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    Platform For AI:Clustering Model Evaluation

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    Platform For AI:Clustering Model Evaluation

    Last Updated:May 17, 2024

    The Clustering Model Evaluation component is used to evaluate clustering models and generate evaluation metrics based on raw data and clustering results.

    Limits

    The report of this component is available only in the Machine Learning Studio console.

    Background information

    The Calinski-Harabasz index is also known as the variance ratio criterion (VRC). The following figure shows the formula used to calculate the VRC. Calculation formula of VRC

    Parameter

    Description

    SSB

    The variance between clusters. The following figure shows the formula used to calculate the variance between clusters. SSBFormula description:

    • k: indicates the number of cluster center points.

    • mi: indicates the center point of the cluster.

    • m: indicates the mean value of the input data.

    SSW

    The variance within a cluster. The following figure shows the formula used to calculate the variance within a cluster. SSWFormula description:

    • k: indicates the number of cluster center points.

    • x: indicates data points.

    • ci: indicates the ith cluster.

    • mi: indicates the center point of the cluster.

    N

    The total number of records.

    k

    The number of cluster center points.

    Configure the component

    You can use the following methods to configure the component parameters:

    Method 1: Use Machine Learning Designer

    Configure the component parameters on the pipeline configuration tab of Machine Learning Designer.

    Tab

    Parameter

    Description

    Fields Setting

    Evaluation Columns

    The columns that are selected from the input table for evaluation. The value of this parameter must be consistent with the feature columns in the model.

    Input Sparse Format

    Specifies whether the input data is sparse. Sparse data is presented by using key-value pairs.

    KV Pair Delimiter

    The delimiter that is used to separate key-value pairs. By default, commas (,) are used.

    KV Delimiter

    The delimiter that is used to separate keys and values. By default, colons (:) are used.

    Tuning

    Cores

    The number of cores. This parameter must be used together with the Memory Size per Core parameter. The value of this parameter must be a positive integer.

    Memory Size per Core

    The memory size of each core. This parameter must be used together with the Cores parameter. Unit: MB.

    Method 2: Use PAI commands

    Configure the parameters of this component by using a Machine Learning Platform for AI (PAI) command. You can use the SQL Script component to call these commands. For more information, see SQL Script. The following table describes the parameters of the command. 

    PAI -name cluster_evaluation
    -project algo_public
    -DinputTableName=pai_cluster_evaluation_test_input
    -DselectedColNames=f0,f3
    -DmodelName=pai_kmeans_test_model
    -DoutputTableName=pai_ft_cluster_evaluation_out;

    Parameter

    Required

    Description

    Default value

    inputTableName

    Yes

    The name of the input table.

    N/A

    selectedColNames

    No

    The names of the columns that are selected from the input table for evaluation. Separate multiple columns with commas (,). The value of this parameter must be consistent with the feature columns in the model.

    All columns

    inputTablePartitions

    No

    The partitions selected from the input table for training. Specify this parameter in one of the following formats:

    • Partition_name=value

    • name1=value1/name2=value2: multi-level partitions

    Note

    If you specify multiple partitions, separate these partitions with commas (,).

    Full table

    enableSparse

    No

    Specifies whether the input data is sparse. Valid values: true and false.

    false

    itemDelimiter

    No

    The delimiter that is used to separate sparse key-value pairs.

    ,

    kvDelimiter

    No

    The delimiter that is used to separate sparse keys and values.

    :

    modelName

    Yes

    The name of the input clustering model.

    N/A

    outputTableName

    Yes

    The name of the output table.

    N/A

    lifecycle

    No

    The lifecycle of the output table.

    N/A

    Example

    1. Execute the following SQL statements to generate test data: 

      create table if not exists pai_cluster_evaluation_test_input as
select * from
(
  select 1 as id, 1 as f0,2 as f3
  union all
  select 2 as id, 1 as f0,3 as f3
  union all
  select 3 as id, 1 as f0,4 as f3
  union all
  select 4 as id, 0 as f0,3 as f3
  union all
  select 5 as id, 0 as f0,4 as f3
)tmp;

    2. Run the following PAI command to build a clustering model. A k-means clustering model is built in this example. 

      PAI -name kmeans
    -project algo_public
    -DinputTableName=pai_cluster_evaluation_test_input
    -DselectedColNames=f0,f3
    -DcenterCount=3
    -Dloop=10
    -Daccuracy=0.00001
    -DdistanceType=euclidean
    -DinitCenterMethod=random
    -Dseed=1
    -DmodelName=pai_kmeans_test_model
    -DidxTableName=pai_kmeans_test_idx

    3. Run the following PAI command to submit the parameters configured for the Clustering Model Evaluation component: 

      PAI -name cluster_evaluation
    -project algo_public
    -DinputTableName=pai_cluster_evaluation_test_input
    -DselectedColNames=f0,f3
    -DmodelName=pai_kmeans_test_model
    -DoutputTableName=pai_ft_cluster_evaluation_out;

    4. View the output evaluation table pai_ft_cluster_evaluation_out and the following visualized graph. Statistical resultsThe following table describes the fields displayed in the graph.

      Field

      Description

      count

      The total number of returned entries.

      centerCount

      The number of cluster centers.

      calinhara

      The VRC.