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

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

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

This issue follows #463 which has a partial implementation in #536.

One additional PR is needed to get #436 into a consistent and complete state for end users. Currently #536 makes changes to Feast Core by adding support for labels to feature sets and features, but it doesn't add support for labels to feature sets in the Python SDK.

The task here is to add this support and update any end-to

• Unable to supply validation_data to a Keras CVExperiment via model_extra_params[“fit”]
• This is because HyperparameterHunter automatically sets validation_data to be the OOF data produced by the cross validation scheme
• I can imagine this would be unexpected behavior, so I’d love to hear any thoughts on how to clear this up

Note

• This issue (along with several others) was ori

Hello, I just browsed the Udemy course about feature engineering recommended by you, and found a blog written by the course instructor. So I provide it here, maybe it can be helpful to someone.

Feature Engineering for Machine Learning: A Comprehensive Overview

[Feature Engineering: Best Resources to Learn Feature

Add tests for ensemble save and load. It can be done:

• by using some existing learner
• or by writing simple learner framework mockup

I will happily add this to the documentation, but I need help figuring out how to use it ;).

Basically I want to extract a quadrilateral region from an image and perspective transform it to be a rectangle. Per my other open issue, I've been pointed to the undocumented gm.perspectiveProjection.

Per this operation
https://github.com/PeculiarVentures/GammaCV/blob/8ffa723ef54b297cda8ffb5b21b027