def read_featureframe(self, spark):
"""
Reads a training dataset in petastorm format from HopsFS
Args:
:spark: the spark session
Returns:
dataframe with the data of the training dataset
Raises:
:TrainingDatasetNotFound: if the requested training dataset could not be found
"""
if hasattr(self, 'training_dataset') and \
self.training_dataset.training_dataset_type != constants.REST_CONFIG.JSON_TRAINING_DATASET_EXTERNAL_TYPE:
if hdfs.exists(self.path):
spark_df = spark.read.parquet(self.path)
elif hdfs.exists(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_PETASTORM_SUFFIX):
spark_df = spark.read.parquet(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_PETASTORM_SUFFIX)
if not hdfs.exists(self.path) and not hdfs.exists(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_PETASTORM_SUFFIX):
raise TrainingDatasetNotFound(
"Could not find a training dataset in folder {} "
"or in file {}".format(
self.path, self.path + constants.FEATURE_STORE.
TRAINING_DATASET_PETASTORM_SUFFIX))
else:
spark_df = spark.read.parquet(self.path)
return fs_utils._return_dataframe_type(spark_df, self.dataframe_type)
def read_featureframe(self, spark):
"""
Reads a training dataset in CSV format from HopsFS
Args:
:spark: the spark session
Returns:
dataframe with the data of the training dataset
Raises:
:TrainingDatasetNotFound: if the requested training dataset could not be found
"""
if self.training_dataset.training_dataset_type != constants.REST_CONFIG.JSON_TRAINING_DATASET_EXTERNAL_TYPE:
if hdfs.exists(self.path):
spark_df = spark.read.format(constants.FEATURE_STORE.TRAINING_DATASET_CSV_FORMAT).option(
constants.SPARK_CONFIG.SPARK_WRITE_HEADER, "true").option(
constants.SPARK_CONFIG.SPARK_WRITE_DELIMITER,
constants.DELIMITERS.COMMA_DELIMITER).load(self.path)
elif hdfs.exists(self.path + constants.FEATURE_STORE.TRAINING_DATASET_CSV_SUFFIX):
spark_df = spark.read.format(constants.FEATURE_STORE.TRAINING_DATASET_CSV_FORMAT).option(
constants.SPARK_CONFIG.SPARK_WRITE_HEADER, "true").option(
constants.SPARK_CONFIG.SPARK_WRITE_DELIMITER,
constants.DELIMITERS.COMMA_DELIMITER).load(self.path +
constants.FEATURE_STORE.TRAINING_DATASET_CSV_SUFFIX)
if not hdfs.exists(self.path) and not hdfs.exists(self.path +
constants.FEATURE_STORE.TRAINING_DATASET_CSV_SUFFIX):
raise TrainingDatasetNotFound("Could not find a training dataset in folder {} or in file {}".format(
self.path, self.path + constants.FEATURE_STORE.TRAINING_DATASET_CSV_SUFFIX))
else:
spark_df = spark.read.format(constants.FEATURE_STORE.TRAINING_DATASET_CSV_FORMAT).option(
constants.SPARK_CONFIG.SPARK_WRITE_HEADER, "true").option(
constants.SPARK_CONFIG.SPARK_WRITE_DELIMITER,
constants.DELIMITERS.COMMA_DELIMITER).load(self.path)
return fs_utils._return_dataframe_type(spark_df, self.dataframe_type)
def read_featureframe(self, spark):
"""
Reads a training dataset in hdf5 format from HopsFS
Args:
:spark: the spark session
Returns:
dataframe with the data of the training dataset
Raises:
:TrainingDatasetNotFound: if the requested training dataset could not be found
:CouldNotConvertDataframe: if the hdf5 dataset could not be converted to a spark dataframe
:HDF5DatasetFormatNotSupportedForExternalTrainingDatasets: if the user tries to read an
external training dataset in the .hdf5 format.
"""
if not hasattr(self, 'training_dataset') or \
self.training_dataset.training_dataset_type \
== constants.REST_CONFIG.JSON_TRAINING_DATASET_EXTERNAL_TYPE:
raise HDF5DatasetFormatNotSupportedForExternalTrainingDatasets(
"The .hdf5 dataset format is not "
"supported for external training datasets.")
if not hdfs.exists(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_HDF5_SUFFIX):
raise TrainingDatasetNotFound(
"Could not find a training dataset in file {}".format(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_HDF5_SUFFIX))
tf = TemporaryFile()
data = hdfs.load(self.path +
constants.FEATURE_STORE.TRAINING_DATASET_HDF5_SUFFIX)
tf.write(data)
tf.seek(0)
hdf5_file = h5py.File(tf)
np_array = hdf5_file[self.training_dataset.name][()]
if self.dataframe_type == constants.FEATURE_STORE.DATAFRAME_TYPE_NUMPY:
return np_array
if self.dataframe_type == constants.FEATURE_STORE.DATAFRAME_TYPE_PYTHON:
return np_array.tolist()
if self.dataframe_type == constants.FEATURE_STORE.DATAFRAME_TYPE_SPARK \
or self.dataframe_type == constants.FEATURE_STORE.DATAFRAME_TYPE_PANDAS:
if np_array.ndim != 2:
raise CouldNotConvertDataframe(
"Cannot convert numpy array that do not have two dimensions to a dataframe. "
"The number of dimensions are: {}".format(np_array.ndim))
num_cols = np_array.shape[1]
dataframe_dict = {}
for n_col in list(range(num_cols)):
col_name = "col_" + str(n_col)
dataframe_dict[col_name] = np_array[:, n_col]
pandas_df = pd.DataFrame(dataframe_dict)
sc = spark.sparkContext
sql_context = SQLContext(sc)
return fs_utils._return_dataframe_type(
sql_context.createDataFrame(pandas_df), self.dataframe_type)
def read_featureframe(self, spark):
"""
Reads a training dataset in tfrecords format from HopsFS
Args:
:spark: the spark session
Returns:
dataframe with the data of the training dataset
Raises:
:TrainingDatasetNotFound: if the requested training dataset could not be found
"""
if hasattr(self, 'training_dataset') and self.training_dataset.training_dataset_type != \
constants.REST_CONFIG.JSON_TRAINING_DATASET_EXTERNAL_TYPE:
if hdfs.exists(self.path):
spark_df = spark.read.format(
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_FORMAT
).option(
constants.SPARK_CONFIG.SPARK_TF_CONNECTOR_RECORD_TYPE,
constants.SPARK_CONFIG.
SPARK_TF_CONNECTOR_RECORD_TYPE_EXAMPLE).load(self.path)
elif hdfs.exists(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_SUFFIX):
spark_df = spark.read.format(
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_FORMAT
).option(
constants.SPARK_CONFIG.SPARK_TF_CONNECTOR_RECORD_TYPE,
constants.SPARK_CONFIG.
SPARK_TF_CONNECTOR_RECORD_TYPE_EXAMPLE
).load(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_SUFFIX)
if not hdfs.exists(self.path) and not hdfs.exists(
self.path +
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_SUFFIX):
raise TrainingDatasetNotFound(
"Could not find a training dataset in folder {} or in file {}"
.format(
self.path, self.path + constants.FEATURE_STORE.
TRAINING_DATASET_TFRECORDS_SUFFIX))
else:
spark_df = spark.read.format(
constants.FEATURE_STORE.TRAINING_DATASET_TFRECORDS_FORMAT
).option(
constants.SPARK_CONFIG.SPARK_TF_CONNECTOR_RECORD_TYPE,
constants.SPARK_CONFIG.SPARK_TF_CONNECTOR_RECORD_TYPE_EXAMPLE
).load(self.path)
return fs_utils._return_dataframe_type(spark_df, self.dataframe_type)
