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Methods defined here:
- __init__(self, data=None, sample_id_column=None, attribute_column=None, value_column=None, label_column=None, cost=1.0, bias=0.0, hash=False, hash_buckets=None, class_weights=None, max_step=100, epsilon=0.01, seed=0, data_sequence_column=None, force_mapreduce=False)
- DESCRIPTION:
The SVMSparse function takes training data (in sparse format) and
outputs a predictive model in binary format, which is input to the
functions SVMSparsePredict and SVMSparseSummary.
PARAMETERS:
data:
Required Argument.
Specifies the name of the teradataml DataFrame that contains
the training samples.
sample_id_column:
Required Argument.
Specifies the name of the column in data, teradataml DataFrame
that contains the identifiers of the training samples.
Types: str
attribute_column:
Required Argument.
Specifies the name of the column in data, teradataml DataFrame
that contains the attributes of the samples.
Types: str
value_column:
Optional Argument. Required when teradataml is connected to
Vantage 1.3 version.
Specifies the name of the column in data, teradataml DataFrame
that contains the attribute values.
Types: str
label_column:
Required Argument.
Specifies the name of the column in data, teradataml DataFrame
that contains the classes of the samples.
Types: str
cost:
Optional Argument.
Specifies the regularization parameter in the SVM soft-margin loss function:
Cost must be greater than 0.0.
Default Value: 1.0
Types: float
bias:
Optional Argument.
Specifies a non-negative value. If the value is greater than zero, each sample
x in the training set will be converted to (x, b); that is, it will
add another dimension containing the bias value b. This argument
addresses situations where not all samples center at 0.
Default Value: 0.0
Types: float
hash:
Optional Argument.
Specifies whether to use hash projection on attributes. hash
projection can accelerate processing speed but can slightly decrease
accuracy.
Note: You must use hash projection if the dataset has more
features than fit into memory.
Default Value: False
Types: bool
hash_buckets:
Optional Argument.
Valid only if hash is True. Specifies the number of buckets for
hash projection. In most cases, the function can determine the
appropriate number of buckets from the scale of the input data set.
However, if the dataset has a very large number of features, you
might have to specify buckets_number to accelerate the function.
Types: int
class_weights:
Optional Argument.
Specifies the weights for different classes. The format is:
"classlabel m:weight m, classlabel n:weight n". If weight for a class
is given, the cost parameter for this class is weight * cost. A
weight larger than 1 often increases the accuracy of the
corresponding class; however, it may decrease global accuracy.
Classes not assigned a weight in this argument is assigned a weight
of 1.0.
Types: str OR list of Strings (str)
max_step:
Optional Argument.
Specifies a positive integer value that specifies the maximum number of
iterations of the training process. One step means that each sample
is seen once by the trainer. The input value must be in the range (0,
10000].
Default Value: 100
Types: int
epsilon:
Optional Argument.
Specifies the termination criterion. When the difference between the values of the
loss function in two sequential iterations is less than this number,
the function stops. epsilon must be greater than 0.0.
Default Value: 0.01
Types: float
seed:
Optional Argument.
A long integer value used to order the training set randomly and
consistently. This value can be used to ensure that the same model
will be generated if the function is run multiple times in a given
database with the same arguments. The input value must be in the
range [0, 9223372036854775807].
Default Value: 0
Types: int
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)
force_mapreduce:
Optional Argument.
Specifies whether the function is to use MapReduce. If set to
'False', a lighter version of the function runs for faster results.
Note:
1. The model may be different with "force_mapreduce" set to 'True' and
"force_mapreduce" set to 'False'.
2. "force_mapreduce" argument support is only available when teradataml
is connected to Vantage 1.3 version.
Default Value: False
Types: bool
RETURNS:
Instance of SVMSparse.
Output teradataml DataFrames can be accessed using attribute
references, such as SVMSparseObj.<attribute_name>.
Output teradataml DataFrame attribute names are:
1. model_table
2. output
RAISES:
TeradataMlException, TypeError, ValueError
EXAMPLES:
# Load the data to run the example.
load_example_data("SVMSparse","svm_iris_input_train")
# Create teradataml DataFrame
svm_iris_input_train = DataFrame.from_table("svm_iris_input_train")
# Example 1
svm_sparse_out = SVMSparse(data=svm_iris_input_train,
sample_id_column='id',
attribute_column='attribute',
label_column='species',
value_column='value1',
max_step=150,
seed=0,
)
# Print the result DataFrame
print(svm_sparse_out.model_table)
print(svm_sparse_out.output)
- __repr__(self)
- Returns the string representation for a SVMSparse 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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