nlpatl

nlpgatl is a library for active learning in machine learning experiments. The goal of NLPatl is assisting user to build high quality labeled dataset. It built on top of transformers, scikit-learn and other machine learning package. It can be applied into both cold start scenario (no any labeled data) and limited labeled data scenario.

Learning

nlpatl.learning.mismatch_farthest_learning

class nlpatl.learning.mismatch_farthest_learning.MismatchFarthestLearning(clustering_sampling, embeddings, clustering, classification, embeddings_type=None, embeddings_model_config=None, clustering_model_config=None, classification_model_config=None, multi_label=False, name='mismatch_farthest_learning')[source]

Bases: nlpatl.learning.learning.Learning

Applying mis-match first farthest traversal method apporach (with modification) to annotate the most valuable data points. You may refer to http://zhaoshuyang.com/static/documents/MAL2.pdf . Here is the pseudo:
1. [NLPatl] Convert raw data to features (Embeddings model)
2. [NLPatl] Train model and clustering data points (Clustering model)
3. [NLPatl] Estmiate the most valuable data points (Sampling)
4. [Human] Subject matter exepknrnts annotates the most valuable data points
5. [NLPatl] Train classification model (Classification model)
6. [NLPatl] Classify unlabeled data points and comparing the clustering model result according to the farthest mismatch data points
7. [Human] Subject matter exepknrnts annotates the most valuable data points
8. Repeat Step 2 to 7 until acquire enough data points or reach other exit criteria.
Parameters

  • clustering_sampling (str or function) – Clustering sampling method for stage 1 exploration. Providing certified methods name (nearest_mean) or custom function.

  • embeddings (str or nlpatl.models.embeddings.Embeddings) – Function for converting raw data to embeddings. Providing model name according to embeddings type. For example, multi-qa-MiniLM-L6-cos-v1 for sentence_transformers. bert-base-uncased` for transformers. vgg16 for torch_vision.

  • embeddings_model_config (dict) – Configuration for embeddings models. Optional. Ignored if using custom embeddings class

  • embeddings_type (str) – Type of embeddings. sentence_transformers for text, transformers for text or torch_vision for image

  • clustering (str or nlpatl.models.clustering.Clustering) – Function for clustering inputs. Either providing certified methods (kmeans) or custom function.

  • clustering_model_config (dict) – Configuration for clustering models. Optional. Ignored if using custom clustering class

  • classification (nlpatl.models.classification.Classification) – Function for classifying inputs. Either providing certified methods (logistic_regression, svc, linear_svc, random_forest and xgboost) or custom function.

  • classification_model_config (dict) – Configuration for classification models. Optional. Ignored if using custom classification class

  • multi_label (bool) – Indicate the classification model is multi-label or multi-class (or binary). Default is False.

  • name (str) – Name of this learning.

clear_learn_data()

Clear all learn data points

educate(index, x, x_features, y)

Annotate data point. Only allowing annotate data point one by one. NOT batch.

Parameters

  • index (int) – Index of data point.

  • x (string, int, float or np.ndarray) – Raw data input. It can be text, number or numpy (for image).

  • x_features (int, float or np.ndarray) – Data features

  • y (string, int, list of string (multi-label case) or list or int (multi-label case)) – Label of data point

explore(x, return_type='dict', num_sample=10)

Estimate the most valuable data points for annotation.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

Returns

The most valuable data points.

Return type

nlpatl.dataset.Dataset objects or dict

explore_educate_in_notebook(x, num_sample=5, num_sample_per_cluster=2, data_type='text')[source]

Estimate the most valuable data points for annotation and annotate it in IPython Notebook. Executing explore function and educate function sequentially.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

  • data_type (str) – Indicate the data format for displying in IPython Notebook. Possible values are text and image.

  • num_sample_per_cluster (int) –

get_learn_data()

Get all learn data points

Returns

Learnt data points

Return type

Tuple of index list of int, x (str or numpy.ndarray) x_features (numpy.ndarray) and y (numpy.ndarray)

learn(x, y, include_learn_data=True)

Train the classification model.

Parameters

  • x (list of string, int or float or np.ndarray.) – Raw data inputs. It can be text, number or numpy.

  • y (bool) – Label of data inputs

  • include_learn_data (bool) – Train the model whether including human annotated data and machine learning self annotated data. Default is True.

nlpatl.learning.semi_supervised_learning

class nlpatl.learning.semi_supervised_learning.SemiSupervisedLearning(sampling, embeddings, classification, embeddings_type=None, embeddings_model_config=None, classification_model_config=None, multi_label=False, self_learn_threshold=0.9, name='semi_supervised_learning')[source]

Bases: nlpatl.learning.learning.Learning

Applying both active learning and semi-supervised learning apporach to annotate the most valuable data points. You may refer to https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0162075&type=printable . Here is the pseudo:
1. [NLPatl] Convert raw data to features (Embeddings model)
2. [NLPatl] Train model and classifing data points (Classification model)
3. [NLPatl] Estmiate the most valuable data points (Sampling)
4. [Human] Subject matter experts annotates the most valuable data points
5. [NLPatl] Retrain classification model
6. [NLPatl] Classify unlabeled data points and labeling those confidences are higher than self_learn_threshold
7. Repeat Step 2 to 6 until acquire enough data points.
Parameters

  • sampling (str or function) – Sampling method for get the most valuable data points. Providing certified methods name (most_confidence, entropy, least_confidence, margin, nearest_mean, fathest) or custom function.

  • embeddings (str or nlpatl.models.embeddings.Embeddings) – Function for converting raw data to embeddings. Providing model name according to embeddings type. For example, multi-qa-MiniLM-L6-cos-v1 for sentence_transformers. bert-base-uncased` for transformers. vgg16 for torch_vision.

  • embeddings_model_config (dict) – Configuration for embeddings models. Optional. Ignored if using custom embeddings class

  • embeddings_type (str) – Type of embeddings. sentence_transformers for text, transformers for text or torch_vision for image

  • classification (nlpatl.models.classification.Classification) – Function for classifying inputs. Either providing certified methods (logistic_regression, svc, linear_svc, random_forest and xgboost) or custom function.

  • classification_model_config (dict) – Configuration for classification models. Optional. Ignored if using custom classification class

  • self_learn_threshold (float) – The minimum threshold for classifying probabilities. Data will be labeled automatically if probability is higher than this value. Default is 0.9

  • name (str) – Name of this learning.

  • multi_label (bool) –

clear_learn_data()

Clear all learn data points

educate(index, x, x_features, y)

Annotate data point. Only allowing annotate data point one by one. NOT batch.

Parameters

  • index (int) – Index of data point.

  • x (string, int, float or np.ndarray) – Raw data input. It can be text, number or numpy (for image).

  • x_features (int, float or np.ndarray) – Data features

  • y (string, int, list of string (multi-label case) or list or int (multi-label case)) – Label of data point

explore(x, return_type='dict', num_sample=10)[source]

Estimate the most valuable data points for annotation.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

Returns

The most valuable data points.

Return type

nlpatl.dataset.Dataset objects or dict

explore_educate_in_notebook(x, num_sample=2, data_type='text')[source]

Estimate the most valuable data points for annotation and annotate it in IPython Notebook. Executing explore function and educate function sequentially.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

  • data_type (str) – Indicate the data format for displying in IPython Notebook. Possible values are text and image.

get_learn_data()

Get all learn data points

Returns

Learnt data points

Return type

Tuple of index list of int, x (str or numpy.ndarray) x_features (numpy.ndarray) and y (numpy.ndarray)

get_self_learn_data()[source]

Get all self learnt data points

Returns

Self learnt data points

Return type

Tuple of index list of int, x (numpy.ndarray) and y (numpy.ndarray)

learn(x=None, y=None, include_learn_data=True)[source]

Train the classification model.

Parameters

  • x (list of string, int or float or np.ndarray.) – Raw data inputs. It can be text, number or numpy.

  • y (bool) – Label of data inputs

  • include_learn_data (bool) – Train the model whether including human annotated data and machine learning self annotated data. Default is True.

nlpatl.learning.supervised_learning

class nlpatl.learning.supervised_learning.SupervisedLearning(sampling, embeddings, classification, embeddings_type=None, embeddings_model_config=None, classification_model_config=None, multi_label=False, name='supervised_learning')[source]

Bases: nlpatl.learning.learning.Learning

Applying typical active learning apporach to annotate the most valuable data points. Here is the pseudo:
1. [NLPatl] Convert raw data to features (Embeddings model)
2. [NLPatl] Train model and classifing data points (Classification model)
3. [NLPatl] Estmiate the most valuable data points (Sampling)
4. [Human] Subject matter experts annotates the most valuable data points
5. Repeat Step 2 to 4 until acquire enough data points.
Parameters

  • sampling (str or function) – Sampling method for get the most valuable data points. Providing certified methods name (most_confidence, entropy, least_confidence, margin, nearest_mean, fathest) or custom function.

  • embeddings (str or nlpatl.models.embeddings.Embeddings) – Function for converting raw data to embeddings. Providing model name according to embeddings type. For example, multi-qa-MiniLM-L6-cos-v1 for sentence_transformers. bert-base-uncased` for transformers. vgg16 for torch_vision.

  • embeddings_model_config (dict) – Configuration for embeddings models. Optional. Ignored if using custom embeddings class

  • embeddings_type (str) – Type of embeddings. sentence_transformers for text, transformers for text or torch_vision for image

  • classification (nlpatl.models.classification.Classification) – Function for classifying inputs. Either providing certified methods (logistic_regression, svc, linear_svc, random_forest and xgboost) or custom function.

  • classification_model_config (dict) – Configuration for classification models. Optional. Ignored if using custom classification class

  • multi_label (bool) – Indicate the classification model is multi-label or multi-class (or binary). Default is False.

  • name (str) – Name of this learning.

clear_learn_data()

Clear all learn data points

educate(index, x, x_features, y)

Annotate data point. Only allowing annotate data point one by one. NOT batch.

Parameters

  • index (int) – Index of data point.

  • x (string, int, float or np.ndarray) – Raw data input. It can be text, number or numpy (for image).

  • x_features (int, float or np.ndarray) – Data features

  • y (string, int, list of string (multi-label case) or list or int (multi-label case)) – Label of data point

explore(x, return_type='dict', num_sample=10)[source]

Estimate the most valuable data points for annotation.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

Returns

The most valuable data points.

Return type

nlpatl.dataset.Dataset objects or dict

explore_educate_in_notebook(x, num_sample=2, data_type='text')

Estimate the most valuable data points for annotation and annotate it in IPython Notebook. Executing explore function and educate function sequentially.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

  • data_type (str) – Indicate the data format for displying in IPython Notebook. Possible values are text and image.

get_learn_data()

Get all learn data points

Returns

Learnt data points

Return type

Tuple of index list of int, x (str or numpy.ndarray) x_features (numpy.ndarray) and y (numpy.ndarray)

learn(x, y, include_learn_data=True)[source]

Train the classification model.

Parameters

  • x (list of string, int or float or np.ndarray.) – Raw data inputs. It can be text, number or numpy.

  • y (bool) – Label of data inputs

  • include_learn_data (bool) – Train the model whether including human annotated data and machine learning self annotated data. Default is True.

nlpatl.learning.unsupervised_learning

class nlpatl.learning.unsupervised_learning.UnsupervisedLearning(sampling, embeddings, clustering, embeddings_type=None, embeddings_model_config=None, clustering_model_config=None, multi_label=False, name='unsupervised_learning')[source]

Bases: nlpatl.learning.learning.Learning

Applying unsupervised learning apporach to annotate the most valuable data points. You may refer to https://homepages.tuni.fi/tuomas.virtanen/papers/active-learning-sound.pdf. Here is the pseudo:
1. [NLPatl] Convert raw data to features (Embeddings model)
2. [NLPatl] Train model and clustering data points (Clustering model)
3. [NLPatl] Estmiate the most valuable data points (Sampling)
4. [Human] Subject matter experts annotates the most valuable data points
5. Repeat Step 2 to 4 until acquire enough data points.
Parameters

  • sampling (str or function) – Sampling method for get the most valuable data points. Providing certified methods name (most_confidence, entropy, least_confidence, margin, nearest_mean, fathest) or custom function.

  • embeddings (str or nlpatl.models.embeddings.Embeddings) – Function for converting raw data to embeddings. Providing model name according to embeddings type. For example, multi-qa-MiniLM-L6-cos-v1 for sentence_transformers. bert-base-uncased` for transformers. vgg16 for torch_vision.

  • embeddings_model_config (dict) – Configuration for embeddings models. Optional. Ignored if using custom embeddings class

  • embeddings_type (str) – Type of embeddings. sentence_transformers for text, transformers for text or torch_vision for image

  • clustering (str or nlpatl.models.clustering.Clustering) – Function for clustering inputs. Either providing certified methods (kmeans) or custom function.

  • clustering_model_config (dict) – Configuration for clustering models. Optional. Ignored if using custom clustering class

  • multi_label (bool) – Indicate the classification model is multi-label or multi-class (or binary). Default is False.

  • name (str) – Name of this learning.

clear_learn_data()

Clear all learn data points

educate(index, x, x_features, y)

Annotate data point. Only allowing annotate data point one by one. NOT batch.

Parameters

  • index (int) – Index of data point.

  • x (string, int, float or np.ndarray) – Raw data input. It can be text, number or numpy (for image).

  • x_features (int, float or np.ndarray) – Data features

  • y (string, int, list of string (multi-label case) or list or int (multi-label case)) – Label of data point

explore(inputs, return_type='dict', num_sample=2)[source]

Estimate the most valuable data points for annotation.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

  • inputs (List[str]) –

Returns

The most valuable data points.

Return type

nlpatl.dataset.Dataset objects or dict

explore_educate_in_notebook(x, num_sample=2, data_type='text')

Estimate the most valuable data points for annotation and annotate it in IPython Notebook. Executing explore function and educate function sequentially.

Parameters

  • x (list of string, int or float or np.ndarray) – Raw data inputs. It can be text, number or numpy (for image).

  • return_type (str) – Data type of returning object. If dict is assigned. Return object is dict. Possible values are dict and object.

  • num_sample (int) – Maximum number of data points for annotation.

  • data_type (str) – Indicate the data format for displying in IPython Notebook. Possible values are text and image.

get_learn_data()

Get all learn data points

Returns

Learnt data points

Return type

Tuple of index list of int, x (str or numpy.ndarray) x_features (numpy.ndarray) and y (numpy.ndarray)

learn(x, y, include_learn_data=True)

Train the classification model.

Parameters

  • x (list of string, int or float or np.ndarray.) – Raw data inputs. It can be text, number or numpy.

  • y (bool) – Label of data inputs

  • include_learn_data (bool) – Train the model whether including human annotated data and machine learning self annotated data. Default is True.

Model

Classification

nlpatl.models.classification.sklearn_classification

sci-kit learn classification wrapper

class nlpatl.models.classification.sklearn_classification.SkLearnClassification(model_name='logistic_regression', model_config={}, name='sklearn_classification')[source]

Bases: nlpatl.models.classification.classification.Classification

A wrapper of sci-kit learn classification class.

Parameters
>>> import nlpatl.models.classification as nmcla
>>> model = nmcla.SkLearnClassification()
predict_proba(x, predict_config={})[source]
Parameters
Returns

Feature and probabilities

Return type

nlptatl.dataset.Dataset

train(x, y)[source]
Parameters

  • x (np.ndarray) – Raw features

  • y (list of string, int or float or np.ndarray.) – Label of data inputs

nlpatl.models.classification.xgboost_classification

class nlpatl.models.classification.xgboost_classification.XGBoostClassification(model_config={}, name='xgboost_classification')[source]

Bases: nlpatl.models.classification.sklearn_classification.SkLearnClassification

A wrapper of xgboost classification class.

Parameters
>>> import nlpatl.models.classification as nmcla
>>> model = nmcla.XGBoostClassification()
predict_proba(x, predict_config={})[source]
Parameters
Returns

Feature and probabilities

Return type

nlptatl.dataset.Dataset

train(x, y)
Parameters

  • x (np.ndarray) – Raw features

  • y (list of string, int or float or np.ndarray.) – Label of data inputs

Clustering

nlpatl.models.clustering.sklearn_clustering

class nlpatl.models.clustering.sklearn_clustering.SkLearnClustering(model_name='kmeans', model_config={}, name='sklearn_clustering')[source]

Bases: nlpatl.models.clustering.clustering.Clustering

A wrapper of sci-kit learn clustering class.

Parameters
>>> import nlpatl.models.clustering as nmclu
>>> model = nmclu.SkLearnClustering()
predict_proba(x, predict_config={})[source]
Parameters
Returns

Feature and probabilities

Return type

nlptatl.dataset.Dataset

train(x)[source]
Parameters

x (np.ndarray) – Raw features

Embeddings

nlpatl.models.embeddings.sentence_transformers

class nlpatl.models.embeddings.sentence_transformers.SentenceTransformers(model_name_or_path, batch_size=16, name='sentence_transformers')[source]

Bases: nlpatl.models.embeddings.embeddings.Embeddings

A wrapper of transformers class.

Parameters

  • model_name_or_path (str) – sentence transformers model name.

  • batch_size (int) – Batch size of data processing. Default is 16

  • model_config (dict) – Model paramateters. Refer to https://www.sbert.net/docs/pretrained_models.html

  • name (str) – Name of this embeddings

>>> import nlpatl.models.embeddings as nme
>>> model = nme.SentenceTransformers()
convert(x)[source]
Parameters

x (np.ndarray) – Raw features

Returns

Vectors of features

Return type

np.ndarray

nlpatl.models.embeddings.transformers

class nlpatl.models.embeddings.transformers.Transformers(model_name_or_path, batch_size=16, padding=False, truncation=False, nn_fwk=None, name='transformers')[source]

Bases: nlpatl.models.embeddings.embeddings.Embeddings

A wrapper of transformers class.

Parameters

  • model_name_or_path (str) – transformers model name.

  • batch_size (int) – Batch size of data processing. Default is 16

  • padding (bool) – Inputs may not have same size. Set True to pad it. Default is False

  • truncation (bool) – Inputs may not have same size. Set True to truncate it. Default is False

  • nn_fwk (str) – Neual network framework. Either pt (for PyTorch) or tf (for TensorFlow)

  • model_config (dict) – Model paramateters. Refer to https://huggingface.co/docs/transformers/index

  • name (str) – Name of this embeddings

>>> import nlpatl.models.embeddings as nme
>>> model = nme.Transformers()
convert(x)[source]
Parameters

x (np.ndarray) – Raw features

Returns

Vectors of features

Return type

np.ndarray

nlpatl.models.embeddings.torchvision

class nlpatl.models.embeddings.torchvision.TorchVision(model_name_or_path, batch_size=16, model_config={'pretrained': True}, transform=None, name='torchvision')[source]

Bases: nlpatl.models.embeddings.embeddings.Embeddings

A wrapper of torch vision class.

Parameters

  • model_name_or_path (str) – torch vision model name. Possible values are resnet18, alexnet and vgg16.

  • batch_size (int) – Batch size of data processing. Default is 16

  • model_config (dict) – Model paramateters. Refer to https://pytorch.org/vision/stable/models.html

  • transform – Preprocessing function

  • name (str) – Name of this embeddings

>>> import nlpatl.models.embeddings as nme
>>> model = nme.TrochVision()
convert(x)[source]
Parameters

x (np.ndarray) – Raw features

Returns

Vectors of features

Return type

np.ndarray

Sampling

Certainity Sampling

nlpatl.sampling.certainty.most_confidence

class nlpatl.sampling.certainty.most_confidence.MostConfidenceSampling(threshold=0.85, name='most_confidence_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points if the confidence is higher than threshold. Refer to https://markcartwright.com/files/wang2019active.pdf

Parameters

  • threshold (float) – Minimum probability of model prediction. Default value is 0.85

  • name (str) – Name of this sampling

sample(data, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data (<MagicMock id='140655954062016'>) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

Uncertainity Learning

nlpatl.sampling.uncertainty.least_confidence

class nlpatl.sampling.uncertainty.least_confidence.LeastConfidenceSampling(name='least_confidence_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points according to the least confidence. Pick the lowest

probabilies for the highest class.

Parameters

name (str) – Name of this sampling

sample(data, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data (<MagicMock id='140655953918608'>) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

nlpatl.sampling.uncertainty.entropy

class nlpatl.sampling.uncertainty.entropy.EntropySampling(name='entropy_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points according to the entropy. Pick the highest N data points

Parameters

name (str) – Name of this sampling

sample(data, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data (<MagicMock id='140655953764304'>) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

nlpatl.sampling.uncertainty.margin

class nlpatl.sampling.uncertainty.margin.MarginSampling(name='margin_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points according to the margin confidence. Pick the lowest

probabilies difference between the highest class and second higest class.

Parameters

name (str) – Name of this sampling

sample(data, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data (<MagicMock id='140655953899968'>) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

nlpatl.sampling.uncertainty.mismatch

class nlpatl.sampling.uncertainty.mismatch.MismatchSampling(name='mismatch_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points according to the mismatch. Pick the N data points

randomly.

Parameters

name (str) – Name of this sampling

sample(data1, data2, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data1 (Union[List[str], List[int], List[float], <MagicMock id='140655953612656'>]) –

  • data2 (Union[List[str], List[int], List[float], <MagicMock id='140655953590544'>]) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

Clustering Sampling

nlpatl.sampling.clustering.farthest

class nlpatl.sampling.clustering.farthest.FarthestSampling(name='farthest_sampling')[source]

Bases: nlpatl.sampling.sampling.Sampling

Sampling data points according to the distances of cluster centriod. Picking n

farthest data points per number of cluster. http://zhaoshuyang.com/static/documents/MAL2.pdf

Parameters

name (str) – Name of this sampling

sample(data, groups, num_sample)[source]
Parameters

  • x – Values of determine the sampling

  • num_sample (int) – Total number of sample for labeling

  • data (<MagicMock id='140655953397456'>) –

  • groups (<MagicMock id='140655954027952'>) –

Returns

Tuple of target indices and sampling values

Return type

Tuple of numpy.ndarray, numpy.ndarray

See Module Index for API.

Indices and tables