mlprodict 0.3.1082

Python Runtime for ONNX models, other helpers to convert machine learned models in C++.

mlprodict

The packages explores ways to productionize machine learning predictions. One approach uses ONNX and tries to implement a runtime in python / numpy or wraps onnxruntime into a single class. The package provides tools to compare predictions, to benchmark models converted with sklearn-onnx. The second approach consists in converting a pipeline directly into C and is not much developed.

from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from mlprodict.onnxrt import OnnxInference, measure_relative_difference
import numpy

iris = load_iris()
X = iris.data[:, :2]
y = iris.target
lr = LinearRegression()
lr.fit(X, y)

# Predictions with scikit-learn.
expected = lr.predict(X[:5])
print(expected)

# Conversion into ONNX.
from mlprodict.onnx_conv import to_onnx
model_onnx = to_onnx(lr, X.astype(numpy.float32))

# Predictions with onnxruntime
oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
ypred = oinf.run({'X': X[:5]})
print(ypred)

# Measuring the maximum difference.
print(measure_relative_difference(expected, ypred))

Installation

The project relies on sklearn-onnx which is in active development. Continuous integration relies on a specific branch of this project to benefit from the lastest changes:

pip install git+https://github.com/xadupre/sklearn-onnx.git@jenkins

The project is currently in active development. It is safer to install the package directly from github:

pip install git+https://github.com/sdpython/mlprodict.git

On Linux and Windows, the package must be compiled with openmp. Full instructions to build the module and run the documentation are described in config.yml for Linux. When this project becomes more stable, it will changed to be using official releases. Experiments with float64 are not supported with sklearn-onnx <= 1.5.0. The code is available at GitHub/mlprodict and has online documentation.

History

current - 2020-05-01 - 0.00Mb

• 121: Add function to convert array to bytes and bytes to array (onnx tensor) (2020-04-30)

• 120: Fix discrepencies for SVM classifier (ONNX) (2020-04-30)

• 119: Keep order in topk implementation (2020-04-17)

• 118: opset is not propagated in OnnxTransformer (2020-04-09)

0.3.1070 - 2020-04-07 - 0.29Mb

• 115: Add a function to replay a benchmark when this one was dumped (more accurate) (2020-04-06)

• 116: Makes ZipMapDictionary picklable (2020-03-30)

• 114: Add more parameters to specify benchmark time (2020-03-30)

• 113: Add operators for opset 12 (2020-03-26)

• 112: Number of feature is wrong for problem num-tr-clus (2020-03-20)

0.3.1029 - 2020-03-17 - 0.28Mb

• 111: Reduce the number of allocation in TreeEnsemble when it is parallelized (cache) (2020-03-13)

• 110: Implements runtime for operator Constant-12 (2020-03-06)

• 109: Generate a benchmark with asv to compare different runtime. Update modules in asv. (2020-03-06)

• 108: Add a function to reduce the memory footprint (2020-02-25)

• 106: Add operator Neg (2020-02-25)

• 101: Fix DecisionTreeClassifier disappearance on the benchmark graph (2020-02-25)

• 107: Add operator IsNaN (2020-02-24)

• 105: Support string labels for Linear, TreeEnsemble, SVM classifiers. (2020-02-24)

• 104: Enable / disable parallelisation in topk (2020-02-23)

• 103: Implements plot benchmark ratio depending on two parameters (2020-02-22)

• 102: Fix conversion for xgboost 1.0 (2020-02-21)

0.3.975 - 2020-02-19 - 0.28Mb

• 100: add notebook on TreeEnsemble (2020-02-19)

• 99: Fixes #93, use same code for TreeEnsembleClassifier and TreeEnsembleRegression (2020-02-19)

• 93: Use pointer for TreeClassifier (2020-02-19)

• 98: mlprodict i broken after onnxruntime, skl2onnx update (2020-02-15)

• 97: Add runtime for operator Conv (2020-01-24)

• 96: Fixes #97, add runtime for operator Conv (2020-01-24)

• 95: Fix OnnxInference where an output and an operator share the same name (2020-01-15)

• 94: Raw scores are always positive for TreeEnsembleClassifier (binary) (2020-01-13)

• 90: Implements a C++ runtime for topk (2019-12-17)

• 86: Use pointers to replace treeindex in tree ensemble cpp runtime (2019-12-17)

• 92: Implements a C++ version of ArrayFeatureExtractor (2019-12-14)

• 89: Implements a function which extracts some informations on the models (2019-12-14)

• 88: Fix bug in runtime of GatherElements (2019-12-14)

0.3.853 - 2019-12-13 - 0.24Mb

• 87: Add converter for HistGradientBoostRegressor (2019-12-09)

• 85: Implements a precompiled run method in OnnxInference (runtime=’python_compiled’) (2019-12-07)

• 84: Automatically creates files to profile time_predict function in the benchmark with py-spy (2019-12-04)

• 83: ONNX: includes experimental operators in the benchmark (2019-12-04)

• 82: Function translate_fct2onnx: use of opset_version (2019-12-04)

• 81: ONNX benchmark: track_score returns scores equal to 0 or 1 (unexpected) (2019-12-04)

• 80: ONNX: extend benchmark to decision_function for some models (2019-12-03)

• 77: Improves ONNX benchmark to measure zipmap impact. (2019-12-03)

• 76: Implements ArgMax 12, ArgMax 12 (python onnx runtime) (2019-11-27)

• 75: ONNX: fix random_state whevever it is available when running benchmark (2019-11-27)

0.3.765 - 2019-11-21 - 0.22Mb

• 59: ONNX: Investigate kmeans and opset availability. (2019-11-21)

• 66: ONNX: improves speed of python runtime for decision trees (2019-11-19)

• 74: Function _modify_dimension should return the same dataset if called the same parameter (even if it uses random functions) (2019-11-15)

• 73: ONNX: fix links on benchmark page (opset is missing) (2019-11-07)

• 72: ONNX: support of sparse tensor for a unary and binary python operators (2019-11-06)

• 71: ONNX: add operator Constant (2019-11-06)

• 67: ONNX: improves speed of svm regressor (2019-11-06)

• 70: ONNX: write tools to test convervsion for models in scikit-learn examples (2019-10-29)

• 65: ONNX: investigate discrepencies for k-NN (2019-10-28)

• 69: ONNX: side by side should work by name and not by positions (2019-10-23)

• 68: ONNX: improves speed of SGDClassifier (2019-10-23)

• 61: Implements a function to create a benchmark based on asv (ONNX) (2019-10-17)

• 63: Export asv results to csv (ONNX) + command line (2019-10-11)

• 64: Add an example with lightgbm and categorical variables (ONNX) (2019-10-07)

• 62: Implements command line for the asv benchmark (ONNX) (2019-10-04)

• 60: Improve lightgbm converter (ONNX) (2019-09-30)

• 58: Fix table checking model, merge is wrong in documentation (2019-09-20)

0.2.542 - 2019-09-15 - 0.59Mb

• 57: ONNX: handles dataframe when converting a model (2019-09-15)

• 56: ONNX: implements cdist operator (2019-09-12)

• 54: ONNX: fix summary, it produces multiple row when model are different when opset is different (2019-09-12)

• 51: ONNX: measure the time performance obtained by using optimization (2019-09-11)

• 52: ONNC-cli: add a command line to optimize an onnx model (2019-09-10)

• 49: ONNX optimization: remove redundant subparts of a graph (2019-09-09)

• 48: ONNX optimization: reduce the number of Identity nodes (2019-09-09)

• 47: Implements statistics on onnx graph and sklearn models, add them to the documentation (2019-09-06)

• 46: Implements KNearestNeibhorsRegressor supporting batch mode (ONNX) (2019-08-31)

• 45: KNearestNeighborsRegressor (2019-08-30)

• 44: Add an example to look into the performance of every node for a particular dataset (2019-08-30)

• 43: LGBMClassifier has wrong shape (2019-08-29)

0.2.452 - 2019-08-28 - 0.13Mb

• 42: Adds a graph which visually summarize the validating benchmark (ONNX). (2019-08-27)

• 41: Enables to test multiple number of features at the same time (ONNX) (2019-08-27)

• 40: Add a parameter to change the number of featuress when validating a model (ONNX). (2019-08-26)

• 39: Add a parameter to dump all models even if they don’t produce errors when being validated (ONNX) (2019-08-26)

• 24: support double for TreeEnsembleClassifier (python runtime ONNX) (2019-08-23)

• 38: See issue on onnxmltools. https://github.com/onnx/onnxmltools/issues/321 (2019-08-19)

• 35: Supports parameter time_kwargs in the command line (ONNX) (2019-08-09)

• 34: Add intervals when measuring time ratios between scikit-learn and onnx (ONNX) (2019-08-09)

• 31: Implements shape inference for the python runtime (ONNX) (2019-08-06)

• 15: Tells operator if the execution can be done inplace for unary operators (ONNX). (2019-08-06)

• 27: Bug fix (2019-08-02)

• 23: support double for TreeEnsembleRegressor (python runtime ONNX) (2019-08-02)

0.2.363 - 2019-08-01 - 0.11Mb

• 26: Tests all converters in separate processeses to make it easier to catch crashes (2019-08-01)

• 25: Ensures operator clip returns an array of the same type (ONNX Python Runtime) (2019-07-30)

• 22: Implements a function to shake an ONNX model and test float32 conversion (2019-07-28)

• 21: Add customized converters (2019-07-28)

• 20: Enables support for TreeEnsemble operators in python runtime (ONNX). (2019-07-28)

• 19: Enables support for SVM operators in python runtime (ONNX). (2019-07-28)

• 16: fix documentation, visual graph are not being rendered in notebooks (2019-07-23)

• 18: implements python runtime for SVM (2019-07-20)

0.2.272 - 2019-07-15 - 0.09Mb

• 17: add a mechanism to use ONNX with double computation (2019-07-15)

• 13: add automated benchmark of every scikit-learn operator in the documentation (2019-07-05)

• 12: implements a way to measure time for each node of the ONNX graph (2019-07-05)

• 11: implements a better ZipMap node based on dedicated container (2019-07-05)

• 8: implements runtime for decision tree (2019-07-05)

• 7: implement python runtime for scaler, pca, knn, kmeans (2019-07-05)

• 10: implements full runtime with onnxruntime not node by node (2019-06-16)

• 9: implements a onnxruntime runtime (2019-06-16)

• 6: first draft of a python runtime for onnx (2019-06-15)

• 5: change style highlight-ipython3 (2018-01-05)