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release 0.4.21
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@MamadouSDiallo
MamadouSDiallo committed Jun 19, 2024
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Showing 3 changed files with 32 additions and 98 deletions.
2 changes: 1 addition & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@ authors = ["Mamadou S Diallo <[email protected]>"]
description = "Select, weight and analyze complex sample data"
license = "MIT"
name = "samplics"
version = "0.4.20"
version = "0.4.21"

readme = "README.md"

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2 changes: 1 addition & 1 deletion src/samplics/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,4 +137,4 @@
"Sample",
]

__version__ = "0.4.20"
__version__ = "0.4.21"
126 changes: 30 additions & 96 deletions src/samplics/sampling/selection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@
Springer-Verlag New York, Inc
"""


from __future__ import annotations

import math
Expand Down Expand Up @@ -195,9 +194,7 @@ def _grs_select(
p=probs[units_s] / np.sum(probs[units_s]),
)
sampled_indices_list.append(sampled_indices_s)
sampled_indices = np.array(
[val for sublist in sampled_indices_list for val in sublist]
).flatten()
sampled_indices = np.array([val for sublist in sampled_indices_list for val in sublist]).flatten()

indices_s, hits_s = np.unique(sampled_indices, return_counts=True)
sample[indices_s] = True
Expand Down Expand Up @@ -278,9 +275,7 @@ def _pps_sys_select_core(
cumsize = np.append(0, np.cumsum(mos))
samp_interval = cumsize[-1] / samp_size
random_start = np.random.random_sample() * samp_interval
random_picks = random_start + samp_interval * np.linspace(
0, samp_size - 1, samp_size
)
random_picks = random_start + samp_interval * np.linspace(0, samp_size - 1, samp_size)

hits = np.zeros(samp_unit.size).astype(int)
for k in range(cumsize.size - 1):
Expand Down Expand Up @@ -315,9 +310,7 @@ def _pps_sys_select(
mos=mos[stratum_units],
)
else:
sample, hits = self._pps_sys_select_core(
samp_unit=samp_unit, samp_size=samp_size, mos=mos
)
sample, hits = self._pps_sys_select_core(samp_unit=samp_unit, samp_size=samp_size, mos=mos)

return sample, hits

Expand All @@ -340,17 +333,10 @@ def _pps_hv_select_core(
initial_probs_selection = (
samp_size
* diff_probs
* (
1
+ np.linspace(1, samp_size, samp_size)
* probs_sorted[pop_size - samp_size]
/ s
)
* (1 + np.linspace(1, samp_size, samp_size) * probs_sorted[pop_size - samp_size] / s)
)
probs_sorted = np.delete(probs_sorted, -1)
selected_i = np.random.choice(
np.arange(0, samp_size), size=1, p=initial_probs_selection
)[0]
selected_i = np.random.choice(np.arange(0, samp_size), size=1, p=initial_probs_selection)[0]
sampled_indices = all_indices_sorted[selected_i + 1 : samp_size]

notsampled_indices = np.delete(all_indices_sorted, sampled_indices)
Expand All @@ -366,9 +352,7 @@ def _pps_hv_select_core(
)
p_starts = notsampled_probs / p_denominator
range_part2 = range(pop_size - samp_size, pop_size - samp_size + selected_i)
p_starts[range_part2] = (
probs_sorted[pop_size - samp_size] / p_denominator[range_part2]
)
p_starts[range_part2] = probs_sorted[pop_size - samp_size] / p_denominator[range_part2]
p_starts_sum = np.cumsum(np.flip(p_starts)[range(p_starts.size - 1)])
p_starts_sum = np.append(np.flip(p_starts_sum), 1)
p_double_starts = p_starts / p_starts_sum
Expand All @@ -381,11 +365,7 @@ def _pps_hv_select_core(
p_double_space = p_double_starts[range(start_j, end_j)]
p_double_space = 1 - (selected_i + 1 - ll) * np.append(0, p_double_space)
p_double_space = np.delete(p_double_space, -1)
a_j = (
(samp_size - ll + 1)
* p_starts[range(start_j, end_j)]
* np.cumprod(p_double_space)
)
a_j = (samp_size - ll + 1) * p_starts[range(start_j, end_j)] * np.cumprod(p_double_space)
indice_j = np.random.choice(sampling_space, size=1, p=a_j / np.sum(a_j))[0]
selected_j = notsampled_indices[indice_j]
sampled_indices = np.append(sampled_indices, selected_j)
Expand Down Expand Up @@ -422,9 +402,7 @@ def _pps_hv_select(
mos=mos[stratum_units],
)
else:
sample, hits = self._pps_hv_select_core(
samp_unit=samp_unit, samp_size=samp_size, mos=mos
)
sample, hits = self._pps_hv_select_core(samp_unit=samp_unit, samp_size=samp_size, mos=mos)

return sample, hits

Expand All @@ -444,15 +422,9 @@ def _pps_brewer_select_core(
for s in np.arange(1, samp_size):
remaining_indices = np.delete(all_indices, sampled_indices)
remaining_probs = np.delete(all_probs, sampled_indices)
remaining_probs = (
remaining_probs
* (1 - remaining_probs)
/ (1 - (samp_size - s) * remaining_probs)
)
remaining_probs = remaining_probs * (1 - remaining_probs) / (1 - (samp_size - s) * remaining_probs)
remaining_probs = remaining_probs / sum(remaining_probs)
current_selection = np.random.choice(
remaining_indices, 1, p=remaining_probs
)
current_selection = np.random.choice(remaining_indices, 1, p=remaining_probs)
sampled_indices = np.append(sampled_indices, current_selection)

sample[sampled_indices] = True
Expand Down Expand Up @@ -485,9 +457,7 @@ def _pps_brewer_select(
mos=mos[stratum_units],
)
else:
sample, hits = self._pps_brewer_select_core(
samp_unit=samp_unit, samp_size=samp_size, mos=mos
)
sample, hits = self._pps_brewer_select_core(samp_unit=samp_unit, samp_size=samp_size, mos=mos)

return sample, hits

Expand All @@ -499,9 +469,7 @@ def _pps_murphy_select_core(
) -> tuple[np.ndarray, np.ndarray]:

if samp_size != 2:
raise ValueError(
"For the Murphy's selection algorithm, sample size must be equal to 2"
)
raise ValueError("For the Murphy's selection algorithm, sample size must be equal to 2")
all_indices = np.arange(samp_unit.size)
all_probs = mos / np.sum(mos)
sampled_indices = np.random.choice(all_indices, 1, p=all_probs)
Expand Down Expand Up @@ -542,9 +510,7 @@ def _pps_murphy_select(
mos=mos[stratum_units],
)
else:
sample, hits = self._pps_murphy_select_core(
samp_unit=samp_unit, samp_size=samp_size, mos=mos
)
sample, hits = self._pps_murphy_select_core(samp_unit=samp_unit, samp_size=samp_size, mos=mos)

return sample, hits

Expand All @@ -567,12 +533,8 @@ def _pps_rs_select_core(
remaining_probs = all_probs / (1 - samp_size * all_probs)
remaining_probs = np.delete(remaining_probs, sampled_indices)
remaining_probs = remaining_probs / np.sum(remaining_probs)
remaining_sample = np.random.choice(
remaining_indices, samp_size - 1, p=remaining_probs
)
sampled_indices, counts = np.unique(
np.append(sampled_indices, remaining_sample), return_counts=True
)
remaining_sample = np.random.choice(remaining_indices, samp_size - 1, p=remaining_probs)
sampled_indices, counts = np.unique(np.append(sampled_indices, remaining_sample), return_counts=True)
if (counts == 1).all():
stop = True

Expand Down Expand Up @@ -606,9 +568,7 @@ def _pps_rs_select(
mos=mos[stratum_units],
)
else:
sample, hits = self._pps_rs_select_core(
samp_unit=samp_unit, samp_size=samp_size, mos=mos
)
sample, hits = self._pps_rs_select_core(samp_unit=samp_unit, samp_size=samp_size, mos=mos)

return sample, hits

Expand Down Expand Up @@ -639,13 +599,11 @@ def _sys_selection_method(
raise AssertionError("samp_size or samp_rate must be provided!")

if samp_rate is not None:
samp_size = int(samp_rate * samp_unit.size)
samp_size = round(samp_rate * samp_unit.size)
samp_interval = int(samp_unit.size / samp_size) # same as 1 / samp_rate

random_start = np.random.choice(range(0, samp_interval))
random_picks = random_start + samp_interval * np.linspace(
0, samp_size - 1, samp_size
).astype(int)
random_picks = random_start + samp_interval * np.linspace(0, samp_size - 1, samp_size).astype(int)
hits = np.zeros(samp_unit.size).astype(int)
hits[random_picks] = 1

Expand All @@ -672,15 +630,11 @@ def _sys_select(
(
sample[stratum_units],
hits[stratum_units],
) = self._sys_selection_method(
samp_unit[stratum_units], samp_size_s, samp_rate_s
)
) = self._sys_selection_method(samp_unit[stratum_units], samp_size_s, samp_rate_s)
elif isinstance(samp_size, int) or isinstance(samp_rate, float):
samp_size_n = None if samp_size is None else samp_size
samp_rate_n = None if samp_rate is None else samp_rate
sample, hits = self._sys_selection_method(
samp_unit, samp_size_n, samp_rate_n
)
sample, hits = self._sys_selection_method(samp_unit, samp_size_n, samp_rate_n)

return sample, hits

Expand All @@ -700,9 +654,7 @@ def inclusion_probs(
stratum = numpy_array(stratum)
if isinstance(samp_size, (int, float)):
strata = np.unique(stratum)
samp_size_temp = dict(
zip(strata, np.repeat(int(samp_size), strata.shape[0]))
)
samp_size_temp = dict(zip(strata, np.repeat(int(samp_size), strata.shape[0])))
elif isinstance(samp_size, dict):
samp_size_temp = samp_size.copy()
else:
Expand All @@ -728,13 +680,9 @@ def inclusion_probs(
):
if self._anycertainty(samp_size_temp, stratum, mos):
raise AssertionError("Some clusters are certainties.")
incl_probs = self._pps_inclusion_probs(
samp_unit, samp_size_temp, mos, stratum
)
incl_probs = self._pps_inclusion_probs(samp_unit, samp_size_temp, mos, stratum)
elif self.method == SelectMethod.sys:
incl_probs = self._sys_inclusion_probs(
samp_unit, samp_size_temp, stratum, samp_rate
)
incl_probs = self._sys_inclusion_probs(samp_unit, samp_size_temp, stratum, samp_rate)
else:
raise ValueError("method not valid!")

Expand Down Expand Up @@ -777,23 +725,17 @@ def select(
self.strata = []
self.pop_size = _samp_unit.shape[0]
if samp_rate is None:
self.samp_size = data_to_dict(
data=samp_size, strat=self.strat, stratum=_stratum
)
self.samp_size = data_to_dict(data=samp_size, strat=self.strat, stratum=_stratum)
self.samp_rate = self._calculate_samp_rate(
strat=self.strat, pop_size=self.pop_size, samp_size=self.samp_size
)
else:
self.samp_rate = data_to_dict(
data=samp_rate, strat=self.strat, stratum=_stratum
)
self.samp_rate = data_to_dict(data=samp_rate, strat=self.strat, stratum=_stratum)
self.samp_size = self._calculate_samp_size(
strat=self.strat, pop_size=self.pop_size, samp_rate=self.samp_rate
)

self.fpc = self._calculate_fpc(
strat=self.strat, pop_size=self.pop_size, samp_size=self.samp_size
)
self.fpc = self._calculate_fpc(strat=self.strat, pop_size=self.pop_size, samp_size=self.samp_size)

if self.method == SelectMethod.grs:
_probs = numpy_array(probs)
Expand Down Expand Up @@ -821,24 +763,18 @@ def select(
if self._anycertainty(samp_size=self.samp_size, stratum=_stratum, mos=_mos):
raise CertaintyError("Some clusters are certainties.")

_samp_ids = np.linspace(
start=0, stop=_samp_unit.shape[0] - 1, num=_samp_unit.shape[0], dtype="int"
)
_samp_ids = np.linspace(start=0, stop=_samp_unit.shape[0] - 1, num=_samp_unit.shape[0], dtype="int")

if remove_nan:
items_to_keep = remove_nans(
_samp_ids.shape[0], _samp_ids, _stratum, _mos, _probs
)
items_to_keep = remove_nans(_samp_ids.shape[0], _samp_ids, _stratum, _mos, _probs)
_samp_ids = _samp_ids[items_to_keep]
_stratum = _stratum[items_to_keep]
_mos = _mos[items_to_keep]
_probs = _probs[items_to_keep]

suffled_order = None
if shuffle and self.method in (SelectMethod.sys, SelectMethod.pps_sys):
suffled_order = np.linspace(
start=0, stop=self.pop_size - 1, num=self.pop_size, dtype="int"
)
suffled_order = np.linspace(start=0, stop=self.pop_size - 1, num=self.pop_size, dtype="int")
np.random.shuffle(suffled_order)
_samp_unit = _samp_unit[suffled_order]
if _stratum.shape not in ((), (0,)):
Expand All @@ -847,9 +783,7 @@ def select(
_mos = _mos[suffled_order]

if self.method in (SelectMethod.srs_wr, SelectMethod.srs_wor):
_probs = self._srs_inclusion_probs(
samp_unit=_samp_ids, samp_size=self.samp_size, stratum=_stratum
)
_probs = self._srs_inclusion_probs(samp_unit=_samp_ids, samp_size=self.samp_size, stratum=_stratum)
sample, hits = self._grs_select(
probs=_probs,
samp_unit=_samp_ids,
Expand Down

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