i[*SrSSKJr SSKJr SSKJr SSKJr SSK J r J r J r Jr "S S 5r"S S \5r"S S\5rg)z" Pipeline utils for this package. )proto)SPECIFICATION_VERSION)_feature_management)model)set_classifier_interface_paramsset_regressor_interface_paramsset_training_featuresset_transform_interface_paramsc@\rSrSrSrS SjrSrSrSrSr S r S r g) Pipelinea A pipeline model that exposes a sequence of models as a single model, It requires a set of inputs, a sequence of other models and a set of outputs. This class is the base class for :py:class:`PipelineClassifier` and :py:class:`PipelineRegressor`, which contain a sequence ending in a classifier or regressor and themselves behave like a classifier or regressor. This class may be used directly for a sequence of feature transformer objects. Nc[RR5n[UlUR [ XAX#5nX@lg)a Create a pipeline of models to be executed sequentially. Parameters ---------- input_features: [list of 2-tuples] Name(s) of the input features, given as a list of `('name', datatype)` tuples. The datatypes entry can be any of the data types defined in the :py:mod:`models.datatypes` module. output_features: [list of features] Name(s) of the output features, given as a list of `('name',datatype)` tuples. The datatypes entry can be any of the data types defined in the :py:mod:`models.datatypes` module. All features must be either defined in the inputs or be produced by one of the contained models. N)_proto Model_pb2Model_SPECIFICATION_VERSIONspecificationVersionpipeliner specselfinput_featuresoutput_featurestraining_featuresrs Z/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/coremltools/models/pipeline.py__init__Pipeline.__init__#s?(%%'$:! - /   c<UR(a [S5eg)NzNNew sub-models cannot be added after the pipeline has been marked as updatable) isUpdatable ValueError)rrs r)_validate_updatable_pipeline_on_add_model2Pipeline._validate_updatable_pipeline_on_add_modelDs   `  r cURUR5 [U[R5(a UR nURR nURR5nURU5 ga= Add a protobuf spec or :py:class:`models.MLModel` instance to the pipeline. All input features of this model must either match the input_features of the pipeline, or match the outputs of a previous model. Parameters ---------- spec: [MLModel, Model_pb2] A protobuf spec or MLModel instance containing a model. N) r$r isinstance_modelMLModel_specrmodelsaddCopyFrom)rrr step_specs r add_modelPipeline.add_modelJs_ 66tyyA dFNN + +::D99%%OO'') 4 r c[UR5nUS:a [S5eURSSHnUR(dM[S5e URUS- nUR(d [S5eSUlg)NrzmPipeline does not seem to have any models. It should be marked as updatable only after adding all sub-models.z>Only the last model can be updatable in an updatable pipeline.zEA pipeline can be made updatable only if the last model is updatable.T)lenr,r#r")rrr num_modelsr last_models r'_validate_sub_models_and_make_updatable0Pipeline._validate_sub_models_and_make_updatable`s) > __Sb)E    T* __Z!^4 %%W  r cdURURRUR5 gN)r7rr)rs rmake_updatablePipeline.make_updatablevs  44TYY5G5GSr c2URn[X!5 gz Set the training inputs of the network spec. Parameters ---------- training_input: [tuple] List of training input names and type of the network. Nrr rtraining_inputrs rset_training_inputPipeline.set_training_inputyyyd3r rr:) __name__ __module__ __qualname____firstlineno____doc__rr$r0r7r;rB__static_attributes__r rrrs' B !, ,T 4r rcH^\rSrSrSrSSjrU4SjrU4SjrSrSr U=r $) PipelineRegressora A pipeline model that exposes a sequence of models as a single model, It requires a set of inputs, a sequence of other models and a set of outputs. In this case the pipeline itself behaves as a regression model by designating a real valued output feature as its 'predicted feature'. c[RR5n[UlUR [ XAX#5nX@lg)a Create a set of pipeline models given a set of model specs. The final output model must be a regression model. Parameters ---------- input_features: [list of 2-tuples] Name(s) of the input features, given as a list of `('name', datatype)` tuples. The datatypes entry can be any of the data types defined in the :py:mod:`models.datatypes` module. output_features: [list of features] Name(s) of the output features, given as a list of `('name',datatype)` tuples. The datatypes entry can be any of the data types defined in the :py:mod:`models.datatypes` module. All features must be either defined in the inputs or be produced by one of the contained models. N)rrrrrpipelineRegressorr rrs rrPipelineRegressor.__init__sA*%%'$:! - /   r c$>[TU]UR5 [U[R 5(a UR nURRRnURR5nURU5 gr') superr$rr(r)r*r+rQrr,r-r.rrrr/ __class__s rr0PipelineRegressor.add_modelse 9$))D dFNN + +::D99..77OO'') 4 r cv>[TU]URRRUR5 gr:)rTr7rrQrrrVs rr; PipelineRegressor.make_updatables) 7 II ' ' 0 0$)) r c2URn[X!5 gr>r?r@s rrB$PipelineRegressor.set_training_inputrDr rEr: rFrGrHrIrJrr0r;rBrK __classcell__rVs@rrNrNs"B!, 4 4r rNcJ^\rSrSrSrSSjrU4SjrU4SjrSrSr U=r $) PipelineClassifiera) A pipeline model that exposes a sequence of models as a single model, It requires a set of inputs, a sequence of other models and a set of outputs. In this case the pipeline itself behaves as a classification model by designating a discrete categorical output feature as its 'predicted feature'. c[R"X25n[RR 5n[ Ul[UUUSUU5nUR XPl g)a Create a set of pipeline models given a set of model specs. The last model in this list must be a classifier model. Parameters ---------- input_features: [list of 2-tuples] Name(s) of the input features, given as a list of `('name', datatype)` tuples. The datatypes entry can be any of the data types defined in the :py:mod:`models.datatypes` module. class_labels: [list] A list of string or integer class labels to use in making predictions. This list must match the class labels in the model outputting the categorical predictedFeatureName output_features: [list] A string or a list of two strings specifying the names of the two output features, the first being a class label corresponding to the class with the highest predicted score, and the second being a dictionary mapping each class to its score. If `output_features` is a string, it specifies the predicted class label and the class scores is set to the default value of `"classProbability."` pipelineClassifierN) r.process_or_validate_classifier_output_featuresrrrrrr rdr)rr class_labelsrrrs rrPipelineClassifier.__init__sc:.\\  %%'$:!.         r c$>[TU]UR5 [U[R 5(a UR nURRRnURR5nURU5 gr') rTr$rr(r)r*r+rdrr,r-r.rUs rr0PipelineClassifier.add_modelse 9$))D dFNN + +::D99//88OO'') 4 r c~>[[U] URRR UR5 gr:)rTrar7rrdrrYs rr;!PipelineClassifier.make_updatable&s,  $O II ( ( 1 1499 r c2URn[X!5 gr>r?r@s rrB%PipelineClassifier.set_training_input+rDr rE)NNr]r_s@rraras)UY0d!* 4 4r raN)rJ coremltoolsrrrrrrr)_interface_managementr r r r rrNrarLr rrqsL (>!l4l4^N4N4b^4^4r