def supply_demand(cfg, hh_demand, alternatives, price_col, reg_col=None, units_col=None, iterations=1):
lcm = yaml_to_class(cfg).from_yaml(str_or_buffer=cfg)
demand_frame = hh_demand.to_frame()
alts_frame = alternatives.to_frame(columns=[units_col, price_col])
alts_seg = alts_frame.index.values
new_price, zone_ratios = supplydemand.supply_and_demand(lcm, demand_frame, alts_frame, alts_seg,
price_col, iterations=iterations, reg_col=reg_col, clip_change_low=1,
clip_change_high=100)
alternatives.update_col_from_series(price_col, new_price)
#update building prices from zones
buildings = orca.merge_tables('buildings', tables=['buildings','parcels'], columns=['unit_price_residential',
'unit_price_non_residential',
'zone_id'])
new_price_name = {'avg_unit_price_zone' : 'unit_price_residential',
'avg_nonres_unit_price_zone': 'unit_price_non_residential'}
new_price = new_price.fillna(0)
new_price_df = pd.DataFrame(new_price)
merged = pd.merge(buildings, new_price_df, left_on='zone_id', right_index=True)
orca.get_table('buildings').update_col_from_series(new_price_name[price_col], merged[price_col])
def lcm_simulate(cfg, choosers, buildings, join_tbls, out_fname,
supply_fname, vacant_fname,
enable_supply_correction=None):
"""
Simulate the location choices for the specified choosers
Parameters
----------
cfg : string
The name of the yaml config file from which to read the location
choice model
choosers : DataFrameWrapper
A dataframe of agents doing the choosing
buildings : DataFrameWrapper
A dataframe of buildings which the choosers are locating in and which
have a supply
join_tbls : list of strings
A list of land use dataframes to give neighborhood info around the
buildings - will be joined to the buildings using existing broadcasts.
out_fname : string
The column name to write the simulated location to
supply_fname : string
The string in the buildings table that indicates the amount of
available units there are for choosers, vacant or not
vacant_fname : string
The string in the buildings table that indicates the amount of vacant
units there will be for choosers
enable_supply_correction : Python dict
Should contain keys "price_col" and "submarket_col" which are set to
the column names in buildings which contain the column for prices and
an identifier which segments buildings into submarkets
"""
cfg = misc.config(cfg)
choosers_df = to_frame(choosers, [], cfg, additional_columns=[out_fname])
additional_columns = [supply_fname, vacant_fname]
if enable_supply_correction is not None and \
"submarket_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["submarket_col"]]
if enable_supply_correction is not None and \
"price_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["price_col"]]
locations_df = to_frame(buildings, join_tbls, cfg,
additional_columns=additional_columns)
available_units = buildings[supply_fname]
vacant_units = buildings[vacant_fname]
print "There are %d total available units" % available_units.sum()
print " and %d total choosers" % len(choosers)
print " but there are %d overfull buildings" % \
len(vacant_units[vacant_units < 0])
vacant_units = vacant_units[vacant_units > 0]
# sometimes there are vacant units for buildings that are not in the
# locations_df, which happens for reasons explained in the warning below
indexes = np.repeat(vacant_units.index.values,
vacant_units.values.astype('int'))
isin = pd.Series(indexes).isin(locations_df.index)
missing = len(isin[isin == False])
indexes = indexes[isin.values]
units = locations_df.loc[indexes].reset_index()
check_nas(units)
print " for a total of %d temporarily empty units" % vacant_units.sum()
print " in %d buildings total in the region" % len(vacant_units)
if missing > 0:
print "WARNING: %d indexes aren't found in the locations df -" % \
missing
print " this is usually because of a few records that don't join "
print " correctly between the locations df and the aggregations tables"
movers = choosers_df[choosers_df[out_fname] == -1]
print "There are %d total movers for this LCM" % len(movers)
if enable_supply_correction is not None:
assert isinstance(enable_supply_correction, dict)
assert "price_col" in enable_supply_correction
price_col = enable_supply_correction["price_col"]
assert "submarket_col" in enable_supply_correction
submarket_col = enable_supply_correction["submarket_col"]
lcm = yaml_to_class(cfg).from_yaml(str_or_buffer=cfg)
if enable_supply_correction.get("warm_start", False) is True:
raise NotImplementedError()
multiplier_func = enable_supply_correction.get("multiplier_func", None)
if multiplier_func is not None:
multiplier_func = orca.get_injectable(multiplier_func)
kwargs = enable_supply_correction.get('kwargs', {})
new_prices, submarkets_ratios = supply_and_demand(
lcm,
movers,
units,
submarket_col,
price_col,
base_multiplier=None,
multiplier_func=multiplier_func,
**kwargs)
# we will only get back new prices for those alternatives
# that pass the filter - might need to specify the table in
# order to get the complete index of possible submarkets
submarket_table = enable_supply_correction.get("submarket_table", None)
if submarket_table is not None:
submarkets_ratios = submarkets_ratios.reindex(
orca.get_table(submarket_table).index).fillna(1)
# write final shifters to the submarket_table for use in debugging
orca.get_table(submarket_table)["price_shifters"] = submarkets_ratios
print "Running supply and demand"
print "Simulated Prices"
print buildings[price_col].describe()
print "Submarket Price Shifters"
print submarkets_ratios.describe()
# we want new prices on the buildings, not on the units, so apply
# shifters directly to buildings and ignore unit prices
orca.add_column(buildings.name,
price_col+"_hedonic", buildings[price_col])
new_prices = buildings[price_col] * \
submarkets_ratios.loc[buildings[submarket_col]].values
buildings.update_col_from_series(price_col, new_prices)
print "Adjusted Prices"
print buildings[price_col].describe()
if len(movers) > vacant_units.sum():
print "WARNING: Not enough locations for movers"
print " reducing locations to size of movers for performance gain"
movers = movers.head(vacant_units.sum())
new_units, _ = yaml_to_class(cfg).predict_from_cfg(movers, units, cfg)
# new_units returns nans when there aren't enough units,
# get rid of them and they'll stay as -1s
new_units = new_units.dropna()
# go from units back to buildings
new_buildings = pd.Series(units.loc[new_units.values][out_fname].values,
index=new_units.index)
choosers.update_col_from_series(out_fname, new_buildings)
_print_number_unplaced(choosers, out_fname)
if enable_supply_correction is not None:
new_prices = buildings[price_col]
if "clip_final_price_low" in enable_supply_correction:
new_prices = new_prices.clip(lower=enable_supply_correction[
"clip_final_price_low"])
if "clip_final_price_high" in enable_supply_correction:
new_prices = new_prices.clip(upper=enable_supply_correction[
"clip_final_price_high"])
buildings.update_col_from_series(price_col, new_prices)
vacant_units = buildings[vacant_fname]
print " and there are now %d empty units" % vacant_units.sum()
print " and %d overfull buildings" % len(vacant_units[vacant_units < 0])
def lcm_simulate(cfg,
choosers,
buildings,
join_tbls,
out_fname,
supply_fname,
vacant_fname,
enable_supply_correction=None):
"""
Simulate the location choices for the specified choosers
Parameters
----------
cfg : string
The name of the yaml config file from which to read the location
choice model
choosers : DataFrameWrapper
A dataframe of agents doing the choosing
buildings : DataFrameWrapper
A dataframe of buildings which the choosers are locating in and which
have a supply
join_tbls : list of strings
A list of land use dataframes to give neighborhood info around the
buildings - will be joined to the buildings using existing broadcasts.
out_fname : string
The column name to write the simulated location to
supply_fname : string
The string in the buildings table that indicates the amount of
available units there are for choosers, vacant or not
vacant_fname : string
The string in the buildings table that indicates the amount of vacant
units there will be for choosers
enable_supply_correction : Python dict
Should contain keys "price_col" and "submarket_col" which are set to
the column names in buildings which contain the column for prices and
an identifier which segments buildings into submarkets
"""
cfg = misc.config(cfg)
choosers_df = to_frame(choosers, [], cfg, additional_columns=[out_fname])
additional_columns = [supply_fname, vacant_fname]
if enable_supply_correction is not None and \
"submarket_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["submarket_col"]]
if enable_supply_correction is not None and \
"price_col" in enable_supply_correction:
additional_columns += [enable_supply_correction["price_col"]]
locations_df = to_frame(buildings,
join_tbls,
cfg,
additional_columns=additional_columns)
available_units = buildings[supply_fname]
vacant_units = buildings[vacant_fname]
print "There are %d total available units" % available_units.sum()
print " and %d total choosers" % len(choosers)
print " but there are %d overfull buildings" % \
len(vacant_units[vacant_units < 0])
vacant_units = vacant_units[vacant_units > 0]
# sometimes there are vacant units for buildings that are not in the
# locations_df, which happens for reasons explained in the warning below
indexes = np.repeat(vacant_units.index.values,
vacant_units.values.astype('int'))
isin = pd.Series(indexes).isin(locations_df.index)
missing = len(isin[isin == False])
indexes = indexes[isin.values]
units = locations_df.loc[indexes].reset_index()
check_nas(units)
print " for a total of %d temporarily empty units" % vacant_units.sum()
print " in %d buildings total in the region" % len(vacant_units)
if missing > 0:
print "WARNING: %d indexes aren't found in the locations df -" % \
missing
print " this is usually because of a few records that don't join "
print " correctly between the locations df and the aggregations tables"
movers = choosers_df[choosers_df[out_fname] == -1]
print "There are %d total movers for this LCM" % len(movers)
if enable_supply_correction is not None:
assert isinstance(enable_supply_correction, dict)
assert "price_col" in enable_supply_correction
price_col = enable_supply_correction["price_col"]
assert "submarket_col" in enable_supply_correction
submarket_col = enable_supply_correction["submarket_col"]
lcm = yaml_to_class(cfg).from_yaml(str_or_buffer=cfg)
if enable_supply_correction.get("warm_start", False) is True:
raise NotImplementedError()
multiplier_func = enable_supply_correction.get("multiplier_func", None)
if multiplier_func is not None:
multiplier_func = orca.get_injectable(multiplier_func)
kwargs = enable_supply_correction.get('kwargs', {})
new_prices, submarkets_ratios = supply_and_demand(
lcm,
movers,
units,
submarket_col,
price_col,
base_multiplier=None,
multiplier_func=multiplier_func,
**kwargs)
# we will only get back new prices for those alternatives
# that pass the filter - might need to specify the table in
# order to get the complete index of possible submarkets
submarket_table = enable_supply_correction.get("submarket_table", None)
if submarket_table is not None:
submarkets_ratios = submarkets_ratios.reindex(
orca.get_table(submarket_table).index).fillna(1)
# write final shifters to the submarket_table for use in debugging
orca.get_table(
submarket_table)["price_shifters"] = submarkets_ratios
print "Running supply and demand"
print "Simulated Prices"
print buildings[price_col].describe()
print "Submarket Price Shifters"
print submarkets_ratios.describe()
# we want new prices on the buildings, not on the units, so apply
# shifters directly to buildings and ignore unit prices
orca.add_column(buildings.name, price_col + "_hedonic",
buildings[price_col])
new_prices = buildings[price_col] * \
submarkets_ratios.loc[buildings[submarket_col]].values
buildings.update_col_from_series(price_col, new_prices)
print "Adjusted Prices"
print buildings[price_col].describe()
if len(movers) > vacant_units.sum():
print "WARNING: Not enough locations for movers"
print " reducing locations to size of movers for performance gain"
movers = movers.head(vacant_units.sum())
new_units, _ = yaml_to_class(cfg).predict_from_cfg(movers, units, cfg)
# new_units returns nans when there aren't enough units,
# get rid of them and they'll stay as -1s
new_units = new_units.dropna()
# go from units back to buildings
new_buildings = pd.Series(units.loc[new_units.values][out_fname].values,
index=new_units.index)
choosers.update_col_from_series(out_fname, new_buildings)
_print_number_unplaced(choosers, out_fname)
if enable_supply_correction is not None:
new_prices = buildings[price_col]
if "clip_final_price_low" in enable_supply_correction:
new_prices = new_prices.clip(
lower=enable_supply_correction["clip_final_price_low"])
if "clip_final_price_high" in enable_supply_correction:
new_prices = new_prices.clip(
upper=enable_supply_correction["clip_final_price_high"])
buildings.update_col_from_series(price_col, new_prices)
vacant_units = buildings[vacant_fname]
print " and there are now %d empty units" % vacant_units.sum()
print " and %d overfull buildings" % len(vacant_units[vacant_units < 0])
