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def size(col):
"""
Collection function: returns the length of the array or map stored in the column.
:param col: name of column or expression
>>> df = spark.createDataFrame([([1, 2, 3],),([1],),([],)], ['data'])
>>> df.select(size(df.data)).collect()
[Row(size(data)=3), Row(size(data)=1), Row(... |
def sort_array(col, asc=True):
"""
Collection function: sorts the input array in ascending or descending order according
to the natural ordering of the array elements. Null elements will be placed at the beginning
of the returned array in ascending order or at the end of the returned array in descending... |
def array_repeat(col, count):
"""
Collection function: creates an array containing a column repeated count times.
>>> df = spark.createDataFrame([('ab',)], ['data'])
>>> df.select(array_repeat(df.data, 3).alias('r')).collect()
[Row(r=[u'ab', u'ab', u'ab'])]
"""
sc = SparkContext._active_spa... |
def map_concat(*cols):
"""Returns the union of all the given maps.
:param cols: list of column names (string) or list of :class:`Column` expressions
>>> from pyspark.sql.functions import map_concat
>>> df = spark.sql("SELECT map(1, 'a', 2, 'b') as map1, map(3, 'c', 1, 'd') as map2")
>>> df.select(... |
def sequence(start, stop, step=None):
"""
Generate a sequence of integers from `start` to `stop`, incrementing by `step`.
If `step` is not set, incrementing by 1 if `start` is less than or equal to `stop`,
otherwise -1.
>>> df1 = spark.createDataFrame([(-2, 2)], ('C1', 'C2'))
>>> df1.select(seq... |
def from_csv(col, schema, options={}):
"""
Parses a column containing a CSV string to a row with the specified schema.
Returns `null`, in the case of an unparseable string.
:param col: string column in CSV format
:param schema: a string with schema in DDL format to use when parsing the CSV column.
... |
def udf(f=None, returnType=StringType()):
"""Creates a user defined function (UDF).
.. note:: The user-defined functions are considered deterministic by default. Due to
optimization, duplicate invocations may be eliminated or the function may even be invoked
more times than it is present in the... |
def pandas_udf(f=None, returnType=None, functionType=None):
"""
Creates a vectorized user defined function (UDF).
:param f: user-defined function. A python function if used as a standalone function
:param returnType: the return type of the user-defined function. The value can be either a
:class... |
def to_str(value):
"""
A wrapper over str(), but converts bool values to lower case strings.
If None is given, just returns None, instead of converting it to string "None".
"""
if isinstance(value, bool):
return str(value).lower()
elif value is None:
return value
else:
... |
def _set_opts(self, schema=None, **options):
"""
Set named options (filter out those the value is None)
"""
if schema is not None:
self.schema(schema)
for k, v in options.items():
if v is not None:
self.option(k, v) |
def format(self, source):
"""Specifies the input data source format.
:param source: string, name of the data source, e.g. 'json', 'parquet'.
>>> df = spark.read.format('json').load('python/test_support/sql/people.json')
>>> df.dtypes
[('age', 'bigint'), ('name', 'string')]
... |
def schema(self, schema):
"""Specifies the input schema.
Some data sources (e.g. JSON) can infer the input schema automatically from data.
By specifying the schema here, the underlying data source can skip the schema
inference step, and thus speed up data loading.
:param schema... |
def option(self, key, value):
"""Adds an input option for the underlying data source.
You can set the following option(s) for reading files:
* ``timeZone``: sets the string that indicates a timezone to be used to parse timestamps
in the JSON/CSV datasources or partition valu... |
def options(self, **options):
"""Adds input options for the underlying data source.
You can set the following option(s) for reading files:
* ``timeZone``: sets the string that indicates a timezone to be used to parse timestamps
in the JSON/CSV datasources or partition values... |
def load(self, path=None, format=None, schema=None, **options):
"""Loads data from a data source and returns it as a :class`DataFrame`.
:param path: optional string or a list of string for file-system backed data sources.
:param format: optional string for format of the data source. Default to ... |
def json(self, path, schema=None, primitivesAsString=None, prefersDecimal=None,
allowComments=None, allowUnquotedFieldNames=None, allowSingleQuotes=None,
allowNumericLeadingZero=None, allowBackslashEscapingAnyCharacter=None,
mode=None, columnNameOfCorruptRecord=None, dateFormat=No... |
def parquet(self, *paths):
"""Loads Parquet files, returning the result as a :class:`DataFrame`.
You can set the following Parquet-specific option(s) for reading Parquet files:
* ``mergeSchema``: sets whether we should merge schemas collected from all \
Parquet part-files. T... |
def text(self, paths, wholetext=False, lineSep=None):
"""
Loads text files and returns a :class:`DataFrame` whose schema starts with a
string column named "value", and followed by partitioned columns if there
are any.
The text files must be encoded as UTF-8.
By default, ... |
def csv(self, path, schema=None, sep=None, encoding=None, quote=None, escape=None,
comment=None, header=None, inferSchema=None, ignoreLeadingWhiteSpace=None,
ignoreTrailingWhiteSpace=None, nullValue=None, nanValue=None, positiveInf=None,
negativeInf=None, dateFormat=None, timestampFo... |
def orc(self, path):
"""Loads ORC files, returning the result as a :class:`DataFrame`.
>>> df = spark.read.orc('python/test_support/sql/orc_partitioned')
>>> df.dtypes
[('a', 'bigint'), ('b', 'int'), ('c', 'int')]
"""
if isinstance(path, basestring):
path = [... |
def jdbc(self, url, table, column=None, lowerBound=None, upperBound=None, numPartitions=None,
predicates=None, properties=None):
"""
Construct a :class:`DataFrame` representing the database table named ``table``
accessible via JDBC URL ``url`` and connection ``properties``.
... |
def mode(self, saveMode):
"""Specifies the behavior when data or table already exists.
Options include:
* `append`: Append contents of this :class:`DataFrame` to existing data.
* `overwrite`: Overwrite existing data.
* `error` or `errorifexists`: Throw an exception if data alre... |
def format(self, source):
"""Specifies the underlying output data source.
:param source: string, name of the data source, e.g. 'json', 'parquet'.
>>> df.write.format('json').save(os.path.join(tempfile.mkdtemp(), 'data'))
"""
self._jwrite = self._jwrite.format(source)
re... |
def option(self, key, value):
"""Adds an output option for the underlying data source.
You can set the following option(s) for writing files:
* ``timeZone``: sets the string that indicates a timezone to be used to format
timestamps in the JSON/CSV datasources or partition va... |
def options(self, **options):
"""Adds output options for the underlying data source.
You can set the following option(s) for writing files:
* ``timeZone``: sets the string that indicates a timezone to be used to format
timestamps in the JSON/CSV datasources or partition valu... |
def partitionBy(self, *cols):
"""Partitions the output by the given columns on the file system.
If specified, the output is laid out on the file system similar
to Hive's partitioning scheme.
:param cols: name of columns
>>> df.write.partitionBy('year', 'month').parquet(os.path... |
def sortBy(self, col, *cols):
"""Sorts the output in each bucket by the given columns on the file system.
:param col: a name of a column, or a list of names.
:param cols: additional names (optional). If `col` is a list it should be empty.
>>> (df.write.format('parquet') # doctest: +SK... |
def save(self, path=None, format=None, mode=None, partitionBy=None, **options):
"""Saves the contents of the :class:`DataFrame` to a data source.
The data source is specified by the ``format`` and a set of ``options``.
If ``format`` is not specified, the default data source configured by
... |
def insertInto(self, tableName, overwrite=False):
"""Inserts the content of the :class:`DataFrame` to the specified table.
It requires that the schema of the class:`DataFrame` is the same as the
schema of the table.
Optionally overwriting any existing data.
"""
self._jw... |
def saveAsTable(self, name, format=None, mode=None, partitionBy=None, **options):
"""Saves the content of the :class:`DataFrame` as the specified table.
In the case the table already exists, behavior of this function depends on the
save mode, specified by the `mode` function (default to throwin... |
def json(self, path, mode=None, compression=None, dateFormat=None, timestampFormat=None,
lineSep=None, encoding=None):
"""Saves the content of the :class:`DataFrame` in JSON format
(`JSON Lines text format or newline-delimited JSON <http://jsonlines.org/>`_) at the
specified path.
... |
def parquet(self, path, mode=None, partitionBy=None, compression=None):
"""Saves the content of the :class:`DataFrame` in Parquet format at the specified path.
:param path: the path in any Hadoop supported file system
:param mode: specifies the behavior of the save operation when data already e... |
def text(self, path, compression=None, lineSep=None):
"""Saves the content of the DataFrame in a text file at the specified path.
The text files will be encoded as UTF-8.
:param path: the path in any Hadoop supported file system
:param compression: compression codec to use when saving t... |
def csv(self, path, mode=None, compression=None, sep=None, quote=None, escape=None,
header=None, nullValue=None, escapeQuotes=None, quoteAll=None, dateFormat=None,
timestampFormat=None, ignoreLeadingWhiteSpace=None, ignoreTrailingWhiteSpace=None,
charToEscapeQuoteEscaping=None, encod... |
def orc(self, path, mode=None, partitionBy=None, compression=None):
"""Saves the content of the :class:`DataFrame` in ORC format at the specified path.
:param path: the path in any Hadoop supported file system
:param mode: specifies the behavior of the save operation when data already exists.
... |
def jdbc(self, url, table, mode=None, properties=None):
"""Saves the content of the :class:`DataFrame` to an external database table via JDBC.
.. note:: Don't create too many partitions in parallel on a large cluster;
otherwise Spark might crash your external database systems.
:par... |
def createStream(ssc, kinesisAppName, streamName, endpointUrl, regionName,
initialPositionInStream, checkpointInterval,
storageLevel=StorageLevel.MEMORY_AND_DISK_2,
awsAccessKeyId=None, awsSecretKey=None, decoder=utf8_decoder,
stsAssume... |
def choose_jira_assignee(issue, asf_jira):
"""
Prompt the user to choose who to assign the issue to in jira, given a list of candidates,
including the original reporter and all commentors
"""
while True:
try:
reporter = issue.fields.reporter
commentors = map(lambda x:... |
def standardize_jira_ref(text):
"""
Standardize the [SPARK-XXXXX] [MODULE] prefix
Converts "[SPARK-XXX][mllib] Issue", "[MLLib] SPARK-XXX. Issue" or "SPARK XXX [MLLIB]: Issue" to
"[SPARK-XXX][MLLIB] Issue"
>>> standardize_jira_ref(
... "[SPARK-5821] [SQL] ParquetRelation2 CTAS should check ... |
def _parse_libsvm_line(line):
"""
Parses a line in LIBSVM format into (label, indices, values).
"""
items = line.split(None)
label = float(items[0])
nnz = len(items) - 1
indices = np.zeros(nnz, dtype=np.int32)
values = np.zeros(nnz)
for i in xrange... |
def _convert_labeled_point_to_libsvm(p):
"""Converts a LabeledPoint to a string in LIBSVM format."""
from pyspark.mllib.regression import LabeledPoint
assert isinstance(p, LabeledPoint)
items = [str(p.label)]
v = _convert_to_vector(p.features)
if isinstance(v, SparseVecto... |
def loadLibSVMFile(sc, path, numFeatures=-1, minPartitions=None):
"""
Loads labeled data in the LIBSVM format into an RDD of
LabeledPoint. The LIBSVM format is a text-based format used by
LIBSVM and LIBLINEAR. Each line represents a labeled sparse
feature vector using the followi... |
def saveAsLibSVMFile(data, dir):
"""
Save labeled data in LIBSVM format.
:param data: an RDD of LabeledPoint to be saved
:param dir: directory to save the data
>>> from tempfile import NamedTemporaryFile
>>> from fileinput import input
>>> from pyspark.mllib.reg... |
def loadLabeledPoints(sc, path, minPartitions=None):
"""
Load labeled points saved using RDD.saveAsTextFile.
:param sc: Spark context
:param path: file or directory path in any Hadoop-supported file
system URI
:param minPartitions: min number of partitions
... |
def appendBias(data):
"""
Returns a new vector with `1.0` (bias) appended to
the end of the input vector.
"""
vec = _convert_to_vector(data)
if isinstance(vec, SparseVector):
newIndices = np.append(vec.indices, len(vec))
newValues = np.append(vec.v... |
def convertVectorColumnsToML(dataset, *cols):
"""
Converts vector columns in an input DataFrame from the
:py:class:`pyspark.mllib.linalg.Vector` type to the new
:py:class:`pyspark.ml.linalg.Vector` type under the `spark.ml`
package.
:param dataset:
input datase... |
def generateLinearInput(intercept, weights, xMean, xVariance,
nPoints, seed, eps):
"""
:param: intercept bias factor, the term c in X'w + c
:param: weights feature vector, the term w in X'w + c
:param: xMean Point around which the data X is centered.
... |
def generateLinearRDD(sc, nexamples, nfeatures, eps,
nParts=2, intercept=0.0):
"""
Generate an RDD of LabeledPoints.
"""
return callMLlibFunc(
"generateLinearRDDWrapper", sc, int(nexamples), int(nfeatures),
float(eps), int(nParts), float(... |
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=0.0, regType=None, intercept=False,
validateData=True, convergenceTol=0.001):
"""
Train a linear regression model using Stochastic Gradient
Descent (SGD). This solves t... |
def predict(self, x):
"""
Predict labels for provided features.
Using a piecewise linear function.
1) If x exactly matches a boundary then associated prediction
is returned. In case there are multiple predictions with the
same boundary then one of them is returned. Which ... |
def save(self, sc, path):
"""Save an IsotonicRegressionModel."""
java_boundaries = _py2java(sc, self.boundaries.tolist())
java_predictions = _py2java(sc, self.predictions.tolist())
java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel(
java_boundaries... |
def load(cls, sc, path):
"""Load an IsotonicRegressionModel."""
java_model = sc._jvm.org.apache.spark.mllib.regression.IsotonicRegressionModel.load(
sc._jsc.sc(), path)
py_boundaries = _java2py(sc, java_model.boundaryVector()).toArray()
py_predictions = _java2py(sc, java_mode... |
def train(cls, data, isotonic=True):
"""
Train an isotonic regression model on the given data.
:param data:
RDD of (label, feature, weight) tuples.
:param isotonic:
Whether this is isotonic (which is default) or antitonic.
(default: True)
"""
... |
def columnSimilarities(self, threshold=0.0):
"""
Compute similarities between columns of this matrix.
The threshold parameter is a trade-off knob between estimate
quality and computational cost.
The default threshold setting of 0 guarantees deterministically
correct res... |
def tallSkinnyQR(self, computeQ=False):
"""
Compute the QR decomposition of this RowMatrix.
The implementation is designed to optimize the QR decomposition
(factorization) for the RowMatrix of a tall and skinny shape.
Reference:
Paul G. Constantine, David F. Gleich. "T... |
def computeSVD(self, k, computeU=False, rCond=1e-9):
"""
Computes the singular value decomposition of the RowMatrix.
The given row matrix A of dimension (m X n) is decomposed into
U * s * V'T where
* U: (m X k) (left singular vectors) is a RowMatrix whose
columns a... |
def multiply(self, matrix):
"""
Multiply this matrix by a local dense matrix on the right.
:param matrix: a local dense matrix whose number of rows must match the number of columns
of this matrix
:returns: :py:class:`RowMatrix`
>>> rm = RowMatrix(sc.paral... |
def U(self):
"""
Returns a distributed matrix whose columns are the left
singular vectors of the SingularValueDecomposition if computeU was set to be True.
"""
u = self.call("U")
if u is not None:
mat_name = u.getClass().getSimpleName()
if mat_name... |
def rows(self):
"""
Rows of the IndexedRowMatrix stored as an RDD of IndexedRows.
>>> mat = IndexedRowMatrix(sc.parallelize([IndexedRow(0, [1, 2, 3]),
... IndexedRow(1, [4, 5, 6])]))
>>> rows = mat.rows
>>> rows.first()
Inde... |
def toBlockMatrix(self, rowsPerBlock=1024, colsPerBlock=1024):
"""
Convert this matrix to a BlockMatrix.
:param rowsPerBlock: Number of rows that make up each block.
The blocks forming the final rows are not
required to have the given nu... |
def multiply(self, matrix):
"""
Multiply this matrix by a local dense matrix on the right.
:param matrix: a local dense matrix whose number of rows must match the number of columns
of this matrix
:returns: :py:class:`IndexedRowMatrix`
>>> mat = IndexedRow... |
def entries(self):
"""
Entries of the CoordinateMatrix stored as an RDD of
MatrixEntries.
>>> mat = CoordinateMatrix(sc.parallelize([MatrixEntry(0, 0, 1.2),
... MatrixEntry(6, 4, 2.1)]))
>>> entries = mat.entries
>>> entries... |
def blocks(self):
"""
The RDD of sub-matrix blocks
((blockRowIndex, blockColIndex), sub-matrix) that form this
distributed matrix.
>>> mat = BlockMatrix(
... sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
... ((1, 0), ... |
def persist(self, storageLevel):
"""
Persists the underlying RDD with the specified storage level.
"""
if not isinstance(storageLevel, StorageLevel):
raise TypeError("`storageLevel` should be a StorageLevel, got %s" % type(storageLevel))
javaStorageLevel = self._java_... |
def add(self, other):
"""
Adds two block matrices together. The matrices must have the
same size and matching `rowsPerBlock` and `colsPerBlock` values.
If one of the sub matrix blocks that are being added is a
SparseMatrix, the resulting sub matrix block will also be a
Sp... |
def transpose(self):
"""
Transpose this BlockMatrix. Returns a new BlockMatrix
instance sharing the same underlying data. Is a lazy operation.
>>> blocks = sc.parallelize([((0, 0), Matrices.dense(3, 2, [1, 2, 3, 4, 5, 6])),
... ((1, 0), Matrices.dense(3,... |
def _vector_size(v):
"""
Returns the size of the vector.
>>> _vector_size([1., 2., 3.])
3
>>> _vector_size((1., 2., 3.))
3
>>> _vector_size(array.array('d', [1., 2., 3.]))
3
>>> _vector_size(np.zeros(3))
3
>>> _vector_size(np.zeros((3, 1)))
3
>>> _vector_size(np.zero... |
def parse(s):
"""
Parse string representation back into the DenseVector.
>>> DenseVector.parse(' [ 0.0,1.0,2.0, 3.0]')
DenseVector([0.0, 1.0, 2.0, 3.0])
"""
start = s.find('[')
if start == -1:
raise ValueError("Array should start with '['.")
... |
def dot(self, other):
"""
Compute the dot product of two Vectors. We support
(Numpy array, list, SparseVector, or SciPy sparse)
and a target NumPy array that is either 1- or 2-dimensional.
Equivalent to calling numpy.dot of the two vectors.
>>> dense = DenseVector(array.... |
def squared_distance(self, other):
"""
Squared distance of two Vectors.
>>> dense1 = DenseVector(array.array('d', [1., 2.]))
>>> dense1.squared_distance(dense1)
0.0
>>> dense2 = np.array([2., 1.])
>>> dense1.squared_distance(dense2)
2.0
>>> dense3... |
def parse(s):
"""
Parse string representation back into the SparseVector.
>>> SparseVector.parse(' (4, [0,1 ],[ 4.0,5.0] )')
SparseVector(4, {0: 4.0, 1: 5.0})
"""
start = s.find('(')
if start == -1:
raise ValueError("Tuple should start with '('")
... |
def dot(self, other):
"""
Dot product with a SparseVector or 1- or 2-dimensional Numpy array.
>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
>>> a.dot(a)
25.0
>>> a.dot(array.array('d', [1., 2., 3., 4.]))
22.0
>>> b = SparseVector(4, [2], [1.0])
>>> ... |
def squared_distance(self, other):
"""
Squared distance from a SparseVector or 1-dimensional NumPy array.
>>> a = SparseVector(4, [1, 3], [3.0, 4.0])
>>> a.squared_distance(a)
0.0
>>> a.squared_distance(array.array('d', [1., 2., 3., 4.]))
11.0
>>> a.squar... |
def toArray(self):
"""
Returns a copy of this SparseVector as a 1-dimensional NumPy array.
"""
arr = np.zeros((self.size,), dtype=np.float64)
arr[self.indices] = self.values
return arr |
def asML(self):
"""
Convert this vector to the new mllib-local representation.
This does NOT copy the data; it copies references.
:return: :py:class:`pyspark.ml.linalg.SparseVector`
.. versionadded:: 2.0.0
"""
return newlinalg.SparseVector(self.size, self.indice... |
def dense(*elements):
"""
Create a dense vector of 64-bit floats from a Python list or numbers.
>>> Vectors.dense([1, 2, 3])
DenseVector([1.0, 2.0, 3.0])
>>> Vectors.dense(1.0, 2.0)
DenseVector([1.0, 2.0])
"""
if len(elements) == 1 and not isinstance(elem... |
def fromML(vec):
"""
Convert a vector from the new mllib-local representation.
This does NOT copy the data; it copies references.
:param vec: a :py:class:`pyspark.ml.linalg.Vector`
:return: a :py:class:`pyspark.mllib.linalg.Vector`
.. versionadded:: 2.0.0
"""
... |
def squared_distance(v1, v2):
"""
Squared distance between two vectors.
a and b can be of type SparseVector, DenseVector, np.ndarray
or array.array.
>>> a = Vectors.sparse(4, [(0, 1), (3, 4)])
>>> b = Vectors.dense([2, 5, 4, 1])
>>> a.squared_distance(b)
... |
def parse(s):
"""Parse a string representation back into the Vector.
>>> Vectors.parse('[2,1,2 ]')
DenseVector([2.0, 1.0, 2.0])
>>> Vectors.parse(' ( 100, [0], [2])')
SparseVector(100, {0: 2.0})
"""
if s.find('(') == -1 and s.find('[') != -1:
return... |
def _equals(v1_indices, v1_values, v2_indices, v2_values):
"""
Check equality between sparse/dense vectors,
v1_indices and v2_indices assume to be strictly increasing.
"""
v1_size = len(v1_values)
v2_size = len(v2_values)
k1 = 0
k2 = 0
all_equal = ... |
def _convert_to_array(array_like, dtype):
"""
Convert Matrix attributes which are array-like or buffer to array.
"""
if isinstance(array_like, bytes):
return np.frombuffer(array_like, dtype=dtype)
return np.asarray(array_like, dtype=dtype) |
def toArray(self):
"""
Return an numpy.ndarray
>>> m = DenseMatrix(2, 2, range(4))
>>> m.toArray()
array([[ 0., 2.],
[ 1., 3.]])
"""
if self.isTransposed:
return np.asfortranarray(
self.values.reshape((self.numRows, se... |
def toSparse(self):
"""Convert to SparseMatrix"""
if self.isTransposed:
values = np.ravel(self.toArray(), order='F')
else:
values = self.values
indices = np.nonzero(values)[0]
colCounts = np.bincount(indices // self.numRows)
colPtrs = np.cumsum(np.... |
def asML(self):
"""
Convert this matrix to the new mllib-local representation.
This does NOT copy the data; it copies references.
:return: :py:class:`pyspark.ml.linalg.DenseMatrix`
.. versionadded:: 2.0.0
"""
return newlinalg.DenseMatrix(self.numRows, self.numCo... |
def toArray(self):
"""
Return an numpy.ndarray
"""
A = np.zeros((self.numRows, self.numCols), dtype=np.float64, order='F')
for k in xrange(self.colPtrs.size - 1):
startptr = self.colPtrs[k]
endptr = self.colPtrs[k + 1]
if self.isTransposed:
... |
def asML(self):
"""
Convert this matrix to the new mllib-local representation.
This does NOT copy the data; it copies references.
:return: :py:class:`pyspark.ml.linalg.SparseMatrix`
.. versionadded:: 2.0.0
"""
return newlinalg.SparseMatrix(self.numRows, self.num... |
def sparse(numRows, numCols, colPtrs, rowIndices, values):
"""
Create a SparseMatrix
"""
return SparseMatrix(numRows, numCols, colPtrs, rowIndices, values) |
def fromML(mat):
"""
Convert a matrix from the new mllib-local representation.
This does NOT copy the data; it copies references.
:param mat: a :py:class:`pyspark.ml.linalg.Matrix`
:return: a :py:class:`pyspark.mllib.linalg.Matrix`
.. versionadded:: 2.0.0
"""
... |
def approxNearestNeighbors(self, dataset, key, numNearestNeighbors, distCol="distCol"):
"""
Given a large dataset and an item, approximately find at most k items which have the
closest distance to the item. If the :py:attr:`outputCol` is missing, the method will
transform the data; if th... |
def approxSimilarityJoin(self, datasetA, datasetB, threshold, distCol="distCol"):
"""
Join two datasets to approximately find all pairs of rows whose distance are smaller than
the threshold. If the :py:attr:`outputCol` is missing, the method will transform the data;
if the :py:attr:`outp... |
def from_labels(cls, labels, inputCol, outputCol=None, handleInvalid=None):
"""
Construct the model directly from an array of label strings,
requires an active SparkContext.
"""
sc = SparkContext._active_spark_context
java_class = sc._gateway.jvm.java.lang.String
... |
def from_arrays_of_labels(cls, arrayOfLabels, inputCols, outputCols=None,
handleInvalid=None):
"""
Construct the model directly from an array of array of label strings,
requires an active SparkContext.
"""
sc = SparkContext._active_spark_context
... |
def setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=False,
locale=None):
"""
setParams(self, inputCol=None, outputCol=None, stopWords=None, caseSensitive=false, \
locale=None)
Sets params for this StopWordRemover.
"""
kwargs ... |
def loadDefaultStopWords(language):
"""
Loads the default stop words for the given language.
Supported languages: danish, dutch, english, finnish, french, german, hungarian,
italian, norwegian, portuguese, russian, spanish, swedish, turkish
"""
stopWordsObj = _jvm().org.a... |
def findSynonyms(self, word, num):
"""
Find "num" number of words closest in similarity to "word".
word can be a string or vector representation.
Returns a dataframe with two fields word and similarity (which
gives the cosine similarity).
"""
if not isinstance(wor... |
def findSynonymsArray(self, word, num):
"""
Find "num" number of words closest in similarity to "word".
word can be a string or vector representation.
Returns an array with two fields word and similarity (which
gives the cosine similarity).
"""
if not isinstance(w... |
def install_exception_handler():
"""
Hook an exception handler into Py4j, which could capture some SQL exceptions in Java.
When calling Java API, it will call `get_return_value` to parse the returned object.
If any exception happened in JVM, the result will be Java exception object, it raise
py4j.p... |
def toJArray(gateway, jtype, arr):
"""
Convert python list to java type array
:param gateway: Py4j Gateway
:param jtype: java type of element in array
:param arr: python type list
"""
jarr = gateway.new_array(jtype, len(arr))
for i in range(0, len(arr)):
jarr[i] = arr[i]
retu... |
def require_minimum_pandas_version():
""" Raise ImportError if minimum version of Pandas is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pandas_version = "0.19.2"
from distutils.version import LooseVersion
try:
import pandas
... |
def require_minimum_pyarrow_version():
""" Raise ImportError if minimum version of pyarrow is not installed
"""
# TODO(HyukjinKwon): Relocate and deduplicate the version specification.
minimum_pyarrow_version = "0.12.1"
from distutils.version import LooseVersion
try:
import pyarrow
... |
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