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Methods defined here:
- __init__(self, object=None, newdata=None, attr_table_groupby_columns=None, attr_table_pid_columns=None, attr_table_val_column=None, output_response_probdist=False, accumulate=None, output_responses=None, newdata_sequence_column=None, object_sequence_column=None, newdata_partition_column=None, newdata_order_column=None, object_order_column=None)
- DESCRIPTION:
The DecisionTreePredict function applies a tree model to a data input,
outputting predicted labels for each data point.
Note: This function is available only when teradataml is connected to
Vantage 1.1 or later versions.
PARAMETERS:
object:
Required Argument.
Specifies the name of the teradataml DataFrame containing the output
model from DecisionTree or instance of DecisionTree.
object_order_column:
Optional Argument.
Specifies Order By columns for object.
Values to this argument can be provided as a list, if multiple columns
are used for ordering.
Types: str OR list of Strings (str)
newdata:
Required Argument.
Specifies the name of the teradataml DataFrame containing the
attribute names and the values.
newdata_partition_column:
Required Argument.
Specifies Partition By columns for newdata.
Values to this argument can be provided as a list, if multiple columns
are used for partition.
Types: str OR list of Strings (str)
newdata_order_column:
Optional Argument.
Specifies Order By columns for newdata.
Values to this argument can be provided as a list, if multiple columns
are used for ordering.
Types: str OR list of Strings (str)
attr_table_groupby_columns:
Required Argument.
Specifies the names of the columns on which attribute "newdata" is
partitioned. Each partition contains one attribute of the input data.
Types: str OR list of Strings (str)
attr_table_pid_columns:
Required Argument.
Specifies the names of the columns that define the data point
identifiers.
Types: str OR list of Strings (str)
attr_table_val_column:
Required Argument.
Specifies the name of the column that contains the input values.
Types: str
output_response_probdist:
Optional Argument.
Specifies whether to output probabilities.
Note:
1. 'output_response_probdist' argument can accept input value True
only when teradataml is connected to Vantage 1.0 Maintenance
Update 2 version or later.
2. 'output_response_probdist' can be set to True only when the
DecisionTree function call used to generate the model had
output_response_probdist = True.
Default Value: False
Types: bool
accumulate:
Optional Argument.
Specifies the names of input teradataml DataFrame columns to copy to the output
teradataML DataFrame.
Types: str OR list of Strings (str)
output_responses:
Optional Argument.
Specifies all responses in input teradataml DataFrame.
This argument requires the output_response_probdist argument to be set to True.
Types: str OR list of Strings (str)
newdata_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "newdata". 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)
object_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "object". 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 DecisionTreePredict.
Output teradataml DataFrames can be accessed using attribute
references, such as DecisionTreePredictObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("DecisionTreePredict", ["iris_attribute_train", "iris_response_train", "iris_attribute_test"])
# Create teradataml DataFrame.
iris_attribute_test = DataFrame.from_table("iris_attribute_test")
iris_attribute_train = DataFrame.from_table("iris_attribute_train")
iris_response_train = DataFrame.from_table("iris_response_train")
# Example 1 -
# We will try to predict the labels for each data point
# by the tree model in the train data (iris_attribute_train).
decision_tree_out = DecisionTree(attribute_name_columns = 'attribute',
attribute_value_column = 'attrvalue',
id_columns = 'pid',
attribute_table = iris_attribute_train,
response_table = iris_response_train,
response_column = 'response',
approx_splits = True,
nodesize = 100,
max_depth = 5,
weighted = False,
split_measure = "gini")
# Use the generated tree model to predict labels on the test data
# iris_attribute_test by using decision_tree_out which is already
# in the sparse format.
decision_tree_predict_out = DecisionTreePredict(newdata=iris_attribute_test,
newdata_partition_column='pid',
object=decision_tree_out,
attr_table_groupby_columns='attribute',
attr_table_pid_columns='pid',
attr_table_val_column='attrvalue',
accumulate='attrvalue',
newdata_sequence_column='pid',
newdata_order_column=['pid','attribute']
)
# Print the result DataFrame
print(decision_tree_predict_out.result)
- __repr__(self)
- Returns the string representation for a DecisionTreePredict 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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