From a9a12c506a943d1c4538639a45c7a59d9202976f Mon Sep 17 00:00:00 2001 From: parul100495 Date: Fri, 11 Dec 2020 12:46:51 -0500 Subject: [PATCH] Documentation [WIP] --- __pycache__/create_plots.cpython-37.pyc | Bin 0 -> 1572 bytes __pycache__/equation_parser.cpython-37.pyc | Bin 0 -> 4180 bytes .../equation_parser_extension.cpython-37.pyc | Bin 0 -> 6814 bytes __pycache__/gather_results.cpython-37.pyc | Bin 0 -> 3096 bytes __pycache__/get_bounds.cpython-37.pyc | Bin 0 -> 4922 bytes __pycache__/inequalities.cpython-37.pyc | Bin 0 -> 3897 bytes ...gistic_regression_functions.cpython-37.pyc | Bin 0 -> 3370 bytes __pycache__/qsa.cpython-37.pyc | Bin 0 -> 3196 bytes create_plots.py | 48 ++ docs/_images/bound-no.png | Bin 0 -> 30125 bytes docs/_images/bound-yes.png | Bin 0 -> 31284 bytes docs/_images/const.png | Bin 0 -> 29892 bytes docs/_sources/code.rst.txt | 15 +- docs/_sources/index.rst.txt | 36 +- docs/_sources/intro.rst.txt | 18 + docs/_sources/quickstart.rst.txt | 47 ++ 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True ) + + fig = plt.figure() + + plt.xlim(min(file_ms), max(file_ms)) + plt.xlabel( "Amount of data", fontsize = 16 ) + plt.xscale( 'log' ) + plt.xticks( fontsize = 12 ) + plt.ylabel( ylabel, fontsize = 16 ) + + if is_yAxis_prob: + plt.ylim(-0.1, 1.1) + # else: + # plt.ylim(-0.2, 2.2) + # plt.plot([1, 100000], [1.25, 1.25], ':k'); + # plt.plot([1, 100000], [2.1, 2.1], ':k'); + + plt.plot ( file_ms, file_QSA, 'b-', linewidth = 3, label = 'QSA' ) + plt.errorbar ( file_ms, file_QSA, yerr = file_QSA_stderror, fmt = '.k' ); + plt.plot ( file_ms, file_LS, 'r-', linewidth = 3, label = 'LogRes' ) + plt.errorbar ( file_ms, file_LS, yerr = file_LS_stderror, fmt = '.k' ); + plt.legend ( loc = legend_loc, fontsize = 12 ) + plt.tight_layout () + + plt.savefig( output_file, bbox_inchesstr = 'tight' ) + plt.show( block = False ) + + +if __name__ == "__main__": + gather_results() + + loadAndPlotResults(csv_path + 'fs.csv', 'Log Loss', img_path + 'tutorial7MSE_py.png', False, 'lower right') + 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zkpankIg>zWo57YA$p02oG0hegsC?YVck;0^bpFtWHql-=t7J3@+}qcl=Fph7@$HB} zmu1)*ba~U9B`mj6q?k{RdJ^NfGnU!x%ry_09G8kb3XoIR-!&&q;mX=45Iji_Hi5cr z?x6ATNPP5N&2=DWJ65rt*NU9KVOE+TzoDu(6|H!) zlY~ab{hFp=ZtrQdXGtd-bH>Tn1`*&UK~ICZY;&3Z*p~E;$}@sD7psfkRj08K`b&OW z;!>}X;DwWB=s@*2L!8`03sWZ8*$`~?NU0iX&edDEN@UBBU#m#cfnU}2G}C`|K-h(B z=Vr{OXxkV*o_2Ik;IHFt^R(fVKdjX@V#9SlReonwa;xVfwG%aBG;~>aNQ)eMwWt>8 zQt!$QY0|C#TCv0)Sbd~uq2Y5nhfAtRmR5w>mSH+w60 z>Z0X-ESTTM-a7HIMe1GLR*U)dF>^B{GLY0V{r{Vtt2Fb{>Hazh&jpI)f0!YjESSPW lp>H?TOa31h!&(IC8MWsK-`_ has hosted the mining challenge every year, since 2006. -With this challenge, they aim at everyone interested to apply their tools to a common dataset. -The challenge is for researchers and practitioners to use their mining tools and approaches on a dare. -The goal is to answer questions about a given dataset, that were previously unanswered. +With the growing usage of machine learning and artificial intelligence in real lives, +the need for incorporating the mitigation of unethical and unfair elements in machine +learning models is rising at an alarming rate. +This requires us to develop a tool to evaluate and mitigate unfairness in these models +that would help data scientists in dealing with such problems in the models they develop. -MSR 2020 is a challenge which presents `Software Heritage Graph Dataset `_. -This is one of the largest known existing public archive of software source code and accompanying development history. -More information can be found in 'Dataset' tab. - -Our goal here is to answer certain research questions (mentioned in 'Problem Statement' tab) and gain some meaninful insights from the dataset. +Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility +of regulating the undesirable behavior of machine learning algorithms and making them `fair' +is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML +researcher). +This is implemented for fairness in machine learning for any generic constraint. +In addition, some extensions for optimizing the bounds and making it more efficient +are also present. +More details of Seldonian is present `here `_. .. toctree:: :maxdepth: 2 :caption: Contents: - problem - dataset - pre_req - queries - model + intro + quickstart + variants code - results - references diff --git a/docs/_sources/intro.rst.txt b/docs/_sources/intro.rst.txt new file mode 100644 index 0000000..750dc43 --- /dev/null +++ b/docs/_sources/intro.rst.txt @@ -0,0 +1,18 @@ +Introduction +============ + +With the growing usage of machine learning and artificial intelligence in real lives, +the need for incorporating the mitigation of unethical and unfair elements in machine +learning models is rising at an alarming rate. +This requires us to develop a tool to evaluate and mitigate unfairness in these models +that would help data scientists in dealing with such problems in the models they develop. + +Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility +of regulating the undesirable behavior of machine learning algorithms and making them `fair' +is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML +researcher). +This is implemented for fairness in machine learning for any generic constraint. +In addition, some extensions for optimizing the bounds and making it more efficient +are also present. + +More details of Seldonian is present `here `_. \ No newline at end of file diff --git a/docs/_sources/quickstart.rst.txt b/docs/_sources/quickstart.rst.txt new file mode 100644 index 0000000..21b3a0f --- /dev/null +++ b/docs/_sources/quickstart.rst.txt @@ -0,0 +1,47 @@ +Getting Started +=============== + +This page is a user guide for the developers to quickly +be able to use FairSeldonian Python library in their own project +or, extending/enhancing the codebase to enhance the framework. + +Pre-requisites +--------------- +Library pre-requisites + +* sklearn - machine learning model implementations like logistic regression. + +* matplotlib - visualizations of the plots. + +* numpy - handling data. + +* pandas - handling data in the form of dataframes. + +* ray - for parallelisation of the algorithm. + +* torch - for tensors used in the codebase. + +Quick start +----------- +The complete code resides in `code` folder. +To run the experiment, you need to amend configurations in `main.py`. + + +Go to terminal or any IDE and run that file using the following command: + +.. code-block:: + + python main.py + +The default mode is set as `base`. To understand other modes present in the code, refer to the `Variants` section of this documentation. + +Configuration +------------- +The user must setup the following things to make full use of this framework: + +- **Configuration of the experiment structure** + + +Collaboration +------------- +Please feel free to contribute to this code base. diff --git a/docs/_sources/variants.rst.txt b/docs/_sources/variants.rst.txt new file mode 100644 index 0000000..62ebf6b --- /dev/null +++ b/docs/_sources/variants.rst.txt @@ -0,0 +1,91 @@ +Variants +======== + +The codebase allows you to choose the variant for tuning and experimenting with the framework. + +Basic Seldonian +--------------- +To begin with, we implemented vanilla Seldonian algorithm to classify the datapoint into 2 groups with difference in true positives as the fairness constraint. + +To use this mode, you need to add CLI parameter `base` as: + +.. code-block:: + + python main.py base + +Improvements to confidence interval +----------------------------------- +In the candidate selection process, we used Hoeffding inequality confidence interval as follows- + +.. math:: + + estimate \pm 2 \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}} + +Instead, this interval can be improved by using a separate values for - a.) error in candidate estimate and b.) confidence interval in safety set as follows- + +.. math:: + + estimate \pm \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}} + \sqrt{\frac{ln(1/\delta)}{2 |D_{candidate}|}} + +This will specifically be helpful in cases where the difference between the sizes of the 2 data splits is huge. + +To use this mode, you need to add CLI parameter `mod` as: + +.. code-block:: + + python main.py mod + +Improvement in bound propagation around constant values +------------------------------------------------------- +As constants have fixed value, there is no need to wrap a confidence interval around them. Thus, the +:math:`\delta` value can directly go to other variable child and need not be split equally into half in case of +binary operator when the other child is a constant. The figures below show naive and improved implementation +of bound propagation in case of constant value of a node of the same tree respectively. + +.. image:: images/const.png + +To use this mode, you need to add CLI parameter `const` as: + +.. code-block:: + + python main.py const + +Improvement in bound propagation from union bound +------------------------------------------------- +A user may defined the fairness constraint in such a way that a particular element appears multiple times +in the same tree. Instead of treating all those entities as independent elements, we can combine all the +elements together union bound and then use the final value of :math:`\delta`. This will theoretically improve the +bound and give us better accuracy and more valid solutions. + +Example: Suppose we have A appearing 3 times with :math:`\delta/2`, :math:`\delta/4` and :math:`\delta/8`. We can simply take the + +.. math:: + \delta_{sum} = 7\delta/8 + +and find the confidence interval using that :math:`\delta`. The figures below show the naive and improved implement using this functionality respectively. + +.. image:: images/bound-no.png + +.. image:: images/bound-yes.png + +Optimization with Lagrangian/KKT +-------------------------------- +To use Lagrangian/KKT technique to optimise the objective function to get candidate solution, several additional modification are done: + +- Objective function: The implementation to find the candidate solution and setting the value of the objective function (which is minimized) is changed to the following- + +.. math:: + -fhat + (\mu * upperbound) + +- Value of :math:`\mu` : We calculate the value of :math:`\mu` as + +.. math:: + -\nabla f( \theta^{*})/ \nabla g_{i}( \theta^{*}) + +which must be positive to support the inequality of the fairness constraint and thus, in case the value is negative, then, we hard-code it to some positive value (say, 1). + +- Change prediction to continuous function: Classification is essentially a step function (0/1 in case of binary classifier as in this case). Thus, instead of getting a label, we change the function to give the probability of getting a label instead of exact label value. This helps us find the derivative of the function easily. This change must be made by the user when he/she changes the predict function for their use-case. + +- 2-player approach to solve KKT: One of the ways to solve KKT optimization problem is to use a 2-player approach where we fix a value of :math:`\mu` and then optimize the function w.r.t. :math:`\theta` and then , we fix :math:`\theta` and optimize the function w.r.t. :math:`\mu`. This goes on until we converge to some value or exceed a specific number of iterations. 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div.sidebar { margin: 0 0 0.5em 1em; border: 1px solid #ddb; - padding: 7px; + padding: 7px 7px 0 7px; background-color: #ffe; width: 40%; float: right; - clear: right; - overflow-x: auto; } p.sidebar-title { font-weight: bold; } -div.admonition, div.topic, blockquote { - clear: left; -} - /* -- topics ---------------------------------------------------------------- */ div.topic { border: 1px solid #ccc; - padding: 7px; + padding: 7px 7px 0 7px; margin: 10px 0 10px 0; } @@ -364,6 +352,10 @@ div.admonition dt { font-weight: bold; } +div.admonition dl { + margin-bottom: 0; +} + p.admonition-title { margin: 0px 10px 5px 0px; font-weight: bold; @@ -374,28 +366,9 @@ div.body p.centered { margin-top: 25px; } -/* -- content of sidebars/topics/admonitions -------------------------------- */ - -div.sidebar > :last-child, -div.topic > :last-child, -div.admonition > :last-child { - margin-bottom: 0; -} - -div.sidebar::after, -div.topic::after, -div.admonition::after, -blockquote::after { - 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li:last-child > :last-child { +li > p:last-child { margin-bottom: 0px; } -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - dl.footnote > dt, dl.citation > dt { float: left; - margin-right: 0.5em; } dl.footnote > dd, @@ -571,15 +520,14 @@ dl.citation > dd:after { } dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; + display: flex; + flex-wrap: wrap; } dl.field-list > dt { + flex-basis: 20%; font-weight: bold; word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; } dl.field-list > dt:after { @@ -587,8 +535,8 @@ dl.field-list > dt:after { } dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; + flex-basis: 70%; + padding-left: 1em; margin-left: 0em; margin-bottom: 0em; } @@ -597,7 +545,7 @@ dl { margin-bottom: 15px; } -dd > :first-child { 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Contents:

@@ -108,7 +103,7 @@ @@ -158,166 +153,213 @@
-
-

Code Documentation

-
-
-config.config(filename='database.ini', section='postgresql')
-

This function is used to pick the configuration from the section -in the provided file

+
+

Code Documentation

+
+
+equation_parser.construct_expr_tree_base(rev_polish_notation)
+

Returns root of constructed tree for given postfix expression

Parameters
-
    -
  • filename – The path of the file which contains the configurations

  • -
  • section – The section inside the file which contains the configuration needed to connect to the database

  • -
+

rev_polish_notation – string with space as delimiter ‘ ‘

Returns
-

the database config needed to connect

+

expr_tree node

-
-
-connect_database.connect_and_fetch_data(query, params)
-

This function is used to connect to the database and fetch the rows -from the database, given the query and config params of the database.

-
-
Parameters
-
    -
  • query – The select query to be used to fetch data

  • -
  • params – The parameters used to contact the database server

  • -
-
-
Returns
-

list of rows in database fetched from the query

-
-
+
+
+equation_parser.eval_expr_tree_base(t_node, Y, predicted_Y, T)
+

To evaluate estimate of the expression tree +:param t_node: expr_tree node +:param Y: pandas::Series +:param predicted_Y: tensor +:param T: pandas::Series +:return: estimate value: float

-
-
-data_pre_processing.clean_data(X)
-

This function is used to clean the data in the dataset

-
-
Parameters
-

X – the dataframe containing the features of the datapoints

-
-
Returns
-

the dataframe with the clean, processed values of the features

-
-
+
+
+equation_parser.eval_expr_tree_conf_interval_base(t_node, Y, predicted_Y, T, delta, inequality, candidate_safety_ratio, predict_bound, modified_h)
+

To evaluate confidence interval of the expression tree +:param t_node: expr_tree node +:param Y: pandas::Series +:param predicted_Y: tensor +:param T: pandas::Series +:param conf_prob: float in [0, 1] +:return:

-
-
-data_pre_processing.clean_labels(Y)
-

This function is used to clean the labels in the dataset

-
+
+
+class equation_parser.expr_tree(value)
+

An expression tree node

+
+ +
+
+equation_parser.inorder(t_node)
+

A utility function to do inorder traversal +:param t_node: expr_tree node +:return: None

+
+ +
+
+equation_parser.isFunc(element)
+

A utility function to check if ‘element’ is one of FP, FN, TP, TN +:param element: expr_tree node +:return: bool

+
+ +
+
+equation_parser.isMod(element)
+

A utility function to check if ‘element’ is mod function +:param element: expr_tree node +:return: bool

+
+ +
+
+equation_parser.isOperator(element)
+

# A utility function to check if ‘element’ is an operator +:param element: expr_tree node +:return: bool

+
+ +
+
+get_bounds.eval_abs_bound(l_x, u_x)
+
Parameters
-

Y – the array containing the labels of the datapoints

+
    +
  • l_x – lower bound

  • +
  • u_x – upper bound

  • +
Returns
-

the array of cleaned labels of the datapoints

+

lower and upper bound of abs operation

-
-
-data_pre_processing.split_train_test(X, Y, ratio=0.3)
-

This function is used to split the dataset into training and testing dataset

-
+
+
+get_bounds.eval_add_bound(l_x, u_x, l_y, u_y)
+
Parameters
    -
  • X – the feature set of the datapoints for regression prediction

  • -
  • Y – the labels of the datapoints

  • -
  • ratio – the test dataset ratio

  • +
  • l_x – lower bound of left child

  • +
  • u_x – upper bound of left child

  • +
  • l_y – lower bound of right child

  • +
  • u_y – upper bound of right child

Returns
-

the training and testing datasets

+

lower and upper bound of add operation

-
-
-regression_model.regression_model_fit(X, Y, model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False))
-

This function is used to fit the provided model with the training dataset provided

-
+
+
+get_bounds.eval_div_bound(l_x, u_x, l_y, u_y)
+
Parameters
    -
  • X – the feature set of the datapoints for regression prediction

  • -
  • Y – the labels of the datapoints

  • -
  • model – The regression model to be used

  • +
  • l_x – lower bound of left child

  • +
  • u_x – upper bound of left child

  • +
  • l_y – lower bound of right child

  • +
  • u_y – upper bound of right child

Returns
-

the fitted regression model

+

lower and upper bound of div operation

-
-
-regression_model.regression_model_predict(X, model)
-

This function is used to predict the labels for the test dataset provided

-
+
+
+get_bounds.eval_multiply_bound(l_x, u_x, l_y, u_y)
+
Parameters
    -
  • X – The feature set for which the prediction is to be made

  • -
  • model – The regression model used for the result

  • +
  • l_x – lower bound of left child

  • +
  • u_x – upper bound of left child

  • +
  • l_y – lower bound of right child

  • +
  • u_y – upper bound of right child

Returns
-

The Y labels predicted by the model

+

lower and upper bound of multiply operation

-
-
-error_analysis.get_error(Y_pred, Y_test)
-

This function is get the mean absolute error between the predicted label -and the actual label

-
+
+
+get_bounds.eval_subtract_bound(l_x, u_x, l_y, u_y)
+
Parameters
    -
  • Y_pred – The predicted labels from the regression model

  • -
  • Y_test – The actual labels from the dataset

  • +
  • l_x – lower bound of left child

  • +
  • u_x – upper bound of left child

  • +
  • l_y – lower bound of right child

  • +
  • u_y – upper bound of right child

Returns
-

The mean absolute error value

+

lower and upper bound of subtract operation

-
-
-plot_results.plot(X, Y, Y_predicted, Y_baseline, X_label, Y_label, title)
-

This function plots the graph, given the data values

+
+
+get_bounds.isMod(element)
+

A utility function to check if ‘element’ is mod function +:param element: expr_tree node +:return: bool

+
+ +
+
+class inequalities.Inequality
+

An enumeration.

+
+ +
+
+inequalities.eval_estimate(element, Y, predicted_Y, T)
+

Assumes that Y and predicted_y contain 0,1 binary classification +Suppose we are calculating for FP(A). +Assume X to be an indicator function defined only in case type=A +s.t. x_i = 1 if FP occurred for ith datapoint and x_i = 0 otherwise. +Our data samples can be assumed to be independent and identically distributed. +Our estimate of p, hat{p} = 1/n * sum(x_i). +We can safely count this as binomial random variable. +E[hat{p}] = 1/n * np = p +As we do not know p, we approximate it to hat{p}.

Parameters
    -
  • X – the feature value array to be plotted on X-axis

  • -
  • Y – the true label value array to be plotted on Y-axis

  • -
  • Y_predicted – the predicted label value array to be plotted on Y-axis

  • -
  • Y_baseline – the baseline label value array to be plotted on Y-axis

  • -
  • X_label – The label name to be used for X-axis

  • -
  • Y_label – The label name to be used for Y-axis

  • -
  • title – The title of the plot

  • +
  • element – expr_tree node

  • +
  • Y – pandas::Series

  • +
  • predicted_Y – tensor

  • +
  • T – pandas::Series

Returns
-

None

+

estimate value: float

-
+
@@ -327,10 +369,8 @@ @@ -340,7 +380,7 @@

- © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

diff --git a/docs/genindex.html b/docs/genindex.html index cfb6944..4d2eb30 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -1,5 +1,6 @@ + @@ -7,7 +8,7 @@ - Index — MSR Project 1.0 documentation + Index — Fair-Seldonian 1.0 documentation @@ -26,11 +27,11 @@ - - - - - + + + + + @@ -50,7 +51,7 @@ - MSR Project + Fair-Seldonian @@ -83,14 +84,10 @@

Contents:

@@ -106,7 +103,7 @@ @@ -159,142 +156,101 @@

Index

C - | D | E | G - | M - | P - | R - | S + | I + | L + | Q

C

-

D

+

E

-
- -

E

-

G

-
- -

M

-
-

P

+

I

-

R

+

L

-
    -
  • - regression_model - -
  • -
-

S

+

Q

@@ -312,7 +268,7 @@

S

- © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

diff --git a/docs/index.html b/docs/index.html index 0210179..8303943 100644 --- a/docs/index.html +++ b/docs/index.html @@ -7,7 +7,7 @@ - Welcome to MSR Project! — MSR Project 1.0 documentation + Welcome to FairSeldonian Project! — Fair-Seldonian 1.0 documentation @@ -26,18 +26,18 @@ - - - - - + + + + + - + @@ -51,7 +51,7 @@ - MSR Project + Fair-Seldonian @@ -84,14 +84,10 @@

Contents:

@@ -107,7 +103,7 @@ @@ -138,7 +134,7 @@
  • »
  • -
  • Welcome to MSR Project!
  • +
  • Welcome to FairSeldonian Project!
  • @@ -157,53 +153,41 @@
    -
    -

    Welcome to MSR Project!

    -

    The International Conference on Mining Software Repositories (MSR) has hosted the mining challenge every year, since 2006. -With this challenge, they aim at everyone interested to apply their tools to a common dataset. -The challenge is for researchers and practitioners to use their mining tools and approaches on a dare. -The goal is to answer questions about a given dataset, that were previously unanswered.

    -

    MSR 2020 is a challenge which presents Software Heritage Graph Dataset. -This is one of the largest known existing public archive of software source code and accompanying development history. -More information can be found in ‘Dataset’ tab.

    -

    Our goal here is to answer certain research questions (mentioned in ‘Problem Statement’ tab) and gain some meaninful insights from the dataset.

    +
    +

    Welcome to FairSeldonian Project!

    +

    With the growing usage of machine learning and artificial intelligence in real lives, +the need for incorporating the mitigation of unethical and unfair elements in machine +learning models is rising at an alarming rate. +This requires us to develop a tool to evaluate and mitigate unfairness in these models +that would help data scientists in dealing with such problems in the models they develop.

    +

    Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility +of regulating the undesirable behavior of machine learning algorithms and making them `fair’ +is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML +researcher). +This is implemented for fairness in machine learning for any generic constraint. +In addition, some extensions for optimizing the bounds and making it more efficient +are also present.

    +

    More details of Seldonian is present here.

    @@ -216,7 +200,7 @@

    Welcome to MSR Project! - +

    @@ -227,7 +211,7 @@

    Welcome to MSR Project!

    - © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

    diff --git a/sphinx/_build/html/problem.html b/docs/intro.html similarity index 67% rename from sphinx/_build/html/problem.html rename to docs/intro.html index 5c01934..69e23ae 100644 --- a/sphinx/_build/html/problem.html +++ b/docs/intro.html @@ -7,7 +7,7 @@ - Problem Statement — MSR Project 1.0 documentation + Introduction — Fair-Seldonian 1.0 documentation @@ -37,7 +37,7 @@ - + @@ -52,7 +52,7 @@ - MSR Project + Fair-Seldonian @@ -85,16 +85,10 @@

    Contents:

    @@ -110,7 +104,7 @@ @@ -141,13 +135,13 @@
  • »
  • -
  • Problem Statement
  • +
  • Introduction
  • - View page source + View page source
  • @@ -160,17 +154,21 @@
    -
    -

    Problem Statement

    -

    We are trying to answer the following research question -

    -

    What is the predicted lifespan of a repository, given the github dataset?

    -
    -

    Motivation

    -

    Big organizations spend a lot of resources and money for hosting their code repositories. -If they have a large amount unused repositories, in terms of volume, it will be a financial overhead on them. -So, using our solution, they can easily look for the unused repositories and remove them appropriately. -This will save them the resources which can be better utilised in something else.

    -
    +
    +

    Introduction

    +

    With the growing usage of machine learning and artificial intelligence in real lives, +the need for incorporating the mitigation of unethical and unfair elements in machine +learning models is rising at an alarming rate. +This requires us to develop a tool to evaluate and mitigate unfairness in these models +that would help data scientists in dealing with such problems in the models they develop.

    +

    Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility +of regulating the undesirable behavior of machine learning algorithms and making them `fair’ +is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML +researcher). +This is implemented for fairness in machine learning for any generic constraint. +In addition, some extensions for optimizing the bounds and making it more efficient +are also present.

    +

    More details of Seldonian is present here.

    @@ -181,7 +179,7 @@

    Motivation - + @@ -194,7 +192,7 @@

    Motivation

    - © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

    diff --git a/docs/objects.inv b/docs/objects.inv index db662188c51f271d29101fff323c7e1b9d1ca166..5a4b2d4310bbed1074e0363a0c4abeffb7b78015 100644 GIT binary patch delta 455 zcmV;&0XY7Z1g8X$C=NzpX>u)7Wo%?`ZfRj|kuyert&~Ae<1i3~@A(x*%eB?+l`Du9 z2Sl}%;22HpA@$;T(($;W{~o(;M7n~usrD8>zi<39o`o(4;+vu19MG22uCe7zV}=UBi0QB%#~iF*V(InjcR>W1UKOR zBcC#Vz1G$Xa@MC;8wzDgI|$(`7KnqYgky#Nid5e6;O(9`#7eZE;|#o`$) zAG_xd!(#jWJRbn%YYJk!^dmW%%28{RPp83uY18b4Ja~iojF{^Hr%hQ@LClRIt!ASH z>NfA(k&YdD*-d@wskh$X`wno>pv%ah7VIHER-d+g-I3!3ou&?zkh%xUOLT_31+NxN ze^JX-qJ`;@PQp+>J({>{LX}?Ri$MK$Hf$8;v9zE>TSu$kf&k0-gio~~zfr{cRxPtG xgr*w4-4U{vivLepQ~`nY>M&2yif>u=7tmt00Zy-dSai^e`+2{rJ^u delta 437 zcmV;m0ZRU-1eFAkC<{$fQXo)rZ)#;@bdfejf1Q&(Z^IxEhIju8s&=*2u34Lkr zIwAsRhZchaXQ}@8<-<;J8YO85OYq+F-W_n}M%S_--LPnl=oOG>!i|7xkF9!4Qu##6 zf=906N4)MeM`<*9$qXZ@fg=?R*^W85EBDM%O#;(`)_{B;LszQ?fOZ!AXE-<~^&N-0 ze_*P(0;c~}Z?r151Lgi~cAb()(K4wig2Sr`z*=Md^)NL!>24o%&Q}c}jndU3oia!* zxL)BMC33n7o2$7kG&UUEXzFu-x}E>rQJo94mVc}HJA;1`AvxX{Zvn)iSO~*OnMKKK zh%627ThPdYIX<)l%=uADmSx%$4QQzae|$$U4p2ZRHL-7DS)8yjUq*a@;-&?HhOD+5 zvbN^a|3wc)y5Y=|jvqa%unfe7`rS}Yfg>DVD0LF%*TNysJ4y+D?GdA(7ZdW#!B5;a)Re2$*fL~oA!A7c6) fW^|hQ5sjO|0keER?>uRV+ot%MHBkHjlkxYPHHXiO diff --git a/docs/py-modindex.html b/docs/py-modindex.html index 659d1cb..8e63d9f 100644 --- a/docs/py-modindex.html +++ b/docs/py-modindex.html @@ -7,7 +7,7 @@ - Python Module Index — MSR Project 1.0 documentation + Python Module Index — Fair-Seldonian 1.0 documentation @@ -26,11 +26,11 @@ - - - - - + + + + + @@ -39,7 +39,7 @@ - @@ -57,7 +57,7 @@ - MSR Project + Fair-Seldonian @@ -90,14 +90,10 @@

    Contents:

    @@ -113,7 +109,7 @@ @@ -164,10 +160,11 @@

    Python Module Index

    c | - d | e | - p | - r + g | + i | + l | + q
    @@ -177,44 +174,52 @@

    Python Module Index

    + + - + + + + - + - + - +
    - config + create_plots
     
    + e
    - connect_database + equation_parser
     
    - d
    + g
    - data_pre_processing + gather_results +
    + get_bounds
     
    - e
    + i
    - error_analysis + inequalities
     
    - p
    + l
    - plot_results + logistic_regression_functions
     
    - r
    + q
    - regression_model + qsa
    @@ -230,7 +235,7 @@

    Python Module Index

    - © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

    diff --git a/sphinx/_build/html/pre_req.html b/docs/quickstart.html similarity index 51% rename from sphinx/_build/html/pre_req.html rename to docs/quickstart.html index f5b6091..a0c4dfe 100644 --- a/sphinx/_build/html/pre_req.html +++ b/docs/quickstart.html @@ -7,7 +7,7 @@ - Quick Start — MSR Project 1.0 documentation + Getting Started — Fair-Seldonian 1.0 documentation @@ -37,8 +37,8 @@ - - + + @@ -52,7 +52,7 @@ - MSR Project + Fair-Seldonian @@ -85,18 +85,16 @@

    Contents:

    -

    - © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

    diff --git a/sphinx/_build/html/search.html b/sphinx/_build/html/search.html index 4a26041..7a898f6 100644 --- a/sphinx/_build/html/search.html +++ b/sphinx/_build/html/search.html @@ -7,7 +7,7 @@ - Search — MSR Project 1.0 documentation + Search — Fair-Seldonian 1.0 documentation @@ -52,7 +52,7 @@ - MSR Project + Fair-Seldonian @@ -85,13 +85,10 @@

    Contents:

    @@ -107,7 +104,7 @@ @@ -179,7 +176,7 @@

    - © Copyright 2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty + © Copyright 2020, Parul Gupta

    diff --git a/sphinx/_build/html/searchindex.js b/sphinx/_build/html/searchindex.js index 65257ef..cfb5539 100644 --- a/sphinx/_build/html/searchindex.js +++ b/sphinx/_build/html/searchindex.js @@ -1 +1 @@ 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    + + + +
    + + + + + +
    + +
    + + + + + + + + + + + + + + + + + +
    + + + + +
    +
    +
    +
    + +
    +

    Variants

    +

    The codebase allows you to choose the variant for tuning and experimenting with the framework.

    +
    +

    Basic Seldonian

    +

    To begin with, we implemented vanilla Seldonian algorithm to classify the datapoint into 2 groups with difference in true positives as the fairness constraint.

    +

    To use this mode, you need to add CLI parameter base as:

    +
    python main.py base
    +
    +
    +
    +
    +

    Improvements to confidence interval

    +

    In the candidate selection process, we used Hoeffding inequality confidence interval as follows-

    +
    +\[estimate \pm 2 \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}}\]
    +

    Instead, this interval can be improved by using a separate values for - a.) error in candidate estimate and b.) confidence interval in safety set as follows-

    +
    +\[estimate \pm \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}} + \sqrt{\frac{ln(1/\delta)}{2 |D_{candidate}|}}\]
    +

    This will specifically be helpful in cases where the difference between the sizes of the 2 data splits is huge.

    +

    To use this mode, you need to add CLI parameter mod as:

    +
    python main.py mod
    +
    +
    +
    +
    +

    Improvement in bound propagation around constant values

    +

    As constants have fixed value, there is no need to wrap a confidence interval around them. Thus, the +\(\delta\) value can directly go to other variable child and need not be split equally into half in case of +binary operator when the other child is a constant. The figures below show naive and improved implementation +of bound propagation in case of constant value of a node of the same tree respectively.

    +_images/const.png +

    To use this mode, you need to add CLI parameter const as:

    +
    python main.py const
    +
    +
    +
    +
    +

    Improvement in bound propagation from union bound

    +

    A user may defined the fairness constraint in such a way that a particular element appears multiple times +in the same tree. Instead of treating all those entities as independent elements, we can combine all the +elements together union bound and then use the final value of \(\delta\). This will theoretically improve the +bound and give us better accuracy and more valid solutions.

    +

    Example: Suppose we have A appearing 3 times with \(\delta/2\), \(\delta/4\) and \(\delta/8\). We can simply take the

    +
    +\[\delta_{sum} = 7\delta/8\]
    +

    and find the confidence interval using that \(\delta\). The figures below show the naive and improved implement using this functionality respectively.

    +_images/bound-no.png +_images/bound-yes.png +
    +
    +

    Optimization with Lagrangian/KKT

    +

    To use Lagrangian/KKT technique to optimise the objective function to get candidate solution, several additional modification are done:

    +
      +
    • Objective function: The implementation to find the candidate solution and setting the value of the objective function (which is minimized) is changed to the following-

    • +
    +
    +\[-fhat + (\mu * upperbound)\]
    +
      +
    • Value of \(\mu\) : We calculate the value of \(\mu\) as

    • +
    +
    +\[-\nabla f( \theta^{*})/ \nabla g_{i}( \theta^{*})\]
    +

    which must be positive to support the inequality of the fairness constraint and thus, in case the value is negative, then, we hard-code it to some positive value (say, 1).

    +
      +
    • Change prediction to continuous function: Classification is essentially a step function (0/1 in case of binary classifier as in this case). Thus, instead of getting a label, we change the function to give the probability of getting a label instead of exact label value. This helps us find the derivative of the function easily. This change must be made by the user when he/she changes the predict function for their use-case.

    • +
    • 2-player approach to solve KKT: One of the ways to solve KKT optimization problem is to use a 2-player approach where we fix a value of \(\mu\) and then optimize the function w.r.t. \(\theta\) and then , we fix \(\theta\) and optimize the function w.r.t. \(\mu\). This goes on until we converge to some value or exceed a specific number of iterations. Instead of doing a 2-player approach, to fasten the optimization process, we did one run of this by using a single value of \(\mu\), fetched from derivative of log-loss divided by derivative of fairness constraint with the initial \(\theta\) values and optimizing the Lagrangian value using Powell optimizer.

    • +
    +
    +
    + + +
    + +
    + + +
    +
    + +
    + +
    + + + + + + + + + + + \ No newline at end of file diff --git a/sphinx/code.rst b/sphinx/code.rst index 479eb38..7cffe7a 100644 --- a/sphinx/code.rst +++ b/sphinx/code.rst @@ -1,22 +1,25 @@ Code Documentation ================== -.. automodule:: config +.. automodule:: qsa :members: -.. automodule:: connect_database +.. automodule:: logistic_regression_functions :members: -.. automodule:: data_pre_processing +.. automodule:: equation_parser :members: -.. automodule:: regression_model +.. automodule:: get_bounds :members: -.. automodule:: error_analysis +.. automodule:: inequalities :members: -.. automodule:: plot_results +.. automodule:: gather_results + :members: + +.. automodule:: create_plots :members: diff --git a/sphinx/conf.py b/sphinx/conf.py index 0e8e841..f3a4a0b 100644 --- a/sphinx/conf.py +++ b/sphinx/conf.py @@ -17,9 +17,9 @@ # -- Project information ----------------------------------------------------- -project = 'MSR Project' -copyright = '2020, Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty' -author = 'Parul Gupta, Anjali Ramaprasad, Animesh Sahay, Pradhiksha Ashok Kumar, Soumya Polisetty' +project = 'Fair-Seldonian' +copyright = '2020, Parul Gupta' +author = 'Parul Gupta' # The full version, including alpha/beta/rc tags release = '1.0' diff --git a/sphinx/dataset.rst b/sphinx/dataset.rst deleted file mode 100644 index b3cd19e..0000000 --- a/sphinx/dataset.rst +++ /dev/null @@ -1,208 +0,0 @@ -Dataset -======= -The dataset is provided as part of the MSR 2020 challenge. -The full version spans more than 5 billion unique source code files and 1 billion unique commits, -coming from more than 80 million software projects. Contents come from GitHub and GitLab, FOSS distributions -like Debian, and language-specific package managers like PyPI. -The data is stored in a uniform representation: A Merkle DAG linking source code -files in directories, evolution of commits and snapshots of -the VCS. - -Complete Dataset ----------------- -The Software Heritage Graph Dataset contains a table representation of the full Software Heritage Graph. It is available in the following formats: - -#. PostgreSQL (compressed): - - * Total size: 1.2 TiB - - * URL: :ref:`/graph/latest/sql/` - -#. Apache Parquet: - - * Total size: 1.2 TiB - - * URL: :ref:`/graph/latest/parquet/` - - * S3: :ref:`s3://softwareheritage/graph` - -Teaser Dataset --------------- -The above dataset is too big, so following 'teaser' dataset is mainly used for training and testing purposes in our implementation. - -popular-4k ----------- -The popular-4k teaser contains a subset of 4000 popular repositories from GitHub, Gitlab, PyPI and Debian. The selection criteria to pick the software origins was the following: -* The 1000 most popular GitHub projects (by number of stars) -* The 1000 most popular Gitlab projects (by number of stars) -* The 1000 most popular PyPI projects (by usage statistics, according to the Top PyPI Packages database), -* The 1000 most popular Debian packages (by “votes” according to the Debian Popularity Contest database) - -The teaser has the following configurations: -* PostgreSQL (compressed): - - * Total size: 23 GiB - - * URL: /graph/latest/popular-4k/sql/ - -popular-3k ----------- -The popular-3k-python teaser contains a subset of 3052 popular repositories tagged as being written in the Python language, from GitHub, Gitlab, PyPI and Debian. The selection criteria to pick the software origins was the following, similar to popular-4k: -* the 1000 most popular GitHub projects written in Python (by number of stars), -* the 131 Gitlab projects written in Python that have 2 stars or more, -* the 1000 most popular PyPI projects (by usage statistics, according to the Top PyPI Packages database), -* the 1000 most popular Debian packages with the debtag implemented-in::python (by “votes” according to the Debian Popularity Contest database). - -The teaser has the following configurations: -* PostgreSQL (compressed): - - * Total size: 4.7 GiB - - * URL: /graph/latest/popular-3k-python/sql/ - -Relational Schema ------------------ -The Merkle DAG of the Software Heritage archive is encoded in the dataset as a set of relational tables. A simplified view of the corresponding database schema is shown here: - -.. image:: images/8.png - -The following are details of the schema: - -* content: contains information on the contents stored in the archive. - - * sha1 (bytes): the SHA-1 of the content - - * sha1_git (bytes): the Git SHA-1 of the content - - * length (integer): the length of the content - -* skipped_content: contains information on the contents that were not archived for various reasons. - - * sha1 (bytes): the SHA-1 of the missing content - - * sha1_git (bytes): the Git SHA-1 of the missing content - - * length (integer): the length of the missing content - -* directory: contains the directories stored in the archive. - - * id (bytes): the intrinsic identifier of the directory, recursively computed with the Git SHA-1 algorithm - - * dir_entries (array of integers): the list of directories contained in this directory, as references to an entry in the directory_entry_dir table. - - * file_entries (array of integers): the list of files contained in this directory, as references to an entry in the directory_entry_file table. - - * rev_entries (array of integers): the list of revisions contained in this directory, as references to an entry in the directory_entry_rev table. - -* directory_entry_file: contains information about file entries in directories. - - * id (integer): unique identifier for the entry - - * target (bytes): the Git SHA-1 of the content this entry points to - - * name (bytes): the name of the file (basename of its path) - - * perms (integer): the permissions of the file - -* directory_entry_dir: contains information about directory entries in directories. - - * id (integer): unique identifier for the entry - - * target (bytes): the Git SHA-1 of the directory this entry points to - - * name (bytes): the name of the directory - - * perms (integer): the permissions of the directory - -* directory_entry_rev: contains information about revision entries in directories. - - * id (integer): unique identifier for the entry - - * target (bytes): the Git SHA-1 of the revision this entry points to - - * name (bytes): the name of the directory that contains this revision - - * perms (integer): the permissions of the revision - -* person: deduplicates commit authors by their names and e-mail addresses. For pseudonymization purposes and in order to prevent abuse, these columns were removed from the dataset, and this table only contains the ID of the author. Individual authors may be retrieved using this ID from the Software Heritage api. - - * id (integer): the identifier of the person - -* revision: contains the revisions stored in the archive. - - * id (bytes): the intrinsic identifier of the revision, recursively computed with the Git SHA-1 algorithm. For Git repositories, this corresponds to the revision hash. - - * date (timestamp): the date the revision was authored - - * committer_date (timestamp): the date the revision was committed - - * author (integer): the author of the revision - - * committer (integer): the committer of the revision - - * message (bytes): the revision message - - * directory (bytes): the Git SHA-1 of the directory the revision points to. Every revision points to the root directory of the project source tree to which it corresponds. - -* revision_history: contains the ordered set of parents of each revision. Each revision has an ordered set of parents (0 for the initial commit of a repository, 1 for a regular commit, 2 for a regular merge commit and 3 or more for octopus-style merge commits). - - * id (bytes): the Git SHA-1 identifier of the revision - - * parent_id (bytes): the Git SHA-1 identifier of the parent - - * parent_rank (integer): the rank of the parent which defines the total order of the parents of the revision - -* release: contains the releases stored in the archive. - - * id (bytes): the intrinsic identifier of the release, recursively computed with the Git SHA-1 algorithm. - - * target (bytes): the Git SHA-1 of the object the release points to. - - * date (timestamp): the date the release was created - - * author (integer): the author of the revision - - * name (bytes): the release name - - * message (bytes): the release message - -* snapshot: contains the list of snapshots stored in the archive. - - * id (bytes): the intrinsic identifier of the snapshot, recursively computed with the Git SHA-1 algorithm. - - * object_id (integer): the primary key of the snapshot - -* snapshot_branches: contains the identifiers of branches associated with each snapshot. This is an intermediary table through which is represented the many-to-many relationship between snapshots and branches. - - * snapshot_id (integer): the integer identifier of the snapshot - - * branch_id (integer): the identifier of the branch - - * snapshot_branch: contains the list of branches. - - * object_id (integer): the identifier of the branch - - * name (bytes): the name of the branch - - * target (bytes): the Git SHA-1 of the object the branch points to - - * target_type (string): the type of object the branch points to (either release, revision, directory or content). - -* origin: the software origins from which the projects in the dataset were archived. - - * id (integer): the identifier of the origin - - * url (bytes): the URL of the origin - - * type (string): the type of origin (e.g git, pypi, hg, svn, git, ftp, deb, …) - -* origin_visit: the different visits of each origin. 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zkpankIg>zWo57YA$p02oG0hegsC?YVck;0^bpFtWHql-=t7J3@+}qcl=Fph7@$HB} zmu1)*ba~U9B`mj6q?k{RdJ^NfGnU!x%ry_09G8kb3XoIR-!&&q;mX=45Iji_Hi5cr z?x6ATNPP5N&2=DWJ65rt*NU9KVOE+TzoDu(6|H!) zlY~ab{hFp=ZtrQdXGtd-bH>Tn1`*&UK~ICZY;&3Z*p~E;$}@sD7psfkRj08K`b&OW z;!>}X;DwWB=s@*2L!8`03sWZ8*$`~?NU0iX&edDEN@UBBU#m#cfnU}2G}C`|K-h(B z=Vr{OXxkV*o_2Ik;IHFt^R(fVKdjX@V#9SlReonwa;xVfwG%aBG;~>aNQ)eMwWt>8 zQt!$QY0|C#TCv0)Sbd~uq2Y5nhfAtRmR5w>mSH+w60 z>Z0X-ESTTM-a7HIMe1GLR*U)dF>^B{GLY0V{r{Vtt2Fb{>Hazh&jpI)f0!YjESSPW lp>H?TOa31h!&(IC8MWsK-`_. .. toctree:: :maxdepth: 2 :caption: Contents: - problem - dataset - pre_req - model + intro + quickstart + variants code - results - references diff --git a/sphinx/intro.rst b/sphinx/intro.rst new file mode 100644 index 0000000..750dc43 --- /dev/null +++ b/sphinx/intro.rst @@ -0,0 +1,18 @@ +Introduction +============ + +With the growing usage of machine learning and artificial intelligence in real lives, +the need for incorporating the mitigation of unethical and unfair elements in machine +learning models is rising at an alarming rate. +This requires us to develop a tool to evaluate and mitigate unfairness in these models +that would help data scientists in dealing with such problems in the models they develop. + +Fair-Seldonian leverages Seldonian algorithm to tackles this problem where the responsibility +of regulating the undesirable behavior of machine learning algorithms and making them `fair' +is transferred from user to designer of the algorithm (i.e. at the creation step itself by ML +researcher). +This is implemented for fairness in machine learning for any generic constraint. +In addition, some extensions for optimizing the bounds and making it more efficient +are also present. + +More details of Seldonian is present `here `_. \ No newline at end of file diff --git a/sphinx/model.rst b/sphinx/model.rst deleted file mode 100644 index bd82bdf..0000000 --- a/sphinx/model.rst +++ /dev/null @@ -1,35 +0,0 @@ -Model -===== -High Level Design ------------------ -.. image:: images/10.PNG -As for a high level idea, we start off by preprocessing our data, which includes sampling, labelling, cleaning and splitting it into train and test data. We the use a linear regression approach to model our dataset and get the output. This is further evaluated by metrics such as Mean Absolute Error as explained below. - -Linear Regression ------------------ -Linear regression is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression. - -.. image:: images/9.png - -Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable). Linear regression consists of finding the best-fitting straight line through the points. The best-fitting line is called a regression line. - -Evaluation ----------- -Since we are attempting to perform regression which outputs continuous value within a given range. Our main goal is to minimize the error which is defined by the Loss Function. - -We have chosen average lifespan as our baseline. We compare our other features with this baseline by plotting graphs and using Mean Absolute Error (MAE) as a metric to evaluate the results of the prediction. MAE is the sum of absolute distances between our target variable and predicted values. - -Mathematically, it can be expressed as follows: - -.. math:: - - \frac{ \sum_{i=1}^{N}|y_i - x_i| }{N} - -where -:math:`y_i` -is the predicted value from the regression model, -:math:`x_i` -is the true value of the datapoint and -N is the number of datapoints. - - diff --git a/sphinx/pre_req.rst b/sphinx/pre_req.rst deleted file mode 100644 index b2ea735..0000000 --- a/sphinx/pre_req.rst +++ /dev/null @@ -1,56 +0,0 @@ -Quick Start -=========== - -This page is intended to be a user guide for the developers to quickly -be able to use/re-use the codebase for their own project -or, extending/enhancing the codebase for better performing models. - -Pre-requisities ---------------- - -Knowledge pre-requisites - -* Python: The code is written in Python and thus, some prior knowledge of Python programming is useful. - -* PostGres: The database format of MSR2020 is postgres and hence, familiarity of postgresql is essential for getting started with this project. - -Library pre-requisites - -* psycopg2 - postgres and python connection adapter library to access the database. - -* sklearn - machine learning model implementations like linear regression. - -* matplotlib - visualizations of the plots. - -* sphinx and sphinx-rtd-theme - Python auto-documentation generator. - -* PyCharm - or, any other IDE to edit the code. - -Getting Started ---------------- - -In the main repository, there exists a python file called `main.py`. - -Create a `database.ini` file and store your config there. The config must include the following: - -.. code-block:: - - [postgresql] - host= - port= - database= - user= - password= - - -Go to terminal or any IDE and run that file using the following command: - -.. code-block:: - - python main.py - - -Reference ---------- - -In case you use this code, please cite our repository in your work. diff --git a/sphinx/problem.rst b/sphinx/problem.rst deleted file mode 100644 index 4cd624e..0000000 --- a/sphinx/problem.rst +++ /dev/null @@ -1,13 +0,0 @@ -Problem Statement -================= -We are trying to answer the following research question - - -What is the predicted lifespan of a repository, given the github dataset? - -Motivation ----------- -Big organizations spend a lot of resources and money for hosting their code repositories. -If they have a large amount unused repositories, in terms of volume, it will be a financial overhead on them. -So, using our solution, they can easily look for the unused repositories and remove them appropriately. -This will save them the resources which can be better utilised in something else. - diff --git a/sphinx/queries.rst b/sphinx/queries.rst deleted file mode 100644 index 1be6ab2..0000000 --- a/sphinx/queries.rst +++ /dev/null @@ -1,128 +0,0 @@ -Queries -======= -The following queries were used for: - - -Create final informative table ------------------------------- - -.. highlight:: none - -Query:: - -CREATE TABLE final_table3 -AS -SELECT - o.id as id, - count(snap.object_id) as snapshot_count, - count(snap.target) as total_targets, - ov.date as original_date, - count(snap.revisions) as num_revision, - max(snap.latest_revision) as last_revision, - count(snap.num_releases) as num_releases, -FROM - origin o - JOIN - origin_visit ov - ON - o.is = ov.origin - JOIN - ( - SELECT - snapshot.object_id as object_id, - count(snaptarget.revisions) as num_revision, - count(snaptarget.num_releases) as num_releases, - max(snaptarget.latest_revision) as latest_revision, - count(snaptarget.target) as total_targets - FROM - snapshot s - JOIN - snapshot_branches snapbranch - ON - s.object_id = snapbranch.snapshot_id - JOIN - ( SELECT - sbranch.object_id as object_id, - count(rev.revisions) as num_revision, - count(rev.num_releases) as num_releases, - max(rev.latest_revision) as latest_revision, - count(sbranch.target) as total_targets - FROM - snapshot_branch sbranch - JOIN - ( - SELECT - count(id) as num_releases, - r.id as revisions, - max(r.date) as latest_revision - FROM - release rel - JOIN - revision r - ON - rel.target = r.id - GROUP BY - r.id - ) AS rev - ON - sbranch.target = rev.id - GROUP BY - sbranch.object_id - ) AS snaptarget - ON - snaptarget.object_id = snapbranch.branch_id - GROUP BY - snapshot.object_id - ) AS snap - ON - ov.snapshot_id = snap.object_id -GROUP BY - o.id; - - -Fetch the column name in the final table ----------------------------------------- - -.. highlight:: none - -Query:: - -SELECT - column_name -FROM - INFORMATION_SCHEMA.columns -WHERE - table_name = 'final_table3' - - -Fetching the features for datapoints ------------------------------------- - -.. highlight:: none - -Query:: - -SELECT - snapshot_count, - total_targets, - original_date, - num_revision, - last_revision, - num_releases -FROM - final_table3 - - -Fetching the labels (lifespan) for the datapoints -------------------------------------------------- - -.. highlight:: none - -Query:: - -SELECT - lifespan -FROM - final_table3 - - diff --git a/sphinx/quickstart.rst b/sphinx/quickstart.rst new file mode 100644 index 0000000..21b3a0f --- /dev/null +++ b/sphinx/quickstart.rst @@ -0,0 +1,47 @@ +Getting Started +=============== + +This page is a user guide for the developers to quickly +be able to use FairSeldonian Python library in their own project +or, extending/enhancing the codebase to enhance the framework. + +Pre-requisites +--------------- +Library pre-requisites + +* sklearn - machine learning model implementations like logistic regression. + +* matplotlib - visualizations of the plots. + +* numpy - handling data. + +* pandas - handling data in the form of dataframes. + +* ray - for parallelisation of the algorithm. + +* torch - for tensors used in the codebase. + +Quick start +----------- +The complete code resides in `code` folder. +To run the experiment, you need to amend configurations in `main.py`. + + +Go to terminal or any IDE and run that file using the following command: + +.. code-block:: + + python main.py + +The default mode is set as `base`. To understand other modes present in the code, refer to the `Variants` section of this documentation. + +Configuration +------------- +The user must setup the following things to make full use of this framework: + +- **Configuration of the experiment structure** + + +Collaboration +------------- +Please feel free to contribute to this code base. diff --git a/sphinx/references.rst b/sphinx/references.rst deleted file mode 100644 index 8d2a6b9..0000000 --- a/sphinx/references.rst +++ /dev/null @@ -1,14 +0,0 @@ -References -========== - -The following urls were used for this project - -MSR Project : https://2020.msrconf.org/ - -Software Heritage Dataset : https://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html# - -Research Papers : http://www1.chapman.edu/~linstead/aliMSR2020.pdf - -Sphinx Documentation : https://www.sphinx-doc.org/en/master/ - -Scikit-Learn Documentation : https://scikit-learn.org/0.21/documentation.html diff --git a/sphinx/variants.rst b/sphinx/variants.rst new file mode 100644 index 0000000..62ebf6b --- /dev/null +++ b/sphinx/variants.rst @@ -0,0 +1,91 @@ +Variants +======== + +The codebase allows you to choose the variant for tuning and experimenting with the framework. + +Basic Seldonian +--------------- +To begin with, we implemented vanilla Seldonian algorithm to classify the datapoint into 2 groups with difference in true positives as the fairness constraint. + +To use this mode, you need to add CLI parameter `base` as: + +.. code-block:: + + python main.py base + +Improvements to confidence interval +----------------------------------- +In the candidate selection process, we used Hoeffding inequality confidence interval as follows- + +.. math:: + + estimate \pm 2 \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}} + +Instead, this interval can be improved by using a separate values for - a.) error in candidate estimate and b.) confidence interval in safety set as follows- + +.. math:: + + estimate \pm \sqrt{\frac{ln(1/\delta)}{2 |D_{safety}|}} + \sqrt{\frac{ln(1/\delta)}{2 |D_{candidate}|}} + +This will specifically be helpful in cases where the difference between the sizes of the 2 data splits is huge. + +To use this mode, you need to add CLI parameter `mod` as: + +.. code-block:: + + python main.py mod + +Improvement in bound propagation around constant values +------------------------------------------------------- +As constants have fixed value, there is no need to wrap a confidence interval around them. Thus, the +:math:`\delta` value can directly go to other variable child and need not be split equally into half in case of +binary operator when the other child is a constant. The figures below show naive and improved implementation +of bound propagation in case of constant value of a node of the same tree respectively. + +.. image:: images/const.png + +To use this mode, you need to add CLI parameter `const` as: + +.. code-block:: + + python main.py const + +Improvement in bound propagation from union bound +------------------------------------------------- +A user may defined the fairness constraint in such a way that a particular element appears multiple times +in the same tree. Instead of treating all those entities as independent elements, we can combine all the +elements together union bound and then use the final value of :math:`\delta`. This will theoretically improve the +bound and give us better accuracy and more valid solutions. + +Example: Suppose we have A appearing 3 times with :math:`\delta/2`, :math:`\delta/4` and :math:`\delta/8`. We can simply take the + +.. math:: + \delta_{sum} = 7\delta/8 + +and find the confidence interval using that :math:`\delta`. The figures below show the naive and improved implement using this functionality respectively. + +.. image:: images/bound-no.png + +.. image:: images/bound-yes.png + +Optimization with Lagrangian/KKT +-------------------------------- +To use Lagrangian/KKT technique to optimise the objective function to get candidate solution, several additional modification are done: + +- Objective function: The implementation to find the candidate solution and setting the value of the objective function (which is minimized) is changed to the following- + +.. math:: + -fhat + (\mu * upperbound) + +- Value of :math:`\mu` : We calculate the value of :math:`\mu` as + +.. math:: + -\nabla f( \theta^{*})/ \nabla g_{i}( \theta^{*}) + +which must be positive to support the inequality of the fairness constraint and thus, in case the value is negative, then, we hard-code it to some positive value (say, 1). + +- Change prediction to continuous function: Classification is essentially a step function (0/1 in case of binary classifier as in this case). Thus, instead of getting a label, we change the function to give the probability of getting a label instead of exact label value. This helps us find the derivative of the function easily. This change must be made by the user when he/she changes the predict function for their use-case. + +- 2-player approach to solve KKT: One of the ways to solve KKT optimization problem is to use a 2-player approach where we fix a value of :math:`\mu` and then optimize the function w.r.t. :math:`\theta` and then , we fix :math:`\theta` and optimize the function w.r.t. :math:`\mu`. This goes on until we converge to some value or exceed a specific number of iterations. Instead of doing a 2-player approach, to fasten the optimization process, we did one run of this by using a single value of :math:`\mu`, fetched from derivative of log-loss divided by derivative of fairness constraint with the initial :math:`\theta` values and optimizing the Lagrangian value using Powell optimizer. + +

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