Python StructType.add方法代碼示例

# 需要導入模塊: from pyspark.sql.types import StructType [as 別名]
# 或者: from pyspark.sql.types.StructType import add [as 別名]
def _get_schema(header, schema):
    if schema is None or len(schema) == 0:
        # Use header to generate schema
        if header is None or len(header) == 0:
            return None
        elif len(header) > 4:
            warnings.warn(WARNING_MOVIE_LENS_HEADER)
            header = header[:4]

        schema = StructType()
        try:
            schema.add(StructField(header[0], IntegerType())).add(
                StructField(header[1], IntegerType())
            ).add(StructField(header[2], FloatType())).add(
                StructField(header[3], LongType())
            )
        except IndexError:
            pass
    else:
        if header is not None:
            warnings.warn(WARNING_HAVE_SCHEMA_AND_HEADER)

        if len(schema) > 4:
            warnings.warn(WARNING_MOVIE_LENS_HEADER)
            schema = schema[:4]

    return schema

# 需要導入模塊: from pyspark.sql.types import StructType [as 別名]
# 或者: from pyspark.sql.types.StructType import add [as 別名]
def get_spark_schema(header=DEFAULT_HEADER):
    ## create schema
    schema = StructType()
    ## do label + ints
    n_ints = 14
    for i in range(n_ints):
        schema.add(StructField(header[i], IntegerType()))
    ## do categoricals
    for i in range(26):
        schema.add(StructField(header[i + n_ints], StringType()))
    return schema

# 需要導入模塊: from pyspark.sql.types import StructType [as 別名]
# 或者: from pyspark.sql.types.StructType import add [as 別名]
 def __init__(self, scoreAndLabels):
     sc = scoreAndLabels.ctx
     sql_ctx = SQLContext.getOrCreate(sc)
     numCol = len(scoreAndLabels.first())
     schema = StructType([
         StructField("score", DoubleType(), nullable=False),
         StructField("label", DoubleType(), nullable=False)])
     if numCol == 3:
         schema.add("weight", DoubleType(), False)
     df = sql_ctx.createDataFrame(scoreAndLabels, schema=schema)
     java_class = sc._jvm.org.apache.spark.mllib.evaluation.BinaryClassificationMetrics
     java_model = java_class(df._jdf)
     super(BinaryClassificationMetrics, self).__init__(java_model)

# 需要導入模塊: from pyspark.sql.types import StructType [as 別名]
# 或者: from pyspark.sql.types.StructType import add [as 別名]
 def __init__(self, predAndLabelsWithOptWeight):
     sc = predAndLabelsWithOptWeight.ctx
     sql_ctx = SQLContext.getOrCreate(sc)
     numCol = len(predAndLabelsWithOptWeight.first())
     schema = StructType([
         StructField("prediction", DoubleType(), nullable=False),
         StructField("label", DoubleType(), nullable=False)])
     if (numCol == 3):
         schema.add("weight", DoubleType(), False)
     df = sql_ctx.createDataFrame(predAndLabelsWithOptWeight, schema)
     java_class = sc._jvm.org.apache.spark.mllib.evaluation.MulticlassMetrics
     java_model = java_class(df._jdf)
     super(MulticlassMetrics, self).__init__(java_model)

# 需要導入模塊: from pyspark.sql.types import StructType [as 別名]
# 或者: from pyspark.sql.types.StructType import add [as 別名]
    def _create_from_pandas_with_arrow(self, pdf, schema, timezone):
        """
        Create a DataFrame from a given pandas.DataFrame by slicing it into partitions, converting
        to Arrow data, then sending to the JVM to parallelize. If a schema is passed in, the
        data types will be used to coerce the data in Pandas to Arrow conversion.
        """
        from distutils.version import LooseVersion
        from pyspark.serializers import ArrowStreamPandasSerializer
        from pyspark.sql.types import from_arrow_type, to_arrow_type, TimestampType
        from pyspark.sql.utils import require_minimum_pandas_version, \
            require_minimum_pyarrow_version

        require_minimum_pandas_version()
        require_minimum_pyarrow_version()

        from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
        import pyarrow as pa

        # Create the Spark schema from list of names passed in with Arrow types
        if isinstance(schema, (list, tuple)):
            if LooseVersion(pa.__version__) < LooseVersion("0.12.0"):
                temp_batch = pa.RecordBatch.from_pandas(pdf[0:100], preserve_index=False)
                arrow_schema = temp_batch.schema
            else:
                arrow_schema = pa.Schema.from_pandas(pdf, preserve_index=False)
            struct = StructType()
            for name, field in zip(schema, arrow_schema):
                struct.add(name, from_arrow_type(field.type), nullable=field.nullable)
            schema = struct

        # Determine arrow types to coerce data when creating batches
        if isinstance(schema, StructType):
            arrow_types = [to_arrow_type(f.dataType) for f in schema.fields]
        elif isinstance(schema, DataType):
            raise ValueError("Single data type %s is not supported with Arrow" % str(schema))
        else:
            # Any timestamps must be coerced to be compatible with Spark
            arrow_types = [to_arrow_type(TimestampType())
                           if is_datetime64_dtype(t) or is_datetime64tz_dtype(t) else None
                           for t in pdf.dtypes]

        # Slice the DataFrame to be batched
        step = -(-len(pdf) // self.sparkContext.defaultParallelism)  # round int up
        pdf_slices = (pdf[start:start + step] for start in xrange(0, len(pdf), step))

        # Create list of Arrow (columns, type) for serializer dump_stream
        arrow_data = [[(c, t) for (_, c), t in zip(pdf_slice.iteritems(), arrow_types)]
                      for pdf_slice in pdf_slices]

        jsqlContext = self._wrapped._jsqlContext

        safecheck = self._wrapped._conf.arrowSafeTypeConversion()
        col_by_name = True  # col by name only applies to StructType columns, can't happen here
        ser = ArrowStreamPandasSerializer(timezone, safecheck, col_by_name)

        def reader_func(temp_filename):
            return self._jvm.PythonSQLUtils.readArrowStreamFromFile(jsqlContext, temp_filename)

        def create_RDD_server():
            return self._jvm.ArrowRDDServer(jsqlContext)

        # Create Spark DataFrame from Arrow stream file, using one batch per partition
        jrdd = self._sc._serialize_to_jvm(arrow_data, ser, reader_func, create_RDD_server)
        jdf = self._jvm.PythonSQLUtils.toDataFrame(jrdd, schema.json(), jsqlContext)
        df = DataFrame(jdf, self._wrapped)
        df._schema = schema
        return df