Tpot: Documentation - TPOT vs sklearn - coverage

[mglowacki100]

It would be nice to have table with TPOT vs sklearn operators.
AFAIK not all operators from sklearn are included in tpot. It could be used as:

• roadmap for tpot
• some dim reduction techniques (e.g. tsne) could be applied without tpot

[rhiever]

Good idea. I've added this to the enhancements list.

[danthedaniel]

This is our current coverage:

• [ ] LabelEncoder
• [ ] NearestNeighbors
• [ ] OneVsOneClassifier
• [ ] OneVsRestClassifier
• [ ] AdditiveChi2Sampler
• [X] LogisticRegression
• [ ] PriorProbabilityEstimator
• [ ] NuSVC
• [ ] LinearSVR
• [ ] NeighborsBase
• [ ] LabelSpreading
• [ ] LinearModel
• [ ] AffinityPropagation
• [ ] FunctionTransformer
• [ ] ElasticNetCV
• [ ] SpectralBiclustering
• [ ] VBGMM
• [ ] RandomizedLasso
• [ ] LassoCV
• [ ] PatchExtractor
• [ ] GridSearchCV
• [ ] PLSSVD
• [ ] MiniBatchSparsePCA
• [ ] ZeroEstimator
• [ ] LSHForest
• [ ] MultiLabelBinarizer
• [ ] FeatureHasher
• [X] FastICA
• [ ] Ridge
• [ ] LedoitWolf
• [ ] MiniBatchDictionaryLearning
• [ ] ElasticNet
• [ ] Imputer
• [ ] KernelRidge
• [x] RandomizedPCA
• [X] SelectKBest
• [ ] MultiTaskElasticNet
• [ ] LassoLarsIC
• [ ] GraphLassoCV
• [ ] LabelBinarizer
• [X] MaxAbsScaler
• [ ] VotingClassifier
• [ ] IsotonicRegression
• [ ] FactorAnalysis
• [ ] LabelPropagation
• [X] GradientBoostingClassifier
• [ ] LogOddsEstimator
• [ ] SparsePCA
• [ ] TfidfVectorizer
• [X] PassiveAggressiveClassifier
• [ ] RFECV
• [ ] RandomizedLogisticRegression
• [ ] AgglomerativeClustering
• [ ] ScaledLogOddsEstimator
• [ ] OrthogonalMatchingPursuitCV
• [ ] SVR
• [ ] KernelCenterer
• [X] AdaBoostClassifier
• [ ] NearestCentroid
• [ ] TheilSenRegressor
• [ ] GaussianProcess
• [ ] SVC
• [ ] PLSCanonical
• [ ] SelectFromModel
• [ ] CountVectorizer
• [X] StandardScaler
• [X] GaussianNB
• [ ] FeatureUnion
• [ ] DummyClassifier
• [ ] GaussianRandomProjection
• [ ] KNeighborsRegressor
• [ ] MultiTaskElasticNetCV
• [ ] ForestClassifier
• [X] ExtraTreesClassifier
• [ ] SparseRandomProjection
• [ ] GraphLasso
• [X] FeatureAgglomeration
• [ ] EllipticEnvelope
• [ ] OneHotEncoder
• [ ] NuSVR
• [ ] RadiusNeighborsClassifier
• [ ] RidgeCV
• [ ] MeanShift
• [ ] LatentDirichletAllocation
• [X] Binarizer
• [ ] KMeans
• [ ] LarsCV
• [ ] SGDClassifier
• [ ] BaggingClassifier
• [ ] AdaBoostRegressor
• [ ] RidgeClassifierCV
• [ ] TruncatedSVD
• [ ] Isomap
• [ ] PCA
• [ ] SkewedChi2Sampler
• [ ] ExtraTreesRegressor
• [ ] RandomizedSearchCV
• [ ] Lasso
• [ ] IncrementalPCA
• [ ] LassoLars
• [X] PolynomialFeatures
• [ ] KernelDensity
• [ ] PassiveAggressiveRegressor
• [ ] SpectralCoclustering
• [ ] QuantileEstimator
• [ ] SpectralClustering
• [X] MultinomialNB
• [ ] LinearRegression
• [ ] Pipeline
• [X] SelectFwe
• [ ] GMM
• [ ] MeanEstimator
• [X] Nystroem
• [X] RandomForestClassifier
• [ ] DummyRegressor
• [ ] Perceptron
• [ ] CCA
• [X] DecisionTreeClassifier
• [ ] ExtraTreeRegressor
• [X] RBFSampler
• [ ] BayesianRidge
• [ ] RANSACRegressor
• [ ] DictionaryLearning
• [ ] TSNE
• [ ] HashingVectorizer
• [ ] RidgeClassifier
• [ ] ForestRegressor
• [ ] DecisionTreeRegressor
• [ ] DBSCAN
• [ ] OutputCodeClassifier
• [X] KNeighborsClassifier
• [X] ZeroCount
• [ ] GradientBoostingRegressor
• [ ] ARDRegression
• [ ] LinearDiscriminantAnalysis
• [ ] CalibratedClassifierCV
• [X] SelectPercentile
• [ ] OAS
• [ ] DPGMM
• [ ] SelectFpr
• [ ] DictVectorizer
• [X] BernoulliNB
• [ ] Normalizer
• [X] LinearSVC
• [X] VarianceThreshold
• [ ] MiniBatchKMeans
• [ ] RandomForestRegressor
• [ ] MultiTaskLasso
• [ ] ProjectedGradientNMF
• [ ] RandomTreesEmbedding
• [ ] OneClassSVM
• [ ] SGDRegressor
• [ ] GaussianRandomProjectionHash
• [ ] SparseCoder
• [ ] MultiTaskLassoCV
• [ ] QuadraticDiscriminantAnalysis
• [ ] Birch
• [ ] RadiusNeighborsRegressor
• [ ] ExtraTreeClassifier
• [ ] MinCovDet
• [ ] LinearModelCV
• [X] MinMaxScaler
• [ ] NMF
• [ ] TfidfTransformer
• [ ] SelectFdr
• [ ] KernelPCA
• [ ] BaggingRegressor
• [ ] Lars
• [ ] SpectralEmbedding
• [X] RobustScaler
• [ ] BernoulliRBM
• [ ] ShrunkCovariance
• [ ] OrthogonalMatchingPursuit
• [ ] NewBase
• [ ] GenericUnivariateSelect
• [X] RFE
• [ ] LassoLarsCV
• [ ] LocallyLinearEmbedding
• [ ] PLSRegression
• [ ] EmpiricalCovariance
• [ ] LogisticRegressionCV
• [ ] MDS

[westonplatter]

@teaearlgraycold for someone coming brand new into the project, is there a good example of what needs to be done for the list you just posted?

[danthedaniel]

@westonplatter - here's the code I used

from sklearn import * # Needed to discover all subclasses
import tpot
import sklearn

def all_subclasses(cls):
    return cls.__subclasses__() + [g for s in cls.__subclasses__() for g in all_subclasses(s)]

tpot_estimators = set([x.__name__ for x in tpot.operators.Operator.inheritors()])
sklearn_estimators = set([x.__name__ for x in all_subclasses(sklearn.base.BaseEstimator)
    if x.__name__[0] != '_' and not x.__name__.startswith("Base")])

for est in sklearn_estimators:
    marker = 'X' if est in tpot_estimators else ' '
    print("- [{}] {}".format(marker, est))

[westonplatter]

@teaearlgraycold thanks for the example. I'll take a look.

[danthedaniel]

@westonplatter, that code requires you use the version of TPOT that is currently in the development branch (0.5).