def _run_tst_fromBinary(self, useConfJson=False):
# run this as a single big test so as to avoid repeated setUp and tearDown of the spark context
# data will be a sequence of test data
# all keys and all values in a test data item must be of the same length
# keys get converted to ints regardless of raw input format
DATA = [
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3], [5, 6, 7]], [[11], [12]], 'int16', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int16', 'int32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11, 12, 13]], 'int32', 'int16'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]], 'int16', 'float32'),
SeriesBinaryTestData.fromArrays([[1, 2, 3]], [[11.0, 12.0, 13.0]], 'float32', 'float32'),
SeriesBinaryTestData.fromArrays([[2, 3, 4]], [[11.0, 12.0, 13.0]], 'float32', 'float32'),
]
for itemidx, item in enumerate(DATA):
outSubdir = os.path.join(self.outputdir, 'input%d' % itemidx)
os.mkdir(outSubdir)
fname = os.path.join(outSubdir, 'inputfile%d.bin' % itemidx)
with open(fname, 'wb') as f:
item.writeToFile(f)
loader = SeriesLoader(self.sc)
if not useConfJson:
series = loader.fromBinary(outSubdir, nkeys=item.nkeys, nvalues=item.nvals, keyType=str(item.keyDtype),
valueType=str(item.valDtype))
else:
# write configuration file
conf = {'input': outSubdir,
'nkeys': item.nkeys, 'nvalues': item.nvals,
'valuetype': str(item.valDtype), 'keytype': str(item.keyDtype)}
with open(os.path.join(outSubdir, "conf.json"), 'wb') as f:
json.dump(conf, f, indent=2)
series = loader.fromBinary(outSubdir)
seriesData = series.rdd.collect()
expectedData = item.data
assert_equals(len(expectedData), len(seriesData),
"Differing numbers of k/v pairs in item %d; expected %d, got %d" %
(itemidx, len(expectedData), len(seriesData)))
for expected, actual in zip(expectedData, seriesData):
expectedKeys = tuple(expected[0])
expectedType = smallestFloatType(item.valDtype)
expectedVals = array(expected[1], dtype=expectedType)
assert_equals(expectedKeys, actual[0],
"Key mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedKeys), str(actual[0])))
assert_true(allclose(expectedVals, actual[1]),
"Value mismatch in item %d; expected %s, got %s" %
(itemidx, str(expectedVals), str(actual[1])))
assert_equals(expectedType, str(actual[1].dtype),
"Value type mismatch in item %d; expected %s, got %s" %
(itemidx, expectedType, str(actual[1].dtype)))
def loadSeries(self, dataPath, nkeys=None, nvalues=None, inputFormat='binary', minPartitions=None,
maxPartitionSize='32mb', confFilename='conf.json', keyType=None, valueType=None, keyPath=None,
varName=None):
"""
Loads a Series object from data stored as binary, text, npy, or mat.
For binary and text, supports single files or multiple files stored on a local file system,
a networked file system (mounted and available on all cluster nodes), Amazon S3, or HDFS.
For local formats (npy and mat) only local file systems currently supported.
Parameters
----------
dataPath: string
Path to data files or directory, as either a local filesystem path or a URI.
May include a single '*' wildcard in the filename. Examples of valid dataPaths include
'local/directory/*.stack", "s3n:///my-s3-bucket/data/", or "file:///mnt/another/directory/".
nkeys: int, optional (required if `inputFormat` is 'text'), default = None
Number of keys per record (e.g. 3 for (x, y, z) coordinate keys). Must be specified for
text data; can be specified here or in a configuration file for binary data.
nvalues: int, optional (required if `inputFormat` is 'text')
Number of values per record. Must be specified here or in a configuration file for binary data.
inputFormat: {'text', 'binary', 'npy', 'mat'}. optional, default = 'binary'
inputFormat of data to be read.
minPartitions: int, optional, default = SparkContext.minParallelism
Minimum number of Spark partitions to use, only for text.
maxPartitionSize : int, optional, default = '32mb'
Maximum size of partitions as a Java-style memory string, e.g. '32mb' or '64mb',
indirectly controls the number of Spark partitions, only for binary.
confFilename: string, optional, default 'conf.json'
Path to JSON file with configuration options including 'nkeys', 'nvalues',
'keyType', and 'valueType'. If a file is not found at the given path, then the base
directory in 'dataPath' will be checked. Parameters will override the conf file.
keyType: string or numpy dtype, optional, default = None
Numerical type of keys, will override conf file.
valueType: string or numpy dtype, optional, default = None
Numerical type of values, will override conf file.
keyPath: string, optional, default = None
Path to file with keys when loading from npy or mat.
varName : str, optional, default = None
Variable name to load (for MAT files only)
Returns
-------
data: lambdaimage.rdds.Series
A Series object, wrapping an RDD, with (n-tuples of ints) : (numpy array) pairs
"""
checkParams(inputFormat, ['text', 'binary', 'npy', 'mat'])
from lambdaimage.rdds.fileio.seriesloader import SeriesLoader
loader = SeriesLoader(self._sc, minPartitions=minPartitions)
if inputFormat.lower() == 'binary':
data = loader.fromBinary(dataPath, confFilename=confFilename, nkeys=nkeys, nvalues=nvalues,
keyType=keyType, valueType=valueType, maxPartitionSize=maxPartitionSize)
elif inputFormat.lower() == 'text':
if nkeys is None:
raise Exception('Must provide number of keys per record for loading from text')
data = loader.fromText(dataPath, nkeys=nkeys)
elif inputFormat.lower() == 'npy':
data = loader.fromNpyLocal(dataPath, keyPath)
else:
if varName is None:
raise Exception('Must provide variable name for loading MAT files')
data = loader.fromMatLocal(dataPath, varName, keyPath)
return data
