def _check_counterfactual(self, scenario, scenario_npis):
# Simulate Italy lifted all NPI for this period
start_date_str = "2020-03-20"
end_date_str = "2020-03-26"
countries = ["Italy"]
scenario_df = generate_scenario(start_date_str, end_date_str, self.latest_df, countries, scenario=scenario)
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(), "Not the requested countries")
self.assertFalse(scenario_df["Date"].duplicated().any(), "Expected 1 row per date only")
start_date = pd.to_datetime(start_date_str, format=DATE_FORMAT)
end_date = pd.to_datetime(end_date_str, format=DATE_FORMAT)
before_day = start_date - np.timedelta64(1, 'D')
before_day_npis = scenario_df[scenario_df.Date == before_day][NPI_COLUMNS].reset_index(drop=True)
before_day_npis_truth = self.latest_df[(self.latest_df.CountryName == "Italy") &
(self.latest_df.Date == before_day)][NPI_COLUMNS].reset_index(drop=True)
# Check the day before the scenario is correct
pd.testing.assert_frame_equal(before_day_npis_truth, before_day_npis, "Not the expected frozen NPIs")
# For the right period (+1 to include start and end date)
nb_days = (end_date - start_date).days + 1
for i in range(nb_days):
check_day = start_date + np.timedelta64(i, 'D')
check_day_npis_df = scenario_df[scenario_df.Date == check_day][NPI_COLUMNS].reset_index(drop=True)
check_day_npis = list(check_day_npis_df.values[0])
self.assertListEqual(scenario_npis, check_day_npis)
# Check Mar 27 is different from frozen day
after_day = end_date + np.timedelta64(1, 'D')
after_day_npis_df = scenario_df[scenario_df.Date == after_day][NPI_COLUMNS].reset_index(drop=True)
self.assertTrue((scenario_npis - after_day_npis_df.values[0]).any(),
"Expected NPIs to be different")
# Check 27 is indeed equal to truth
after_day_npis_truth = self.latest_df[(self.latest_df.CountryName == "Italy") &
(self.latest_df.Date == after_day)
][NPI_COLUMNS].reset_index(drop=True)
pd.testing.assert_frame_equal(after_day_npis_truth, after_day_npis_df, "Not the expected unfrozen NPIs")
def test_generate_scenario_future_from_last_known_date_custom(self):
# Simulate Italy uses custom NPIs for 1 week after last known date
countries = ["Italy"]
last_known_date = self.latest_df[self.latest_df.CountryName ==
"Italy"].Date.max()
start_date = last_known_date + np.timedelta64(1, 'D')
# Set end date to 1 week from last known date
end_date_str = (last_known_date +
pd.DateOffset(7)).strftime(DATE_FORMAT)
end_date = pd.to_datetime(end_date_str, format=DATE_FORMAT)
nb_days = (end_date - start_date).days + 1 # +1 to include start date
scenario = [ONE_NPIS] * nb_days
# Generate the scenario
scenario_df = generate_scenario(None,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(start_date_str=None,
end_date_str=end_date_str,
scenario_df=scenario_df,
scenario_npis=scenario[0],
country=countries[0])
def test_generate_scenario_future_from_last_known_date_max(self):
# Simulate Italy maxes out NPIs for 1 week after last known date
countries = ["Italy"]
start_date_str = None
# Set end date to 1 week from last known date
last_known_date = self.latest_df[self.latest_df.CountryName ==
"Italy"].Date.max()
end_date_str = (last_known_date +
pd.DateOffset(7)).strftime(DATE_FORMAT)
scenario = "MAX"
scenario_npis = MAX_NPIS
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def get_predictions(start_date_str, end_date_str, pres_df, countries=None):
# Concatenate prescriptions with historical data
raw_df = get_raw_data(HIST_DATA_FILE_PATH)
hist_df = generate_scenario(start_date_str,
end_date_str,
raw_df,
countries=countries,
scenario='Historical')
start_date = pd.to_datetime(start_date_str, format='%Y-%m-%d')
hist_df = hist_df[hist_df.Date < start_date]
ips_df = pd.concat([hist_df, pres_df])
with tempfile.NamedTemporaryFile() as tmp_ips_file:
# Write ips_df to file
ips_df.to_csv(tmp_ips_file.name)
with tempfile.NamedTemporaryFile() as tmp_pred_file:
# Run script to generate predictions
output_str = subprocess.check_output([
'python3', PREDICT_MODULE, '--start_date', start_date_str,
'--end_date', end_date_str, '--interventions_plan',
tmp_ips_file.name, '--output_file', tmp_pred_file.name
],
stderr=subprocess.STDOUT)
# Print output from running script
print(output_str.decode("utf-8"))
# Load predictions to return
# print(f"Generating predictions from {start_date_str} to {start_date_str} from {tmp_pred_file.name} . {tmp_ips_file.name}..")
# predict(start_date_str, start_date_str, tmp_ips_file.name, tmp_pred_file.name)
df = pd.read_csv(tmp_pred_file)
return df
def test_generate_scenario_all_countries_future_from_last_known_date_freeze(
self):
# Simulate ALL countries uses custom NPIs for 1 week after last known date
countries = None
# Set end date to 1 week from last known date
last_known_date = self.latest_df.Date.max()
end_date_str = (last_known_date +
pd.DateOffset(7)).strftime(DATE_FORMAT)
# Make sure we generate scenarios for enough days
nb_days = 14
scenario = [ONE_NPIS] * nb_days
# Generate the scenarios
scenario_df = generate_scenario(None,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check them
all_countries = self.latest_df.CountryName.unique()
for country in all_countries:
all_regions = self.latest_df[self.latest_df.CountryName ==
country].RegionName.unique()
for region in all_regions:
self._check_future(start_date_str=None,
end_date_str=end_date_str,
scenario_df=scenario_df[
(scenario_df.CountryName == country)
& (scenario_df.RegionName == region)],
scenario_npis=scenario[0],
country=country,
region=region)
def test_generate_scenario_mind_the_gap_custom(self):
# Scenario = Custom
start_date_str = "2021-01-01"
end_date_str = "2021-01-31"
countries = ["Italy"]
# Set all the NPIs to one for each day between start data and end date.
scenario = [ONE_NPIS] * 31
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(),
"Not the requested countries")
# Inception is 2020-01-01. 366 days for 2020 + 31 for Jan 2021
self.assertEqual(
397, len(scenario_df),
"Expected the number of days between inception and end date")
# The last 31 rows must be the same
self.assertEqual(
1,
scenario_df.tail(31)[NPI_COLUMNS].mean().mean(),
"Expected the last 31 rows to have all NPIs set to 1")
def test_generate_scenario_all_countries_future_from_last_known_date_freeze(
self):
# Simulate ALL countries uses custom NPIs for the rest of the year
countries = None
end_date_str = "2020-12-31"
# Make sure we generate scenarios for enough days
nb_days = 180
scenario = [ONE_NPIS] * nb_days
# Generate the scenarios
scenario_df = generate_scenario(None,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check them
all_countries = self.latest_df.CountryName.unique()
for country in all_countries:
all_regions = self.latest_df[self.latest_df.CountryName ==
country].RegionName.unique()
for region in all_regions:
self._check_future(start_date_str=None,
end_date_str=end_date_str,
scenario_df=scenario_df[
(scenario_df.CountryName == country)
& (scenario_df.RegionName == region)],
scenario_npis=scenario[0],
country=country,
region=region)
def test_generate_scenario_mind_the_gap_freeze_all_countries(self):
# Check all countries, with frozen npis for 180 days, 1 week from today
start_date = datetime.now() + timedelta(days=7)
start_date_str = start_date.strftime(DATE_FORMAT)
end_date = start_date + timedelta(days=180)
end_date_str = end_date.strftime(DATE_FORMAT)
inception_date = datetime.strptime(INCEPTION_DATE, DATE_FORMAT)
countries = None
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario="Freeze")
self.assertIsNotNone(scenario_df)
nb_days_since_inception = (end_date - inception_date).days + 1
# For each country, assert the scenario contains the expected number of days
for country in self.latest_df.CountryName.unique():
all_regions = self.latest_df[self.latest_df.CountryName ==
country].RegionName.unique()
for region in all_regions:
ips_gdf = scenario_df[(scenario_df.CountryName == country)
& (scenario_df.RegionName == region)]
self.assertEqual(
nb_days_since_inception, len(ips_gdf),
f"Not the expected number of days"
f" for {country} / {region}")
def test_generate_scenario_future_from_last_known_date_freeze(self):
# Simulate Italy freezes NPIS for the rest of the year
countries = ["Italy"]
start_date_str = None
end_date_str = "2020-12-31"
scenario = "Freeze"
last_known_date = self.latest_df[self.latest_df.CountryName ==
"Italy"].Date.max()
frozen_npis_df = self.latest_df[
(self.latest_df.CountryName == "Italy") &
(self.latest_df.Date == last_known_date)][NPI_COLUMNS].reset_index(
drop=True)
scenario_npis = list(frozen_npis_df.values[0])
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def test_generate_scenario_mind_the_gap_freeze_dates_mismatch(self):
# Check scenario contains all days, for 2 countries, where 1 country has 1 more day of data than the other
# Last known date:
# - Belgium: 20201103
# - Brazil: 20201104
# Make sure we don't skip a day
start_date_str = "2021-01-01"
end_date_str = "2021-01-31"
countries = ["Belgium", "Brazil"]
dates_mismatch_df = get_raw_data(DATES_MISMATCH_DATA_FILE,
latest=False)
scenario_df = generate_scenario(start_date_str,
end_date_str,
dates_mismatch_df,
countries,
scenario="Freeze")
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(),
"Not the requested countries")
# Inception is 2020-01-01. 366 days for 2020 + 31 for Jan 2021
nb_days_since_inception = 397
# For each country, assert the scenario contains the expected number of days
for country in countries:
all_regions = dates_mismatch_df[dates_mismatch_df.CountryName ==
country].RegionName.unique()
for region in all_regions:
ips_gdf = scenario_df[(scenario_df.CountryName == country)
& (scenario_df.RegionName == region)]
self.assertEqual(
nb_days_since_inception, len(ips_gdf),
f"Not the expected number of days"
f" for {country} / {region}")
def test_generate_scenario_future_from_last_known_date_freeze(self):
# Simulate Italy freezes NPIS for 1 week after last known date
countries = ["Italy"]
start_date_str = None
scenario = "Freeze"
# Set end date to 1 week from last known date
last_known_date = self.latest_df[self.latest_df.CountryName ==
"Italy"].Date.max()
end_date_str = (last_known_date +
pd.DateOffset(7)).strftime(DATE_FORMAT)
frozen_npis_df = self.latest_df[
(self.latest_df.CountryName == "Italy") &
(self.latest_df.Date == last_known_date)][NPI_COLUMNS].reset_index(
drop=True)
scenario_npis = list(frozen_npis_df.values[0])
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def test_generate_scenario_future_freeze(self):
# Simulate Italy froze it's NPIS for the second part of the year
countries = ["Italy"]
start_date_str = "2020-07-01"
end_date_str = "2020-12-31"
scenario = "Freeze"
before_day = pd.to_datetime("2020-06-30", format=DATE_FORMAT)
frozen_npis_df = self.latest_df[
(self.latest_df.CountryName == "Italy")
& (self.latest_df.Date == before_day)][NPI_COLUMNS].reset_index(
drop=True)
scenario_npis = list(frozen_npis_df.values[0])
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def get_predictions(start_date_str, end_date_str, pres_df, countries=None):
# Concatenate prescriptions with historical data
raw_df = get_raw_data(HIST_DATA_FILE_PATH)
hist_df = generate_scenario(start_date_str,
end_date_str,
raw_df,
countries=countries,
scenario='Historical')
start_date = pd.to_datetime(start_date_str, format='%Y-%m-%d')
hist_df = hist_df[hist_df.Date < start_date]
ips_df = pd.concat([hist_df, pres_df])
with tempfile.NamedTemporaryFile() as tmp_ips_file:
xp = XPrizePredictor()
ips_df.to_csv(tmp_ips_file.name)
x = xp.predict(start_date_str, end_date_str, tmp_ips_file.name)
return x
def test_generate_scenario_mind_the_gap_freeze_2_countries(self):
# Check 2 countries
start_date_str = "2021-01-01"
end_date_str = "2021-01-31"
countries = ["France", "Italy"]
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario="Freeze")
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(),
"Not the requested countries")
# Inception is 2020-01-01. 366 days for 2020 + 31 for Jan 2021
self.assertEqual(
397 * 2, len(scenario_df),
"Not the expected number of days between inception and end date")
def get_predictions(start_date_str, end_date_str, pres_df, countries=None):
# Concatenate prescriptions with historical data
raw_df = get_raw_data(HIST_DATA_FILE_PATH)
hist_df = generate_scenario(start_date_str, end_date_str, raw_df,
countries=countries, scenario='Historical')
start_date = pd.to_datetime(start_date_str, format='%Y-%m-%d')
hist_df = hist_df[hist_df.Date < start_date]
ips_df = pd.concat([hist_df, pres_df])
# Write ips_df to file
ips_df.to_csv(TMP_PRESCRIPTION_FILE)
# Use full path of the local file passed as ip_file
ip_file_full_path = os.path.abspath(TMP_PRESCRIPTION_FILE)
# Go to covid-xprize root dir to access predict script
wd = os.getcwd()
os.chdir("../../../..")
# Run script to generate predictions
output_str = subprocess.check_output(
[
'python', PREDICT_MODULE,
'--start_date', start_date_str,
'--end_date', end_date_str,
'--interventions_plan', ip_file_full_path,
'--output_file', TMP_PRED_FILE_NAME
],
stderr=subprocess.STDOUT
)
# Print output from running script
print(output_str.decode("utf-8"))
# Load predictions to return
df = pd.read_csv(TMP_PRED_FILE_NAME)
# Return to prescriptor dir
os.chdir(wd)
return df
def test_generate_scenario_mind_the_gap_freeze_2_countries(self):
# Check 2 countries
nb_days = 31
# We have 2 countries, and they may have different last know dates.
# Set start date to 7 days from now to guarantee a gap
start_date = datetime.now() + timedelta(days=7)
start_date_str = start_date.strftime(DATE_FORMAT)
end_date = start_date + timedelta(days=nb_days)
end_date_str = end_date.strftime(DATE_FORMAT)
inception_date = pd.to_datetime(INCEPTION_DATE, format=DATE_FORMAT)
countries = ["France", "Italy"]
scenario_df = generate_scenario(start_date_str, end_date_str, self.latest_df, countries, scenario="Freeze")
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(), "Not the requested countries")
# Check we get the expected number of days
expected_days = (end_date - inception_date).days + 1 # +1 because inception_date and end_date are included
self.assertEqual(expected_days * 2, len(scenario_df),
"Not the expected number of days between inception and end date")
def test_generate_scenario_mind_the_gap_max(self):
# Scenario = MAX
nb_days = 31
countries = ["Italy"]
last_known_date = self.latest_df[self.latest_df.CountryName == countries[0]].Date.max()
start_date = last_known_date + timedelta(days=7)
start_date_str = start_date.strftime(DATE_FORMAT)
end_date = start_date + timedelta(days=nb_days)
end_date_str = end_date.strftime(DATE_FORMAT)
inception_date = pd.to_datetime(INCEPTION_DATE, format=DATE_FORMAT)
scenario_df = generate_scenario(start_date_str, end_date_str, self.latest_df, countries, scenario="MAX")
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(), "Not the requested countries")
# Check we get the expected number of days
expected_days = (end_date - inception_date).days + 1 # +1 because inception_date and end_date are included
self.assertEqual(expected_days, len(scenario_df), "Expected the number of days between inception and end date")
# The last nb_days rows must be the same
self.assertEqual(sum(MAX_NPIS), scenario_df.tail(nb_days)[NPI_COLUMNS].mean().sum(),
f"Expected the last {nb_days} rows to have NPIs set to their max value")
def test_generate_scenario_future_from_last_known_date_max(self):
# Simulate Italy maxes out NPIs for the rest of the year
countries = ["Italy"]
start_date_str = None
end_date_str = "2020-12-31"
scenario = "MAX"
scenario_npis = MAX_NPIS
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def test_generate_scenario_mind_the_gap_min(self):
# Scenario = MIN
start_date_str = "2021-01-01"
end_date_str = "2021-01-31"
countries = ["Italy"]
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario="MIN")
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(),
"Not the requested countries")
# Inception is 2020-01-01. 366 days for 2020 + 31 for Jan 2021
self.assertEqual(
397, len(scenario_df),
"Expected the number of days between inception and end date")
# The last 31 rows must be the same
self.assertEqual(0,
scenario_df.tail(31)[NPI_COLUMNS].sum().sum(),
"Expected the last 31 rows to have NPIs set to 0")
def test_generate_scenario_future_max(self):
# Simulate Italy maxed out all NPIs for a period
countries = ["Italy"]
start_date_str = "2020-07-01"
end_date_str = "2020-12-31"
scenario = "MAX"
scenario_npis = MAX_NPIS
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario_npis,
country=countries[0])
def test_generate_scenario_future_custom(self):
# Simulate Italy used custom NPIs for a period: each NPI set to 1 for 7 consecutive days
countries = ["Italy"]
start_date_str = "2020-07-01"
end_date_str = "2020-12-31"
start_date = pd.to_datetime(start_date_str, format=DATE_FORMAT)
end_date = pd.to_datetime(end_date_str, format=DATE_FORMAT)
nb_days = (end_date - start_date).days + 1 # +1 to include start date
scenario = [ONE_NPIS] * nb_days
# Generate the scenario
scenario_df = generate_scenario(start_date_str,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(
start_date_str=start_date_str,
end_date_str=end_date_str,
scenario_df=scenario_df[scenario_df.CountryName == countries[0]],
scenario_npis=scenario[0],
country=countries[0])
def test_generate_scenario_future_from_last_known_date_custom(self):
# Simulate Italy uses custom NPIs for the rest of the year
countries = ["Italy"]
last_known_date = self.latest_df[self.latest_df.CountryName ==
"Italy"].Date.max()
start_date = last_known_date + np.timedelta64(1, 'D')
end_date_str = "2020-12-31"
end_date = pd.to_datetime(end_date_str, format=DATE_FORMAT)
nb_days = (end_date - start_date).days + 1 # +1 to include start date
scenario = [ONE_NPIS] * nb_days
# Generate the scenario
scenario_df = generate_scenario(None,
end_date_str,
self.latest_df,
countries,
scenario=scenario)
# Check it
self._check_future(start_date_str=None,
end_date_str=end_date_str,
scenario_df=scenario_df,
scenario_npis=scenario[0],
country=countries[0])
def test_generate_scenario_mind_the_gap_custom(self):
# Scenario = Custom
nb_days = 31
start_lag = 7
countries = ["Italy"]
last_known_date = self.latest_df[self.latest_df.CountryName == countries[0]].Date.max()
start_date = last_known_date + timedelta(days=start_lag)
start_date_str = start_date.strftime(DATE_FORMAT)
end_date = start_date + timedelta(days=nb_days)
end_date_str = end_date.strftime(DATE_FORMAT)
inception_date = pd.to_datetime(INCEPTION_DATE, format=DATE_FORMAT)
# Set all the NPIs to one for each day between start date and end date, as well as from last known date.
scenario = [ONE_NPIS] * (nb_days + start_lag)
scenario_df = generate_scenario(start_date_str, end_date_str, self.latest_df, countries, scenario=scenario)
self.assertIsNotNone(scenario_df)
# Misleading name but checks the elements, regardless of order
self.assertCountEqual(countries, scenario_df.CountryName.unique(), "Not the requested countries")
# Check we get the expected number of days
expected_days = (end_date - inception_date).days + 1 # +1 because inception_date and end_date are included
self.assertEqual(expected_days, len(scenario_df), "Expected the number of days between inception and end date")
# The last 31 rows must be the same
self.assertEqual(1, scenario_df.tail(nb_days)[NPI_COLUMNS].mean().mean(),
f"Expected the last {nb_days} rows to have all NPIs set to 1")
# Evaluate some example submissions.
# __NOTE: Please run the corresponding example notebooks first in order to train the models that are used in this section.__
# In[ ]:
IP_FILE = "predictions/robojudge_test_scenario.csv"
predictions = {}
# In[ ]:
from covid_xprize.validation.scenario_generator import generate_scenario
countries = None
scenario_df = generate_scenario(start_date_str,
end_date_str,
latest_df,
countries,
scenario="Freeze")
# Remove countries that weren't there at the beginning of the challenge or duplicates
ignore_countries = ["Tonga", "United States Virgin Islands"]
scenario_df = scenario_df[scenario_df.CountryName.isin(ignore_countries) ==
False]
# IP_FILE = "covid_xprize/validation/data/2020-09-30_historical_ip.csv"
scenario_df.to_csv(IP_FILE, index=False)
# ## Linear
# In[ ]:
# Check a model has been trained
if not os.path.isfile(