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Methods defined here:
- __init__(self, coefficient=None, meta_table=None, input_columns=None, sort_column=None, partition_columns=None, coefficient_sequence_column=None, meta_table_sequence_column=None)
- DESCRIPTION:
The IDWT function is the inverse of DWT; that is, IDWT applies
inverse wavelet transforms on multiple sequences simultaneously.
IDWT takes as input the output teradataml DataFrame and meta
DataFrame output by DWT and outputs the sequences in time domain.
(Because the IDWT output is comparable to the DWT input, the
inverse transformation is also called the reconstruction.)
PARAMETERS:
coefficient:
Required Argument.
Specifies the name of the input teradataml DataFrame that
contains the coefficients generated by DWT. Typically, this
teradataml DataFrame is the output teradataml DataFrame of
DWT.
meta_table:
Required Argument.
Specifies the name of the input teradataml DataFrame that
contains the meta information used in DWT. Typically, this
teradataml DataFrame is the meta teradataml DataFrame output
by DWT.
input_columns:
Required Argument.
Specifies the names of the columns, present in the 'coefficent'
teradataml DataFrame, that contain the data to be transformed.
These columns must contain numeric values between -1e308 and
1e308. The function treats NULL in columns as 0.
Types: str OR list of Strings (str)
sort_column:
Required Argument.
Specifies the name of the input column that represents the
order of coefficients in each sequence (the waveletid column
in the DWT output teradataml DataFrame). The column must
contain a sequence of integer values that start from 1 for
each sequence. If a value is missing from the sequence, then
the function treats the corresponding data column as 0.
Types: str
partition_columns:
Optional Argument.
Specifies the names of the partition_columns, which identify
the sequences. Rows with the same partition_columns values
belong to the same sequence. If you specify multiple
partition_columns, then the function treats the first one as
the distribute key of the output and meta teradataml DataFrames.
By default, all rows belong to one sequence, and the function
generates a distribute key column named 'dwt_idrandom_name' in
both the output teradataml DataFrame and the meta teradataml
DataFrame. In both teradataml DataFrames, every cell of
'dwt_idrandom_name' has the value 1.
Types: str OR list of Strings (str)
coefficient_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each
row of the input argument "coefficient". 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)
meta_table_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each
row of the input argument "meta_table". 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 IDWT.
Output teradataml DataFrames can be accessed using attribute
references, such as IDWTObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. output_table
2. output
RAISES:
TeradataMlException
EXAMPLES:
# Load example data of "DWT".
load_example_data("dwt", ["ville_climatedata", "dwt_filter_dim"])
# The table "ville_climatedata" contains hourly climate data for five
# cities on a given day. The table "dwt_filter_dim" contains wavelet
# filter information.
# Example 1 : Apply inverse wavelet transform on the output of
# DWT, to generate time series sequence.
# Create teradataml DataFrame objects.
ville_climatedata = DataFrame.from_table("ville_climatedata")
dwt_filter_dim = DataFrame.from_table("dwt_filter_dim")
DWT_out = DWT(data = ville_climatedata,
input_columns = ["temp_f","pressure_mbar","dewpoint_f"],
wavelet_filter=dwt_filter_dim,
sort_column = "period",
level = 2,
partition_columns = "city",
wavelet_filter_sequence_column="filtername"
)
IDWT_out = IDWT(coefficient = DWT_out.coefficient,
meta_table = DWT_out.meta_table,
input_columns = ["temp_f","pressure_mbar","dewpoint_f"],
sort_column = "waveletid",
partition_columns = ["city"]
)
# Print the results
print(IDWT_out.output_table)
# Example 2 : Alternatively, persist the outputs of DWT in
# Vantage and use persisted tables to perform IDWT.
# Persisting DWT_out.coefficient to table named as 'dwt_coef_table'
# and DWT_out.meta_table to table named as 'dwt_meta_table'.
copy_to_sql(DWT_out.coefficient, "dwt_coef_table")
copy_to_sql(DWT_out.meta_table, "dwt_meta_table")
# Create teradataml DataFrame objects.
dwt_coef_table = DataFrame.from_table("dwt_coef_table")
dwt_meta_table = DataFrame.from_table("dwt_meta_table")
IDWT_out = IDWT(coefficient = dwt_coef_table,
meta_table = dwt_meta_table,
input_columns = ["temp_f","pressure_mbar","dewpoint_f"],
sort_column = "waveletid",
partition_columns = ["city"]
)
# Print the results
print(IDWT_out)
- __repr__(self)
- Returns the string representation for a IDWT 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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