def sample_choice(prob_array, method="MC"):
"""sample chosen index given probability in prob_array
prob_array - 2-d array for probability of being chosen, with probablities for a agent at
one row, and probabilities for alternatives at columns
method - the method used to sample choice, either MC (Monte Carlo) or max_prob
"""
if prob_array.ndim <> 2:
raise RuntimeError, "prob_array must be a 2d array"
rows, columns = prob_array.shape
sum_prob_by_col = sum(prob_array, axis=1, dtype=float64)
if not ma.allclose(sum_prob_by_col, ones((rows, ))):
strange_rows = where(sum_prob_by_col != ones((rows, )))
raise RuntimeError, "prob_array must add up to 1 for each row. Abnormal rows: %s" % prob_array[
strange_rows, :]
if method.lower() == "mc":
cum_prob = ncumsum(prob_array, axis=1)
R = uniform(0, 1, rows)
R.resize((rows, 1))
match = (R < cum_prob)
choices = argmax(
match, axis=1) # return the first index of 1 in each row
elif method.lower() == "max_prob":
choices = argmax(prob_array)
if choices.size <> rows:
raise RuntimeError, "having problems sample choice"
return (arange(rows), choices)
def run(self, individual_dataset, fraction_dataset, id_name1='blockgroup_id',
id_name2='zone_id', fraction_attribute_name='fraction'):
"""
"""
assert id_name1 in individual_dataset.get_known_attribute_names()
if id_name2 not in individual_dataset.get_known_attribute_names():
individual_dataset.add_primary_attribute(-1*ones(individual_dataset.size()), id_name2)
fraction_id1 = fraction_dataset.get_attribute(id_name1)
individual_id1 = individual_dataset.get_attribute(id_name1)
unique_ids = unique(fraction_id1)
for id1 in unique_ids:
individual_of_id1 = where(individual_id1==id1)[0]
n = individual_of_id1.size
logger.log_status("Processing %s %s: %s individuals" % (id_name1, id1, n) )
if n > 0:
fractions = fraction_dataset.get_attribute(fraction_attribute_name)[fraction_id1==id1]
id2 = fraction_dataset.get_attribute(id_name2)[fraction_id1==id1]
## ignore households in geography with sum of fractions less than 1.0e-6
if fractions.sum() < 1.0e-2:
continue
if not allclose(fractions.sum(), 1.0, rtol=1.e-2):
fractions = normalize(fractions)
fractions_cumsum = ncumsum(fractions)
R = random(n)
index = searchsorted(fractions_cumsum, R)
individual_dataset.modify_attribute(id_name2, id2[index], index=individual_of_id1)
def __init__(self, config):
ss = SimulationState(new_instance=True)
ss.set_current_time(config['base_year'])
ss.set_cache_directory(config['cache_directory'])
SessionConfiguration(new_instance=True,
package_order=config['dataset_pool_configuration'].package_order,
in_storage=AttributeCache())
#if not os.path.exists(config['cache_directory']): ## if cache exists, it will automatically skip
cacher = CreateBaseyearCache()
cache_dir = cacher.run(config)
if 'estimation_database_configuration' in config:
db_server = DatabaseServer(config['estimation_database_configuration'])
db = db_server.get_database(config['estimation_database_configuration'].database_name)
out_storage = StorageFactory().get_storage(
'sql_storage',
storage_location = db)
else:
output_cache = os.path.join(config['cache_directory'], str(config['base_year']+1))
out_storage = StorageFactory().get_storage('flt_storage', storage_location=output_cache)
dataset_pool = SessionConfiguration().get_dataset_pool()
households = dataset_pool.get_dataset("household")
buildings = dataset_pool.get_dataset("building")
zones = dataset_pool.get_dataset("zone")
zone_ids = zones.get_id_attribute()
capacity_attribute_name = "residential_units" #_of_use_id_%s" % id
capacity_variable_name = "%s=sanfrancisco.zone.aggregate_%s_from_building" % \
(capacity_attribute_name, capacity_attribute_name)
buildings.compute_variables("sanfrancisco.building.zone_id", dataset_pool=dataset_pool)
zones.compute_variables(capacity_variable_name, dataset_pool=dataset_pool)
building_zone_id = buildings.get_attribute('zone_id')
# is_household_unplace = datasets['household'].get_attribute("building_id") <= 0
is_household_unplaced = 1 #all households are unplaced
household_building_id = zeros(households.size(), dtype='int32')-1 #datasets['household'].get_attribute("building_id")
for zone_id in zone_ids:
capacity = zones.get_attribute_by_id(capacity_attribute_name, zone_id)
is_household_in_this_zone = (households.get_attribute('zone_id') == zone_id)
is_unplaced_household_in_this_zone = is_household_in_this_zone * is_household_unplaced
is_building_in_this_zone = (building_zone_id == zone_id)
# if not is_household_in_this_zone.sum() <= capacity:
if capacity == 0 or is_household_in_this_zone.sum()==0:
print "WARNING: zone %s has %s households but only %s units" % (zone_id, is_household_in_this_zone.sum(), capacity)
continue
prob = buildings.get_attribute(capacity_attribute_name) * is_building_in_this_zone / array(capacity, dtype=float64)
r = random(sum(is_unplaced_household_in_this_zone))
prob_cumsum = ncumsum(prob)
index_to_bldg = searchsorted(prob_cumsum, r)
household_building_id[where(is_unplaced_household_in_this_zone)] = buildings.get_attribute_by_index('building_id', index_to_bldg)
# import pdb;pdb.set_trace()
households.set_values_of_one_attribute('building_id', household_building_id)
households.write_dataset(out_table_name='households', out_storage=out_storage)
def sample_choice(prob_array, method="MC"):
"""sample chosen index given probability in prob_array
prob_array - 2-d array for probability of being chosen, with probablities for a agent at
one row, and probabilities for alternatives at columns
method - the method used to sample choice, either MC (Monte Carlo) or max_prob
"""
if prob_array.ndim <> 2:
raise RuntimeError, "prob_array must be a 2d array"
rows, columns = prob_array.shape
if not ma.allclose(sum(prob_array, axis=1, dtype=float64), ones((rows,))):
raise RuntimeError, "prob_array must add up to 1 for each row"
if method.lower() == "mc":
cum_prob = ncumsum(prob_array, axis=1)
R = uniform(0, 1, rows) ## new R spec
R.resize((rows, 1))
# R = random((rows,1)) ## preOPUS4 specification of R - added 8 jul 09
match = R < cum_prob
choices = argmax(match, axis=1) # return the first index of 1 in each row
elif method.lower() == "max_prob":
choices = argmax(prob_array)
if choices.size <> rows:
raise RuntimeError, "having problems sample choice"
return (arange(rows), choices)
def run(self, individual_dataset, fraction_dataset, id_name1='blockgroup_id',
id_name2='zone_id', fraction_attribute_name='fraction', dataset_pool=None):
"""
"""
if dataset_pool is None:
dataset_pool = SessionConfiguration().get_dataset_pool()
if isinstance(individual_dataset, str):
individual_dataset = dataset_pool[individual_dataset]
if isinstance(fraction_dataset, str):
fraction_dataset = dataset_pool[fraction_dataset]
assert id_name1 in individual_dataset.get_known_attribute_names()
if id_name2 not in individual_dataset.get_known_attribute_names():
dtype = fraction_dataset.get_attribute(id_name2).dtype
default_values = -1*ones(individual_dataset.size(), dtype=dtype)
individual_dataset.add_primary_attribute(default_values, id_name2)
fraction_id1 = fraction_dataset.get_attribute(id_name1)
individual_id1 = individual_dataset.get_attribute(id_name1)
unique_ids = unique(fraction_id1)
for id1 in unique_ids:
individual_of_id1 = where(individual_id1==id1)[0]
n = individual_of_id1.size
logger.log_status("Processing %s %s: %s individuals" % (id_name1, id1, n) )
if n > 0:
fractions = fraction_dataset.get_attribute(fraction_attribute_name)[fraction_id1==id1]
id2 = fraction_dataset.get_attribute(id_name2)[fraction_id1==id1]
## ignore individuals in geography with sum of fractions less than 1.0e-2
if fractions.sum() < 1.0e-2:
continue
if not allclose(fractions.sum(), 1.0, rtol=1.e-2):
fractions = normalize(fractions)
fractions_cumsum = ncumsum(fractions)
R = random(n)
index = searchsorted(fractions_cumsum, R)
individual_dataset.modify_attribute(id_name2, id2[index], index=individual_of_id1)
individual_dataset.flush_dataset()
def probsample_replace(source_array, size, prob_array, return_index=False):
"""Unequal probability sampling; with replacement case.
Using numpy searchsorted function, suitable for large array"""
if not isinstance(source_array, ndarray):
try:
source_array = asarray(source_array)
except:
raise TypeError, "source_array must be of type ndarray"
if prob_array is None:
return sample_replace(source_array, size, return_index=return_index)
if prob_array.sum() == 0:
raise ValueError, "there aren't non-zero weights in prob_array"
cum_prob = ncumsum(prob_array)
sample_prob = uniform(0, 1, size)
sampled_index = searchsorted(cum_prob, sample_prob)
if return_index:
return sampled_index
else:
return source_array[sampled_index]
def probsample_noreplace(source_array, sample_size, prob_array=None,
exclude_index=None, return_index=False):
"""generate non-repeat random 1d samples from source_array of sample_size, excluding
indices appeared in exclude_index.
return indices to source_array if return_index is true.
source_array - the source array to sample from
sample_size - scalar representing the sample size
prob_array - the array used to weight sample
exclude_index - array representing indices should not appear in resulted array,
which can be used, for example, to exclude current choice from sampling,
indexed to source_array
"""
if not isinstance(source_array, ndarray):
try:
source_array = asarray(source_array)
except:
raise TypeError, "source_array must be of type ndarray"
pop_size = source_array.size
if pop_size < sample_size:
logger.log_warning("There are less or equal indices (%s) in source_array than the sample_size (%s). Use probsample_replace. " %
(pop_size, sample_size))
#sample_size = max
return probsample_replace(source_array, sample_size, prob_array=prob_array, return_index=return_index)
elif pop_size == sample_size:
if return_index:
return arange(source_array.size)
else:
return source_array
if sample_size <= 0:
#logger.log_warning("sample_size is %s. Nothing is sampled." % sample_size)
return array([], dtype='i')
if prob_array is None:
return sample_noreplace(source_array, sample_size, return_index=return_index)
if not isinstance(prob_array, ndarray):
try:
prob_array = asarray(prob_array)
except:
raise TypeError, "prob_array must be of type ndarray"
p_array = prob_array.astype(float64) # creates a copy (not just a pointer)
p_array_sum = p_array.sum()
if not ma.allclose(p_array_sum, 1.0):
p_array = p_array / p_array_sum
if abs(1.0 - p_array_sum) > 0.01:
# print this message only if it is a serious difference
logger.log_warning("prob_array doesn't sum up to 1, and is normalized. Sum: %s" % p_array_sum)
#import pdb; pdb.set_trace()
prob_array_size = nonzerocounts(prob_array)
if prob_array_size < sample_size:
logger.log_warning("There are less or equal non-zero weight (%s) in prob_array than the sample_size (%s). Use probsample_replace. " %
(prob_array_size, sample_size))
return probsample_replace(source_array, sample_size, prob_array=p_array, return_index=return_index)
elif prob_array_size == sample_size:
if return_index:
return where(prob_array>0)[0]
else:
return source_array[prob_array>0]
totalmass = 1.0
to_be_sampled = sample_size
sampled_index = array([], dtype='i') #initialize sampled_index
if exclude_index is not None:
try:
totalmass -= asarray(p_array[exclude_index]).sum()
p_array[exclude_index] = 0
except IndexError:
logger.log_warning("The exclude_index (%s) is not in prob array" % (exclude_index))
#raise IndexError, "Having problem to apply exclude_index values"
cum_prob = ncumsum(p_array/p_array.sum()) * totalmass
if not ma.allclose(cum_prob[-1], totalmass):
raise ValueError, "prob_array doesn't sum up to 1 even after normalization"
while True:
sample_prob = uniform(0, totalmass, to_be_sampled).astype(float32)
proposed_index = searchsorted(cum_prob, sample_prob) #, 0, cum_prob.size-1)
# dup_indicator = find_duplicates_self(proposed_index)
# valid_index = proposed_index[dup_indicator==0]
# sampled_index = concatenate((sampled_index, source_array[valid_index]))
# if not sometrue(dup_indicator):
# return sampled_index
i = 0
if numpy.__version__ >= '1.2.0':
## numpy.unique1d in version 1.2.0 has reversed the return, changed [0]->[1]
i = 1
uniqueidx = unique1d(proposed_index, True)[i]
valid_index = proposed_index[sort(uniqueidx)]
#valid_index = unique_values(proposed_index)
#import pdb; pdb.set_trace()
sampled_index = concatenate((sampled_index, valid_index))
if valid_index.size == to_be_sampled:
if return_index:
return sampled_index
else:
return source_array[sampled_index]
totalmass -= asarray(p_array[valid_index]).sum()
p_array[valid_index] = 0
cum_prob = ncumsum(p_array/p_array.sum()) * totalmass
assert ma.allclose(totalmass, cum_prob[-1])
to_be_sampled -= valid_index.size
def prob2dsample(source_array, sample_size, prob_array=None, exclude_index=None,
replace=False, return_index=False):
"""generate non-repeat random 2d samples from source_array of sample_size, not including
indices appeared in exclude_index; sample column by column, more efficient when there are more
rows than columns in sample_size.
return elements in source_array of shape sample_size.
source_array - the source array to sample from
sample_size - tuple representing the sample size with (rows, columns), non-repeat at each row
exclude_index - array representing indices should not appear in resulted array, used to exclude current choice from sampling
prob_array - the array used to weight sample
"""
rows, columns = sample_size
source_array_size = source_array.size
if source_array_size <= columns:
logger.log_warning("There are less or equal indices (%s) in source_array than the sample_size (%s). Sample %s." %
(source_array_size, columns, source_array_size))
return ones((rows,1)) * source_array[newaxis,:]
if prob_array is None:
prob_array = ones(source_array_size)
if not (isinstance(prob_array, ndarray) or is_masked_array(prob_array)):
raise TypeError, "prob_array must be of type ndarray"
# prob_array_size = nonzerocounts(prob_array)
# if prob_array_size <= columns:
# logger.log_warning( "there are less or equal non-zero weight (%s) in prob_array than the sample_size (%s). Sample %s instead. " %\
# (prob_array_size, columns, prob_array_size))
# return ones((rows,1)) * source_array[nonzero(prob_array)][newaxis,:]
p_array = prob_array
p_array_sum = p_array.sum(dtype="float64")
if not ma.allclose(p_array_sum, 1.0):
if abs(1.0 - p_array_sum) > 0.01:
# print this message only if it is a serious difference
logger.log_warning("prob_array doesn't sum up to 1, and is normalized. Sum: %s" % p_array_sum)
p_array = p_array / p_array_sum
cum_prob = ncumsum(p_array)
sampled_choiceset_index = zeros(sample_size, dtype="int32") - 1 #initialize output
if not replace:
if exclude_index is not None:
if not isinstance(exclude_index, ndarray):
try:
exclude_index = asarray(exclude_index)
except:
raise TypeError, "exclude_index must be of type ndarray"
if exclude_index.shape[0] <> rows:
raise ValueError, "exclude_index should have the same number of rows as sample_size[0]"
if rank(exclude_index) == 1:
exclude_index = exclude_index[:, newaxis]
#sampled_choiceset_index = concatenate((exclude_index,sampled_choiceset_index),axis=1)
#attach exclude_index to the beginning of sampled_choiceset_index
else:
exclude_index = zeros(shape=(sample_size[0],1), dtype="int32")
for j in range(columns):
slots_to_be_sampled = arange(rows)
#proposed_index = zeros((rows,1)) - 1
while True:
proposed_index = probsample_replace(arange(source_array_size), slots_to_be_sampled.size, p_array)
try:
exclude_array = exclude_index[slots_to_be_sampled,]
except:
exclude_array = None
duplicate_indicator = find_duplicates_others(proposed_index, exclude_array)
valid_index = slots_to_be_sampled[duplicate_indicator==0]
sampled_choiceset_index[valid_index, j] = proposed_index[duplicate_indicator==0]
if nonzerocounts(duplicate_indicator) == 0:
break
slots_to_be_sampled = slots_to_be_sampled[duplicate_indicator>0]
exclude_index = concatenate((exclude_index, take(sampled_choiceset_index,(j,), axis=1)), axis = 1)
else:
for j in range(columns):
sampled_choiceset_index[:,j] = probsample_replace(arange(source_array_size), rows, p_array)
if return_index:
return sampled_choiceset_index
else:
return source_array[sampled_choiceset_index]
def prob2dsample(source_array,
sample_size,
prob_array=None,
exclude_index=None,
replace=False,
return_index=False):
"""generate non-repeat random 2d samples from source_array of sample_size, not including
indices appeared in exclude_index; sample column by column, more efficient when there are more
rows than columns in sample_size.
return elements in source_array of shape sample_size.
source_array - the source array to sample from
sample_size - tuple representing the sample size with (rows, columns), non-repeat at each row
exclude_index - array representing indices should not appear in resulted array, used to exclude current choice from sampling
prob_array - the array used to weight sample
"""
rows, columns = sample_size
source_array_size = source_array.size
if source_array_size <= columns and not replace:
logger.log_warning(
"There are less or equal indices (%s) in source_array than the sample_size (%s). Sample %s."
% (source_array_size, columns, source_array_size))
if return_index:
return ones(
(rows, 1), dtype='i') * arange(source_array_size)[newaxis, :]
else:
return ones((rows, 1), dtype='i') * source_array[newaxis, :]
if prob_array is None:
prob_array = ones(source_array_size)
if not (isinstance(prob_array, ndarray) or is_masked_array(prob_array)):
raise TypeError, "prob_array must be of type ndarray"
# prob_array_size = nonzerocounts(prob_array)
# if prob_array_size <= columns:
# logger.log_warning( "there are less or equal non-zero weight (%s) in prob_array than the sample_size (%s). Sample %s instead. " %\
# (prob_array_size, columns, prob_array_size))
# return ones((rows,1)) * source_array[nonzero(prob_array)][newaxis,:]
p_array = prob_array
p_array_sum = p_array.sum(dtype="float64")
if not ma.allclose(p_array_sum, 1.0):
if abs(1.0 - p_array_sum) > 0.01:
# print this message only if it is a serious difference
logger.log_warning(
"prob_array doesn't sum up to 1, and is normalized. Sum: %s" %
p_array_sum)
p_array = p_array / p_array_sum
cum_prob = ncumsum(p_array)
sampled_choiceset_index = zeros(
sample_size, dtype="int32") - 1 #initialize output
if not replace:
if exclude_index is not None:
if not isinstance(exclude_index, ndarray):
try:
exclude_index = asarray(exclude_index)
except:
raise TypeError, "exclude_index must be of type ndarray"
if exclude_index.shape[0] <> rows:
raise ValueError, "exclude_index should have the same number of rows as sample_size[0]"
if rank(exclude_index) == 1:
exclude_index = exclude_index[:, newaxis]
#sampled_choiceset_index = concatenate((exclude_index,sampled_choiceset_index),axis=1)
#attach exclude_index to the beginning of sampled_choiceset_index
else:
exclude_index = zeros(shape=(sample_size[0], 1), dtype="int32")
for j in range(columns):
slots_to_be_sampled = arange(rows)
#proposed_index = zeros((rows,1)) - 1
while True:
proposed_index = probsample_replace(
arange(source_array_size), slots_to_be_sampled.size,
p_array)
try:
exclude_array = exclude_index[slots_to_be_sampled, ]
except:
exclude_array = None
duplicate_indicator = find_duplicates_others(
proposed_index, exclude_array)
valid_index = slots_to_be_sampled[duplicate_indicator == 0]
sampled_choiceset_index[valid_index, j] = proposed_index[
duplicate_indicator == 0]
if nonzerocounts(duplicate_indicator) == 0:
break
slots_to_be_sampled = slots_to_be_sampled[
duplicate_indicator > 0]
exclude_index = concatenate(
(exclude_index, take(sampled_choiceset_index, (j, ), axis=1)),
axis=1)
else:
for j in range(columns):
sampled_choiceset_index[:, j] = probsample_replace(
arange(source_array_size), rows, p_array)
if return_index:
return sampled_choiceset_index
else:
return source_array[sampled_choiceset_index]
def probsample_noreplace(source_array, sample_size, prob_array=None, exclude_index=None, return_index=False):
"""generate non-repeat random 1d samples from source_array of sample_size, excluding
indices appeared in exclude_index.
return indices to source_array if return_index is true.
source_array - the source array to sample from
sample_size - scalar representing the sample size
prob_array - the array used to weight sample
exclude_index - array representing indices should not appear in resulted array,
which can be used, for example, to exclude current choice from sampling,
indexed to source_array
"""
if not isinstance(source_array, ndarray):
try:
source_array = asarray(source_array)
except:
raise TypeError, "source_array must be of type ndarray"
pop_size = source_array.size
if pop_size < sample_size:
logger.log_warning(
"There are less or equal indices (%s) in source_array than the sample_size (%s). Use probsample_replace. "
% (pop_size, sample_size)
)
# sample_size = max
return probsample_replace(source_array, sample_size, prob_array=prob_array, return_index=return_index)
elif pop_size == sample_size:
if return_index:
return arange(source_array.size)
else:
return source_array
if sample_size <= 0:
# logger.log_warning("sample_size is %s. Nothing is sampled." % sample_size)
return array([], dtype="i")
if prob_array is None:
return sample_noreplace(source_array, sample_size, return_index=return_index)
if not isinstance(prob_array, ndarray):
try:
prob_array = asarray(prob_array)
except:
raise TypeError, "prob_array must be of type ndarray"
p_array = prob_array.astype(float64) # creates a copy (not just a pointer)
p_array_sum = p_array.sum()
if not ma.allclose(p_array_sum, 1.0):
p_array = p_array / p_array_sum
if abs(1.0 - p_array_sum) > 0.01:
# print this message only if it is a serious difference
logger.log_warning("prob_array doesn't sum up to 1, and is normalized. Sum: %s" % p_array_sum)
# import pdb; pdb.set_trace()
prob_array_size = nonzerocounts(prob_array)
if prob_array_size < sample_size:
logger.log_warning(
"There are less or equal non-zero weight (%s) in prob_array than the sample_size (%s). Use probsample_replace. "
% (prob_array_size, sample_size)
)
return probsample_replace(source_array, sample_size, prob_array=p_array, return_index=return_index)
elif prob_array_size == sample_size:
if return_index:
return where(prob_array > 0)[0]
else:
return source_array[prob_array > 0]
totalmass = 1.0
to_be_sampled = sample_size
sampled_index = array([], dtype="i") # initialize sampled_index
if exclude_index is not None:
try:
totalmass -= asarray(p_array[exclude_index]).sum()
p_array[exclude_index] = 0
except IndexError:
logger.log_warning("The exclude_index (%s) is not in prob array" % (exclude_index))
# raise IndexError, "Having problem to apply exclude_index values"
cum_prob = ncumsum(p_array / p_array.sum()) * totalmass
if not ma.allclose(cum_prob[-1], totalmass):
raise ValueError, "prob_array doesn't sum up to 1 even after normalization"
while True:
sample_prob = uniform(0, totalmass, to_be_sampled).astype(float32)
proposed_index = searchsorted(cum_prob, sample_prob) # , 0, cum_prob.size-1)
# dup_indicator = find_duplicates_self(proposed_index)
# valid_index = proposed_index[dup_indicator==0]
# sampled_index = concatenate((sampled_index, source_array[valid_index]))
# if not sometrue(dup_indicator):
# return sampled_index
i = 0
if numpy.__version__ >= "1.2.0":
## numpy.unique1d in version 1.2.0 has reversed the return, changed [0]->[1]
i = 1
uniqueidx = unique1d(proposed_index, True)[i]
valid_index = proposed_index[sort(uniqueidx)]
# valid_index = unique_values(proposed_index)
# import pdb; pdb.set_trace()
sampled_index = concatenate((sampled_index, valid_index))
if valid_index.size == to_be_sampled:
if return_index:
return sampled_index
else:
return source_array[sampled_index]
totalmass -= asarray(p_array[valid_index]).sum()
p_array[valid_index] = 0
cum_prob = ncumsum(p_array / p_array.sum()) * totalmass
assert ma.allclose(totalmass, cum_prob[-1])
to_be_sampled -= valid_index.size
