[DOCS] Makes PUT inference API docs collapsible (#54653)
Co-authored-by: lcawl <lcawley@elastic.co>
This commit is contained in:
István Zoltán Szabó
parent
11afead21e
commit
d025b90cd1
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@ -42,7 +42,7 @@ Regression configuration for inference.
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`results_field`::
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`results_field`::
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(Optional, string)
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-regression-results-field]
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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`num_top_feature_importance_values`::
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`num_top_feature_importance_values`::
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(Optional, integer)
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(Optional, integer)
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@ -65,7 +65,7 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-
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`results_field`::
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`results_field`::
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(Optional, string)
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-classification-results-field]
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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`top_classes_results_field`::
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`top_classes_results_field`::
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(Optional, string)
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(Optional, string)
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@ -61,15 +61,272 @@ The {infer} definition for the model. If `definition` is specified, then
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.Properties of `definition`
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.Properties of `definition`
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[%collapsible%open]
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[%collapsible%open]
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====
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====
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`preprocessors`:::
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//Begin preprocessors
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`preprocessors`::
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(Optional, object)
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(Optional, object)
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Collection of preprocessors. See <<ml-put-inference-preprocessors>> for the full
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Collection of preprocessors. See <<ml-put-inference-preprocessor-example>>.
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list of available preprocessors.
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+
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.Properties of `preprocessors`
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`trained_model`:::
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[%collapsible%open]
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=====
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//Begin frequency encoding
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`frequency_encoding`::
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(Required, object)
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(Required, object)
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The definition of the trained model. See <<ml-put-inference-trained-model>> for
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Defines a frequency encoding for a field.
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details.
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+
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.Properties of `frequency_encoding`
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[%collapsible%open]
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======
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`feature_name`::
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(Required, string)
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The name of the resulting feature.
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`field`::
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(Required, string)
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The field name to encode.
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`frequency_map`::
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(Required, object map of string:double)
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Object that maps the field value to the frequency encoded value.
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======
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//End frequency encoding
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//Begin one hot encoding
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`one_hot_encoding`::
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(Required, object)
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Defines a one hot encoding map for a field.
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+
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.Properties of `one_hot_encoding`
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[%collapsible%open]
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======
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`field`::
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(Required, string)
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The field name to encode.
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`hot_map`::
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(Required, object map of strings)
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String map of "field_value: one_hot_column_name".
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======
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//End one hot encoding
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//Begin target mean encoding
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`target_mean_encoding`::
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(Required, object)
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Defines a target mean encoding for a field.
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+
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.Properties of `target_mean_encoding`
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[%collapsible%open]
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======
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`default_value`:::
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(Required, double)
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The feature value if the field value is not in the `target_map`.
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`feature_name`:::
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(Required, string)
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The name of the resulting feature.
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`field`:::
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(Required, string)
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The field name to encode.
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`target_map`:::
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(Required, object map of string:double)
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Object that maps the field value to the target mean value.
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======
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//End target mean encoding
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=====
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//End preprocessors
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//Begin trained model
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`trained_model`::
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(Required, object)
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The definition of the trained model.
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+
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.Properties of `trained_model`
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[%collapsible%open]
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=====
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//Begin tree
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`tree`::
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(Required, object)
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The definition for a binary decision tree.
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+
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.Properties of `tree`
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[%collapsible%open]
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======
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`classification_labels`:::
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(Optional, string) An array of classification labels (used for
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`classification`).
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`feature_names`:::
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(Required, string)
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Features expected by the tree, in their expected order.
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`target_type`:::
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(Required, string)
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String indicating the model target type; `regression` or `classification`.
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`tree_structure`:::
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(Required, object)
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An array of `tree_node` objects. The nodes must be in ordinal order by their
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`tree_node.node_index` value.
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======
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//End tree
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//Begin tree node
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`tree_node`::
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(Required, object)
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The definition of a node in a tree.
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--
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There are two major types of nodes: leaf nodes and not-leaf nodes.
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* Leaf nodes only need `node_index` and `leaf_value` defined.
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* All other nodes need `split_feature`, `left_child`, `right_child`,
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`threshold`, `decision_type`, and `default_left` defined.
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--
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+
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.Properties of `tree_node`
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[%collapsible%open]
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======
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`decision_type`::
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(Optional, string)
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Indicates the positive value (in other words, when to choose the left node)
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decision type. Supported `lt`, `lte`, `gt`, `gte`. Defaults to `lte`.
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`default_left`::
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(Optional, boolean)
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Indicates whether to default to the left when the feature is missing. Defaults
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to `true`.
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`leaf_value`::
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(Optional, double)
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The leaf value of the of the node, if the value is a leaf (in other words, no
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children).
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`left_child`::
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(Optional, integer)
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The index of the left child.
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`node_index`::
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(Integer)
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The index of the current node.
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`right_child`::
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(Optional, integer)
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The index of the right child.
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`split_feature`::
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(Optional, integer)
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The index of the feature value in the feature array.
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`split_gain`::
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(Optional, double) The information gain from the split.
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`threshold`::
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(Optional, double)
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The decision threshold with which to compare the feature value.
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======
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//End tree node
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//Begin ensemble
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`ensemble`::
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(Optional, object)
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The definition for an ensemble model. See <<ml-put-inference-model-example>>.
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+
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.Properties of `ensemble`
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[%collapsible%open]
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======
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//Begin aggregate output
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`aggregate_output`::
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(Required, object)
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An aggregated output object that defines how to aggregate the outputs of the
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`trained_models`. Supported objects are `weighted_mode`, `weighted_sum`, and
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`logistic_regression`. See <<ml-put-inference-aggregated-output-example>>.
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+
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.Properties of `aggregate_output`
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[%collapsible%open]
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=======
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//Begin logistic regression
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`logistic_regression`::
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(Optional, object)
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This `aggregated_output` type works with binary classification (classification
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for values [0, 1]). It multiplies the outputs (in the case of the `ensemble`
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model, the inference model values) by the supplied `weights`. The resulting
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vector is summed and passed to a
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https://en.wikipedia.org/wiki/Sigmoid_function[`sigmoid` function]. The result
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of the `sigmoid` function is considered the probability of class 1 (`P_1`),
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consequently, the probability of class 0 is `1 - P_1`. The class with the
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highest probability (either 0 or 1) is then returned. For more information about
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logistic regression, see
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https://en.wikipedia.org/wiki/Logistic_regression[this wiki article].
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+
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.Properties of `logistic_regression`
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[%collapsible%open]
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========
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`weights`:::
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(Required, double)
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The weights to multiply by the input values (the inference values of the trained
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models).
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========
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//End logistic regression
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//Begin weighted sum
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`weighted_sum`::
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(Optional, object)
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This `aggregated_output` type works with regression. The weighted sum of the
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input values.
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.Properties of `weighted_sum`
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[%collapsible%open]
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========
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`weights`:::
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(Required, double)
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The weights to multiply by the input values (the inference values of the trained
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models).
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========
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//End weighted sum
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//Begin weighted mode
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`weighted_mode`::
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(Optional, object)
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This `aggregated_output` type works with regression or classification. It takes
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a weighted vote of the input values. The most common input value (taking the
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weights into account) is returned.
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.Properties of `weighted_mode`
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[%collapsible%open]
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========
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`weights`:::
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(Required, double)
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The weights to multiply by the input values (the inference values of the trained
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models).
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========
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//End weighted mode
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=======
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//End aggregate output
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`classification_labels`::
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(Optional, string)
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An array of classification labels.
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`feature_names`::
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(Optional, string)
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Features expected by the ensemble, in their expected order.
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`target_type`::
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(Required, string)
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String indicating the model target type; `regression` or `classification.`
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`trained_models`::
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(Required, object)
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An array of `trained_model` objects. Supported trained models are `tree` and
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`ensemble`.
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======
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//End ensemble
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=====
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//End trained model
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====
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====
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//End definition
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//End definition
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@ -100,7 +357,7 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-regression-num-top-
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`results_field`::::
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`results_field`::::
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(Optional, string)
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-regression-results-field]
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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=====
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=====
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`classification`:::
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`classification`:::
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@ -120,7 +377,7 @@ include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-classification-num-
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`results_field`::::
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`results_field`::::
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(Optional, string)
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(Optional, string)
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-classification-results-field]
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include::{docdir}/ml/ml-shared.asciidoc[tag=inference-config-results-field]
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`top_classes_results_field`::::
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`top_classes_results_field`::::
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(Optional, string)
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(Optional, string)
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@ -151,208 +408,6 @@ An object map that contains metadata about the model.
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(Optional, string)
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(Optional, string)
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An array of tags to organize the model.
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An array of tags to organize the model.
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[[ml-put-inference-preprocessors]]
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===== {infer-cap} preprocessor definitions
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`frequency_encoding`::
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(Required, object)
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Defines a frequency encoding for a field.
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`frequency_encoding`.`field`:::
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(Required, string)
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The field name to encode.
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`frequency_encoding`.`feature_name`:::
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(Required, string)
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The name of the resulting feature.
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`frequency_encoding`.`frequency_map`:::
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(Required, object map of string:double)
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Object that maps the field value to the frequency encoded value.
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`one_hot_encoding`::
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(Required, object)
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Defines a one hot encoding map for a field.
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`one_hot_encoding`.`field`:::
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(Required, string)
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The field name to encode.
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`one_hot_encoding`.`hot_map`:::
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(Required, object map of strings)
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String map of "field_value: one_hot_column_name".
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`target_mean_encoding`::
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(Required, object)
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Defines a target mean encoding for a field.
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`target_mean_encoding`.`field`:::
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(Required, string)
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The field name to encode.
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`target_mean_encoding`.`feature_name`:::
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(Required, string)
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The name of the resulting feature.
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`target_mean_encoding`.`target_map`:::
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(Required, object map of string:double)
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Object that maps the field value to the target mean value.
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`target_mean_encoding`.`default_value`:::
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(Required, double)
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The feature value if the field value is not in the `target_map`.
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See <<ml-put-inference-preprocessor-example>> for more details.
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[[ml-put-inference-trained-model]]
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===== {infer-cap} trained model definitions
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`tree`::
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(Required, object)
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The definition for a binary decision tree.
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`tree`.`feature_names`:::
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(Required, string)
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Features expected by the tree, in their expected order.
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`tree`.`tree_structure`:::
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(Required, object)
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An array of `tree_node` objects. The nodes must be in ordinal order by their
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`tree_node.node_index` value.
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`tree`.`classification_labels`:::
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(Optional, string) An array of classification labels (used for
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`classification`).
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`tree`.`target_type`:::
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(Required, string)
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String indicating the model target type; `regression` or `classification`.
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There are two major types of nodes: leaf nodes and not-leaf nodes.
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* Leaf nodes only need `node_index` and `leaf_value` defined.
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* All other nodes need `split_feature`, `left_child`, `right_child`,
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`threshold`, `decision_type`, and `default_left` defined.
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|
||||||
`tree_node`::
|
|
||||||
(Required, object)
|
|
||||||
The definition of a node in a tree.
|
|
||||||
|
|
||||||
`tree_node`.`decision_type`:::
|
|
||||||
(Optional, string)
|
|
||||||
Indicates the positive value (in other words, when to choose the left node)
|
|
||||||
decision type. Supported `lt`, `lte`, `gt`, `gte`. Defaults to `lte`.
|
|
||||||
|
|
||||||
`tree_node`.`threshold`:::
|
|
||||||
(Optional, double)
|
|
||||||
The decision threshold with which to compare the feature value.
|
|
||||||
|
|
||||||
`tree_node`.`left_child`:::
|
|
||||||
(Optional, integer)
|
|
||||||
The index of the left child.
|
|
||||||
|
|
||||||
`tree_node`.`right_child`:::
|
|
||||||
(Optional, integer)
|
|
||||||
The index of the right child.
|
|
||||||
|
|
||||||
`tree_node`.`default_left`:::
|
|
||||||
(Optional, boolean)
|
|
||||||
Indicates whether to default to the left when the feature is missing. Defaults
|
|
||||||
to `true`.
|
|
||||||
|
|
||||||
`tree_node`.`split_feature`:::
|
|
||||||
(Optional, integer)
|
|
||||||
The index of the feature value in the feature array.
|
|
||||||
|
|
||||||
`tree_node`.`node_index`:::
|
|
||||||
(Integer)
|
|
||||||
The index of the current node.
|
|
||||||
|
|
||||||
`tree_node`.`split_gain`:::
|
|
||||||
(Optional, double) The information gain from the split.
|
|
||||||
|
|
||||||
`tree_node`.`leaf_value`:::
|
|
||||||
(Optional, double)
|
|
||||||
The leaf value of the of the node, if the value is a leaf (in other words, no
|
|
||||||
children).
|
|
||||||
|
|
||||||
`ensemble`::
|
|
||||||
(Optional, object)
|
|
||||||
The definition for an ensemble model.
|
|
||||||
|
|
||||||
`ensemble`.`feature_names`:::
|
|
||||||
(Optional, string)
|
|
||||||
Features expected by the ensemble, in their expected order.
|
|
||||||
|
|
||||||
`ensemble`.`trained_models`:::
|
|
||||||
(Required, object)
|
|
||||||
An array of `trained_model` objects. Supported trained models are `tree` and
|
|
||||||
`ensemble`.
|
|
||||||
|
|
||||||
`ensemble`.`classification_labels`:::
|
|
||||||
(Optional, string)
|
|
||||||
An array of classification labels.
|
|
||||||
|
|
||||||
`ensemble`.`target_type`:::
|
|
||||||
(Required, string)
|
|
||||||
String indicating the model target type; `regression` or `classification.`
|
|
||||||
|
|
||||||
`ensemble`.`aggregate_output`:::
|
|
||||||
(Required, object)
|
|
||||||
An aggregated output object that defines how to aggregate the outputs of the
|
|
||||||
`trained_models`. Supported objects are `weighted_mode`, `weighted_sum`, and
|
|
||||||
`logistic_regression`.
|
|
||||||
|
|
||||||
See <<ml-put-inference-model-example>> for more details.
|
|
||||||
|
|
||||||
|
|
||||||
[[ml-put-inference-aggregated-output]]
|
|
||||||
===== Aggregated output types
|
|
||||||
|
|
||||||
`logistic_regression`::
|
|
||||||
(Optional, object)
|
|
||||||
This `aggregated_output` type works with binary classification (classification
|
|
||||||
for values [0, 1]). It multiplies the outputs (in the case of the `ensemble`
|
|
||||||
model, the inference model values) by the supplied `weights`. The resulting
|
|
||||||
vector is summed and passed to a
|
|
||||||
https://en.wikipedia.org/wiki/Sigmoid_function[`sigmoid` function]. The result
|
|
||||||
of the `sigmoid` function is considered the probability of class 1 (`P_1`),
|
|
||||||
consequently, the probability of class 0 is `1 - P_1`. The class with the
|
|
||||||
highest probability (either 0 or 1) is then returned. For more information about
|
|
||||||
logistic regression, see
|
|
||||||
https://en.wikipedia.org/wiki/Logistic_regression[this wiki article].
|
|
||||||
|
|
||||||
`logistic_regression`.`weights`:::
|
|
||||||
(Required, double)
|
|
||||||
The weights to multiply by the input values (the inference values of the trained
|
|
||||||
models).
|
|
||||||
|
|
||||||
`weighted_sum`::
|
|
||||||
(Optional, object)
|
|
||||||
This `aggregated_output` type works with regression. The weighted sum of the
|
|
||||||
input values.
|
|
||||||
|
|
||||||
`weighted_sum`.`weights`:::
|
|
||||||
(Required, double)
|
|
||||||
The weights to multiply by the input values (the inference values of the trained
|
|
||||||
models).
|
|
||||||
|
|
||||||
`weighted_mode`::
|
|
||||||
(Optional, object)
|
|
||||||
This `aggregated_output` type works with regression or classification. It takes
|
|
||||||
a weighted vote of the input values. The most common input value (taking the
|
|
||||||
weights into account) is returned.
|
|
||||||
|
|
||||||
`weighted_mode`.`weights`:::
|
|
||||||
(Required, double)
|
|
||||||
The weights to multiply by the input values (the inference values of the trained
|
|
||||||
models).
|
|
||||||
|
|
||||||
See <<ml-put-inference-aggregated-output-example>> for more details.
|
|
||||||
|
|
||||||
|
|
||||||
[[ml-put-inference-example]]
|
[[ml-put-inference-example]]
|
||||||
==== {api-examples-title}
|
==== {api-examples-title}
|
||||||
|
|
|
||||||
|
|
@ -1224,11 +1224,6 @@ importance] values per document. By default, it is zero and no feature
|
||||||
importance calculation occurs.
|
importance calculation occurs.
|
||||||
end::inference-config-classification-num-top-feature-importance-values[]
|
end::inference-config-classification-num-top-feature-importance-values[]
|
||||||
|
|
||||||
tag::inference-config-classification-results-field[]
|
|
||||||
The field that is added to incoming documents to contain the inference
|
|
||||||
prediction. Defaults to `predicted_value`.
|
|
||||||
end::inference-config-classification-results-field[]
|
|
||||||
|
|
||||||
tag::inference-config-classification-top-classes-results-field[]
|
tag::inference-config-classification-top-classes-results-field[]
|
||||||
Specifies the field to which the top classes are written. Defaults to
|
Specifies the field to which the top classes are written. Defaults to
|
||||||
`top_classes`.
|
`top_classes`.
|
||||||
|
|
@ -1241,10 +1236,10 @@ importance] values per document. By default, it is zero and no feature importanc
|
||||||
calculation occurs.
|
calculation occurs.
|
||||||
end::inference-config-regression-num-top-feature-importance-values[]
|
end::inference-config-regression-num-top-feature-importance-values[]
|
||||||
|
|
||||||
tag::inference-config-regression-results-field[]
|
tag::inference-config-results-field[]
|
||||||
Specifies the field to which the inference prediction is written. Defaults to
|
The field that is added to incoming documents to contain the inference
|
||||||
`predicted_value`.
|
prediction. Defaults to `predicted_value`.
|
||||||
end::inference-config-regression-results-field[]
|
end::inference-config-results-field[]
|
||||||
|
|
||||||
tag::influencers[]
|
tag::influencers[]
|
||||||
A comma separated list of influencer field names. Typically these can be the by,
|
A comma separated list of influencer field names. Typically these can be the by,
|
||||||
|
|
|
||||||
Loading…
Reference in New Issue