automl

What happened + What you expected to happen

The shim tune.create_scheduler() does not properly parse the keyword parameters passed in a dictionary for the pb2 scheduler. For this call

pb2_parm_dict = {"time_attr": "time_total_s", "metric": "metric_score", "mode": "min",
                 "hyperparam_bounds": {"param1": [0, 1]}}

pb2_scheduler = create_scheduler("pb2", **pb2_pa

Describe the issue:
During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct

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

Is there an existing issue for this?

• I have searched the existing issues

Is your feature request related to a problem? Please describe.

I think would be helpful supporting elasticsearch because is one of the most used search engines

Describe the solution you'd like.

No response

Describe an alternate solution.

No response

Anything else? (Additional Context)

_N

I have been trying to use AutoSklearn with Multi-class classification

so my labels are like this

0 1 2 3 4 ... 200
1 0 1 1 1 ... 1
0 1 0 0 1 ... 0
1 0 0 1 0 ... 0
1 1 0 1 0 ... 1
0 1 1 0 1 ... 0
1 1 1 0 0 ... 1
1 0 1 0 1 ... 0

I used this code

`
y = y[:, (65,67,54,133,122,63,102

• 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

We would like to forward a particular 'key' column which is part of the features to appear alongside the predictions - this is to be able to identify to which set of features a particular prediction belongs to. Here is an example of predictions output using the tensorflow.contrib.estimator.multi_class_head:

{"classes": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"],
 "scores": [0.068196

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning

Contact Details [Optional]

support@zenml.io

Describe the feature you'd like

Currently our CLI offers a way to install the python packages that are required for a given integration. However, some of our integrations also have system requirements that are necessary to make them work (graphviz, kubectl, etc. ).
All system requirements should be listed on an integration level, just

In order to reduce overfitting, I would like to ask for a new parameter: "n_repetitions". This parameter sets the number of complete sets of folds to compute for repeated k-fold cross-validation.

Cross-validation example:

{
    "validation_type": "kfold",
    "k_folds": 5,
    "n_repetitions": 3, # new
    "shuffle": True,
    "stratify": True,
    "random_seed": 123
}

Discussed in microsoft/FLAML#543

https://www.usenix.org/conference/opml19/program

Prepare to read papers