通过

OneVsRestClassifier Class

  • Reference

One-vs-All macro (OVA)

Note

This algorithm can be treated as a wrapper for all the binary

classifiers in nimbusml. A few binary

classifiers already have implementation for multi-class problems,

thus users can choose either one

depending on the context. The OVA version of a binary classifier,

such as wrapping a LightGbmBinaryClassifier ,

can be different from LightGbmClassifier ,

which develops a multi-class classifier directly.

Inheritance
nimbusml.internal.core.multiclass._onevsrestclassifier.OneVsRestClassifier
OneVsRestClassifier
nimbusml.base_predictor.BasePredictor
OneVsRestClassifier
sklearn.base.ClassifierMixin
OneVsRestClassifier

Constructor

OneVsRestClassifier(classifier, output_for_sub_graph=0, use_probabilities=True, normalize='Auto', caching='Auto', feature=None, label=None, weight=None, **params)

Parameters

Name Description
feature

see Columns.
label

see Columns.
weight

see Columns.
classifier

The subgraph for the binary trainer used to construct the OVA learner. This should be a TrainBinary node.
output_for_sub_graph

The training subgraph output.
use_probabilities

Use probabilities in OVA combiner.
normalize

If Auto, the choice to normalize depends on the preference declared by the algorithm. This is the default choice. If No, no normalization is performed. If Yes, normalization always performed. If Warn, if normalization is needed by the algorithm, a warning message is displayed but normalization is not performed. If normalization is performed, a MaxMin normalizer is used. This normalizer preserves sparsity by mapping zero to zero.
caching

Whether trainer should cache input training data.
params

Additional arguments sent to compute engine.

Examples


   ###############################################################################
   # OneVsRestClassifier
   from nimbusml import Pipeline, FileDataStream
   from nimbusml.datasets import get_dataset
   from nimbusml.ensemble import FastTreesBinaryClassifier
   from nimbusml.feature_extraction.categorical import OneHotVectorizer
   from nimbusml.multiclass import OneVsRestClassifier

   # data input (as a FileDataStream)
   path = get_dataset('infert').as_filepath()
   data = FileDataStream.read_csv(path)
   print(data.head())
   #    age  case education  induced  parity ... row_num  spontaneous  ...
   # 0   26     1    0-5yrs        1       6 ...       1            2  ...
   # 1   42     1    0-5yrs        1       1 ...       2            0  ...
   # 2   39     1    0-5yrs        2       6 ...       3            0  ...
   # 3   34     1    0-5yrs        2       4 ...       4            0  ...
   # 4   35     1   6-11yrs        1       3 ...       5            1  ...

   # define the training pipeline
   pipeline = Pipeline([
       OneHotVectorizer(columns={'edu': 'education'}),
       OneVsRestClassifier(
           # using a binary classifier + OVR for multiclass dataset
           FastTreesBinaryClassifier(),
           # True = class probabilities will sum to 1.0
           # False = raw scores, unknown range
           use_probabilities=True,
           feature=['age', 'edu'], label='induced')
   ])

   # train, predict, and evaluate
   metrics, predictions = pipeline.fit(data).test(data, output_scores=True)

   # print predictions
   print(predictions.head())
   #   PredictedLabel   Score.0   Score.1   Score.2
   # 0               2  0.084504  0.302600  0.612897
   # 1               0  0.620235  0.379226  0.000538
   # 2               2  0.077734  0.061426  0.860840
   # 3               0  0.657593  0.012318  0.330088
   # 4               0  0.743427  0.090343  0.166231

   # print evaluation metrics
   print(metrics)
   #   Accuracy(micro-avg)  Accuracy(macro-avg)  Log-loss  Log-loss reduction  ...
   # 0             0.641129             0.515541  0.736198           22.99994  ...

Remarks

OneVsRestClassifier https://en.wikipedia.org/wiki/Multiclass_classification converts any binary classifiers into mult-class. A multi-class classification problem (with K classes) can be decomposed into K binary classification problems per class, with label as 0 or 1 (if a sample belongs to the class). OneVsRestClassifier predicts the label with the highest score from the basic learners.

Methods

decision_function

Returns score values
get_params

Get the parameters for this operator.
predict_proba

Returns probabilities

decision_function

Returns score values

decision_function(X, **params)

get_params

Get the parameters for this operator.

get_params(deep=False)

Parameters

Name Description
deep
Default value: False

predict_proba

Returns probabilities

predict_proba(X, **params)