
import logging
from app.logic.helpers import *
from app.logic.active_learning import *
from app.logic.classify import *
from app.logic.feature_selection import *
from app.logic.model_selection import *
from app.logic.train import *

logger = logging.getLogger(__name__)

resolvers = {
    'Query': {
        #'train': lambda value, info, **args: train(args['trainingTask']),
        'train_batch': lambda value, info, **args: train_batch(args['candidates'], args['training_data'],
            args['params'], args['model_id']),
        'train_from_local_data': lambda value, info, **args: train_from_local_data(args['candidates'],
                args['schema'], args['training_data_file_name'], args['params'], args['model_id']),
        'get_training_results': lambda value, info, **args: get_training_results(args['model_id']),
        'all_training_results': lambda value, info, **args: all_training_results(),
        'delete_training_results': lambda value, info, **args: delete_training_results(args['model_id']),
        #'modelSelection': lambda value, info, **args: model_selection(args['models'], args['modelSel']),
        'classify': lambda value, info, **args: classify(args['cachedModelID'], args['data']),

        'loadModelSelectionResults': lambda value, info, **args: loadModelSelectionResults(args['obj']),
        'modelSelectionResultsToObject': lambda value, info, **args: modelSelectionResultsToObject(args['savedId'], args['msr']),

        'defaultModelConfiguration': lambda value, info, **args: defaultModelConfiguration(),
        'defaultModelSelection': lambda value, info, **args: defaultModelSelection(),
        'defaultFeatures': lambda value, info, **args:  defaultFeatures(args['dataset']),
        'defaultFeaturizers': lambda value, info, **args:  defaultFeaturizers(args['features']),
        'defaultCandidate': lambda value, info, **args:  defaultCandidate(args['dataset']),
        'createModelConfiguration':  lambda value, info, **args: createModelConfiguration(
            args['type'], args['weighting'], args['tokenizer'], args['ngrams'], args['tf'],
            args['df'], args['penalty'], args['multiclass'], args['solver'], args['primal_dual'],
            args['fitIntercept'], args['max_df'], args['min_df'], args['stopwords'], args['C'], args['max_iter']
        ),
        'createModelSelection':  lambda value, info, **args: createModelSelection(
            args['metric'], args['method'], args['evalMode'], args['numFolds']
        ),
        'createCandidate':  lambda value, info, **args: createCandidate(args['features'], args['featurizers'], args['config']),
        'addCandidate':  lambda value, info, **args: addToList(args['addThis'], args.get('toThis', None)),
        #'createTrainingTasks': lambda value, info, **args:  createTrainingTasks(args['candidates'],
        #        args['training_data'], args['params']),

        'mergeDatasets':  lambda value, info, **args: mergeDatasets(args['datasets']),
        'mergeLabeledDatasets':  lambda value, info, **args: mergeLabeledDatasets(args['datasets']),

        'addFeaturizer':  lambda value, info, **args: addToList(args['addThis'], args.get('toThis', None)),
        'subsetFeatures': lambda value, info, **args:  subsetFeatures(args['dataset'], args['selectedFeatures']),
        'topNCorrelatedFeatures': lambda value, info, **args:  top_correlated_features(args['dataset'], args['config'], args['topN']),
        'topNPctCorrelatedFeatures': lambda value, info, **args:  top_pct_correlated_features(args['dataset'], args['config'], args['pct']),
        'topNRFEFeatures': lambda value, info, **args:  top_rfe_features(args['dataset'], args['config'], args['topN']),
        'topNPctRFEFeatures': lambda value, info, **args:  top_pct_rfe_features(args['dataset'], args['config'], args['pct']),

        'numericalFeatureType': lambda value, info, **args: 'NUMERICAL',
        'categoricalFeatureType': lambda value, info, **args: 'CATEGORICAL',
        'textFeatureType': lambda value, info, **args: 'TEXT',
        'setFeatureType': lambda value, info, **args: 'SET',
        'booleanFeatureType': lambda value, info, **args: 'BOOLEAN',
        'labelFeatureType': lambda value, info, **args: 'LABEL',

        'noopFeaturizerType': lambda value, info, **args: 'NOOP',
        'minMaxScalerFeaturizerType': lambda value, info, **args: 'MIN_MAX_SCALER',
        'labelBinarizerFeaturizerType': lambda value, info, **args: 'LABEL_BINARIZER',
        'tfidfVectorizerFeaturizerType': lambda value, info, **args: 'TFIDF_VECTORIZER',
        'multilabelBinarizerFeaturizerType': lambda value, info, **args: 'MULTILABEL_BINARIZER',
        'labelEncoderFeaturizerType': lambda value, info, **args: 'LABEL',
        'textToVectorFeaturizerType': lambda value, info, **args: 'TEXT_TO_VECTOR',
        
        'correlationFeatureSelectionMode': lambda value, info, **args: 'CORRELATION',
        'rfeFeatureSelectionMode': lambda value, info, **args: 'RFE',
        
        'logisticRegressionModelType': lambda value, info, **args: 'LOGISTIC_REGRESSION',
        'linearSVCModelType': lambda value, info, **args: 'LINEAR_SVC',
        
        'noneClassWeightingType': lambda value, info, **args: 'NONE',
        'balancedClassWeightingType': lambda value, info, **args: 'BALANCED',
        
        'unigramNGramType': lambda value, info, **args: 'UNIGRAM',
        'bigramNGramType': lambda value, info, **args: 'BIGRAM',
        'bothNGramType': lambda value, info, **args: 'BOTH',
        
        'linearTermFreqType': lambda value, info, **args: 'LINEAR',
        'sublinearTermFreqType': lambda value, info, **args: 'SUBLINEAR',
        
        'defaultDocumentFreqType': lambda value, info, **args: 'DEFAULT',
        'smoothDocumentFreqType': lambda value, info, **args: 'SMOOTH',
        
        'l1PenaltyType': lambda value, info, **args: 'L1',
        'l2PenaltyType': lambda value, info, **args: 'L2',
        
        'ovrMultiClassType': lambda value, info, **args: 'OVR',
        'multinomialMultiClassType': lambda value, info, **args: 'MULTINOMIAL',
        'autoMultiClassType': lambda value, info, **args: 'AUTO',
        
        'liblinearSolverType': lambda value, info, **args: 'LIBLINEAR',
        'newtonCGSolverType': lambda value, info, **args: 'NEWTON_CG',
        'lbfgsSolverType': lambda value, info, **args: 'LBFGS',
        'sagSolverType': lambda value, info, **args: 'SAG',
        'sagaSolverType': lambda value, info, **args: 'SAGA',
        
        'primalMode': lambda value, info, **args: 'PRIMAL',
        'dualMode': lambda value, info, **args: 'DUAL',
        
        'wordTokenizerType': lambda value, info, **args: 'WORD_TOKENIZER',
        'stemmerTokenizerType': lambda value, info, **args: 'STEMMER',
        'lemmatizerTokenizerType': lambda value, info, **args: 'LEMMATIZER',
        
        'precisionModelSelectionMetric': lambda value, info, **args: 'PRECISION',
        'recallModelSelectionMetric': lambda value, info, **args: 'RECALL',
        'f1ModelSelectionMetric': lambda value, info, **args: 'F1',
        
        'bestModelModelSelectionMethod': lambda value, info, **args: 'BEST',
        'kneePointModelSelectionMethod': lambda value, info, **args: 'KNEE_POINT',
        'oneStdevModelSelectionMethod': lambda value, info, **args: 'ONE_STDEV',
        'twoStdevModelSelectionMethod': lambda value, info, **args: 'TWO_STDEV',
        
        'looModelEvaluationType': lambda value, info, **args: 'LEAVE_ONE_OUT',
        'kFoldsModelEvaluationType': lambda value, info, **args: 'K_FOLDS',
        
        'noneStopWordType': lambda value, info, **args: 'NONE',
        'englishStopWordType': lambda value, info, **args: 'ENGLISH',
    },
    'Object': {
    },
    'Mutation': {
    },
    'Scalar': {
    },
}






8b1f39b6d5a05cc812bb72479b26db383d0aa345
<mask token>

<mask token>
logger = logging.getLogger(__name__)
resolvers = {'Query': {'train_batch': lambda value, info, **args:
    train_batch(args['candidates'], args['training_data'], args['params'],
    args['model_id']), 'train_from_local_data': lambda value, info, **args:
    train_from_local_data(args['candidates'], args['schema'], args[
    'training_data_file_name'], args['params'], args['model_id']),
    'get_training_results': lambda value, info, **args:
    get_training_results(args['model_id']), 'all_training_results': lambda
    value, info, **args: all_training_results(), 'delete_training_results':
    lambda value, info, **args: delete_training_results(args['model_id']),
    'classify': lambda value, info, **args: classify(args['cachedModelID'],
    args['data']), 'loadModelSelectionResults': lambda value, info, **args:
    loadModelSelectionResults(args['obj']), 'modelSelectionResultsToObject':
    lambda value, info, **args: modelSelectionResultsToObject(args[
    'savedId'], args['msr']), 'defaultModelConfiguration': lambda value,
    info, **args: defaultModelConfiguration(), 'defaultModelSelection': lambda
    value, info, **args: defaultModelSelection(), 'defaultFeatures': lambda
    value, info, **args: defaultFeatures(args['dataset']),
    'defaultFeaturizers': lambda value, info, **args: defaultFeaturizers(
    args['features']), 'defaultCandidate': lambda value, info, **args:
    defaultCandidate(args['dataset']), 'createModelConfiguration': lambda
    value, info, **args: createModelConfiguration(args['type'], args[
    'weighting'], args['tokenizer'], args['ngrams'], args['tf'], args['df'],
    args['penalty'], args['multiclass'], args['solver'], args['primal_dual'
    ], args['fitIntercept'], args['max_df'], args['min_df'], args[
    'stopwords'], args['C'], args['max_iter']), 'createModelSelection': lambda
    value, info, **args: createModelSelection(args['metric'], args['method'
    ], args['evalMode'], args['numFolds']), 'createCandidate': lambda value,
    info, **args: createCandidate(args['features'], args['featurizers'],
    args['config']), 'addCandidate': lambda value, info, **args: addToList(
    args['addThis'], args.get('toThis', None)), 'mergeDatasets': lambda
    value, info, **args: mergeDatasets(args['datasets']),
    'mergeLabeledDatasets': lambda value, info, **args:
    mergeLabeledDatasets(args['datasets']), 'addFeaturizer': lambda value,
    info, **args: addToList(args['addThis'], args.get('toThis', None)),
    'subsetFeatures': lambda value, info, **args: subsetFeatures(args[
    'dataset'], args['selectedFeatures']), 'topNCorrelatedFeatures': lambda
    value, info, **args: top_correlated_features(args['dataset'], args[
    'config'], args['topN']), 'topNPctCorrelatedFeatures': lambda value,
    info, **args: top_pct_correlated_features(args['dataset'], args[
    'config'], args['pct']), 'topNRFEFeatures': lambda value, info, **args:
    top_rfe_features(args['dataset'], args['config'], args['topN']),
    'topNPctRFEFeatures': lambda value, info, **args: top_pct_rfe_features(
    args['dataset'], args['config'], args['pct']), 'numericalFeatureType': 
    lambda value, info, **args: 'NUMERICAL', 'categoricalFeatureType': lambda
    value, info, **args: 'CATEGORICAL', 'textFeatureType': lambda value,
    info, **args: 'TEXT', 'setFeatureType': lambda value, info, **args:
    'SET', 'booleanFeatureType': lambda value, info, **args: 'BOOLEAN',
    'labelFeatureType': lambda value, info, **args: 'LABEL',
    'noopFeaturizerType': lambda value, info, **args: 'NOOP',
    'minMaxScalerFeaturizerType': lambda value, info, **args:
    'MIN_MAX_SCALER', 'labelBinarizerFeaturizerType': lambda value, info,
    **args: 'LABEL_BINARIZER', 'tfidfVectorizerFeaturizerType': lambda
    value, info, **args: 'TFIDF_VECTORIZER',
    'multilabelBinarizerFeaturizerType': lambda value, info, **args:
    'MULTILABEL_BINARIZER', 'labelEncoderFeaturizerType': lambda value,
    info, **args: 'LABEL', 'textToVectorFeaturizerType': lambda value, info,
    **args: 'TEXT_TO_VECTOR', 'correlationFeatureSelectionMode': lambda
    value, info, **args: 'CORRELATION', 'rfeFeatureSelectionMode': lambda
    value, info, **args: 'RFE', 'logisticRegressionModelType': lambda value,
    info, **args: 'LOGISTIC_REGRESSION', 'linearSVCModelType': lambda value,
    info, **args: 'LINEAR_SVC', 'noneClassWeightingType': lambda value,
    info, **args: 'NONE', 'balancedClassWeightingType': lambda value, info,
    **args: 'BALANCED', 'unigramNGramType': lambda value, info, **args:
    'UNIGRAM', 'bigramNGramType': lambda value, info, **args: 'BIGRAM',
    'bothNGramType': lambda value, info, **args: 'BOTH',
    'linearTermFreqType': lambda value, info, **args: 'LINEAR',
    'sublinearTermFreqType': lambda value, info, **args: 'SUBLINEAR',
    'defaultDocumentFreqType': lambda value, info, **args: 'DEFAULT',
    'smoothDocumentFreqType': lambda value, info, **args: 'SMOOTH',
    'l1PenaltyType': lambda value, info, **args: 'L1', 'l2PenaltyType': lambda
    value, info, **args: 'L2', 'ovrMultiClassType': lambda value, info, **
    args: 'OVR', 'multinomialMultiClassType': lambda value, info, **args:
    'MULTINOMIAL', 'autoMultiClassType': lambda value, info, **args: 'AUTO',
    'liblinearSolverType': lambda value, info, **args: 'LIBLINEAR',
    'newtonCGSolverType': lambda value, info, **args: 'NEWTON_CG',
    'lbfgsSolverType': lambda value, info, **args: 'LBFGS', 'sagSolverType':
    lambda value, info, **args: 'SAG', 'sagaSolverType': lambda value, info,
    **args: 'SAGA', 'primalMode': lambda value, info, **args: 'PRIMAL',
    'dualMode': lambda value, info, **args: 'DUAL', 'wordTokenizerType': lambda
    value, info, **args: 'WORD_TOKENIZER', 'stemmerTokenizerType': lambda
    value, info, **args: 'STEMMER', 'lemmatizerTokenizerType': lambda value,
    info, **args: 'LEMMATIZER', 'precisionModelSelectionMetric': lambda
    value, info, **args: 'PRECISION', 'recallModelSelectionMetric': lambda
    value, info, **args: 'RECALL', 'f1ModelSelectionMetric': lambda value,
    info, **args: 'F1', 'bestModelModelSelectionMethod': lambda value, info,
    **args: 'BEST', 'kneePointModelSelectionMethod': lambda value, info, **
    args: 'KNEE_POINT', 'oneStdevModelSelectionMethod': lambda value, info,
    **args: 'ONE_STDEV', 'twoStdevModelSelectionMethod': lambda value, info,
    **args: 'TWO_STDEV', 'looModelEvaluationType': lambda value, info, **
    args: 'LEAVE_ONE_OUT', 'kFoldsModelEvaluationType': lambda value, info,
    **args: 'K_FOLDS', 'noneStopWordType': lambda value, info, **args:
    'NONE', 'englishStopWordType': lambda value, info, **args: 'ENGLISH'},
    'Object': {}, 'Mutation': {}, 'Scalar': {}}

import logging
from app.logic.helpers import *
from app.logic.active_learning import *
from app.logic.classify import *
from app.logic.feature_selection import *
from app.logic.model_selection import *
from app.logic.train import *
logger = logging.getLogger(__name__)
resolvers = {'Query': {'train_batch': lambda value, info, **args:
    train_batch(args['candidates'], args['training_data'], args['params'],
    args['model_id']), 'train_from_local_data': lambda value, info, **args:
    train_from_local_data(args['candidates'], args['schema'], args[
    'training_data_file_name'], args['params'], args['model_id']),
    'get_training_results': lambda value, info, **args:
    get_training_results(args['model_id']), 'all_training_results': lambda
    value, info, **args: all_training_results(), 'delete_training_results':
    lambda value, info, **args: delete_training_results(args['model_id']),
    'classify': lambda value, info, **args: classify(args['cachedModelID'],
    args['data']), 'loadModelSelectionResults': lambda value, info, **args:
    loadModelSelectionResults(args['obj']), 'modelSelectionResultsToObject':
    lambda value, info, **args: modelSelectionResultsToObject(args[
    'savedId'], args['msr']), 'defaultModelConfiguration': lambda value,
    info, **args: defaultModelConfiguration(), 'defaultModelSelection': lambda
    value, info, **args: defaultModelSelection(), 'defaultFeatures': lambda
    value, info, **args: defaultFeatures(args['dataset']),
    'defaultFeaturizers': lambda value, info, **args: defaultFeaturizers(
    args['features']), 'defaultCandidate': lambda value, info, **args:
    defaultCandidate(args['dataset']), 'createModelConfiguration': lambda
    value, info, **args: createModelConfiguration(args['type'], args[
    'weighting'], args['tokenizer'], args['ngrams'], args['tf'], args['df'],
    args['penalty'], args['multiclass'], args['solver'], args['primal_dual'
    ], args['fitIntercept'], args['max_df'], args['min_df'], args[
    'stopwords'], args['C'], args['max_iter']), 'createModelSelection': lambda
    value, info, **args: createModelSelection(args['metric'], args['method'
    ], args['evalMode'], args['numFolds']), 'createCandidate': lambda value,
    info, **args: createCandidate(args['features'], args['featurizers'],
    args['config']), 'addCandidate': lambda value, info, **args: addToList(
    args['addThis'], args.get('toThis', None)), 'mergeDatasets': lambda
    value, info, **args: mergeDatasets(args['datasets']),
    'mergeLabeledDatasets': lambda value, info, **args:
    mergeLabeledDatasets(args['datasets']), 'addFeaturizer': lambda value,
    info, **args: addToList(args['addThis'], args.get('toThis', None)),
    'subsetFeatures': lambda value, info, **args: subsetFeatures(args[
    'dataset'], args['selectedFeatures']), 'topNCorrelatedFeatures': lambda
    value, info, **args: top_correlated_features(args['dataset'], args[
    'config'], args['topN']), 'topNPctCorrelatedFeatures': lambda value,
    info, **args: top_pct_correlated_features(args['dataset'], args[
    'config'], args['pct']), 'topNRFEFeatures': lambda value, info, **args:
    top_rfe_features(args['dataset'], args['config'], args['topN']),
    'topNPctRFEFeatures': lambda value, info, **args: top_pct_rfe_features(
    args['dataset'], args['config'], args['pct']), 'numericalFeatureType': 
    lambda value, info, **args: 'NUMERICAL', 'categoricalFeatureType': lambda
    value, info, **args: 'CATEGORICAL', 'textFeatureType': lambda value,
    info, **args: 'TEXT', 'setFeatureType': lambda value, info, **args:
    'SET', 'booleanFeatureType': lambda value, info, **args: 'BOOLEAN',
    'labelFeatureType': lambda value, info, **args: 'LABEL',
    'noopFeaturizerType': lambda value, info, **args: 'NOOP',
    'minMaxScalerFeaturizerType': lambda value, info, **args:
    'MIN_MAX_SCALER', 'labelBinarizerFeaturizerType': lambda value, info,
    **args: 'LABEL_BINARIZER', 'tfidfVectorizerFeaturizerType': lambda
    value, info, **args: 'TFIDF_VECTORIZER',
    'multilabelBinarizerFeaturizerType': lambda value, info, **args:
    'MULTILABEL_BINARIZER', 'labelEncoderFeaturizerType': lambda value,
    info, **args: 'LABEL', 'textToVectorFeaturizerType': lambda value, info,
    **args: 'TEXT_TO_VECTOR', 'correlationFeatureSelectionMode': lambda
    value, info, **args: 'CORRELATION', 'rfeFeatureSelectionMode': lambda
    value, info, **args: 'RFE', 'logisticRegressionModelType': lambda value,
    info, **args: 'LOGISTIC_REGRESSION', 'linearSVCModelType': lambda value,
    info, **args: 'LINEAR_SVC', 'noneClassWeightingType': lambda value,
    info, **args: 'NONE', 'balancedClassWeightingType': lambda value, info,
    **args: 'BALANCED', 'unigramNGramType': lambda value, info, **args:
    'UNIGRAM', 'bigramNGramType': lambda value, info, **args: 'BIGRAM',
    'bothNGramType': lambda value, info, **args: 'BOTH',
    'linearTermFreqType': lambda value, info, **args: 'LINEAR',
    'sublinearTermFreqType': lambda value, info, **args: 'SUBLINEAR',
    'defaultDocumentFreqType': lambda value, info, **args: 'DEFAULT',
    'smoothDocumentFreqType': lambda value, info, **args: 'SMOOTH',
    'l1PenaltyType': lambda value, info, **args: 'L1', 'l2PenaltyType': lambda
    value, info, **args: 'L2', 'ovrMultiClassType': lambda value, info, **
    args: 'OVR', 'multinomialMultiClassType': lambda value, info, **args:
    'MULTINOMIAL', 'autoMultiClassType': lambda value, info, **args: 'AUTO',
    'liblinearSolverType': lambda value, info, **args: 'LIBLINEAR',
    'newtonCGSolverType': lambda value, info, **args: 'NEWTON_CG',
    'lbfgsSolverType': lambda value, info, **args: 'LBFGS', 'sagSolverType':
    lambda value, info, **args: 'SAG', 'sagaSolverType': lambda value, info,
    **args: 'SAGA', 'primalMode': lambda value, info, **args: 'PRIMAL',
    'dualMode': lambda value, info, **args: 'DUAL', 'wordTokenizerType': lambda
    value, info, **args: 'WORD_TOKENIZER', 'stemmerTokenizerType': lambda
    value, info, **args: 'STEMMER', 'lemmatizerTokenizerType': lambda value,
    info, **args: 'LEMMATIZER', 'precisionModelSelectionMetric': lambda
    value, info, **args: 'PRECISION', 'recallModelSelectionMetric': lambda
    value, info, **args: 'RECALL', 'f1ModelSelectionMetric': lambda value,
    info, **args: 'F1', 'bestModelModelSelectionMethod': lambda value, info,
    **args: 'BEST', 'kneePointModelSelectionMethod': lambda value, info, **
    args: 'KNEE_POINT', 'oneStdevModelSelectionMethod': lambda value, info,
    **args: 'ONE_STDEV', 'twoStdevModelSelectionMethod': lambda value, info,
    **args: 'TWO_STDEV', 'looModelEvaluationType': lambda value, info, **
    args: 'LEAVE_ONE_OUT', 'kFoldsModelEvaluationType': lambda value, info,
    **args: 'K_FOLDS', 'noneStopWordType': lambda value, info, **args:
    'NONE', 'englishStopWordType': lambda value, info, **args: 'ENGLISH'},
    'Object': {}, 'Mutation': {}, 'Scalar': {}}


import logging
from app.logic.helpers import *
from app.logic.active_learning import *
from app.logic.classify import *
from app.logic.feature_selection import *
from app.logic.model_selection import *
from app.logic.train import *

logger = logging.getLogger(__name__)

resolvers = {
    'Query': {
        #'train': lambda value, info, **args: train(args['trainingTask']),
        'train_batch': lambda value, info, **args: train_batch(args['candidates'], args['training_data'],
            args['params'], args['model_id']),
        'train_from_local_data': lambda value, info, **args: train_from_local_data(args['candidates'],
                args['schema'], args['training_data_file_name'], args['params'], args['model_id']),
        'get_training_results': lambda value, info, **args: get_training_results(args['model_id']),
        'all_training_results': lambda value, info, **args: all_training_results(),
        'delete_training_results': lambda value, info, **args: delete_training_results(args['model_id']),
        #'modelSelection': lambda value, info, **args: model_selection(args['models'], args['modelSel']),
        'classify': lambda value, info, **args: classify(args['cachedModelID'], args['data']),

        'loadModelSelectionResults': lambda value, info, **args: loadModelSelectionResults(args['obj']),
        'modelSelectionResultsToObject': lambda value, info, **args: modelSelectionResultsToObject(args['savedId'], args['msr']),

        'defaultModelConfiguration': lambda value, info, **args: defaultModelConfiguration(),
        'defaultModelSelection': lambda value, info, **args: defaultModelSelection(),
        'defaultFeatures': lambda value, info, **args:  defaultFeatures(args['dataset']),
        'defaultFeaturizers': lambda value, info, **args:  defaultFeaturizers(args['features']),
        'defaultCandidate': lambda value, info, **args:  defaultCandidate(args['dataset']),
        'createModelConfiguration':  lambda value, info, **args: createModelConfiguration(
            args['type'], args['weighting'], args['tokenizer'], args['ngrams'], args['tf'],
            args['df'], args['penalty'], args['multiclass'], args['solver'], args['primal_dual'],
            args['fitIntercept'], args['max_df'], args['min_df'], args['stopwords'], args['C'], args['max_iter']
        ),
        'createModelSelection':  lambda value, info, **args: createModelSelection(
            args['metric'], args['method'], args['evalMode'], args['numFolds']
        ),
        'createCandidate':  lambda value, info, **args: createCandidate(args['features'], args['featurizers'], args['config']),
        'addCandidate':  lambda value, info, **args: addToList(args['addThis'], args.get('toThis', None)),
        #'createTrainingTasks': lambda value, info, **args:  createTrainingTasks(args['candidates'],
        #        args['training_data'], args['params']),

        'mergeDatasets':  lambda value, info, **args: mergeDatasets(args['datasets']),
        'mergeLabeledDatasets':  lambda value, info, **args: mergeLabeledDatasets(args['datasets']),

        'addFeaturizer':  lambda value, info, **args: addToList(args['addThis'], args.get('toThis', None)),
        'subsetFeatures': lambda value, info, **args:  subsetFeatures(args['dataset'], args['selectedFeatures']),
        'topNCorrelatedFeatures': lambda value, info, **args:  top_correlated_features(args['dataset'], args['config'], args['topN']),
        'topNPctCorrelatedFeatures': lambda value, info, **args:  top_pct_correlated_features(args['dataset'], args['config'], args['pct']),
        'topNRFEFeatures': lambda value, info, **args:  top_rfe_features(args['dataset'], args['config'], args['topN']),
        'topNPctRFEFeatures': lambda value, info, **args:  top_pct_rfe_features(args['dataset'], args['config'], args['pct']),

        'numericalFeatureType': lambda value, info, **args: 'NUMERICAL',
        'categoricalFeatureType': lambda value, info, **args: 'CATEGORICAL',
        'textFeatureType': lambda value, info, **args: 'TEXT',
        'setFeatureType': lambda value, info, **args: 'SET',
        'booleanFeatureType': lambda value, info, **args: 'BOOLEAN',
        'labelFeatureType': lambda value, info, **args: 'LABEL',

        'noopFeaturizerType': lambda value, info, **args: 'NOOP',
        'minMaxScalerFeaturizerType': lambda value, info, **args: 'MIN_MAX_SCALER',
        'labelBinarizerFeaturizerType': lambda value, info, **args: 'LABEL_BINARIZER',
        'tfidfVectorizerFeaturizerType': lambda value, info, **args: 'TFIDF_VECTORIZER',
        'multilabelBinarizerFeaturizerType': lambda value, info, **args: 'MULTILABEL_BINARIZER',
        'labelEncoderFeaturizerType': lambda value, info, **args: 'LABEL',
        'textToVectorFeaturizerType': lambda value, info, **args: 'TEXT_TO_VECTOR',
        
        'correlationFeatureSelectionMode': lambda value, info, **args: 'CORRELATION',
        'rfeFeatureSelectionMode': lambda value, info, **args: 'RFE',
        
        'logisticRegressionModelType': lambda value, info, **args: 'LOGISTIC_REGRESSION',
        'linearSVCModelType': lambda value, info, **args: 'LINEAR_SVC',
        
        'noneClassWeightingType': lambda value, info, **args: 'NONE',
        'balancedClassWeightingType': lambda value, info, **args: 'BALANCED',
        
        'unigramNGramType': lambda value, info, **args: 'UNIGRAM',
        'bigramNGramType': lambda value, info, **args: 'BIGRAM',
        'bothNGramType': lambda value, info, **args: 'BOTH',
        
        'linearTermFreqType': lambda value, info, **args: 'LINEAR',
        'sublinearTermFreqType': lambda value, info, **args: 'SUBLINEAR',
        
        'defaultDocumentFreqType': lambda value, info, **args: 'DEFAULT',
        'smoothDocumentFreqType': lambda value, info, **args: 'SMOOTH',
        
        'l1PenaltyType': lambda value, info, **args: 'L1',
        'l2PenaltyType': lambda value, info, **args: 'L2',
        
        'ovrMultiClassType': lambda value, info, **args: 'OVR',
        'multinomialMultiClassType': lambda value, info, **args: 'MULTINOMIAL',
        'autoMultiClassType': lambda value, info, **args: 'AUTO',
        
        'liblinearSolverType': lambda value, info, **args: 'LIBLINEAR',
        'newtonCGSolverType': lambda value, info, **args: 'NEWTON_CG',
        'lbfgsSolverType': lambda value, info, **args: 'LBFGS',
        'sagSolverType': lambda value, info, **args: 'SAG',
        'sagaSolverType': lambda value, info, **args: 'SAGA',
        
        'primalMode': lambda value, info, **args: 'PRIMAL',
        'dualMode': lambda value, info, **args: 'DUAL',
        
        'wordTokenizerType': lambda value, info, **args: 'WORD_TOKENIZER',
        'stemmerTokenizerType': lambda value, info, **args: 'STEMMER',
        'lemmatizerTokenizerType': lambda value, info, **args: 'LEMMATIZER',
        
        'precisionModelSelectionMetric': lambda value, info, **args: 'PRECISION',
        'recallModelSelectionMetric': lambda value, info, **args: 'RECALL',
        'f1ModelSelectionMetric': lambda value, info, **args: 'F1',
        
        'bestModelModelSelectionMethod': lambda value, info, **args: 'BEST',
        'kneePointModelSelectionMethod': lambda value, info, **args: 'KNEE_POINT',
        'oneStdevModelSelectionMethod': lambda value, info, **args: 'ONE_STDEV',
        'twoStdevModelSelectionMethod': lambda value, info, **args: 'TWO_STDEV',
        
        'looModelEvaluationType': lambda value, info, **args: 'LEAVE_ONE_OUT',
        'kFoldsModelEvaluationType': lambda value, info, **args: 'K_FOLDS',
        
        'noneStopWordType': lambda value, info, **args: 'NONE',
        'englishStopWordType': lambda value, info, **args: 'ENGLISH',
    },
    'Object': {
    },
    'Mutation': {
    },
    'Scalar': {
    },
}





