featuretools | open source python library for automated feature | Machine Learning library

Recently I've updated featuretools to v1.0.0 and faced the following issue. I have instances that vary within time and I want to build time dependent features for them. Besides, I want to save some historical characteristics of those instances. So my cutoff time dataset consists of such columns as: time, instance_id and feature1, feature2, ..., target

When I tried to to run dfs, I got the error 'NoneType' object has no attribute 'logical_types'

I have found out that it is caused by the inner function get_ww_types_from_features

It tries to get the column types of cutoff time df, assuming it has woodwork type

I tried to learn featuretools following documentation from featuretools.com.

A error came up: AttributeError: 'EntitySet' object has no attribute 'entity_from_dataframe'

Could you help me? Thank you.

Code:

New to feature tools, getting this error while creating entity

I am trying to work with Featuretools to develop an automated feature engineering workflow for the customer churn dataset. The end outcome is a function that takes in a dataset and label times for customers and builds a feature matrix that can be used to train a machine learning model.

As part of this exercise I am trying to execute the below code for plotting a histogram and got "TypeError: import_optional_dependency() got an unexpected keyword argument 'errors' ". Please help resolve this TypeError.

I have an imbalanced dataset which has 200 million data from class 0 and 8000 data from class 1. I followed two different approaches to build a model.

1. Randomly sample a new dataset which has a ratio of 1:4. Meaning 32000 from class 0 and 8000 from class 1. Then use featuretools to generate features(70 features generated in my case) and split dataset into train and test set with test_size = 0.2 and stratify minority class. Build a model with Random Forest algorithm and predict the test set.

Code:

I have more than 100,000 rows of training data with timestamps and would like to calculate a feature matrix for new test data, of which there are only 10 rows. Some of the features in the test data will end up aggregating some of the training data. I need the implementation to be fast since this is one step in a real-time inference pipeline.

I can think of two ways this can be implemented:

1. Concatenating the train and test entity sets and running DFS and then only using the last 10 rows and throwing away the rest. This is very time consuming. Is there a way to calculate a subset of an entity set while using data from the entire entity set?

2. Using the steps outlined in Calculating Feature Matrix for New Data section on the Featuretools Deployment page. However, as demonstrated below, this doesn't seem to work.

Create all/train/test entity sets:

Using Featuretools, I want to convert the value of a certain feature to rank.

This will be the exact question. If anyone can help me, please answer.

First, the following code uses the rank function of pandas and displays the result. I believe this result is correct.

In the classic mock customer dataset example in featuretools, if I have to derive trans_primitives like month, day, year etc. of transaction_time attribute of transactions entity. How do I do that?

As of version v0.12.0, FeatureTools allows you to assign custom names to multi-output primitives: https://github.com/alteryx/featuretools/pull/794. By default, the when you define custom multi-output primitives, the column names for the generated features are appended with a [0], [1], [2], etc. So let us say that I have the following code to output a multi-output primitive: