Stop mapping of query cells to child views if no cells are mapped in the parent view

[emdann]

Now it throws a big error:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In [20], line 5
      3     organ_obs = adata_query.obs['organ'].copy()
      4     adata_query.obs.drop('organ', 1, inplace=True)
----> 5 mvatlas_mapped = mva.tl.add_query(mvatlas, adata_query)

File /nfs/team205/ed6/bin/multi-view-atlas/src/multi_view_atlas/tl/map_query.py:78, in add_query(mvatlas, adata_query)
     74     if depth > 0:
     75         adata_query_concat = adata_query_concat[
     76             new_view_assign.loc[adata_query_concat.obs_names, current_view] == 1
     77         ].copy()
---> 78     v_assign, next_view_adata = map_next_view(
     79         mvatlas, adata_query_concat, current_view=current_view, next_view=next_view
     80     )
     81     vdata_dict[next_view] = next_view_adata
     82 new_view_assign = pd.concat([new_view_assign, v_assign], 1)

File /nfs/team205/ed6/bin/multi-view-atlas/src/multi_view_atlas/tl/map_query.py:192, in map_next_view(mvatlas, adata_query, current_view, next_view, batch_key, batch_categories)
    189 # Get assignment to next view
    190 Y_assign_atlas = v_assign.loc[curr_view_adata.obs_names].values.ravel()
--> 192 v_assign_query = assign_from_similarity(Y_assign_atlas, X_similarity_atlas, X_similarity_query, v_assign)
    193 v_assign_query.index = adata_query.obs_names
    195 v_assign = pd.concat([v_assign, v_assign_query], axis=0)

File /nfs/team205/ed6/bin/multi-view-atlas/src/multi_view_atlas/tl/map_query.py:127, in assign_from_similarity(Y_assign_atlas, X_similarity_atlas, X_similarity_query, v_assign, k)
    124     raise ValueError("v_assign must have only one column")
    126 clf = KNeighborsClassifier(n_neighbors=k, metric="euclidean").fit(X_similarity_atlas, Y_assign_atlas)
--> 127 Y_assign_query = clf.predict(X_similarity_query)
    128 assign_predict = pd.DataFrame(Y_assign_query, columns=v_assign.columns)
    129 return assign_predict

File ~/my-conda-envs/patho-signatures-2/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:226, in KNeighborsClassifier.predict(self, X)
    210 """Predict the class labels for the provided data.
    211 
    212 Parameters
   (...)
    221     Class labels for each data sample.
    222 """
    223 if self.weights == "uniform":
    224     # In that case, we do not need the distances to perform
    225     # the weighting so we do not compute them.
--> 226     neigh_ind = self.kneighbors(X, return_distance=False)
    227     neigh_dist = None
    228 else:

File ~/my-conda-envs/patho-signatures-2/lib/python3.9/site-packages/sklearn/neighbors/_base.py:745, in KNeighborsMixin.kneighbors(self, X, n_neighbors, return_distance)
    743         X = _check_precomputed(X)
    744     else:
--> 745         X = self._validate_data(X, accept_sparse="csr", reset=False, order="C")
    747 n_samples_fit = self.n_samples_fit_
    748 if n_neighbors > n_samples_fit:

File ~/my-conda-envs/patho-signatures-2/lib/python3.9/site-packages/sklearn/base.py:577, in BaseEstimator._validate_data(self, X, y, reset, validate_separately, **check_params)
    575     raise ValueError("Validation should be done on X, y or both.")
    576 elif not no_val_X and no_val_y:
--> 577     X = check_array(X, input_name="X", **check_params)
    578     out = X
    579 elif no_val_X and not no_val_y:

File ~/my-conda-envs/patho-signatures-2/lib/python3.9/site-packages/sklearn/utils/validation.py:909, in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)
    907     n_samples = _num_samples(array)
    908     if n_samples < ensure_min_samples:
--> 909         raise ValueError(
    910             "Found array with %d sample(s) (shape=%s) while a"
    911             " minimum of %d is required%s."
    912             % (n_samples, array.shape, ensure_min_samples, context)
    913         )
    915 if ensure_min_features > 0 and array.ndim == 2:
    916     n_features = array.shape[1]

ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required by KNeighborsClassifier.