From dcb7209ca9595acc014b7952d68642db63c81ece Mon Sep 17 00:00:00 2001 From: greyhypotheses <10231132+greyhypotheses@users.noreply.github.com> Date: Mon, 22 Apr 2024 19:16:15 +0100 Subject: [PATCH 1/3] Update .gitignore --- .gitignore | 1 - 1 file changed, 1 deletion(-) diff --git a/.gitignore b/.gitignore index 3aa199f..db6ffcb 100644 --- a/.gitignore +++ b/.gitignore @@ -8,7 +8,6 @@ __pycache__/ # Distribution / packaging .Python -build/ develop-eggs/ dist/ downloads/ From 42440443ff9657355df143e7eea0db66f4b30283 Mon Sep 17 00:00:00 2001 From: greyhypotheses <10231132+greyhypotheses@users.noreply.github.com> Date: Mon, 22 Apr 2024 19:16:28 +0100 Subject: [PATCH 2/3] Update docs/source/index.rst --- docs/source/index.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 89df2ac..a393da8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -20,10 +20,12 @@ Projects & Machine Learning .. container:: 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zlLtRSS@H9sr7TUs*Xxa+3JTJ~(l1wT)34X{X$AyXxUgjEL&X?rDYr(x__%WNUC)lu z$yru6PvsV8xVnv~9tzZlqK4kjpBJV8wrv`L{`jA9{^w!>83{}K;l~dNMc&u>A*H;z zcse-o;V2M?`)L|pK$)V&+)q}x4#lZOvqHO%JSTAPQ}b~puuJ^D0Y~Y*sV=ds!^_P|TuUYCX7BIC<5c(m3iACwe-g8Dub$zQj86KOu=nrz^3S!L z9WY!t>3sibAo}P1e_zNs0UriwQvCe<i=^uF>+6RA + +# Project Constraints + +An inexhaustive set of constraints. + +
+ +## Solution Constraints + +These are "… constraints on the way that the problem must be solved." (4a., [Volere Template](https://homepages.laas.fr/kader/Robertson.pdf)) + +
+ +## Time Constraints + +What is the client team's project time limit? Beware, time and budget are critical to determining whether a feasible solution exists. + +
+ +## Implementation Environment Constraints + +Is there a cloud platform, or on-premises, infrastructure restriction? + +
+ +## Budget Constraint + +During the viability/feasibility assessment compare the stated budget with the estimated cost. Remember, the cost is determined by all elements that have a cost; both project delivery costs and lifecycle costs are critical. + +
+
+
+
+ +
+
+
+
diff --git a/docs/build/html/_sources/data/collection.md.txt b/docs/build/html/_sources/data/collection.md.txt new file mode 100644 index 0000000..cf827ed --- /dev/null +++ b/docs/build/html/_sources/data/collection.md.txt @@ -0,0 +1,28 @@ +
+ +# Data Collection + +
+ +What was the data acquisition mechanism? Per data instance, was data acquisition via + +

+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/data/composition.md.txt b/docs/build/html/_sources/data/composition.md.txt new file mode 100644 index 0000000..4b7f11e --- /dev/null +++ b/docs/build/html/_sources/data/composition.md.txt @@ -0,0 +1,208 @@ +
+ +# Composition + +In general, the questions herein should be studied before data collection. The datasheets paper [1] notes that most of the questions: + +
+ "… are intended to provide dataset consumers with the information they need to make informed decisions about using the dataset for their chosen tasks. … the questions are designed to elicit information about compliance with the EU's General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions." +
+ +
+ +
References +
    +
  1. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    2. +
+
+ +
+
+ +## What does each data set instance represent? + +Describe what each instance, i.e., row, of the data set represents. Although peculiar, if the data set is multi-representational, e.g., an instance in a merchant's data base table might represent one of: (a) an online reading event, (b) an online product purchasing event, or \(c\) an online download event. Each representation must be described. + +
+
+ +## The # of Instances + +How many instances does the data set have? + +
+
+ +## Pre-processed? + +Are any aspects of the data set pre-processed? If yes: + +
    +
  • Document the pre-processing steps.
    • +
  • State whether the underlying raw data is available, and provide a link to the data.
    • +
  • If available, provide a link to the pre-processing programs.
    • +
+ +
+
+ +## Is the data set a sample of a larger data set? + +**If yes:** + +
    +
  • If the data set is representative of the larger data set: How was representativeness verified/validated?
    • +
  • If the data set is not representative of the larger data set, e.g., is a geographically focused subset, explain why.
    • +
+ +
+
+ +## Lineage + +Summarise the data set's lineage, including linkage options. [1, 2] + +
+ +
References +
    +
  1. QLIK: What is data lineage?
    2. +
  2. IBM: What is data lineage?
    3. +
+
+ +
+
+ +## Licences & Fees + +If applicable, summarise the data's costs. + +
+
+ +## Profiles of Instances + +Herein, the focus is a summary of the instances of a data set, e.g., for a tabular data set: + +**By Field** + +
    +
  • The field name.
    • +
  • Description: What does the element of an instance denote/represent?
    • +
  • Data type.
    • +
  • Dictionary of a categorical data type.
    • +
  • Unit of measure.
    • +
  • Is this a raw data field or a feature?
    • +
  • Is this a target field?
    • +
  • Does the field identify a sub-population?
    • +
  • Column Profile: Note column profiling "… provides statistical information regarding the distribution of data values and associated patterns that are assigned to each data attribute, …". [1]     If a field/column has missing elements, explain why.
    • +
  • A graph of the field's data distribution.
    • +
+ +
+ +**Across Fields** + +
    +
  • Cross-Column Profiles: Relationships between columns.
    • +
+ +
+
+ +
References +
    +
  1. 5.5.2 Profiling for Data Quality Assessment, in Master Data Management, Page 96, The MK/OMG Press, 2008
    2. +
  2. Data Profiling
    3. +
+
+ +
+
+ + +## Errors + +Please detail any errors, sources of noise, or redundancies. + + +
+
+ + +## Recommended Data Splits for Machine Learning + +Are there recommended data splits? + +
+
+ +## Confidentiality + +Does the data set contain data that might be considered confidential? For example, + +
    +
  • Is the data protected by legal privilege or by doctor–patient confidentiality?
    • +
  • Does the data include the content of private/non-public communications of individuals.
    • +
+ +
+
+ +## Identification of Individuals + +Is it possible to identify individuals directly or indirectly? + +
+
+ +## Data Sensitivity + +Does the data set include sensitive data elements? Describe. Examples of sensitive data elements are +elements that directly/indirectly reveal: + +
    +
  • Locations.
    • +
  • Financial details.
    • +
  • Health details.
    • +
  • Biometric profiles.
    • +
  • Genetic profiles.
    • +
  • Government identification codes of individuals.
    • +
  • Criminal history.
    • +
  • Institutionally and/or commercially sensitive data.
    • +
  • Race or ethnic origin.
    • +
  • Sexual orientations.
    • +
  • Religious beliefs.
    • +
  • Political opinions.
    • +
  • Trade union memberships.
    • +
  • And more.
    • +
+ +
+
+ +## Distressing Data Elements + +Does ``… the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?'' [1, 2] + +
+ +
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/data/controls.md.txt b/docs/build/html/_sources/data/controls.md.txt new file mode 100644 index 0000000..da4c4fe --- /dev/null +++ b/docs/build/html/_sources/data/controls.md.txt @@ -0,0 +1,29 @@ +
+ +# Controls + +
+ +Do "… any export controls or other regulatory restrictions apply to the dataset or to individual instances?" [1, 2]   +Please, study **Section 3.6 of** [2] for more details. + +
+ +
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/data/data.md.txt b/docs/build/html/_sources/data/data.md.txt new file mode 100644 index 0000000..91eea7b --- /dev/null +++ b/docs/build/html/_sources/data/data.md.txt @@ -0,0 +1,49 @@ +
+ +# Data & Datasheets + +
+ +The datasheet of a dataset outlines the data set's provenance, lineage, and a bit more. Each dataset must have its own datasheet. Gebru & colleagues first proposed Datasheets for Datasets, for machine learning products or projects, during the years 2018/2019. Following feedback from a variety of institutions, industries, agencies, etc., a baseline Datasheets for Datasets outline was released.[^acm], [^arXiv] Each datasheet consists of a set of questions, and the datasheet's primary objective vis-à-vis data set creator is to: + +> … encourage data set creators to reflect carefully upon (a) the "process of creating, distributing, and maintaining a dataset", and (b) "any underlying assumptions, potential risks or harms, and implications of use“ + +The primary objective vis-à-vis data set user is to: + +> … ensure that the data set user has the information required to "… make informed decisions about using a dataset." + +This chapter consists of a set of sections. The sections, except the Natural Language Processing section, reflect the groupings of the questions in the latest datasheet version. [^acm], [^arXiv], [^applicability] The questions of the natural language processing (NLP) section apply to NLP projects only. The questions were developed by Bender & Friedman. [^bender] + +
+
+ +```{toctree} +:caption: Content +:glob: + +motivation +composition +collection +controls +maintenance +nlp +``` + +
+
+ +
+
+ +
+
+ +
+
+ +[^acm]: [Datasheets for Datasets](https://dl.acm.org/doi/10.1145/3458723), Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92 +[^arXiv]: [Datasheets for Datasets](https://arxiv.org/abs/1803.09010v8), arXiv:1803.09010v8, 2021, updated datasheet appendix +[^applicability]: If a question is inapplicable, note down its inapplicability. +[^bender]: [Data Statements for Natural Language Processing:Toward Mitigating System Bias and Enabling Better Science](https://doi.org/10.1162/tacl_a_00041), Transactions of the Association for Computational Linguistics, 2018, 6: 587–604 + +
\ No newline at end of file diff --git a/docs/build/html/_sources/data/maintenance.md.txt b/docs/build/html/_sources/data/maintenance.md.txt new file mode 100644 index 0000000..b01733d --- /dev/null +++ b/docs/build/html/_sources/data/maintenance.md.txt @@ -0,0 +1,39 @@ +
+ +# Maintenance + +
+ +The focus herein is the maintenance details of the data set. Please, study **Section 3.7 of** [2] for more details. A few +examples: + +
    +
  • Who is the data set owner, curator, or manager?
    • +
  • Does the data set have an erratum point?
    • +
  • Does the data set have an ad-hoc/regular/irregular update schedule? If yes, please describe.
    • +
  • Does the data set have retention restrictions, deletion norms, etc.? If yes, please describe.
    • +
  • And more.
    • +
+ +In a nutshell, describe all details pertinent to model operations. + +
+ +
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
\ No newline at end of file diff --git a/docs/build/html/_sources/data/motivation.md.txt b/docs/build/html/_sources/data/motivation.md.txt new file mode 100644 index 0000000..dbaa4b7 --- /dev/null +++ b/docs/build/html/_sources/data/motivation.md.txt @@ -0,0 +1,56 @@ +
+ +# Motivation + +
+ +The motivation, or deliberations, that led to the collection and storage of a data set can impact its appropriateness +for model development. For example, the outcome of a deliberation might have been to: + +
    +
  • Focus on, collect, and store, the data of a particular strata of a population.
    • +
  • Retain aggregated records only.
    • +
+ +The former will limit the applicability of any models that depend on the data set, using the latter is ill-advised due to lost information and ecological fallacy risks. Additionally, it is important to beware of funding/sponsorship bias. + +Hence + +
    +
  1. Why was the data set created? +
      +
    • Was there a specific task in mind?
      • +
    • Was there a specific gap that needed to be filled?
      • +
    +
    2. +
  2. Who created the data set? +
      +
    • In brief, which team, organisation, research group, etc., created the dataset? Was it created for another entity?
      • +
    +
    3. +
  3. Who funded the creation of the dataset?
    4. +
+ +
+
+ +
References +
    +
  1. External Validity, Research Methods Knowledge Base
    2. +
  2. Industry Sponsorship Bias
    3. +
  3. What is epistemically wrong with research affected by sponsorship bias? The evidential account.
    4. +
+
+ + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/data/nlp.md.txt b/docs/build/html/_sources/data/nlp.md.txt new file mode 100644 index 0000000..5699793 --- /dev/null +++ b/docs/build/html/_sources/data/nlp.md.txt @@ -0,0 +1,89 @@ +
+ +# Natural Language Processing + +## Background + +This section only applies to data sets for natural language processing (NLP). The focus being the *proposed data +statements* of Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science by E Bender & B Friedman. + +Please study the description of each statement within [Section 5](https://direct.mit.edu/tacl/article/doi/10.162/tacl_a_00041/43452/Data-Statements-for-Natural-Language-Processing). Subsequently, and per data set, record the +applicable data statements. Brief excerpts below. + + +
+ +## Data Statement Queries in Brief + +### Curation Rationale + +Why was the data collected? In brief, which "… texts were included and what were the goals in selecting +texts, both in the original collection and in any further sub-selection?" Impacts inference. + +
+ +### Language Variety + +This is important because languages "… differ from each other in structural ways that can interact with NLP algorithms". +Additionally, and within a language, "… regional or social dialects can also show great variation". + +
+ +### Speaker Demographic + +Important in cases wherein "… variation (in pronunciation, prosody, word choice, and grammar) correlates with *speaker-demographic* characteristics". + +
+ + +### Annotator Demographic + +In brief, what are the "… demographic characteristics of the annotators and [the] annotation guideline developers?" These are critical questions because they give an insight into "… their experience with language and thus their perception of what they are annotating". + +
+ +### Speech Situation + +Mainly because "… characteristics of the speech situation can affect linguistic structure and patterns …". + +
+ +### Text Characteristics + +Mainly because "… genre and topic influence the vocabulary and structural characteristics of texts". + +
+ +### Recording Quality + +If applicable, outline recording quality. + +
+ +### Provenance Appendix + +If the data set was derived from an existing data set "… the data statements for the source datasets should be included as an appendix" + +
+ +### Other Pertinent Details + +Record pertinent details that the previous statements do not request. + +
+
+ +
+
+ +
+
+ +
+
+ +[^text]: The text Dimensions of Register Variation: A Cross-Linguistic Comparison might be helpful. + +
+
+ diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt new file mode 100644 index 0000000..a393da8 --- /dev/null +++ b/docs/build/html/_sources/index.rst.txt @@ -0,0 +1,65 @@ +.. image:: _static/logo.svg + + +Projects & Machine Learning +=========================== + +.. container:: .large + + Questions and notes for potential machine learning dependent business projects + + +.. raw:: html + +
+
+
+
+ + +.. container:: .buttons + + `Introduction `_ + :ref:`genindex` + + +.. image:: https://img.shields.io/github/v/tag/premodelling/systems?label=Release&style=flat-square&labelColor=292b2e&color=292b2e + + +.. toctree:: + :maxdepth: 2 + :caption: INTRODUCTION + :hidden: + + introduction/introduction + introduction/plausible + +.. toctree:: + :maxdepth: 3 + :caption: PROJECT DETAILS + :hidden: + + project/project + model/model + data/data + stakeholders/stakeholders + requirements/requirements + constraints/constraints + risks/risks + +.. raw:: html +
+
+
+
+ +
+
+
+
+ + +.. + Comment + :ref:`genindex` + :ref:`search` diff --git a/docs/build/html/_sources/introduction/introduction.md.txt b/docs/build/html/_sources/introduction/introduction.md.txt new file mode 100644 index 0000000..25f6bdb --- /dev/null +++ b/docs/build/html/_sources/introduction/introduction.md.txt @@ -0,0 +1,72 @@ +# Introduction + + +## Background + +**These pages** complement an in-development online *project details hub*. The hub's focus is business machine learning +projects, and its aim is the simplification of requirements elicitation; it has sets of questions pertinent to machine +learning dependent projects. An external partner is developing the online hub, which will be a component of the Scottish +Artificial Intelligence Register platform. [^business] + +In future, we hope the *project details hub* will reduce the likelihood that a project will fail, i.e., will aid +successful deployments of machine learning solutions within + +
    +
  • Applications that directly or indirectly serve the public.
    • +
  • Back-end operations or internal applications.
    • +
  • Policy teams, vis-à-vis providing evidence/insights based on the automatic and continuous executions of simple and complex models.
    • +
+ +
+ +These pages are not as comprehensive as the hub. The objective: share a few help notes with colleagues whilst we await +the hub's first release. However, because these pages might be a helpful reference point, we will continuously update its content. + +
+
+ +## Purpose + +These pages complement the upcoming *project details hub* by explaining selections of the questions therein. Together, +the expectation is that prospective clients/collaborators will **(a)** have well detailed project requirements, **(b)** carefully consider the time commitment, collaboration commitment, cost, range of expertise, project details, etc., that are required for machine learning dependent projects. Additionally, stakeholders will be able to make well informed decisions about whether to proceed with a project. + +
+ +A project's details are especially critical to conducting a well-informed technical feasibility & economic +viability assessment before proceeding. It is also important to beware that the data science aspect of the project is only a part of the project, ignoring this leads to [project failures](https://www.kdnuggets.com/survey-machine-learning-projects-still-routinely-fail-to-deploy) due to, e.g., + +
    +
  • Under-planning.
    • +
  • Ambiguous project scope/design details.
    • +
  • Project pilots that are neither informed by nor tests the deployment goal.
    • +
  • Underestimating, or ignoring, cost.
    • +
  • Ignoring the critical importance of intensive collaboration between the business, data science, operations, etc., +teams.
    • +
+ +
+ +One of the first sections of these pages is **Deployment Goal**. Colleagues may question why. Here are a few references that +capture our experience of why business data science projects fail, and the advantages of starting with ``the end in mind'' [^deployment] + + + +
+
+
+
+ +
+
+
+
+ +[^business]: The word _business_ indicates that the focus is not research. +[^deployment]: An upcoming update will include more notes. + +
+
diff --git a/docs/build/html/_sources/introduction/plausible.md.txt b/docs/build/html/_sources/introduction/plausible.md.txt new file mode 100644 index 0000000..f0e21d5 --- /dev/null +++ b/docs/build/html/_sources/introduction/plausible.md.txt @@ -0,0 +1,33 @@ +# Project Plausibility + +
+ +```{mermaid} +%%{ init: { 'flowchart': { 'curve': 'monotoneX'} } }%% +flowchart LR + id0([start]) --> id1{budget} + id1 -- yes --> id2{budget,
time} + id1 -- no --> id3([terminate]) + id2 -- no --> id3 + id2 -- yes --> id5(project
details) + id5 --> id6{feasible?} + id6 -- no --> id3 + id6 -- yes --> id8(next
steps) + + classDef default fill:#333333,stroke:#333333,stroke-width:0px,color:#ffffff,font-size:11pt; +``` + +
+ +
An illustration of considerations vis-à-vis a potential machine learning dependent project. The project details +inform the feasibility assessment.
+ +
+
+
+
+ +
+
+
+
diff --git a/docs/build/html/_sources/model/aim.md.txt b/docs/build/html/_sources/model/aim.md.txt new file mode 100644 index 0000000..daf22e5 --- /dev/null +++ b/docs/build/html/_sources/model/aim.md.txt @@ -0,0 +1,28 @@ +
+ +# Aim + +
+ +What is the machine learning model's aim? For example: + +> To determine the probability that a retinal image is a member of a diabetic retinopathy class; the classes are _no_ +> (diabetic retinopathy), _mild_, _moderate_, _severe_, _proliferative_.
+> $\sum\limits_{l \: \in \: L} p(\mathcal{C}_{l}) = 1$, $\quad L =$ {no, mild, moderate, severe, proliferative} + +
+ +**Note**, the model's aim matches the deployed product's model output +expectations. + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/model/business.md.txt b/docs/build/html/_sources/model/business.md.txt new file mode 100644 index 0000000..e7c3fab --- /dev/null +++ b/docs/build/html/_sources/model/business.md.txt @@ -0,0 +1,31 @@ +
+ +# Business Metrics + +
+ +:::{note} +:class: margin +In progress +::: + +A later version of this hub will have more details, and an example. In brief, business performance metrics may focus on, e.g., + +
    +
  • Financial savings, return on investment, due time saving, error reduction, etc.
    • +
  • The cost of mistakes over time, e.g., misdiagnosis over time.
    • +
+ +Considering the retinal images classification example. The client may use the relationship between diabetic retinopathy severity, and the life-time cost of sight loss prevention/sight loss delay, to define a set of business metrics for model evaluation. + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/model/model.md.txt b/docs/build/html/_sources/model/model.md.txt new file mode 100644 index 0000000..8a40261 --- /dev/null +++ b/docs/build/html/_sources/model/model.md.txt @@ -0,0 +1,25 @@ +
+ +# Model + + +```{toctree} +:glob: + +aim +business +performance +``` + + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/model/performance.md.txt b/docs/build/html/_sources/model/performance.md.txt new file mode 100644 index 0000000..d3e152a --- /dev/null +++ b/docs/build/html/_sources/model/performance.md.txt @@ -0,0 +1,56 @@ +
+ +# Performance Metrics + +:::{admonition} In Progress +:class: margin +
+::: + +
+ +## Model Performance Metrics + +These are the standard technical metrics. **Referring back to the retinal images classification example**, we may define the performance metrics criteria as follows: [^metrics] + +
+ +
EXAMPLE
Based on the (a) error matrix measures, i. +e., True Positive, False Negative, False Positive, True Negative, and (b) a threshold defined as the intersection of precision & sensitivity, the expected criteria are:
+
Overarching Criteria
+
Precision > 0.9, Sensitivity (True Positive Rate) > 0.9, Specificity (True Negative Rate) > 0.9, Youden's J Statistic > 0.9, and False Positive Rate < 0.1.
+
Disaggregated Criteria
+
For each class, and by the same overarching threshold value, ensure that each criterion metric value is within its defined range; as outlined above.
+
+ +
+
+ +## Model Card + +This is for auditing purposes. A model's model card should summarise the artefacts of the released, in-use, +in-production, model. A model card example: + + + +
+
+ +
+
+ +
+
+ +
+
+ + +[^metrics]: **N**: # of negative cases, **P**: # of positive cases. | **TN**: True Negative, **FP**: False Positive, **FN**: False Negative, **TP**: True Positive | **TNR = TN/N**: True Negative Rate (specificity) | **FPR = FP/N**: False Positive Rate (fall out) | **FNR = FN/P**: False Negative Rate (miss rate) | **TPR = TP/P**: True Positive Rate (hit rate, sensitivity, recall) + + +
+
diff --git a/docs/build/html/_sources/project/project.md.txt b/docs/build/html/_sources/project/project.md.txt new file mode 100644 index 0000000..662a0c3 --- /dev/null +++ b/docs/build/html/_sources/project/project.md.txt @@ -0,0 +1,64 @@ +
+ +# Critical Preliminaries + +## Problem Statement + +What is the problem, and why is it important to solve this problem? + +
+ +## Outcome Expectations + +What are the potential product's outcome expectations, e.g., + +
+ An increase in the detection of early-stage, i.e. mild, diabetic retinopathy, hence the prevention of sight loss amongst a greater percentage of diabetic patients. +
+ +
+ +The underlying aim might be, e.g., + +
+ To reduce the mis-classification rate of retinal images of diabetic patients to ≤ 1.25% per class per quarter. +
+ +
+ +## Deployment Goal + +**What is the aim, expected output, of the product's model?** Example: + +
+ A retinal image's diabetic retinopathy classification probability per class. +
+ +
+ +**What will the deployed product present to the optician?** **Or** rather, **how will the model's output be used?** Considering the example above, the client's opticians might expect: [^deployment] + +
    +
  • The classification probability per class.
    • +
  • The definition of each class.
    • +
  • The classification; vis-à-vis the highest classification probability.
    • +
  • The preliminary treatment pathway of the classification, according to the rules of the optician's institution.
    • +
  • The retinal image.
    • +
+ +
+
+ +
+
+ +
+
+ +
+
+ +[^deployment]: This is not machine learning. This is about integrating a machine learning model's output into a business product, and hence business operations. + +
+
diff --git a/docs/build/html/_sources/requirements/additional.md.txt b/docs/build/html/_sources/requirements/additional.md.txt new file mode 100644 index 0000000..c783521 --- /dev/null +++ b/docs/build/html/_sources/requirements/additional.md.txt @@ -0,0 +1,29 @@ +
+ +# Non Functional Requirements + +
+ +```{toctree} +:glob: + +ca +security +ua +ps +rmra +cost +``` + + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/availability.md.txt b/docs/build/html/_sources/requirements/availability.md.txt new file mode 100644 index 0000000..a5eb9ec --- /dev/null +++ b/docs/build/html/_sources/requirements/availability.md.txt @@ -0,0 +1,58 @@ +
+ +# Availability + +The [systems engineering body of knowledge defines availability](https://sebokwiki.org/wiki/System_Reliability,_Availability,_and_Maintainability#Availability) as the + +> "… probability that a repairable system or system element is operational at a given point-in-time under a given set of environmental conditions" + +**Herein, define the system's availability expectations**. Availability is a function of reliability and maintainability, i.e., + +$$ +availability = \frac{MTBF}{MTBF + MTTR} +$$ + +whereby + +
    +
  • The mean time between failure is a reliability metric.
    • +
  • The mean time to repair is a maintainability metric.
    • +
+ +
+
+ +```{image} ../../../assets/availability-metrics.png +:alt: Availability Metrics +:width: 80% + +``` + +
+
+ +
+
Availability metrics illustration by Amazon. Note: MTBF (Mean Time Between Failure) is a reliability metric, MTTR (Mean Time To Repair) is a maintainability metric, MTBF/(MTBF + MTTR) is a definition of availability, MTTD (Mean Time To Detection) +
+
+ +
+
+ +
References +
    +
  1. Availability
    2. +
  2. Understanding Availability
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/ca.md.txt b/docs/build/html/_sources/requirements/ca.md.txt new file mode 100644 index 0000000..a1926aa --- /dev/null +++ b/docs/build/html/_sources/requirements/ca.md.txt @@ -0,0 +1,43 @@ +# Context & Architecture + +Additionally, include data flow diagrams. + +
+ +## System Context Diagram + +The system context diagram illustrates how the service node of the system interacts with the world it is serving; a +[slightly complex example](https://docs.conda.io/projects/conda/en/4.14.x/architecture.html#level-1-context). + +
+ +## Architecture + +The architecture, which outlines integrations with other systems, modelling stages, data flows, monitoring node, +re-training node, etc. + +```{image} ../../../assets/ml-lifecycle.png +:alt: Machine Learning Dependent Business Project +:width: 80% + +``` + +
+
+ +
+
A platform agnostic example courtesy of Amazon Web Services; scroll down. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/cost.md.txt b/docs/build/html/_sources/requirements/cost.md.txt new file mode 100644 index 0000000..e102108 --- /dev/null +++ b/docs/build/html/_sources/requirements/cost.md.txt @@ -0,0 +1,21 @@ +
+ +# Cost + +:::{note} +:class: margin +In progress +::: + +
+
+ +
+
+ +
+
+ +
+
+ diff --git a/docs/build/html/_sources/requirements/functional.md.txt b/docs/build/html/_sources/requirements/functional.md.txt new file mode 100644 index 0000000..f1c1e29 --- /dev/null +++ b/docs/build/html/_sources/requirements/functional.md.txt @@ -0,0 +1,23 @@ +
+ +# Functional Requirements + +A **functional requirement** specifies what a deployed product/system must do. + +**Note, (a)** a system may have more than one requirement, and **(b)** each requirement must have at least one criterion. The [Volere Specification](https://homepages.laas.fr/kader/Robertson.pdf) defines a criterion, i.e., fit criterion, as an objective measure + +> "… for defining the meaning of a requirement, and eventually testing whether a given solution satisfies the original requirement." ([Volere Specification](https://homepages.laas.fr/kader/Robertson.pdf)) + +The *deployment goal* dictates what the product/system will be, and the functional requirements thereof. + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/mr.md.txt b/docs/build/html/_sources/requirements/mr.md.txt new file mode 100644 index 0000000..d33af86 --- /dev/null +++ b/docs/build/html/_sources/requirements/mr.md.txt @@ -0,0 +1,75 @@ +
+ +# Maintainability & Resilience + +## Definitions + +### Maintainability + +In the systems engineering body of knowledge the [basic definition of maintainability](https://sebokwiki.org/wiki/System_Reliability,_Availability,_and_Maintainability#Maintainability) is the + +> “ … probability that a given maintenance action for an item under given usage conditions can be performed within a stated time interval when the maintenance is performed under stated conditions using stated procedures and resources.” + +Note that **maintainability comprises** serviceability & repairability, i.e., + +> "… the ease of conducting scheduled inspections and servicing" + +**and** + +> "… the ease of restoring service after a failure" + +
+ +### Resilience + +The resilience of a system can be defined as "… the ability to maintain [capability](https://sebokwiki.org/wiki/Capability_(glossary)) in the face of a [disruption](https://sebokwiki.org/wiki/Disruption_(glossary))" [[The Systems Engineering Body of Knowledge.](https://sebokwiki.org/wiki/System_Resilience#Definition)] + +The body of knowledge outlines the objectives of a system's resilience plan. Study the Achieving Resilience details, therein the fundamental objectives are: + +
    +
  • Avoid: eliminate or reduce exposure to [stress](https://sebokwiki.org/wiki/Stress_(glossary))
    • +
  • Withstand: resist capability degradation when stressed
    • +
  • Recover: replenish lost capability after degradation
    • +
+ +
+
+ +## Outlining Specifications for Machine Learning Systems + +**In the context of machine learning**, the foci herein are + +
    +
  • The model drift and data drift strategy. The blog Model Drift: Best Practices to Improve Machine Learning Model Performance includes an excellent discussion about how to monitor machine learning systems. Drift details must be outlined vis-à-vis model drift and data drift. Model drift monitoring is via the evaluation metrics detailed in the model performance metrics section. Whereas data drift is generally via statistical tests or custom models, depending on the data types in question.
    • +
  • Continuous performance improvement strategy. For continuous model improvement purposes, the system architecture should include a re-training component. If it does not, the monitoring system should have an automatic end-of-life alert, triggered by one or more evaluation metrics falling-out of their constraints.
    • +
  • Maintenance services/agreements. For general maintainability concepts, study the reliability, maintainability, and availability page of the systems engineering body of knowledge for the expectations herein; and the maintenance and resilience metrics thereof.
    • +
+ +
+ +Altogether, define maintenance and resilience metrics vis-à-vis the system/product, model drift & data drift. The +references link to metrics definitions examples. + +
+
+ +
References +
    +
  1. Maintenance Metrics
    2. +
  2. Resilience Metrics
    3. +
  3. Detection of data drift and outliers affecting machine learning model performance over time. +
    4. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/ps.md.txt b/docs/build/html/_sources/requirements/ps.md.txt new file mode 100644 index 0000000..fb5e82a --- /dev/null +++ b/docs/build/html/_sources/requirements/ps.md.txt @@ -0,0 +1,38 @@ +
+ +# Performance & Scalability + +## Performance + +What are the [performance](https://www.boxuk.com/insight/guide-to-non-functional-requirements-types-and-examples/#:~:text=of%20the%20application-,Performance,-What%20is%20performance) expectations? Considerations include:[^cost] + +
    +
  • Latency: timeinput - timeoutput
    • +
  • Timeliness
    • +
  • Throughput/Request Rate
    • +
  • The daily data flow expectations
    • +
  • The size expectations per data set.
    • +
+ +
+ +## Scalability + +What are the [scalability](https://www.gartner.com/en/information-technology/glossary/scalability) expectations of the system? + +
+
+ +
+
+ +
+
+ +
+
+ +[^cost]: High performance can have high cost implications. + +
+
diff --git a/docs/build/html/_sources/requirements/reliability.md.txt b/docs/build/html/_sources/requirements/reliability.md.txt new file mode 100644 index 0000000..e802159 --- /dev/null +++ b/docs/build/html/_sources/requirements/reliability.md.txt @@ -0,0 +1,32 @@ +
+ +# Reliability + +In the systems engineering body of knowledge the [basic definition of reliability](https://sebokwiki.org/wiki/System_Reliability,_Availability,_and_Maintainability#Reliability) is + +> “ … the probability of a product performing its intended function under stated conditions without failure for a given time period”. + +In the context of machine learning systems, the reliability specification of each intended function, i.e., each functional requirement, must detail/specify the + +
    +
  • Function in question, and its operating environment.
    • +
  • Time scales; for evaluating the reliability metrics.
    • +
  • Definition of failure; for evaluating the reliability metrics, and for in-built failure mitigation considerations.
    • +
  • Reliability metrics
    • +
+ +
+ +This aids (a) the design of possible in-built solutions that mitigate factors that affect reliability, and (b) system monitoring strategy, and the operations thereof. Study the [reliability, maintainability, and availability page](https://sebokwiki.org/wiki/System_Reliability,_Availability,_and_Maintainability) of the systems engineering body of knowledge for the expectations herein. + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/requirements.md.txt b/docs/build/html/_sources/requirements/requirements.md.txt new file mode 100644 index 0000000..1997f91 --- /dev/null +++ b/docs/build/html/_sources/requirements/requirements.md.txt @@ -0,0 +1,25 @@ +
+ +# Requirements + +
+ + +```{toctree} +:glob: + +functional +additional +``` + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/rmra.md.txt b/docs/build/html/_sources/requirements/rmra.md.txt new file mode 100644 index 0000000..9c19b89 --- /dev/null +++ b/docs/build/html/_sources/requirements/rmra.md.txt @@ -0,0 +1,44 @@ +
+ +# Reliability, Maintainability, Resilience, Availability + +The [systems engineering body of knowledge](https://sebokwiki.org/wiki/Guide_to_the_Systems_Engineering_Body_of_Knowledge_(SEBoK)) notes that reliability, maintenance, and availability attributes are three of the core attributes that affect the [utility of a system and its economic life-cycle cost](https://sebokwiki.org/wiki/System_Reliability,_Availability,_and_Maintainability). In the context of machine learning products, reliability, maintenance, availability, and resilience specifications are critical to items such as + +
    +
  • continuous service,
    • +
  • drift monitoring & continuous model performance improvement
    • +
+ +and the costs thereof. The tasks herein will require the aid of a reliability engineer, or similar. The aim being to + +
    +
  • Define the reliability, maintenance, availability, and resilience expectations via metrics constraints.
    • +
  • Outline in-built designs/solutions that minimise the probability of constraints breaches.
    • +
+ +
+
+ +```{toctree} +:caption: Content +:glob: + +reliability +mr +availability +``` + +
+
+ +
+
+ +
+
+ +
+
+ + +[^utility]: [A definition of utility](https://www.oed.com/search/dictionary/?scope=Entries&q=utility) diff --git a/docs/build/html/_sources/requirements/security.md.txt b/docs/build/html/_sources/requirements/security.md.txt new file mode 100644 index 0000000..599f36f --- /dev/null +++ b/docs/build/html/_sources/requirements/security.md.txt @@ -0,0 +1,41 @@ + +
+ +# System Security + +System security addresses + +
    +
  • Confidentiality: The system's data is accessible to authorised users only.
    • +
  • Integrity: The system prevents unauthorised access or modification of the model and/or the entire system; by (a) adhering to best security practices, (b) keeping abreast of vulnerabilities, (c) being non-repudiation proof.
    • +
  • Accountability: All system actions are tracked and stored, hence breaches can be investigated.
    • +
+ +
+ +Therefore, the detail the prospective system's: + +
    +
  1. Activity Tracking System: For auditability requirements, etc.
    2. +
  2. System Protection & Privacy Layer: Ensuring adherence to laws and regulations, e.g., GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), etc. +
      +
    • Legal Compliance: "… adherence to laws and regulations that are enforced by the government".
      • +
    • Regulatory Compliance: "… adherence to rules and regulations set forth by regulatory bodies that oversee specific industries".
      • +
    • Privacy: Encryption
      • +
    +
    3. +
  3. Authentication System: That ascertains a user's authenticity.
    4. +
  4. Authorisation System: That ascertains, per user, access to authorised areas only.
    5. +
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/requirements/ua.md.txt b/docs/build/html/_sources/requirements/ua.md.txt new file mode 100644 index 0000000..4d5ab4e --- /dev/null +++ b/docs/build/html/_sources/requirements/ua.md.txt @@ -0,0 +1,37 @@ +
+ +# Usability & Accessibility + +The foci herein are the requirements/constraints vis-à-vis + +
    +
  • Interface: The constraints herein depend on (a) the audience, e.g., children, young adults, executives, (b) the organisation's presentation or branding norms, (c) and more. [1]
    • +
  • Style: The audience, e.g., children, young adults, executives, dictates the constraints herein. [1]
    • +
  • Ease of use: Each ease of use constraint must have fit criteria for acceptance testing. [1]
    • +
  • Learnability: Again, each constraint must have fit criteria for acceptance testing. [1]
    • +
  • Accessibility; For more details study the Web Content Accessibility Guidelines (WCAG) [2]
    • +
+ +The client team should have an unambiguous outline per requirement/constraint. + +
+
+ +
References +
    +
  1. Volere
    2. +
  2. Web Content Accessibility Guidelines (WCAG)
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/risks/risks.md.txt b/docs/build/html/_sources/risks/risks.md.txt new file mode 100644 index 0000000..edecc6a --- /dev/null +++ b/docs/build/html/_sources/risks/risks.md.txt @@ -0,0 +1,20 @@ +
+ +# Risks + +```{admonition} In Progress +:class: margin +
+``` + +The online *project details hub* focuses on both machine learning algorithm risks and the risks of the overarching project. + +
+
+
+
+ +
+
+
+
diff --git a/docs/build/html/_sources/stakeholders/people.md.txt b/docs/build/html/_sources/stakeholders/people.md.txt new file mode 100644 index 0000000..38847ab --- /dev/null +++ b/docs/build/html/_sources/stakeholders/people.md.txt @@ -0,0 +1,49 @@ +
+ +# People + +## Aim + +Identify and record all the project's stakeholders. + +
+ +## Notes + +The stakeholders are the people who (Volere) + +
+ … can affect the outcome/success of a project and, or, are affected by its outcome/success. +
+ +Examples include: + +
    +
  • The client. The entity (a) the system/product is being built for, and (b) that will pay for the system/product.
    • +
  • The signatories. The people who need to sign-off aspects of the project, e.g., system security, data access, etc.
    • +
  • The end users. The people who will interact with the product.
    • +
+ +The Open Group Architecture Framework (TOGAF) hosts a detailed discussion of [stakeholder management](https://pubs.opengroup.org/architecture/togaf9-doc/arch/chap21.html). The discussion includes a [sample stakeholder map for an imaginary project](https://pubs.opengroup.org/architecture/togaf9-doc/arch/chap21.html#:~:text=21.3.1.1-,Sample%20Stakeholder%20Analysis,-A%20sample%20stakeholder). It is a good guide to discerning a project's stakeholders and their import. + +
+
+ +
References +
    +
  1. Stakeholder Management, Chapter 21, The Open Group Architecture Framework (TOGAF) Standard, Version 9.2
    2. +
  2. Got stake?, by Forman, J. B. & Discenza, R., 2012
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sources/stakeholders/register.md.txt b/docs/build/html/_sources/stakeholders/register.md.txt new file mode 100644 index 0000000..02b6d4a --- /dev/null +++ b/docs/build/html/_sources/stakeholders/register.md.txt @@ -0,0 +1,50 @@ +
+ +# Stakeholders Register + +## Aim + +Create a stakeholder register. + +## Notes + +The stakeholder register is a + +
+ … project document that includes information about project stakeholders including an assessment and classification of project stakeholders. [1] +
+ +The details per stakeholder should include: [2] + +
    +
  • Name
    • +
  • Role in employing organisation.
    • +
  • The stakeholder's employing organisation department.
    • +
  • Role in project.
    • +
  • And, any other items pertinent to constructive stakeholder interactions.
    • +
+ +
+
+ +
References +
    +
  1. A Guide to the Project Management Body of Knowledge, Seventh Edition, 2021, The Standard for Project Management.
    2. +
  2. Got stake?, by J. B. Forman, & R. Discenza, 2012
    3. +
+
+ +
+
+ +
+
+ +
+
+ +
+
+ + + diff --git a/docs/build/html/_sources/stakeholders/stakeholders.md.txt b/docs/build/html/_sources/stakeholders/stakeholders.md.txt new file mode 100644 index 0000000..d376e09 --- /dev/null +++ b/docs/build/html/_sources/stakeholders/stakeholders.md.txt @@ -0,0 +1,24 @@ +
+ +# Stakeholders + +
+ +```{toctree} +:glob: + +* +``` + + +
+
+ +
+
+ +
+
+ +
+
diff --git a/docs/build/html/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/docs/build/html/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css new file mode 100644 index 0000000..eb19f69 --- /dev/null +++ b/docs/build/html/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) 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// [C]VC[V] is m=1 + var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 + var s_v = "^(" + C + ")?" + v; // vowel in stem + + this.stemWord = function (w) { + var stem; + var suffix; + var firstch; + var origword = w; + + if (w.length < 3) + return w; + + var re; + var re2; + var re3; + var re4; + + firstch = w.substr(0,1); + if (firstch == "y") + w = firstch.toUpperCase() + w.substr(1); + + // Step 1a + re = /^(.+?)(ss|i)es$/; + re2 = /^(.+?)([^s])s$/; + + if (re.test(w)) + w = w.replace(re,"$1$2"); + else if (re2.test(w)) + w = w.replace(re2,"$1$2"); + + // Step 1b + re = /^(.+?)eed$/; + re2 = /^(.+?)(ed|ing)$/; + if (re.test(w)) { + var fp = re.exec(w); + re = new RegExp(mgr0); + if (re.test(fp[1])) { + re = /.$/; + w = w.replace(re,""); + } + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1]; + re2 = new RegExp(s_v); + if (re2.test(stem)) { + w = stem; + re2 = /(at|bl|iz)$/; + re3 = new RegExp("([^aeiouylsz])\\1$"); + re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re2.test(w)) + w = w + "e"; + else if (re3.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + else if (re4.test(w)) + w = w + "e"; + } + } + + // Step 1c + re = /^(.+?)y$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(s_v); + if (re.test(stem)) + w = stem + "i"; + } + + // Step 2 + re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step2list[suffix]; + } + + // Step 3 + re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + suffix = fp[2]; + re = new RegExp(mgr0); + if (re.test(stem)) + w = stem + step3list[suffix]; + } + + // Step 4 + re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; + re2 = /^(.+?)(s|t)(ion)$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + if (re.test(stem)) + w = stem; + } + else if (re2.test(w)) { + var fp = re2.exec(w); + stem = fp[1] + fp[2]; + re2 = new RegExp(mgr1); + if (re2.test(stem)) + w = stem; + } + + // Step 5 + re = /^(.+?)e$/; + if (re.test(w)) { + var fp = re.exec(w); + stem = fp[1]; + re = new RegExp(mgr1); + re2 = new RegExp(meq1); + re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); + if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) + w = stem; + } + re = /ll$/; + re2 = new RegExp(mgr1); + if (re.test(w) && re2.test(w)) { + re = /.$/; + w = w.replace(re,""); + } + + // and turn initial Y back to y + if (firstch == "y") + w = firstch.toLowerCase() + w.substr(1); + return w; + } +} + diff --git a/docs/build/html/_static/logo.svg b/docs/build/html/_static/logo.svg new file mode 100644 index 0000000..48d68e8 --- /dev/null +++ b/docs/build/html/_static/logo.svg @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/docs/build/html/_static/minus.png b/docs/build/html/_static/minus.png new file mode 100644 index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu=nj kDsEF_5m^0CR;1wuP-*O&G^0G}KYk!hp00i_>zopr08q^qX#fBK literal 0 HcmV?d00001 diff --git a/docs/build/html/_static/plus.png b/docs/build/html/_static/plus.png new file mode 100644 index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d GIT binary patch literal 90 zcmeAS@N?(olHy`uVBq!ia0vp^+#t*WBp7;*Yy1LIik>cxAr*|t7R?Mi>2?kWtu>-2 m3q%Vub%g%s<8sJhVPMczOq}xhg9DJoz~JfX=d#Wzp$Pyb1r*Kz literal 0 HcmV?d00001 diff --git a/docs/build/html/_static/pygments.css b/docs/build/html/_static/pygments.css new file mode 100644 index 0000000..190e57c --- /dev/null +++ b/docs/build/html/_static/pygments.css @@ -0,0 +1,45 @@ +pre { line-height: 125%; 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+} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlinks", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + let score = Math.round(100 * queryLower.length / title.length) + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/build/html/_static/sphinx_highlight.js new file mode 100644 index 0000000..8a96c69 --- /dev/null +++ b/docs/build/html/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/docs/build/html/constraints/constraints.html b/docs/build/html/constraints/constraints.html new file mode 100644 index 0000000..0051188 --- /dev/null +++ b/docs/build/html/constraints/constraints.html @@ -0,0 +1,251 @@ + + + + + + + + + + Project Constraints — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Project Constraints

+

An inexhaustive set of constraints.

+
+
+

Solution Constraints

+

These are “… constraints on the way that the problem must be solved.” (4a., Volere Template)

+
+
+
+

Time Constraints

+

What is the client team’s project time limit? Beware, time and budget are critical to determining whether a feasible solution exists.

+
+
+
+

Implementation Environment Constraints

+

Is there a cloud platform, or on-premises, infrastructure restriction?

+
+
+
+

Budget Constraint

+

During the viability/feasibility assessment compare the stated budget with the estimated cost. Remember, the cost is determined by all elements that have a cost; both project delivery costs and lifecycle costs are critical.

+
+
+
+
+
+
+
+
+
+
+ + +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/data/collection.html b/docs/build/html/data/collection.html new file mode 100644 index 0000000..5feb04c --- /dev/null +++ b/docs/build/html/data/collection.html @@ -0,0 +1,244 @@ + + + + + + + + + + Data Collection — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Data Collection

+
+

What was the data acquisition mechanism? Per data instance, was data acquisition via

+
    +
  • A sensor.
    • +
  • An application programming interface.
    • +
  • Interviews.
    • +
  • Questionnaires.
    • +
  • A mix of mechanisms.
    • +
  • etc.
    • +
+
+
+
+
+
+
+
+
+
+ + +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/data/composition.html b/docs/build/html/data/composition.html new file mode 100644 index 0000000..aeb8c73 --- /dev/null +++ b/docs/build/html/data/composition.html @@ -0,0 +1,407 @@ + + + + + + + + + + Composition — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Composition

+

In general, the questions herein should be studied before data collection. The datasheets paper [1] notes that most of the questions:

+
+ "… are intended to provide dataset consumers with the information they need to make informed decisions about using the dataset for their chosen tasks. … the questions are designed to elicit information about compliance with the EU's General Data Protection Regulation (GDPR) or comparable regulations in other jurisdictions." +
+
+
References +
    +
  1. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    2. +
+
+
+
+
+

What does each data set instance represent?

+

Describe what each instance, i.e., row, of the data set represents. Although peculiar, if the data set is multi-representational, e.g., an instance in a merchant’s data base table might represent one of: (a) an online reading event, (b) an online product purchasing event, or (c) an online download event. Each representation must be described.

+
+
+
+
+

The # of Instances

+

How many instances does the data set have?

+
+
+
+
+

Pre-processed?

+

Are any aspects of the data set pre-processed? If yes:

+
    +
  • Document the pre-processing steps.
    • +
  • State whether the underlying raw data is available, and provide a link to the data.
    • +
  • If available, provide a link to the pre-processing programs.
    • +
+
+
+
+
+

Is the data set a sample of a larger data set?

+

If yes:

+
    +
  • If the data set is representative of the larger data set: How was representativeness verified/validated?
    • +
  • If the data set is not representative of the larger data set, e.g., is a geographically focused subset, explain why.
    • +
+
+
+
+
+

Lineage

+

Summarise the data set’s lineage, including linkage options. [1, 2]

+
+
References +
    +
  1. QLIK: What is data lineage?
    2. +
  2. IBM: What is data lineage?
    3. +
+
+
+
+
+
+

Licences & Fees

+

If applicable, summarise the data’s costs.

+
+
+
+
+

Profiles of Instances

+

Herein, the focus is a summary of the instances of a data set, e.g., for a tabular data set:

+

By Field

+
    +
  • The field name.
    • +
  • Description: What does the element of an instance denote/represent?
    • +
  • Data type.
    • +
  • Dictionary of a categorical data type.
    • +
  • Unit of measure.
    • +
  • Is this a raw data field or a feature?
    • +
  • Is this a target field?
    • +
  • Does the field identify a sub-population?
    • +
  • Column Profile: Note column profiling "… provides statistical information regarding the distribution of data values and associated patterns that are assigned to each data attribute, …". [1]     If a field/column has missing elements, explain why.
    • +
  • A graph of the field's data distribution.
    • +
+
+

Across Fields

+
    +
  • Cross-Column Profiles: Relationships between columns.
    • +
+
+
+
References +
    +
  1. 5.5.2 Profiling for Data Quality Assessment, in Master Data Management, Page 96, The MK/OMG Press, 2008
    2. +
  2. Data Profiling
    3. +
+
+
+
+
+
+

Errors

+

Please detail any errors, sources of noise, or redundancies.

+
+
+
+ +
+

Confidentiality

+

Does the data set contain data that might be considered confidential? For example,

+
    +
  • Is the data protected by legal privilege or by doctor–patient confidentiality?
    • +
  • Does the data include the content of private/non-public communications of individuals.
    • +
+
+
+
+
+

Identification of Individuals

+

Is it possible to identify individuals directly or indirectly?

+
+
+
+
+

Data Sensitivity

+

Does the data set include sensitive data elements? Describe. Examples of sensitive data elements are +elements that directly/indirectly reveal:

+
    +
  • Locations.
    • +
  • Financial details.
    • +
  • Health details.
    • +
  • Biometric profiles.
    • +
  • Genetic profiles.
    • +
  • Government identification codes of individuals.
    • +
  • Criminal history.
    • +
  • Institutionally and/or commercially sensitive data.
    • +
  • Race or ethnic origin.
    • +
  • Sexual orientations.
    • +
  • Religious beliefs.
    • +
  • Political opinions.
    • +
  • Trade union memberships.
    • +
  • And more.
    • +
+
+
+
+
+

Distressing Data Elements

+

Does ``… the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?‘’ [1, 2]

+
+
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+
+
+
+
+
+
+
+
+
+
+ + +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/data/controls.html b/docs/build/html/data/controls.html new file mode 100644 index 0000000..5b9be3a --- /dev/null +++ b/docs/build/html/data/controls.html @@ -0,0 +1,244 @@ + + + + + + + + + + Controls — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Controls

+
+

Do “… any export controls or other regulatory restrictions apply to the dataset or to individual instances?” [1, 2]   +Please, study Section 3.6 of [2] for more details.

+
+
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+
+
+
+
+
+
+
+
+
+ + +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/data/data.html b/docs/build/html/data/data.html new file mode 100644 index 0000000..6a62472 --- /dev/null +++ b/docs/build/html/data/data.html @@ -0,0 +1,307 @@ + + + + + + + + + + Data & Datasheets — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Data & Datasheets

+
+

The datasheet of a dataset outlines the data set’s provenance, lineage, and a bit more. Each dataset must have its own datasheet. Gebru & colleagues first proposed Datasheets for Datasets, for machine learning products or projects, during the years 2018/2019. Following feedback from a variety of institutions, industries, agencies, etc., a baseline Datasheets for Datasets outline was released.[1], [2] Each datasheet consists of a set of questions, and the datasheet’s primary objective vis-à-vis data set creator is to:

+
+

… encourage data set creators to reflect carefully upon (a) the “process of creating, distributing, and maintaining a dataset”, and (b) “any underlying assumptions, potential risks or harms, and implications of use“

+
+

The primary objective vis-à-vis data set user is to:

+
+

… ensure that the data set user has the information required to “… make informed decisions about using a dataset.”

+
+

This chapter consists of a set of sections. The sections, except the Natural Language Processing section, reflect the groupings of the questions in the latest datasheet version. [1], [2], [3] The questions of the natural language processing (NLP) section apply to NLP projects only. The questions were developed by Bender & Friedman. [4]

+
+
+
+

Content

+ +
+
+
+
+
+
+
+
+
+
+
+ +
+ + +
+ +
+ + +
+
+ +
+ +
+ + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/data/maintenance.html b/docs/build/html/data/maintenance.html new file mode 100644 index 0000000..cd3748d --- /dev/null +++ b/docs/build/html/data/maintenance.html @@ -0,0 +1,252 @@ + + + + + + + + + + Maintenance — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+ + + + + +
+ +
+ +
+ + + +
+
+
+ +
+
+

Maintenance

+
+

The focus herein is the maintenance details of the data set. Please, study Section 3.7 of [2] for more details. A few +examples:

+
    +
  • Who is the data set owner, curator, or manager?
    • +
  • Does the data set have an erratum point?
    • +
  • Does the data set have an ad-hoc/regular/irregular update schedule? If yes, please describe.
    • +
  • Does the data set have retention restrictions, deletion norms, etc.? If yes, please describe.
    • +
  • And more.
    • +
+

In a nutshell, describe all details pertinent to model operations.

+
+
References +
    +
  1. Datasheets for Datasets, Communications of the ACM, 2021, Volume 64, Issue 12, pages 86 – 92
    2. +
  2. Datasheets for Datasets, arXiv:1803.09010v8, 2021, updated datasheet appendix
    3. +
+
+
+
+
+
+
+
+
+
+
+ + +
+ +
+ + +
+
+ +
+ +
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+ +
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+

Motivation

+
+

The motivation, or deliberations, that led to the collection and storage of a data set can impact its appropriateness +for model development. For example, the outcome of a deliberation might have been to:

+
    +
  • Focus on, collect, and store, the data of a particular strata of a population.
    • +
  • Retain aggregated records only.
    • +
+

The former will limit the applicability of any models that depend on the data set, using the latter is ill-advised due to lost information and ecological fallacy risks. Additionally, it is important to beware of funding/sponsorship bias.

+

Hence

+
    +
  1. Why was the data set created? +
      +
    • Was there a specific task in mind?
      • +
    • Was there a specific gap that needed to be filled?
      • +
    +
    2. +
  2. Who created the data set? +
      +
    • In brief, which team, organisation, research group, etc., created the dataset? Was it created for another entity?
      • +
    +
    3. +
  3. Who funded the creation of the dataset?
    4. +
+
+
+
References +
    +
  1. External Validity, Research Methods Knowledge Base
    2. +
  2. Industry Sponsorship Bias
    3. +
  3. What is epistemically wrong with research affected by sponsorship bias? The evidential account.
    4. +
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+ +
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+

Natural Language Processing

+
+

Background

+

This section only applies to data sets for natural language processing (NLP). The focus being the proposed data +statements of Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science by E Bender & B Friedman.

+

Please study the description of each statement within Section 5. Subsequently, and per data set, record the +applicable data statements. Brief excerpts below.

+
+
+
+

Data Statement Queries in Brief

+
+

Curation Rationale

+

Why was the data collected? In brief, which “… texts were included and what were the goals in selecting +texts, both in the original collection and in any further sub-selection?” Impacts inference.

+
+
+
+

Language Variety

+

This is important because languages “… differ from each other in structural ways that can interact with NLP algorithms”.
+Additionally, and within a language, “… regional or social dialects can also show great variation”.

+
+
+
+

Speaker Demographic

+

Important in cases wherein “… variation (in pronunciation, prosody, word choice, and grammar) correlates with speaker-demographic characteristics”.

+
+
+
+

Annotator Demographic

+

In brief, what are the “… demographic characteristics of the annotators and [the] annotation guideline developers?” These are critical questions because they give an insight into “… their experience with language and thus their perception of what they are annotating”.

+
+
+
+

Speech Situation

+

Mainly because “… characteristics of the speech situation can affect linguistic structure and patterns …”.

+
+
+
+

Text Characteristics

+

Mainly because “… genre and topic influence the vocabulary and structural characteristics of texts”.

+
+
+
+

Recording Quality

+

If applicable, outline recording quality.

+
+
+
+

Provenance Appendix

+

If the data set was derived from an existing data set “… the data statements for the source datasets should be included as an appendix”

+
+
+
+

Other Pertinent Details

+

Record pertinent details that the previous statements do not request.

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+ + + +
+ + + + + +
+ +
+ +
+ +
    + + +
  • Systems ⁄ 
    • + +
  • Index
    • + + + +
  • + + + +
    • + +
+ +
+
+
+ + +

Index

+ +
+ +
+ + +
+ +
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+ + +
+

+ + © Copyright 2024, greyhypotheses +
+ + + + Built with Sphinx using a + + theme + + provided by Revitron. + +

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+
+ + _images/logo.svg
+

Projects & Machine Learning

+
+

Questions and notes for potential machine learning dependent business projects

+
+
+
+
+
+

Introduction +Index

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+https://img.shields.io/github/v/tag/premodelling/systems?label=Release&style=flat-square&labelColor=292b2e&color=292b2e +
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+ + © Copyright 2024, greyhypotheses +
+ + + + Built with Sphinx using a + + theme + + provided by Revitron. + +

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+ + + + \ No newline at end of file diff --git a/docs/build/html/introduction/introduction.html b/docs/build/html/introduction/introduction.html new file mode 100644 index 0000000..0846ee1 --- /dev/null +++ b/docs/build/html/introduction/introduction.html @@ -0,0 +1,283 @@ + + + + + + + + + + Introduction — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
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+ +
+

Introduction

+
+

Background

+

These pages complement an in-development online project details hub. The hub’s focus is business machine learning +projects, and its aim is the simplification of requirements elicitation; it has sets of questions pertinent to machine +learning dependent projects. An external partner is developing the online hub, which will be a component of the Scottish +Artificial Intelligence Register platform. [1]

+

In future, we hope the project details hub will reduce the likelihood that a project will fail, i.e., will aid +successful deployments of machine learning solutions within

+
    +
  • Applications that directly or indirectly serve the public.
    • +
  • Back-end operations or internal applications.
    • +
  • Policy teams, vis-à-vis providing evidence/insights based on the automatic and continuous executions of simple and complex models.
    • +
+
+

These pages are not as comprehensive as the hub. The objective: share a few help notes with colleagues whilst we await +the hub’s first release. However, because these pages might be a helpful reference point, we will continuously update its content.

+
+
+
+
+

Purpose

+

These pages complement the upcoming project details hub by explaining selections of the questions therein. Together, +the expectation is that prospective clients/collaborators will (a) have well detailed project requirements, (b) carefully consider the time commitment, collaboration commitment, cost, range of expertise, project details, etc., that are required for machine learning dependent projects. Additionally, stakeholders will be able to make well informed decisions about whether to proceed with a project.

+
+

A project’s details are especially critical to conducting a well-informed technical feasibility & economic +viability assessment before proceeding. It is also important to beware that the data science aspect of the project is only a part of the project, ignoring this leads to project failures due to, e.g.,

+
    +
  • Under-planning.
    • +
  • Ambiguous project scope/design details.
    • +
  • Project pilots that are neither informed by nor tests the deployment goal.
    • +
  • Underestimating, or ignoring, cost.
    • +
  • Ignoring the critical importance of intensive collaboration between the business, data science, operations, etc., +teams.
    • +
+
+

One of the first sections of these pages is Deployment Goal. Colleagues may question why. Here are a few references that +capture our experience of why business data science projects fail, and the advantages of starting with ``the end in mind’’ [2]

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Project Plausibility

+
+
+ %%{ init: { 'flowchart': { 'curve': 'monotoneX'} } }%% +flowchart LR + id0([start]) --> id1{<span title="Does the potential project have a budget?">budget</span>} + id1 -- yes --> id2{<span title="Is the budget, project timebox, and the collaboration commitment plausible?">budget,<br>time</span>} + id1 -- no --> id3([terminate]) + id2 -- no --> id3 + id2 -- yes --> id5(<span title="The project scope/design details">project<br>details</span>) + id5 --> id6{<span title="Is the potential project addressable via machine learning, technically feasible, and economically viable?">feasible?</span>} + id6 -- no --> id3 + id6 -- yes --> id8(next <br> steps) + + classDef default fill:#333333,stroke:#333333,stroke-width:0px,color:#ffffff,font-size:11pt; +

+
An illustration of considerations vis-à-vis a potential machine learning dependent project. The project details +inform the feasibility assessment.
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+ + + +
+ + + + + +
+ +
+ +
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    + + +
  • Systems ⁄ 
    • + +
  • Model ⁄ 
    • + +
  • Aim
    • + + + +
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    • + +
+ +
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+ +
+
+

Aim

+
+

What is the machine learning model’s aim? For example:

+
+

To determine the probability that a retinal image is a member of a diabetic retinopathy class; the classes are no +(diabetic retinopathy), mild, moderate, severe, proliferative.
+\(\sum\limits_{l \: \in \: L} p(\mathcal{C}_{l}) = 1\), \(\quad L =\) {no, mild, moderate, severe, proliferative}

+
+
+

Note, the model’s aim matches the deployed product’s model output +expectations.

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+ +
+
+

Business Metrics

+
+
+

Note

+

In progress

+
+

A later version of this hub will have more details, and an example. In brief, business performance metrics may focus on, e.g.,

+
    +
  • Financial savings, return on investment, due time saving, error reduction, etc.
    • +
  • The cost of mistakes over time, e.g., misdiagnosis over time.
    • +
+

Considering the retinal images classification example. The client may use the relationship between diabetic retinopathy severity, and the life-time cost of sight loss prevention/sight loss delay, to define a set of business metrics for model evaluation.

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+ + + +
+ + + + + +
+ +
+ +
+ +
    + + +
  • Systems ⁄ 
    • + +
  • Model
    • + + + +
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Model

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Performance Metrics

+
+

In Progress

+
+
+
+
+

Model Performance Metrics

+

These are the standard technical metrics. Referring back to the retinal images classification example, we may define the performance metrics criteria as follows: [1]

+
+
EXAMPLE
Based on the (a) error matrix measures, i. +e., True Positive, False Negative, False Positive, True Negative, and (b) a threshold defined as the intersection of precision & sensitivity, the expected criteria are:
+
Overarching Criteria
+
Precision > 0.9, Sensitivity (True Positive Rate) > 0.9, Specificity (True Negative Rate) > 0.9, Youden's J Statistic > 0.9, and False Positive Rate < 0.1.
+
Disaggregated Criteria
+
For each class, and by the same overarching threshold value, ensure that each criterion metric value is within its defined range; as outlined above.
+
+
+
+
+
+

Model Card

+

This is for auditing purposes. A model’s model card should summarise the artefacts of the released, in-use, +in-production, model. A model card example:

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Critical Preliminaries

+
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Problem Statement

+

What is the problem, and why is it important to solve this problem?

+
+
+
+

Outcome Expectations

+

What are the potential product’s outcome expectations, e.g.,

+
+ An increase in the detection of early-stage, i.e. mild, diabetic retinopathy, hence the prevention of sight loss amongst a greater percentage of diabetic patients. +
+
+

The underlying aim might be, e.g.,

+
+ To reduce the mis-classification rate of retinal images of diabetic patients to ≤ 1.25% per class per quarter. +
+
+
+
+

Deployment Goal

+

What is the aim, expected output, of the product’s model? Example:

+
+ A retinal image's diabetic retinopathy classification probability per class. +
+
+

What will the deployed product present to the optician? Or rather, how will the model’s output be used? Considering the example above, the client’s opticians might expect: [1]

+
    +
  • The classification probability per class.
    • +
  • The definition of each class.
    • +
  • The classification; vis-à-vis the highest classification probability.
    • +
  • The preliminary treatment pathway of the classification, according to the rules of the optician's institution.
    • +
  • The retinal image.
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Availability

+

The systems engineering body of knowledge defines availability as the

+
+

“… probability that a repairable system or system element is operational at a given point-in-time under a given set of environmental conditions”

+
+

Herein, define the system’s availability expectations. Availability is a function of reliability and maintainability, i.e.,

+
+\[ +availability = \frac{MTBF}{MTBF + MTTR} +\]
+

whereby

+
    +
  • The mean time between failure is a reliability metric.
    • +
  • The mean time to repair is a maintainability metric.
    • +
+
+
+Availability Metrics +
+
+
+
Availability metrics illustration by Amazon. Note: MTBF (Mean Time Between Failure) is a reliability metric, MTTR (Mean Time To Repair) is a maintainability metric, MTBF/(MTBF + MTTR) is a definition of availability, MTTD (Mean Time To Detection) +
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References +
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  1. Availability
    2. +
  2. Understanding Availability
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Context & Architecture

+

Additionally, include data flow diagrams.

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System Context Diagram

+

The system context diagram illustrates how the service node of the system interacts with the world it is serving; a +slightly complex example.

+
+
+
+

Architecture

+

The architecture, which outlines integrations with other systems, modelling stages, data flows, monitoring node, +re-training node, etc.

+Machine Learning Dependent Business Project +
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A platform agnostic example courtesy of Amazon Web Services; scroll down. +
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In progress

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Functional Requirements

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A functional requirement specifies what a deployed product/system must do.

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Note, (a) a system may have more than one requirement, and (b) each requirement must have at least one criterion. The Volere Specification defines a criterion, i.e., fit criterion, as an objective measure

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“… for defining the meaning of a requirement, and eventually testing whether a given solution satisfies the original requirement.” (Volere Specification)

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The deployment goal dictates what the product/system will be, and the functional requirements thereof.

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Maintainability & Resilience

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Definitions

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Maintainability

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In the systems engineering body of knowledge the basic definition of maintainability is the

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“ … probability that a given maintenance action for an item under given usage conditions can be performed within a stated time interval when the maintenance is performed under stated conditions using stated procedures and resources.”

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Note that maintainability comprises serviceability & repairability, i.e.,

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“… the ease of conducting scheduled inspections and servicing”

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and

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“… the ease of restoring service after a failure”

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Resilience

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The resilience of a system can be defined as “… the ability to maintain capability in the face of a disruption” [The Systems Engineering Body of Knowledge.]

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The body of knowledge outlines the objectives of a system’s resilience plan. Study the Achieving Resilience details, therein the fundamental objectives are:

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  • Avoid: eliminate or reduce exposure to [stress](https://sebokwiki.org/wiki/Stress_(glossary))
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Outlining Specifications for Machine Learning Systems

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In the context of machine learning, the foci herein are

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  • The model drift and data drift strategy. The blog Model Drift: Best Practices to Improve Machine Learning Model Performance includes an excellent discussion about how to monitor machine learning systems. Drift details must be outlined vis-à-vis model drift and data drift. Model drift monitoring is via the evaluation metrics detailed in the model performance metrics section. Whereas data drift is generally via statistical tests or custom models, depending on the data types in question.
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  • Continuous performance improvement strategy. For continuous model improvement purposes, the system architecture should include a re-training component. If it does not, the monitoring system should have an automatic end-of-life alert, triggered by one or more evaluation metrics falling-out of their constraints.
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  • Maintenance services/agreements. For general maintainability concepts, study the reliability, maintainability, and availability page of the systems engineering body of knowledge for the expectations herein; and the maintenance and resilience metrics thereof.
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Altogether, define maintenance and resilience metrics vis-à-vis the system/product, model drift & data drift. The +references link to metrics definitions examples.

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  1. Maintenance Metrics
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Performance & Scalability

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Performance

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What are the performance expectations? Considerations include:[1]

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Scalability

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What are the scalability expectations of the system?

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Reliability

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In the systems engineering body of knowledge the basic definition of reliability is

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“ … the probability of a product performing its intended function under stated conditions without failure for a given time period”.

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In the context of machine learning systems, the reliability specification of each intended function, i.e., each functional requirement, must detail/specify the

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  • Reliability metrics
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This aids (a) the design of possible in-built solutions that mitigate factors that affect reliability, and (b) system monitoring strategy, and the operations thereof. Study the reliability, maintainability, and availability page of the systems engineering body of knowledge for the expectations herein.

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Reliability, Maintainability, Resilience, Availability

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The systems engineering body of knowledge notes that reliability, maintenance, and availability attributes are three of the core attributes that affect the utility of a system and its economic life-cycle cost. In the context of machine learning products, reliability, maintenance, availability, and resilience specifications are critical to items such as

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  • drift monitoring & continuous model performance improvement
    • +
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and the costs thereof. The tasks herein will require the aid of a reliability engineer, or similar. The aim being to

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  • Define the reliability, maintenance, availability, and resilience expectations via metrics constraints.
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Content

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System Security

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System security addresses

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  • Accountability: All system actions are tracked and stored, hence breaches can be investigated.
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Therefore, the detail the prospective system’s:

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  1. Activity Tracking System: For auditability requirements, etc.
    2. +
  2. System Protection & Privacy Layer: Ensuring adherence to laws and regulations, e.g., GDPR (General Data Protection Regulation), HIPAA (Health Insurance Portability and Accountability Act), etc. +
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    • Legal Compliance: "… adherence to laws and regulations that are enforced by the government".
      • +
    • Regulatory Compliance: "… adherence to rules and regulations set forth by regulatory bodies that oversee specific industries".
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    • Privacy: Encryption
      • +
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  3. Authentication System: That ascertains a user's authenticity.
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Usability & Accessibility

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The foci herein are the requirements/constraints vis-à-vis

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  • Interface: The constraints herein depend on (a) the audience, e.g., children, young adults, executives, (b) the organisation's presentation or branding norms, (c) and more. [1]
    • +
  • Style: The audience, e.g., children, young adults, executives, dictates the constraints herein. [1]
    • +
  • Ease of use: Each ease of use constraint must have fit criteria for acceptance testing. [1]
    • +
  • Learnability: Again, each constraint must have fit criteria for acceptance testing. [1]
    • +
  • Accessibility; For more details study the Web Content Accessibility Guidelines (WCAG) [2]
    • +
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The client team should have an unambiguous outline per requirement/constraint.

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Risks

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In Progress

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The online project details hub focuses on both machine learning algorithm risks and the risks of the overarching project.

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+ + © Copyright 2024, greyhypotheses +
+ + + + Built with Sphinx using a + + theme + + provided by Revitron. + +

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+ + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js new file mode 100644 index 0000000..22735ff --- /dev/null +++ b/docs/build/html/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Aim": [[11, "aim"], [29, "aim"], [30, "aim"]], "Annotator Demographic": [[7, "annotator-demographic"]], "Architecture": [[18, "architecture"]], "Availability": [[17, "availability"]], "Background": [[7, "background"], [9, "background"]], "Budget Constraint": [[0, "budget-constraint"]], "Business Metrics": [[12, "business-metrics"]], "Composition": [[2, "composition"]], "Confidentiality": [[2, "confidentiality"]], "Content": [[4, null], [25, null]], "Context & Architecture": [[18, "context-architecture"]], "Controls": [[3, "controls"]], "Cost": [[19, "cost"]], "Critical Preliminaries": [[15, "critical-preliminaries"]], "Curation Rationale": [[7, "curation-rationale"]], "Data & Datasheets": [[4, "data-datasheets"]], "Data Collection": [[1, "data-collection"]], "Data Sensitivity": [[2, "data-sensitivity"]], "Data Statement Queries in Brief": [[7, "data-statement-queries-in-brief"]], "Definitions": [[21, "definitions"]], "Deployment Goal": [[15, "deployment-goal"]], "Distressing Data Elements": [[2, "distressing-data-elements"]], "Errors": [[2, "errors"]], "Functional Requirements": [[20, "functional-requirements"]], "Identification of Individuals": [[2, "identification-of-individuals"]], "Implementation Environment Constraints": [[0, "implementation-environment-constraints"]], "In Progress": [[14, null], [28, null]], "Introduction": [[9, "introduction"]], "Is the data set a sample of a larger data set?": [[2, "is-the-data-set-a-sample-of-a-larger-data-set"]], "Language Variety": [[7, "language-variety"]], "Licences & Fees": [[2, "licences-fees"]], "Lineage": [[2, "lineage"]], "Maintainability": [[21, "maintainability"]], "Maintainability & Resilience": [[21, "maintainability-resilience"]], "Maintenance": [[5, "maintenance"]], "Model": [[13, "model"]], "Model Card": [[14, "model-card"]], "Model Performance Metrics": [[14, "model-performance-metrics"]], "Motivation": [[6, "motivation"]], "Natural Language Processing": [[7, "natural-language-processing"]], "Non Functional Requirements": [[16, "non-functional-requirements"]], "Notes": [[29, "notes"], [30, "notes"]], "Other Pertinent Details": [[7, "other-pertinent-details"]], "Outcome Expectations": [[15, "outcome-expectations"]], "Outlining Specifications for Machine Learning Systems": [[21, "outlining-specifications-for-machine-learning-systems"]], "People": [[29, "people"]], "Performance": [[22, "performance"]], "Performance & Scalability": [[22, "performance-scalability"]], "Performance Metrics": [[14, "performance-metrics"]], "Pre-processed?": [[2, "pre-processed"]], "Problem Statement": [[15, "problem-statement"]], "Profiles of Instances": [[2, "profiles-of-instances"]], "Project 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"scalabl": 22, "secur": 26, "sensit": 2, "set": 2, "situat": 7, "solut": 0, "speaker": 7, "specif": 21, "speech": 7, "split": 2, "stakehold": [30, 31], "statement": [7, 15], "system": [18, 21, 26], "text": 7, "time": 0, "usabl": 27, "varieti": 7, "what": 2}}) \ No newline at end of file diff --git a/docs/build/html/stakeholders/people.html b/docs/build/html/stakeholders/people.html new file mode 100644 index 0000000..ddf6fb0 --- /dev/null +++ b/docs/build/html/stakeholders/people.html @@ -0,0 +1,261 @@ + + + + + + + + + + People — <img src="../_static/icon.svg" alt="Systems"> 0.1 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Aim

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Identify and record all the project’s stakeholders.

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The stakeholders are the people who (Volere)

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Examples include:

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  • The client. The entity (a) the system/product is being built for, and (b) that will pay for the system/product.
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  • The signatories. The people who need to sign-off aspects of the project, e.g., system security, data access, etc.
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  • The end users. The people who will interact with the product.
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The Open Group Architecture Framework (TOGAF) hosts a detailed discussion of stakeholder management. The discussion includes a sample stakeholder map for an imaginary project. It is a good guide to discerning a project’s stakeholders and their import.

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References +
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  1. Stakeholder Management, Chapter 21, The Open Group Architecture Framework (TOGAF) Standard, Version 9.2
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  2. Got stake?, by Forman, J. B. & Discenza, R., 2012
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Stakeholders Register

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Aim

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Create a stakeholder register.

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Notes

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The stakeholder register is a

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+ … project document that includes information about project stakeholders including an assessment and classification of project stakeholders. [1] +
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The details per stakeholder should include: [2]

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  • Name
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  • Role in employing organisation.
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  • The stakeholder's employing organisation department.
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  • Role in project.
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  • And, any other items pertinent to constructive stakeholder interactions.
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References +
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  1. A Guide to the Project Management Body of Knowledge, Seventh Edition, 2021, The Standard for Project Management.
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  2. Got stake?, by J. B. Forman, & R. Discenza, 2012
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