4951808959

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github

Committed by GitHub

Commit Message

Merge efe6b42df into ef7906c5e

Pull Request Pull Request #13792: SPARKNLP-825 Adding multilabel param

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/src/main/scala/com/johnsnowlabs/nlp/HasClassifierActivationProperties.scala

/*
 * Copyright 2017-2022 John Snow Labs
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package com.johnsnowlabs.nlp

import org.apache.spark.ml.param.{FloatParam, Param}

trait HasClassifierActivationProperties extends ParamsAndFeaturesWritable {

  /** Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
    *
    * @group param
    */
  val activation: Param[String] = new Param(
    this,
    "activation",
    "Whether to calculate logits via Softmax or Sigmoid. Default is Softmax")

  /** Choose the threshold to determine which logits are considered to be positive or negative.
    * (Default: `0.5f`). The value should be between 0.0 and 1.0. Changing the threshold value
    * will affect the resulting labels and can be used to adjust the balance between precision and
    * recall in the classification process.
    *
    * @group param
    */
  val threshold = new FloatParam(
    this,
    "threshold",
    "Choose the threshold to determine which logits are considered to be positive or negative")

  /** Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
   *  multi-label (each label has a probability between 0.0 to 1.0).
   *  Default is False i.e. multi-class
   *
   * @group param
   */
  val multilabel: Param[Boolean] = new Param(
    this,
    "multilabel",
    "Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class")

  setDefault(activation -> ActivationFunction.softmax, threshold -> 0.5f, multilabel -> false)

  /** @group getParam */
  def getActivation: String = $(activation)

  /** @group setParam */
  def setActivation(value: String): this.type = {

    value match {
      case ActivationFunction.softmax =>
        set(this.activation, ActivationFunction.softmax)
      case ActivationFunction.sigmoid =>
        set(this.activation, ActivationFunction.sigmoid)
      case _ =>
        set(this.activation, ActivationFunction.softmax)
    }

  }

  /** Choose the threshold to determine which logits are considered to be positive or negative.
    * (Default: `0.5f`). The value should be between 0.0 and 1.0. Changing the threshold value
    * will affect the resulting labels and can be used to adjust the balance between precision and
    * recall in the classification process.
    *
    * @group param
    */
  def setThreshold(threshold: Float): this.type =
    set(this.threshold, threshold)

  /** Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
   * multi-label (each label has a probability between 0.0 to 1.0).
   * Default is False i.e. multi-class
   *
   * @group param
   */
  def setMultilabel(value: Boolean): this.type = {
    if (value) {
      setActivation(ActivationFunction.sigmoid)
    } else setActivation(ActivationFunction.softmax)
    set(this.multilabel, value)
  }

}

object ActivationFunction {

  val softmax = "softmax"
  val sigmoid = "sigmoid"

}

1	/*
2	* Copyright 2017-2022 John Snow Labs
3	*
4	* Licensed under the Apache License, Version 2.0 (the "License");
5	* you may not use this file except in compliance with the License.
6	* You may obtain a copy of the License at
7	*
8	* http://www.apache.org/licenses/LICENSE-2.0
9	*
10	* Unless required by applicable law or agreed to in writing, software
11	* distributed under the License is distributed on an "AS IS" BASIS,
12	* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13	* See the License for the specific language governing permissions and
14	* limitations under the License.
15	*/
16
17	package com.johnsnowlabs.nlp
18
19	import org.apache.spark.ml.param.{FloatParam, Param}
20
21	trait HasClassifierActivationProperties extends ParamsAndFeaturesWritable {
22
23	/** Whether to enable caching DataFrames or RDDs during the training (Default depends on model).
24	*
25	* @group param
26	*/
27	val activation: Param[String] = new Param(	×
28	this,
29	"activation",	×
30	"Whether to calculate logits via Softmax or Sigmoid. Default is Softmax")	×
31
32	/** Choose the threshold to determine which logits are considered to be positive or negative.
33	* (Default: `0.5f`). The value should be between 0.0 and 1.0. Changing the threshold value
34	* will affect the resulting labels and can be used to adjust the balance between precision and
35	* recall in the classification process.
36	*
37	* @group param
38	*/
39	val threshold = new FloatParam(	×
40	this,
41	"threshold",	×
42	"Choose the threshold to determine which logits are considered to be positive or negative")	×
43
44	/** Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
45	* multi-label (each label has a probability between 0.0 to 1.0).
46	* Default is False i.e. multi-class
47	*
48	* @group param
49	*/
50	val multilabel: Param[Boolean] = new Param(	×
51	this,
52	"multilabel",	×
53	"Whether or not the result should be multi-class (the sum of all probabilities is 1.0) or multi-label (each label has a probability between 0.0 to 1.0). Default is False i.e. multi-class")	×
54
55	setDefault(activation -> ActivationFunction.softmax, threshold -> 0.5f, multilabel -> false)	×
56
57	/** @group getParam */
58	def getActivation: String = $(activation)	×
59
60	/** @group setParam */
61	def setActivation(value: String): this.type = {
62
63	value match {
64	case ActivationFunction.softmax =>
65	set(this.activation, ActivationFunction.softmax)	×
66	case ActivationFunction.sigmoid =>
67	set(this.activation, ActivationFunction.sigmoid)	×
68	case _ =>
69	set(this.activation, ActivationFunction.softmax)	×
70	}
71
72	}
73
74	/** Choose the threshold to determine which logits are considered to be positive or negative.
75	* (Default: `0.5f`). The value should be between 0.0 and 1.0. Changing the threshold value
76	* will affect the resulting labels and can be used to adjust the balance between precision and
77	* recall in the classification process.
78	*
79	* @group param
80	*/
81	def setThreshold(threshold: Float): this.type =
82	set(this.threshold, threshold)	×
83
84	/** Set whether or not the result should be multi-class (the sum of all probabilities is 1.0) or
85	* multi-label (each label has a probability between 0.0 to 1.0).
86	* Default is False i.e. multi-class
87	*
88	* @group param
89	*/
90	def setMultilabel(value: Boolean): this.type = {
91	if (value) {
92	setActivation(ActivationFunction.sigmoid)	×
93	} else setActivation(ActivationFunction.softmax)	×
94	set(this.multilabel, value)	×
95	}
96
97	}
98
99	object ActivationFunction {
100
101	val softmax = "softmax"	×
102	val sigmoid = "sigmoid"	×
103
104	}

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