| | |
- RegressionEvaluator(data=None, observation_column=None, prediction_column=None, metrics=None, independent_features_num=None, freedom_degrees=None)
- DESCRIPTION:
The RegressionEvaluator() function computes metrics to evaluate and compare
multiple models and summarizes how close predictions are to their expected
values.
Notes:
* This function requires the UTF8 client character set for UNICODE data.
* This function does not support Pass Through Characters (PTCs).
* For information about PTCs, see Teradata Vantage™ - Analytics Database
International Character Set Support.
* This function does not support KanjiSJIS or Graphic data types.
PARAMETERS:
data:
Required Argument.
Specifies the input teradataml DataFrame.
Types: teradataml DataFrame
observation_column:
Required Argument.
Specifies the column name in "data" containing observation labels.
Types: str
prediction_column:
Required Argument.
Specifies the column name in "data" containing predicted labels.
Types: str
metrics:
Optional Argument.
Specifies the list of evaluation metrics. The function returns
the following metrics if the list is not provided:
MAE:
Mean absolute error (MAE) is the arithmetic average of the
absolute errors between observed values and predicted values.
MSE:
Mean squared error (MSE) is the average of the squares of
the errors between observed values and predicted values.
MSLE:
Mean Square Log Error (MSLE) is the relative difference
between the log-transformed observed values and predicted
values.
MAPE:
Mean Absolute Percentage Error (MAPE) is the mean or
average of the absolute percentage errors of forecasts.
MPE:
Mean percentage error (MPE) is the computed average
of percentage errors by which predicted values differ from
observed values.
RMSE:
Root means squared error (MSE) is the square root of the
average of the squares of the errors between observed
values and predicted values.
RMSLE:
Root means Square Log Error (MSLE) is the square root
of the relative difference between the log-transformed
observed values and predicted values.
R2:
R Squared (R2) is the proportion of the variation in the
dependent variable that is predictable from the independent
variable(s).
AR2:
Adjusted R-squared (AR2) is a modified version of R-squared
that has been adjusted for the independent variable(s) in
the model.
EV:
Explained variation (EV) measures the proportion to which
a mathematical model accounts for the variation (dispersion)
of a given data set.
ME:
Max-Error (ME) is the worst-case error between observed
values and predicted values.
MPD:
Mean Poisson Deviance (MPD) is equivalent to Tweedie
Deviances when the power parameter value is 1.
MGD:
Mean Gamma Deviance (MGD) is equivalent to Tweedie
Deviances when the power parameter value is 2.
FSTAT:
F-statistics (FSTAT) conducts an F-test. An F-test
is any statistical test in which the test statistic has an
F-distribution under the null hypothesis.
* F_score:
F_score value from the F-test.
* F_Critcialvalue:
F critical value from the F-test. (alpha, df1, df2,
UPPER_TAILED), alpha = 95%
* p_value:
Probability value associated with the F_score value
(F_score, df1, df2, UPPER_TAILED)
* F_conclusion:
F-test result, either 'reject null hypothesis' or
'fail to reject null hypothesis'. If F_score >
F_Critcialvalue, then 'reject null hypothesis'
Else 'fail to reject null hypothesis'
Types: str OR list of strs
independent_features_num:
Optional Argument.
Specifies the number of independent variables in the model.
Required with Adjusted R Squared metric, else ignored.
Types: int
freedom_degrees:
Optional Argument.
Specifies the numerator degrees of freedom (df1) and denominator
degrees of freedom (df2). Required with fstat metric, else ignored.
Types: int OR list of ints
**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: bool
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: bool
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 RegressionEvaluator.
Output teradataml DataFrames can be accessed using attribute
references, such as RegressionEvaluatorObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
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.
from teradataml import valib
# Set the 'configure.val_install_location' variable.
from teradataml import configure
configure.val_install_location = "val"
# Create required teradataml DataFrame.
# Load the example data.
load_example_data("teradataml", "titanic")
# Create teradataml DataFrame objects.
titanic = DataFrame.from_table("titanic")
# First generate linear regression model using LinReg() function from 'valib'.
lin_reg_obj = valib.LinReg(data=titanic,
columns=["age", "survived", "pclass"],
response_column="fare")
# Score the data using the linear regression model generated above.
obj = valib.LinRegPredict(data=titanic,
model=lin_reg_obj.model,
accumulate = "fare",
response_column="fare_prediction")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1 : Compute 'RMSE', 'R2' and 'FSTAT' metrics to evaluate
# the model.
RegressionEvaluator_out = RegressionEvaluator(data = obj.result,
observation_column = "fare",
prediction_column = "fare_prediction",
freedom_degrees = [1, 2],
independent_features_num = 2,
metrics = ['RMSE','R2','FSTAT'])
# Print the result DataFrame.
print(RegressionEvaluator_out.result)
|