def estimate(dset,
indvars,
depvar_name='price',
max_segment_size=15000,
estimation_table='buildings',
output_names=None,
agents_groupby=[
'building_type_id',
]):
output_csv, output_title, coeff_name, output_varname = output_names
choosers = dset.fetch(estimation_table)
segments = choosers.groupby(agents_groupby)
num_segments = len(segments.size().index)
if num_segments != len(indvars):
print "ERROR: number of segments does not match number of sets of independent variable"
indvar_dict = dict(zip(segments.size().index.values, range(num_segments)))
for name, segment in segments:
ind_vars = indvars[indvar_dict[name]]
name = str(name)
segment = segment[segment[depvar_name] > 0]
if len(segment[depvar_name]) > max_segment_size:
segment = segment.ix[np.random.choice(segment.index,
max_segment_size,
replace=False)]
depvar = segment[depvar_name].apply(np.log)
est_data = pd.DataFrame(index=segment.index)
for varname in indvars:
est_data[varname] = segment[varname]
est_data = est_data.fillna(0)
est_data = sm.add_constant(est_data, prepend=False)
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv % name, output_title % name, coeff_name % name
print "Estimating hedonic for %s with %d observations" % (
name, len(segment.index))
model = sm.OLS(depvar, est_data)
results = model.fit()
print results.summary()
print ' '
tmp_outcsv = output_csv % name
tmp_outtitle = output_title % name
misc.resultstocsv((results.rsquared, results.rsquared_adj),
est_data.columns,
zip(results.params, results.bse, results.tvalues),
tmp_outcsv,
hedonic=1,
tblname=output_title)
dset.store_coeff(tmp_coeffname, results.params.values,
results.params.index)
def estimate(dset,config,year,show=True,variables=None):
choosers = fetch_table(dset,config)
if 'est_sample_size' in config:
choosers = choosers.ix[np.random.choice(choosers.index, config['est_sample_size'],replace=False)]
output_csv, output_title, coeff_name, output_varname = config["output_names"]
assert 'alternatives' in config
alternatives = eval(config['alternatives'])
alternatives = merge(dset,alternatives,config)
t1 = time.time()
segments = [(None,choosers)]
if 'segment' in config:
for varname in config['segment']:
if varname not in choosers.columns:
choosers[varname] = calcvar(choosers,config,dset,varname)
segments = choosers.groupby(config['segment'])
for name, segment in segments:
name = str(name)
if name is not None: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv%name, output_title%name, coeff_name%name
else: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv, output_title, coeff_name
assert "dep_var" in config
depvar = config["dep_var"]
global SAMPLE_SIZE
SAMPLE_SIZE = config["alt_sample_size"] if "alt_sample_size" in config else SAMPLE_SIZE
sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(
segment,alternatives,SAMPLE_SIZE,chosenalts=segment[depvar])
print "Estimating parameters for segment = %s, size = %d" % (name, len(segment.index))
data = spec(alternative_sample,config,submodel=name)
if show: print data.describe()
data = data.as_matrix()
fnames = config['ind_vars']
fnames = config['ind_var_names'] if 'ind_var_names' in config else fnames
fit, results = interaction.estimate(data,est_params,SAMPLE_SIZE)
fnames = interaction.add_fnames(fnames,est_params)
if show: print misc.resultstotable(fnames,results)
misc.resultstocsv(fit,fnames,results,tmp_outcsv,tblname=tmp_outtitle)
dset.store_coeff(tmp_coeffname,zip(*results)[0],fnames)
print "Finished executing in %f seconds" % (time.time()-t1)
def estimate(dset,config,year,show=True,variables=None):
choosers = fetch_table(dset,config)
if 'est_sample_size' in config:
choosers = choosers.ix[np.random.choice(choosers.index, config['est_sample_size'],replace=False)]
output_csv, output_title, coeff_name, output_varname = config["output_names"]
assert 'alternatives' in config
alternatives = eval(config['alternatives'])
alternatives = merge(dset,alternatives,config)
t1 = time.time()
segments = [(None,choosers)]
if 'segment' in config:
for varname in config['segment']:
if varname not in choosers.columns:
choosers[varname] = calcvar(choosers,config,dset,varname)
segments = choosers.groupby(config['segment'])
for name, segment in segments:
name = str(name)
if name is not None: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv%name, output_title%name, coeff_name%name
else: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv, output_title, coeff_name
assert "dep_var" in config
depvar = config["dep_var"]
global SAMPLE_SIZE
SAMPLE_SIZE = config["alt_sample_size"] if "alt_sample_size" in config else SAMPLE_SIZE
sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(
segment,alternatives,SAMPLE_SIZE,chosenalts=segment[depvar])
print "Estimating parameters for segment = %s, size = %d" % (name, len(segment.index))
data = spec(alternative_sample,config,submodel=name)
if show: print data.describe()
data = data.as_matrix()
fnames = config['ind_vars']
fnames = config['ind_var_names'] if 'ind_var_names' in config else fnames
fit, results = interaction.estimate(data,est_params,SAMPLE_SIZE)
fnames = interaction.add_fnames(fnames,est_params)
if show: print misc.resultstotable(fnames,results)
misc.resultstocsv(fit,fnames,results,tmp_outcsv,tblname=tmp_outtitle)
dset.store_coeff(tmp_coeffname,zip(*results)[0],fnames)
print "Finished executing in %f seconds" % (time.time()-t1)
def estimate (dset,indvars,depvar = 'building_id',alternatives=None,SAMPLE_SIZE=100,max_segment_size = 1200,estimation_table = 'households_for_estimation',
output_names=None,agents_groupby = ['income_3_tenure',]):
##HCLM ESTIMATION
output_csv, output_title, coeff_name, output_varname = output_names
choosers = dset.fetch(estimation_table)
segments = choosers.groupby(agents_groupby)
num_segments = len(segments.size().index)
if num_segments != len(indvars):
print "ERROR: number of segments does not match number of sets of independent variable"
indvar_dict = dict(zip(segments.size().index.values,range(num_segments)))
alts = alternatives
for name, segment in segments:
ind_vars = indvars[indvar_dict[name]]
name = str(name)
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv%name, output_title%name, coeff_name%name
if len(segment[depvar]) > max_segment_size: #reduce size of segment if too big so things don't bog down
segment = segment.ix[np.random.choice(segment.index, max_segment_size,replace=False)]
#,weight_var='residential_units')
sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(segment,alts,SAMPLE_SIZE,chosenalts=segment[depvar])
##Interaction variables
interaction_vars = [(var, var.split('_x_')) for var in ind_vars if '_x_' in var]
for ivar in interaction_vars:
alternative_sample[ivar[0]] = ((alternative_sample[ivar[1][0]])*alternative_sample[ivar[1][1]])
print "Estimating parameters for segment = %s, size = %d" % (name, len(segment.index))
if len(segment.index) > 50:
est_data = pd.DataFrame(index=alternative_sample.index)
for varname in ind_vars:
est_data[varname] = alternative_sample[varname]
est_data = est_data.fillna(0)
data = est_data.as_matrix()
try:
fit, results = interaction.estimate(data, est_params, SAMPLE_SIZE)
fnames = interaction.add_fnames(ind_vars,est_params)
print misc.resultstotable(fnames,results)
misc.resultstocsv(fit,fnames,results,tmp_outcsv,tblname=tmp_outtitle)
coefficients = zip(*results)[0]+(.0001,.0001,.0001,.0001,.0001,.0001,.0001,.0001,.0001,.0001,.0001,)
varnames = fnames+['county8001','county8005','county8013','county8014','county8019','county8031','county8035','county8039','county8047','county8059','county8123']
dset.store_coeff(tmp_coeffname,coefficients,varnames)
except:
print 'SINGULAR MATRIX OR OTHER DATA/ESTIMATION PROBLEM'
else:
print 'SAMPLE SIZE TOO SMALL'
def estimate (dset,indvars,depvar_name = 'price',max_segment_size = 15000,estimation_table = 'buildings',output_names=None,agents_groupby = ['building_type_id',]):
output_csv, output_title, coeff_name, output_varname = output_names
choosers = dset.fetch(estimation_table)
segments = choosers.groupby(agents_groupby)
num_segments = len(segments.size().index)
if num_segments != len(indvars):
print "ERROR: number of segments does not match number of sets of independent variable"
indvar_dict = dict(zip(segments.size().index.values,range(num_segments)))
for name, segment in segments:
ind_vars = indvars[indvar_dict[name]]
name = str(name)
segment = segment[segment[depvar_name]>0]
if len(segment[depvar_name]) > max_segment_size:
segment = segment.ix[np.random.choice(segment.index, max_segment_size,replace=False)]
depvar = segment[depvar_name].apply(np.log)
est_data = pd.DataFrame(index=segment.index)
for varname in indvars:
est_data[varname] = segment[varname]
est_data = est_data.fillna(0)
est_data = sm.add_constant(est_data,prepend=False)
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv%name, output_title%name, coeff_name%name
print "Estimating hedonic for %s with %d observations" % (name,len(segment.index))
model = sm.OLS(depvar,est_data)
results = model.fit()
print results.summary()
print ' '
tmp_outcsv = output_csv%name
tmp_outtitle = output_title%name
misc.resultstocsv((results.rsquared,results.rsquared_adj),est_data.columns,
zip(results.params,results.bse,results.tvalues),tmp_outcsv,hedonic=1,
tblname=output_title)
dset.store_coeff(tmp_coeffname,results.params.values,results.params.index)
def estimate(dset,config,year=None,show=True,simulate=0,variables=None):
t1 = time.time()
buildings = fetch_table(dset,config,simulate)
buildings = merge(dset,buildings,config)
assert "output_names" in config
output_csv, output_title, coeff_name, output_varname = config["output_names"]
print "Finished specifying in %f seconds" % (time.time()-t1)
t1 = time.time()
simrents = []
segments = [(None,buildings)]
if 'segment' in config: segments = buildings.groupby(config['segment'])
for name, segment in segments:
est_data = spec(segment,config,submodel=name,dset=dset)
if name is not None: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv%name, output_title%name, coeff_name%name
else: tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv, output_title, coeff_name
if not simulate:
assert "dep_var" in config
depvar = segment[config["dep_var"]]
if "dep_var_transform" in config: depvar = depvar.apply(eval(config['dep_var_transform']))
if name: print "Estimating hedonic for %s with %d observations" % (name,len(segment.index))
if show : print est_data.describe()
model = sm.OLS(depvar,est_data)
results = model.fit()
if show: print results.summary()
tmp_outcsv = output_csv if name is None else output_csv%name
tmp_outtitle = output_title if name is None else output_title%name
misc.resultstocsv((results.rsquared,results.rsquared_adj),est_data.columns,
zip(results.params,results.bse,results.tvalues),tmp_outcsv,hedonic=1,
tblname=output_title)
dset.store_coeff(tmp_coeffname,results.params.values,results.params.index)
else:
print "Generating rents on %d buildings" % (est_data.shape[0])
vec = dset.load_coeff(tmp_coeffname)
vec = np.reshape(vec,(vec.size,1))
rents = est_data.dot(vec).astype('f4')
if "output_transform" in config: rents = rents.apply(eval(config['output_transform']))
simrents.append(rents[rents.columns[0]])
if simulate:
simrents = pd.concat(simrents)
dset.buildings[output_varname] = simrents.reindex(dset.buildings.index)
dset.store_attr(output_varname,year,simrents)
print "Finished executing in %f seconds" % (time.time()-t1)
def lcmnl_estimate(cmdata,numclasses,csdata,numalts,chosen,maxiter=MAXITER,emtol=EMTOL,\
skipprep=False,csbeta=None,cmbeta=None,csfnames=None,cmfnames=None):
loglik = -999999
l_0 = None
if csbeta is None:
csbeta = [np.random.rand(csdata.shape[1]) for i in range(numclasses)]
if csfnames is None:
csfnames = ['cs%d' % i for i in range(csdata.shape[1])]
if cmfnames is None:
cmfnames = ['cm%d' % i for i in range(cmdata.shape[1])]
if not skipprep:
cmdata, cmfnames = prep_cm_data(cmdata, numclasses, cmfnames)
if cmbeta is None: cmbeta = np.random.rand(cmdata.shape[1]) * 10.0 - 5.0
results_d = {}
for i in range(maxiter):
print "Running iteration %d" % (i + 1)
print time.ctime()
# EXPECTATION
def expectation(cmbeta, csbeta):
print "Running class membership model"
cmprobs = mnl.mnl_simulate(cmdata,
cmbeta,
numclasses,
GPU=GPU,
returnprobs=1)
csprobs = []
for cno in range(numclasses):
tmp = mnl.mnl_simulate(csdata,
csbeta[cno],
numalts,
GPU=GPU,
returnprobs=1)
tmp = np.sum(tmp * chosen, axis=1) # keep only chosen probs
csprobs.append(np.reshape(tmp, (-1, 1)))
csprobs = np.concatenate(csprobs, axis=1)
h = csprobs * cmprobs
loglik = np.sum(np.log(np.sum(h, axis=1)))
wts = h / np.reshape(np.sum(h, axis=1), (-1, 1))
return loglik, wts
oldloglik = loglik
loglik, wts = expectation(cmbeta, csbeta)
if l_0 is None: l_0 = loglik
print "current cmbeta", cmbeta
print "current csbeta", csbeta
print "current loglik", loglik, i + 1, "\n\n"
if abs(loglik - oldloglik) < emtol: break
# MAXIMIZATION
for cno in range(numclasses):
print "Estimating class specific model for class %d" % (cno + 1)
t1 = time.time()
weights = np.reshape(wts[:, cno], (-1, 1))
fit, results = mnl.mnl_estimate(csdata,
chosen,
numalts,
GPU=GPU,
weights=weights,
beta=csbeta[cno])
print "Finished in %fs" % (time.time() - t1)
csbeta[cno] = zip(*results)[0]
results_d['cs%d' % cno] = results
print "Estimating class membership model"
t1 = time.time()
fit, results = mnl.mnl_estimate(cmdata,None,numclasses,GPU=GPU,weights=wts,lcgrad=True, \
beta=cmbeta,coeffrange=(-1000,1000))
print "Finished in %fs" % (time.time() - t1)
cmbeta = zip(*results)[0]
results_d['cm'] = results
l_1 = loglik
l_0, foo = expectation(np.zeros(len(cmbeta)),
[np.zeros(len(a)) for a in csbeta])
ll_ratio = 1 - (l_1 / l_0)
print "Null Log-liklihood: %f" % l_0
print "Log-liklihood at convergence: %f" % l_1
print "Log-liklihood ratio: %f" % ll_ratio
a = []
fnames = []
fnames += cmfnames
a += results_d['cm']
for i in range(numclasses):
fnames += ['%s cls%d' % (s, i) for s in csfnames]
a += results_d['cs%d' % i]
print misc.resultstotable(fnames, a)
fit = (l_0, l_1, ll_ratio)
misc.resultstocsv(fit,
fnames,
a,
"lc-coeff.csv",
tblname="Latent Class Model Coefficients")
return (l_0, l_1, ll_ratio), results_d
def lcmnl_estimate(cmdata,numclasses,csdata,numalts,chosen,maxiter=MAXITER,emtol=EMTOL,\
skipprep=False,csbeta=None,cmbeta=None,csfnames=None,cmfnames=None):
loglik = -999999
l_0 = None
if csbeta is None: csbeta = [np.random.rand(csdata.shape[1]) for i in range(numclasses)]
if csfnames is None: csfnames = ['cs%d'%i for i in range(csdata.shape[1])]
if cmfnames is None: cmfnames = ['cm%d'%i for i in range(cmdata.shape[1])]
if not skipprep: cmdata,cmfnames = prep_cm_data(cmdata,numclasses,cmfnames)
if cmbeta is None: cmbeta = np.random.rand(cmdata.shape[1])*10.0-5.0
results_d = {}
for i in range(maxiter):
print "Running iteration %d" % (i+1)
print time.ctime()
# EXPECTATION
def expectation(cmbeta,csbeta):
print "Running class membership model"
cmprobs = mnl.mnl_simulate(cmdata,cmbeta,numclasses,GPU=GPU,returnprobs=1)
csprobs = []
for cno in range(numclasses):
tmp = mnl.mnl_simulate(csdata,csbeta[cno],numalts,GPU=GPU,returnprobs=1)
tmp = np.sum(tmp*chosen,axis=1) # keep only chosen probs
csprobs.append(np.reshape(tmp,(-1,1)))
csprobs = np.concatenate(csprobs,axis=1)
h = csprobs * cmprobs
loglik = np.sum(np.log(np.sum(h,axis=1)))
wts = h / np.reshape(np.sum(h,axis=1),(-1,1))
return loglik, wts
oldloglik = loglik
loglik, wts = expectation(cmbeta,csbeta)
if l_0 is None: l_0 = loglik
print "current cmbeta", cmbeta
print "current csbeta", csbeta
print "current loglik", loglik, i+1, "\n\n"
if abs(loglik-oldloglik) < emtol: break
# MAXIMIZATION
for cno in range(numclasses):
print "Estimating class specific model for class %d" % (cno+1)
t1 = time.time()
weights=np.reshape(wts[:,cno],(-1,1))
fit, results = mnl.mnl_estimate(csdata,chosen,numalts,GPU=GPU,weights=weights,beta=csbeta[cno])
print "Finished in %fs" % (time.time()-t1)
csbeta[cno] = zip(*results)[0]
results_d['cs%d'%cno] = results
print "Estimating class membership model"
t1 = time.time()
fit, results = mnl.mnl_estimate(cmdata,None,numclasses,GPU=GPU,weights=wts,lcgrad=True, \
beta=cmbeta,coeffrange=(-1000,1000))
print "Finished in %fs" % (time.time()-t1)
cmbeta = zip(*results)[0]
results_d['cm'] = results
l_1 = loglik
l_0, foo = expectation(np.zeros(len(cmbeta)),[np.zeros(len(a)) for a in csbeta])
ll_ratio = 1-(l_1/l_0)
print "Null Log-liklihood: %f" % l_0
print "Log-liklihood at convergence: %f" % l_1
print "Log-liklihood ratio: %f" % ll_ratio
a = []
fnames = []
fnames += cmfnames
a += results_d['cm']
for i in range(numclasses):
fnames += ['%s cls%d'%(s,i) for s in csfnames]
a += results_d['cs%d'%i]
print misc.resultstotable(fnames,a)
fit = (l_0,l_1,ll_ratio)
misc.resultstocsv(fit,fnames,a,"lc-coeff.csv",tblname="Latent Class Model Coefficients")
return (l_0,l_1,ll_ratio), results_d
def estimate(
dset,
indvars,
depvar="building_id",
alternatives=None,
SAMPLE_SIZE=100,
max_segment_size=1200,
estimation_table="households_for_estimation",
output_names=None,
agents_groupby=["income_3_tenure"],
):
##HCLM ESTIMATION
output_csv, output_title, coeff_name, output_varname = output_names
choosers = dset.fetch(estimation_table)
segments = choosers.groupby(agents_groupby)
num_segments = len(segments.size().index)
if num_segments != len(indvars):
print "ERROR: number of segments does not match number of sets of independent variable"
indvar_dict = dict(zip(segments.size().index.values, range(num_segments)))
alts = alternatives
for name, segment in segments:
ind_vars = indvars[indvar_dict[name]]
name = str(name)
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv % name, output_title % name, coeff_name % name
if len(segment[depvar]) > max_segment_size: # reduce size of segment if too big so things don't bog down
segment = segment.ix[np.random.choice(segment.index, max_segment_size, replace=False)]
# ,weight_var='residential_units')
sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(
segment, alts, SAMPLE_SIZE, chosenalts=segment[depvar]
)
##Interaction variables
interaction_vars = [(var, var.split("_x_")) for var in ind_vars if "_x_" in var]
for ivar in interaction_vars:
alternative_sample[ivar[0]] = (alternative_sample[ivar[1][0]]) * alternative_sample[ivar[1][1]]
print "Estimating parameters for segment = %s, size = %d" % (name, len(segment.index))
if len(segment.index) > 50:
est_data = pd.DataFrame(index=alternative_sample.index)
for varname in ind_vars:
est_data[varname] = alternative_sample[varname]
est_data = est_data.fillna(0)
data = est_data.as_matrix()
try:
fit, results = interaction.estimate(data, est_params, SAMPLE_SIZE)
fnames = interaction.add_fnames(ind_vars, est_params)
print misc.resultstotable(fnames, results)
misc.resultstocsv(fit, fnames, results, tmp_outcsv, tblname=tmp_outtitle)
coefficients = zip(*results)[0] + (
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
0.0001,
)
varnames = fnames + [
"county8001",
"county8005",
"county8013",
"county8014",
"county8019",
"county8031",
"county8035",
"county8039",
"county8047",
"county8059",
"county8123",
]
dset.store_coeff(tmp_coeffname, coefficients, varnames)
except:
print "SINGULAR MATRIX OR OTHER DATA/ESTIMATION PROBLEM"
else:
print "SAMPLE SIZE TOO SMALL"
def estimate(dset, config, year=None, show=True, simulate=0, variables=None):
t1 = time.time()
buildings = fetch_table(dset, config, simulate)
buildings = merge(dset, buildings, config)
assert "output_names" in config
output_csv, output_title, coeff_name, output_varname = config[
"output_names"]
print "Finished specifying in %f seconds" % (time.time() - t1)
t1 = time.time()
simrents = []
segments = [(None, buildings)]
if 'segment' in config: segments = buildings.groupby(config['segment'])
for name, segment in segments:
est_data = spec(segment, config, submodel=name, dset=dset)
if name is not None:
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv % name, output_title % name, coeff_name % name
else:
tmp_outcsv, tmp_outtitle, tmp_coeffname = output_csv, output_title, coeff_name
if not simulate:
assert "dep_var" in config
depvar = segment[config["dep_var"]]
if "dep_var_transform" in config:
depvar = depvar.apply(eval(config['dep_var_transform']))
if name:
print "Estimating hedonic for %s with %d observations" % (
name, len(segment.index))
if show: print est_data.describe()
model = sm.OLS(depvar, est_data)
results = model.fit()
if show: print results.summary()
tmp_outcsv = output_csv if name is None else output_csv % name
tmp_outtitle = output_title if name is None else output_title % name
misc.resultstocsv((results.rsquared, results.rsquared_adj),
est_data.columns,
zip(results.params, results.bse,
results.tvalues),
tmp_outcsv,
hedonic=1,
tblname=output_title)
dset.store_coeff(tmp_coeffname, results.params.values,
results.params.index)
else:
print "Generating rents on %d buildings" % (est_data.shape[0])
vec = dset.load_coeff(tmp_coeffname)
vec = np.reshape(vec, (vec.size, 1))
rents = est_data.dot(vec).astype('f4')
if "output_transform" in config:
rents = rents.apply(eval(config['output_transform']))
simrents.append(rents[rents.columns[0]])
if simulate:
simrents = pd.concat(simrents)
dset.buildings[output_varname] = simrents.reindex(dset.buildings.index)
dset.store_attr(output_varname, year, simrents)
print "Finished executing in %f seconds" % (time.time() - t1)
print segment.reset_index().building_id.describe()
alts.index = alts.index.astype('int32')
#sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(segment,alts,SAMPLE_SIZE,chosenalts=segment[depvar],weight_var='non_residential_sqft')
sample, alternative_sample, est_params = interaction.mnl_interaction_dataset(segment,alts,SAMPLE_SIZE,chosenalts=segment[depvar])
#alternative_sample['paris_x_employees'] = (alternative_sample.in_paris*alternative_sample.employees)
print "Estimating parameters for segment = %s, size = %d" % (name, len(segment.index))
if len(segment.index) > 50:
est_data = pd.DataFrame(index=alternative_sample.index)
for varname in ind_vars:
est_data[varname] = alternative_sample[varname]
est_data = est_data.fillna(0)
data = est_data.as_matrix()
try:
fit, results = interaction.estimate(data, est_params, SAMPLE_SIZE)
#print fit
#print results
fnames = interaction.add_fnames(ind_vars,est_params)
print misc.resultstotable(fnames,results)
misc.resultstocsv(fit,fnames,results,tmp_outcsv,tblname=tmp_outtitle)
dset.store_coeff(tmp_coeffname,zip(*results)[0],fnames)
except:
print 'SINGULAR MATRIX OR OTHER DATA/ESTIMATION PROBLEM'
else:
print 'SAMPLE SIZE TOO SMALL'
print dset.coeffs[('emp_location_6','coeffs')]
print dset.coeffs[('emp_location_6','coeffs')][0]
print dset.coeffs[('emp_location_6','coeffs')][1]
print dset.coeffs[('emp_location_6','coeffs')][2]
print dset.coeffs[('emp_location_6','coeffs')][3]
print dset.coeffs[('emp_location_6','coeffs')][4]
print dset.coeffs[('emp_location_6','coeffs')][5]
