def _profile_1d_histogram(self, name, hist):
is_num = is_numeric(hist)
is_ts = is_timestamp(hist) or name in self.var_timestamp
bin_labels = np.array(get_bin_centers(hist)[0])
bin_counts = np.array([v.entries for v in get_bin_centers(hist)[1]])
if len(bin_counts) == 0:
self.logger.warning(f'Histogram "{name}" is empty; skipping.')
return dict()
if is_ts:
to_timestamp = np.vectorize(lambda x: pd.to_datetime(x).value)
bin_labels = to_timestamp(bin_labels)
profile = dict()
profile["filled"] = bin_counts.sum()
if hasattr(hist, "nanflow"):
profile["nan"] = hist.nanflow.entries
elif hasattr(hist, "bins") and "NaN" in hist.bins:
profile["nan"] = hist.bins["NaN"].entries
else:
profile["nan"] = 0
profile["overflow"] = hist.overflow.entries if hasattr(
hist, "overflow") else 0
profile["underflow"] = (hist.underflow.entries if hasattr(
hist, "underflow") else 0)
profile["count"] = profile["filled"] + profile["nan"]
profile["distinct"] = len(np.unique(bin_labels))
mpv = bin_labels[np.argmax(bin_counts)] # most probable value
profile["most_probable_value"] = mpv if not is_ts else pd.Timestamp(
mpv)
if is_num and profile["filled"] > 0:
for f_names, func in self.stats_functions.items():
names = f_names.split(",")
results = func(bin_labels, bin_counts)
if len(names) == 1:
results = [results]
if is_ts:
results = [
pd.Timedelta(result)
if f_name == "std" else pd.Timestamp(result)
for f_name, result in zip(name, results)
]
profile.update({k: v for k, v in zip(names, results)})
elif not is_num:
profile["fraction_true"] = pm_np.fraction_of_true(
bin_labels, bin_counts)
return profile
def test_fraction_of_true():
res = fraction_of_true([], [])
assert np.isnan(res)
res = fraction_of_true(["a"], [10])
assert np.isnan(res)
res = fraction_of_true(["a", "b", "c"], [10, 10, 10])
assert np.isnan(res)
res = fraction_of_true(np.array(["True", "False"]), np.array([0, 0]))
assert np.isnan(res)
res = fraction_of_true(np.array(["True", "False"]), np.array([10, 10]))
assert res == 0.5
res = fraction_of_true(np.array([True, False]), [10, 10])
assert res == 0.5
res = fraction_of_true(np.array(["True"]), np.array([10]))
assert res == 1.0
res = fraction_of_true(np.array([True]), np.array([10]))
assert res == 1.0
res = fraction_of_true(np.array(["False"]), np.array([10]))
assert res == 0.0
res = fraction_of_true(np.array([False]), np.array([10]))
assert res == 0.0
def test_fraction_of_true():
res = fraction_of_true([], [])
assert np.isnan(res)
res = fraction_of_true(['a'], [10])
assert np.isnan(res)
res = fraction_of_true(['a', 'b', 'c'], [10, 10, 10])
assert np.isnan(res)
res = fraction_of_true(np.array(['True', 'False']), np.array([0, 0]))
assert np.isnan(res)
res = fraction_of_true(np.array(['True', 'False']), np.array([10, 10]))
assert res == 0.5
res = fraction_of_true(np.array([True, False]), [10, 10])
assert res == 0.5
res = fraction_of_true(np.array(['True']), np.array([10]))
assert res == 1.0
res = fraction_of_true(np.array([True]), np.array([10]))
assert res == 1.0
res = fraction_of_true(np.array(['False']), np.array([10]))
assert res == 0.0
res = fraction_of_true(np.array([False]), np.array([10]))
assert res == 0.0
