automated-machine-learning

https://github.com/microsoft/nni/blob/8d5f643c64580bb26a7b10a3c4c9accf617f65b1/nni/compression/pytorch/speedup/jit_translate.py#L382

While trying to speedup my single shot detector, the following error comes up. Any way to fix this,

/usr/local/lib/python3.8/dist-packages/nni/compression/pytorch/speedup/jit_translate.py in forward(self, *args)
    363 
    364         def forward(self, *

Feature Description

We want to enable the users to specify the value ranges for any argument in the blocks.
The following code example shows a typical use case.
The users can specify the number of units in a DenseBlock to be either 10 or 20.

Code Example

import au

We currently fit and predict upon loading autosklearn.experimental.askl2 for the first time. In environments with a non-persistent filesystem (autosklearn is installed into a new filesystem each time), this can add quite a bit of time delay as experienced in #1362

It seems more applicable to export the

• As a user, I wish featuretools dfs would take a string as cutoff_time aswell as a datetime object

Code Example

fm, features = ft.dfs(entityset=es,
                      target_dataframe_name='customers',
                      cutoff_time="2014-1-1 05:00",
                      instance_ids=[1],
                      cutoff_time_in_index=True)

as well as

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

Problem
Some of our transformers & estimators are not thoroughly tested or not tested at all.

Solution
Use OpTransformerSpec and OpEstimatorSpec base test specs to provide tests for all existing transformers & estimators.

When using r2 as eval metric for regression task (with 'Explain' mode) the metric values reported in Leaderboard (at README.md file) are multiplied by -1.
For instance, the metric value for some model shown in the Leaderboard is -0.41, while when clicking the model name leads to the detailed results page - and there the value of r2 is 0.41.
I've noticed that when one of R2 metric values in the L

I published a new v0.1.12 release of HCrystalBall, that updated some package dependencies and fixed some bugs in cross validation.

Should the original pin for 0.1.10 be updated? Unfortunately won't have time soon to submit a PR for this.

Current type hints are using np.array which is... actually not a even class, but a convenience function for creation of np.ndarray.
But instead of just substituting usages of np.array, it's better to use types from numpy.typing module, which was introduced in Numpy 1.20. (https://numpy.org/devdocs/reference/typing.html#module-numpy.typing). Besides parameterized NDArray[DType] it also i

It would help to have download option to get a list of used packages:

For example:
Download list of ML Packages used in Model Training, with all corresponding citations in CSV Format.