Generate Partial Dependency Plot Data (Operator Toolbox)
SynopsisPartial Dependency Plots are used to understand the influence of a single attribute on a prediction.
In order to get PDP data for an attribute k (only possible for numerical attributes) we:
- 1. take a value x between the minimum and maximum of attribute k.
- 2. set the attribute value of all examples in the ExampleSet to this value
- 3. score the ExampleSet and calculate the average response. The response is either the predicted regression value or the average confidence of the positive class.
For more information on PDP see https://christophm.github.io/interpretable-ml-book/pdp.html.
- exa (Data Table)
The input ExampleSet.
- col (Data Table)
A collection of ExampleSets, each having PDP data for one attribute.
This parameter allows you to select the attribute selection filter; the method you want to use for selecting attributes. It has the following options:
- all: This option selects all the attributes of the ExampleSet, no attributes are removed. This is the default option.
- single: This option allows the selection of a single attribute. The required attribute is selected by the attribute parameter.
- subset: This option allows the selection of multiple attributes through a list (see parameter attributes). If the meta data of the ExampleSet is known all attributes are present in the list and the required ones can easily be selected.
- regular_expression: This option allows you to specify a regular expression for the attribute selection. The regular expression filter is configured by the parameters regular expression, use except expression and except expression.
- value_type: This option allows selection of all the attributes of a particular type. It should be noted that types are hierarchical. For example real and integer types both belong to the numeric type. The value type filter is configured by the parameters value type, use value type exception, except value type.
- block_type: This option allows the selection of all the attributes of a particular block type. It should be noted that block types may be hierarchical. For example value_series_start and value_series_end block types both belong to the value_series block type. The block type filter is configured by the parameters block type, use block type exception, except block type.
- no_missing_values: This option selects all attributes of the ExampleSet which do not contain a missing value in any example. attributes that have even a single missing value are removed.
- numeric_value_filter: All numeric attributes whose examples all match a given numeric condition are selected. The condition is specified by the numeric condition parameter. Please note that all nominal attributes are also selected irrespective of the given numerical condition.
The required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter if the meta data is known.Range:
The required attributes can be selected from this option. This opens a new window with two lists. All attributes are present in the left list. They can be shifted to the right list, which is the list of selected attributes that will make it to the output port.Range:
Attributes whose names match this expression will be selected. The expression can be specified through the edit and preview regular expression menu. This menu gives a good idea of regular expressions and it also allows you to try different expressions and preview the results simultaneously.Range:
If enabled, an exception to the first regular expression can be specified. This exception is specified by the except regular expression parameter.Range:
This option allows you to specify a regular expression. Attributes matching this expression will be filtered out even if they match the first expression (expression that was specified in regular expression parameter).Range:
This option allows to select a type of attribute. One of the following types can be chosen: nominal, numeric, integer, real, text, binominal, polynominal, file_path, date_time, date, time.Range:
If enabled, an exception to the selected type can be specified. This exception is specified by the except value type parameter.Range:
The attributes matching this type will be removed from the final output even if they matched the before selected type, specified by the value type parameter. One of the following types can be selected here: nominal, numeric, integer, real, text, binominal, polynominal, file_path, date_time, date, time.Range:
This option allows to select a block type of attribute. One of the following types can be chosen: single_value, value_series, value_series_start, value_series_end, value_matrix, value_matrix_start, value_matrix_end, value_matrix_row_start.Range:
If enabled, an exception to the selected block type can be specified. This exception is specified by the except block type parameter.Range:
The attributes matching this block type will be removed from the final output even if they matched the before selected type by the block type parameter. One of the following block types can be selected here: single_value, value_series, value_series_start, value_series_end, value_matrix, value_matrix_start, value_matrix_end, value_matrix_row_start.Range:
The numeric condition used by the numeric condition filter type. A numeric attribute is kept if all examples match the specified condition for this attribute. For example the numeric condition '> 6' will keep all numeric attributes having a value of greater than 6 in every example. A combination of conditions is possible: '> 6 && < 11' or '<= 5 || < 0'. But && and || cannot be used together in one numeric condition. Conditions like '(> 0 && < 2) || (>10 && < 12)' are not allowed because they use both && and ||. Nominal attributes are always kept, regardless of the specified numeric condition.Range:
Special attributes are attributes with special roles. These are: id, label, prediction, cluster, weight and batch. Also custom roles can be assigned to attributes. By default all special attributes are delivered to the output port irrespective of the conditions in the attribute selection. If this parameter is set to true, special attributes are also tested against conditions specified in the attribute selection and only those attributes are selected that match the conditions.Range:
If this parameter is set to true the selection is reversed. In that case all attributes matching the specified condition are removed and the other attributes remain in the output ExampleSet. Special attributes are kept independent of the invert selection parameter as along as the include special attributes parameter is not set to true. If so the condition is also applied to the special attributes and the selection is reversed if this parameter is checked.Range:
- number_of_bins Number of bins in which the values of each attribute is divided to calculate the partial dependency plot data. Range:
Generate PDP Data for Titanic
This tutorial process demonstrate the basic usage of the Generate Partial Dependency Plot Data operator. A Deep Learning model is trained on the Titanic Training data set. The Generate Partial Dependency Plot Data operator is used to create partial dependency plots for the numerical attributes on which the Deep Learning model is built on.