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- Shap(data=None, object=None, training_function='TD_GLM', model_type='Regression', input_columns=None, detailed=False, accumulate=None, num_parallel_trees=1000, num_boost_rounds=10, **generic_arguments)
- DESCRIPTION:
Function to get explanation for individual predictions
(feature contributions) in a machine learning model based on the
co-operative game theory optimal Shapley values.
PARAMETERS:
data:
Required Argument.
Specifies the teradataml DataFrame.
Types: teradataml DataFrame
object:
Required Argument.
Specifies the teradataml DataFrame containing the model data.
Types: teradataml DataFrame
training_function:
Required Argument.
Specifies the model type name.
Default Value: "TD_GLM"
Permitted Values: TD_GLM, TD_DECISIONFOREST, TD_XGBOOST
Types: str
model_type:
Required Argument.
Specifies the operation to be performed on input data.
Default Value: "Regression"
Permitted Values: Regression, Classification
Types: str
input_columns:
Required Argument.
Specifies the names of the columns in "data" used for
training the model (predictors, features or independent variables).
Types: str OR list of Strings (str)
detailed:
Optional Argument.
Specifies whether to output detailed shap information about the
forest trees.
Default Value: False
Types: bool
accumulate:
Optional Argument.
Specifies the names of the input columns to copy to the output teradataml DataFrame.
Types: str OR list of Strings (str)
num_parallel_trees:
Optional Argument.
Specify the number of parallel boosted trees. Each boosted tree
operates on a sample of data that fits in an AMPs memory.
Note:
* By default, "num_parallel_trees" is chosen equal to the number of AMPs with
data.
Default Value: 1000
Types: int
num_boost_rounds:
Optional Argument.
Specifies the number of iterations to boost the weak classifiers. The
iterations must be an int in the range [1, 100000].
Default Value: 10
Types: int
**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 Shap.
Output teradataml DataFrames can be accessed using attribute
references, such as ShapObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
1. output
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
# Vantage user is connected to.
# 3. To check the list of analytic functions available on
# Vantage user connected to, use
# "display_analytic_functions()".
# Load the example data.
load_example_data("byom", "iris_input")
load_example_data("teradataml", ["cal_housing_ex_raw"])
# Create teradataml DataFrame objects.
iris_input = DataFrame("iris_input")
data_input = DataFrame.from_table("cal_housing_ex_raw")
# Check the list of available analytic functions.
display_analytic_functions()
# Import function Shap.
from teradataml import Shap, XGBoost, DecisionForest, SVM
# Example 1: Shap for classification model.
XGBoost_out = XGBoost(data=iris_input,
input_columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
response_column = 'species',
model_type='Classification',
iter_num=25)
Shap_out = Shap(data=iris_input,
object=XGBoost_out.result,
id_column='id',
training_function="TD_XGBOOST",
model_type="Classification",
input_columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'],
detailed=True)
# Print the result DataFrame.
print(Shap_out.output_data)
# Example 2: Shap for regression model.
from teradataml import ScaleFit, ScaleTransform
# Scale "target_columns" with respect to 'STD' value of the column.
fit_obj = ScaleFit(data=data_input,
target_columns=['MedInc', 'HouseAge', 'AveRooms',
'AveBedrms', 'Population', 'AveOccup',
'Latitude', 'Longitude'],
scale_method="STD")
# Transform the data.
transform_obj = ScaleTransform(data=data_input,
object=fit_obj.output,
accumulate=["id", "MedHouseVal"])
decision_forest_out = DecisionForest(data=transform_obj.result,
input_columns=['MedInc', 'HouseAge', 'AveRooms',
'AveBedrms', 'Population', 'AveOccup',
'Latitude', 'Longitude'],
response_column="MedHouseVal",
model_type="Regression",
max_depth = 10
)
Shap_out2 = Shap(data=transform_obj.result,
object=decision_forest_out.result,
id_column='id',
training_function="TD_DECISIONFOREST",
model_type="Regression",
input_columns=['MedInc', 'HouseAge', 'AveRooms','AveBedrms', 'Population', 'AveOccup','Latitude', 'Longitude'],
detailed=True)
# Print the result DataFrame.
print(Shap_out2.output_data)
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