def test_write_single_time(self):
fpath = _get_tempfile()
array = np.arange(0, 100, dtype='float32').reshape(-1, 5)
with MmapArrayWriter(path=fpath,
shape=array.shape,
dtype=array.dtype,
remove_exist=True) as f:
f.write(array)
x = MmapArray(fpath)
self.assertTrue(np.all(array == x))
with MmapArrayWriter(path=fpath, remove_exist=False) as f:
f.write(array)
x = MmapArray(fpath)
self.assertTrue(np.all(np.concatenate([array, array], axis=0) == x))
def test_write_multiple_time(self):
fpath = _get_tempfile()
array = np.arange(0, 1000, dtype='float32').reshape(-1, 2, 5)
with MmapArrayWriter(path=fpath,
shape=(0, ) + array.shape[1:],
dtype=array.dtype,
remove_exist=True) as f:
for i in range(0, array.shape[0], 8):
f.write(array[i:i + 8])
x = MmapArray(fpath)
self.assertTrue(np.all(array == x))
array1 = np.arange(0, 100, dtype='float32').reshape(-1, 2, 5)
array[10:10 + array1.shape[0]] = array1
with MmapArrayWriter(path=fpath, remove_exist=False) as f:
f.write(array1, start_position=10)
x = MmapArray(fpath)
self.assertTrue(np.all(array == x))
def __init__(self,
path='~/tensorflow_datasets/3dshapes.h5',
cache_dir=None,
seed=8):
path = os.path.abspath(os.path.expanduser(path))
assert os.path.exists(path), "Path to file %s must exists" % path
self.path = path
if cache_dir is None:
cache_dir = os.path.dirname(path)
if not os.path.exists(cache_dir):
os.mkdir(cache_dir)
image_path = os.path.join(cache_dir, '3dshapes.images')
label_path = os.path.join(cache_dir, '3dshapes.labels')
# ====== read the dataset and cache it again ====== #
if not os.path.exists(image_path) or not os.path.exists(label_path):
import h5py
with h5py.File(path, 'r') as dataset:
images = dataset['images']
labels = dataset['labels']
with MmapArrayWriter(image_path,
shape=images.shape,
dtype=images.dtype,
remove_exist=True) as img, \
MmapArrayWriter(label_path,
shape=labels.shape,
dtype=labels.dtype,
remove_exist=True) as lab:
for start, end in tqdm(list(
batching(8000, n=images.shape[0])),
desc="Caching data"):
img.write(images[start:end])
lab.write(labels[start:end])
# ====== load the data ====== #
self.images = MmapArray(image_path)
self.factors = MmapArray(label_path)
# ====== split the dataset ====== #
rand = np.random.RandomState(seed=seed)
n = len(self.images)
ids = rand.permutation(n)
# train:85% valid:5% test:10%
self.train_indices = ids[:int(0.85 * n)]
self.valid_indices = ids[int(0.85 * n):int(0.9 * n)]
self.test_indices = ids[int(0.9 * n):]
def test_write_multiprocessing(self):
fpath = _get_tempfile()
jobs = [(i, np.random.rand(12, 25, 8), fpath, (300, 25, 8))
for i in range(25)]
with Pool(2) as pool:
pool.map(_fn_write, jobs)
# checking the output
array = np.concatenate([x[1] for x in jobs], axis=0)
x = MmapArray(fpath)
self.assertTrue(np.all(array == x))
def test_read_multiprocessing(self):
fpath = _get_tempfile()
array = np.random.rand(1200, 25, 8)
# first write the array
with MmapArrayWriter(fpath, (None, 25, 8), array.dtype) as f:
f.write(array)
x = MmapArray(fpath)
self.assertTrue(np.all(array == x))
# use multiprocessing to randomly read the array
jobs = [(x,
sorted(
np.random.randint(0,
array.shape[0],
size=(2, ),
dtype='int32'))) for i in range(25)]
with Pool(2) as pool:
for start, end, data in pool.map(_fn_read, jobs):
data = zlib.decompress(data)
data = np.frombuffer(data).reshape(-1, 25, 8)
self.assertTrue(np.all(data == array[start:end]))
# ====== reading ====== #
print()
start = timeit.default_timer()
with open(numpy_path, 'rb') as f:
y = np.load(f)
numpy_open_time = timeit.default_timer() - start
print('Load Numpy array:', numpy_open_time, 's')
start = timeit.default_timer()
hdf5 = h5py.File(hdf5_path, 'r')
h5py_open_time = timeit.default_timer() - start
print('Load HDF5 data :', h5py_open_time, 's')
start = timeit.default_timer()
mmap = MmapArray(mmap_path)
mmap_open_time = timeit.default_timer() - start
print('Load Memmap data:', mmap_open_time, 's')
print()
print('Test correctness of stored data')
print('Numpy :', np.all(y == X))
print('HDF5 :', np.all(hdf5['X'][:] == X))
print('Memmap:', np.all(mmap[:] == X))
# ====== iterating over dataset ====== #
print()
start = timeit.default_timer()
for epoch in range(0, 3):
for i in range(0, N, 256):
x = X[i:i + 256]
def transform(self,
X,
indices=None,
sad=None,
save_ivecs=False,
keep_stats=False,
name=None):
"""
Parameters
----------
X : ndarray
Training data [n_samples, n_features]
indices : {Mapping, tuple, list}
in case the data is given by a list of files, `indices`
act as file indicator mapping from
'file_name' -> (start_index_in_X, end_index_in_X)
This mapping can be provided by a dictionary, or list of
tuple.
sad : ndarray
inspired by the "Speech Activity Detection" (SAD) indexing,
this array is indicator of which samples will be taken into
training; the shape should be [n_samples,] or [n_samples, 1]
save_ivecs : bool
if True, save extracted i-vectors to disk at path `ivec_[name]`
if False, return directly the i-vectors without saving
keep_stats : bool
if True, keep the zero and first order statistics.
The first order statistics could consume huge amount
of disk space. Otherwise, they are deleted after training
name : {None, str}
identity of the i-vectors (for re-using in future).
If None, a random name is used
"""
if not self.is_fitted:
raise ValueError(
"Ivector has not been fitted, call Ivector.fit(...) first")
n_files = X.shape[0] if indices is None else len(indices)
if name is None:
name = uuid(length=8)
else:
name = str(name)
# ====== init ====== #
z_path = self.get_z_path(name)
f_path = self.get_f_path(name)
if save_ivecs:
i_path = self.get_i_path(name)
else:
i_path = None
name_path = self.get_name_path(name)
# ====== check exist i-vector file ====== #
if i_path is not None and os.path.exists(i_path):
ivec = MmapArray(path=i_path)
assert ivec.shape[0] == n_files and ivec.shape[1] == self.tv_dim,\
"Need i-vectors for %d files, found exists data at path:'%s' with shape:%s" % \
(n_files, i_path, ivec.shape)
return ivec
# ====== extract Z and F ====== #
if os.path.exists(z_path) and os.path.exists(f_path):
pass
else:
if os.path.exists(z_path):
os.remove(z_path)
if os.path.exists(f_path):
os.remove(f_path)
if os.path.exists(name_path):
os.remove(name_path)
_extract_zero_and_first_stats(X=X,
sad=sad,
indices=indices,
gmm=self.gmm,
z_path=z_path,
f_path=f_path,
name_path=name_path)
Z = MmapArray(path=z_path)
F = MmapArray(path=f_path)
# ====== extract I-vec ====== #
ivec = self.tmat.transform_to_disk(path=i_path,
Z=Z,
F=F,
dtype='float32')
# ====== clean ====== #
Z.close()
F.close()
if not keep_stats:
if os.path.exists(z_path):
os.remove(z_path)
if os.path.exists(f_path):
os.remove(f_path)
else:
print("Zero-order stats saved at:", ctext(z_path, 'cyan'))
print("First-order stats saved at:", ctext(f_path, 'cyan'))
return ivec
def fit(self,
X,
indices=None,
sad=None,
refit_gmm=False,
refit_tmat=False,
extract_ivecs=False,
keep_stats=False):
"""
Parameters
----------
X : ndarray
Training data [n_samples, n_features]
indices : {Mapping, tuple, list}
in case the data is given by a list of files, `indices`
act as file indicator mapping from
'file_name' -> (start_index_in_X, end_index_in_X)
This mapping can be provided by a dictionary, or list of
tuple.
Note: the order provided in indices will be preserved
sad : ndarray
inspired by the "Speech Activity Detection" (SAD) indexing,
this array is indicator of which samples will be taken into
training; the shape should be [n_samples,] or [n_samples, 1]
refit_gmm : bool
if True, re-fit the GMM even though it is fitted,
consequently, the T-matrix will be re-fitted
refit_tmat : bool
if True, re-fit the T-matrix even though it is fitted
extract_ivecs : bool
if True, extract the i-vector for training data
keep_stats : bool
if True, keep the zero and first order statistics.
The first order statistics could consume huge amount
of disk space. Otherwise, they are deleted after training
"""
new_gmm = (not self.gmm.is_fitted or refit_gmm)
# ====== clean error files ====== #
if os.path.exists(self.z_path):
Z = MmapArray(self.z_path)
if Z.shape[0] == 0: # empty file
os.remove(self.z_path)
Z.close()
if os.path.exists(self.f_path):
F = MmapArray(self.f_path)
if F.shape[0] == 0: # empty file
os.remove(self.f_path)
F.close()
if os.path.exists(self.ivec_path):
ivec = MmapArray(self.ivec_path)
if ivec.shape[0] == 0: # empty file
os.remove(self.ivec_path)
ivec.close()
# ====== Training the GMM first ====== #
if new_gmm:
input_data = [X]
if sad is not None:
input_data.append(sad)
if indices is not None:
input_data.append(indices)
self.gmm.fit(input_data)
# ====== some fun, and confusing logics ====== #
# GMM need to be fitted before creating T-matrix model
new_tmat = (not self.tmat.is_fitted or new_gmm or refit_tmat)
# New I-vector is need when:
# - only when `extract_ivecs=True`
# - and new T-matrix is trained but no I-vector is extracted
new_ivec = extract_ivecs and \
(new_tmat or not os.path.exists(self.ivec_path))
# new stats is only needed when
# - GMM is updated
# - training new Tmatrix and the Z and F not exist
# - extracting new I-vector and the Z and F not exist
if not new_gmm and \
(os.path.exists(self.z_path) and os.path.exists(self.f_path)):
new_stats = False
else:
new_stats = new_gmm or new_tmat or new_ivec
# ====== extract the statistics ====== #
if new_stats:
_extract_zero_and_first_stats(X=X,
sad=sad,
indices=indices,
gmm=self.gmm,
z_path=self.z_path,
f_path=self.f_path,
name_path=self.name_path)
# ====== Training the T-matrix and extract i-vector ====== #
if new_tmat or new_ivec:
Z = MmapArray(path=self.z_path)
F = MmapArray(path=self.f_path)
if new_tmat:
self.tmat.fit((Z, F))
if new_ivec:
self.tmat.transform_to_disk(path=self.ivec_path,
Z=Z,
F=F,
dtype='float32',
device='gpu',
override=True)
Z.close()
F.close()
# ====== clean ====== #
if not keep_stats:
if os.path.exists(self.z_path):
os.remove(self.z_path)
if os.path.exists(self.f_path):
os.remove(self.f_path)
return self
def _parse_data_descriptor(path, read_only):
""" Return mapping: name -> (dtype, shape, Data, path) """
if not os.path.isfile(path):
return None
file_ext = os.path.splitext(path)[-1].lower()
file_name = os.path.basename(path)
# ====== ignore ====== #
if os.path.basename(path) in _ignore_files:
return None
# ====== audio file ====== #
if file_ext in _audio_ext:
return [(file_name, ('audio', 'unknown', None, path))]
# ====== image file ====== #
if file_ext in _image_ext:
return [(file_name, ('image', 'unknown', None, path))]
# ====== text file .txt ====== #
if file_ext in ('.txt', ):
return [(file_name, ('txt', 'unknown', None, path))]
# ====== check if is csv file ====== #
if file_ext in ('.csv', '.tsv'):
sep = _infer_separator(path)
data = []
# read by manually open file much faster than numpy.genfromtxt
with open(path, 'r') as f:
for line in f:
line = line[:-1]
data.append(line.split(sep))
data = np.array(data, dtype=str)
return [('.'.join(file_name.split('.')[:-1]), ('csv', data.shape, data,
path))]
# ====== check if a file is Data ====== #
try:
dtype, shape = read_mmaparray_header(path)
# ensure read-only mode here,
# i.e. change only applied in memory, not saved to disk
kw = dict(mode='c') if read_only else dict()
data = MmapArray(path, **kw)
assert np.dtype(dtype) == data.dtype and shape == data.shape, \
"Metadata mismatch for MmapArray"
return [(file_name, (data.dtype, data.shape, data, path))]
except Exception: # cannot read the header of MmapArray
pass
# ====== try to load pickle file if possible ====== #
try: # try with unpickling
with open(path, 'rb') as f:
data = cPickle.load(f)
shape_info = 0
if hasattr(data, 'shape'):
shape_info = data.shape
elif hasattr(data, '__len__'):
shape_info = len(data)
return [(file_name, (str(data.dtype) if hasattr(data, 'dtype') else
type(data).__name__, shape_info, data, path))]
except cPickle.UnpicklingError:
try: # try again with numpy load
with open(path, 'rb') as f:
data = np.load(f)
return [(file_name,
(str(data.dtype)
if hasattr(data, 'dtype') else type(data).__name__,
len(data) if hasattr(data, '__len__') else 0, data,
path))]
except Exception:
pass
# ====== load memmap dict ====== #
try:
data = MmapDict(path, read_only=read_only)
return [(file_name, ('memdict', len(data), data, path))]
except Exception as e:
pass
# ====== load SQLiteDict ====== #
if '.db' in os.path.splitext(path)[1]:
try:
db = SQLiteDict(path, read_only=read_only)
name = os.path.basename(path).replace('.db', '')
return [(tab if tab != SQLiteDict._DEFAULT_TABLE else name,
('sqlite', len(db.set_table(tab)), db.as_table(tab),
path)) for tab in db.get_all_tables()]
except Exception as e:
pass
# ====== unknown datatype ====== #
return [(file_name, ('unknown', 'unknown', None, path))]
def read_dataset10x(name,
filtered_cells=True,
filtered_genes=True,
override=False,
verbose=True) -> SingleCellOMIC:
r""" Predefined procedure for download and preprocessing 10x dataset into
`SingleCellOMIC` i.e. scanpy.AnnData object
Reference:
https://artyomovlab.wustl.edu/publications/supp_materials/4Oleg/2019_sc_ATAC_seq_DT1634_Denis/sc-atacseq-explorer-Denis-121119.html
"""
### prepare the URL
name = str(name).lower().strip()
spec = 'filtered' if filtered_cells else 'raw'
flatten_datasets = [(exp, version, dsname)
for exp, i in all_datasets.items()
for version, j in i.items() for dsname in j]
found = []
for exp, version, dsname in flatten_datasets:
if name == dsname:
found.append((exp, version, dsname))
if not found:
raise ValueError(f"Cannot find data with name {name}, "
f"all available datasets are: {flatten_datasets}")
if len(found) > 1:
raise RuntimeError(
f"Found multiple datasets {found} with name='{name}'")
exp, version, name = found[0]
dataset_name = name + '_' + spec
url = group_to_url_skeleton[exp][version].format(version, name, name, spec)
### prepare the output path
filename = os.path.basename(url)
# download path
download_path = os.path.join(DOWNLOAD_DIR, exp, version)
if not os.path.exists(download_path):
os.makedirs(download_path)
# preprocessing path
preprocessed_path = os.path.join(DATA_DIR,
f'10x_{exp}_{name}_{spec}_preprocessed')
if override and os.path.exists(preprocessed_path):
if verbose:
print("Overriding path: %s" % preprocessed_path)
shutil.rmtree(preprocessed_path)
if not os.path.exists(preprocessed_path):
os.mkdir(preprocessed_path)
# ******************** preprocessed ******************** #
if len(os.listdir(preprocessed_path)) == 0:
if verbose:
print("Dataset10X:")
print(" Meta :", found)
print(" File :", filename)
print(" URL :", url)
print(" Download :", download_path)
print(" Preprocess :", preprocessed_path)
### download the tar file
path = download_file(url=url,
filename=os.path.join(download_path, filename),
override=False,
md5=_MD5.get(f"{exp}*{version}*{name}*{spec}",
None))
if not tarfile.is_tarfile(path):
raise RuntimeError("Expecting tarfile but received: %s" % path)
contents = {}
with tarfile.open(path, mode="r:gz") as f:
all_files = [(path, info.name, info.size, verbose) for info in f
if info.isfile()]
for name, data in MPI(jobs=all_files,
func=_read_tarinfo,
batch=1,
ncpu=4):
contents[name] = data
# cell barcodes
barcodes = contents['barcodes']
### cell-atac
if exp == 'cell-atac':
n_top_genes = 20000 # this is ad-hoc value
X = contents['matrix'].T.todense()
peaks = contents['peaks']
X_peaks = peaks[:, 2].astype(np.float32) - peaks[:, 1].astype(
np.float32)
X_col_name = np.array([':'.join(i) for i in peaks])
save_data = [(OMIC.atac.name, X)]
save_metadata = dict(main_omic=OMIC.atac.name,
barcodes=barcodes,
chromatin_var=X_col_name)
sco = SingleCellOMIC(X,
cell_id=barcodes,
gene_id=X_col_name,
omic=OMIC.atac,
name=name)
### cell-exp and cell-vdj
elif exp in ('cell-exp', 'cell-vdj'):
n_top_genes = 2000
# feature (Id, Name, Type(antibody or gene-expression))
X_col = contents[
'features'] if 'features' in contents else contents['genes']
# data matrix
X = contents['matrix'].T
if not isinstance(X, csr_matrix) and hasattr(X, 'tocsr'):
X = X.tocsr()
X = X.astype('float32')
assert X.shape[0] == barcodes.shape[0] and X.shape[
1] == X_col.shape[0]
# antibody and gene are provided
prot_ids = []
pmhc_ids = []
gene_ids = []
if X_col.shape[1] == 3:
for idx, (feat_id, feat_name, feat_type) in enumerate(X_col):
if feat_type == 'Antibody Capture':
if exp == "cell-vdj" and "_TotalSeqC" not in feat_name:
pmhc_ids.append(idx)
else:
prot_ids.append(idx)
elif feat_type == 'Gene Expression':
gene_ids.append(idx)
else:
raise ValueError(
f"Unknown feature type:{feat_id}-{feat_name}-{feat_type}"
)
elif X_col.shape[1] == 2:
gene_ids = slice(None, None)
else:
raise ValueError(f"No support for features matrix\n{X_col}")
# Antibody ID, Antibody Name
y = X[:, prot_ids]
y_col = X_col[prot_ids][:, 0] # the id
y_col_name = X_col[prot_ids][:, 1] # the name
# pMHC peptide
if len(pmhc_ids) > 0:
z = X[:, pmhc_ids]
z_col = X_col[pmhc_ids][:, 0] # the id
z_col_name = X_col[pmhc_ids][:, 1] # the name
# Gene ID, Gene Name
X = X[:, gene_ids].todense()
X_col_name = X_col[gene_ids][:, 1] # the name
X_col = X_col[gene_ids][:, 0] # the id
assert np.min(X) >= 0 and np.max(X) < 65000, \
f"Only support uint16 data type, given data with max={np.max(X)}"
# data and metadata
sco = SingleCellOMIC(X,
cell_id=barcodes,
gene_id=X_col_name,
omic=OMIC.transcriptomic,
name=name)
save_data = [(OMIC.transcriptomic.name, X),
(OMIC.proteomic.name, y)]
save_metadata = {
'main_omic': OMIC.transcriptomic.name,
'barcodes': barcodes,
f"{OMIC.transcriptomic.name}_var": X_col_name,
f"{OMIC.proteomic.name}_var": y_col_name
}
if len(pmhc_ids) > 0:
save_data.append((OMIC.pmhc.name, z))
save_metadata[f"{OMIC.pmhc.name}_var"] = z_col_name
### others
else:
raise NotImplementedError(f"No support for experiment: {exp}")
### save data and metadata
for name, data in save_data:
outpath = os.path.join(preprocessed_path, name)
n_samples, n_features = data.shape
if n_samples == 0 or n_features == 0:
continue
with MmapArrayWriter(outpath,
shape=(0, n_features),
dtype=np.uint16,
remove_exist=True) as f:
if verbose:
prog = tqdm(f"Saving {outpath}",
total=n_samples,
unit='samples')
for s, e in batching(batch_size=5120, n=n_samples):
x = data[s:e]
if hasattr(x, 'todense'):
x = x.todense()
f.write(x)
if verbose:
prog.update(e - s)
if verbose:
prog.clear()
prog.close()
# save metadata
outpath = os.path.join(preprocessed_path, 'metadata')
with open(outpath, 'wb') as f:
pickle.dump(save_metadata, f)
if verbose:
print(f"Saved metadata to path {outpath}")
### filter genes, follow 10x and use Cell Ranger recipe,
# this is copied from Scanpy
n_genes = sco.shape[1]
sc.pp.filter_genes(sco, min_counts=1)
# normalize with total UMI count per cell
sc.pp.normalize_total(sco, key_added='n_counts_all')
filter_result = sc.pp.filter_genes_dispersion(sco.X,
flavor='cell_ranger',
n_top_genes=n_top_genes,
log=False)
gene_subset = filter_result.gene_subset
indices = sco.get_var_indices()
markers = (MARKER_GENES
if sco.current_omic == OMIC.transcriptomic else MARKER_ATAC)
for name in markers:
idx = indices.get(name, None)
if idx is not None:
gene_subset[idx] = True
sco._inplace_subset_var(gene_subset) # filter genes
if verbose:
print(
f"Filtering genes {n_genes} to {sco.shape[1]} variated genes.")
with open(os.path.join(preprocessed_path, 'top_genes'), 'wb') as f:
pickle.dump(sco.var_names.values, f)
# ******************** load and return the dataset ******************** #
omics = [
name for name in os.listdir(preprocessed_path)
if name not in ('metadata', 'top_genes') and '_' not in name
]
with open(os.path.join(preprocessed_path, 'metadata'), 'rb') as f:
metadata = pickle.load(f)
with open(os.path.join(preprocessed_path, 'top_genes'), 'rb') as f:
top_genes = pickle.load(f)
data = {
name: MmapArray(os.path.join(preprocessed_path,
name)).astype(np.float32)
for name in omics
}
main_omic = metadata['main_omic']
X = data[main_omic]
var_names = metadata[f'{main_omic}_var']
if filtered_genes:
var_ids = {j: i for i, j in enumerate(var_names)}
ids = [var_ids[i] for i in top_genes]
X = X[:, ids]
var_names = var_names[ids]
sco = SingleCellOMIC(
X,
cell_id=metadata['barcodes'],
gene_id=var_names,
omic=main_omic,
name=f"{dataset_name}{'' if filtered_genes else 'all'}")
for o in omics:
if o != main_omic:
sco.add_omic(omic=o,
X=data[o],
var_names=np.asarray(metadata[f'{o}_var']))
return sco
mmap.write(X)
print('Writing data to Memmap:', timeit.default_timer() - start, 's')
hdf5.flush()
hdf5.close()
mmap.flush()
mmap.close()
# ====== reading ====== #
print()
start = timeit.default_timer()
hdf5 = h5py.File('tmp.hdf5', 'r')
print('Load HDF5 data :', timeit.default_timer() - start, 's')
start = timeit.default_timer()
mmap = MmapArray('tmp.mmap')
print('Load Memmap data:', timeit.default_timer() - start, 's')
print()
print('Test correctness of stored data')
print('HDF5 :', np.all(hdf5['X'][:] == X))
print('Memmap:', np.all(mmap[:] == X))
# ====== iterating over dataset ====== #
print()
start = timeit.default_timer()
for epoch in range(0, 3):
for i in range(0, N, 256):
x = X[i:i + 256]
print('Iterate Numpy data :', timeit.default_timer() - start, 's')
