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spatially-conditioned permutations #261

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spatially-conditioned permutations #261

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26 changes: 17 additions & 9 deletions segregation/inference/comparative.py
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Original file line number Diff line number Diff line change
Expand Up @@ -338,9 +338,13 @@ def _prepare_random_label(seg_class_1, seg_class_2):
seg_class_1.group_pop_var,
seg_class_1.total_pop_var,
"grouping_variable",
data_1.geometry.name if hasattr(data_1, "geometry") else None,
]
]
data_1.columns = ["group", "total", "grouping_variable"]
cols = ["group", "total", "grouping_variable"]
if hasattr(data_1, "geometry") :
cols += 'geometry',
data_1.columns = cols

data_2.loc[:, (seg_class_2.group_pop_var, seg_class_2.total_pop_var)] = (
data_2.loc[:, (seg_class_2.group_pop_var, seg_class_2.total_pop_var)]
Expand All @@ -352,9 +356,10 @@ def _prepare_random_label(seg_class_1, seg_class_2):
seg_class_2.group_pop_var,
seg_class_2.total_pop_var,
"grouping_variable",
data_2.geometry.name if hasattr(data_2, "geometry") else None,
]
]
data_2.columns = ["group", "total", "grouping_variable"]
data_2.columns = cols

stacked_data = pd.concat([data_1, data_2], axis=0)

Expand All @@ -367,11 +372,9 @@ def _prepare_random_label(seg_class_1, seg_class_2):

if seg_class_1.groups != seg_class_2.groups:
raise ValueError("MultiGroup groups should be the same")
# geometry has been discarded, but the CRS can cause concatenation problems
data_1.crs = None
data_2.crs = None

stacked_data = pd.concat([data_1, data_2], ignore_index=True)
return stacked_data
return stacked_data.reset_index(drop=True)


def _estimate_random_label_difference(data):
Expand Down Expand Up @@ -403,9 +406,14 @@ def _estimate_random_label_difference(data):
stacked_data["grouping_variable"] = grouping

else:
stacked_data["grouping_variable"] = np.random.permutation(
stacked_data["grouping_variable"].values
)
shuffled_indices = np.random.permutation(len(stacked_data))
if groups:
stacked_data[groups] = stacked_data.iloc[shuffled_indices][groups].values

else:
# these two cols need to be permuted together to maintain the relationship between group and total population counts
stacked_data[['group', 'total']] = stacked_data.iloc[shuffled_indices][['group','total']].values


stacked_data_1 = stacked_data[stacked_data["grouping_variable"] == "Group_1"]
stacked_data_2 = stacked_data[stacked_data["grouping_variable"] == "Group_2"]
Expand Down
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