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- VectorDistance(target_data=None, reference_data=None, target_id_column=None, target_feature_columns=None, ref_id_column=None, ref_feature_columns=None, distance_measure='COSINE', topk=10, **generic_arguments)
- DESCRIPTION:
The VectorDistance() function accepts a dataframe of target vectors and a dataframe of reference vectors and
returns output containing the distance between target-reference pairs.
The function computes the distance between the target pair and the reference pair from the same input
if you provide only one input.
You must have the same column order in the "target_feature_columns" argument and the "ref_feature_columns"
argument. The function ignores the feature values during distance computation if the value is either
None, NAN, or INF.
The function returns N*N output if you use the "topk" value as -1 because the function includes
all reference vectors in the output.
Notes:
* The algorithm used in this function is of the order of N*N (where N is the number of rows).
Hence, expect the function to run significantly longer as the number of rows increases in either
the "target_data" or the "reference_data".
* Because the reference data is copied to the spool for each AMP before running the query, the user
spool limits the size and scalability of the input.
* This function requires the UTF8 client character set for UNICODE data.
* This function does not support Pass Through Characters (PTCs).
For information about PTCs, see Teradata Vantage™ - Analytics Database International Character Set
Support.
* This function does not support KanjiSJIS or Graphic data types.
PARAMETERS:
target_data:
Required Argument.
Specifies the teradataml DataFrame containing target data vectors.
Types: teradataml DataFrame
reference_data:
Optional Argument.
Specifies the teradataml DataFrame containing reference data vectors.
Types: teradataml DataFrame
target_id_column:
Required Argument.
Specifies the name of the "target_data" column that contains
identifiers of the target data vectors.
Types: str
target_feature_columns:
Required Argument.
Specifies the names of the "target_data" columns that contain features
of the target data vectors.
Note:
You can specify up to 2018 feature columns.
Types: str OR list of Strings (str)
ref_id_column:
Optional Argument.
Specifies the name of the "reference_data" column that contains
identifiers of the reference data vectors.
Types: str
ref_feature_columns:
Optional Argument.
Specifies the names of the "reference_data" columns that contain
features of the reference data vectors.
Note:
You can specify up to 2018 feature columns.
Types: str OR list of Strings (str)
distance_measure:
Optional Argument.
Specifies the distance type to compute between the target and the reference vector.
Default Value: "COSINE"
Permitted Values:
* Cosine: Cosine distance between the target vector and the reference vector.
* Euclidean: Euclidean distance between the target vector and the reference vector.
* Manhattan: Manhattan distance between the target vector and the reference vector.
Types: str OR list of strs
topk:
Optional Argument.
Specifies the maximum number of closest reference vectors to include in the output table
for each target vector. The value k is an integer between 1 and 100.
Default Value: 10
Types: int
**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 SQLE Engine function supports, else an
exception is raised.
RETURNS:
Instance of VectorDistance.
Output teradataml DataFrames can be accessed using attribute
references, such as VectorDistanceObj.<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("vectordistance", ["target_mobile_data_dense", "ref_mobile_data_dense"])
# Create teradataml DataFrame objects.
target_mobile_data_dense=DataFrame("target_mobile_data_dense")
ref_mobile_data_dense=DataFrame("ref_mobile_data_dense")
# Check the list of available analytic functions.
display_analytic_functions()
# Example 1 : Compute the cosine, euclidean, manhattan distance between the target and reference vectors.
VectorDistance_out = VectorDistance(target_id_column="userid",
target_feature_columns=['CallDuration', 'DataCounter', 'SMS'],
ref_id_column="userid",
ref_feature_columns=['CallDuration', 'DataCounter', 'SMS'],
distance_measure=['Cosine', 'Euclidean', 'Manhattan'],
topk=2,
target_data=target_mobile_data_dense,
reference_data=ref_mobile_data_dense)
# Print the result DataFrame.
print(VectorDistance_out.result)
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