Creating integration tests for quick-start for ranking

[gabrielspmoreira]

Closes #667
This PR creates the integration tests for quick-start for ranking scripts, which includes preprocessing the TenRec dataset with different options and training ranking models on the preprocessed data.

Preprocessing tests

• Check for basic preprocessing + target encoding, and proper tagging, dtype and number of rows and max values
• Tests the available data split strategies: random, random_by_user, temporal
• Tests the available filtering strategies: query string and min/max frequency for users and items
• Tests frequency capping

Model building, training and evaluation tests

• Trains single task-learning models with the model specific options: MLP, DLRM, DCN-v2, Wide&Deep, DeepFM
• Trains single task-learning models with the model specific options: DLRM, MMOE, PLE

Data setup

These integration tests require a 10M rows sample of the TenRec dataset, which is available in this internal Google Drive (tenrec_ci.zip).
The data needs to be downloaded in the CI machine and uncompressed to /raid/data/tenrec_ci/, which is the standard path where our other CI datasets are (e.g. /raid/data/lastfm/preprocessed).
P.s. If needed, the path for the TenRec sample data can be set by using the CI_TENREC_DATA_PATH env variable

[github-actions]

Documentation preview

https://nvidia-merlin.github.io/Merlin/review/pr-1015