def save(self, fname: str):
"""
Save database as feather file.
:param fname: The name of the file to create.
"""
assert not os.path.exists(fname)
df = self._df.copy()
df.index.name = INDEX_NAME
df.reset_index(
inplace=True
) # Index is not stored in feather format. https://github.com/wesm/feather/issues/200
write_dataframe(df, fname)
def invert(cls,
db: Type[RankingDatabase],
fname: str,
top_n_identifiers: int = 50000) -> None:
"""
Create an inverted whole genome rankings database keeping only the top n genes/regions for a feature.
Inverted design: not storing the rankings for all regions in the dataframe but instead store the identifier of the
top n genes/regions in the dataframe introduces an enormous reduction in disk and memory size.
:param db: The rankings database.
:param fname: the filename of the inverted database to be created.
:param top_n_identifiers: The number of genes to keep in the inverted database.
"""
df_original = db.load_full()
n_features = len(df_original)
index_fname = InvertedRankingDatabase._derive_identifiers_fname(fname)
assert not os.path.exists(
index_fname), "Database index {0:s} already exists.".format(
index_fname)
identifiers = df_original.columns.values
with open(index_fname, 'w') as f:
f.write('\n'.join(identifiers))
identifier2idx = {
identifier: idx
for idx, identifier in enumerate(identifiers)
}
inverted_data = np.empty(shape=(n_features, top_n_identifiers),
dtype=INVERTED_DB_DTYPE)
df_original.columns = [
identifier2idx[identifier] for identifier in df_original.columns
]
for idx, (_, row) in tqdm(enumerate(df_original.iterrows())):
inverted_data[idx, :] = np.array(
row.sort_values(ascending=True).head(top_n_identifiers).index,
dtype=INVERTED_DB_DTYPE)
df = pd.DataFrame(data=inverted_data,
index=df_original.index,
columns=list(range(top_n_identifiers)))
df.index.name = INDEX_NAME
df.reset_index(
inplace=True
) # Index is not stored in feather format. https://github.com/wesm/feather/issues/200
write_dataframe(df, fname)
def convert2feather(fname: str,
out_folder: str,
name: str,
extension: str = "feather") -> str:
"""
Convert a whole genome rankings database to a feather format based database.
More information on this format can be found here:
.. feather-format: https://blog.rstudio.com/2016/03/29/feather/
:param fname: The filename of the legacy
:param out_folder: The name of the folder to write the new database to.
:param name: The name of the rankings database.
:param extension: The extension of the new database file.
:return: The filename of the new database.
"""
assert os.path.isfile(fname), "{} does not exist.".format(fname)
assert os.path.isdir(out_folder), "{} is not a directory.".format(
out_folder)
feather_fname = os.path.join(
out_folder, "{}.{}".format(
os.path.splitext(os.path.basename(fname))[0], extension))
assert not os.path.exists(feather_fname), "{} already exists.".format(
feather_fname)
# Load original database into memory.
# Caveat: the original storage format of whole genome rankings does not store the metadata, i.e. name and gene
# nomenclature.
# The avoid having to specify nomenclature it is set as unknown.
db = SQLiteRankingDatabase(fname=fname,
name=name,
nomenclature=UNKNOWN_NOMENCLATURE)
df = db.load_full()
df.index.name = INDEX_NAME
df.reset_index(
inplace=True
) # Index is not stored in feather format. https://github.com/wesm/feather/issues/200
write_dataframe(df, feather_fname)
return feather_fname