Python-terrier

Minor update to support activities for [CIKM 2021 tutorial](https://github.com/terrier-org/cikm2021tutorial/). In particular:
- `pt.debug.print_num_rows()` added
- Terrier Data Repository support for TREC Covid test collection.

Notable update: containing a small number of new features, and numerous other minor updates. New features include new operators on Terrier indices (196), as well as the ability to express a Terrier weighting model in a Python lambda (208, 215). A number of upstream fixes in [terrier-core](https://github.com/terrier-org/terrier-core) supported multi-lingual settings, gridsearch etc. There was also enchanced support for [ir_measures](https://github.com/terrierteam/ir_measures)

- 227, 230 dataframe type coercion during evaluation
- 226, 228 pt.Experiment consistency in missing qids in topics/qrels/runs
- 224 Using PyTerrier when not connected to Internet
- 220, 221 pt.debug utility transformers
- 219 pt.apply.new_column fails on empty dataframes
- 216 Mirrors for datasets
- 208, 215 Terrier weighting models expressed in Python
- 211 only compute ranks for score column
- 209 pt.apply.by_query verbose support
- 202, 231 pandas 1.3.0 compatibility
- 201 ParallelIndexer should use indexer.setExternalParalllism()
- 200 BatchRetrieve.from_dataset() support for http://data.terrier.org/
- 197 meaningful error message when passing corpus_iter to TRECCollectionIndexer
- 196 add + and len() support on a Terrier index.
- 195 intersect operator returns extra columns
- 192 more documentation on how to tune weightmodels
- 191 Revised GridSearch documentation
- 188 Global properties for BatchRetrieve
- 187 dataset indices with different variants dont get different dirs
- 185 Addititional dataset.get_topicsqrels() to make pt.Experiment faster to write
- 178 rank accuracy under negative scalar multiple
- 177 add a dataset search feature
- 176 Evaluating empty resultset
- 136 more pre-built indices?
- 113 datasets to support mirrors
- 21, 222 Alias BatchRetrieve to TerrierRetrieve

Significant update: Windows support, new evaluation measures, GridScan/Search, multi-threaded retrieval for Terrier's BatchRetrieve.

* Windows support - 135
* Use [ir_measures](https://github.com/terrierteam/ir_measures/) for calculating measures -- thanks to Sean MacAvaney, University of Glasgow
* GridScan and GridSearch - 97 -- thanks to Chentao Xu, University of Glasgow
* Conducting experiments in batches of queries, and dropping unused queries - 98, 81
* Keep previous formulations of the query - 126
* Terrier retrieval using multiple threads - 3
* Retrieve more than 1000 results from Terrier - 140, thanks to DayalStrub
* Update pandas version in requirements.txt (159), thanks to Alberto Ueda, UFMG
* Fixes to FilesIndexer, thanks to Chirag Shag, University of Washington
plus other minor updates

This release includes:
- multiple testing correction in pt.Experiment() (99)
- Terrier Index API needs documentation (103)
- Tokenising/escaping queries from IR Datasets (104), from Sean MacAvaney, University of Glasgow
- BatchRetrieve should warn and ignore empty queries (110), with thanks to TimMo-prog
- Indexing pipelines (118)
- pt.apply operations for making a new column and dropping a column (121), with thanks to Sean MacAvaney, University of Glasgow
- detect LTR learned model use that differs in number of features from fitting time (123), with thanks to Iadh Ounis, University of Glasgow

There are also now many additional neural ranking and re-ranking plugins for PyTerrier - see list at https://pyterrier.readthedocs.io/en/latest/neural.html#available-neural-re-ranking-integrations

This release includes:
- [IR Datasets](https://ir-datasets.com) support (#90, 100) - thanks to Sean MacAvaney, University of Glasgow
- faster indexing via multi-threading and fifos (92) - thanks to Sean MacAvaney, University of Glasgow
- Easier ways of operating on text of documents (88)
- Documentation improvements - thanks to Iadh Ounis, University of Glasgow
- ensuring Precisioncutoff metrics are reported (89) - thanks to Xiao Wang, University of Glasgow
- rounding of pt.Experiment values - suggestion by Iadh Ounis, University of Glasgow

This PyTerrier release contains significant updates from the last public release in March 2020. It is intended to serve as a stable release for PyTerrier, while further improvements are merged in a new stable release.