Package for generating and evaluating patterns in quantitative reports
Project description
data-patterns
Package for generating and evaluating data-patterns in quantitative reports
Free software: MIT/X license
Documentation: https://data-patterns.readthedocs.io.
Features
Here is what the package does:
Generating and evaluating patterns in structured datasets and exporting to Excel and JSON
Transforming generated patterns into Pandas code
Quick overview
To install the package
pip install data_patterns
To introduce the features of the this package define the following Pandas DataFrame:
df = pd.DataFrame(columns = ['Name', 'Type', 'Assets', 'TV-life', 'TV-nonlife' , 'Own funds', 'Excess'], data = [['Insurer 1', 'life insurer', 1000, 800, 0, 200, 200], ['Insurer 2', 'non-life insurer', 4000, 0, 3200, 800, 800], ['Insurer 3', 'non-life insurer', 800, 0, 700, 100, 100], ['Insurer 4', 'life insurer', 2500, 1800, 0, 700, 700], ['Insurer 5', 'non-life insurer', 2100, 0, 2200, 200, 200], ['Insurer 6', 'life insurer', 9000, 8800, 0, 200, 200], ['Insurer 7', 'life insurer', 9000, 0, 8800, 200, 200], ['Insurer 8', 'life insurer', 9000, 8800, 0, 200, 200], ['Insurer 9', 'non-life insurer', 9000, 0, 8800, 200, 200], ['Insurer 10', 'non-life insurer', 9000, 0, 8800, 200, 199.99]]) df.set_index('Name', inplace = True)
Start by defining a PatternMiner:
miner = data_patterns.PatternMiner(df)
To generate patterns use the find-function of this object:
df_patterns = miner.find({'name' : 'equal values', 'pattern' : '=', 'parameters': {"min_confidence": 0.5, "min_support" : 2, "decimal" : 8}})
The result is a DataFrame with the patterns that were found. The first part of the DataFrame now contains
id |
pattern_id |
pattern_def |
support |
exceptions |
confidence |
---|---|---|---|---|---|
0 |
equal values |
{Own funds} = {Excess} |
9 |
1 |
0.9 |
The miner finds one patterns; it states that the ‘Own funds’-column is identical to the ‘Excess’-column in 9 of the 10 cases (with a confidence of 90 %, there is one case where the equal-pattern does not hold).
To analyze data with the generated set of data-patterns use the analyze function with the dataframe with the data as input:
df_results = miner.analyze(df)
The result is a DataFrame with the results. If we select result_type = False then the first part of the output contains
index |
result_type |
pattern_id |
pattern_def |
support |
exceptions |
confidence |
P values |
Q values |
Insurer 10 |
False |
equal values |
{Own funds} = {Excess} |
9 |
1 |
0.9 |
200 |
199.99 |
Other patterns you can use are ‘>’, ‘<’, ‘<=’, ‘>=’, ‘!=’, ‘sum’, and ‘–>’.
Read the documentation for more features.
Upload to Pypi (for developers)
Change the version in setup.py
Go to github.com and navigate to the repository. Next, click on the tab “releases” and then on “Create a new release”. Now, define a Tag verion (it is best to use the same number as you used in your setup.py version-field: v0.1.15 for example). Then click on “publish release”.
Make a Pypi account here: https://pypi.org/manage/projects/
Download twine by typing in your command prompt:
pip install twine
Get admin rights of the owner of the data_patterns package.
Open your command prompt and go to the folder of data_patterns. Then type
python setup.py sdist
twine upload dist/*
A good reference is here: https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
History
0.1.0 (2019-10-27)
Development release.
0.1.11 (2019-11-6)
First release on PyPI.
Project details
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