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def train(train_dir, model_save_path=None, n_neighbors=None, knn_algo='ball_tree', verbose=False):
"""
Trains a k-nearest neighbors classifier for face recognition.
:param train_dir: directory that contains a sub-directory for each known person, with its name.
(View in source code to see train_dir ex... |
def predict(X_img_path, knn_clf=None, model_path=None, distance_threshold=0.6):
"""
Recognizes faces in given image using a trained KNN classifier
:param X_img_path: path to image to be recognized
:param knn_clf: (optional) a knn classifier object. if not specified, model_save_path must be specified.
... |
def show_prediction_labels_on_image(img_path, predictions):
"""
Shows the face recognition results visually.
:param img_path: path to image to be recognized
:param predictions: results of the predict function
:return:
"""
pil_image = Image.open(img_path).convert("RGB")
draw = ImageDraw.... |
def _rect_to_css(rect):
"""
Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order
:param rect: a dlib 'rect' object
:return: a plain tuple representation of the rect in (top, right, bottom, left) order
"""
return rect.top(), rect.right(), rect.bottom(), rect.left() |
def _trim_css_to_bounds(css, image_shape):
"""
Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image.
:param css: plain tuple representation of the rect in (top, right, bottom, left) order
:param image_shape: numpy shape of the image array
:return: a trimmed plain... |
def face_distance(face_encodings, face_to_compare):
"""
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
for each comparison face. The distance tells you how similar the faces are.
:param faces: List of face encodings to compare
:param face_to_compa... |
def load_image_file(file, mode='RGB'):
"""
Loads an image file (.jpg, .png, etc) into a numpy array
:param file: image file name or file object to load
:param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported.
:return: image contents as... |
def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller face... |
def face_locations(img, number_of_times_to_upsample=1, model="hog"):
"""
Returns an array of bounding boxes of human faces in a image
:param img: An image (as a numpy array)
:param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces.
... |
def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128):
"""
Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector
If you are using a GPU, this can give you much faster results since the GPU
can process batches of images at once. If you aren'... |
def face_landmarks(face_image, face_locations=None, model="large"):
"""
Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image
:param face_image: image to search
:param face_locations: Optionally provide a list of face locations to check.
:param model:... |
def face_encodings(face_image, known_face_locations=None, num_jitters=1):
"""
Given an image, return the 128-dimension face encoding for each face in the image.
:param face_image: The image that contains one or more faces
:param known_face_locations: Optional - the bounding boxes of each face if you al... |
def _parse_datatype_string(s):
"""
Parses the given data type string to a :class:`DataType`. The data type string format equals
to :class:`DataType.simpleString`, except that top level struct type can omit
the ``struct<>`` and atomic types use ``typeName()`` as their format, e.g. use ``byte`` instead
... |
def _int_size_to_type(size):
"""
Return the Catalyst datatype from the size of integers.
"""
if size <= 8:
return ByteType
if size <= 16:
return ShortType
if size <= 32:
return IntegerType
if size <= 64:
return LongType |
def _infer_type(obj):
"""Infer the DataType from obj
"""
if obj is None:
return NullType()
if hasattr(obj, '__UDT__'):
return obj.__UDT__
dataType = _type_mappings.get(type(obj))
if dataType is DecimalType:
# the precision and scale of `obj` may be different from row to... |
def _infer_schema(row, names=None):
"""Infer the schema from dict/namedtuple/object"""
if isinstance(row, dict):
items = sorted(row.items())
elif isinstance(row, (tuple, list)):
if hasattr(row, "__fields__"): # Row
items = zip(row.__fields__, tuple(row))
elif hasattr(ro... |
def _has_nulltype(dt):
""" Return whether there is NullType in `dt` or not """
if isinstance(dt, StructType):
return any(_has_nulltype(f.dataType) for f in dt.fields)
elif isinstance(dt, ArrayType):
return _has_nulltype((dt.elementType))
elif isinstance(dt, MapType):
return _has_... |
def _create_converter(dataType):
"""Create a converter to drop the names of fields in obj """
if not _need_converter(dataType):
return lambda x: x
if isinstance(dataType, ArrayType):
conv = _create_converter(dataType.elementType)
return lambda row: [conv(v) for v in row]
elif i... |
def _make_type_verifier(dataType, nullable=True, name=None):
"""
Make a verifier that checks the type of obj against dataType and raises a TypeError if they do
not match.
This verifier also checks the value of obj against datatype and raises a ValueError if it's not
within the allowed range, e.g. u... |
def to_arrow_type(dt):
""" Convert Spark data type to pyarrow type
"""
import pyarrow as pa
if type(dt) == BooleanType:
arrow_type = pa.bool_()
elif type(dt) == ByteType:
arrow_type = pa.int8()
elif type(dt) == ShortType:
arrow_type = pa.int16()
elif type(dt) == Integ... |
def to_arrow_schema(schema):
""" Convert a schema from Spark to Arrow
"""
import pyarrow as pa
fields = [pa.field(field.name, to_arrow_type(field.dataType), nullable=field.nullable)
for field in schema]
return pa.schema(fields) |
def from_arrow_type(at):
""" Convert pyarrow type to Spark data type.
"""
import pyarrow.types as types
if types.is_boolean(at):
spark_type = BooleanType()
elif types.is_int8(at):
spark_type = ByteType()
elif types.is_int16(at):
spark_type = ShortType()
elif types.is_... |
def from_arrow_schema(arrow_schema):
""" Convert schema from Arrow to Spark.
"""
return StructType(
[StructField(field.name, from_arrow_type(field.type), nullable=field.nullable)
for field in arrow_schema]) |
def _check_series_localize_timestamps(s, timezone):
"""
Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone.
If the input series is not a timestamp series, then the same series is returned. If the input
series is a timestamp series, then a converted series is... |
def _check_dataframe_localize_timestamps(pdf, timezone):
"""
Convert timezone aware timestamps to timezone-naive in the specified timezone or local timezone
:param pdf: pandas.DataFrame
:param timezone: the timezone to convert. if None then use local timezone
:return pandas.DataFrame where any time... |
def _check_series_convert_timestamps_internal(s, timezone):
"""
Convert a tz-naive timestamp in the specified timezone or local timezone to UTC normalized for
Spark internal storage
:param s: a pandas.Series
:param timezone: the timezone to convert. if None then use local timezone
:return panda... |
def _check_series_convert_timestamps_localize(s, from_timezone, to_timezone):
"""
Convert timestamp to timezone-naive in the specified timezone or local timezone
:param s: a pandas.Series
:param from_timezone: the timezone to convert from. if None then use local timezone
:param to_timezone: the tim... |
def add(self, field, data_type=None, nullable=True, metadata=None):
"""
Construct a StructType by adding new elements to it to define the schema. The method accepts
either:
a) A single parameter which is a StructField object.
b) Between 2 and 4 parameters as (name, data_... |
def _cachedSqlType(cls):
"""
Cache the sqlType() into class, because it's heavy used in `toInternal`.
"""
if not hasattr(cls, "_cached_sql_type"):
cls._cached_sql_type = cls.sqlType()
return cls._cached_sql_type |
def asDict(self, recursive=False):
"""
Return as an dict
:param recursive: turns the nested Row as dict (default: False).
>>> Row(name="Alice", age=11).asDict() == {'name': 'Alice', 'age': 11}
True
>>> row = Row(key=1, value=Row(name='a', age=2))
>>> row.asDict(... |
def summary(self):
"""
Gets summary (e.g. residuals, mse, r-squared ) of model on
training set. An exception is thrown if
`trainingSummary is None`.
"""
if self.hasSummary:
return LinearRegressionTrainingSummary(super(LinearRegressionModel, self).summary)
... |
def evaluate(self, dataset):
"""
Evaluates the model on a test dataset.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame`
"""
if not isinstance(dataset, DataFrame):
raise ValueErro... |
def summary(self):
"""
Gets summary (e.g. residuals, deviance, pValues) of model on
training set. An exception is thrown if
`trainingSummary is None`.
"""
if self.hasSummary:
return GeneralizedLinearRegressionTrainingSummary(
super(GeneralizedL... |
def evaluate(self, dataset):
"""
Evaluates the model on a test dataset.
:param dataset:
Test dataset to evaluate model on, where dataset is an
instance of :py:class:`pyspark.sql.DataFrame`
"""
if not isinstance(dataset, DataFrame):
raise ValueErro... |
def _get_local_dirs(sub):
""" Get all the directories """
path = os.environ.get("SPARK_LOCAL_DIRS", "/tmp")
dirs = path.split(",")
if len(dirs) > 1:
# different order in different processes and instances
rnd = random.Random(os.getpid() + id(dirs))
random.shuffle(dirs, rnd.random)... |
def _get_spill_dir(self, n):
""" Choose one directory for spill by number n """
return os.path.join(self.localdirs[n % len(self.localdirs)], str(n)) |
def mergeValues(self, iterator):
""" Combine the items by creator and combiner """
# speedup attribute lookup
creator, comb = self.agg.createCombiner, self.agg.mergeValue
c, data, pdata, hfun, batch = 0, self.data, self.pdata, self._partition, self.batch
limit = self.memory_limit... |
def mergeCombiners(self, iterator, limit=None):
""" Merge (K,V) pair by mergeCombiner """
if limit is None:
limit = self.memory_limit
# speedup attribute lookup
comb, hfun, objsize = self.agg.mergeCombiners, self._partition, self._object_size
c, data, pdata, batch = 0... |
def _spill(self):
"""
dump already partitioned data into disks.
It will dump the data in batch for better performance.
"""
global MemoryBytesSpilled, DiskBytesSpilled
path = self._get_spill_dir(self.spills)
if not os.path.exists(path):
os.makedirs(pat... |
def items(self):
""" Return all merged items as iterator """
if not self.pdata and not self.spills:
return iter(self.data.items())
return self._external_items() |
def _external_items(self):
""" Return all partitioned items as iterator """
assert not self.data
if any(self.pdata):
self._spill()
# disable partitioning and spilling when merge combiners from disk
self.pdata = []
try:
for i in range(self.partitio... |
def _recursive_merged_items(self, index):
"""
merge the partitioned items and return the as iterator
If one partition can not be fit in memory, then them will be
partitioned and merged recursively.
"""
subdirs = [os.path.join(d, "parts", str(index)) for d in self.localdi... |
def _get_path(self, n):
""" Choose one directory for spill by number n """
d = self.local_dirs[n % len(self.local_dirs)]
if not os.path.exists(d):
os.makedirs(d)
return os.path.join(d, str(n)) |
def sorted(self, iterator, key=None, reverse=False):
"""
Sort the elements in iterator, do external sort when the memory
goes above the limit.
"""
global MemoryBytesSpilled, DiskBytesSpilled
batch, limit = 100, self._next_limit()
chunks, current_chunk = [], []
... |
def _spill(self):
""" dump the values into disk """
global MemoryBytesSpilled, DiskBytesSpilled
if self._file is None:
self._open_file()
used_memory = get_used_memory()
pos = self._file.tell()
self._ser.dump_stream(self.values, self._file)
self.values... |
def _spill(self):
"""
dump already partitioned data into disks.
"""
global MemoryBytesSpilled, DiskBytesSpilled
path = self._get_spill_dir(self.spills)
if not os.path.exists(path):
os.makedirs(path)
used_memory = get_used_memory()
if not self.... |
def _merge_sorted_items(self, index):
""" load a partition from disk, then sort and group by key """
def load_partition(j):
path = self._get_spill_dir(j)
p = os.path.join(path, str(index))
with open(p, 'rb', 65536) as f:
for v in self.serializer.load_s... |
def worker(sock, authenticated):
"""
Called by a worker process after the fork().
"""
signal.signal(SIGHUP, SIG_DFL)
signal.signal(SIGCHLD, SIG_DFL)
signal.signal(SIGTERM, SIG_DFL)
# restore the handler for SIGINT,
# it's useful for debugging (show the stacktrace before exit)
signal.... |
def portable_hash(x):
"""
This function returns consistent hash code for builtin types, especially
for None and tuple with None.
The algorithm is similar to that one used by CPython 2.7
>>> portable_hash(None)
0
>>> portable_hash((None, 1)) & 0xffffffff
219750521
"""
if sys.ve... |
def _parse_memory(s):
"""
Parse a memory string in the format supported by Java (e.g. 1g, 200m) and
return the value in MiB
>>> _parse_memory("256m")
256
>>> _parse_memory("2g")
2048
"""
units = {'g': 1024, 'm': 1, 't': 1 << 20, 'k': 1.0 / 1024}
if s[-1].lower() not in units:
... |
def ignore_unicode_prefix(f):
"""
Ignore the 'u' prefix of string in doc tests, to make it works
in both python 2 and 3
"""
if sys.version >= '3':
# the representation of unicode string in Python 3 does not have prefix 'u',
# so remove the prefix 'u' for doc tests
literal_re ... |
def cache(self):
"""
Persist this RDD with the default storage level (C{MEMORY_ONLY}).
"""
self.is_cached = True
self.persist(StorageLevel.MEMORY_ONLY)
return self |
def persist(self, storageLevel=StorageLevel.MEMORY_ONLY):
"""
Set this RDD's storage level to persist its values across operations
after the first time it is computed. This can only be used to assign
a new storage level if the RDD does not have a storage level set yet.
If no stor... |
def unpersist(self, blocking=False):
"""
Mark the RDD as non-persistent, and remove all blocks for it from
memory and disk.
.. versionchanged:: 3.0.0
Added optional argument `blocking` to specify whether to block until all
blocks are deleted.
"""
se... |
def getCheckpointFile(self):
"""
Gets the name of the file to which this RDD was checkpointed
Not defined if RDD is checkpointed locally.
"""
checkpointFile = self._jrdd.rdd().getCheckpointFile()
if checkpointFile.isDefined():
return checkpointFile.get() |
def map(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each element of this RDD.
>>> rdd = sc.parallelize(["b", "a", "c"])
>>> sorted(rdd.map(lambda x: (x, 1)).collect())
[('a', 1), ('b', 1), ('c', 1)]
"""
def func(_, iterat... |
def flatMap(self, f, preservesPartitioning=False):
"""
Return a new RDD by first applying a function to all elements of this
RDD, and then flattening the results.
>>> rdd = sc.parallelize([2, 3, 4])
>>> sorted(rdd.flatMap(lambda x: range(1, x)).collect())
[1, 1, 1, 2, 2,... |
def mapPartitions(self, f, preservesPartitioning=False):
"""
Return a new RDD by applying a function to each partition of this RDD.
>>> rdd = sc.parallelize([1, 2, 3, 4], 2)
>>> def f(iterator): yield sum(iterator)
>>> rdd.mapPartitions(f).collect()
[3, 7]
"""
... |
def mapPartitionsWithSplit(self, f, preservesPartitioning=False):
"""
Deprecated: use mapPartitionsWithIndex instead.
Return a new RDD by applying a function to each partition of this RDD,
while tracking the index of the original partition.
>>> rdd = sc.parallelize([1, 2, 3, 4]... |
def distinct(self, numPartitions=None):
"""
Return a new RDD containing the distinct elements in this RDD.
>>> sorted(sc.parallelize([1, 1, 2, 3]).distinct().collect())
[1, 2, 3]
"""
return self.map(lambda x: (x, None)) \
.reduceByKey(lambda x, _: x, n... |
def sample(self, withReplacement, fraction, seed=None):
"""
Return a sampled subset of this RDD.
:param withReplacement: can elements be sampled multiple times (replaced when sampled out)
:param fraction: expected size of the sample as a fraction of this RDD's size
without r... |
def randomSplit(self, weights, seed=None):
"""
Randomly splits this RDD with the provided weights.
:param weights: weights for splits, will be normalized if they don't sum to 1
:param seed: random seed
:return: split RDDs in a list
>>> rdd = sc.parallelize(range(500), 1... |
def takeSample(self, withReplacement, num, seed=None):
"""
Return a fixed-size sampled subset of this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
>>> rdd = sc.parallelize(... |
def _computeFractionForSampleSize(sampleSizeLowerBound, total, withReplacement):
"""
Returns a sampling rate that guarantees a sample of
size >= sampleSizeLowerBound 99.99% of the time.
How the sampling rate is determined:
Let p = num / total, where num is the sample size and to... |
def union(self, other):
"""
Return the union of this RDD and another one.
>>> rdd = sc.parallelize([1, 1, 2, 3])
>>> rdd.union(rdd).collect()
[1, 1, 2, 3, 1, 1, 2, 3]
"""
if self._jrdd_deserializer == other._jrdd_deserializer:
rdd = RDD(self._jrdd.uni... |
def intersection(self, other):
"""
Return the intersection of this RDD and another one. The output will
not contain any duplicate elements, even if the input RDDs did.
.. note:: This method performs a shuffle internally.
>>> rdd1 = sc.parallelize([1, 10, 2, 3, 4, 5])
>>... |
def repartitionAndSortWithinPartitions(self, numPartitions=None, partitionFunc=portable_hash,
ascending=True, keyfunc=lambda x: x):
"""
Repartition the RDD according to the given partitioner and, within each resulting partition,
sort records by their ke... |
def sortByKey(self, ascending=True, numPartitions=None, keyfunc=lambda x: x):
"""
Sorts this RDD, which is assumed to consist of (key, value) pairs.
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortByKey().first()
('1', 3)
>>> sc.p... |
def sortBy(self, keyfunc, ascending=True, numPartitions=None):
"""
Sorts this RDD by the given keyfunc
>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]
>>> sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]
... |
def cartesian(self, other):
"""
Return the Cartesian product of this RDD and another one, that is, the
RDD of all pairs of elements C{(a, b)} where C{a} is in C{self} and
C{b} is in C{other}.
>>> rdd = sc.parallelize([1, 2])
>>> sorted(rdd.cartesian(rdd).collect())
... |
def groupBy(self, f, numPartitions=None, partitionFunc=portable_hash):
"""
Return an RDD of grouped items.
>>> rdd = sc.parallelize([1, 1, 2, 3, 5, 8])
>>> result = rdd.groupBy(lambda x: x % 2).collect()
>>> sorted([(x, sorted(y)) for (x, y) in result])
[(0, [2, 8]), (1,... |
def pipe(self, command, env=None, checkCode=False):
"""
Return an RDD created by piping elements to a forked external process.
>>> sc.parallelize(['1', '2', '', '3']).pipe('cat').collect()
[u'1', u'2', u'', u'3']
:param checkCode: whether or not to check the return value of the... |
def foreach(self, f):
"""
Applies a function to all elements of this RDD.
>>> def f(x): print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreach(f)
"""
f = fail_on_stopiteration(f)
def processPartition(iterator):
for x in iterator:
f(x)
... |
def foreachPartition(self, f):
"""
Applies a function to each partition of this RDD.
>>> def f(iterator):
... for x in iterator:
... print(x)
>>> sc.parallelize([1, 2, 3, 4, 5]).foreachPartition(f)
"""
def func(it):
r = f(it)
... |
def collect(self):
"""
Return a list that contains all of the elements in this RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
"""
with SCCallSiteSync(self.context) as ... |
def reduce(self, f):
"""
Reduces the elements of this RDD using the specified commutative and
associative binary operator. Currently reduces partitions locally.
>>> from operator import add
>>> sc.parallelize([1, 2, 3, 4, 5]).reduce(add)
15
>>> sc.parallelize((2 ... |
def treeReduce(self, f, depth=2):
"""
Reduces the elements of this RDD in a multi-level tree pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, 4], 10)
>>> rdd.treeReduce(ad... |
def fold(self, zeroValue, op):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given associative function and a neutral "zero value."
The function C{op(t1, t2)} is allowed to modify C{t1} and return it
as its result value to avo... |
def aggregate(self, zeroValue, seqOp, combOp):
"""
Aggregate the elements of each partition, and then the results for all
the partitions, using a given combine functions and a neutral "zero
value."
The functions C{op(t1, t2)} is allowed to modify C{t1} and return it
as i... |
def treeAggregate(self, zeroValue, seqOp, combOp, depth=2):
"""
Aggregates the elements of this RDD in a multi-level tree
pattern.
:param depth: suggested depth of the tree (default: 2)
>>> add = lambda x, y: x + y
>>> rdd = sc.parallelize([-5, -4, -3, -2, -1, 1, 2, 3, ... |
def max(self, key=None):
"""
Find the maximum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([1.0, 5.0, 43.0, 10.0])
>>> rdd.max()
43.0
>>> rdd.max(key=str)
5.0
"""
if key is None:
... |
def min(self, key=None):
"""
Find the minimum item in this RDD.
:param key: A function used to generate key for comparing
>>> rdd = sc.parallelize([2.0, 5.0, 43.0, 10.0])
>>> rdd.min()
2.0
>>> rdd.min(key=str)
10.0
"""
if key is None:
... |
def sum(self):
"""
Add up the elements in this RDD.
>>> sc.parallelize([1.0, 2.0, 3.0]).sum()
6.0
"""
return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add) |
def stats(self):
"""
Return a L{StatCounter} object that captures the mean, variance
and count of the RDD's elements in one operation.
"""
def redFunc(left_counter, right_counter):
return left_counter.mergeStats(right_counter)
return self.mapPartitions(lambda... |
def histogram(self, buckets):
"""
Compute a histogram using the provided buckets. The buckets
are all open to the right except for the last which is closed.
e.g. [1,10,20,50] means the buckets are [1,10) [10,20) [20,50],
which means 1<=x<10, 10<=x<20, 20<=x<=50. And on the input ... |
def countByValue(self):
"""
Return the count of each unique value in this RDD as a dictionary of
(value, count) pairs.
>>> sorted(sc.parallelize([1, 2, 1, 2, 2], 2).countByValue().items())
[(1, 2), (2, 3)]
"""
def countPartition(iterator):
counts = de... |
def top(self, num, key=None):
"""
Get the top N elements from an RDD.
.. note:: This method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driver's memory.
.. note:: It returns the list sorted in descending order.
... |
def takeOrdered(self, num, key=None):
"""
Get the N elements from an RDD ordered in ascending order or as
specified by the optional key function.
.. note:: this method should only be used if the resulting array is expected
to be small, as all the data is loaded into the driv... |
def take(self, num):
"""
Take the first num elements of the RDD.
It works by first scanning one partition, and use the results from
that partition to estimate the number of additional partitions needed
to satisfy the limit.
Translated from the Scala implementation in RD... |
def saveAsNewAPIHadoopDataset(self, conf, keyConverter=None, valueConverter=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the new Hadoop OutputFormat API (mapreduce package). Keys/values are
converted for output using either u... |
def saveAsNewAPIHadoopFile(self, path, outputFormatClass, keyClass=None, valueClass=None,
keyConverter=None, valueConverter=None, conf=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the new Hadoop OutputF... |
def saveAsSequenceFile(self, path, compressionCodecClass=None):
"""
Output a Python RDD of key-value pairs (of form C{RDD[(K, V)]}) to any Hadoop file
system, using the L{org.apache.hadoop.io.Writable} types that we convert from the
RDD's key and value types. The mechanism is as follows:... |
def saveAsPickleFile(self, path, batchSize=10):
"""
Save this RDD as a SequenceFile of serialized objects. The serializer
used is L{pyspark.serializers.PickleSerializer}, default batch size
is 10.
>>> tmpFile = NamedTemporaryFile(delete=True)
>>> tmpFile.close()
... |
def saveAsTextFile(self, path, compressionCodecClass=None):
"""
Save this RDD as a text file, using string representations of elements.
@param path: path to text file
@param compressionCodecClass: (None by default) string i.e.
"org.apache.hadoop.io.compress.GzipCodec"
... |
def reduceByKey(self, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative and commutative reduce function.
This will also perform the merging locally on each mapper before
sending results to a reducer, similarly to a "combiner" ... |
def reduceByKeyLocally(self, func):
"""
Merge the values for each key using an associative and commutative reduce function, but
return the results immediately to the master as a dictionary.
This will also perform the merging locally on each mapper before
sending results to a red... |
def partitionBy(self, numPartitions, partitionFunc=portable_hash):
"""
Return a copy of the RDD partitioned using the specified partitioner.
>>> pairs = sc.parallelize([1, 2, 3, 4, 2, 4, 1]).map(lambda x: (x, x))
>>> sets = pairs.partitionBy(2).glom().collect()
>>> len(set(sets[... |
def combineByKey(self, createCombiner, mergeValue, mergeCombiners,
numPartitions=None, partitionFunc=portable_hash):
"""
Generic function to combine the elements for each key using a custom
set of aggregation functions.
Turns an RDD[(K, V)] into a result of type RDD... |
def aggregateByKey(self, zeroValue, seqFunc, combFunc, numPartitions=None,
partitionFunc=portable_hash):
"""
Aggregate the values of each key, using given combine functions and a neutral
"zero value". This function can return a different result type, U, than the type
... |
def foldByKey(self, zeroValue, func, numPartitions=None, partitionFunc=portable_hash):
"""
Merge the values for each key using an associative function "func"
and a neutral "zeroValue" which may be added to the result an
arbitrary number of times, and must not change the result
(e... |
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