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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_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.
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 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: 'output_response_probdist' argument can accept input value True
only when teradataml is connected to Vantage 1.0 Maintenance
Update 2 version or later.
Default Value: False
Types: bool
accumulate:
Optional Argument.
Specifies the names of newdata columns to copy to the output
teradataml DataFrame.
Types: str OR list of Strings (str)
output_responses:
Optional Argument.
Required if output_response_probdist is True.
Specifies all responses in newdata.
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 -
# First train the data, i.e., create a decision tree Model
td_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",
output_response_probdist = False)
# Run predict on the output of decision tree
decision_tree_predict_out = DecisionTreePredict(newdata=iris_attribute_test,
newdata_partition_column='pid',
object=td_decision_tree_out,
attr_table_groupby_columns='attribute',
attr_table_pid_columns='pid',
attr_table_val_column='attrvalue',
accumulate='pid',
output_response_probdist=False,
output_responses=['pid','attribute'])
# Print output dataframes
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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