adding file to read models from yaml

urbansim/models/lcm.py

@@ -162,9 +162,9 @@ def from_yaml(cls, yaml_str=None, str_or_buffer=None):
             name=cfg.get('name', None)
         )
 
-        if cfg['log_likelihoods']:
+        if cfg.get('log_likelihoods', None):
             model.log_likelihoods = cfg['log_likelihoods']
-        if cfg['fit_parameters']:
+        if cfg.get('fit_parameters', None):
             model.fit_parameters = pd.DataFrame(cfg['fit_parameters'])
 
         return model

urbansim/models/yamlmodelrunner.py

@@ -0,0 +1,184 @@
+import numpy as np
+from urbansim.utils import misc
+from urbansim.models import RegressionModel, MNLLocationChoiceModel, \
+    GrowthRateTransition
+
+
+def hedonic_estimate(df, cfgname):
+    """
+    Parameters
+    ----------
+    df : DataFrame
+        The dataframe which contains the columns to use for the estimation.
+    cfgname : string
+        The name of the yaml config file which describes the hedonic model.
+    """
+    print "Running hedonic estimation\n"
+    cfg = misc.config(cfgname)
+    hm = RegressionModel.from_yaml(str_or_buffer=cfg)
+    print hm.fit(df).summary()
+    hm.to_yaml(str_or_buffer=cfg)
+
+
+def hedonic_simulate(df, cfgname, outdf, outfname):
+    """
+    Parameters
+    ----------
+    df : DataFrame
+        The dataframe which contains the columns to use for the estimation.
+    cfgname : string
+        The name of the yaml config file which describes the hedonic model.
+    outdf : DataFrame
+        The dataframe to write the simulated price/rent to.
+    outfname : string
+        The column name to write the simulated price/rent to.
+    """
+    print "Running hedonic simulation\n"
+    cfg = misc.config(cfgname)
+    hm = RegressionModel.from_yaml(str_or_buffer=cfg)
+    price_or_rent = hm.predict(df)
+    print price_or_rent.describe()
+    outdf[outfname] = price_or_rent.reindex(outdf.index)
+
+
+def lcm_estimate(choosers, chosen_fname, alternatives, cfgname):
+    """
+    Parameters
+    ----------
+    choosers : DataFrame
+        A dataframe of rows of agents which have locations assigned.
+    chosen_fname : string
+        A string indicating the column in the choosers dataframe which
+        gives which location the choosers have chosen.
+    alternatives : DataFrame
+        A dataframe of locations which should include the chosen locations
+        from the choosers dataframe as well as some other locations from
+        which to sample.  Values in choosers[chosen_fname] should index
+        into the alternatives dataframe.
+    cfgname : string
+        The name of the yaml config file from which to read the location
+        choice model.
+    """
+    print "Running location choice model estimation\n"
+    cfg = misc.config(cfgname)
+    lcm = MNLLocationChoiceModel.from_yaml(str_or_buffer=cfg)
+    lcm.fit(choosers, alternatives, choosers[chosen_fname])
+    lcm.report_fit()
+    lcm.to_yaml(str_or_buffer=cfg)
+
+
+def get_vacant_units(choosers, location_fname, locations, supply_fname):
+    """
+    This is a bit of a nuanced method for this skeleton which computes
+    the vacant units from a building dataset for both households and jobs.
+
+    Parameters
+    ----------
+    choosers : DataFrame
+        A dataframe of agents doing the choosing.
+    location_fname : string
+        A string indicating a column in choosers which indicates the locations
+        from the locations dataframe that these agents are located in.
+    locations : DataFrame
+        A dataframe of locations which the choosers are location in and which
+        have a supply.
+    supply_fname : string
+        A string indicating a column in locations which is an integer value
+        representing the number of agents that can be located at that location.
+    """
+    vacant_units = locations[supply_fname].sub(
+        choosers.groupby(location_fname).size(), fill_value=0)
+    print "There are %d total available units" % locations[supply_fname].sum()
+    print "    and %d total choosers" % len(choosers.index)
+    print "    but there are %d overfull buildings" % \
+        len(vacant_units[vacant_units < 0].index)
+    vacant_units = vacant_units[vacant_units > 0]
+    alternatives = locations.loc[np.repeat(vacant_units.index,
+                                 vacant_units.values.astype('int'))] \
+        .reset_index()
+    print "    for a total of %d empty units" % vacant_units.sum()
+    print "    in %d buildings total in the region" % len(vacant_units)
+    return alternatives
+
+
+def _print_number_unplaced(df, fieldname="building_id"):
+    """
+    Just an internal function to use to compute and print info on the number
+    of unplaced agents.
+    """
+    counts = df[fieldname].isnull().value_counts()
+    count = 0 if True not in counts else counts[True]
+    print "Total currently unplaced: %d" % count
+
+
+def lcm_simulate(choosers, locations, cfgname, outdf, output_fname):
+    """
+    Simulate the location choices for the specified choosers
+
+    Parameters
+    ----------
+    choosers : DataFrame
+        A dataframe of agents doing the choosing.
+    locations : DataFrame
+        A dataframe of locations which the choosers are location in and which
+        have a supply.
+    cfgname : string
+        The name of the yaml config file from which to read the location
+        choice model.
+    outdf : DataFrame
+        The dataframe to write the simulated location to.
+    outfname : string
+        The column name to write the simulated location to.
+    """
+    print "Running location choice model simulation\n"
+    cfg = misc.config(cfgname)
+    lcm = MNLLocationChoiceModel.from_yaml(str_or_buffer=cfg)
+    movers = choosers[choosers[output_fname].isnull()]
+    new_units = lcm.predict(movers, locations)
+    print "Assigned %d choosers to new units" % len(new_units.index)
+    outdf[output_fname].loc[new_units.index] = \
+        locations.loc[new_units.values][output_fname].values
+    _print_number_unplaced(outdf, output_fname)
+
+
+def simple_relocation(choosers, relocation_rate, fieldname='building_id'):
+    """
+    Parameters
+    ----------
+    choosers : DataFrame
+        A dataframe of people which might be relocating.
+    relocation_rate : float
+        A number less than one describing the percent of rows to mark for
+        relocation.
+    fieldname : string
+        The field name in the choosers dataframe to set to np.nan for those
+        rows to mark for relocation.
+    """
+    print "Running relocation\n"
+    _print_number_unplaced(choosers, fieldname)
+    chooser_ids = np.random.choice(choosers.index, size=relocation_rate *
+                                   len(choosers.index), replace=False)
+    choosers[fieldname].loc[chooser_ids] = np.nan
+    _print_number_unplaced(choosers, fieldname)
+
+
+def simple_transition(dset, dfname, rate):
+    """
+    Parameters
+    ----------
+    choosers : dataset
+        The dataset object, in order to write the resulting transitioned
+        dataframe
+    dfname : string
+        The name of the dataframe in the dataset to read and write the the
+        dataframe.
+    rate : float
+        The rate at which to grow the dataframe using a simple growth rate
+        transition model.
+    """
+    transition = GrowthRateTransition(rate)
+    df = dset.fetch(dfname)
+    print "%d agents before transition" % len(df.index)
+    df, added, copied, removed = transition.transition(df, None)
+    print "%d agents after transition" % len(df.index)
+    dset.save_tmptbl(dfname, df)