def weight_matrix_by_user_feature_counts(dataMatrix: sps.csr_matrix,
UCM: sps.csr_matrix,
strategy="linear"):
"""
Assumes that rows of dataMatrix are users and it returns the weighted dataMatrix based on user feature counts of
UCM
:param dataMatrix:
:param UCM:
:param strategy: strategy to use in order to put weights
:return:
"""
if UCM.shape[0] != dataMatrix.shape[0]:
raise ValueError("UCM does not contain all users in dataMatrix")
UCM_popularity = (UCM > 0).sum(axis=0)
UCM_popularity = np.array(UCM_popularity).squeeze()
user_list = UCM.tocoo().row
feature_list = UCM.tocoo().col
user_feature_list = np.full(shape=dataMatrix.shape[0], fill_value=1)
user_feature_list[user_list] = UCM_popularity[feature_list]
users = dataMatrix.tocoo().row
feature_list_for_user = np.array(user_feature_list[users],
dtype=np.float32)
return _weight_matrix(dataMatrix, feature_list_for_user, strategy)
def sparse_adj_to_edge(adj_matrix: sp.csr_matrix):
"""Convert a Scipy sparse matrix to (edge_index, edge_weight) representation"""
adj_matrix = adj_matrix.tocoo(copy=False)
edge_index = np.asarray((adj_matrix.row, adj_matrix.col))
edge_weight = adj_matrix.data.copy()
return edge_index, edge_weight
def _save(
filepath: Union[str, Path],
matrix: csr_matrix,
cell_ids: pd.DataFrame,
features: pd.DataFrame,
save_pickle: bool = False,
save_rds: bool = False,
save_h5ad: bool = False,
save_loom: bool = False,
meta: Optional[pd.DataFrame] = None,
):
filepath = Path(filepath)
if save_pickle:
build_counts_store(matrix.tocoo(),
cell_ids,
features,
save_path=filepath)
if save_rds:
Convert.pickle_to_rds_dir(filepath.parent)
if save_h5ad or save_loom:
cell_ids = meta if meta is not None else pd.DataFrame(
cell_ids).set_index(0)
cell_ids.index.name = "index"
features = features.rename(columns={
"ensgs": "gene_ids",
"genes": "index"
}).set_index("index")
adata = AnnData(matrix.tocsr(), cell_ids, features)
if save_h5ad:
adata.write_h5ad(filepath.parent / "rna.h5ad")
if save_loom:
adata.write_loom(filepath.parent / "rna.loom")
def convert_URM_to_FM(URM: csr_matrix):
"""
Convert positive interactions of an URM in the way that is needed for the FM model.
- In each row there are 3 interactions: 1 for the user, 1 for the item
- Only positive samples are encoded here
Note: this method works only for implicit dataset
:param URM: URM to be preprocessed
:return: csr_matrix containing the URM preprocessed in the described way
"""
n_users = URM.shape[0]
n_items = URM.shape[1]
n_sample = len(URM.data)
FM_matrix = sps.coo_matrix((n_sample, n_users + n_items))
# Setting rows
FM_matrix.row = np.repeat(np.arange(n_sample), 2) # one row has two ones
# Setting cols
row = np.reshape(URM.tocoo().row, newshape=(n_sample, 1))
col = np.reshape(URM.tocoo().col + n_users, newshape=(n_sample, 1))
row_col = np.concatenate([row, col], axis=1)
unrolled_row_col = np.reshape(row_col, newshape=len(FM_matrix.row))
FM_matrix.col = unrolled_row_col
# Setting data
FM_matrix.data = np.ones(len(FM_matrix.row), dtype=np.float32)
return FM_matrix.tocsr()
def sparse2dict(matrix: csr_matrix):
""" csr_matrix을 dictionary format으로 변환
"""
coo = matrix.tocoo()
return dict(data=coo.data.tolist(),
row=coo.row.tolist(),
col=coo.col.tolist(),
shape=coo.shape)
def calculate_normalized_affinity(
W: csr_matrix
) -> Tuple[csr_matrix, np.array, np.array]:
diag = W.sum(axis=1).A1
diag_half = np.sqrt(diag)
W_norm = W.tocoo(copy=True)
W_norm.data /= diag_half[W_norm.row]
W_norm.data /= diag_half[W_norm.col]
W_norm = W_norm.tocsr()
return W_norm, diag, diag_half
def fit(self, graph: sp.csr_matrix):
"""
Fitting a NodeSketch model.
"""
self._graph = graph
self._num_nodes = graph.shape[0]
self._hash_values = self._generate_hash_values()
self._sla = graph.tocoo()
self._sla.data = np.array([1 for _ in range(len(self._sla.data))])
self._sla_original = self._sla.copy()
self._do_single_sketch()
for _ in range(self.iterations - 1):
self._augment_sla()
self._do_single_sketch()
def _get_log_distances(self,
y_distances: csr_matrix,
base=0.5) -> csr_matrix:
"""
Returns the logarithmic version (base default: 0.5) of the distance matrix returned by TagEmebddingClassifier.
This must be used in order to compute valid precision@k scores
since small Distances should be ranked better than great ones.
:param y_distances: sparse distance matrix (multilabel matrix with distances instead of binary indicators)
:param base: base of the log function (must be smaller then one)
:return: sparse matrix with the log values
"""
log_y_distances = y_distances.tocoo()
log_y_distances.data = np.log(log_y_distances.data) / np.log(base)
return log_y_distances.tocsr()
def csr2tensor(self, matrix: sp.csr_matrix):
r"""Convert csr_matrix to tensor.
Args:
matrix (scipy.csr_matrix): Sparse matrix to be converted.
Returns:
torch.sparse.FloatTensor: Transformed sparse matrix.
"""
matrix = matrix.tocoo()
x = torch.sparse.FloatTensor(
torch.LongTensor(np.array([matrix.row, matrix.col])),
torch.FloatTensor(matrix.data.astype(np.float32)),
matrix.shape).to(self.device)
return x
def dropcols_coo(csr_mat: sp.csr_matrix, idx_to_drop):
"""
Drop columns of sparse matrix
http://stackoverflow.com/questions/23966923/delete-columns-of-matrix-of-csr-format-in-python
"""
idx_to_drop = np.unique(idx_to_drop)
coo_mat = csr_mat.tocoo()
keep = ~np.in1d(coo_mat.col, idx_to_drop)
coo_mat.data = coo_mat.data[keep]
coo_mat.row = coo_mat.row[keep]
coo_mat.col = coo_mat.col[keep]
# decrement column indices
coo_mat.col -= idx_to_drop.searchsorted(coo_mat.col)
coo_mat._shape = (coo_mat.shape[0], coo_mat.shape[1] - len(idx_to_drop))
return coo_mat.tocsr()
def weight_matrix_by_user_profile(dataMatrix: sps.csr_matrix,
URM,
strategy="linear"):
"""
:param dataMatrix:
:param URM:
:param strategy: strategy to use in order to put weights
:return:
"""
if URM.shape[0] != dataMatrix.shape[0]:
raise ValueError("URM does not contain all users in dataMatrix")
user_activity = (URM > 0).sum(axis=1)
user_activity = np.array(user_activity).squeeze()
users = dataMatrix.tocoo().row
user_profile_for_user = np.array(user_activity[users], dtype=np.float32)
return _weight_matrix(dataMatrix, user_profile_for_user, strategy)
def weight_matrix_by_item_feature_value(dataMatrix: sps.csr_matrix,
ICM: sps.csr_matrix,
strategy="linear"):
"""
Assumes that rows of dataMatrix are items and it returns the weighted dataMatrix based on item feature value
of ICM
:param dataMatrix:
:param ICM: ICM with only one columns
:param strategy:
:return:
"""
if ICM.shape[0] != dataMatrix.shape[0]:
raise ValueError("ICM does not contain all items in dataMatrix")
item_list = dataMatrix.tocoo().row
item_feature_weights = np.array(ICM[item_list].todense()).squeeze()
mean_value = item_feature_weights.mean()
item_feature_weights[item_feature_weights == 0] = mean_value
return _weight_matrix(dataMatrix, item_feature_weights, strategy)
def weight_matrix_by_item_popularity(dataMatrix: sps.csr_matrix,
R_iu: sps.csr_matrix,
strategy="linear"):
"""
Assumes that dataMatrix has items as row
:param dataMatrix: csr matrix with items as rows
:param R_iu: Rating item x user matrix
:param strategy: strategy to use in order to put weights
:return:
"""
if R_iu.shape[0] != dataMatrix.shape[0]:
raise ValueError("R_iu does not contain all items in dataMatrix")
item_popularity = (R_iu > 0).sum(axis=1)
item_popularity = np.array(item_popularity).squeeze()
items = dataMatrix.tocoo().row
item_popularity_for_item = np.array(item_popularity[items],
dtype=np.float32)
return _weight_matrix(dataMatrix, item_popularity_for_item, strategy)
def _reweight_values(self,
doc_term_matrix: sp.csr_matrix) -> sp.csr_matrix:
"""
Re-weight values in a doc-term matrix according to parameters specified
in :class:`Vectorizer` initialization: binary or tf-idf weighting,
sublinear term-frequency, document-normalized weights.
Args:
doc_term_matrix
Returns:
Reweighted doc-term matrix.
"""
# re-weight the local components (term freqs)
if self.tf_type == "binary":
doc_term_matrix.data.fill(1)
elif self.tf_type == "bm25":
if not self.dl_type:
doc_term_matrix.data = (doc_term_matrix.data *
(BM25_K1 + 1.0) /
(BM25_K1 + doc_term_matrix.data))
else:
dls = get_doc_lengths(doc_term_matrix, type_=self.dl_type)
length_norm = (1 - BM25_B) + (BM25_B *
(dls / self._avg_doc_length))
doc_term_matrix = doc_term_matrix.tocoo(copy=False)
doc_term_matrix.data = (
doc_term_matrix.data * (BM25_K1 + 1.0) /
(doc_term_matrix.data +
(BM25_K1 * length_norm[doc_term_matrix.row])))
doc_term_matrix = doc_term_matrix.tocsr(copy=False)
elif self.tf_type == "sqrt":
_ = np.sqrt(doc_term_matrix.data,
doc_term_matrix.data,
casting="unsafe")
elif self.tf_type == "log":
_ = np.log(doc_term_matrix.data,
doc_term_matrix.data,
casting="unsafe")
doc_term_matrix.data += 1.0
elif self.tf_type == "linear":
pass # tfs are already linear
else:
# this should never raise, i'm just being a worrywart
raise ValueError(
errors.value_invalid_msg(
"tf_type", self.tf_type,
{"binary", "bm25", "sqrt", "log", "linear"}))
# apply the global component (idfs), column-wise
if self.idf_type:
doc_term_matrix = doc_term_matrix * self._idf_diag
# apply normalizations, row-wise
# unless we've already handled it for bm25-style tf
if self.dl_type and self.tf_type != "bm25":
n_docs, _ = doc_term_matrix.shape
dls = get_doc_lengths(doc_term_matrix, type_=self.dl_type)
dl_diag = sp.spdiags(1.0 / dls,
diags=0,
m=n_docs,
n=n_docs,
format="csr")
doc_term_matrix = dl_diag * doc_term_matrix
if self.norm is not None:
doc_term_matrix = normalize_mat(doc_term_matrix,
norm=self.norm,
axis=1,
copy=False)
return doc_term_matrix
def augment_adj(adj_matrix: sp.csr_matrix, nodes: Union[list, int, np.ndarray],
edge_weight: np.ndarray = None, *,
nbrs_to_link: Union[list, np.ndarray, None] = None,
common_nbrs: Union[list, np.ndarray, None] = None,
fill_weight: float = 1.0) -> sp.csr_matrix:
"""Augment a specified adjacency matrix by linking nodes to
each element in `nbrs_to_link`.
Examples
----------
# add 2 nodes adjacent to [2,3] and 3, respectively.
>>> augmented_adj = augment_adj(adj_matrix, nodes=2,
nbrs_to_link=[[2,3],3],
fill_weight=1.0)
# add 2 nodes all adjacent to [1,2,3].
>>> augmented_adj = augment_adj(adj_matrix, nodes=2,
common_nbrs=[1,2,3],
fill_weight=1.0)
# add 3 edges, [3,1], [4,2], [5,3].
>>> augmented_adj = augment_adj(adj_matrix, nodes=[3,4,5],
common_nbrs=[1,2,3],
fill_weight=1.0)
Parameters
----------
adj_matrix: shape [num_nodes, num_nodes].
A Scipy sparse adjacency matrix.
nodes: the nodes that will be linked to the graph.
list or np.array: the nodes connected to `nbrs_to_link`
int: new added nodes connected to `nbrs_to_link`,
node ids [num_nodes, ..., num_nodes+nodes-1].
nbrs_to_link: a list of N elements,
where N is the length of 'nodes'.
the specified neighbor(s) for each added node.
if `None`, it will be set to `[0, ..., N-1]`.
common_nbrs: shape [None,].
specified common neighbors for each added node.
fill_weight: edge weight for the augmented edges.
NOTE:
----------
Both `nbrs_to_link` and `common_nbrs` should not be specified together.
See Also
----------
graphgallery.functional.augment_edge
"""
adj_matrix = adj_matrix.tocoo(copy=False)
edge_index = adj_matrix.row, adj_matrix.col
augmented_edge_index, augmented_edge_weight = augment_edge(edge_index, nodes,
edge_weight=adj_matrix.data,
nbrs_to_link=nbrs_to_link,
common_nbrs=common_nbrs,
fill_weight=fill_weight)
N = augmented_edge_index.max() + 1
augmented_adj = sp.csr_matrix((augmented_edge_weight, augmented_edge_index),
shape=(N, N))
augmented_adj.eliminate_zeros()
return augmented_adj