| | |
- OutlierFilterFit(data=None, target_columns=None, group_columns=None, lower_percentile=None, upper_percentile=None, iqr_multiplier=1.5, outlier_method=None, replacement_value=None, remove_tail='BOTH', percentile_method=None, **generic_arguments)
- DESCRIPTION:
The OutlierFilterFit() function calculates the lower percentile, upper percentile,
count of rows and median for all the "target_columns" provided by the user.
These metrics for each column help the function OutlierTransform() detect
outliers in data. It stores parameters from arguments into an output used
during transformation.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
target_columns:
Required Argument.
Specifies the name(s) of the column(s) in "data" for which to compute the metrics.
Types: str OR list of Strings (str)
lower_percentile:
Required Argument.
Specifies the lower range of percentile to be used.
Types: float
upper_percentile:
Required Argument.
Specifies the upper range of percentile to be used.
Types: float
outlier_method:
Required Argument.
Specifies the method for filtering the outliers.
Permitted Values:
* PERCENTILE - [min_value, max_value].
* TUKEY - [Q1 - k*(Q3-Q1), Q1 + k*(Q3-Q1)]
where:
Q1 = 25th quartile of data
Q3 = 75th quartile of data
k = interquantile range multiplier (see "iqr_multiplier")
* CARLING - Q2 ± c*(Q3-Q1)
where:
Q2 = median of data
Q1 = 25th quartile of data
Q3 = 75th quartile of data
c = (17.63*r - 23.64) / (7.74*r - 3.71)
r = count of rows in group_columns if you specify "group_columns",
otherwise count of rows in "data"
Types: str
replacement_value:
Required Argument.
Specifies the method to handle outliers.
Permitted Values:
* DELETE - Do not copy row to output DataFrame.
* NULL - Copy row to output DataFrame, replacing each outlier with NULL.
* MEDIAN - Copy row to output DataFrame, replacing each outlier with median
value for its group.
* replacement value - Copy row to output DataFrame, replacing each outlier with
a replacement value. Replacement value must be numeric.
Types: str, int, float
percentile_method:
Required Argument.
Specifies the teradata percentile methods to be used for calculating the upper
and lower percentiles of the "target_columns".
Permitted Values:
* PERCENTILECONT - Considering continuous distribution.
* PERCENTILEDISC - Considering discrete distibution.
Types: str
group_columns:
Optional Argument.
Specifies the name(s) of the column(s) in "data" to group.
Types: str
iqr_multiplier:
Optional Argument.
Specifies the multiplier of interquartile range for 'TUKEY' filtering.
Default Value: 1.5
Types: float
remove_tail:
Optional Argument.
Specifies the tail of the distribution to remove.
Permitted Values:
* LOWER - The lower tail.
* UPPER - The upper tail.
* BOTH - Both tails.
Default Value: "BOTH"
Types: 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 table or not.
When set to True, results are persisted in 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 volatile table or not.
When set to True, results are stored in 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
SQLE Engine function supports, else an exception is raised.
RETURNS:
Instance of OutlierFilterFit.
Output teradataml DataFrames can be accessed using attribute
references, such as OutlierFilterFitObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Notes:
# 1. Get the connection to Vantage, before importing the function in user space.
# 2. User can import the function, if it is available on the Vantage user is connected to.
# 3. To check the list of analytic functions available on the Vantage user connected to,
# use "display_analytic_functions()".
# Load the example data.
load_example_data("teradataml", ["titanic"])
# Create teradataml DataFrame object.
titanic_data = DataFrame.from_table("titanic")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function OutlierFilterFit.
from teradataml import OutlierFilterFit
# Example 1: Generating fit object to find outlier values in column "fare".
fit_obj = OutlierFilterFit(data=titanic_data,
target_columns="fare",
lower_percentile=0.1,
upper_percentile=0.9,
outlier_method="PERCENTILE",
replacement_value="MEDIAN",
percentile_method="PERCENTILECONT")
# Print the result DataFrame.
print(fit_obj.result)
|