Search engine and evaluation modules, options for pivoted document length normalization and tokenization
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.gitignore
06.topics.851-900.txt
README.md
__init__.py
evaluator.py
pivot.py
qrels.february
search.py
tokenizer.py

README.md

About

An information retrieval engine built for the task of finding web documents from the 2006 TREC blog track collection. Additional modules evaluate the IR engine and perform pivoted document length normalization (see Singhal et al. 1996).

The IR engine follows the vector space model and uses log TF*IDF weights.

Corpus

The corpus is NOT included with this code, but the 2006 TREC blog track test collection can be acquired here: http://ir.dcs.gla.ac.uk/test_collections/blogs06info.html.

However, the search engine should still work on any corpus where each document is stored in a single text file and all of these are stored in a directory called corpus/ (the evaluutor will not work).

Usage

SearchEngine

The main object for querying is SearchEngine. It should be called as follows:

>>> s = SearchEngine(**options)

Querying is called with via the method query(query_string, num_of_results).

Example of instantiating and querying:

# create an object for a sample of 1500 documents, with otherwise defaults
>>> s = SearchEngine(docs=1500) 

# get ranked list of 5 docs	
>>> s.query('cheney hunting',5)	 
              0                              id
1085  23.781539  BLOG06-20060217-020-0019557550
675   23.589061  BLOG06-20060214-022-0031627413
875   23.560228  BLOG06-20060216-035-0019518477
1421  22.525424  BLOG06-20060216-022-0021420409
748   21.452589  BLOG06-20060221-017-0005368642
Keyword options
  • corpus_dir - The name of the directory where the corpus is stored, each as a single text file ending in .txt. Default is 'corpus/'.

  • sample - Boolean, whether to run on entire collection or not. Default True.

  • docs - The number of docs to use in a sample. Will be ignored if sample is False. Default is 200.

  • normalize - Boolean, whether to normalize the weights by document length or not. Default is False.

  • biwords - Boolean, whether to index biwords or not. Default is False.

  • english_only - Boolean, whether or not to index only English documents as determined by stopwords-based language detection. Defaults to False.

Tokenization options

  • lowercase - Boolean, whether to fold all words to lowercase. Default is True.

  • tokenize - 'no_digits', 'symbols' or 'no_symbols'. 'no_digits' has alphabetic characters only. 'symbols' includes apostrophes and hyphens as well as digits. 'no_symbols' includes alphabetic and digit word chapters but not apostrophes or hyphens. Default is 'no_digits'.

  • stopwords - Boolean, whether to INCLUDE stopwords - True means stopwords included, False means stopwords removed. Default is True.

  • stemming - None, 'porter', 'lancaster' or 'snowball'. Which stemmer to use, or no stemmer as None. Default is None.

  • lemmatize - True or False. Whether to lemmatize. Cannot be used in conjunction with stemming. Default is False.

IREvaluator

The SearchEngine can be evaluated with IREvaluator(search_engine, QRELS_filename, query_filename, num_documents_k). Only text files in the format given for this project will parse correctly. Example:

# create an IREvaluator object passing a search engine object, with k = 10
>>> s = SearchEngine()
>>> e = IREvaluator(s,'qrels.february','06.topics.851-900.txt',10)

The IREvaluator object has methods and attributes for easy evaluation and inspection:

# get the mean of metrics across the queries
>>> e.averages
AP           0.344866
F.2          0.515084
F1           0.453895
RR           0.771124
p@5          0.520833
precision    0.475000
r-prec       0.381769
recall       0.471608

# see individual query results
>>> e.results
           AP       F.2        F1   RR  p@5  precision    r-prec    recall
851  0.428571  0.303502  0.352941  1.0  0.6        0.3  0.428571  0.428571
852  0.500000  0.308300  0.461538  0.5  0.4        0.3  0.666667  1.000000
853  0.099405  0.445205  0.192308  1.0  0.8        0.5  0.119048  0.119048
854  0.400000  0.945455  0.571429  1.0  1.0        1.0  0.400000  0.400000
855  0.154464  0.390977  0.307692  1.0  0.4        0.4  0.250000  0.250000
...

# read a query manually and see if it was marked relevant (string must match that in the topic file exactly including random whitespace characters)

>>> e.query_read('cheney hunting')
BLOG06-20060217-020-0019557550
RELEVANT
JustOneMinute: Guns Don't Shoot People; Dick Cheney Shoots PeopleGuns Don't Shoot People; Dick Cheney Shoots People
The gang that couldn't shoot straight takes its act on the road, and the most dangerous man in Washington DC shows he is also the most dangerous man in South Texas :
...

# compare an old query with a different one

>>> e.compare('"letting india into the club?"','india nuclear power')
Old Precision: 0.0
New Precision: 0.4
Old P@5: 0.0
New P@5: 0.4

# get a pr curve for up to a specified # of documents

>>> e.pr_curve(10)
   precision    recall
0   0.583333  0.071967
1   0.562500  0.151687
2   0.562500  0.211425
3   0.552083  0.255722
4   0.520833  0.288804
5   0.517361  0.330253
6   0.497024  0.363176
7   0.492188  0.403322
8   0.488426  0.447407
9   0.475000  0.471608

Pivoted Document Normalization

Use the pivot module's find_pivot method to calculate the pivot paramaters. Then pass that to SearchEngine's pivot(slope, pivot_factor) method to recalculate all the weights.

# create a normalized SearchEngine
>>> s = SearchEngine(sample=False, normalize=True)

# find pivot with 100 docs to be retrieved
# doc lengths go into 10 bins of equal length
>>> find_pivot(s,1000,10)
Pivot slope: 0.00162740912633
Found pivot factor: 19.8401716844

Then you can use the pivot factors to recalculate weights.

>>> s.pivot(0.00162740912633, 19.8401716844)