class TestLoadData:
fips_claims_data = load_claims_data(DATA_FILEPATH, DROP_DATE, "fips")
hrr_claims_data = load_claims_data(DATA_FILEPATH, DROP_DATE, "hrr")
fips_data = load_data(DATA_FILEPATH, DROP_DATE, "fips")
hrr_data = load_data(DATA_FILEPATH, DROP_DATE, "hrr")
def test_base_unit(self):
with pytest.raises(AssertionError):
load_claims_data(DATA_FILEPATH, DROP_DATE, "foo")
with pytest.raises(AssertionError):
load_data(DATA_FILEPATH, DROP_DATE, "foo")
def test_claims_columns(self):
assert "hrr" in self.hrr_claims_data.index.names
assert "fips" in self.fips_claims_data.index.names
assert "date" in self.hrr_claims_data.index.names
assert "date" in self.fips_claims_data.index.names
expected_claims_columns = ["Denominator", "Covid_like"]
for col in expected_claims_columns:
assert col in self.fips_claims_data.columns
assert col in self.hrr_claims_data.columns
assert len(set(self.fips_claims_data.columns) - set(expected_claims_columns)) == 0
assert len(set(self.hrr_claims_data.columns) - set(expected_claims_columns)) == 0
def test_data_columns(self):
assert "hrr" in self.hrr_data.columns
assert "fips" in self.fips_data.columns
assert "date" in self.hrr_data.columns
assert "date" in self.fips_data.columns
expected_columns = ["num", "den"]
for col in expected_columns:
assert col in self.fips_data.columns
assert col in self.hrr_data.columns
def test_edge_values(self):
for data in [self.hrr_claims_data, self.fips_claims_data]:
assert data.index.get_level_values('date').max() >= Config.FIRST_DATA_DATE
assert data.index.get_level_values('date').min() < DROP_DATE
for data in [self.hrr_data, self.fips_data]:
assert data.date.max() >= Config.FIRST_DATA_DATE
assert data.date.min() < DROP_DATE
def test_hrrs_values(self):
assert len(self.hrr_data.hrr.unique()) <= CONSTANTS.NUM_HRRS
assert len(self.hrr_claims_data.index.get_level_values(
'hrr').unique()) <= CONSTANTS.NUM_HRRS
assert self.hrr_data.isna().sum().sum() == 0
assert self.hrr_data["num"].sum() == self.hrr_claims_data["Covid_like"].sum()
assert self.hrr_data["den"].sum() == self.hrr_claims_data["Denominator"].sum()
def test_fips_values(self):
assert len(self.fips_data.fips.unique()) <= CONSTANTS.NUM_COUNTIES
assert len(self.fips_claims_data.index.get_level_values(
'fips').unique()) <= CONSTANTS.NUM_COUNTIES
assert self.fips_data.isna().sum().sum() == 0
assert self.fips_data["num"].sum() == self.fips_claims_data["Covid_like"].sum()
assert self.fips_data["den"].sum() == self.fips_claims_data["Denominator"].sum()
def test_base_unit(self):
with pytest.raises(AssertionError):
load_claims_data(DATA_FILEPATH, DROP_DATE, "foo")
with pytest.raises(AssertionError):
load_data(DATA_FILEPATH, DROP_DATE, "foo")
class TestLoadData:
fips_data = load_data(DATA_FILEPATH, DROP_DATE, "fips")
hrr_data = load_data(DATA_FILEPATH, DROP_DATE, "hrr")
def test_backwards_pad(self):
num0 = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8],
dtype=float).reshape(-1, 1)
den0 = np.array([0, 10, 10, 10, 10, 10, 10, 100, 101], dtype=float)
num1, den1 = ClaimsHospIndicator.backwards_pad(num0,
den0,
k=7,
min_visits_to_fill=0)
assert np.array_equal(num0, num1)
assert np.array_equal(den0, den1)
num2, den2 = ClaimsHospIndicator.backwards_pad(num0,
den0,
k=7,
min_visits_to_fill=11)
exp_num2 = np.array([0, 1, 3, 5, 7, 9, 11, 7, 8],
dtype=float).reshape(-1, 1)
exp_den2 = np.array([0, 10, 20, 20, 20, 20, 20, 100, 101], dtype=float)
assert np.array_equal(exp_num2, num2)
assert np.array_equal(exp_den2, den2)
num3, den3 = ClaimsHospIndicator.backwards_pad(num0,
den0,
k=7,
min_visits_to_fill=100)
exp_num3 = np.array([0, 1, 3, 6, 10, 15, 21, 7, 8],
dtype=float).reshape(-1, 1)
exp_den3 = np.array([0, 10, 20, 30, 40, 50, 60, 100, 101], dtype=float)
assert np.array_equal(exp_num3, num3)
assert np.array_equal(exp_den3, den3)
num4, den4 = ClaimsHospIndicator.backwards_pad(num0,
den0,
k=3,
min_visits_to_fill=100)
exp_num4 = np.array([0, 1, 3, 6, 10, 14, 18, 7, 8],
dtype=float).reshape(-1, 1)
exp_den4 = np.array([0, 10, 20, 30, 40, 40, 40, 100, 101], dtype=float)
assert np.array_equal(exp_num4, num4)
assert np.array_equal(exp_den4, den4)
def test_fit_fips(self):
date_range = pd.date_range("2020-05-01", "2020-05-20")
all_fips = self.fips_data.fips.unique()
loc_index_fips_data = self.fips_data.set_index(["fips", "date"])
sample_fips = nr.choice(all_fips, 10)
for fips in sample_fips:
sub_data = loc_index_fips_data.loc[fips]
sub_data = sub_data.reindex(date_range, fill_value=0)
res0 = ClaimsHospIndicator.fit(sub_data, date_range[0], fips)
# first value is burn-in
assert np.min(res0["rate"][1:]) > 0
assert np.max(res0["rate"][1:]) <= 100
if np.all(np.isnan(res0["se"])):
assert res0["incl"].sum() == 0
else:
# binomial standard error, hence largest possible value is
# 100 * (0.5 / sqrt(MIN_DEN))
assert np.nanmax(
res0["se"]) <= 100 * (0.5 / np.sqrt(Config.MIN_DEN))
assert np.nanmin(res0["se"]) > 0
assert res0["incl"].sum() > 0
def test_fit_hrrs(self):
date_range = pd.date_range("2020-05-01", "2020-05-20")
all_hrrs = self.hrr_data.hrr.unique()
loc_index_hrr_data = self.hrr_data.set_index(["hrr", "date"])
sample_hrrs = nr.choice(all_hrrs, 10)
for hrr in sample_hrrs:
sub_data = loc_index_hrr_data.loc[hrr]
sub_data = sub_data.reindex(date_range, fill_value=0)
res0 = ClaimsHospIndicator.fit(sub_data, date_range[0], hrr)
# first value is burn-in
assert np.min(res0["rate"][1:]) > 0
assert np.max(res0["rate"][1:]) <= 100
if np.all(np.isnan(res0["se"])):
assert res0["incl"].sum() == 0
else:
# binomial standard error, hence largest possible value is
# 100 * (0.5 / sqrt(MIN_DEN))
assert np.nanmax(
res0["se"]) <= 100 * (0.5 / np.sqrt(Config.MIN_DEN))
assert np.nanmin(res0["se"]) > 0
assert res0["incl"].sum() > 0