Hi,
I'm new to tpot but I got this error. I understand that score function can take strings, but I got the following error when using TPOTClassifier.

---

ValueError Traceback (most recent call last)
in
----> 1 tpot.score(X_test, y_test)

~/miniconda3/envs/ml

Adding types on the public API surface would allow us to do some runtime type checking later on and would allow user's IDE to have more info for static analysis.

The functions/signatures to type are the ones listed here https://github.com/keras-team/autokeras/blob/master/autokeras/__init__.py

For the context, see #856 where I add some type information on a ImageClassifier.

This issue can

For example, if there is a relationship transaction.session_id -> sessions.id and we are calculating a feature transactions: sessions.SUM(transactions.value) any rows for which there is no corresponding session should be given the default value of 0 instead of NaN.

Of course this should not normally occur, but when it does it seems more reasonable to use the default_value.

`DirectF

i want to know whether autosklearn support multilabel task, thank you!

I tried building the docs, but was met with a graphviz error. Typically this means I can spend a few hours pecking away at the dependencies until I get stable build... or someone that has it working can export their environment, and publish an environment.yml that we can use with the build instructions.
I was going off of the d2l book since that's a dep here, but their [environment.yml](https://g

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.

I run this code

import os
os.environ['is_test_suite']="True" # this is writen due to bug for multiprocessing and pickling I issued. #426 
from auto_ml import Predictor
from auto_ml.utils import get_boston_dataset
from auto_ml.utils_models import load_ml_model

# Load data
df_train, df_test = get_boston_dataset()

# Tell auto_ml which column is 'output'
# Also note columns t

Sklearn has a TON of estimators, metrics, and cv iterators that could trivially be added to the xcessiv.presets package. I'm a bit focused on other issues to bother adding them all.

Anyone who can help add to the list can easily do so.

Adding preset estimators/metrics/cvs is very easy. There's literally no need to understand how the rest of Xcessiv works, just take a look and copy the patt

Expected Results

List of cost of all configs, or run if more then one run per config

Actual Results

Don't know where it is

Versions

0.11.1

1. installation
2. example csv header table
3. list of available header options

do all of this before explaining the benefits, etc.

showing them what it can do is way more powerful than explaining it.

Hi PBA Team,

Spotted this warning in my IDE when looking through your code:

https://github.com/arcelien/pba/blob/a9cc308d936dee488aab7e3582ab7a47167b587a/pba/augmentation_transforms.py#L224

getargspec() has been deprecated since Python 3.0 in favour of getfullargspec()

It may be worthwhile adding a try/except block for this import because getfullargspec() isn't available in Pyt

The download buttons for the examples on readthedocs are not working. I cloned the repo and built the docs locally and it works fine. I guess just something in the build process went wrong and it should be working when you build it again.

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.