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Methods defined here:
- __init__(self, object=None, data=None, attribute_columns=None, summary=False, data_sequence_column=None, object_sequence_column=None, data_order_column=None, object_order_column=None)
- DESCRIPTION:
SVMDenseSummary extracts readable information from the model
produced by SVMDense. The function can display either a
summary of the model training results or a teradataml DataFrame
containing the weights for each attribute.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model
data generated by SVMDense or instance of SVMDense,
which contains the model.
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)
data:
Required Argument.
Specifies the teradataml DataFrame containing the input test data.
It should be the training dataset, otherwise the result may be
incomplete.
data_order_column:
Optional Argument.
Specifies Order By columns for data.
Values to this argument can be provided as a list, if multiple
columns are used for ordering.
Types: str OR list of Strings (str)
attribute_columns:
Required Argument.
Specifies the input teradataml DataFrame columns that contain the
attributes of the test samples. Attribute columns must be
numeric (int, real, bigint,smallint, or float).
Python teradataml DataFrame column types accepted: (int, float, long).
Types: str OR list of Strings (str)
summary:
Optional Argument.
If True, the output contains only summary information of the model.
If False, the output contains the weight of each attribute in the
model.
Default Value: False
Types: bool
data_sequence_column:
Optional Argument.
Specifies the list of column(s) that uniquely identifies each row of
the input argument "data". 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 SVMDenseSummary.
Output teradataml DataFrames can be accessed using attribute
references, such as SVMDenseSummaryObj.<attribute_name>.
Output teradataml DataFrame attribute name is:
result
RAISES:
TeradataMlException
EXAMPLES:
# Load the data to run the example.
load_example_data("svmdense","nb_iris_input_train")
# Create teradataml DataFrame
nb_iris_input_train = DataFrame.from_table("nb_iris_input_train")
# Generate Radial Basis Model (RBF) Model
densesvm_iris_rbf_model = SVMDense(data = nb_iris_input_train,
sample_id_column = "id",
attribute_columns = ['sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'],
kernel_function = "rbf",
gamma = 0.1,
subspace_dimension = 120,
hash_bits = 512,
label_column = "species",
cost = 1.0,
bias = 0.0,
max_step = 100,
seed = 1
)
# Example 1 - Display the model parameters (weights, attributes etc).
svm_dense_summary_out1 = SVMDenseSummary(object = densesvm_iris_rbf_model,
data = nb_iris_input_train,
attribute_columns=['sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'],
summary = False
)
# Print the result DataFrame
print(svm_dense_summary_out1)
- __repr__(self)
- Returns the string representation for a SVMDenseSummary class instance.
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