def test_rank():
s = mdf.MicroSeries([1, 2, 3], weights=[4, 5, 6])
assert np.array_equal(s.rank().values, [4, 9, 15])
s = mdf.MicroSeries([3, 1, 2], weights=[6, 4, 5])
assert np.array_equal(s.rank().values, [15, 4, 9])
s = mdf.MicroSeries([2, 1, 3], weights=[5, 4, 6])
assert np.array_equal(s.rank().values, [9, 4, 15])
def test_cumsum():
s = mdf.MicroSeries([1, 2, 3], weights=[4, 5, 6])
assert np.array_equal(s.cumsum().values, [4, 14, 32])
s = mdf.MicroSeries([2, 1, 3], weights=[5, 4, 6])
assert np.array_equal(s.cumsum().values, [10, 14, 32])
s = mdf.MicroSeries([3, 1, 2], weights=[6, 4, 5])
assert np.array_equal(s.cumsum().values, [18, 22, 32])
def test_top_pct():
x = list(range(1, 11)) # 1 to 10. Sum = 10 * 11 / 2 = 55.
df = pd.DataFrame({"x": x})
ms = mdf.MicroSeries(x)
RES = 10 / 55
assert mdf.top_10_pct_share(df, "x") == RES
assert ms.top_10_pct_share() == RES
x = list(range(1, 4))
df = pd.DataFrame({"x": x, "w": x})
ms = mdf.MicroSeries(x, weights=x)
# This is equivalent to [1, 2, 2, 3, 3, 3]
# Sum = 14, top half is 9.
RES = 9 / 14
assert mdf.top_50_pct_share(df, "x", "w") == RES
assert ms.top_50_pct_share() == RES
def test_series_getitem():
arr = np.array([0, 1, 1])
w = np.array([3, 0, 9])
s = mdf.MicroSeries(arr, weights=w)
assert s[[1, 2]].sum() == np.sum(arr[[1, 2]] * w[[1, 2]])
assert s[1:3].sum() == np.sum(arr[1:3] * w[1:3])
def test_mean():
arr = np.array([3, 0, 2])
w = np.array([4, 1, 1])
series = mdf.MicroSeries(arr, weights=w)
assert series.mean() == np.average(arr, weights=w)
arr = np.linspace(-20, 100, 100)
w = np.linspace(1, 3, 100)
series = mdf.MicroSeries(arr)
series.set_weights(w)
assert series.mean() == np.average(arr, weights=w)
w = np.linspace(1, 3, 101)
series = mdf.MicroSeries(arr)
try:
series.set_weights(w)
assert False
except Exception:
pass
def test_sum():
arr = np.array([0, 1, 1])
w = np.array([3, 0, 9])
series = mdf.MicroSeries(arr, weights=w)
assert series.sum() == (arr * w).sum()
arr = np.linspace(-20, 100, 100)
w = np.linspace(1, 3, 100)
series = mdf.MicroSeries(arr)
series.set_weights(w)
assert series.sum() == (arr * w).sum()
# Verify that an error is thrown when passing weights of different size
# from the values.
w = np.linspace(1, 3, 101)
series = mdf.MicroSeries(arr)
try:
series.set_weights(w)
assert False
except Exception:
pass
def calc(self, var, map_to=None, period="2020", verbose=False):
try:
result = self.model.calculate(var, period)
except:
if verbose:
print(
f"Initial period calculation failed for {var}; attempting to gross up periods"
)
try:
result = self.model.calculate_add(var, period)
except:
if verbose:
print(
f"Grossing up period calculation failed for {var}; attempting to divide period"
)
result = self.model.calculate_divide(var, period)
entity = self.model.tax_benefit_system.variables[var].entity.key
result = self.map_to(result, entity=entity, target_entity=map_to)
if not self.use_microdf or self.weight_vars is None:
return result
return mdf.MicroSeries(
result, weights=self.weight_vars[map_to or entity]
)
def calc(
self,
var: str,
period: Union[str, int] = 2020,
weighted: bool = True,
map_to: str = None,
how: str = None,
dp: int = 2,
) -> MicroSeries:
if period is None:
period = self.year
try:
var_metadata = self.simulation.tax_benefit_system.variables[var]
arr = self.simulation.calculate(var, period)
except Exception as e:
try:
arr = self.simulation.calculate_add(var, period)
if var_metadata.value_type == bool:
arr = arr >= 52
except:
try:
arr = self.simulation.calculate_divide(var, period)
except:
raise e
if var_metadata.value_type == float:
arr = arr.round(dp)
if var_metadata.value_type == Enum:
arr = arr.decode_to_str()
if not weighted:
return arr
else:
entity = var_metadata.entity.key
if map_to:
arr = self.map_to(arr, entity, map_to, how=how)
entity = map_to
return mdf.MicroSeries(arr, weights=self.entity_weights[entity])
import pandas as pd
import pytest
import microdf as mdf
X = [1, 5, 2]
Y = [0, -6, 3]
W = [4, 1, 1]
df = pd.DataFrame({"x": X, "y": Y, "w": W})
ms = mdf.MicroSeries(X, weights=W)
md = mdf.MicroDataFrame(df[["x", "y"]], weights=W)
# Also make a version with groups.
df2 = df.copy(deep=True)
df2.x *= 2
df2.y *= 1.5
dfg = pd.concat([df, df2])
dfg["g"] = ["a"] * 3 + ["b"] * 3
mdg = mdf.MicroDataFrame(dfg[["x", "y", "g"]], weights=W)
def test_weighted_quantile():
Q = [0, 0.5, 1]
mdf.weighted_quantile(df, "x", "w", Q).tolist()
def test_weighted_median():
assert mdf.weighted_median(df, "x") == 2
mdf.weighted_median(df, "x", "w")
# Test with groups.
mdf.weighted_median(dfg, "x", "w", "g")
def test_median():
# 1, 2, 3, 4, *4*, 4, 5, 5, 5
arr = np.array([1, 2, 3, 4, 5])
w = np.array([1, 1, 1, 3, 3])
series = mdf.MicroSeries(arr, weights=w)
assert series.median() == 4
def test_decile_rank_rank():
s = mdf.MicroSeries(
[5, 4, 3, 2, 1, 6, 7, 8, 9],
weights=[10, 20, 10, 10, 10, 10, 10, 10, 10, 10],
)
assert np.array_equal(s.decile_rank().values, [6, 5, 3, 2, 1, 7, 8, 9, 10])
def test_quintile_rank():
s = mdf.MicroSeries([4, 2, 3], weights=[20, 60, 20])
assert np.array_equal(s.quintile_rank().values, [5, 3, 4])
def test_quartile_rank():
s = mdf.MicroSeries([4, 2, 3], weights=[25, 50, 25])
assert np.array_equal(s.quartile_rank().values, [4, 2, 3])
def test_percentile_rank():
s = mdf.MicroSeries([4, 2, 3, 1], weights=[20, 40, 20, 20])
assert np.array_equal(s.percentile_rank().values, [100, 60, 80, 20])