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

Expected Behavior

Ingestion and batch retrieval should use either "datetime" or "event_timestamp" and stick to one convention.

Current Behavior

During ingestion, the function expects the input dataframe to have a column named "datetime". However, this column is later renamed in-place to "event_timestamp" and saved as avro.

During batch retrieval, "event_timestamp" column is returne

• 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