[DO NOT MERGE] Tracking scikit-learn failure fixes #6451
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This update introduces a new function, `support_array_like`, to handle conversion of list and tuple inputs to NumPy arrays when the accelerator is active. Additionally, the `CumlArray` class has been modified to support this conversion during initialization, ensuring compatibility with array-like data types. Changes include: - New `support_array_like` function in `api_decorators.py`. - Updated `CumlArray` to convert list/tuple inputs to NumPy arrays when the accelerator is active. - Detect lists and tuples as array-like in `input_utils.py` in accel mode.
This commit introduces a new test file, `test_array_like_input.py`, which includes tests for the `support_array_like` function and the `CumlArray` class. The tests validate the conversion of list and tuple inputs to NumPy arrays, as well as the handling of NumPy and CuPy arrays when the accelerator is active.
- Introduced a new method in KMeans to dispatch CPU implementation when sparse arrays are detected during fitting. - Updated the is_sparse function to use cupyx' and scipy's issparse method for better compatibility.
- Introduced a new test to verify that KMeans correctly dispatches to CPU when fitting with sparse input. - Ensured that the model's attributes and predictions are validated as numpy arrays when using sparse data.
- Changed default solver from 'eig' to 'auto', allowing automatic selection of 'eig'. - Updated documentation to reflect new solver options: 'auto', 'eig', 'svd', and 'cd'. - Refactored solver selection logic into a new method `_select_solver` for better clarity and maintainability.
- Check for non-scalar intercept values and ensuring correct coefficient array shapes for matrix multiplication. - Dispatch Ridge to CPU for multi-target training.
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Updated the args_to_cpu method to allow NoneType as a valid argument alongside numbers and strings. Fixes skl tests: - test_inplace_data_preprocessing[42-False-csr_matrix] - test_linear_regression_sample_weight_consistency[42-False-csr_matrix]
Updated the input_to_cuml_array function to convert scalar values into arrays of appropriate shape. This enhancement improves the function's flexibility in handling different input types. Fixes skl tests: - test_linear_regression_sample_weight_consistency[42-False-None - test_linear_regression_sample_weight_consistency[42-True-None]
Updated the Ridge class to include a check for wide datasets (n_features > n_samples) when using the SVD solver. This addition improves the decision-making process for dispatching to the CPU implementation during fit-related functions, ensuring better performance for specific data shapes. Fixes skl tests: - test_ridge_sample_weight_consistency[42-svd-wide-None-False] - test_ridge_sample_weight_consistency[42-svd-wide-None-True]
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