Functions

Summary

function	class parent	truncated documentation
ClassFactory		Dynamically creates a class for a specific operator.
OnnxReduceL2_typed		Adds operator ReduceL2 for float or double.
OnnxReduceSumApi11		Adds operator ReduceSum with opset>=13 following API from opset 12.
OnnxReshapeApi13		Adds operator Reshape with opset>=14 following API from opset 13.
OnnxSplitApi11		Adds operator Split with opset>=13 following API from opset 11.
OnnxSqueezeApi11		Adds operator Squeeze with opset>=13 following API from opset 11.
OnnxUnsqueezeApi11		Adds operator Unsqueeze with opset>=13 following API from opset 11.
_1d_problem
__pep8
_additional_imports		Adds additional imports for experimental models.
_analyse_tree		Extract information from a tree.
_analyse_tree_h		Extract information from a tree in a HistGradientBoosting.
_apply_einsum_matmul		Decomposes the generic matrix multiplication into numpy operations depending on the operator to use for matrix multiplication …
_apply_optimisation_on_graph		Applies an optimisation function fct on a graph and not on the model.
_apply_remove_node_fct_node		Applies an optimizing function on a subgraphs.
_apply_squeeze_transpose		Puts output dimension in the expected order.
_apply_transpose_reshape		Put all dimensions in the same order.
_argmax
_argmax_use_numpy_select_last_index
_argmin
_argmin_use_numpy_select_last_index
_array_feature_extrator		Implementation of operator ArrayFeatureExtractor with numpy.
_asv_class_name
_basic_verification
_batchnorm_test_mode
_batchnorm_training_mode
_build_schemas
_call_conv_runtime_opset
_call_runtime		Private.
_call_validate
_capture_output
_check_dtype
_check_run_benchmark
_clean_initializer_name
_clean_operator_name
_clean_script		Comments out all lines containing .show().
_clean_values_optim
_clean_variable_name
_common_check_numpy_extended_dot		Common verifications for all implementations of numpy_extended_dot().
_common_converter_begin
_common_converter_int_t
_common_converter_t
_common_shape_calculator_int_t
_common_shape_calculator_t
_compare_expected		Compares the expected output against the runtime outputs. This is specific to onnxruntime or mlprodict. …
_compute_negative_log_likelihood_loss		Modified version of softmaxcrossentropy.py …
_converter_classifier		Default converter for a classifier with one input and two outputs, label and probabilities of the same input type. …
_converter_cluster		Default converter for a clustering with one input and two outputs, label and distances of the same input type. It …
_converter_regressor		Default converter for a regressor with one input and one output of the same type. It assumes instance operator …
_converter_transformer		Default converter for a transformer with one input and one output of the same type. It assumes instance operator …
_coor_to_str
_copy_attributes
_copy_inout
_copy_value_info_proto
_create_asv_benchmark_file		Creates a benchmark file based in the information received through the argument. It uses one of the templates like …
_create_column		Creates a column from values with dtype
_create_node_id
_custom_parser_xgboost		Custom parser for XGBClassifier and LGBMClassifier.
_decompose_einsum_equation_simple		Applies strategy simple, numpy defined in by function decompose_einsum_equation().
_default_OPSET_TO_IR_VERSION		Returns the default mapping between opset and ir_version.
_dict2str
_dictionary2str
_display_code_lines
_dispsimple
_dofit_model
_domain_to_class_name		Converts domain into a name.
_download_url
_dropout
_dynamic_class_creation		Automatically generates classes for each of the operators module onnx defines and described at Operators …
_edit_distance
_einsum
_elem_type_as_str
_element_unary		Infers shape for an element wise operator. The function returns but updates known_shapes.
_element_wise		Infers shape for an element wise operator. The function returns but updates known_shapes.
_enumerate_asv_benchmark_all_models		Loops over all possible models and fills a folder with benchmarks following asv concepts.
_enumerate_classes		Extracts the classes of a file.
_enumerate_fit_info		Extracts the name of the fitted models and the data used to train it.
_enumerate_validated_operator_opsets_ops
_enumerate_validated_operator_opsets_version
_figures2dict		Converts the data from list to dictionaries.
_finalize
_find_operator_domain		Determines the domain of an operator. Raises an exception if not found or if there is an ambiguity.
_fix_opset_skl2onnx
_fix_tree_ensemble
_fix_tree_ensemble_node		Fixes a node for old versionsof skl2onnx.
_format_dict		Formats a dictionary as code.
_generate_op_doc
_get_all_schemas_with_history
_get_doc_template
_get_doc_template
_get_info_lgb		Get informations from and lightgbm trees.
_get_info_xgb		Get informations from and lightgbm trees.
_get_onnx_function		Returns the list of functions defined in ONNX package.
_get_output_shape
_get_output_shape_no_ceil
_get_pad_shape
_get_problem_data
_get_shape
_get_type
_get_typed_class_attribute		Converts an attribute into a C++ value.
_global_average_pool
_guess_noshape
_guess_type
_handle_init_files		Returns created, location_model, prefix_import.
_has_decision_function
_has_predict_proba
_has_transform_model
_hash_obj_content		Hash the content of an object.
_insert_diff		Splits a using split, insert HTML differences between pieces. The function relies on package pyquickhelper. …
_internal_decorator
_internal_method_decorator
_is_rotation
_jsonify
_leaky_relu
_leaky_relu_inplace
_make_att_graph
_make_callable		Same function as make_callable() but deals with function which an undefined number of arguments.
_make_einsum_model
_make_inputs
_make_node		Constructs a NodeProto.
_make_opset
_measure_time		Measures the execution time for a function.
_measure_time		Measures a statement and returns the results as a dictionary.
_merge_options
_model_name		Extracts the main component of a model, removes suffixes such Classifier, Regressor, CV.
_modify_dimension		Modifies the number of features to increase or reduce the number of features.
_multiply_time_kwargs		Multiplies values in time_kwargs following strategy time_kwargs_fact for a given model inst.
_nodes
_noshapevar
_numpy_array		Single function to create an array.
_numpy_dot_inplace_left		Subpart of @see fn numpy_dot_inplace.
_numpy_dot_inplace_right		Subpart of @see fn numpy_dot_inplace.
_numpy_extended_dot_equation		Returns the equation equivalent to an extended version of an aligned matrix multiplication (see numpy_extended_dot()). …
_numpy_extended_dot_python_intermediate
_numpy_extended_dot_python_l1l2l3
_numpy_extended_dot_python_update_broadcast
_op_type_domain_classifier		Defines op_type and op_domain based on dtype.
_op_type_domain_classifier		Defines op_type and op_domain based on dtype.
_op_type_domain_regressor		Defines op_type and op_domain based on dtype.
_op_type_domain_regressor		Defines op_type and op_domain based on dtype.
_pad_impl
_parse_node		Parses nodes.
_parse_tree_structure		The pool of all nodes’ indexes created when parsing a single tree. Different tree use different pools.
_pool
_pool_get_output_shape
_pool_impl
_populate__get_all_schemas_with_history
_populate_schemas		Populates all schemas.
_post_process_output		Applies post processings before running the comparison such as changing type from list to arrays.
_private_get_file		Retrieves one template.
_problem_for_cl_decision_function		Returns X, y, intial_types, method, name, X runtime for a scoring problem. It is based on Iris dataset.
_problem_for_cl_decision_function_binary		Returns X, y, intial_types, method, name, X runtime for a scoring problem. Binary classification. It is based …
_problem_for_clnoproba		Returns X, y, intial_types, method, name, X runtime for a scoring problem. It is based on Iris dataset.
_problem_for_clnoproba_binary		Returns X, y, intial_types, method, name, X runtime for a scoring problem. Binary classification. It is based …
_problem_for_clustering		Returns X, intial_types, method, name, X runtime for a clustering problem. It is based on Iris dataset.
_problem_for_clustering_scores		Returns X, intial_types, method, name, X runtime for a clustering problem, the score part, not the cluster. It …
_problem_for_dict_vectorizer		Returns a problem for the sklearn.feature_extraction.DictVectorizer.
_problem_for_feature_hasher		Returns a problem for the sklearn.feature_extraction.DictVectorizer.
_problem_for_label_encoder		Returns a problem for the sklearn.preprocessing.LabelEncoder.
_problem_for_mixture		Returns X, y, intial_types, method, node name, X runtime for a m-cl classification problem. It is based on Iris …
_problem_for_numerical_scoring		Returns X, y, intial_types, method, name, X runtime for a scoring problem. It is based on Iris dataset.
_problem_for_numerical_trainable_transform		Returns X, intial_types, method, name, X runtime for a transformation problem. It is based on Iris dataset.
_problem_for_numerical_trainable_transform_cl		Returns X, intial_types, method, name, X runtime for a transformation problem. It is based on Iris dataset.
_problem_for_numerical_transform		Returns X, intial_types, method, name, X runtime for a transformation problem. It is based on Iris dataset.
_problem_for_numerical_transform_positive		Returns X, intial_types, method, name, X runtime for a transformation problem. It is based on Iris dataset.
_problem_for_one_hot_encoder		Returns a problem for the sklearn.preprocessing.OneHotEncoder.
_problem_for_outlier		Returns X, intial_types, method, name, X runtime for a transformation problem. It is based on Iris dataset.
_problem_for_predictor_binary_classification		Returns X, y, intial_types, method, node name, X runtime for a binary classification problem. It is based on Iris …
_problem_for_predictor_multi_classification		Returns X, y, intial_types, method, node name, X runtime for a m-cl classification problem. It is based on Iris …
_problem_for_predictor_multi_classification_label		Returns X, y, intial_types, method, node name, X runtime for a m-cl classification problem. It is based on Iris …
_problem_for_predictor_multi_regression		Returns X, y, intial_types, method, name, X runtime for a mregression problem. It is based on Iris dataset.
_problem_for_predictor_regression		Returns X, y, intial_types, method, name, X runtime for a regression problem. It is based on Iris dataset.
_problem_for_tfidf_transformer		Returns a problem for the :epkg:`sklearn:feature_extraction:text:TfidfTransformer`.
_problem_for_tfidf_vectorizer		Returns a problem for the :epkg:`sklearn:feature_extraction:text:TfidfVectorizer`.
_random_input
_raw_score_binary_classification
_read_patterns		Reads the testing pattern.
_reduce_infos		Produces agregates features.
_register_converters_lightgbm		This functions registers additional converters for lightgbm.
_register_converters_mlinsights		This functions registers additional converters for mlinsights.
_register_converters_skl2onnx		This functions registers additional converters for skl2onnx.
_register_converters_xgboost		This functions registers additional converters for xgboost.
_relu
_rename_graph_input		Renames an input and adds an Identity node to connect the dots.
_rename_graph_output		Renames an output and adds an Identity node to connect the dots.
_rename_node_input		Renames an input from a node.
_rename_node_output		Renames an output from a node.
_replace
_replace_tensor_type
_retrieve_class_parameters		Imports files in bdir, compile files and extra metadata from them.
_retrieve_problems_extra		Use by enumerate_compatible_opset().
_run_skl_prediction
_save_model_dump
_select_pattern_problem		Selects a benchmark type based on the problem kind.
_shape_calculator_classifier		Default shape calculator for a classifier with one input and two outputs, label (int64) and probabilites of the same …
_shape_calculator_cluster		Default shape calculator for a clustering with one input and two outputs, label (int64) and distances of the same type. …
_shape_calculator_regressor		Default shape calculator for a regressor with one input and one output of the same type.
_shape_calculator_transformer		Default shape calculator for a transformer with one input and one output of the same type.
_shape_exc
_side_by_side_by_values_inputs
_skl2onnx_add_to_container		Adds ONNX graph to skl2onnx container and scope.
_sklearn_subfolder		Returns the list of subfolders for a model.
_sparse_array		Single function to create an sparse array (coo_matrix).
_split_tree_ensemble_atts		Splits the attributes of a TreeEnsembleRegressor into multiple trees in order to do the summation in double instead …
_summary_report_indices
_to_array
_translate_split_criterion
_try_onnx		Tries onnx conversion.
_type_to_string		Converts a type into a readable string.
_update_module		Dynamically updates the module with operators defined by ONNX.
_update_test_metadata
_validate_runtime_dict
_validate_runtime_separate_process
_var_as_dict		Converts a protobuf object into something readable. The current implementation relies on json. That’s not …
_vcelu1
abs		See numpy.abs().
acos		See numpy.acos().
acosh		See numpy.acosh().
add_model_import_init		Modifies a template such as TemplateBenchmarkClassifier with code associated to the model model.
add_onnx_graph		Adds a whole ONNX graph to an existing one following skl2onnx API assuming this ONNX graph implements an …
amax		See numpy.amax().
amin		See numpy.amin().
analyse_einsum_equation		Analyses an einsum equation.
analyze_model		Returns informations, statistics about a model, its number of nodes, its size…
annotate_heatmap		Annotates a heatmap. See plot_benchmark_metrics() for an example.
apply_einsum_sequence		Applies a sequence of operations on a list of inputs. The sequence of operations is produced by function decompose_einsum_equation(). …
arange		See numpy.arange(), start, stop must be specified.
argmax		See numpy.argmax().
argmax_use_numpy_select_last_index		Needed or operator ArgMax.
argmin		See numpy.argmin().
argmin_use_numpy_select_last_index		Needed or operator ArgMin.
array_feature_extrator		Implementation of operator ArrayFeatureExtractor with numpy.
asin		See numpy.asin().
asinh		See numpy.asinh().
assert_almost_equal_string		Compares two arrays knowing they contain strings. Raises an exception if the test fails.
astype_range		Computes ranges for every number in an array once converted into float32. The function returns two matrices which …
asv2csv		Converts results produced by asv into csv.
asv_bench		Creates an asv benchmark in a folder but does not run it.
atan		See numpy.atan().
atanh		See numpy.atanh().
benchmark_doc		Runs the benchmark published into the documentation (see Availability of scikit-learn model for runtime onnxruntime1 and Availability of scikit-learn model for runtime python_compiled). …
benchmark_fct		Benchmarks a function which takes an array as an input and changes the number of rows.
benchmark_replay		The command rerun a benchmark if models were stored by command line vaidate_runtime.
binary_array_to_string		Used to compare decision path.
build_custom_scenarios		Defines parameters values for some operators.
build_custom_scenarios		Defines parameters values for some operators.
build_key_split		Used for documentation.
cache_folder		Returns this folder.
calculate_lightgbm_output_shapes		Shape calculator for LightGBM Booster (see lightgbm).
ceil		See numpy.ceil().
change_input_first_dimension		Some models are converted under the assumption batch prediction is not necessary. This function changes the first …
change_style		Switches from AaBb into aa_bb.
change_style		Switches from AaBb into aa_bb.
check		Checks the library is working. It raises an exception. If you want to disable the logs:
check_is_almost_equal		Checks that two floats or two arrays are almost equal.
check_model_representation		Checks that a trained model can be exported in a specific list of formats and produces the same outputs if the representation …
check_type		Raises an exception if the model is not of the expected type.
classifier		Returns any classifier from scikit-learn converted into ONNX assuming a converter is registered with sklearn-onnx. …
clean_error_msg		Removes EOL from error messages in dataframes.
clip		See numpy.clip().
cluster		Returns any cluster from scikit-learn converted into ONNX assuming a converter is registered with sklearn-onnx. …
common_reference_implementation
compare_backend		The function compares the expected output (computed with the model before being converted to ONNX) and the ONNX output. …
compare_outputs		Compares expected values and output. Returns None if no error, an exception message otherwise.
compare_runtime		The function compares the expected output (computed with the model before being converted to ONNX) and the ONNX output …
compare_runtime		The function compares the expected output (computed with the model before being converted to ONNX) and the ONNX output …
compare_runtime_session		The function compares the expected output (computed with the model before being converted to ONNX) and the ONNX output …
compile_c_function		Compiles a C function with cffi. It takes one features vector.
compose_page_onnxrt_ops		Writes page Python Runtime for ONNX operators.
compress		See numpy.compress(). numpy.compress(condition, x) or npnx.compress(x, condition).
compute_benchmark		Compares the processing time several functions.
compute_transposition_features		Given a shape and a permutation, computes many features used to predict the cost of the transposition.
concat		Operator concat, handle numpy.vstack() and numpy.hstack().
convert_lightgbm		This converters reuses the code from LightGbm.py …
convert_model		Runs the appropriate conversion method.
convert_score_cdist_sum		Converts function score_cdist_sum() into ONNX.
convert_scorer		Converts a scorer into ONNX assuming there exists a converter associated to it. The function wraps the function …
convert_transfer_transformer		Converters for TransferTransformer.
convert_validate		Converts a model stored in pkl file and measure the differences between the model and the ONNX predictions.
convert_xgboost		This converters reuses the code from XGBoost.py …
converter_lightgbm_concat		Converter for operator LightGBMConcat.
cos		See numpy.cos().
cosh		See numpy.cosh().
create_asv_benchmark		Creates an asv benchmark in a folder but does not run it.
create_tensor		Creates a tensor.
cst		Creates a constant. log(x) + numpy.float32(1) works but numpy.float32(32) + log(x) fails because Python calls …
cumsum		See numpy.cumsum().
custom_einsum		Experimental implementation of operator Einsum when it does a matrix multiplication. Case: bsnh,btnh->bnts with …
custom_pad		Implements function pad in python, only …
custom_scorer_transform_converter		Selects the appropriate converter for a @see cl CustomScorerTransform.
custom_scorer_transform_parser		This function updates the inputs and the outputs for a @see cl CustomScorerTransform.
custom_scorer_transform_shape_calculator		Computes the output shapes for a @see cl CustomScorerTransform.
debug_dump		Dumps an object for debug purpose.
debug_onnx_object		__dict__ is not in most of onnx objects. This function uses function dir to explore this object.
debug_print		Displays informations on an object.
decompose_einsum_equation		Decomposes an equation used in numpy.einsum knowing the input shapes. It returns a sequence of operations …
default_time_kwargs		Returns default values number and repeat to measure the execution of a function.
det		See numpy.linalg:det().
device_to_providers		Returns the corresponding providers for a specific device.
dgemm_dot		dgemm_dot(double[:,
display_onnx		Returns a shortened string of the model.
dot		See numpy.dot()
download_model_data		Downloads a model and returns a link to the local ONNX file and data which can be used as inputs.
dtype_name		Returns the name of a numpy dtype.
dump_binary_classification		Trains and dumps a model for a binary classification problem. The function trains a model and calls dump_data_and_model(). …
dump_booster_model		Dumps Booster to JSON format. Parameters ———- self: booster num_iteration : int or None, optional …
dump_data_and_model		Saves data with pickle, saves the model with pickle and onnx, runs and saves the predictions for the given model. …
dump_into_folder		Dumps information when an error was detected using pickle.
dump_lgbm_booster		Dumps a Lightgbm booster into JSON.
dump_multilabel_classification		Trains and dumps a model for a binary classification problem. The function trains a model and calls dump_data_and_model(). …
dump_multiple_classification		Trains and dumps a model for a binary classification problem. The function trains a model and calls dump_data_and_model(). …
dump_multiple_regression		Trains and dumps a model for a multi regression problem. The function trains a model and calls dump_data_and_model(). …
dump_one_class_classification		Trains and dumps a model for a One Class outlier problem. The function trains a model and calls dump_data_and_model(). …
dump_single_regression		Trains and dumps a model for a regression problem. The function trains a model and calls dump_data_and_model(). …
dynamic_doc		Generates the documentation for ONNX operators.
einsum		See numpy.einsum().
einsum		Proposes a new implementation of numpy.einsum. It does not allow expresion using … and expects a right …
einsum_benchmark		Investigates whether or not the decomposing einsum is faster.
einsum_test		Investigates whether or not the decomposing einsum is faster.
empty_shape_calculator		Does nothing.
ensure_topological_order		Ensures and modifies the order of nodes to have a topological order (every node in the list can only be an input …
enumerate_benchmark_replay		Replays a benchmark stored with function enumerate_validated_operator_opsets …
enumerate_cached_einsum		Enumerates all cached einsum function.
enumerate_compatible_opset		Lists all compatible opsets for a specific model.
enumerate_export_asv_json		Looks into asv results and wraps all of them into a dataframe or flat data.
enumerate_fitted_arrays		Enumerate all fitted arrays included in a scikit-learn object.
enumerate_model_node_outputs		Enumerates all the nodes of a model.
enumerate_models		Enumerates models with models.
enumerate_onnx_tests		Collects test from a sub folder of onnx/backend/test. Works as an enumerator to start processing them without …
enumerate_pipeline_models		Enumerates all the models within a pipeline.
enumerate_random_inputs		Enumerates random matrices.
enumerate_validated_operator_opsets		Tests all possible configurations for all possible operators and returns the results.
enumerate_visual_onnx_representation_into_rst		Returns content for pages such as linear_model.
erf		See scipy.special.erf.
evaluate_condition		Evaluates a condition such as StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')
exp		See numpy.exp().
expand_dims		See numpy.expand_dims().
expand_onnx_options		Expands shortened options. Long names hide some part of graphs in asv benchmark. This trick converts a string …
expit		See scipy.special.expit.
export2numpy		Exports an ONNX model to the numpy syntax. The exports does not work with all operators.
export2onnx		Exports an ONNX model to the onnx syntax.
export2tf2onnx		Exports an ONNX model to the tensorflow-onnx syntax.
export2xop		Exports an ONNX model to the onnx syntax.
export_asv_json		Looks into asv results and wraps all of them into a dataframe or flat data.
export_template		Exports an ONNX model to the onnx syntax.
extract_information_from_filename		Returns a dictionary with information extracted from a filename. An example is better:
extract_options		Extracts comparison option from filename. As example, Binarizer-SkipDim1 means options SkipDim1 is enabled. …
filter_rows		Used for documentation.
find_missing_sklearn_imports		Finds in scikit-learn the missing pieces.
find_node_input_name		Finds a node input by its name.
find_node_name		Finds a node by its name.
find_sklearn_module		Finds the corresponding modulee for an element of scikit-learn.
find_suitable_problem		Defines suitables problems for additional converters.
find_suitable_problem		Determines problems suitable for a given scikit-learn operator. It may be
fit_classification_model		Fits a classification model.
fit_classification_model_simple		Fits a classification model.
fit_multilabel_classification_model		Fits a classification model.
fit_regression_model		Fits a regression model.
fix_missing_imports		The execution of a file through function exec does not import new modules. They must be there when it is …
floor		See numpy.floor().
from_array		Converts an array into an ONNX tensor.
from_bytes		Retrieves an array from bytes then protobuf.
from_pb		Extracts tensor description from a protobuf.
gather_numpy		Gathers values along an axis specified by dim. For a 3-D tensor the output is specified by:
gather_numpy_2
gemm_dot		Implements dot product with gemm when possible.
get_args_kwargs		Extracts arguments and optional parameters of a function.
get_default_context		Returns a default context useful for most of the conversion from a function using numpy into ONNX.
get_default_context_cpl		Returns a default useful context to compile the converter returned by translate_fct2onnx().
get_defined_inputs		Retrieves defined inputs in already declared variables bsed on their names.
get_defined_outputs		Gets types of predefined outputs when they cannot be inferred. Some part of it should be automated based on type …
get_domain_list		Returns the list of available domains.
get_dtype_shape		Returns the shape of a tensor.
get_inputs_from_data		Produces input data for onnx runtime.
get_ir_version		Returns the corresponding IR_VERSION based on the selected opset. See ONNX Version.
get_max_value		Returns the maximum value for a specific type.
get_numpy_template		Template to export ONNX into numpy code.
get_onnx_example		Retrieves examples associated to one operator stored in onnx packages.
get_onnx_schema		Returns the operator schema for a specific operator.
get_onnx_template		Template to export ONNX into onnx code.
get_operator_schemas		Returns all schemas mapped to an operator name.
get_ort_device		Converts device into C_OrtDevice.
get_rst_doc		Returns a documentation in RST format for all OnnxOperator.
get_rst_doc		Returns a documentation in RST format for all OnnxOperator.
get_tf2onnx_template		Template to export ONNX into tensorflow-onnx code.
get_xop_template		Template to export ONNX into a code based on XOP API.
guess_dtype		Converts a proto type into a numpy type.
guess_initial_types		Guesses initial types from an array or a dataframe.
guess_numpy_type		Guesses the corresponding numpy type based on data_type.
guess_numpy_type_from_dtype		Converts a string (such as ‘dtype(float32)’) into a numpy dtype.
guess_numpy_type_from_string		Converts a string (such as ‘float’) into a numpy dtype.
guess_proto_dtype		Guesses the ONNX dtype given a numpy dtype.
guess_proto_dtype_name		Returns a string equivalent to onnx_dtype.
guess_schema_from_data		Guesses initial types from a dataset.
guess_schema_from_model		Guesses initial types from a model.
hash_onnx_object		Hash the content of an object.
heatmap		Creates a heatmap from a numpy array and two lists of labels. See plot_benchmark_metrics() for an example.
hstack		See numpy.hstack().
identify_interpreter		Identifies the interpreter for a scikit-learn model.
identity		Identity.
insert_node		Inserts a node before one node input.
insert_results_into_onnx		Inserts results into an ONNX graph to produce an extended ONNX graph. It can saved and looked into with a tool such …
inspect_sklearn_model		Inspects a scikit-learn model and produces some figures which tries to represent the complexity of it.
interpret_options_from_string		Converts a string into a dictionary.
iris_data		Returns (X, y) for iris data.
is_backend_enabled		Tells if a backend is enabled. Raises an exception if backend != ‘onnxruntime’. Unit tests only test models against …
is_last_schema		Tells if this is the most recent schema for this operator.
is_numpy_dtype		Tells if a dtype is a numpy dtype.
is_transpose_identity		Tells if the permutation perm does nothing (itentity).
isnan		See numpy.isnan().
latency		Measures the latency of a model (python API).
latency		Measures the latency of a model (python API).
lightgbm_parser		Agnostic parser for LightGBM Booster.
linear_regression		Returns a linear regression converted into ONNX.
load_audit		Use to test conversion of sklearn.ensemble.GradientBoostingClassifier into ONNX.
load_data		Restores protobuf data stored in a folder.
load_data_and_model		Loads every file in a dictionary {key: filename}. The extension is either pkl and onnx and determines how it …
load_ipython_extension		To allow the call %load_ext mlprodict
load_op		Sets up a class for a specific ONNX operator.
load_op		Gets the operator related to the onnx node.
load_op		Gets the operator related to the onnx node. This runtime does nothing and never complains.
load_op		Gets the operator related to the onnx node.
loadop		Dynamically creates a class for a every operator type in the given list.
log		See numpy.log().
log1p		See numpy.log1p().
logistic_regression		Returns a logistic regression converted into ONNX, option zipmap is set to false.
main		Implements python -m mlprodict <command> <args>.
make_callable		Creates a callable function able to cope with default values as the combination of functions compile and exec …
make_hash_bytes		Creates a hash of length length.
make_n_rows		Multiplies or reduces the rows of x to get exactly n rows.
make_name		Creates a unique name.
make_numpy_code		Converts an ONNX operators into numpy code.
make_readable_title		Creates a readable title based on the test information.
make_slice		Implements operator slice in numpy.
make_sure		Raises an exception if cond is not verified.
make_tf2onnx_code		Converts an ONNX operators into tf2onnx code.
make_value_info		Converts a variable defined by its name, type and shape into onnx.ValueInfoProto.
map_onnx_to_numpy_type		Converts ONNX type into numpy type.
matmul		See numpy.matmul().
max_depth		Retrieves the max depth assuming the estimator is a decision tree.
max_supported_opset		Returns the latest supported opset for the main domain.
mean		See numpy.mean().
measure_relative_difference		Measures the relative difference between predictions between two ways of computing them. The functions returns nan …
measure_time		Measures a statement and returns the results as a dictionary.
merge_benchmark		Merges several benchmarks run with command line validate_runtime.
merge_results		Merges results by name. The first ones are used to keep the order.
modify_tree_for_rule_in_set		LightGBM produces sometimes a tree with a node set to use rule == to a set of values (= in set), the values …
modules_list		Returns modules and versions currently used.
new_calculate_sklearn_function_transformer_output_shapes		Rewrites the converters implemented in sklearn-onnx to support custom functions implemented with Complete Numpy API for ONNX. …
new_convert_sklearn_decision_tree_classifier		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_decision_tree_regressor		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_function_transformer		Rewrites the converters implemented in sklearn-onnx to support custom functions implemented with Complete Numpy API for ONNX. …
new_convert_sklearn_gradient_boosting_classifier		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_gradient_boosting_regressor		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_random_forest_classifier		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_random_forest_regressor		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_svm_classifier		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
new_convert_sklearn_svm_regressor		Rewrites the converters implemented in sklearn-onnx to support an operator supporting doubles.
numpy_diagonal		Extracts diagonal coefficients from an array.
numpy_dot_inplace		Implements a dot product, deals with inplace information. See numpy.dot.
numpy_extended_dot		Extended version of a matrix multiplication (numpy.dot) with two matrices m1, m2 of the same dimensions. …
numpy_extended_dot_matrix		Implementation of numpy_extended_dot() using dot product, multiplication, transpose and reduction but not …
numpy_extended_dot_ouput_shape		Computes the output shape of results produced by function numpy_extended_dot …
numpy_extended_dot_python		Implementation of numpy_extended_dot() in pure python. This implementation is not efficient but shows how to …
numpy_matmul_inplace		Implements a matmul product, deals with inplace information. See numpy.matmul. Inplace computation does …
numpy_max		Returns the maximum of an array. Deals with text as well.
numpy_max		Returns the maximum of an array. Deals with text as well.
numpy_min		Returns the minimum of an array. Deals with text as well.
numpy_min		Returns the maximum of an array. Deals with text as well.
numpy_min_max		Returns the minimum of an array. Deals with text as well.
numpy_type_prototype		Converts a numpy dtyp into a TensorProto dtype.
numpy_type_prototype		Converts a numpy dtyp into a TensorProto dtype.
onnx2bigraph		Converts an ONNX graph into a graph representation, edges, vertices.
onnx_code		Exports an ONNX graph into a python code creating the same graph.
onnx_documentation_folder		Creates documentation in a folder for all known ONNX operators or a subset.
onnx_graph_distance		Computes a distance between two ONNX graphs. They must not be too big otherwise this function might take for ever. …
onnx_if		Implements a test with onnx syntax.
onnx_model_opsets		Extracts opsets in a dictionary.
onnx_optim		Optimizes an ONNX model.
onnx_optimisations		Calls several possible optimisations including onnx_remove_node().
onnx_pad		Implements numpy.pad based on ONNX signature.
onnx_remove_node		Removes as many nodes as possible without changing the outcome. It applies onnx_remove_node_unused(), onnx_remove_node_identity(), …
onnx_remove_node_identity		Removes as many Identity nodes as possible. The function looks into every node and subgraphs if recursive is …
onnx_remove_node_redundant		Removes redundant part of the graph. A redundant part is a set of nodes which takes the same inputs and produces …
onnx_remove_node_unused		Removes unused nodes of the graph. An unused node is not involved in the output computation.
onnx_rename_names		Renames all names except the inputs and outputs.
onnx_shaker		Shakes a model ONNX. Explores the ranges for every prediction. Uses astype_range()
onnx_simple_text_plot		Displays an ONNX graph into text.
onnx_statistics		Computes statistics on ONNX models, extracts informations about the model such as the number of nodes.
onnx_stats		Computes statistics on an ONNX model.
onnx_text_plot		Uses onnx2bigraph() to convert the ONNX graph into text.
onnx_text_plot_io		Displays information about input and output types.
onnx_text_plot_tree		Gives a textual representation of a tree ensemble.
onnxnumpy		Decorator to declare a function implemented using numpy syntax but executed with ONNX operators. …
onnxnumpy_default		Decorator with options to declare a function implemented using numpy syntax but executed with ONNX …
onnxnumpy_np		Decorator to declare a function implemented using numpy syntax but executed with ONNX operators. …
onnxsklearn_class		Decorator to declare a converter for a class derivated from scikit-learn, implementing inference method …
onnxsklearn_classifier		Decorator to declare a converter for a classifier implemented using numpy syntax but executed with ONNX …
onnxsklearn_cluster		Decorator to declare a converter for a cluster implemented using numpy syntax but executed with ONNX …
onnxsklearn_regressor		Decorator to declare a converter for a regressor implemented using numpy syntax but executed with ONNX …
onnxsklearn_transformer		Decorator to declare a converter for a transformer implemented using numpy syntax but executed with ONNX …
onnxview		Displays an ONNX graph into a notebook.
optimize_decompose_einsum_equation		Proposes a new implementation of numpy.einsum. It does not allow expresion using … and expects a right …
overwrite_opset		Overwrites the main opset in an ONNX file. Does not change any node definition.
pad		It does not implement numpy.pad() but the ONNX version onnx_pad. …
pairwise_array_distances		Computes pairwise distances between two lists of arrays l1 and l2. The distance is 1e9 if shapes are not equal.
parser_transfer_transformer		Parser for TransferTransformer.
plot_benchmark_metrics		Plots a heatmap which represents a benchmark. See example below.
plot_onnx		Plots an ONNX graph on the standard output.
plot_onnx		Plots an ONNX graph into a matplotlib graph.
plot_validate_benchmark		Plots a graph which summarizes the performances of a benchmark validating a runtime for ONNX.
predict_transposition_cost		Given a shape and a permutation, predicts the cost of the transposition.
prepare_c_profiling		Prepares model and data to be profiled with tool perftest …
print_code		Returns the code with line number.
prod		See numpy.prod().
proto2dtype		Converts a proto type into a numpy type.
proto2vars		Converts proto values to Variables.
py_make_float_array		Creates an array with a single element from a constant.
py_mul		Function for python operator *.
py_opp		Function for python unary operator -.
py_pow		Function for python operator **.
pycelu		Computes function celu(x).
pygemm		Pure python implementatin of GEMM.
random_feed		Creates a dictionary of random inputs.
reciprocal		See numpy.reciprocal().
register_converters		This functions registers additional converters to the list of converters sklearn-onnx declares.
register_onnx_magics		Register magics function, can be called from a notebook.
register_operator		Registers a new runtime operator.
register_rewritten_operators		Registers modified operators and returns the old values.
register_scorers		Registers operators for @see cl CustomScorerTransform.
regressor		Returns any regressor from scikit-learn converted into ONNX assuming a converter is registered with sklearn-onnx. …
relu		relu
reorder_nodes_for_display		Reorders the node with breadth first seach (BFS).
reshape_reference_implementation
restore_lgbm_info		Restores speed up information to help modifying the structure of the tree.
round		See numpy.round().
scatter_elements
score_cdist_sum		Computes the sum of pairwise distances between expected_values and predictions. It has no particular purpose …
select_attribute		Returns the list of the same attribute. [el.att for el in ens].
select_model_inputs_outputs		Takes a model and changes its outputs.
set_n_jobs		Looks into model signature and add parameter n_jobs if available. The function does not overwrite the parameter.
set_random_state		Sets all possible parameter random_state to 0.
setup		Sphinx extension mlprodict.npy.xop_sphinx displays documentation on ONN Operators.
sgemm_dot		sgemm_dot(float[:,
shape_abs		Infers shape for operator Abs.
shape_acos		Infers shape for operator Acos.
shape_acosh		Infers shape for operator Acosh.
shape_add		Infers shape for operator Add.
shape_and		Infers shape for operator And.
shape_asin		Infers shape for operator Asin.
shape_asinh		Infers shape for operator Asinh.
shape_atan		Infers shape for operator Atan.
shape_atanh		Infers shape for operator Atanh.
shape_calculator_lightgbm_concat		Shape calculator for operator LightGBMConcat.
shape_calculator_transfer_transformer		Shape calculator for TransferTransformer.
shape_castlike		Infers shape for operator CastLike.
shape_ceil		Infers shape for operator Ceil.
shape_celu		Infers shape for operator Celu.
shape_clip		Infers shape for operator Clip.
shape_cos		Infers shape for operator Cos.
shape_cosh		Infers shape for operator Cosh.
shape_det		Infers shape for operator Abs.
shape_dispatch		Calls the corresponding fucntion for every node.
shape_div		Infers shape for operator Div.
shape_elu		Infers shape for operator Elu.
shape_equal		Infers shape for operator Equal.
shape_erf		Infers shape for operator Erf.
shape_exp		Infers shape for operator Exp.
shape_floor		Infers shape for operator Floor.
shape_greater		Infers shape for operator Greater.
shape_greaterorequal		Infers shape for operator GreaterOrEqual.
shape_hardmax		Infers shape for operator Hardmax.
shape_hardsigmoid		Infers shape for operator HardSigmoid.
shape_identity		Infers shape for operator Identity.
shape_isinf		Infers shape for operator IsInf.
shape_isnan		Infers shape for operator IsNan.
shape_leakyrelu		Infers shape for operator LeakyRelu.
shape_less		Infers shape for operator Less.
shape_lessorequal		Infers shape for operator LessOrEqual.
shape_log		Infers shape for operator Log.
shape_logsoftmax		Infers shape for operator LogSoftmax.
shape_max		Infers shape for operator Max.
shape_min		Infers shape for operator Min.
shape_mod		Infers shape for operator Mod.
shape_mul		Infers shape for operator Mul.
shape_neg		Infers shape for operator Neg.
shape_not		Infers shape for operator Not.
shape_or		Infers shape for operator Or.
shape_pow		Infers shape for operator Pow.
shape_reciprocal		Infers shape for operator Reciprocal.
shape_relu		Infers shape for operator Relu.
shape_round		Infers shape for operator Round.
shape_selu		Infers shape for operator Selu.
shape_sigmoid		Infers shape for operator Sigmoid.
shape_sign		Infers shape for operator Sigmoid.
shape_sin		Infers shape for operator Sin.
shape_sinh		Infers shape for operator Sinh.
shape_softmax		Infers shape for operator Softmax.
shape_sqrt		Infers shape for operator Sqrt.
shape_sub		Infers shape for operator Sub.
shape_tan		Infers shape for operator Tan.
shape_tanh		Infers shape for operator Tanh.
shape_trilu		Infers shape for operator Trilu.
shape_xor		Infers shape for operator Xor.
short_list_zoo_models		Returns a short list from ONNX Zoo.
shorten_onnx_options		Shortens onnx options into a string. Long names hide some part of graphs in asv benchmark.
side_by_side_by_values		Compares the execution of two sessions. It calls method OnnxInference.run …
sigmoid		See scipy.special.expit.
sign		See numpy.sign().
sin		See numpy.sin().
single_axes		axes contains positive values, then it is the position of this axis in the original matrix, otherwise it is -1 …
sinh		See numpy.sinh().
sizeof_dtype
sklearn2graph		Converts any kind of scikit-learn model into a grammar model.
sklearn_decision_tree_regressor		Converts a DecisionTreeRegressor …
sklearn_linear_regression		Converts a linear regression …
sklearn_logistic_regression		Interprets a logistic regression …
sklearn_operators		Builds the list of operators from scikit-learn. The function goes through the list of submodule and get …
sklearn_standard_scaler		Converts a standard scaler …
softmaxcrossentropy		Modified version of softmaxcrossentropy.py …
split_columns_subsets		Functions used in the documentation to split a dataframe by columns into multiple dataframe to reduce the scrolling. …
sqrt		See numpy.sqrt().
squareform_pdist		Replacements for squareform …
squeeze		See numpy.squeeze().
sum		See numpy.sum().
summary_report		Finalizes the results computed by function enumerate_validated_operator_opsets().
tan		See numpy.tan().
tanh		See numpy.tanh().
test_qlinear_conv		Checks a runtime for operator QLinearConv.
timeexec		Measures the time for a given expression.
timeit_repeat		Returns a series of repeat time measures for number executions of code assuming fct is a function.
to_bytes		Converts an array into protobuf and then into bytes.
to_onnx		Converts a model using on sklearn-onnx.
to_skl2onnx_type		Converts name, elem_type, shape into a sklearn-onnx type.
topk		See numpy.argsort().
topk_sorted_implementation		Retrieves the top-k elements.
topk_sorted_implementation_cpp		Retrieves the top-k elements using a C++ implementation when the axis is the last dimension, otherwise, it falls …
transformer		Returns any transformer from scikit-learn converted into ONNX assuming a converter is registered with sklearn-onnx. …
translate_fct2onnx		Translates a function into ONNX. The code it produces is using classes OnnxAbs, OnnxAdd, …
transpose		See numpy.transpose().
type_mapping		Mapping between types name and type integer value.
unsqueeze		See numpy.expand_dims().
update_registered_converter_npy		Registers or updates a converter for a new model so that it can be converted when inserted in a scikit-learn pipeline. …
validate_python_inference		Validates the code produced by method to_python. …
validate_runtime		Walks through most of scikit-learn operators or model or predictor or transformer, tries to convert them …
verify_code		Verifies python code.
verify_model		Verifies a model.
verify_script		Checks that models fitted in an example from scikit-learn documentation can be converted into ONNX.
version2number		Converts a version number into a real number.
visual_rst_template		Returns a jinja2 template to display DOT graph for each converter from sklearn-onnx.
vstack		See numpy.vstack().
where		See numpy.where().

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