Splunk® Machine Learning Toolkit

ML-SPL API Guide

This documentation does not apply to the most recent version of Splunk® Machine Learning Toolkit. For documentation on the most recent version, go to the latest release.

Agglomerative Clustering

This example covers the following:

  • using the BaseAlgo class
  • validating search syntax
  • converting parameters
  • using df_util utilities
  • adding a custom metric to the algorithm

In this example, you will add scikit-learn's AgglomerativeClustering algorithm to the Splunk Machine Learning Toolkit. See the scikit-learn documentation for details on the AgglomerativeClustering algorithm.

In addition to inheriting from the BaseAlgo class, this example uses the convert_params utility and the df_util module. Additionally, you use scikit-learn's silhouette_samples function to create silhouette scores for each cluster label. See the scikit-learn documentation for more details on the silhouette_samples function.

This example uses the ML-SPL API available in the Splunk Machine Learning Toolkit version 2.2.0 and later. Verify your Splunk Machine Learning Toolkit version before using this example.

Steps

Do the following:

  1. Register the algorithm in algos.conf.
    1. Register the algorithm using the REST API: 
      $ curl -k -u admin:<admin pass> https://localhost:8089/servicesNS/nobody/Splunk_ML_Toolkit/configs/conf-algos -d name="AgglomerativeClustering"
    2. Register the algorithm manually:
      Modify or create the algos.conf file located in $SPLUNK_HOME/etc/apps/Splunk_ML_Toolkit/local/ and add the following stanza to register your algorithm 
       [AgglomerativeClustering]

      When you register the algorithm with this method, you will need to restart Splunk Enterprise.

  2. Create the python file in the algos folder. For this example, you create $SPLUNK_HOME/etc/apps/Splunk_ML_Toolkit/bin/algos/AgglomerativeClustering.py
    Ensure any needed code is imported. Import the convert_params utility and df_util module. 
    import numpy as np
from sklearn.metrics import silhouette_sample
from sklearn.cluster import AgglomerativeClustering as AgClustering
 
from base import BaseAlgo
from util.param_util import convert_params
from util import df_util
  3. Define the class.
    Inherit from the BaseAlgo class: 
    class AgglomerativeClustering(BaseAlgo):
	"""Use scikit-learn's AgglomerativeClustering algorithm to cluster data."""
  4. Define the __init__ method.
    • Check for valid syntax
    • Convert parameters
      • The convert_params utility tries to convert parameters into the declared type.
      • In this example, the user will pass k=<some integer> to the estimator -- however, when it is passed in via the search query, it is treated as a string.
      • The convert_params utility will try to convert the k parameter to an integer and error accordingly if it cannot.
      • The alias lets users define the number of clusters with k instead of n_clusters.
    • Attach the initialized estimator to self with the converted parameters.
        def __init__(self, options):

        feature_variables = options.get('feature_variables', {})
        target_variable = options.get('target_variable', {})

        # Ensure fields are present
        if len(feature_variables) == 0:
            raise RuntimeError('You must supply one or more fields')

        # No from clause allowed
        if len(target_variable) > 0:
            raise RuntimeError('AgglomerativeClustering does not support the from clause')

        # Convert params & alias k to n_clusters
        params = options.get('params', {})
        out_params = convert_params(
            params,
            ints=['k'],
            strs=['linkage', 'affinity'],
            aliases={'k': 'n_clusters'}
        )

        # Check for valid linkage
        if 'linkage' in out_params:
            valid_linkage = ['ward', 'complete', 'average']
            if out_params['linkage'] not in valid_linkage:
                raise RuntimeError('linkage must be one of: {}'.format(', '.join(valid_linkage)))

        # Check for valid affinity
        if 'affinity' in out_params:
            valid_affinity = ['l1', 'l2', 'cosine', 'manhattan',
                              'precomputed', 'euclidean']

            if out_params['affinity'] not in valid_affinity:
                raise RuntimeError('affinity must be one of: {}'.format(', '.join(valid_affinity)))

        # Check for invalid affinity & linkage combination
        if 'linkage' in out_params and 'affinity' in out_params:
            if out_params['linkage'] == 'ward':
                if out_params['affinity'] != 'euclidean':
                    raise RuntimeError('ward linkage (default) must use euclidean affinity (default)')

        # Initialize the estimator
        self.estimator = AgClustering(**out_params)

    The convert_params utility is small and simple. When it is passed parameters from the search, they're received as strings. If you would like to pass them to an algorithm or estimator, you need to convert them to the proper type (e.g. an int or a boolean). The function does exactly this.

    So when convert_params is called, it will convert the parameters from the search to the proper type if they are one of the following:

    • float
    • int
    • string
    • boolean
  5. Define the fit method.
    • To merge predictions with the original data, first make a copy.
    • Use the df_util's prepare_features method.
    • After making the predictions, create an output dataframe. Use the nans mask returned from prepare_features to know where to insert the rows if there were any nulls present.
    • Lastly, merge with the original dataframe and return.
        def fit(self, df, options):
        """Do the clustering and merge labels with original data."""
        # Make a copy of the input data
        X = df.copy()

        # Use the df_util prepare_features method to
        # - drop null columns & rows
        # - convert categorical columns into dummy indicator columns
        # X is our cleaned data, nans is a mask of the null value locations
        X, nans, columns = df_util.prepare_features(X, self.feature_variables)

        # Do the actual clustering
        y_hat = self.estimator.fit_predict(X.values)

        # attach silhouette coefficient score for each row
        silhouettes = silhouette_samples(X, y_hat)

        # Combine the two arrays, and transpose them.
        y_hat = np.vstack([y_hat, silhouettes]).T

        # Assign default output names
        default_name = 'cluster'

        # Get the value from the as-clause if present
        output_name = options.get('output_name', default_name)

        # There are two columns - one for the labels, for the silhouette scores
        output_names = [output_name, 'silhouette_score']

        # Use the predictions and nans-mask to create a new dataframe
        output_df = df_util.create_output_dataframe(y_hat, nans, output_names)

        # Merge the dataframe with the original input data
        df = df_util.merge_predictions(df, output_df)
        return df

    The prepare features does a number of things, and is just one of the utility methods in df_util.py.

    prepare_features(X, variables, final_columns=None, get_dummies=True)

    This method defines conventional steps to prepare features: 

    - drop unused columns
- drop rows that have missing values
- optionally (if get_dummies==True)
- convert categorical fields into indicator dummy variables
- optionally (if final_column is provided)
- make the resulting dataframe match final_columns

    Args: 

    X (dataframe): input dataframe
variables (list): column names
final_columns (list): finalized column names - default is None
get_dummies (bool): indicate if categorical variable should be converted - default is True

    Returns: 

    X (dataframe): prepared feature dataframe
nans (np array): boolean array to indicate which rows have missing values in the original dataframe
columns (list): sorted list of feature column names

    Output shape: In this example, you add two columns rather than just one column to the output. You need to make sure that the output_names passed to the create_output_dataframe method has two names in it.

    Finished example

    import numpy as np
from sklearn.cluster import AgglomerativeClustering as AgClustering
from sklearn.metrics import silhouette_samples

from base import BaseAlgo
from util.param_util import convert_params
from util import df_util


class AgglomerativeClustering(BaseAlgo):
    """Use scikit-learn's AgglomerativeClustering algorithm to cluster data."""

    def __init__(self, options):

        feature_variables = options.get('feature_variables', {})
        target_variable = options.get('target_variable', {})

        # Ensure fields are present
        if len(feature_variables) == 0:
            raise RuntimeError('You must supply one or more fields')

        # No from clause allowed
        if len(target_variable) > 0:
            raise RuntimeError('AgglomerativeClustering does not support the from clause')

        # Convert params & alias k to n_clusters
        params = options.get('params', {})
        out_params = convert_params(
            params,
            ints=['k'],
            strs=['linkage', 'affinity'],
            aliases={'k': 'n_clusters'}
        )

        # Check for valid linkage
        if 'linkage' in out_params:
            valid_linkage = ['ward', 'complete', 'average']
            if out_params['linkage'] not in valid_linkage:
                raise RuntimeError('linkage must be one of: {}'.format(', '.join(valid_linkage)))

        # Check for valid affinity
        if 'affinity' in out_params:
            valid_affinity = ['l1', 'l2', 'cosine', 'manhattan',
                              'precomputed', 'euclidean']

            if out_params['affinity'] not in valid_affinity:
                raise RuntimeError('affinity must be one of: {}'.format(', '.join(valid_affinity)))

        # Check for invalid affinity & linkage combination
        if 'linkage' in out_params and 'affinity' in out_params:
            if out_params['linkage'] == 'ward':
                if out_params['affinity'] != 'euclidean':
                    raise RuntimeError('ward linkage (default) must use euclidean affinity (default)')

        # Initialize the estimator
        self.estimator = AgClustering(**out_params)

    def fit(self, df, options):
        """Do the clustering & merge labels with original data."""
        # Make a copy of the input data
        X = df.copy()

        # Use the df_util prepare_features method to
        # - drop null columns & rows
        # - convert categorical columns into dummy indicator columns
        # X is our cleaned data, nans is a mask of the null value locations
        X, nans, columns = df_util.prepare_features(X, self.feature_variables)

        # Do the actual clustering
        y_hat = self.estimator.fit_predict(X.values)

        # attach silhouette coefficient score for each row
        silhouettes = silhouette_samples(X, y_hat)

        # Combine the two arrays, and transpose them.
        y_hat = np.vstack([y_hat, silhouettes]).T

        # Assign default output names
        default_name = 'cluster'

        # Get the value from the as-clause if present
        output_name = options.get('output_name', default_name)

        # There are two columns - one for the labels, for the silhouette scores
        output_names = [output_name, 'silhouette_score']

        # Use the predictions & nans-mask to create a new dataframe
        output_df = df_util.create_output_dataframe(y_hat, nans, output_names)

        # Merge the dataframe with the original input data
        df = df_util.merge_predictions(df, output_df)
        return df

    Silhouette plot examples

    You can now make a silhouette plot. These can be useful for selecting the number of clusters if not known a priori.

    Agglomerative search.png

    Often the global average is useful for such a plot. It is added in the following screenshot as a chart overlay:

    Agglomerative search2.png

Last modified on 03 July, 2019
Correlation Matrix   Support Vector Regressor

This documentation applies to the following versions of Splunk® Machine Learning Toolkit: 2.4.0, 3.0.0, 3.1.0, 3.2.0, 3.3.0, 3.4.0, 4.0.0, 4.1.0, 4.2.0, 4.3.0

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