def test_new_match_should_set_header_in_psm(self):
job = Job(user=self.user,
covariate_variables='ttime_min,slope',
outcome_variables='forest_cov')
abstractfeature = AbstractFeature()
data = Data(abstractfeature)
sma = job.process(data)
self.assertEqual(sma.names, job.covariate_variables.split(','))
def test_job_process_should_return_Statistical_Matching_Adapter(self):
job = Job(user=self.user,
covariate_variables='forest_cov',
outcome_variables='forest_cov')
abstractfeature = AbstractFeature()
data = Data(abstractfeature)
sma = job.process(data)
self.assertIsInstance(sma, StatisticalMatchingAdapter)
def test_get_matched_should_return_ids(self):
job = Job(user=self.user,
covariate_variables='forest_cov',
outcome_variables='forest_cov')
abstractfeature = AbstractFeature()
data = Data(abstractfeature)
sma = job.process(data)
self.assertTrue(
all([(match in self.control_set) for match in sma.matches]))
def create_job(request):
body = json.loads(request.body)
user_map = Map.objects.get(pk=request.session['mid'])
_clear_job(user_map)
caliper = body['caliper']
support = body['support']
covariates = body['covariates']
estimator = body['estimator']
method = body['method']
outcome = body['outcome']
low_outcome_year = body['low_outcome_year']
high_outcome_year = body['high_outcome_year']
error_type = body['error_type']
country = body['country'].encode('latin1')
region_type = body['region_type']
state = body['state']
min_forest_cover = body['min_forest_cover']
max_forest_cover = body['max_forest_cover']
agroforest = body['agroforest']
agriculture = body['agriculture']
forest = body['forest']
treatment_area_option = body['treatment_area_option']
control_area_option = body['control_area_option']
session_start = datetime.fromtimestamp(
int(body['user_start_time']) / 1000,
timezone.utc) # Convert epoch ms to seconds
# This user instance does not affect other user identification since we use usermap for identification
user = User.objects.get(username='******')
layer_services.clear_matched_points(user_map)
job = Job(user=user,
usermap=user_map,
caliper_distance=caliper,
common_support=support,
covariate_variables=covariates,
matching_estimator=estimator,
matching_method=method,
outcome_variables=outcome,
standard_error_type=error_type,
low_outcome_year=low_outcome_year,
high_outcome_year=high_outcome_year,
current=True)
job.save()
job_stats = JobStats(job_id=job,
country=country,
session_start=session_start,
region_type=region_type,
state=state,
min_forest_cover=min_forest_cover,
max_forest_cover=max_forest_cover,
agroforest=agroforest,
agriculture=agriculture,
forest=forest,
treatment_area_option=treatment_area_option,
control_area_option=control_area_option)
job_stats.save()
abstractfeature = AbstractFeature()
data = Data(abstractfeature)
data.retrieve(user_map, job.outcome_variables, job.covariate_variables)
stat_match = job.process(data)
matched_feature_ids = stat_match.matches
layer_services.set_matched_points(user_map, matched_feature_ids)
# While the outcome variable could be assigned to stat_match,
# this lets us change the outcome without refitting the match
results = stat_match.results(
data.outcome_column(job.low_outcome_year, job.high_outcome_year))
balance_statistics = stat_match.balance_statistics()
bounds = stat_match.bounds(
data.outcome_column(job.low_outcome_year, job.high_outcome_year))
CBSmeans.objects.create_table(job, balance_statistics)
CBStests.objects.create_table(job, balance_statistics)
# Results.objects.create_table(job, results, data.outcome_names[0])
Results.objects.create_table(job, results, "Forest Loss")
# ResultsChart.objects.create_table(job, results, data.outcome_names[0])
ResultsChart.objects.create_table(job, results, "Forest Loss")
CheckSensitivity.objects.create_table(job, bounds)
return HttpResponse('success', status=200)
