* python 3.8 has outdated skl version (1.3) and we should use newer python for Coverage step
* fix in-place warning
* fix 'UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow.'
* skipping docgen entirely on non-main
* fixing Warning: 'Node.js 16 actions are deprecated. Please update the following actions to use Node.js 20:'
* s/base_estimator/estimator/g
* s/sparse/sparse_output/g
* in progress, todo implement parser for function
* first attempt at _parse_sklearn_function_transformer
* s/boston/cali/g
* removing the Normalizer with wrong params as it is no longer possible
* Whiten parameter of PCA must now be bool
* lint
* pulling from github for skl to unblock for now
* trying again with skl2onnx
* skl2onnx install keeps getting overwritten, must be in two places for now
* we skipped identity in parse, causing it to miss probab
* lint
* feat: dynamic axes
* feat: add test for varying batch size
* chore: cleanup variable naming
* chore: lint
`pre-commit run --all-files` was quite aggressive
* fix: check onnx conv, not torch
* chore: remove `fix_graph`
* chore: lint
* fix: skip onnx test if not installed
* chore: add more tests
notably, the multi-classification models appear to still have the previous behavior.
* Revert "chore: lint"
This reverts commit 8b4ea4ed33.
* fix: limit array properly
* chore: clean up remaining print statements
* fix: set first dimension in tests to custom
Necessary to support arbitrary batch sizes
Also refactors tests to properly separate out model conversion and testing
* chore: update LGBM-ONNXML notebook
Must set first dimension to None in the ONNXMLTools conversion.
* fix: more bumpversion locations
* Added support for more decision conditions in trees and ONNX conversion
* Added test for BRANCH_LT in ONNXML
* Use convert_model from onnxmltools instead of manually picking converter for test
* remove `import enable_hist_gradient_boosting` because it's not needed since skl 1.0
* remove the `except` part and use `n_features_in_` for HistGradientBoosting
* use `var_` instead of `sigma_` because it was deprecated in skl 1.0 and will be removed in skl 1.2
* Use `algorithm=lloyd` for KMeans instead of `algorithm=full/auto`
* Use `csr_matrix` from the `scipy.sparse` namespace because the `scipy.sparse.csr` namespace is deprecated.
* Use `loss='log_loss'` instead of `loss='log'` because it was deprecated in v1.1 and will be removed in version 1.3.
* Use `eigenvalues_` instead of `lambdas_` because it was deprecated in version 1.0 and will be removed in 1.2.
* Use `eigenvectors_` instead of `alphas_` because it was deprecated in version 1.0 and will be removed in 1.2.
* check for model NotFitted before conversion
* check for model type
* better model type check and basic tests for conversion without fit()
Co-authored-by: SangamSwadiK <sangamswadiK@users.noreply.github.com>
* Testing skl 1.1.1 again
* scikit-learn 1.1.1 requires Python (>= 3.8). I think it's best to deprecate 3.7
* checking errors if remove pin
* pulling two tests for now
* putting this back before i forget. TODO: fix and add back 2 onnx tests
* adding raises for falsefalse scaler.
* simplifying test
* debugging skl hist gb
* skip test for dbg
* for now, bad hack around extra params
* adding back others as well
* adding back others as well
* n_components == 10, must be <= 3; updates to PCA in skl 1.1.1
* matteo's changes
* wip
* fixing param order
* fix atol for regression chain
* more fixes
* fixing flake issues
* test data in float32
* test data in float32
* limiting tree depth for perf_tree_trav
* rtol 1e-4
* converting back to float32 after float64 tree operations
* better renaming of variables
* fix order
* wip
* explicitly specify dtype
* removing .predict test. increasing tolerance
* small refactoring
* fix missing self
* just the tensor without dtype is sufficient
* per-init one
Co-authored-by: Matteo Interlandi <mainterl@microsoft.com>
Co-authored-by: snakanda <snakanda@node.testvm.orion-pg0.wisc.cloudlab.us>
Masahiro Hiramori
Karla Saur
Pusunuru Giri Prasad
Masahiro Hiramori
Joppe Geluykens
Chris Hua
Chris Hua
Matteo Interlandi
Rafail Giavrimis
Roman Bredehoft
Luis Montero
SangamSwadik
Matteo Interlandi
Akshay Jain
Ziheng Wang
Xavier Dupré
Supun Nakandala