Tutorials

Seven runnable end-to-end examples, ordered from simplest to most involved. Each pairs a TweetDataset method with the kind of input it expects and shows the actual output you'll see.

#	Tutorial	Method exercised	Notes
01	Quickstart: top hashtags	hashtag_histogram_alt_python	Minimum viable usage. Start here.
02	Mention histogram	mention_histogram_alt_python	Same shape as #01 but for @user tokens.
03	Bilingual n-grams	ngram_histogram_alt_python with lan='spanish' / lan='english'	Stopword handling per language.
04	Spanish sentiment range	sentiment_range_spanish_alt_python	First run is slow: loads a TensorFlow model.
05	Hashtag co-occurrence network	hashtag_weighted_coonet	Returns an igraph.Graph.
06	Mention co-occurrence network	mention_weighted_coonet	Same as #05, on mentions.
07	R-bridge: top hashtags	hashtag_histogram_r	The R-bridge path. Worker container only.

The pages here are auto-generated from each examples/<slug>/README.md by scripts/sync_tutorials.py. Edit the source README; CI fails on drift.

How to read them

Each tutorial has the same three sections:

1. What you'll see: the actual stdout the example prints, so you can match it against your run.
2. How it works: the call chain from TweetDataset method down through base_algs.
3. Run it: exact shell commands to bring up the cluster, run the example, and tear it down.

Prerequisites

• Whistlerlib installed on the client (see Install from PyPI).
• A running Whistlerlib cluster on localhost:8786 (see Install with Docker).
• For tutorial 07: the cluster must be running the albertogarob/whistlerlib image (R lives only there).

Related

• Algorithm families, which method routes to which base primitive.
• Architecture, what runs where.