Skip to main content

Python package for uploading models to Scailable toolchain.

Project description

sclblpy

Last edited 23-02-2020; McK.

Python package for Scailable uploads

Functionally this package allows one to upload fitted models to Scailable after authentication using JWT:

# Import the package:
import sclblpy as sp

# Upload a model
sp.upload(mod)

Note that upon first upload the user will be prompted to provide the Scailable username and password (users can signup for an account at https://www.scailable.net/admin/signup).

Further exposed functions

Next to the main upload() function, the package also exposes the following functions to administer endpoints:

# List all endpoints owned by the current user
sp.endpoints()

# Remove an endpoint
sp.delete("cfid-cfid-cfid")

Finally, the package exposes

# Remove stored user credentials

Notes:

Install package locally pip install -e .

Dependencies

  • requests

Currently supported models:

StatsModels [link]

Name Package Direct transpile (at 21-02-2020) ONNX transpile (sometime 2020)
Generalized Least Squares StatsModels Yes
Ordinary Least Squares StatsModels Yes
Weighted Least Squares StatsModels Yes

Scikit-learn [link]

Name Package Direct transpile (at 21-02-2020) ONNX transpile (sometime 2020)
ARDRegression linear_model Yes Yes
AdaBoostClassifier ensemble Yes
AdaBoostRegressor ensemble Yes
AdditiveChi2Sampler kernel_approximation
AffinityPropagation cluster
AgglomerativeClustering cluster
BaggingClassifier ensemble Yes
BaggingRegressor ensemble Yes
BaseDecisionTree tree
BaseEnsemble ensemble
BayesianGaussianMixture mixture Yes
BayesianRidge linear_model Yes Yes
BernoulliNB naive_bayes Yes
BernoulliRBM neural_network
Binarizer preprocessing Yes
Birch cluster
CCA cross_decomposition
CalibratedClassifierCV calibration Yes
CategoricalNB naive_bayes
ClassifierChain multioutput
ComplementNB naive_bayes Yes
DBSCAN cluster
DecisionTreeClassifier tree Yes Yes
DecisionTreeRegressor tree Yes Yes
DictVectorizer feature_extraction Yes
DictionaryLearning decomposition
ElasticNet linear_model Yes Yes
ElasticNetCV linear_model Yes Yes
EllipticEnvelope covariance
EmpiricalCovariance covariance
ExtraTreeClassifier tree Yes Yes
ExtraTreeRegressor tree Yes Yes
ExtraTreesClassifier ensemble Yes Yes
ExtraTreesRegressor ensemble Yes Yes
FactorAnalysis decomposition
FastICA decomposition
FeatureAgglomeration cluster
FeatureHasher feature_extraction
FunctionTransformer preprocessing Yes
GaussianMixture mixture Yes
GaussianNB naive_bayes Yes
GaussianProcessClassifier gaussian_process
GaussianProcessRegressor gaussian_process Yes
GaussianRandomProjection random_projection
GenericUnivariateSelect feature_selection Yes
GradientBoostingClassifier ensemble Yes
GradientBoostingRegressor ensemble Yes
GraphicalLasso covariance
GraphicalLassoCV covariance
GridSearchCV model_selection Yes
HuberRegressor linear_model Yes Yes
IncrementalPCA decomposition Yes
IsolationForest ensemble
IsotonicRegression isotonic
KBinsDiscretizer preprocessing Yes
KMeans cluster Yes
KNNImputer impute
KNeighborsClassifier neighbors Yes
KNeighborsRegressor neighbors Yes
KNeighborsTransformer neighbors
KernelCenterer preprocessing
KernelDensity neighbors
KernelPCA decomposition
KernelRidge kernel_ridge
LabelBinarizer preprocessing Yes
LabelEncoder preprocessing Yes
LabelPropagation semi_supervised
LabelSpreading semi_supervised
Lars linear_model Yes Yes
LarsCV linear_model Yes Yes
Lasso linear_model Yes Yes
LassoCV linear_model Yes Yes
LassoLars linear_model Yes Yes
LassoLarsCV linear_model Yes Yes
LassoLarsIC linear_model Yes Yes
LatentDirichletAllocation decomposition
LedoitWolf covariance
LinearDiscriminantAnalysis discriminant_analysis Yes
LinearRegression linear_model Yes Yes
LinearSVC svm Yes Yes
LinearSVR svm Yes Yes
LocalOutlierFactor neighbors
LogisticRegression linear_model Yes
LogisticRegressionCV linear_model Yes
MLPClassifier neural_network Yes
MLPRegressor neural_network Yes
MaxAbsScaler preprocessing Yes
MeanShift cluster
MinCovDet covariance
MinMaxScaler preprocessing Yes
MiniBatchDictionaryLearning decomposition
MiniBatchKMeans cluster Yes
MiniBatchSparsePCA decomposition
MissingIndicator impute
MultiLabelBinarizer preprocessing
MultiOutputClassifier multioutput
MultiOutputRegressor multioutput
MultiTaskElasticNet linear_model Yes
MultiTaskElasticNetCV linear_model Yes
MultiTaskLasso linear_model Yes
MultiTaskLassoCV linear_model Yes
MultinomialNB naive_bayes Yes
NMF decomposition
NearestCentroid neighbors
NearestNeighbors neighbors Yes
NeighborhoodComponentsAnalysis neighbors
Normalizer preprocessing Yes
NuSVC svm Yes Yes
NuSVR svm Yes Yes
Nystroem kernel_approximation
OAS covariance
OPTICS cluster
OneClassSVM svm Yes
OneHotEncoder preprocessing Yes
OneVsOneClassifier multiclass
OneVsRestClassifier multiclass Yes
OrdinalEncoder preprocessing Yes
OrthogonalMatchingPursuit linear_model Yes Yes
OrthogonalMatchingPursuitCV linear_model Yes Yes
OutputCodeClassifier multiclass
PCA decomposition Yes
PLSCanonical cross_decomposition
PLSRegression cross_decomposition
PLSSVD cross_decomposition
PassiveAggressiveClassifier linear_model Yes Yes
PassiveAggressiveRegressor linear_model Yes Yes
Perceptron linear_model Yes
PolynomialFeatures preprocessing Yes
PowerTransformer preprocessing
QuadraticDiscriminantAnalysis discriminant_analysis
QuantileTransformer preprocessing
RANSACRegressor linear_model Yes
RBFSampler kernel_approximation
RFE feature_selection Yes
RFECV feature_selection Yes
RadiusNeighborsClassifier neighbors
RadiusNeighborsRegressor neighbors
RadiusNeighborsTransformer neighbors
RandomForestClassifier ensemble Yes Yes
RandomForestRegressor ensemble Yes Yes
RandomTreesEmbedding ensemble
RandomizedSearchCV model_selection
RANSACRegressor linear_model Yes
RegressorChain multioutput
Ridge linear_model Yes Yes
RidgeCV linear_model Yes Yes
RidgeClassifier linear_model Yes
RidgeClassifierCV linear_model Yes
RobustScaler preprocessing Yes
SGDClassifier linear_model Yes Yes
SGDRegressor linear_model Yes Yes
SVC svm Yes Yes
SVR svm Yes Yes
SelectFdr feature_selection Yes
SelectFpr feature_selection Yes
SelectFromModel feature_selection Yes
SelectFwe feature_selection Yes
SelectKBest feature_selection Yes
SelectPercentile feature_selection Yes
ShrunkCovariance covariance
SimpleImputer impute Yes
SkewedChi2Sampler kernel_approximation
SparseCoder decomposition
SparsePCA decomposition
SparseRandomProjection random_projection
SpectralBiclustering cluster
SpectralClustering cluster
SpectralCoclustering cluster
StackingClassifier ensemble
StackingRegressor ensemble
StandardScaler preprocessing Yes
TheilSenRegressor linear_model Yes Yes
TransformedTargetRegressor compose
TruncatedSVD decomposition Yes
VarianceThreshold feature_selection Yes
VotingClassifier ensemble Yes
VotingRegressor ensemble Yes
XGBClassifier tree Yes
XGBRegressor tree Yes
XGBRFClassifier tree Yes
XGBRFRegressor tree Yes

References:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

sclblpy-0.0.1-py3.7.egg (14.6 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page