def test_supplemental_arg_changes_content_of_config_file(tmpdir, monkeypatch):
monkeypatch.setattr(initialize_city, 'geocode_address', mockreturn)
# Generate a test config for Brisbane
initialize_city.make_config_file(
tmpdir.join('/test_config_brisbane_supplemental.yml'),
'Brisbane, Australia', 'Australia/Brisbane', 'brisbane',
'test_crashes.csv', ['parking_tickets_dummy_file_1.csv'])
with open(tmpdir.join('/test_config_brisbane_supplemental.yml'),
'r') as test_file:
expected_file_contents = test_file.read()
with open(tmpdir.join('/test_config_brisbane_supplemental.yml'),
'r') as test_file:
test_file_contents = test_file.read()
assert test_file_contents == expected_file_contents
def test_initialize_city_brisbane_no_supplemental(tmpdir, monkeypatch):
monkeypatch.setattr(initialize_city, 'geocode_address', mockreturn)
# Generate a test config for Brisbane
initialize_city.make_config_file(
tmpdir.join('/test_config_brisbane_no_supplemental.yml'),
'Brisbane, Australia', 'Australia/Brisbane', 'brisbane',
'test_crashes.csv', 'test_concerns.csv', False)
# check that the file contents generated is identical to a pre-built string
with open(tmpdir.join('/test_config_brisbane_no_supplemental.yml'),
'r') as test_file:
test_file_contents = test_file.read()
with open(
os.path.join(TEST_FP, 'data',
'config_brisbane_no_supplemental.yml'),
'r') as test_file:
expected_file_contents = test_file.read()
assert test_file_contents == expected_file_contents
def test_initialize_city_brisbane_no_supplemental(monkeypatch):
monkeypatch.setattr(initialize_city, 'geocode_address', mockreturn)
tmpdir = py.path.local('/tmp')
# Generate a test config for Brisbane
initialize_city.make_config_file(tmpdir.join('/test_config_brisbane.yml'),
'Brisbane, Australia', 'brisbane',
'test_crashes.csv', 'test_concerns.csv')
# check that the file contents generated is identical to a pre-built string
expected_file_contents = """# City name
city: Brisbane, Australia
# City centerpoint latitude & longitude (default geocoded values set)
city_latitude: -27.4697707
city_longitude: 153.0251235
# Radius of city's road network from centerpoint in km, required if OSM has no polygon data (defaults to 20km)
city_radius: 20
# The folder under data where this city's data is stored
name: brisbane
# If given, limit crashes to after start_year and before end_year
# Recommended to limit to just a few years for now
start_year:
end_year:
# level of predictions, either 'week' or 'segment'
level: 'segment'
#################################################################
# Configuration for data standardization
# crash file configurations
crashes_files:
test_crashes.csv:
required:
id:
latitude:
longitude:
# If date supplied in single column:
date_complete:
# If date is separated into year/month/day:
date_year:
date_month:
# Leave date_day empty if not available
date_day:
# If time is available and separate from date:
time:
# If time specified, time_format is one of:
# default (HH:MM:SS)
# seconds (since midnight)
# military (HHMM)
time_format:
optional:
summary:
address:
vehicles:
bikes:
# List of concern type information
concern_files:
- name: concern
filename: test_concerns.csv
latitude:
longitude:
time:
# week on which to predict crashes (week, year)
# Best practice is to choose a week towards the end of your crash data set
# in format [month, year]
time_target: [30, 2017]
# specify how many weeks back to predict in output of train_model
weeks_back: 1"""
with open(tmpdir.join('/test_config_brisbane.yml'), 'r') as test_file:
test_file_contents = test_file.read()
assert test_file_contents == expected_file_contents
def test_supplemental_argument_should_change_content_of_config_file(
monkeypatch):
monkeypatch.setattr(initialize_city, 'geocode_address', mockreturn)
tmpdir = py.path.local('/tmp')
# Generate a test config for Brisbane
initialize_city.make_config_file(tmpdir.join('/test_config_brisbane.yml'),
'Brisbane, Australia',
'Australia/Brisbane', 'brisbane',
'test_crashes.csv', 'test_concerns.csv',
['parking_tickets_dummy_file_1.csv'])
# check that the file contents generated is identical to a pre-built string
expected_file_contents = """# City name
city: Brisbane, Australia
# City centerpoint latitude & longitude (default geocoded values set)
city_latitude: -27.4697707
city_longitude: 153.0251235
# City's time zone: defaults to the local time zone of computer initializing the city's config file
timezone: Australia/Brisbane
# Radius of city's road network from centerpoint in km, required if OSM has no polygon data (defaults to 20km)
city_radius: 20
# The folder under data where this city's data is stored
name: brisbane
# If given, limit crashes to after startdate and no later than enddate
# Recommended to limit to just a few years for now
startdate:
enddate:
# The type of predictions to generate, 'segment' is default, 'week' is legacy
level: 'segment'
#################################################################
# Configuration for data standardization
# crash file configurations
crashes_files:
test_crashes.csv:
required:
id:
latitude:
longitude:
# If date supplied in single column:
date_complete:
# If date is separated into year/month/day:
date_year:
date_month:
# Leave date_day empty if not available
date_day:
# If time is available and separate from date:
time:
# If time specified, time_format is one of:
# default (HH:MM:SS)
# seconds (since midnight)
# military (HHMM)
time_format:
optional:
summary:
address:
vehicles:
bikes:
# List of concern type information
concern_files:
- name: concern
filename: test_concerns.csv
latitude:
longitude:
time:
# Additional data sources
data_source:
- name:
filename: parking_tickets_dummy_file_1.csv
address:
date:
time:
category:
notes:
# Feature is categorical (f_cat) or continuous (f_cont)
feat:
# feat_agg (feature aggregation) can be total count 'default' or 'latest value'
feat_agg:
# if latest, the column name where the value can be found
value:
# If using legacy 'week' predictions:
# specify year & week on which to predict crashes (best practice is year & week towards the end of your crash data set
# in format [month, year]
time_target: [30, 2017]
# specify how many weeks back to predict in output of train_model
weeks_back: 1"""
with open(tmpdir.join('/test_config_brisbane.yml'), 'r') as test_file:
test_file_contents = test_file.read()
assert test_file_contents == expected_file_contents
