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DISAtool-1.0.1
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مشاهده بیشترتوضیحات
A library used to assess the informative and discriminative properties of subspaces/patterns
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DISAtool-1.0.1 |
| نام | DISAtool |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | L. Alexandre, R.S. Costa, R. Henriques |
| ایمیل نویسنده | leonardoalexandre@tecnico.ulisboa.pt |
| آدرس صفحه اصلی | https://github.com/JupitersMight/DISA |
| آدرس اینترنتی | https://pypi.org/project/DISAtool/ |
| مجوز | - |
# DISA
DISA (**D**iscriminative and **I**nformative **S**ubspace **A**nalysis), a software package in Python (v3.7) capable of assessing patterns with numerical outputs by statistically testing the correlation gain of the subspace against the overall space
## Input parameters
data : pandas.Dataframe
patterns : A python list where each position is a dictionary with the pattern properties:
[i] : {
"lines" : list (mandatory)
"columns" : list (mandatory)
"column_values": list (optional)
"noise": list (optional)
"type" : string (optional)
}
Description of parameters:
- "lines" refers to the observations of the pattern
- "columns" refers to the variables of the pattern
- "column_values" refers to the pattern coherence on columns
- "noise" refers to the noise allowed in each column
- "type" refers to the type of coherence (by default we assume constant coherence)
outcome : dict
{
"values": pandas.Series (mandatory)
"outcome_value" : int (optional)
"type": string (optional)
"method": string (optional)
"heuristic": boolean (optional)
}
Description of parameters:
- "values" the outcome variable
- "outcome_value" : if the outcome variable is categorical the user can force DISA to analyse according to a specific category, we assume the category is represented by a discrete value. (by default it will select the best category per pattern)
- "type": if the user wishes to analyse a continuous outcome variable this field should take the value "Numerical" (by default it is assumed to be categorical)
- "method": if in the "type" parameter the user inputted "Numerical" then this field can be filled in with:
- "min_max" - uses the minimum and the maximum values of each pattern to define the pattern-conditioned outcome intervals
- "average" - uses the average +- standard deviation of each pattern to define the pattern-conditioned outcome intervals
- "gaussian" (default) - assumes both the pattern-conditioned outcome and the outcome variable follow a normal distribution to define the pattern-conditioned outcome intervals
- "empirical" - uses the empirical distribution of the pattern-conditioned outcome and outcome variables to define the pattern-conditioned outcome intervals
- "heuristic": if in the "method" parameter the user inputted "empirical" then this field can be used to optimize the discriminative properties of each pattern
output_configurations : dict (optional)
{
"print_table" : boolean
"file_path_table" : string
"show_plots" : boolean
"file_path_plots" : string
"print_numeric_intervals" : boolean
"file_path_numeric_intervals" : string
}
Description of parameters:
- "print_table" : if set to True will output a table of results (by default it is set to True)
- "file_path_table" : path to file to write the output table to (by default it is set to None)
- "show_plots" : if the "method" parameters in the outcome parameter was set to "gaussian" or "empirical" then this parameter if set to True will plot a figure (by default it is set to False)
- "file_path_plots" : if the "method" parameters in the outcome parameter was set to "gaussian" or "empirical" then the path to a folder can be set in this parameter to output a PNG of the figures (by default it is set to None)
- "print_numeric_intervals" : if the "type" parameter in the outcome parameter was set to "Numerical" then this parameter can be set to True to output the interval that the pattern discriminates (by default it is set to False)
- "file_path_numeric_intervals" : if the "type" parameter in the outcome parameter was set to "Numerical" then the path to a folder can be set in this parameter to output to a file the intervals that each pattern discriminates
## Dataset examples
Four examples on how to use DISA are provided in the folder "Example", the Echocardiogram, the Liver Disorders, the Breast Cancer Wisconsin (diagnostic), and Dodecanol datasets. Inside each of the datasets corresponding folder you will find a set of files and a folder. The python and jupyter notebook files provide the code to analyse patterns using DISA. The patterns are contained in the .txt files and the processed datasets in both the .csv and .arff files.
## Package dependencies
pandas - 1.4.3
numpy - 1.23.1
scipy - 1.8.1
prettytable - 3.3.0
matplotlib - 3.5.1
## Metrics
A list of all the implemented metrics in DISA and the corresponding DOI (some but not all of these metrics are futher explained in https://mhahsler.github.io/arules/docs/measures).
Information Gain: https://doi.org/10.1016/S0306-4379(03)00072-3
Chi-squared: https://doi.org/10.1145/253260.253327
Gini index: https://doi.org/10.1016/S0306-4379(03)00072-3
Difference in Support: 10.1109/TKDE.2010.241
Bigger Support: 10.1109/TKDE.2010.241
Confidence: 10.1145/170036.170072
All-Confidence: 10.1109/TKDE.2003.1161582
Lift: 10.1145/170036.170072
Standardised Lift: https://doi.org/10.1016/j.csda.2008.03.013
Collective Strength: https://dl.acm.org/doi/pdf/10.1145/275487.275490
Cosine: https://doi.org/10.1016/S0306-4379(03)00072-3
Interestingness: arXiv:1202.3215
Comprehensibility: arXiv:1202.3215
Completeness: arXiv:1202.3215
Added Value: https://doi.org/10.1016/S0306-4379(03)00072-3
Casual Confidence: https://doi.org/10.1007/3-540-44673-7_1
Casual Support: https://doi.org/10.1007/3-540-44673-7_1
Certainty Factor: 10.3233/IDA-2002-6303
Conviction: 10.1145/170036.170072
Coverage (Support): 10.1145/170036.170072
Descriptive Confirmed Confidence: https://doi.org/10.1016/S0306-4379(03)00072-3
Difference of Proportions: https://doi.org/10.1007/s001800100075
Example and Counter Example: SEBAG, M.; SCHOENAUER, M. Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In: Proc. of EKAW. 1988. p. 28.
Imbalance Ratio: https://doi.org/10.1007/s10618-009-0161-2
Fisher's Exact Test (p-value): 10.3233/IDA-2007-11502
Hyper Confidence: 10.3233/IDA-2007-11502
Hyper Lift: 10.3233/IDA-2007-11502
Laplace Corrected Confidence: https://doi.org/10.1016/S0306-4379(03)00072-3
Importance: https://docs.microsoft.com/en-us/analysis-services/data-mining/microsoft-association-algorithm-technical-reference?view=asallproducts-allversions&viewFallbackFrom=sql-server-ver15
Jaccard Coefficient: https://doi.org/10.1016/S0306-4379(03)00072-3
J-Measure: NII Article ID (NAID) 10011699020
Kappa: https://doi.org/10.1016/S0306-4379(03)00072-3
Klosgen: https://doi.org/10.1016/S0306-4379(03)00072-3
Kulczynski: https://doi.org/10.1007/s10618-009-0161-2
Goodman-Kruskal's Lambda: https://doi.org/10.1016/S0306-4379(03)00072-3
Least Contradiction: (2004) Extraction de pepites de connaissances dans les donnees: Une nouvelle approche et une etude de sensibilite au bruit. In Mesures de Qualite pour la fouille de donnees. Revue des Nouvelles Technologies de l’Information, RNTI author : Aze, J. and Y. Kodratoff
Lerman Similarity: (1981) Classification et analyse ordinale des données. Author : Lerman, Israel-César.
Piatetsky-Shapiro: NII Article ID (NAID) 10000000985
Max Confidence: https://doi.org/10.1016/S0306-4379(03)00072-3
Odds Ratio: https://doi.org/10.1016/S0306-4379(03)00072-3
Phi Correlation Coefficient: https://doi.org/10.1016/S0306-4379(03)00072-3
Ralambondrainy: DIATTA, Jean; RALAMBONDRAINY, Henri; TOTOHASINA, André. Towards a unifying probabilistic implicative normalized quality measure for association rules. In: Quality Measures in Data Mining. Springer, Berlin, Heidelberg, 2007. p. 237-250.
Relative Linkage Disequilibrium: https://doi.org/10.1007/978-3-540-70720-2_15
Relative Risk: https://doi.org/10.1148/radiol.2301031028
Rule Power Factor:https://doi.org/10.1016/j.procs.2016.07.175
Sebag-Schoenauer : SEBAG, M.; SCHOENAUER, M. Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In: Proc. of EKAW. 1988. p. 28.
Yule Q: https://doi.org/10.1016/S0306-4379(03)00072-3
Yule Y: https://doi.org/10.1016/S0306-4379(03)00072-3
Weighted Support: https://doi.org/10.1016/j.patcog.2021.107900
Weighted Rule Support: https://doi.org/10.1016/j.patcog.2021.107900
Weighted Confidence: https://doi.org/10.1016/j.patcog.2021.107900
Weighted Lift: https://doi.org/10.1016/j.patcog.2021.107900
Statistical Significance: https://doi.org/10.1007/s10618-017-0521-2
FleBiC Score: https://doi.org/10.1016/j.patcog.2021.107900
## Authors
DISA was developed by:
L. Alexandre (leonardoalexandre@tecnico.ulisboa.pt), R.S. Costa (rs.costa@fct.unl.pt) and R. Henriques
نیازمندی
| مقدار | نام |
|---|---|
| ==1.8.1 | scipy |
| - | prettytable |
| - | matplotlib |
| - | numpy |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl DISAtool-1.0.1:
pip install DISAtool-1.0.1.whl
نصب پکیج tar.gz DISAtool-1.0.1:
pip install DISAtool-1.0.1.tar.gz