Python pyspark.RDD屬性代碼示例

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def clean_claims(claims: RDD, b_item_map: Broadcast):
    def clean(claim):
        item_map = b_item_map.value
        if claim.datatype == 'wikibase-item':
            if claim.object in item_map:
                claim = claim._replace(object=item_map[claim.object])
                return claim
            else:
                return None
        elif claim.datatype == 'quantity':
            unit = claim.object.unit
            unit = unit.split('/')[-1]
            if unit in item_map:
                claim = claim._replace(object=item_map[unit])
                return claim
            else:
                return None
        return claim

    dt_filter = {'wikibase-item', 'string', 'monolingualtext', 'quantity', 'time'}

    return claims.filter(lambda c: c.datatype in dt_filter).map(clean).filter(lambda c: c is not None) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def partition_per_row(rdd: RDD) -> RDD:
    """Place each row in an RDD into a separate partition.

    Only useful if that row represents something large to be computed over,
    perhaps an external resource such as a multi-gb training dataset. The spark
    part of the dataset is expected to be tiny and easily fit in a single
    partition.
    """
    num_rows = rdd.count()
    # Help out mypy. Also don't use `identity`, as it somehow fails serialization
    partition_fn = cast(Callable[[int], int], lambda x: x)

    return (
        # bring everything together and assign each row a partition id
        rdd.repartition(1)
        .mapPartitions(lambda rows: enumerate(rows))
        # Partition by the new parition_id
        .partitionBy(num_rows, partition_fn)
        # Drop the partition id, giving back the origional shape
        .map(lambda pair: pair[1])
    )


# Shared joins 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def apply(self, data_points: RDD, fault_tolerant: bool = False) -> np.ndarray:
        """Label PySpark RDD of data points with LFs.

        Parameters
        ----------
        data_points
            PySpark RDD containing data points to be labeled by LFs
        fault_tolerant
            Output ``-1`` if LF execution fails?

        Returns
        -------
        np.ndarray
            Matrix of labels emitted by LFs
        """
        f_caller = _FunctionCaller(fault_tolerant)

        def map_fn(args: Tuple[DataPoint, int]) -> RowData:
            return apply_lfs_to_data_point(*args, lfs=self._lfs, f_caller=f_caller)

        labels = data_points.zipWithIndex().map(map_fn).collect()
        return self._numpy_from_row_data(labels) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def _py2java(sc, obj):
    """ Convert Python object into Java """
    if isinstance(obj, RDD):
        obj = _to_java_object_rdd(obj)
    elif isinstance(obj, DataFrame):
        obj = obj._jdf
    elif isinstance(obj, SparkContext):
        obj = obj._jsc
    elif isinstance(obj, list):
        obj = [_py2java(sc, x) for x in obj]
    elif isinstance(obj, JavaObject):
        pass
    elif isinstance(obj, (int, long, float, bool, bytes, unicode)):
        pass
    else:
        data = bytearray(PickleSerializer().dumps(obj))
        obj = sc._jvm.org.apache.spark.ml.python.MLSerDe.loads(data)
    return obj 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def train(cls, data, lambda_=1.0):
        """
        Train a Naive Bayes model given an RDD of (label, features)
        vectors.

        This is the Multinomial NB (U{http://tinyurl.com/lsdw6p}) which
        can handle all kinds of discrete data.  For example, by
        converting documents into TF-IDF vectors, it can be used for
        document classification. By making every vector a 0-1 vector,
        it can also be used as Bernoulli NB (U{http://tinyurl.com/p7c96j6}).
        The input feature values must be nonnegative.

        :param data:
          RDD of LabeledPoint.
        :param lambda_:
          The smoothing parameter.
          (default: 1.0)
        """
        first = data.first()
        if not isinstance(first, LabeledPoint):
            raise ValueError("`data` should be an RDD of LabeledPoint")
        labels, pi, theta = callMLlibFunc("trainNaiveBayesModel", data, lambda_)
        return NaiveBayesModel(labels.toArray(), pi.toArray(), numpy.array(theta)) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def predict(self, x):
        """
        Predict the label of one or more examples.

        .. note:: In Python, predict cannot currently be used within an RDD
            transformation or action.
            Call predict directly on the RDD instead.

        :param x:
          Data point (feature vector), or an RDD of data points (feature
          vectors).
        """
        if isinstance(x, RDD):
            return self.call("predict", x.map(_convert_to_vector))

        else:
            return self.call("predict", _convert_to_vector(x)) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
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 one is
        undefined (same as java.util.Arrays.binarySearch).
        2) If x is lower or higher than all boundaries then first or
        last prediction is returned respectively. In case there are
        multiple predictions with the same boundary then the lowest
        or highest is returned respectively.
        3) If x falls between two values in boundary array then
        prediction is treated as piecewise linear function and
        interpolated value is returned. In case there are multiple
        values with the same boundary then the same rules as in 2)
        are used.

        :param x:
          Feature or RDD of Features to be labeled.
        """
        if isinstance(x, RDD):
            return x.map(lambda v: self.predict(v))
        return np.interp(x, self.boundaries, self.predictions) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def _py2java(sc, obj):
    """ Convert Python object into Java """
    if isinstance(obj, RDD):
        obj = _to_java_object_rdd(obj)
    elif isinstance(obj, DataFrame):
        obj = obj._jdf
    elif isinstance(obj, SparkContext):
        obj = obj._jsc
    elif isinstance(obj, list):
        obj = [_py2java(sc, x) for x in obj]
    elif isinstance(obj, JavaObject):
        pass
    elif isinstance(obj, (int, long, float, bool, bytes, unicode)):
        pass
    else:
        data = bytearray(PickleSerializer().dumps(obj))
        obj = sc._jvm.org.apache.spark.mllib.api.python.SerDe.loads(data)
    return obj 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
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()
        IndexedRow(0, [1.0,2.0,3.0])
        """
        # We use DataFrames for serialization of IndexedRows from
        # Java, so we first convert the RDD of rows to a DataFrame
        # on the Scala/Java side. Then we map each Row in the
        # DataFrame back to an IndexedRow on this side.
        rows_df = callMLlibFunc("getIndexedRows", self._java_matrix_wrapper._java_model)
        rows = rows_df.rdd.map(lambda row: IndexedRow(row[0], row[1]))
        return rows 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
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.first()
        MatrixEntry(0, 0, 1.2)
        """
        # We use DataFrames for serialization of MatrixEntry entries
        # from Java, so we first convert the RDD of entries to a
        # DataFrame on the Scala/Java side. Then we map each Row in
        # the DataFrame back to a MatrixEntry on this side.
        entries_df = callMLlibFunc("getMatrixEntries", self._java_matrix_wrapper._java_model)
        entries = entries_df.rdd.map(lambda row: MatrixEntry(row[0], row[1], row[2]))
        return entries 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
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), Matrices.dense(3, 2, [7, 8, 9, 10, 11, 12]))]), 3, 2)
        >>> blocks = mat.blocks
        >>> blocks.first()
        ((0, 0), DenseMatrix(3, 2, [1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 0))

        """
        # We use DataFrames for serialization of sub-matrix blocks
        # from Java, so we first convert the RDD of blocks to a
        # DataFrame on the Scala/Java side. Then we map each Row in
        # the DataFrame back to a sub-matrix block on this side.
        blocks_df = callMLlibFunc("getMatrixBlocks", self._java_matrix_wrapper._java_model)
        blocks = blocks_df.rdd.map(lambda row: ((row[0][0], row[0][1]), row[1]))
        return blocks 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def pprint(self, num=10):
        """
        Print the first num elements of each RDD generated in this DStream.

        @param num: the number of elements from the first will be printed.
        """
        def takeAndPrint(time, rdd):
            taken = rdd.take(num + 1)
            print("-------------------------------------------")
            print("Time: %s" % time)
            print("-------------------------------------------")
            for record in taken[:num]:
                print(record)
            if len(taken) > num:
                print("...")
            print("")

        self.foreachRDD(takeAndPrint) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def transformWith(self, func, other, keepSerializer=False):
        """
        Return a new DStream in which each RDD is generated by applying a function
        on each RDD of this DStream and 'other' DStream.

        `func` can have two arguments of (`rdd_a`, `rdd_b`) or have three
        arguments of (`time`, `rdd_a`, `rdd_b`)
        """
        if func.__code__.co_argcount == 2:
            oldfunc = func
            func = lambda t, a, b: oldfunc(a, b)
        assert func.__code__.co_argcount == 3, "func should take two or three arguments"
        jfunc = TransformFunction(self._sc, func, self._jrdd_deserializer, other._jrdd_deserializer)
        dstream = self._sc._jvm.PythonTransformed2DStream(self._jdstream.dstream(),
                                                          other._jdstream.dstream(), jfunc)
        jrdd_serializer = self._jrdd_deserializer if keepSerializer else self._sc.serializer
        return DStream(dstream.asJavaDStream(), self._ssc, jrdd_serializer) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def window(self, windowDuration, slideDuration=None):
        """
        Return a new DStream in which each RDD contains all the elements in seen in a
        sliding window of time over this DStream.

        @param windowDuration: width of the window; must be a multiple of this DStream's
                              batching interval
        @param slideDuration:  sliding interval of the window (i.e., the interval after which
                              the new DStream will generate RDDs); must be a multiple of this
                              DStream's batching interval
        """
        self._validate_window_param(windowDuration, slideDuration)
        d = self._ssc._jduration(windowDuration)
        if slideDuration is None:
            return DStream(self._jdstream.window(d), self._ssc, self._jrdd_deserializer)
        s = self._ssc._jduration(slideDuration)
        return DStream(self._jdstream.window(d, s), self._ssc, self._jrdd_deserializer) 

# 需要導入模塊: import pyspark [as 別名]
# 或者: from pyspark import RDD [as 別名]
def countByValueAndWindow(self, windowDuration, slideDuration, numPartitions=None):
        """
        Return a new DStream in which each RDD contains the count of distinct elements in
        RDDs in a sliding window over this DStream.

        @param windowDuration: width of the window; must be a multiple of this DStream's
                              batching interval
        @param slideDuration:  sliding interval of the window (i.e., the interval after which
                              the new DStream will generate RDDs); must be a multiple of this
                              DStream's batching interval
        @param numPartitions:  number of partitions of each RDD in the new DStream.
        """
        keyed = self.map(lambda x: (x, 1))
        counted = keyed.reduceByKeyAndWindow(operator.add, operator.sub,
                                             windowDuration, slideDuration, numPartitions)
        return counted.filter(lambda kv: kv[1] > 0)