Examples for case 0a

Author: jnothman

slep006/cases_opt0a.py

@@ -1,6 +1,98 @@
+import numpy as np
+
 from defs import (accuracy, group_cv, make_scorer, SelectKBest,
                   LogisticRegressionCV, cross_validate,
                   make_pipeline, X, y, my_groups, my_weights,
                   my_other_weights)
 
-# TODO
+# %%
+# Case A: weighted scoring and fitting
+
+
+GROUPS_IDX = -1
+WEIGHT_IDX = -2
+
+
+def unwrap_X(X):
+    return X[:, -2:]
+
+
+class WrappedGroupCV:
+    def __init__(self, base_cv, groups_idx=GROUPS_IDX):
+        self.base_cv = base_cv
+        self.groups_idx = groups_idx
+
+    def split(self, X, y, groups=None):
+        groups = X[:, self.groups_idx]
+        return self.base_cv.split(unwrap_X(X), y, groups=groups)
+
+    def get_n_splits(self, X, y, groups=None):
+        groups = X[:, self.groups_idx]
+        return self.base_cv.split(unwrap_X(X), y, groups=groups)
+
+
+wrapped_group_cv = WrappedGroupCV(group_cv)
+
+
+class WrappedLogisticRegressionCV(LogisticRegressionCV):
+    def fit(self, X, y):
+        return super().fit(unwrap_X(X), y, sample_weight=X[:, WEIGHT_IDX])
+
+
+weighted_acc = make_scorer(accuracy, request_props=['sample_weight'])
+
+
+def wrapped_weighted_acc(est, X, y, sample_weight=None):
+    return weighted_acc(est, unwrap_X(X), y, sample_weight=X[:, WEIGHT_IDX])
+
+
+lr = WrappedLogisticRegressionCV(
+    cv=wrapped_group_cv,
+    scoring=wrapped_weighted_acc,
+).set_props_request(['sample_weight'])
+cross_validate(lr, np.hstack([X, my_weights, my_groups]), y,
+               cv=wrapped_group_cv,
+               scoring=wrapped_weighted_acc)
+
+# %%
+# Case B: weighted scoring and unweighted fitting
+
+class UnweightedWrappedLogisticRegressionCV(LogisticRegressionCV):
+    def fit(self, X, y):
+        return super().fit(unwrap_X(X), y)
+
+
+lr = UnweightedWrappedLogisticRegressionCV(
+    cv=wrapped_group_cv,
+    scoring=wrapped_weighted_acc,
+).set_props_request(['sample_weight'])
+cross_validate(lr, np.hstack([X, my_weights, my_groups]), y,
+               cv=wrapped_group_cv,
+               scoring=wrapped_weighted_acc)
+
+
+# %%
+# Case C: unweighted feature selection
+
+class UnweightedWrappedSelectKBest(SelectKBest):
+    def fit(self, X, y):
+        return super().fit(unwrap_X(X), y)
+
+
+lr = WrappedLogisticRegressionCV(
+    cv=wrapped_group_cv,
+    scoring=weighted_acc,
+).set_props_request(['sample_weight'])
+sel = UnweightedWrappedSelectKBest()
+pipe = make_pipeline(sel, lr)
+cross_validate(pipe, np.hstack([X, my_weights, my_groups]), y,
+               cv=wrapped_group_cv,
+               scoring=wrapped_weighted_acc)
+
+# %%
+# Case D: different scoring and fitting weights
+
+SCORING_WEIGHT_IDX = -3
+
+# TODO: proceed from here. Note that this change implies the need to add
+# a parameter to unwrap_X, since we will now append an additional column to X.