def _generate_tables(self, files):
for i, file in enumerate(files):
if self.config.field is not None:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
# We keep only the field we are interested in
dataset = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(dataset, (list, tuple)):
pa_table = paj.read_json(
BytesIO("\n".join(json.dumps(row) for row in dataset).encode("utf-8")),
read_options=self.config.pa_read_options,
parse_options=self.config.pa_parse_options,
)
else:
pa_table = pa.Table.from_pydict(mapping=dataset, schema=self.config.schema)
else:
try:
pa_table = paj.read_json(
file,
read_options=self.config.pa_read_options,
parse_options=self.config.pa_parse_options,
)
except pa.ArrowInvalid:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
f"Select the correct one and provide it as `field='XXX'` to the `load_dataset` method. "
)
yield i, pa_table
def stream_json(fn, parquet_fn, schema=None, chunk_size=10000000):
if isinstance(fn, str):
fn = [fn]
if schema is None:
schema = read_json(fn[0]).schema
writer = pq.ParquetWriter(parquet_fn, schema)
for _f in fn:
check_gz = _f.endswith('.gz')
if check_gz:
f = gzip.open(_f, 'r')
else:
f = open(_f, 'r')
while True:
chunk = f.readlines(chunk_size)
if not chunk:
break
tbl = read_json(io.BytesIO(''.join(chunk).encode()))
assert tbl.schema == schema # make sure the read table schema is the same as the parsed schema
writer.write_table(tbl)
f.close()
writer.close()
def convert_to_arrow(file_paths,
save_path,
cache_path_prefix="./data_chunk",
no_combine=False):
converted_tables = []
if len(file_paths) == 1:
mmap = pa.memory_map(file_paths[0])
json_input = json.read_json(mmap)
writer = nlp.arrow_writer.ArrowWriter(path=save_path)
writer.write_table(json_input)
else:
for idx, file in enumerate(file_paths):
cache_path = cache_path_prefix + "." + str(idx)
mmap = pa.memory_map(file)
json_input = json.read_json(mmap)
writer = nlp.arrow_writer.ArrowWriter(path=cache_path)
writer.write_table(json_input)
mmap = pa.memory_map(cache_path)
f = pa.ipc.open_stream(mmap)
pa_table = f.read_all()
converted_tables.append(pa_table)
if not no_combine:
pa_table = pa.concat_tables(converted_tables, promote=False)
writer = nlp.arrow_writer.ArrowWriter(path=save_path)
writer.write_table(pa_table)
def convert_ndjsons_to_parquet(files: List[Path], file_name: str,
out_dir: Union[Path,
str], schema: pa.Schema) -> Path:
pq_file = Path(f"{out_dir}/{file_name}.parquet")
if not schema:
schema = pa_json.read_json(files[0]).schema
with pq.ParquetWriter(pq_file, schema) as writer:
parse_options = pa_json.ParseOptions(explicit_schema=schema)
for f in files:
logger.debug(f"Processing {f}")
table = pa_json.read_json(f, parse_options=parse_options)
writer.write_table(table)
remove(f)
return pq_file
def _generate_tables(self, files):
for i, file in enumerate(files):
if self.config.field is not None:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
# We keep only the field we are interested in
dataset = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(dataset, (list, tuple)):
pa_table = paj.read_json(
BytesIO("\n".join(json.dumps(row)
for row in dataset).encode("utf-8")),
read_options=self.config.pa_read_options,
parse_options=self.config.pa_parse_options,
)
else:
pa_table = pa.Table.from_pydict(mapping=dataset)
else:
try:
pa_table = paj.read_json(
file,
read_options=self.config.pa_read_options,
parse_options=self.config.pa_parse_options,
)
except pa.ArrowInvalid:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "
)
if self.config.features:
# Encode column if ClassLabel
for i, col in enumerate(self.config.features.keys()):
if isinstance(self.config.features[col],
datasets.ClassLabel):
pa_table = pa_table.set_column(
i, self.config.schema.field(col),
[self.config.features[col].str2int(pa_table[col])])
# Cast allows str <-> int/float, while parse_option explicit_schema does NOT
# Before casting, rearrange JSON field names to match passed features schema field names order
pa_table = pa.Table.from_arrays(
[pa_table[name] for name in self.config.features],
schema=self.config.schema)
yield i, pa_table
def arrow_from_json(r, *args, **kwargs):
""" Read the stream from s3 and turn JSON into an Arrow Table. """
table = json.read_json(r)
print("Created Dataframe with dimensions: (nrow, ncol) = %s" %
str(table.shape),
file=sys.stderr)
return table
def from_jsonl(
cls,
json_path: str,
identifier: Identifier = None,
dataset_fmt: str = "in_memory",
) -> Dataset:
"""Load a dataset from a .jsonl file on disk, where each line of the
json file consists of a single example."""
if dataset_fmt == "in_memory":
# Load the .jsonl file
with open(json_path) as f:
data = [json.loads(line) for line in f]
return cls(
data,
identifier=identifier,
dataset_fmt=dataset_fmt,
)
elif dataset_fmt == "datasets":
# Use jsonarrow to directly load the json
return cls(
jsonarrow.read_json(json_path),
identifier=identifier,
dataset_fmt=dataset_fmt,
)
else:
raise NotImplementedError
def pa_read_json(
input_file: Union[IO, str],
schema: Union[pa.Schema, Metadata, dict] = None,
expect_full_schema: bool = True,
**kwargs,
):
"""Read a jsonlines file into an Arrow table.
Args:
input_file (Union[IO, str]): the JSONL you want to read. string, path or
file-like object.
schema (pyarrow.Schema): pyarrow Schema with the expected columns wanted.
If unset pyarrow will infer datatypes.
expect_full_schema (bool, optional): if True, pyarrow reader will
expect the input schema to have fields for every col in the
input file. If False, then will only cast columns that
are listed in the schema, leaving all other columns to their
default type on read.
**kwargs (optional): Additional kwargs are passed to pyarrow.json.read_json
Returns:
pyarrow.Table: the jsonl file in pyarrow format casted to the specified schema
"""
if schema:
schema = _get_arrow_schema(schema)
pa_json_table = json.read_json(input_file, **kwargs)
if schema:
pa_json_table = cast_arrow_table_to_schema(
pa_json_table,
schema=schema,
expect_full_schema=expect_full_schema)
return pa_json_table
def from_json(cls, json_path: str, identifier: Identifier) -> Dataset:
"""Load a dataset from a JSON file on disk, where each line of the json
file consists of a single example."""
return cls(
jsonarrow.read_json(json_path),
identifier=identifier,
)
def _generate_tables(self, files):
for i, file in enumerate(files):
pa_table = paj.read_json(
file,
read_options=self.config.pa_read_options,
parse_options=self.config.pa_parse_options,
)
yield i, pa_table
def _read_file(self, f: "pyarrow.NativeFile", **arrow_reader_args):
from pyarrow import json
read_options = arrow_reader_args.pop(
"read_options", json.ReadOptions(use_threads=False))
return json.read_json(f,
read_options=read_options,
**arrow_reader_args)
def _generate_tables(self, files):
for file_idx, file in enumerate(files):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
# We keep only the field we are interested in
dataset = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(dataset, (list, tuple)):
mapping = {
col: [dataset[i][col] for i in range(len(dataset))]
for col in dataset[0].keys()
}
else:
mapping = dataset
pa_table = pa.Table.from_pydict(mapping=mapping)
yield file_idx, self._cast_classlabels(pa_table)
# If the file has one json object per line
else:
with open(file, "rb") as f:
batch_idx = 0
while True:
batch = f.read(self.config.chunksize)
if not batch:
break
batch += f.readline() # finish current line
try:
pa_table = paj.read_json(BytesIO(batch))
except json.JSONDecodeError as e:
logger.error(
f"Failed to read file '{file}' with error {type(e)}: {e}"
)
try:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
except json.JSONDecodeError:
raise e
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "
)
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx,
batch_idx), self._cast_classlabels(pa_table)
batch_idx += 1
def json_read(read_paths: List[str]):
logger.debug(f"Reading {len(read_paths)} files.")
tables = []
for read_path in read_paths:
with filesystem.open_input_file(read_path) as f:
tables.append(
json.read_json(
f,
read_options=json.ReadOptions(use_threads=False),
**arrow_json_args))
block = ArrowBlock(pa.concat_tables(tables))
return block, block.get_metadata(input_files=read_paths)
def import_table(source: str):
if not source:
return sample_table()
if source.endswith(".csv"):
from pyarrow import csv
return csv.read_csv(source)
if source.endswith(".json"):
from pyarrow import json
return json.read_json(source)
if source.endswith(".parquet"):
return pq.read_table(source)
raise ValueError("source must be csv, json or parquet")
def readZippedFile(file: str, verbose: bool = False) -> pd.DataFrame:
"""Read a zipped file.
Reads a dataset export file as exported and downloaded from Pega. The export
file is formatted as a zipped multi-line JSON file or CSV file
and the data is read into a pandas dataframe.
Parameters
----------
file : str
The full path to the file
verbose : str, default=False
Whether to print the names of the files within the unzipped file for debugging purposes
Returns
-------
pd.DataFrame
A pandas dataframe with the contents.
"""
with zipfile.ZipFile(file, mode="r") as z:
files = z.namelist()
if verbose:
print(files) # pragma: no cover
if "data.json" in files:
with z.open("data.json") as zippedfile:
try:
from pyarrow import json
return json.read_json(
zippedfile).to_pandas() # pragma: no cover
except ImportError: # pragma: no cover
try:
dataset = pd.read_json(zippedfile, lines=True)
return dataset
except ValueError:
dataset = pd.read_json(zippedfile)
return dataset
if "csv.json" in files: # pragma: no cover
with z.open("data.csv") as zippedfile:
try:
from pyarrow import csv
return csv.read_json(zippedfile).to_pandas()
except ImportError:
return pd.read_csv(zippedfile)
else: # pragma: no cover
raise FileNotFoundError(
"Cannot find a 'data' file in the zip folder.")
def _read_table_from_source(
source: Union[pd.DataFrame, str]) -> Tuple[pa.Table, List[str]]:
"""
Infers a data source type (path or Pandas DataFrame) and reads it in as
a PyArrow Table.
Args:
source (Union[pd.DataFrame, str]):
Either a string path or Pandas DataFrame.
Returns:
Tuple[pa.Table, List[str]]:
Tuple containing PyArrow table of dataset, and column names of PyArrow table.
"""
# Pandas DataFrame detected
if isinstance(source, pd.DataFrame):
table = pa.Table.from_pandas(df=source)
# Inferring a string path
elif isinstance(source, str):
file_path = source
filename, file_ext = os.path.splitext(file_path)
if ".csv" in file_ext:
from pyarrow import csv
table = csv.read_csv(filename)
elif ".json" in file_ext:
from pyarrow import json
table = json.read_json(filename)
else:
table = pq.read_table(file_path)
else:
raise ValueError(
f"Unknown data source provided for ingestion: {source}")
# Ensure that PyArrow table is initialised
assert isinstance(table, pa.lib.Table)
column_names = table.column_names
return table, column_names
def _init_table_from_path(self):
if '.jsonl' in self.path.suffixes:
# Can read ".jsonl" or ".jsonl.gz"
import pyarrow.json as paj
self.table = paj.read_json(
str(self.path),
read_options=paj.ReadOptions(
# magic constants:
# 894 - estimated average number of bytes per JSON item manifest
# 10000 - how many items we want to have in a chunk (Arrow's "batch")
block_size=894 * 10000))
elif '.arrow' == self.path.suffixes[-1]:
# Can read ".arrow"
import pyarrow as pa
mmap = pa.memory_map(str(self.path))
stream = pa.ipc.open_file(mmap)
self.table = stream.read_all()
else:
raise ValueError(f"Unknown LazyDict file format : '{self.path}'")
def _generate_tables(self, files):
for file_idx, file in enumerate(files):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
# We keep only the field we are interested in
dataset = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(dataset, (list, tuple)):
mapping = {
col: [dataset[i][col] for i in range(len(dataset))]
for col in dataset[0].keys()
}
else:
mapping = dataset
pa_table = pa.Table.from_pydict(mapping=mapping)
yield file_idx, self._cast_classlabels(pa_table)
# If the file has one json object per line
else:
with open(file, "rb") as f:
batch_idx = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
block_size = max(self.config.chunksize // 32, 16 << 10)
while True:
batch = f.read(self.config.chunksize)
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(f)
try:
while True:
try:
pa_table = paj.read_json(
io.BytesIO(batch),
read_options=paj.ReadOptions(
block_size=block_size))
break
except (pa.ArrowInvalid,
pa.ArrowNotImplementedError) as e:
if (isinstance(e, pa.ArrowInvalid)
and "straddling" not in str(e)
or block_size > len(batch)):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f"Batch of {len(batch)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."
)
block_size *= 2
except pa.ArrowInvalid as e:
logger.error(
f"Failed to read file '{file}' with error {type(e)}: {e}"
)
try:
with open(file, encoding="utf-8") as f:
dataset = json.load(f)
except json.JSONDecodeError:
raise e
raise ValueError(
f"Not able to read records in the JSON file at {file}. "
f"You should probably indicate the field of the JSON file containing your records. "
f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
f"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "
) from None
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx,
batch_idx), self._cast_classlabels(pa_table)
batch_idx += 1
def query_json_file(f, column, val):
table = pj.read_json(f)
return [i for i in table.column(column) if i == val]
def readDSExport(
filename: Union[pd.DataFrame, str],
path: str = ".",
verbose: bool = True,
force_pandas: bool = False,
**kwargs,
) -> pd.DataFrame:
"""Read a Pega dataset export file.
Can accept either a Pandas DataFrame or one of the following formats:
- .csv
- .json
- .zip (zipped json or CSV)
It automatically infers the default file names for both model data as well as predictor data.
If you supply either 'modelData' or 'predictorData' as the 'file' argument, it will search for them.
If you supply the full name of the file in the 'path' directory, it will import that instead.
Parameters
----------
filename : [pd.DataFrame, str]
Either a Pandas DataFrame with the source data (for compatibility),
or a string, in which case it can either be:
- The name of the file (if a custom name) or
- Whether we want to look for 'modelData' or 'predictorData' in the path folder.
path : str, default = '.'
The location of the file
verbose : bool, default = True
Whether to print out which file will be imported
Keyword arguments:
Any arguments to plug into the read csv or json function, from either PyArrow or Pandas.
Returns
-------
pd.DataFrame
The read data from the given file
Examples:
>>> df = readDSExport(file = 'modelData', path = './datamart')
>>> df = readDSExport(file = 'ModelSnapshot.json', path = 'data/ADMData')
>>> df = pd.read_csv('file.csv')
>>> df = readDSExport(file = df)
"""
if isinstance(filename, pd.DataFrame):
return filename
is_url = False
if os.path.isfile(os.path.join(path, filename)):
file = os.path.join(path, filename)
else:
file = get_latest_file(path, filename)
if file == "Target not found":
import requests
try:
response = requests.get(f"{path}/{filename}")
is_url = True if response.status_code == 200 else False
except:
is_url = False
if is_url:
file = f"{path}/{filename}"
if file.split(".")[-1] == "zip":
file = urllib.request.urlopen(f"{path}/{filename}")
if verbose:
print("File found through URL")
if file in [None, "Target not found"]:
if verbose:
print(f"File {filename} not found in dir {path}")
return None
if isinstance(file, str):
extension = file.split(".")[-1]
elif isinstance(file, http.client.HTTPResponse):
extension = "zipped"
if verbose:
print(f"Importing: {os.path.join(path,filename)}"
) if is_url else print(f"Importing: {file}")
if extension == "parquet": # pragma: no cover
try:
import pyarrow.parquet as pq
return pq.read_table(file).to_pandas()
except ImportError:
print("You need to import pyarrow to read parquet files.")
if extension == "csv":
try:
if force_pandas or is_url:
raise ImportError("Forcing pandas.")
from pyarrow import csv, ArrowInvalid
try: # pragma: no cover
return csv.read_csv(
file,
parse_options=csv.ParseOptions(
delimiter=kwargs.get("sep", ",")),
).to_pandas()
except ArrowInvalid: # pragma: no cover
raise ImportError()
except ImportError:
if not is_url:
if verbose:
print(
"Can't import pyarrow, so defaulting to pandas. For faster imports, please install pyarrow."
)
return pd.read_csv(file, **kwargs)
except OSError: # pragma: no cover
raise FileNotFoundError(f"File {file} is not found.")
elif extension == "json":
try: # pragma: no cover
if force_pandas:
raise ImportError("Forcing pandas.")
from pyarrow import json, ArrowInvalid
try:
return json.read_json(file, **kwargs).to_pandas()
except ArrowInvalid:
raise ImportError()
except ImportError: # pragma: no cover
if verbose:
print(
"Can't import pyarrow, so defaulting to pandas. For faster imports, please install pyarrow."
)
try:
return pd.read_json(file, lines=True, **kwargs)
except ValueError:
return pd.read_json(file, **kwargs)
except OSError: # pragma: no cover
raise FileNotFoundError(f"File {file} is not found.")
else:
try:
if is_url and extension == "zipped":
return readZippedFile(file=BytesIO(file.read()))
elif extension == "zip":
return readZippedFile(file=file)
else:
return FileNotFoundError(
f"File {file} is not found.") # pragma: no cover
except OSError: # pragma: no cover
raise FileNotFoundError(f"File {file} is not found.")
def read_json(self, *args, **kwargs):
read_options = kwargs.setdefault('read_options', ReadOptions())
read_options.use_threads = True
table = read_json(*args, **kwargs)
table.validate()
return table
def read_json(self, *args, **kwargs):
read_options = kwargs.setdefault('read_options', ReadOptions())
read_options.use_threads = True
table = read_json(*args, **kwargs)
table._validate()
return table
def _read_table_from_source(source: Union[pd.DataFrame, str], chunk_size: int,
max_workers: int) -> Tuple[str, str]:
"""
Infers a data source type (path or Pandas DataFrame) and reads it in as
a PyArrow Table.
The PyArrow Table that is read will be written to a parquet file with row
group size determined by the minimum of:
* (table.num_rows / max_workers)
* chunk_size
The parquet file that is created will be passed as file path to the
multiprocessing pool workers.
Args:
source (Union[pd.DataFrame, str]):
Either a string path or Pandas DataFrame.
chunk_size (int):
Number of worker processes to use to encode values.
max_workers (int):
Amount of rows to load and ingest at a time.
Returns:
Tuple[str, str]:
Tuple containing parent directory path and destination path to
parquet file.
"""
# Pandas DataFrame detected
if isinstance(source, pd.DataFrame):
table = pa.Table.from_pandas(df=source)
# Inferring a string path
elif isinstance(source, str):
file_path = source
filename, file_ext = os.path.splitext(file_path)
if ".csv" in file_ext:
from pyarrow import csv
table = csv.read_csv(filename)
elif ".json" in file_ext:
from pyarrow import json
table = json.read_json(filename)
else:
table = pq.read_table(file_path)
else:
raise ValueError(
f"Unknown data source provided for ingestion: {source}")
# Ensure that PyArrow table is initialised
assert isinstance(table, pa.lib.Table)
# Write table as parquet file with a specified row_group_size
dir_path = tempfile.mkdtemp()
tmp_table_name = f"{int(time.time())}.parquet"
dest_path = f"{dir_path}/{tmp_table_name}"
row_group_size = min(ceil(table.num_rows / max_workers), chunk_size)
pq.write_table(table=table, where=dest_path, row_group_size=row_group_size)
# Remove table from memory
del table
return dir_path, dest_path
def _split_generators(self, dl_manager):
if not self.config.data_files:
raise ValueError(
f"At least one data file must be specified, but got data_files={self.config.data_files}"
)
# Do an early pass if:
# * `features` are not specified, to infer the class labels
# * `drop_metadata` is False, to find the metadata files
do_analyze = (
self.config.features is None
and not self.config.drop_labels) or not self.config.drop_metadata
if do_analyze:
labels = set()
metadata_files = collections.defaultdict(list)
def analyze(files_or_archives, downloaded_files_or_dirs, split):
if len(downloaded_files_or_dirs) == 0:
return
# The files are separated from the archives at this point, so check the first sample
# to see if it's a file or a directory and iterate accordingly
if os.path.isfile(downloaded_files_or_dirs[0]):
original_files, downloaded_files = files_or_archives, downloaded_files_or_dirs
for original_file, downloaded_file in zip(
original_files, downloaded_files):
original_file, downloaded_file = str(
original_file), str(downloaded_file)
_, original_file_ext = os.path.splitext(original_file)
if original_file_ext.lower() in self.IMAGE_EXTENSIONS:
labels.add(
os.path.basename(
os.path.dirname(original_file)))
elif os.path.basename(
original_file) == self.METADATA_FILENAME:
metadata_files[split].append(
(original_file, downloaded_file))
else:
original_file_name = os.path.basename(
original_file)
logger.debug(
f"The file '{original_file_name}' was ignored: it is not an image, and is not {self.METADATA_FILENAME} either."
)
else:
archives, downloaded_dirs = files_or_archives, downloaded_files_or_dirs
for archive, downloaded_dir in zip(archives,
downloaded_dirs):
archive, downloaded_dir = str(archive), str(
downloaded_dir)
for downloaded_dir_file in dl_manager.iter_files(
downloaded_dir):
_, downloaded_dir_file_ext = os.path.splitext(
downloaded_dir_file)
if downloaded_dir_file_ext in self.IMAGE_EXTENSIONS:
labels.add(
os.path.basename(
os.path.dirname(downloaded_dir_file)))
elif os.path.basename(downloaded_dir_file
) == self.METADATA_FILENAME:
metadata_files[split].append(
(None, downloaded_dir_file))
else:
archive_file_name = os.path.basename(archive)
original_file_name = os.path.basename(
downloaded_dir_file)
logger.debug(
f"The file '{original_file_name}' from the archive '{archive_file_name}' was ignored: it is not an image, and is not {self.METADATA_FILENAME} either."
)
if not self.config.drop_labels:
logger.info("Inferring labels from data files...")
if not self.config.drop_metadata:
logger.info("Analyzing metadata files...")
data_files = self.config.data_files
splits = []
for split_name, files in data_files.items():
if isinstance(files, str):
files = [files]
files, archives = self._split_files_and_archives(files)
downloaded_files = dl_manager.download(files)
downloaded_dirs = dl_manager.download_and_extract(archives)
if do_analyze:
analyze(files, downloaded_files, split_name)
analyze(archives, downloaded_dirs, split_name)
splits.append(
datasets.SplitGenerator(
name=split_name,
gen_kwargs={
"files": [(file, downloaded_file)
for file, downloaded_file in zip(
files, downloaded_files)] +
[(None, dl_manager.iter_files(downloaded_dir))
for downloaded_dir in downloaded_dirs],
"metadata_files":
metadata_files
if not self.config.drop_metadata else None,
"split_name":
split_name,
},
))
if not self.config.drop_metadata and metadata_files:
# Verify that:
# * all metadata files have the same set of features
# * the `file_name` key is one of the metadata keys and is of type string
features_per_metadata_file: List[Tuple[str,
datasets.Features]] = []
for _, downloaded_metadata_file in itertools.chain.from_iterable(
metadata_files.values()):
with open(downloaded_metadata_file, "rb") as f:
pa_metadata_table = paj.read_json(f)
features_per_metadata_file.append(
(downloaded_metadata_file,
datasets.Features.from_arrow_schema(
pa_metadata_table.schema)))
for downloaded_metadata_file, metadata_features in features_per_metadata_file:
if metadata_features != features_per_metadata_file[0][1]:
raise ValueError(
f"Metadata files {downloaded_metadata_file} and {features_per_metadata_file[0][0]} have different features: {features_per_metadata_file[0]} != {metadata_features}"
)
metadata_features = features_per_metadata_file[0][1]
if "file_name" not in metadata_features:
raise ValueError(
"`file_name` must be present as dictionary key in metadata files"
)
if metadata_features["file_name"] != datasets.Value("string"):
raise ValueError("`file_name` key must be a string")
del metadata_features["file_name"]
else:
metadata_features = None
# Normally, we would do this in _info, but we need to know the labels and/or metadata
# before building the features
if self.config.features is None:
if not self.config.drop_labels and not metadata_files:
self.info.features = datasets.Features({
"image":
datasets.Image(),
"label":
datasets.ClassLabel(names=sorted(labels))
})
task_template = ImageClassification(image_column="image",
label_column="label")
task_template = task_template.align_with_features(
self.info.features)
self.info.task_templates = [task_template]
else:
self.info.features = datasets.Features(
{"image": datasets.Image()})
if not self.config.drop_metadata and metadata_files:
# Verify that there are no duplicated keys when compared to the existing features ("image", optionally "label")
duplicated_keys = set(
self.info.features) & set(metadata_features)
if duplicated_keys:
raise ValueError(
f"Metadata feature keys {list(duplicated_keys)} are already present as the image features"
)
self.info.features.update(metadata_features)
return splits
def _generate_examples(self, files, metadata_files, split_name):
if not self.config.drop_metadata and metadata_files:
split_metadata_files = metadata_files.get(split_name, [])
image_empty_metadata = {
k: None
for k in self.info.features if k != "image"
}
last_checked_dir = None
metadata_dir = None
metadata_dict = None
downloaded_metadata_file = None
file_idx = 0
for original_file, downloaded_file_or_dir in files:
if original_file is not None:
_, original_file_ext = os.path.splitext(original_file)
if original_file_ext.lower() in self.IMAGE_EXTENSIONS:
# If the file is an image, and we've just entered a new directory,
# find the nereast metadata file (by counting path segments) for the directory
current_dir = os.path.dirname(original_file)
if last_checked_dir is None or last_checked_dir != current_dir:
last_checked_dir = current_dir
metadata_file_candidates = [
(
os.path.relpath(
original_file,
os.path.dirname(
metadata_file_candidate)),
metadata_file_candidate,
downloaded_metadata_file,
) for metadata_file_candidate,
downloaded_metadata_file in
split_metadata_files
if metadata_file_candidate is
not None # ignore metadata_files that are inside archives
and not os.path.relpath(
original_file,
os.path.dirname(metadata_file_candidate)
).startswith("..")
]
if metadata_file_candidates:
_, metadata_file, downloaded_metadata_file = min(
metadata_file_candidates,
key=lambda x: count_path_segments(x[0]))
with open(downloaded_metadata_file, "rb") as f:
pa_metadata_table = paj.read_json(f)
pa_file_name_array = pa_metadata_table[
"file_name"]
pa_file_name_array = pc.replace_substring(
pa_file_name_array,
pattern="\\",
replacement="/")
pa_metadata_table = pa_metadata_table.drop(
["file_name"])
metadata_dir = os.path.dirname(metadata_file)
metadata_dict = {
file_name: image_metadata
for file_name, image_metadata in zip(
pa_file_name_array.to_pylist(),
pa_table_to_pylist(pa_metadata_table))
}
else:
raise ValueError(
f"One or several metadata.jsonl were found, but not in the same directory or in a parent directory of {downloaded_file_or_dir}."
)
if metadata_dir is not None and downloaded_metadata_file is not None:
file_relpath = os.path.relpath(
original_file, metadata_dir)
file_relpath = file_relpath.replace("\\", "/")
if file_relpath not in metadata_dict:
raise ValueError(
f"Image at {file_relpath} doesn't have metadata in {downloaded_metadata_file}."
)
image_metadata = metadata_dict[file_relpath]
else:
raise ValueError(
f"One or several metadata.jsonl were found, but not in the same directory or in a parent directory of {downloaded_file_or_dir}."
)
yield file_idx, {
**image_empty_metadata,
"image": downloaded_file_or_dir,
**image_metadata,
}
file_idx += 1
else:
for downloaded_dir_file in downloaded_file_or_dir:
_, downloaded_dir_file_ext = os.path.splitext(
downloaded_dir_file)
if downloaded_dir_file_ext.lower(
) in self.IMAGE_EXTENSIONS:
current_dir = os.path.dirname(downloaded_dir_file)
if last_checked_dir is None or last_checked_dir != current_dir:
last_checked_dir = current_dir
metadata_file_candidates = [
(
os.path.relpath(
downloaded_dir_file,
os.path.dirname(
downloaded_metadata_file)),
metadata_file_candidate,
downloaded_metadata_file,
) for metadata_file_candidate,
downloaded_metadata_file in
split_metadata_files
if metadata_file_candidate is
None # ignore metadata_files that are not inside archives
and not os.path.relpath(
downloaded_dir_file,
os.path.dirname(
downloaded_metadata_file)
).startswith("..")
]
if metadata_file_candidates:
_, metadata_file, downloaded_metadata_file = min(
metadata_file_candidates,
key=lambda x: count_path_segments(x[0]
))
with open(downloaded_metadata_file,
"rb") as f:
pa_metadata_table = paj.read_json(f)
pa_file_name_array = pa_metadata_table[
"file_name"]
pa_file_name_array = pc.replace_substring(
pa_file_name_array,
pattern="\\",
replacement="/")
pa_metadata_table = pa_metadata_table.drop(
["file_name"])
metadata_dir = os.path.dirname(
downloaded_metadata_file)
metadata_dict = {
file_name: image_metadata
for file_name, image_metadata in zip(
pa_file_name_array.to_pylist(),
pa_table_to_pylist(
pa_metadata_table))
}
else:
raise ValueError(
f"One or several metadata.jsonl were found, but not in the same directory or in a parent directory of {downloaded_dir_file}."
)
if metadata_dir is not None and downloaded_metadata_file is not None:
downloaded_dir_file_relpath = os.path.relpath(
downloaded_dir_file, metadata_dir)
downloaded_dir_file_relpath = downloaded_dir_file_relpath.replace(
"\\", "/")
if downloaded_dir_file_relpath not in metadata_dict:
raise ValueError(
f"Image at {downloaded_dir_file_relpath} doesn't have metadata in {downloaded_metadata_file}."
)
image_metadata = metadata_dict[
downloaded_dir_file_relpath]
else:
raise ValueError(
f"One or several metadata.jsonl were found, but not in the same directory or in a parent directory of {downloaded_dir_file}."
)
yield file_idx, {
**image_empty_metadata,
"image": downloaded_dir_file,
**image_metadata,
}
file_idx += 1
else:
file_idx = 0
for original_file, downloaded_file_or_dir in files:
if original_file is not None:
_, original_file_ext = os.path.splitext(original_file)
if original_file_ext.lower() in self.IMAGE_EXTENSIONS:
if self.config.drop_labels or metadata_files:
yield file_idx, {
"image": downloaded_file_or_dir,
}
else:
yield file_idx, {
"image":
downloaded_file_or_dir,
"label":
os.path.basename(
os.path.dirname(original_file)),
}
file_idx += 1
else:
for downloaded_dir_file in downloaded_file_or_dir:
_, downloaded_dir_file_ext = os.path.splitext(
downloaded_dir_file)
if downloaded_dir_file_ext.lower(
) in self.IMAGE_EXTENSIONS:
if self.config.drop_labels or metadata_files:
yield file_idx, {
"image": downloaded_dir_file,
}
else:
yield file_idx, {
"image":
downloaded_dir_file,
"label":
os.path.basename(
os.path.dirname(downloaded_dir_file)),
}
file_idx += 1
from pyarrow import json
import pyarrow.parquet as pq
import pyarrow as pa
from glob import glob
import time
t = time.time_ns()
fn = "tests/data/formats/tweets"
table = json.read_json(fn + ".jsonl")
pq.write_table(table, "parc.parquet", compression="ZSTD")
# table = pq.read_table(fn + '.parquet')
# print(table.schema)
# print(table.column_names)
# print(table.select(['username']))
# print(table.take([0]).to_pydict())
print(table.take([0, 500, 9000])["username"])
# print(table.filter())
def iter_arrow(tbl):
for batch in tbl.to_batches():
dict_batch = batch.to_pydict()
for index in range(len(batch)):
yield {k: v[index] for k, v in dict_batch.items()}
# , filter=([('user_verified', '=', True)]))
# table = pq.read_table(fn + '.parquet')
def read_to_object(self, f: IOBase):
at = pa_json.read_json(f.name)
return at
#! /usr/bin/env nix-shell
#! nix-shell -i python3 -p python3 withPackages(ps: [ ps.pandas ps.numpy ps.pyarrow])
# nix-shell -p "python3.withPackages(ps: [ ps.pandas ps.numpy ps.pyarrow ps.yapf])"
from pyarrow import json as paj
import json as js
import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
print("Start ....")
x = np.random.randn(10000)
df = pd.DataFrame({'one': x, 'two': x + 5, 'three': x > 0.1}, )
table = pa.Table.from_pandas(df)
pq.write_table(table, 'example.parquet')
with open("exemplary.json", "w") as f:
js.dump(dict(x=list(x), y=[dict(a=1, b=5, c=False) for i in list(x)]), f)
table = paj.read_json(f"exemplary.json")
pq.write_table(table, 'example_nested.parquet')
print("Stop ....")
def from_jsonl(cls, path: Pathlike) -> 'LazyDict':
_check_arrow()
import pyarrow.json as paj
table = paj.read_json(str(path))
return cls(table)
def test_json_file_to_arrow(f):
return pa_json.read_json(f)