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- OneClassSVMPredict(object=None, newdata=None, id_column=None, accumulate=None, output_prob=False, output_responses=None, **generic_arguments)
- DESCRIPTION:
The OneClassSVMPredict() function uses the model generated
by the function OneClassSVM() to predicts target class labels
(classification) on new input data. Output values are 0 and 1.
A value of 1 corresponds to a 'normal' observation, and a value
of 0 is assigned to 'outlier' observations.
Notes:
* Standardize input features using ScaleFit() and ScaleTransform()
before using the function.
* The function takes only numeric features.
* The categorical features must be converted to numeric values prior to prediction.
* Rows with missing (null) values are skipped by the function during prediction.
* For prediction results evaluation, user can use ClassificationEvaluator() or
ROC() as the postprocessing step.
PARAMETERS:
object:
Required Argument.
Specifies the teradataml DataFrame containing the model data
generated by OneClassSVM() function or an instance of OneClassSVM.
Types: teradataml DataFrame or OneClassSVM
newdata:
Required Argument.
Specifies the teradataml DataFrame containing the input data.
Types: teradataml DataFrame
id_column:
Required Argument.
Specifies the name of the column that uniquely identifies an
observation in the test data.
Types: str
accumulate:
Optional Argument.
Specifies the name(s) of input teradataml DataFrame column(s) to copy to the
output.
Types: str OR list of Strings (str)
output_prob:
Optional Argument.
Specifies whether the function should output the probability for each
response.
Note:
* Only applicable if "model_type" is 'Classification'.
Default Value: False
Types: bool
output_responses:
Optional Argument.
Specifies the class labels for which to output probabilities.
A label must be 0 or 1. If not specified, the function outputs the
probability of the predicted response.
Note:
* Only applicable if "output_prob" is 'True'.
Types: str OR list of Strings (str)
**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 OneClassSVMPredict.
Output teradataml DataFrames can be accessed using attribute
references, such as OneClassSVMPredictObj.<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.
# Load the example data.
load_example_data("teradataml", ["cal_housing_ex_raw"])
# Create teradataml DataFrame objects.
data_input = DataFrame.from_table("cal_housing_ex_raw")
# Check the list of available analytic functions.
display_analytic_functions()
# 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"])
# Train the input data by OneClassSVM which helps model
# to find anomalies in transformed data.
one_class_svm=OneClassSVM(data=transform_obj.result,
input_columns=['MedInc', 'HouseAge', 'AveRooms',
'AveBedrms', 'Population', 'AveOccup',
'Latitude', 'Longitude'],
local_sgd_iterations=537,
batch_size=1,
learning_rate='constant',
initial_eta=0.01,
lambda1=0.1,
alpha=0.0,
momentum=0.0,
iter_max=1
)
# Example 1 : Using trained data by OneClassSVM model, predict whether observation
# is outlier or normal in the form of '0' or '1' on "newdata".
OneClassSVMPredict_out1 = OneClassSVMPredict(object = one_class_svm.result,
newdata = transform_obj.result,
id_column = "id"
)
# Print the result DataFrame.
print(OneClassSVMPredict_out1.result)
# Example 2 : Using trained data by OneClassSVM model, predict whether observation
# is outlier or normal in the form of '0' or '1' also provides probability
# of outcome of '0' and '1' on "newdata".
OneClassSVMPredict_out2 = OneClassSVMPredict(object = one_class_svm,
newdata = transform_obj.result,
id_column = "id",
accumulate="MedInc",
output_prob=True,
output_responses=["0", "1"]
)
# Print the result DataFrame.
print(OneClassSVMPredict_out2.result)
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