note base_probabilities could all be zero since we allowed all probs for nests to be zero
# check here to print a clear message but make_choices will raise error if probs don't sum to 1
BAD_PROB_THRESHOLD = 0.001
no_choices = \
base_probabilities.sum(axis=1).sub(np.ones(len(base_probabilities.index))).abs() \
> BAD_PROB_THRESHOLD * np.ones(len(base_probabilities.index))
if no_choices.any():
report_bad_choices(no_choices,
base_probabilities,
tracing.extend_trace_label(trace_label, 'eval_nl'),
tag='bad_probs',
msg="base_probabilities all zero")
choices = make_choices(base_probabilities,
trace_label,
trace_choosers=choosers)
if trace_label:
tracing.trace_df(choosers, '%s.choosers' % trace_label)
tracing.trace_df(raw_utilities,
'%s.raw_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(nested_exp_utilities,
'%s.nested_exp_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(nested_probabilities,
'%s.nested_probabilities' % trace_label,
column_labels=['alternative', 'probability'])
tracing.trace_df(base_probabilities,
'%s.base_probabilities' % trace_label,
column_labels=['alternative', 'probability'])
tracing.trace_df(choices,
'%s.choices' % trace_label,
columns=[None, trace_choice_name])
tracing.trace_df(model_design,
'%s.model_design' % trace_label,
column_labels=['expression', None])
# dump whole df - for software development debugging
# tracing.trace_df(raw_utilities, "%s.raw_utilities" % trace_label,
# slicer='NONE', transpose=False)
# tracing.trace_df(nested_exp_utilities, "%s.nested_exp_utilities" % trace_label,
# slicer='NONE', transpose=False)
# tracing.trace_df(nested_probabilities, "%s.nested_probabilities" % trace_label,
# slicer='NONE', transpose=False)
# tracing.trace_df(base_probabilities, "%s.base_probabilities" % trace_label,
# slicer='NONE', transpose=False)
# tracing.trace_df(unnested_probabilities, "%s.unnested_probabilities" % trace_label,
# slicer='NONE', transpose=False)
return choices
def eval_mnl(choosers,
spec,
locals_d=None,
trace_label=None,
trace_choice_name=None):
"""
Run a simulation for when the model spec does not involve alternative
specific data, e.g. there are no interactions with alternative
properties and no need to sample from alternatives.
Each row in spec computes a partial utility for each alternative,
by providing a spec expression (often a boolean 0-1 trigger)
and a column of utility coefficients for each alternative.
We compute the utility of each alternative by matrix-multiplication of eval results
with the utility coefficients in the spec alternative columns
yielding one row per chooser and one column per alternative
Parameters
----------
choosers : pandas.DataFrame
spec : pandas.DataFrame
A table of variable specifications and coefficient values.
Variable expressions should be in the table index and the table
should have a column for each alternative.
locals_d : Dict
This is a dictionary of local variables that will be the environment
for an evaluation of an expression that begins with @
trace_label: str
This is the label to be used for trace log file entries and dump file names
when household tracing enabled. No tracing occurs if label is empty or None.
trace_choice_name: str
This is the column label to be used in trace file csv dump of choices
Returns
-------
choices : pandas.Series
Index will be that of `choosers`, values will match the columns
of `spec`.
"""
trace_label = tracing.extend_trace_label(trace_label, 'mnl')
check_for_variability = tracing.check_for_variability()
model_design = eval_variables(spec.index, choosers, locals_d)
if check_for_variability:
_check_for_variability(model_design, trace_label)
# matrix product of spec expression evals with utility coefficients of alternatives
# sums the partial utilities (represented by each spec row) of the alternatives
# resulting in a dataframe with one row per chooser and one column per alternative
# pandas dot matrix-multiply depends on column names of model_design matching spec index values
utilities = model_design.dot(spec)
probs = utils_to_probs(utilities,
trace_label=trace_label,
trace_choosers=choosers)
choices = make_choices(probs,
trace_label=trace_label,
trace_choosers=choosers)
if trace_label:
tracing.trace_df(choosers, '%s.choosers' % trace_label)
tracing.trace_df(utilities,
'%s.utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(probs,
'%s.probs' % trace_label,
column_labels=['alternative', 'probability'])
tracing.trace_df(choices,
'%s.choices' % trace_label,
columns=[None, trace_choice_name])
tracing.trace_df(model_design,
'%s.model_design' % trace_label,
column_labels=['expression', None])
return choices
def eval_nl(choosers,
spec,
nest_spec,
locals_d,
trace_label=None,
trace_choice_name=None):
"""
Run a nested-logit simulation for when the model spec does not
involve alternative
specific data, e.g. there are no interactions with alternative
properties and no need to sample from alternatives.
Parameters
----------
choosers : pandas.DataFrame
spec : pandas.DataFrame
A table of variable specifications and coefficient values.
Variable expressions should be in the table index and the table
should have a column for each alternative.
nest_spec:
dictionary specifying nesting structure and nesting coefficients
(from the model spec yaml file)
locals_d : Dict or None
This is a dictionary of local variables that will be the environment
for an evaluation of an expression that begins with @
trace_label: str
This is the label to be used for trace log file entries
and dump file names
when household tracing enabled. No tracing occurs if
label is empty or None.
trace_choice_name: str
This is the column label to be used in trace file csv dump of choices
Returns
-------
choices : pandas.Series
Index will be that of `choosers`, values will match the columns
of `spec`.
"""
trace_label = tracing.extend_trace_label(trace_label, 'nl')
check_for_variability = tracing.check_for_variability()
t0 = tracing.print_elapsed_time()
# column names of expression_values match spec index values
expression_values = eval_variables(spec.index, choosers, locals_d)
t0 = tracing.print_elapsed_time("eval_variables", t0, debug=True)
if check_for_variability:
_check_for_variability(expression_values, trace_label)
t0 = tracing.print_elapsed_time("_check_for_variability", t0, debug=True)
# raw utilities of all the leaves
raw_utilities = compute_utilities(expression_values, spec)
t0 = tracing.print_elapsed_time("expression_values.dot", t0, debug=True)
# exponentiated utilities of leaves and nests
nested_exp_utilities = compute_nested_exp_utilities(
raw_utilities, nest_spec)
t0 = tracing.print_elapsed_time("compute_nested_exp_utilities",
t0,
debug=True)
# probabilities of alternatives relative to siblings sharing the same nest
nested_probabilities = compute_nested_probabilities(
nested_exp_utilities, nest_spec, trace_label=trace_label)
t0 = tracing.print_elapsed_time("compute_nested_probabilities",
t0,
debug=True)
# global (flattened) leaf probabilities based on relative nest coefficients
base_probabilities = compute_base_probabilities(nested_probabilities,
nest_spec)
t0 = tracing.print_elapsed_time("compute_base_probabilities",
t0,
debug=True)
# note base_probabilities could all be zero since we allowed all probs
# for nests to be zero check here to print a clear message but
# make_choices will raise error if probs don't sum to 1
BAD_PROB_THRESHOLD = 0.001
no_choices = \
base_probabilities.sum(axis=1).sub(
np.ones(len(base_probabilities.index))).abs() \
> BAD_PROB_THRESHOLD * np.ones(len(base_probabilities.index))
if no_choices.any():
logit.report_bad_choices(no_choices,
base_probabilities,
tracing.extend_trace_label(
trace_label, 'eval_nl'),
tag='bad_probs',
msg="base_probabilities all zero")
t0 = tracing.print_elapsed_time("report_bad_choices", t0, debug=True)
choices, rands = logit.make_choices(base_probabilities,
trace_label,
trace_choosers=choosers)
t0 = tracing.print_elapsed_time("logit.make_choices", t0, debug=True)
if trace_label:
tracing.trace_df(choosers, '%s.choosers' % trace_label)
tracing.trace_df(raw_utilities,
'%s.raw_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(nested_exp_utilities,
'%s.nested_exp_utilities' % trace_label,
column_labels=['alternative', 'utility'])
tracing.trace_df(nested_probabilities,
'%s.nested_probabilities' % trace_label,
column_labels=['alternative', 'probability'])
tracing.trace_df(base_probabilities,
'%s.base_probabilities' % trace_label,
column_labels=['alternative', 'probability'])
tracing.trace_df(choices,
'%s.choices' % trace_label,
columns=[None, trace_choice_name])
tracing.trace_df(rands,
'%s.rands' % trace_label,
columns=[None, 'rand'])
tracing.trace_df(expression_values,
'%s.expression_values' % trace_label,
column_labels=['expression', None])
return choices
