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Methods defined here:
- __init__(self, data=None, partition_columns=None, duplicate_rows_count=1, order_by_columns=None, split_count=4, rows_per_split=1000, accumulate=None, split_id_column='split_id', return_stats_table=True, values_before_first='-1', values_after_last='null', duplicate_column=None, partial_split_id=False, data_sequence_column=None)
- DESCRIPTION:
The SeriesSplitter function splits partitions into subpartitions
(called splits) to balance the partitions for time series
manipulation. The function creates an additional column that contains
split identifiers. Each row contains the identifier of the split to
which the row belongs. Optionally, the function also copies a
specified number of boundary rows to each split.
PARAMETERS:
data:
Required Argument.
Specifies the name of the input teradataml DataFrame to be split.
partition_columns:
Required Argument.
Specifies the partitioning columns of teradataml DataFrame.
These columns determines the identity of a partition.
Types: str OR list of Strings (str)
duplicate_rows_count:
Optional Argument.
Specifies the number of rows to duplicate across split boundaries.
By default, the function duplicates one row from the previous
partition and one row from the next partition. If you specify
only one value v1, then the function duplicates v1 rows from the
previous partition and v1 rows from the next partition. If you
specify two values v1 and v2, then the function duplicates v1
rows from the previous partition and v2 rows from the next
partition. Each argument value must be non-negative integer
less than or equal to 1000.
Default Value: 1
Types: int or list of Integers (int)
order_by_columns:
Optional Argument.
Specifies the ordering columns of teradataml DataFrame. These
columns establish the order of the rows and splits. Without
this argument, the function can split the rows in any order.
Types: str OR list of Strings (str)
split_count:
Optional Argument.
Specifies the desired number of splits in a partition of the output
table. The value of "split_count" must be a positive int, and
its upper bound is the number of rows in the partition.
Note: If underlying table on Vantage, pointed by 'data', has
multiple partitions, then you cannot specify "split_count". Instead,
specify "rows_per_split". Base the value of "split_count" on the
desired amount of parallelism.
For example, for a cluster with 10 vworkers, make "split_count"
a multiple of 10. If the number of rows in teradataml DataFrame
(n) is not exactly divisible by "split_count", then the function
estimates the number of splits in the partition, using this formula:
ceiling (n / ceiling (n / split_count) )
Default Value: 4
Types: int
rows_per_split:
Optional Argument.
Specifies the desired maximum number of rows in each split in
the output teradataml DataFrame. If the number of rows in
input table is not exactly divisible by "rows_per_split", then
the last split contains fewer than "rows_per_split" rows, but
no row contains more than "rows_per_split" rows. The value of
"rows_per_split" must be a positive int.
Note: If underlying table on Vantage, pointed by 'data', has
multiple partitions, then specify "rows_per_split" instead of
"split_count".
Default Value: 1000
Types: int
accumulate:
Optional Argument.
Specifies the names of teradataml DataFrame columns (other than
those specified by "partition_columns" and "order_by_columns") to
copy to the output teradataml DataFrame. By default, only the
columns specified by "partition_columns" and "order_by_columns" are
copied to the output teradataml DataFrame.
Types: str OR list of Strings (str)
split_id_column:
Optional Argument.
Specifies the name for the output teradataml DataFrame column
to contain the split identifiers. If the output teradataml
DataFrame has another column name as that specified in
"split_id_column", the function returns an error. Therefore, if
the output teradataml DataFrame has a column named 'split_id'
(specified by "accumulate", "partition_columns", or
"order_by_columns"), you must use "split_id_column" to specify
a different value.
Default Value: "split_id"
Types: str
return_stats_table:
Optional Argument.
Specifies whether the function returns the data in "stats_table"
output teradataml DataFrame. When this value is "False", the
function returns only the data in "output_table" output
teradataml DataFrame.
Default Value: True
Types: bool
values_before_first:
Optional Argument.
If "duplicate_rows_count" is nonzero and "order_by_columns" is
specified, then "values_before_first" specifies the values to be
stored in the ordering columns that precede the first row of
the first split in a partition as a result of duplicating rows
across split boundaries. If "values_before_first" specifies only
one value and "order_by_columns" specifies multiple ordering columns,
then the specified value is stored in every ordering column.
If "values_before_first" specifies multiple values, then it must
specify a value for each ordering column. The value and the
ordering column must have the same data type. For the data type
str, the values are case-insensitive. The values for different
data types are:
int: -1,
str : "-1",
Date or time-based: 1900-01-01 0:00:00,
Boolean: False
Default Value: "-1"
Types: str
values_after_last:
Optional Argument.
If "duplicate_rows_count" is nonzero and orde_by_columns is
specified, then "values_after_last" specifies the values to be
stored in the ordering columns that follow the last row of the
last split in a partition as a result of duplicating rows across
split boundaries. If "values_after_last" specifies only one value
and "order_by_columns" specifies multiple ordering columns, then
the specified value is stored in every ordering column. If
"values_after_last" specifies multiple values, then it must specify
a value for each ordering column. The value and the ordering
column must have the same data type. For the data type str, the
values are case-insensitive.
Default Value: "null"
Types: str
duplicate_column:
Optional Argument.
Specifies the name of the column that indicates whether a row is
duplicated from the neighboring split. If the row is duplicated,
this column contains 1; otherwise it contains 0.
Types: str
partial_split_id:
Optional Argument.
Specifies whether "split_id_column" contains only the numeric split
identifier. If the value is "True", then "split_id_column"
contains a numeric representation of the split identifier that
is unique for each partition. To distribute the output
teradataml DataFrame by split, use a combination of all
partitioning columns and "split_id_column". If the value is "False",
then "split_id_column" contains a string representation of the
split that is unique across all partitions. The function
generates the string representation by concatenating the
partitioning columns with the order of the split inside the
partition (the numeric representation). In the string
representation, hyphens separate partitioning column names from
each other and from the order. For example, "pcol1- pcol2-3".
Default Value: False
Types: bool
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "data". The argument is used to ensure
deterministic results for functions which produce results that vary
from run to run.
Types: str OR list of Strings (str)
RETURNS:
Instance of SeriesSplitter.
Output teradataml DataFrames can be accessed using attribute
references, such as SeriesSplitterObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. output_table
2. stats_table
3. output
When the argument "return_stats_table" is set to True, all the three
output teradataml DataFrames are generated. But, when the argument
'return_stats_table' is False, the "stats_table" output teradataml
DataFrame will not be generated.
RAISES:
TeradataMlException
EXAMPLES:
# Load example data.
load_example_data("seriessplitter", "ibm_stock")
# Create teradataml DataFrame objects.
# The input table has the daily stock prices from 1961 to 1962.
ibm_stock = DataFrame.from_table("ibm_stock")
# Example 1 - This examples splits the time series stock data into
# subpartitions.
SeriesSplitter_out1 = SeriesSplitter(data = ibm_stock,
partition_columns = ["name"],
order_by_columns = ["period"],
split_count = 50,
accumulate = ["stockprice"]
)
# Print the results
print(SeriesSplitter_out1.output_table)
print(SeriesSplitter_out1.stats_table)
print(SeriesSplitter_out1.output)
# Example 2 - In the example "return_stats_table" is False which returns
# only the output_table.
SeriesSplitter_out2 = SeriesSplitter(data=ibm_stock,
partition_columns='name',
order_by_columns = 'period',
split_count = 9,
split_id_column = 'split_id',
duplicate_rows_count = [1,1],
return_stats_table = False,
accumulate = 'stockprice',
values_before_first = "1961-01-01",
values_after_last = "NULL",
partial_split_id = False
)
# Print the results
print(SeriesSplitter_out1.output_table)
print(SeriesSplitter_out1.output)
# Note: When argument return_stats_table is False output teradataml DataFrame,
# (stats_table) is not created. If tried to access this attribute
# an INFO message will be thrown mentioning the same.
- __repr__(self)
- Returns the string representation for a SeriesSplitter class instance.
- get_build_time(self)
- Function to return the build time of the algorithm in seconds.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_prediction_type(self)
- Function to return the Prediction type of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- get_target_column(self)
- Function to return the Target Column of the algorithm.
When model object is created using retrieve_model(), then the value returned is
as saved in the Model Catalog.
- show_query(self)
- Function to return the underlying SQL query.
When model object is created using retrieve_model(), then None is returned.
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