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- ScaleFit(data=None, target_columns=None, scale_method=None, miss_value='KEEP', global_scale=False, multiplier='1', intercept='0', parameter_data=None, attribute_data=None, partition_columns=None, ignoreinvalid_locationscale=False, unused_attributes='UNSCALED', attribute_name_column=None, attribute_value_column=None, target_attributes=None, **generic_arguments)
- DESCRIPTION:
ScaleFit() function outputs statistics to input to ScaleTransform() function,
which scales specified input DataFrame columns.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
target_columns:
Required Argument.
Specifies the input teradataml DataFrame column(s) for which to output statistics.
The columns must contain numeric data in the range (-1e³⁰⁸, 1e³⁰⁸).
Note:
* This argument cannot be used with "target_attributes", "attribute_name_column",
"attribute_value_column".
Types: str OR list of Strings (str)
scale_method:
Required Argument.
Specifies the scale method to be used for scaling. If one value is
provided, it applies to all target columns. If more than one value is
specified, scale method values applies to target columns values in the
order specified by the user.
ScaleTransform() function uses the location and scale values in the
following formula to scale target column value X to scaled value X':
X' = intercept + multiplier * ((X - location)/scale)
"intercept" and "multiplier" arguments determine intercept and multiplier.
In the table, Xmin, Xmax, and XMean are the minimum, maximum, and mean
values of the target column.
+--------------+---------------------------+-----------------+------------------------------+
| scale_method | Description | location | scale |
+--------------+---------------------------+-----------------+------------------------------+
| MAXABS | Maximum absolute value. | 0 | Maximum |X| |
| | | | |
| MEAN | Mean. | XMean | 1 |
| | | | |
| MIDRANGE | Midrange. | (Xmax+Xmin)/2 | (Xmax-Xmin)/2 |
| | | | |
| RANGE | Range. | Xmin | Xmax-Xmin |
| | | | |
| RESCALE | Rescale using specified | See table after | See table after |
| | lower bound, upper bound, | RESCALE syntax. | RESCALE syntax. |
| | or both.See syntax | | |
| | after this table. | | |
| | | | |
| STD | Standard deviation. | XMean | √(∑((Xi - Xmean)2)/ N) |
| | | | where N is count of |
| | | | valid values. |
| | | | |
| SUM | Sum. | 0 | ΣX |
| | | | |
| USTD | Unbiased standard | XMean | √(∑((Xi - Xmean)2)/ (N - 1)) |
| | deviation. | | where N is count of |
| | | | valid values. |
+--------------+---------------------------+-----------------+------------------------------+
RESCALE ({ lb=lower_bound | ub=upper_bound | lb=lower_bound, ub=upper_bound })
+------------------------+-----------------------------+----------------------------+
| | location | scale |
+------------------------+-----------------------------+----------------------------+
| Lower bound only | Xmin - lower_bound | 1 |
| | | |
| Upper bound only | Xmax - upper_bound | 1 |
| | | |
| Lower and upper bounds | Xmin - (lower_bound/ | (Xmax - Xmin)/ |
| | (upper_bound- lower_bound)) | (upper_bound- lower_bound) |
+------------------------+-----------------------------+----------------------------+
Permitted Values:
* MAXABS
* MEAN
* MIDRANGE
* RANGE
* RESCALE
* STD
* SUM
* USTD
Types: str OR list of Strings (str)
miss_value:
Optional Argument.
Specifies how to process NULL values in input.
Permitted Values:
* KEEP: Keep NULL values.
* ZERO: Replace each NULL value with zero.
* LOCATION: Replace each NULL value with its location value.
Default Value: "KEEP"
Types: str
global_scale:
Optional Argument.
Specifies whether all input columns are scaled to the same location
and scale. When set to False, each input column is scaled separately.
Default Value: False
Types: bool
multiplier:
Optional Argument.
Specifies one or more multiplying factors(multiplier) to apply to the input data.
If only one multiplier is specified, it applies to all target columns.
If a list of multipliers is specified, each multiplier applies to the
corresponding target column.
Default Value: "1"
Types: str OR list of String (str)
intercept:
Optional Argument.
Specifies one or more addition factors(intercept) incrementing the scaled results.
If only one intercept specified, it applies to all target columns.
If a list of intercepts is specified, each intercept applies to the
corresponding target column.
The syntax of intercept is:
[-]{number | min | mean | max }
where min, mean, and max are the global minimum, maximum, mean values
in the corresponding columns. The function scales the values of min,
mean, and max.
For example, if intercept is "- min" and multiplier is
1, the scaled result is transformed to a non-negative sequence
according to this formula, where scaledmin is the scaled value:
X = -scaledmin + 1 * (X - location)/scale.
Default Value: "0"
Types: str OR list of String (str)
parameter_data:
Optional Argument.
Specifies the input teradataml DataFrame containing the parameters.
Note:
* This is valid when "data_partition_column" is used.
Types: teradataml DataFrame
attribute_data:
Optional Argument.
Specifies the input teradataml DataFrame containing the attributes.
Note:
* This is valid when "data_partition_column" is used.
Types: teradataml DataFrame
partition_columns:
Optional Argument.
Specifies the column name in the "data" to partition the input.
Types: str OR list of Strings (str)
ignoreinvalid_locationscale:
Optional Argument.
Specifies whether to ignore invalid values of location and scale parameters.
Default Value: False
Types: bool
unused_attributes:
Optional Argument.
Specifies whether to emit out unused attributes of different partitions
as unscaled values or NULLs (for dense input).
Permitted Values: 'NULLIFY', 'UNSCALED'
Default Value: 'UNSCALED'
Types: str
attribute_name_column:
Optional Argument.
Specifies the column name in the "attribute_data" which contains attribute names.
Note:
* This is required for sparse input.
Types: str
attribute_value_column:
Optional Argument.
Specifies the column name in the "attribute_data" which contains attribute values.
Note:
* This is required for sparse input.
Types: str
target_attributes:
Optional Argument.
Specifies the attributes for which scaling should be performed.
Note:
* This is required for sparse input.
Types: str OR list of Strings (str)
**generic_arguments:
Specifies the generic keyword arguments SQLE functions accept.
Below are the generic keyword arguments:
persist:
Optional Argument.
Specifies whether to persist the results of the function in a table or not.
When set to True, results are persisted in a table; otherwise, results
are garbage collected at the end of the session.
Default Value: False
Types: boolean
volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not.
When set to True, results are stored in a volatile table, otherwise not.
Default Value: False
Types: boolean
Function allows the user to partition, hash, order or local order the input
data. These generic arguments are available for each argument that accepts
teradataml DataFrame as input and can be accessed as:
* "<input_data_arg_name>_partition_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_hash_column" accepts str or list of str (Strings)
* "<input_data_arg_name>_order_column" accepts str or list of str (Strings)
* "local_order_<input_data_arg_name>" accepts boolean
Note:
These generic arguments are supported by teradataml if the underlying SQL Engine
function supports, else an exception is raised.
RETURNS:
Instance of ScaleFit.
Output teradataml DataFrames can be accessed using attribute
references, such as ScaleFitObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. output
2. output_data
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage to execute the function.
# 2. One must import the required functions mentioned in
# the example from teradataml.
# 3. Function will raise error if not supported on the Vantage
# user is connected to.
# Load the example data.
load_example_data("teradataml", ["scale_housing"])
load_example_data('scale', ["scale_attributes", "scale_parameters",
"scale_input_partitioned", "scale_input_sparse","scale_input_part_sparse"])
# Create teradataml DataFrame.
scaling_house = DataFrame.from_table("scale_housing")
scale_attribute = DataFrame.from_table("scale_attributes")
scale_parameter = DataFrame.from_table("scale_parameters")
scale_inp_part = DataFrame.from_table("scale_input_partitioned")
scale_inp_sparse = DataFrame.from_table("scale_input_sparse")
scale_inp_part_sparse = DataFrame.from_table("scale_input_part_sparse")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1: Create statistics to scale "lotsize" with respect to
# mean value of the column.
fit_obj = ScaleFit(data=scaling_house,
target_columns="lotsize",
scale_method="MEAN",
miss_value="KEEP",
global_scale=False,
multiplier="1",
intercept="0")
# Print the result DataFrame.
print(fit_obj.output)
print(fit_obj.output_data)
# Example 2: Create statistics to scale "fare" and "age" columns
# with respect to maximum absolute value with partition column
# for dense input.
fit_obj = ScaleFit(data=scale_inp_part,
attribute_data=scale_attribute,
parameter_data=scale_parameter,
target_columns=['fare', 'age'],
scale_method="maxabs",
miss_value="zero",
global_scale=False,
data_partition_column='pid',
attribute_data_partition_column='pid',
parameter_data_partition_column='pid')
# Print the result DataFrame.
print(fit_obj.output)
print(fit_obj.output_data)
# Example 3: Create statistics to scale "fare" column with respect to
# range for sparse input.
fit_obj = ScaleFit(data=scale_inp_sparse,
target_attribute=['fare'],
scale_method="range",
miss_value="keep",
global_scale=False,
attribute_name_column='attribute_column',
attribute_value_column='attribute_value')
# Print the result DataFrame.
print(fit_obj.output)
print(fit_obj.output_data)
# Example 4: Create statistics to scale "fare" column with respect to
# maximum absolute value for sparse input with partition column.
fit_obj = ScaleFit(data=scale_inp_part_sparse,
parameter_data=scale_parameter,
attribute_data=scale_attribute,
scale_method="maxabs",
miss_value="zero",
global_scale=False,
attribute_name_column='attribute_column',
attribute_value_column='attribute_value',
data_partition_column='pid',
attribute_data_partition_column='pid',
parameter_data_partition_column='pid')
# Print the result DataFrame.
print(fit_obj.output)
print(fit_obj.output_data)
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