def recall(X_true: csr_matrix, X_top_k: np.array, R=100) -> np.array:
selected = np.take_along_axis(X_true, X_top_k[:, :R], axis=-1)
hit = selected.sum(axis=-1)
maxhit = np.minimum(X_true.getnnz(axis=1), R)
return np.squeeze(np.asarray(hit)) / maxhit
def _trim_empty(X: sparse.csr_matrix,
rows: bool = True,
cols: bool = True) -> sparse.csr_matrix:
if cols:
X = X[:, :np.max(X.indices) + 1]
if rows:
X = X[:np.where(X.getnnz(axis=1))[0][-1] + 1, :]
return X
def recall(X_true: csr_matrix, X_top_k: np.array, R=100) -> np.array:
""" Calculates recall@R for each users in X_true and X_top_k matrices
Args:
X_true: Matrix containing True values for user-item interactions
X_top_k: Matrix containing indices picked by model
R: Number of elements taken into consideration
Returns:
Numpy array containing calculated recall@R for each user
"""
selected = np.take_along_axis(X_true, X_top_k[:, :R], axis=-1)
hit = selected.sum(axis=-1)
maxhit = np.minimum(X_true.getnnz(axis=1), R)
return np.squeeze(np.asarray(hit)) / maxhit
def collect_basic_statistics(
X: csr_matrix,
cluster_labels: List[str],
cond_labels: List[str],
gene_names: List[str],
n_jobs: int,
temp_folder: str,
verbose: bool,
) -> List[pd.DataFrame]:
""" Collect basic statistics, triggering calc_basic_stat in parallel
"""
start = time.perf_counter()
sum_vec = cnt_vec = None
if cond_labels is None:
sum_vec = X.sum(axis=0).A1
cnt_vec = X.getnnz(axis=0)
result_list = Parallel(n_jobs=n_jobs, max_nbytes=1e7, temp_folder=temp_folder)(
delayed(calc_basic_stat)(
clust_id,
X.data,
X.indices,
X.indptr,
X.shape,
cluster_labels,
cond_labels,
gene_names,
sum_vec,
cnt_vec,
verbose,
)
for clust_id in cluster_labels.categories
)
end = time.perf_counter()
if verbose:
logger.info(
"Collecting basic statistics is done. Time spent = {:.2f}s.".format(
end - start
)
)
return result_list
def fisher_test(
X: csr_matrix,
cluster_labels: List[str],
cond_labels: List[str],
gene_names: List[str],
n_jobs: int,
temp_folder: str,
verbose: bool,
) -> List[pd.DataFrame]:
""" Run Fisher's exact test, triggering calc_fisher in parallel
"""
start = time.time()
cnt_vec = None
if cond_labels is None:
cnt_vec = X.getnnz(axis=0)
result_list = Parallel(n_jobs=n_jobs,
max_nbytes=1e7,
temp_folder=temp_folder)(delayed(calc_fisher)(
clust_id,
X.data,
X.indices,
X.indptr,
X.shape,
cluster_labels,
cond_labels,
gene_names,
cnt_vec,
verbose,
) for clust_id in cluster_labels.categories)
end = time.time()
if verbose:
logger.info(
"Fisher's exact test is done. Time spent = {:.2f}s.".format(end -
start))
return result_list
def ndcg(X_true: csr_matrix, X_top_k: np.array, R=100) -> np.array:
""" Calculate ndcg@R for each users in X_true and X_pred matrices
Args:
X_true: Matrix containing True values for user-item interactions
X_top_k: Matrix containing inidices picked by model
R: Number of elements taken into consideration
Returns:
Numpy array containing calculated ndcg@R for each user
"""
penalties = 1. / np.log2(np.arange(2, R + 2))
selected = np.take_along_axis(X_true, X_top_k[:, :R], axis=-1)
DCG = selected * penalties
cpenalties = np.empty(R + 1)
np.cumsum(penalties, out=cpenalties[1:])
cpenalties[0] = 0
maxhit = np.minimum(X_true.getnnz(axis=1), R)
IDCG = cpenalties[maxhit]
return DCG / IDCG
def _mutual_proximity_empiric_sparse(S: csr_matrix,
test_set_ind: np.ndarray = None,
min_nnz=0,
verbose: int = 0,
log=None,
n_jobs=None):
"""MP empiric for sparse similarity matrices.
Please do not directly use this function, but invoke via
mutual_proximity_empiric()
"""
if verbose and log:
log.message("Starting MP empiric for sparse matrices.")
self_value = 1. # similarity matrix
n = S.shape[0]
if not n_jobs:
n_jobs = 1
elif n_jobs == -1:
n_jobs = cpu_count()
else:
pass
# This will become S_mp.data
shared_data = Array(ctypes.c_double, S.data.size)
shared_data_np = np.ctypeslib.as_array(shared_data.get_obj())
if verbose and log:
log.message("Spawning processes and starting MP computation.")
with Pool(processes=n_jobs,
initializer=_mpes_init,
initargs=(S, shared_data)) as pool:
S_nonzero = filterfalse(lambda ij: ij[0] > ij[1], zip(*S.nonzero()))
for _ in pool.imap(func=partial(_mpes_sec_dist,
args=(verbose, log, n, min_nnz)),
iterable=S_nonzero,
chunksize=int(1e5)):
pass # output stored by function in shared array
pool.join()
if verbose and log:
log.message("Assemble upper-triangular MP matrix.")
S_mp = csr_matrix((shared_data_np, S.indices, S.indptr),
shape=S.shape,
copy=False).tolil()
del shared_data, shared_data_np
if verbose and log:
log.message("Symmetrizing matrix.")
S_mp += S_mp.T
# Retain original distances for objects with too few neighbors.
# That is, keep distances FROM these objects to others (rows), but
# set distances of other objects TO them to NaN (columns).
# Returned matrix is thus NOT SYMMETRIC.
if verbose and log:
log.message(("Retain original similarities for objects with too few "
"neighbors. If there are any, the output matrix will "
"not be symmetric anymore! (Rows corresponding to these "
"objects will be in original space; corresponding "
"columns will contain NaN)."))
for row in np.argwhere(S.getnnz(axis=1) <= min_nnz):
row = row[0] # use scalar for indexing instead of array
S_mp[row, :] = S.getrow(row)
if verbose and log:
log.message("Setting self similarities.")
for i in range(n):
S_mp[i, i] = self_value #need to set self values
if verbose and log:
log.message("Converting to CSR matrix and returning.")
return S_mp.tocsr()