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Python package that computes similarity between two items based on their tag relevance scores and writes to file the list of items along with their corresponding top item neighbors

Project Description
# SimilarityCalculator

*Uses parallel processing and efficient C implementations to quickly compute similarities between a list of items; and
prints the top neighbors (most similar items) of each item to file.*

##### Travis Continuous Integration Testing status

[![Build Status](https://travis-ci.org/SMLtahir/SimilarityCalculator.svg?branch=master)](https://travis-ci.org/SMLtahir/SimilarityCalculator)

For maximum assurance against bugs, Travis CI testing is linked to the source Github repository. Travis runs several
unit tests on the latest code pushed to Github and displays a badge indicating **Build_Passing** or
**Build_Failing**. Since this project is currently under continuous development, it is suggested however to use only
stable builds released as code versions.

##### Compatible with Python versions

SimilarityCalculator currently runs on Python versions: *2.6*, *2.7*, *pypy*

#### How to run:

First, set **PYTHONPATH** environment variable to your Python bin directory

**Runnable modules**:

- load_neighbors.py
- test/unit_tests_load_neighbors.py

###### Run from the command line

LOAD_NEIGHBORS:

```
python load_neighbors.py
```

UNIT_TESTS:

```
python test/unit_tests_load_neighbors.py
```

To get information about any runnable module:

* python <moduleName.py> -h
* python <moduleName.py> --help

This will tell you how to run the program along with its available command line parameters.

###### Run from external module (as a function)
**SimilarityCalculator** can also be run as a function from an external module
The sample file **runAsFunction.py** located in SimilarityCalculator/data/samples/ should be followed as a model for
running the tool from an external module.

##### Parallel execution

The SimilarityCalculator has been designed to run on maximum available CPUs and using several C implementations
over native Python. Due to this, it is extremely fast and efficient. In case the user wants less CPUs to be used during
similarity and nearest neighbor computation, the **NUMBER_OF_CPU** configurational parameter should be set appropriately.
Its default value is **MAX**.


##### Similarity measures currently supported

Currently, Similarity between items is calculated using the Cosine Similarity Measure. We are currently working to
extend the code to include more measures.

##### Configuration Parameters

There are 3 json files in the SimilarityCalculator/config/ directory:
1. **config.json** - This is the most general config file. Store parameters whose values you do not plan to change
very often, here. These usually include input/output file paths, etc.
2. **config.test.json** - This file should be used for test parameters or parameters whose values you plan to change
very frequently. This can include number of CPUs, neighborhood size, etc.
3. **config.local.json** - This file is not written to the Github repository and will have to be created when
the code is first used. This is used to store local and private parameters and their values. **DO NOT** store these
parameters in any of the other config files.

In case the same configuration parameter is set in two or more files than the following is the order of assignment.
config.local.json > config.test.json > config.json
This means that if a parameter is set in both, local as well as test config files, then the value of the
parameter in config.test will be overwritten by config.local and so on.

The following parameters are to be modified as desired in these files.

1. **NUMBER_OF_CPU**

*Example entry*:

"NUMBER_OF_CPU": 2

*Default entry*:

"NUMBER_OF_CPU": "MAX"


This is used to indicate the number of CPUs to be used in parallel by the program. If not changed by the user,
the default is "MAX" which means that maximum available CPUs will be used for computation. Use this option for
large datasets and when speedy computation is required. Minimum value for this parameter is 1.

2. **FILE_RELEVANCE_PREDICTIONS**

*Example entry*:

"FILE_RELEVANCE_PREDICTIONS": "correct/path/inserted/here/sample_file.csv"

*Default entry*:

"FILE_RELEVANCE_PREDICTIONS": "data/relevance_predictions.txt"

This is the path to the main input file. It is suggested to store all data files in the SimilarityCalculator/data/
directory. The input file can be a .txt, .csv (comma separated) or .tsv (tab separated) file.
It contains 3 values:

item1_id,item2_id,relevance_score

Please reserve the first line of the file as the Header line for better readability.
The comma (,) can be replaced by any **INPUT_FIELD_SEPARATOR** as long as it is declared in the
**INPUT_FIELD_SEPARATOR** configuration parameter (see below).

Item1 is an entity of a set on which you need to perform the nearest-neighbor analysis. Item2 (henceforth called
**tag**) is an entity that is used as a metric to compare two entities of the item1 set.

* Example 1:

MovieId,Genre,RelevanceScore
1,"Action",0.50
1,"Comedy",0.75
2,"Action",0.67
2,"Comedy",0.30
3,"Action",0.98
3,"Comedy",0.10

The example above can be used to check how similar two movies are based on how highly they can be classified to the
different genres. Normalization of scores is done within the program and so this should not be a worry at the time
of preparing the needed input file.

* Example 2:

UserId,MovieId,Rating
1,96,4.5
1,54,2.0
2,96,5.0
2,54,1.0
3,96,3.0
3,54,4.0

The example above can be used to find how closely matched the tastes of different users are, on the basis of the
ratings they assign to movies.

Fully working examples (input + output files) can be found in the SimilarityCalculator/data/samples/ directory.

This code has been tested successfully on data files containing up to 2.5 million lines.

3. **INPUT_FIELD_SEPARATOR**

*Example entry* (tab-space):

"INPUT_FIELD_SEPARATOR": "\t"

*Default entry* (comma):

"INPUT_FIELD_SEPARATOR": ","

This is the token (space, tab-space, comma, colon, semi-colon, etc.) that is used to separate two fields of the
input files. Examples can be seen above.

4. **TAG_WEIGHTED**

*Valid entries*:

"TAG_WEIGHTED": "T"
"TAG_WEIGHTED": "F"

*Default entry*:

"TAG_WEIGHTED": "F"

This configuration parameter can take only two values - "T" (True) and "F" (False). By default its value is F which
means that tags will be all equally weighted (= 1.0). Assign a value of "T" when some tags (as described above) need
to be weighted differently than others. This could happen if you want some tags to play a bigger role in determining
the nearest neighbors than others.

5. **FILE_TAG_WEIGHTS**

*Example entry*:

"FILE_TAG_WEIGHTS": "correct/path/inserted/here/sample_tag_file.csv"

*Default entry*:

"FILE_TAG_WEIGHTS": "data/tagWeights.txt"

This is an optional input file. The value of this parameter will be considered only when the parameter
**TAG_WEIGHTED** is set to "T" (True). The format of this file is as follows:

tag,tag_weight

Please reserve the first line of the file as the Header line for better readability.
The comma (,) can be replaced by any **INPUT_FIELD_SEPARATOR** as long as it is declared in the
**INPUT_FIELD_SEPARATOR** configuration parameter (see above). The below is a tab-delimited example.

*Example*:

tag weight
"tagA" 2.3
"tagB" 1.0
"tagC" -2.0

**Please note**: If this file is included, **ALL** tags must be assigned weights.

6. **FILE_NEIGHBORS**

*Example entry*:

"FILE_NEIGHBORS": "correct/path/inserted/here/sample_file.txt"

*Default entry*:

"FILE_NEIGHBORS": "data/neighbors.txt"

This configuration parameter tells the program where to store the final output file. It is advised to store this in
the SimilarityCalculator/data/ directory as is done by default. The format of the file produced will be as below:

itemId,neighbor1,Similarity_Score

*Example*:

1 1 4.7657
1 2 4.6790
1 4 4.4423
1 5 4.4208
1 3 2.8345
2 2 4.7657
2 1 4.6790
2 4 4.5840
2 5 4.5061
2 3 2.9758

As can be seen in the example above, the neighbors of a particular itemId are printed in decreasing order of
similarity score. This means that the nearest neighbors will be displayed on top.

7. **NEIGHBORHOOD_SIZE**

*Example entry*:

"NEIGHBORHOOD_SIZE": 50

*Default entry*:

"NEIGHBORHOOD_SIZE": 250

This determines the number of top neighbors that you would want the program to calculate per item. If this value is
set higher than the total number of items, all items will be printed along with their similarity scores as
neighbors for every item.

8. **LOG_NAME**

*Example entry*:

"LOG_NAME": "correct/path/inserted/here/sample_file.txt"

*Default entry*:

"LOG_NAME": "logs/load_neighbors.txt"

This tells the program where to store the runtime logs of the program. It is advised to store this in the
SimilarityCalculator/logs/ directory as is done by default.

9. **ITEM1_COLUMN_NO**

*Example entry*:

"ITEM1_COLUMN_NO": 4

*Default entry*:

"ITEM1_COLUMN_NO": 0

This parameter tells the program in which column of the .csv/ .tsv *RELEVANCE_PREDICTIONS* file it can find
entities belonging to the itemId1 set (as defined earlier). It is particularly useful when the input file is a
multi-column one not prepared specially as input for the SimilarityCalculator program.

*Example*:

MovieId,Budget,BoxOfficeSales,Genre,relevanceScore(GenreToMovieId)
1,87,76,"Action",0.50
1,87,76,"Comedy",0.75
2,45,30,"Action",0.67
2,45,30,"Comedy",0.30
3,95,110,"Action",0.98
3,95,110,"Comedy",0.10

In this case, if we want to find the top-nearest neighbors of the different movies in our list, we would set

"ITEM1_COLUMN_NO": 0
"ITEM2_COLUMN_NO": 3
"RELEVANCE_SCORE_COLUMN_NO": 4

**Note**: Column numbering here starts from 0, and not from 1.
For the description of the two other parameters shown in the example above, see their respective sections below.

10. **ITEM2_COLUMN_NO**

*Example entry*:

"ITEM2_COLUMN_NO": 4

*Default entry*:

"ITEM2_COLUMN_NO": 1

This parameter tells the program in which column of the .csv/ .tsv *RELEVANCE_PREDICTIONS* file it can find
entities belonging to the itemId2 set (as defined earlier). It is particularly useful when the input file is a
multi-column one not prepared specially as input for the SimilarityCalculator program.

Please refer to the example above to see the usage of this parameter.

11. **RELEVANCE_SCORE_COLUMN_NO**

*Example entry*:

"RELEVANCE_SCORE_COLUMN_NO": 1

*Default entry*:

"RELEVANCE_SCORE_COLUMN_NO": 2

This parameter tells the program in which column of the .csv/ .tsv *RELEVANCE_PREDICTIONS* file it can find
entities belonging to the relevance_score set (as defined earlier). It is particularly useful when the input file
is a multi-column one not prepared specially as input for the SimilarityCalculator program.

Please refer to the example above to see the usage of this parameter.
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