def test_parse_sample_selection(self):
input = Input()
(crises_dates, normal_dates) = input.parse_sample_selection("./foc/forecaster/tests/test_sample_selection.xls")
expected_crises = {"USA":[2009,2001], "DEU":[]}
expected_normal = {"USA":[1994,1995,1996], "DEU":[1994,1995]}
self.assertEqual(crises_dates, expected_crises)
self.assertEqual(normal_dates, expected_normal)
def __init__(self, db_location):
'''
Constructor
'''
self.input = Input()
self.crises, self.normals = self.input.parse_sample_selection(
db_location)
class CrisisSeer(object):
'''
Knows when a crisis occured in a certain country (based
on the crisis database)
'''
def __init__(self, db_location):
'''
Constructor
'''
self.input = Input()
self.crises, self.normals = self.input.parse_sample_selection(
db_location)
def get_crisis_years(self, country_code):
"""
return a list of years when this country had crises
"""
events_list = []
try:
events_list = self.crises[country_code]
except KeyError:
# this country has no noted crises so we'll suppose
# it's crisis-free
pass
return events_list
class CrisisSeer(object):
'''
Knows when a crisis occured in a certain country (based
on the crisis database)
'''
def __init__(self, db_location):
'''
Constructor
'''
self.input = Input()
self.crises, self.normals = self.input.parse_sample_selection(db_location)
def get_crisis_years(self, country_code):
"""
return a list of years when this country had crises
"""
events_list = []
try:
events_list = self.crises[country_code]
except KeyError:
# this country has no noted crises so we'll suppose
# it's crisis-free
pass
return events_list
def build_from_crises_file(self, country_codes, feature_indicators,
test_percentage):
"""
Entry method that builds a samples set by fetching the data using the extractor.
Classes are determined from a crisis XLS file.
sparse - if True it fetches the data for the necessary years only. Shown to be non-efficient.
"""
# clear the sample sets
self.crisis_samples = []
self.normal_samples = []
# get the years classified as crises / normal periods
dates_input = Input()
t_crises, t_normal = dates_input.parse_sample_selection(self.t_loc)
crises_list, normal_list = dates_input.parse_sample_selection_to_list(
self.t_loc)
if country_codes[
0] == "EVERYTHING": # we take everything available in the samples set
wb_countries = self.extractor.grab_metadata("countries")
wb_country_codes = set([country.code for country in wb_countries])
samples_definition_codes = set(t_crises.keys()) | set(
t_normal.keys())
country_codes = list(wb_country_codes & samples_definition_codes)
country_codes.sort()
# we fetch all the data here
# boundaries
start_date = min(min(crises_list),
min(normal_list)) - conf.look_back_years
end_date = max(max(crises_list), max(normal_list))
arg = self.extractor.arg()
arg["country_codes"] = country_codes
arg["indicator_codes"] = feature_indicators
arg["interval"] = (start_date, end_date)
arg["pause"] = conf.wb_pause
countries = self.extractor.grab(arg)
if self.cache_enabled and self.extractor.was_cached():
print("Cache was hit, didn't have to query the World Bank API.")
elif self.cache_enabled:
print("Data wasn't cached, queried the World Bank API.")
# assign the samples
for country in countries:
# fetch all the indicators for target country
indicators = []
for ind_code in feature_indicators:
indicator = country.get_indicator(ind_code)
indicators.append(indicator)
# create samples from those indicators - in crises...
try:
crisis_years = t_crises[country.code]
except KeyError:
continue # we skip this country
new_samples = self.assign_samples(indicators, crisis_years,
CRISIS_CLASS, country.code)
self.crisis_samples.extend(new_samples)
# ... and in normal periods
normal_years = t_normal[country.code]
new_samples = self.assign_samples(indicators, normal_years,
NORMAL_CLASS, country.code)
self.normal_samples.extend(new_samples)
return self.divide(self.crisis_samples, self.normal_samples,
test_percentage)
def build_from_crises_file(self, country_codes, feature_indicators, test_percentage):
"""
Entry method that builds a samples set by fetching the data using the extractor.
Classes are determined from a crisis XLS file.
sparse - if True it fetches the data for the necessary years only. Shown to be non-efficient.
"""
# clear the sample sets
self.crisis_samples = []
self.normal_samples = []
# get the years classified as crises / normal periods
dates_input= Input()
t_crises, t_normal = dates_input.parse_sample_selection(self.t_loc)
crises_list, normal_list = dates_input.parse_sample_selection_to_list(self.t_loc)
if country_codes[0]=="EVERYTHING": # we take everything available in the samples set
wb_countries = self.extractor.grab_metadata("countries")
wb_country_codes = set([country.code for country in wb_countries])
samples_definition_codes = set(t_crises.keys()) | set(t_normal.keys())
country_codes = list(wb_country_codes & samples_definition_codes)
country_codes.sort()
# we fetch all the data here
# boundaries
start_date = min(min(crises_list), min(normal_list))-conf.look_back_years
end_date = max(max(crises_list), max(normal_list))
arg = self.extractor.arg()
arg["country_codes"] = country_codes
arg["indicator_codes"] = feature_indicators
arg["interval"] = (start_date, end_date)
arg["pause"] = conf.wb_pause
countries = self.extractor.grab(arg)
if self.cache_enabled and self.extractor.was_cached():
print("Cache was hit, didn't have to query the World Bank API.")
elif self.cache_enabled:
print("Data wasn't cached, queried the World Bank API.")
# assign the samples
for country in countries:
# fetch all the indicators for target country
indicators = []
for ind_code in feature_indicators:
indicator = country.get_indicator(ind_code)
indicators.append(indicator)
# create samples from those indicators - in crises...
try:
crisis_years = t_crises[country.code]
except KeyError:
continue # we skip this country
new_samples = self.assign_samples(indicators,
crisis_years,
CRISIS_CLASS,
country.code)
self.crisis_samples.extend(new_samples)
# ... and in normal periods
normal_years = t_normal[country.code]
new_samples = self.assign_samples(indicators,
normal_years,
NORMAL_CLASS,
country.code)
self.normal_samples.extend(new_samples)
return self.divide(self.crisis_samples, self.normal_samples, test_percentage)
def __init__(self, db_location):
'''
Constructor
'''
self.input = Input()
self.crises, self.normals = self.input.parse_sample_selection(db_location)