docstring fixes

Author: cosmic-cortex

modAL/disagreement.py

@@ -149,10 +149,10 @@ def vote_entropy_sampling(committee, X, n_instances=1, **disagreement_measure_kw
     Returns
     -------
     query_idx: numpy.ndarray of shape (n_instances, )
-        The indices of the instances from X_pool chosen to be labelled.
+        The indices of the instances from X chosen to be labelled.
 
     X[query_idx]: numpy.ndarray of shape (n_instances, n_features)
-        The instances from X_pool chosen to be labelled.
+        The instances from X chosen to be labelled.
     """
     disagreement = vote_entropy(committee, X, **disagreement_measure_kwargs)
     query_idx = multi_argmax(disagreement, n_instances=n_instances)
@@ -181,10 +181,10 @@ def consensus_entropy_sampling(committee, X, n_instances=1, **disagreement_measu
     Returns
     -------
     query_idx: numpy.ndarray of shape (n_instances, )
-        The indices of the instances from X_pool chosen to be labelled.
+        The indices of the instances from X chosen to be labelled.
 
     X[query_idx]: numpy.ndarray of shape (n_instances, n_features)
-        The instances from X_pool chosen to be labelled.
+        The instances from X chosen to be labelled.
     """
     disagreement = consensus_entropy(committee, X, **disagreement_measure_kwargs)
     query_idx = multi_argmax(disagreement, n_instances=n_instances)
@@ -213,10 +213,10 @@ def max_disagreement_sampling(committee, X, n_instances=1, **disagreement_measur
     Returns
     -------
     query_idx: numpy.ndarray of shape (n_instances, )
-        The indices of the instances from X_pool chosen to be labelled.
+        The indices of the instances from X chosen to be labelled.
 
     X[query_idx]: numpy.ndarray of shape (n_instances, n_features)
-        The instances from X_pool chosen to be labelled.
+        The instances from X chosen to be labelled.
     """
     disagreement = KL_max_disagreement(committee, X, **disagreement_measure_kwargs)
     query_idx = multi_argmax(disagreement, n_instances=n_instances)
@@ -242,10 +242,10 @@ def max_std_sampling(regressor, X, n_instances=1, **predict_kwargs):
     Returns
     -------
     query_idx: numpy.ndarray of shape (n_instances, )
-        The indices of the instances from X_pool chosen to be labelled.
+        The indices of the instances from X chosen to be labelled.
 
     X[query_idx]: numpy.ndarray of shape (n_instances, n_features)
-        The instances from X_pool chosen to be labelled.
+        The instances from X chosen to be labelled.
     """
     _, std = regressor.predict(X, return_std=True, **predict_kwargs)
     std = std.reshape(len(X), )