All modules for which code is available
71 |- diPLSlib.functions 72 |
- diPLSlib.models 73 |
- diPLSlib.utils.misc 74 |
69 |
70 |
77 |
28 |
53 |
54 |
58 |
59 |
132 |
133 |
138 |
139 |
140 |
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/doc/_build/html/_static/language_data.js:
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1 | /*
2 | * This script contains the language-specific data used by searchtools.js,
3 | * namely the list of stopwords, stemmer, scorer and splitter.
4 | */
5 |
6 | var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
7 |
8 |
9 | /* Non-minified version is copied as a separate JS file, if available */
10 |
11 | /**
12 | * Porter Stemmer
13 | */
14 | var Stemmer = function() {
15 |
16 | var step2list = {
17 | ational: 'ate',
18 | tional: 'tion',
19 | enci: 'ence',
20 | anci: 'ance',
21 | izer: 'ize',
22 | bli: 'ble',
23 | alli: 'al',
24 | entli: 'ent',
25 | eli: 'e',
26 | ousli: 'ous',
27 | ization: 'ize',
28 | ation: 'ate',
29 | ator: 'ate',
30 | alism: 'al',
31 | iveness: 'ive',
32 | fulness: 'ful',
33 | ousness: 'ous',
34 | aliti: 'al',
35 | iviti: 'ive',
36 | biliti: 'ble',
37 | logi: 'log'
38 | };
39 |
40 | var step3list = {
41 | icate: 'ic',
42 | ative: '',
43 | alize: 'al',
44 | iciti: 'ic',
45 | ical: 'ic',
46 | ful: '',
47 | ness: ''
48 | };
49 |
50 | var c = "[^aeiou]"; // consonant
51 | var v = "[aeiouy]"; // vowel
52 | var C = c + "[^aeiouy]*"; // consonant sequence
53 | var V = v + "[aeiou]*"; // vowel sequence
54 |
55 | var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
56 | var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
57 | var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
58 | var s_v = "^(" + C + ")?" + v; // vowel in stem
59 |
60 | this.stemWord = function (w) {
61 | var stem;
62 | var suffix;
63 | var firstch;
64 | var origword = w;
65 |
66 | if (w.length < 3)
67 | return w;
68 |
69 | var re;
70 | var re2;
71 | var re3;
72 | var re4;
73 |
74 | firstch = w.substr(0,1);
75 | if (firstch == "y")
76 | w = firstch.toUpperCase() + w.substr(1);
77 |
78 | // Step 1a
79 | re = /^(.+?)(ss|i)es$/;
80 | re2 = /^(.+?)([^s])s$/;
81 |
82 | if (re.test(w))
83 | w = w.replace(re,"$1$2");
84 | else if (re2.test(w))
85 | w = w.replace(re2,"$1$2");
86 |
87 | // Step 1b
88 | re = /^(.+?)eed$/;
89 | re2 = /^(.+?)(ed|ing)$/;
90 | if (re.test(w)) {
91 | var fp = re.exec(w);
92 | re = new RegExp(mgr0);
93 | if (re.test(fp[1])) {
94 | re = /.$/;
95 | w = w.replace(re,"");
96 | }
97 | }
98 | else if (re2.test(w)) {
99 | var fp = re2.exec(w);
100 | stem = fp[1];
101 | re2 = new RegExp(s_v);
102 | if (re2.test(stem)) {
103 | w = stem;
104 | re2 = /(at|bl|iz)$/;
105 | re3 = new RegExp("([^aeiouylsz])\\1$");
106 | re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
107 | if (re2.test(w))
108 | w = w + "e";
109 | else if (re3.test(w)) {
110 | re = /.$/;
111 | w = w.replace(re,"");
112 | }
113 | else if (re4.test(w))
114 | w = w + "e";
115 | }
116 | }
117 |
118 | // Step 1c
119 | re = /^(.+?)y$/;
120 | if (re.test(w)) {
121 | var fp = re.exec(w);
122 | stem = fp[1];
123 | re = new RegExp(s_v);
124 | if (re.test(stem))
125 | w = stem + "i";
126 | }
127 |
128 | // Step 2
129 | re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
130 | if (re.test(w)) {
131 | var fp = re.exec(w);
132 | stem = fp[1];
133 | suffix = fp[2];
134 | re = new RegExp(mgr0);
135 | if (re.test(stem))
136 | w = stem + step2list[suffix];
137 | }
138 |
139 | // Step 3
140 | re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
141 | if (re.test(w)) {
142 | var fp = re.exec(w);
143 | stem = fp[1];
144 | suffix = fp[2];
145 | re = new RegExp(mgr0);
146 | if (re.test(stem))
147 | w = stem + step3list[suffix];
148 | }
149 |
150 | // Step 4
151 | re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
152 | re2 = /^(.+?)(s|t)(ion)$/;
153 | if (re.test(w)) {
154 | var fp = re.exec(w);
155 | stem = fp[1];
156 | re = new RegExp(mgr1);
157 | if (re.test(stem))
158 | w = stem;
159 | }
160 | else if (re2.test(w)) {
161 | var fp = re2.exec(w);
162 | stem = fp[1] + fp[2];
163 | re2 = new RegExp(mgr1);
164 | if (re2.test(stem))
165 | w = stem;
166 | }
167 |
168 | // Step 5
169 | re = /^(.+?)e$/;
170 | if (re.test(w)) {
171 | var fp = re.exec(w);
172 | stem = fp[1];
173 | re = new RegExp(mgr1);
174 | re2 = new RegExp(meq1);
175 | re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
176 | if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
177 | w = stem;
178 | }
179 | re = /ll$/;
180 | re2 = new RegExp(mgr1);
181 | if (re.test(w) && re2.test(w)) {
182 | re = /.$/;
183 | w = w.replace(re,"");
184 | }
185 |
186 | // and turn initial Y back to y
187 | if (firstch == "y")
188 | w = firstch.toLowerCase() + w.substr(1);
189 | return w;
190 | }
191 | }
192 |
193 |
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/doc/_build/html/_static/sphinx_highlight.js:
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1 | /* Highlighting utilities for Sphinx HTML documentation. */
2 | "use strict";
3 |
4 | const SPHINX_HIGHLIGHT_ENABLED = true
5 |
6 | /**
7 | * highlight a given string on a node by wrapping it in
8 | * span elements with the given class name.
9 | */
10 | const _highlight = (node, addItems, text, className) => {
11 | if (node.nodeType === Node.TEXT_NODE) {
12 | const val = node.nodeValue;
13 | const parent = node.parentNode;
14 | const pos = val.toLowerCase().indexOf(text);
15 | if (
16 | pos >= 0 &&
17 | !parent.classList.contains(className) &&
18 | !parent.classList.contains("nohighlight")
19 | ) {
20 | let span;
21 |
22 | const closestNode = parent.closest("body, svg, foreignObject");
23 | const isInSVG = closestNode && closestNode.matches("svg");
24 | if (isInSVG) {
25 | span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
26 | } else {
27 | span = document.createElement("span");
28 | span.classList.add(className);
29 | }
30 |
31 | span.appendChild(document.createTextNode(val.substr(pos, text.length)));
32 | const rest = document.createTextNode(val.substr(pos + text.length));
33 | parent.insertBefore(
34 | span,
35 | parent.insertBefore(
36 | rest,
37 | node.nextSibling
38 | )
39 | );
40 | node.nodeValue = val.substr(0, pos);
41 | /* There may be more occurrences of search term in this node. So call this
42 | * function recursively on the remaining fragment.
43 | */
44 | _highlight(rest, addItems, text, className);
45 |
46 | if (isInSVG) {
47 | const rect = document.createElementNS(
48 | "http://www.w3.org/2000/svg",
49 | "rect"
50 | );
51 | const bbox = parent.getBBox();
52 | rect.x.baseVal.value = bbox.x;
53 | rect.y.baseVal.value = bbox.y;
54 | rect.width.baseVal.value = bbox.width;
55 | rect.height.baseVal.value = bbox.height;
56 | rect.setAttribute("class", className);
57 | addItems.push({ parent: parent, target: rect });
58 | }
59 | }
60 | } else if (node.matches && !node.matches("button, select, textarea")) {
61 | node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
62 | }
63 | };
64 | const _highlightText = (thisNode, text, className) => {
65 | let addItems = [];
66 | _highlight(thisNode, addItems, text, className);
67 | addItems.forEach((obj) =>
68 | obj.parent.insertAdjacentElement("beforebegin", obj.target)
69 | );
70 | };
71 |
72 | /**
73 | * Small JavaScript module for the documentation.
74 | */
75 | const SphinxHighlight = {
76 |
77 | /**
78 | * highlight the search words provided in localstorage in the text
79 | */
80 | highlightSearchWords: () => {
81 | if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
82 |
83 | // get and clear terms from localstorage
84 | const url = new URL(window.location);
85 | const highlight =
86 | localStorage.getItem("sphinx_highlight_terms")
87 | || url.searchParams.get("highlight")
88 | || "";
89 | localStorage.removeItem("sphinx_highlight_terms")
90 | url.searchParams.delete("highlight");
91 | window.history.replaceState({}, "", url);
92 |
93 | // get individual terms from highlight string
94 | const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
95 | if (terms.length === 0) return; // nothing to do
96 |
97 | // There should never be more than one element matching "div.body"
98 | const divBody = document.querySelectorAll("div.body");
99 | const body = divBody.length ? divBody[0] : document.querySelector("body");
100 | window.setTimeout(() => {
101 | terms.forEach((term) => _highlightText(body, term, "highlighted"));
102 | }, 10);
103 |
104 | const searchBox = document.getElementById("searchbox");
105 | if (searchBox === null) return;
106 | searchBox.appendChild(
107 | document
108 | .createRange()
109 | .createContextualFragment(
110 | '
60 |
131 |
61 |
70 |
130 |
71 |
115 |
129 |
72 |
73 |
74 |
81 |
111 |
112 |
113 |
114 | Python Module Index
75 | 76 |
77 | d
78 |
79 |
80 | | 83 | d | ||
| 87 | |
88 | diPLSlib | 89 | |
| 92 | |
93 | diPLSlib.functions | 94 | |
| 97 | |
98 | diPLSlib.models | 99 | |
| 102 | |
103 | diPLSlib.utils | 104 | |
| 107 | |
108 | diPLSlib.utils.misc | 109 | |
' + 111 | '' + 112 | _("Hide Search Matches") + 113 | "
" 114 | ) 115 | ); 116 | }, 117 | 118 | /** 119 | * helper function to hide the search marks again 120 | */ 121 | hideSearchWords: () => { 122 | document 123 | .querySelectorAll("#searchbox .highlight-link") 124 | .forEach((el) => el.remove()); 125 | document 126 | .querySelectorAll("span.highlighted") 127 | .forEach((el) => el.classList.remove("highlighted")); 128 | localStorage.removeItem("sphinx_highlight_terms") 129 | }, 130 | 131 | initEscapeListener: () => { 132 | // only install a listener if it is really needed 133 | if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; 134 | 135 | document.addEventListener("keydown", (event) => { 136 | // bail for input elements 137 | if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; 138 | // bail with special keys 139 | if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; 140 | if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { 141 | SphinxHighlight.hideSearchWords(); 142 | event.preventDefault(); 143 | } 144 | }); 145 | }, 146 | }; 147 | 148 | _ready(() => { 149 | /* Do not call highlightSearchWords() when we are on the search page. 150 | * It will highlight words from the *previous* search query. 151 | */ 152 | if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); 153 | SphinxHighlight.initEscapeListener(); 154 | }); 155 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # diPLSlib: A Python library for domain adaptation in multivariate calibration 2 | 3 |  4 |  5 | 6 |  7 | 8 | diPLSlib has been created to build privacy-preserving regression models and to perform calibration model maintenance by domain adaptation. It has a scikit-learn compatible API and features following methods: 9 | 10 | - (Multiple) Domain-invariant partial least squares regression (di-PLS/mdi-PLS) 11 | - Graph-based calibration transfer (GCT-PLS) 12 | - $(\epsilon, \delta)$-differentially private partial least squares regression (edPLS) 13 | 14 | **Citation**: If you use this library in your research, please cite the following reference: 15 | 16 | ``` 17 | Nikzad-Langerodi R.(2024). diPLSlib : A Python library for domain adaptation in multivariate calibration (version 2.4.1). URL: https://di-pls.readthedocs.io/ 18 | ``` 19 | or in Bibtex format : 20 | ```bibtex 21 | @misc{nikzad2024diPLSlib, 22 | author = {Nikzad-Langerodi, Ramin}, 23 | month = {12}, 24 | title = {diPLSlib: A Python library for domain adaptation in multivariate calibration}, 25 | url = {https://di-pls.readthedocs.io/}, 26 | year = {2024} 27 | } 28 | ``` 29 | 30 | # Installation 31 | ```bash 32 | pip install diPLSlib 33 | ``` 34 | 35 | # Quick Start 36 | ## How to apply di-PLS 37 | Train regression model 38 | ```python 39 | from diPLSlib.models import DIPLS 40 | from diPLSlib.utils import misc 41 | 42 | l = 100000 # or l = (10000, 100) Regularization 43 | m = DIPLS(A=2, l=l) 44 | m.fit(X, y, X_source, X_target) 45 | 46 | # Typically X=X_source and y are the corresponding response values 47 | ``` 48 | Apply the model 49 | ```python 50 | yhat_dipls = m.predict(X_test) 51 | err = misc.rmse(y_test, yhat_dipls) 52 | ``` 53 | 54 | ## How to apply mdi-PLS 55 | ```python 56 | from diPLSlib.models import DIPLS 57 | 58 | l = 100000 # or l = (5, 100, 1000) Regularization 59 | m = DIPLS(A=3, l=l, target_domain=2) 60 | m.fit(X, y, X_source, X_target) 61 | 62 | # X_target = [X1, X2, ... , Xk] is a list of target domain data 63 | # The parameter target_domain specifies for which domain the model should be trained (here X2). 64 | ``` 65 | 66 | ## How to apply GCT-PLS 67 | ```python 68 | from diPLSlib.models import GCTPLS 69 | 70 | # Training 71 | l = 10 # or l = (10, 10) Regularization 72 | m = GCTPLS(A=2, l=l) 73 | m.fit(X, y, X_source, X_target) 74 | 75 | # X_source and X_target hold the same samples measured in the source and target domain, respectively. 76 | ``` 77 | 78 | ## How to apply EDPLS 79 | ```python 80 | from diPLSlib.models import EDPLS 81 | 82 | # Training 83 | epsilon = 1 # Privacy loss 84 | delta = 0.05 # Failure probability of the privacy guarantee 85 | m = EDPLS(A=2, epsilon=epsilon, delta=delta) 86 | m.fit(X, y) 87 | ``` 88 | 89 | ## How to apply KDAPLS 90 | ```python 91 | from diPLSlib.models import KDAPLS 92 | 93 | # Training 94 | model = KDAPLS(A=2, l=0.5, kernel_params={"type": "rbf", "gamma": 0.001}) 95 | model.fit(x, y, xs, xt) 96 | ``` 97 | 98 | ## Examples 99 | For more examples, please refer to the [Notebooks](notebooks): 100 | 101 | - [Domain adaptation with di-PLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_diPLS.ipynb) 102 | - [Including multiple domains (mdi-PLS)](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_mdiPLS.ipynb) 103 | - [Implicit calibration transfer with GCT-PLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_gctPLS.ipynb) 104 | - [Model selection (with `scikit-learn`)](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_ModelSelection.ipynb) 105 | - [Privacy-preserving regression with EDPLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_edPLS.ipynb) 106 | - [Non-parametric domain adaptation with KDAPLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_daPLS.ipynb) 107 | 108 | # Documentation 109 | The documentation can be found [here](https://di-pls.readthedocs.io/en/latest/diPLSlib.html). 110 | 111 | # Acknowledgements 112 | The first version of di-PLS was developed by Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, Bernhard Moser and Susanne Saminger-Platz 113 | and published in: 114 | 115 | - *Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, and Susanne Saminger-Platz 116 | Analytical Chemistry 2018 90 (11), 6693-6701 https://doi.org/10.1021/acs.analchem.8b00498* 117 | 118 | Further refinements to the initial algorithm were published in: 119 | 120 | - *R. Nikzad-Langerodi, W. Zellinger, S. Saminger-Platz and B. Moser, "Domain-Invariant Regression Under Beer-Lambert's Law," 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 581-586, https://doi.org/10.1109/ICMLA.2019.00108.* 121 | 122 | - *Ramin Nikzad-Langerodi, Werner Zellinger, Susanne Saminger-Platz, Bernhard A. Moser, 123 | Domain adaptation for regression under Beer–Lambert’s law, 124 | Knowledge-Based Systems, Volume 210, 2020, https://doi.org/10.1016/j.knosys.2020.106447.* 125 | 126 | - *Bianca Mikulasek, Valeria Fonseca Diaz, David Gabauer, Christoph Herwig, Ramin Nikzad-Langerodi, 127 | "Partial least squares regression with multiple domains" Journal of Chemometrics 2023 37 (5), e3477, https://doi.org/10.13140/RG.2.2.23750.75845* 128 | 129 | - *Ramin Nikzad-Langerodi & Florian Sobieczky (2021). Graph‐based calibration transfer. Journal of Chemometrics, 35(4), e3319. https://doi.org/10.1002/cem.3319* 130 | 131 | - *Ramin Nikzad-Langerodi, Mohit Kumar, Du Nguyen Duy, and Mahtab Alghasi (2024), "(epsilon, delta)-Differentially Private Partial Least Squares Regression", unpublished.* 132 | 133 | # Contact us 134 | Bottleneck Analytics GmbH 135 | info@bottleneck-analytics.com 136 | 137 | -------------------------------------------------------------------------------- /diPLSlib.egg-info/PKG-INFO: -------------------------------------------------------------------------------- 1 | Metadata-Version: 2.1 2 | Name: diPLSlib 3 | Version: 2.5.0 4 | Summary: Python package for domain adaptation in multivariate regression 5 | Home-page: https://github.com/B-Analytics/di-PLS 6 | Author: Ramin Nikzad-Langerodi 7 | Author-email: ramin.nikzad-langerodi@scch.at 8 | Classifier: Programming Language :: Python :: 3 9 | Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3) 10 | Classifier: Operating System :: OS Independent 11 | Requires-Python: >=3.6 12 | Description-Content-Type: text/markdown 13 | License-File: LICENSE 14 | Requires-Dist: numpy 15 | Requires-Dist: matplotlib 16 | Requires-Dist: scipy 17 | Requires-Dist: scikit-learn 18 | 19 | # diPLSlib: A Python library for domain adaptation in multivariate calibration 20 | 21 |  22 |  23 | 24 |  25 | 26 | diPLSlib has been created to build privacy-preserving regression models and to perform calibration model maintenance by domain adaptation. It has a scikit-learn compatible API and features following methods: 27 | 28 | - (Multiple) Domain-invariant partial least squares regression (di-PLS/mdi-PLS) 29 | - Graph-based calibration transfer (GCT-PLS) 30 | - $(\epsilon, \delta)$-differentially private partial least squares regression (edPLS) 31 | 32 | **Citation**: If you use this library in your research, please cite the following reference: 33 | 34 | ``` 35 | Nikzad-Langerodi R.(2024). diPLSlib : A Python library for domain adaptation in multivariate calibration (version 2.4.1). URL: https://di-pls.readthedocs.io/ 36 | ``` 37 | or in Bibtex format : 38 | ```bibtex 39 | @misc{nikzad2024diPLSlib, 40 | author = {Nikzad-Langerodi, Ramin}, 41 | month = {12}, 42 | title = {diPLSlib: A Python library for domain adaptation in multivariate calibration}, 43 | url = {https://di-pls.readthedocs.io/}, 44 | year = {2024} 45 | } 46 | ``` 47 | 48 | # Installation 49 | ```bash 50 | pip install diPLSlib 51 | ``` 52 | 53 | # Quick Start 54 | ## How to apply di-PLS 55 | Train regression model 56 | ```python 57 | from diPLSlib.models import DIPLS 58 | from diPLSlib.utils import misc 59 | 60 | l = 100000 # or l = (10000, 100) Regularization 61 | m = DIPLS(A=2, l=l) 62 | m.fit(X, y, X_source, X_target) 63 | 64 | # Typically X=X_source and y are the corresponding response values 65 | ``` 66 | Apply the model 67 | ```python 68 | yhat_dipls = m.predict(X_test) 69 | err = misc.rmse(y_test, yhat_dipls) 70 | ``` 71 | 72 | ## How to apply mdi-PLS 73 | ```python 74 | from diPLSlib.models import DIPLS 75 | 76 | l = 100000 # or l = (5, 100, 1000) Regularization 77 | m = DIPLS(A=3, l=l, target_domain=2) 78 | m.fit(X, y, X_source, X_target) 79 | 80 | # X_target = [X1, X2, ... , Xk] is a list of target domain data 81 | # The parameter target_domain specifies for which domain the model should be trained (here X2). 82 | ``` 83 | 84 | ## How to apply GCT-PLS 85 | ```python 86 | from diPLSlib.models import GCTPLS 87 | 88 | # Training 89 | l = 10 # or l = (10, 10) Regularization 90 | m = GCTPLS(A=2, l=l) 91 | m.fit(X, y, X_source, X_target) 92 | 93 | # X_source and X_target hold the same samples measured in the source and target domain, respectively. 94 | ``` 95 | 96 | ## How to apply EDPLS 97 | ```python 98 | from diPLSlib.models import EDPLS 99 | 100 | # Training 101 | epsilon = 1 # Privacy loss 102 | delta = 0.05 # Failure probability of the privacy guarantee 103 | m = EDPLS(A=2, epsilon=epsilon, delta=delta) 104 | m.fit(X, y) 105 | ``` 106 | 107 | ## How to apply KDAPLS 108 | ```python 109 | from diPLSlib.models import KDAPLS 110 | 111 | # Training 112 | model = KDAPLS(A=2, l=0.5, kernel_params={"type": "rbf", "gamma": 0.001}) 113 | model.fit(x, y, xs, xt) 114 | ``` 115 | 116 | ## Examples 117 | For more examples, please refer to the [Notebooks](notebooks): 118 | 119 | - [Domain adaptation with di-PLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_diPLS.ipynb) 120 | - [Including multiple domains (mdi-PLS)](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_mdiPLS.ipynb) 121 | - [Implicit calibration transfer with GCT-PLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_gctPLS.ipynb) 122 | - [Model selection (with `scikit-learn`)](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_ModelSelection.ipynb) 123 | - [Privacy-preserving regression with EDPLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_edPLS.ipynb) 124 | - [Non-parametric domain adaptation with KDAPLS](https://github.com/B-Analytics/diPLSlib/blob/main/notebooks/demo_daPLS.ipynb) 125 | 126 | # Documentation 127 | The documentation can be found [here](https://di-pls.readthedocs.io/en/latest/diPLSlib.html). 128 | 129 | # Acknowledgements 130 | The first version of di-PLS was developed by Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, Bernhard Moser and Susanne Saminger-Platz 131 | and published in: 132 | 133 | - *Ramin Nikzad-Langerodi, Werner Zellinger, Edwin Lughofer, and Susanne Saminger-Platz 134 | Analytical Chemistry 2018 90 (11), 6693-6701 https://doi.org/10.1021/acs.analchem.8b00498* 135 | 136 | Further refinements to the initial algorithm were published in: 137 | 138 | - *R. Nikzad-Langerodi, W. Zellinger, S. Saminger-Platz and B. Moser, "Domain-Invariant Regression Under Beer-Lambert's Law," 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 581-586, https://doi.org/10.1109/ICMLA.2019.00108.* 139 | 140 | - *Ramin Nikzad-Langerodi, Werner Zellinger, Susanne Saminger-Platz, Bernhard A. Moser, 141 | Domain adaptation for regression under Beer–Lambert’s law, 142 | Knowledge-Based Systems, Volume 210, 2020, https://doi.org/10.1016/j.knosys.2020.106447.* 143 | 144 | - *Bianca Mikulasek, Valeria Fonseca Diaz, David Gabauer, Christoph Herwig, Ramin Nikzad-Langerodi, 145 | "Partial least squares regression with multiple domains" Journal of Chemometrics 2023 37 (5), e3477, https://doi.org/10.13140/RG.2.2.23750.75845* 146 | 147 | - *Ramin Nikzad-Langerodi & Florian Sobieczky (2021). Graph‐based calibration transfer. Journal of Chemometrics, 35(4), e3319. https://doi.org/10.1002/cem.3319* 148 | 149 | - *Ramin Nikzad-Langerodi, Mohit Kumar, Du Nguyen Duy, and Mahtab Alghasi (2024), "(epsilon, delta)-Differentially Private Partial Least Squares Regression", unpublished.* 150 | 151 | # Contact us 152 | Bottleneck Analytics GmbH 153 | info@bottleneck-analytics.com 154 | 155 | -------------------------------------------------------------------------------- /doc/_build/html/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
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1 | '''
2 | Some helper functions for diPLSlib
3 | '''
4 |
5 | import numpy as np
6 | from scipy.stats import norm
7 | from scipy.stats import f
8 | from math import exp, sqrt
9 | from scipy.special import erf
10 |
11 | def gengaus(length, mu, sigma, mag, noise=0):
12 | """
13 | Generate a Gaussian spectrum-like signal with optional random noise.
14 |
15 | Parameters
16 | ----------
17 |
18 | length : int
19 | Length of the generated signal.
20 |
21 | mu : float
22 | Mean of the Gaussian function.
23 |
24 | sigma : float
25 | Standard deviation of the Gaussian function.
26 |
27 | mag : float
28 | Magnitude of the Gaussian signal.
29 |
30 | noise : float, optional (default=0)
31 | Standard deviation of the Gaussian noise to be added to the signal.
32 |
33 | Returns
34 | -------
35 |
36 | signal : ndarray of shape (length,)
37 | The generated Gaussian signal with noise.
38 |
39 | Examples
40 | --------
41 |
42 | >>> from diPLSlib.utils.misc import gengaus
43 | >>> import numpy as np
44 | >>> import scipy.stats
45 | >>> signal = gengaus(100, 50, 10, 5, noise=0.1)
46 | """
47 |
48 | s = mag*norm.pdf(np.arange(length),mu,sigma)
49 | n = noise*np.random.rand(length)
50 | signal = s + n
51 |
52 | return signal
53 |
54 |
55 | def hellipse(X, alpha=0.05):
56 | """
57 | Compute the 95% confidence interval ellipse for a 2D scatter plot.
58 |
59 | Parameters
60 | ----------
61 |
62 | X : ndarray of shape (n_samples, 2)
63 | Matrix of data points.
64 |
65 | alpha : float, optional (default=0.05)
66 | Significance level for the confidence interval.
67 |
68 | Returns
69 | -------
70 |
71 | el : ndarray of shape (2, 100)
72 | Coordinates of the ellipse's points. To plot, use `plt.plot(el[0, :], el[1, :])`.
73 |
74 | Examples
75 | --------
76 |
77 | >>> import matplotlib.pyplot as plt
78 | >>> import numpy as np
79 | >>> from diPLSlib.utils.misc import hellipse
80 | >>> X = np.random.random((100, 2))
81 | >>> el = hellipse(X)
82 | >>> plt.scatter(X[:,0], X[:,1], label='Data points') # doctest: +ELLIPSIS
83 |
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77 |
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102 |
103 |
104 | diPLSlib documentation
76 |Introduction
78 |diPLSlib is a Python library designed for domain adaptation in multivariate calibration, with a focus on privacy-preserving regression and calibration model maintenance. It provides a scikit-learn compatible API and implements advanced methods for aligning data distributions across different domains, enabling robust and transferable regression models.
79 |The library features several state-of-the-art algorithms, including:
80 |-
81 |
Domain-Invariant Partial Least Squares (di-PLS/mdi-PLS): Aligns feature distributions between source and target domains to improve model generalization.
82 | Graph-based Calibration Transfer (GCT-PLS): Minimizes discrepancies between paired samples from different domains in the latent variable space.
83 | Kernel Domain Adaptive PLS (KDAPLS): Projects data into a reproducing kernel Hilbert space for non-parametric domain adaptation.
84 | Differentially Private PLS (EDPLS): Ensures privacy guarantees for sensitive data using the \((\epsilon, \delta)\)-differential privacy framework.
85 |
diPLSlib is suitable for chemometrics, analytical chemistry, and other fields where robust calibration transfer and privacy-preserving modeling are required. For more details, usage examples, and API documentation, please refer to the sections below.
87 |
88 |
99 | Contents:
89 |-
90 |
- diPLSlib package
-
91 |
- diPLSlib.functions module 92 |
- diPLSlib.models module 93 |
- diPLSlib.utils subpackage 94 |
- Module contents 95 |
97 |
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- Languages 18 | ${languages 19 | .map( 20 | (translation) => ` 21 |
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85 |
86 | Read the Docs
87 | v: ${config.versions.current.slug}
88 |
89 |
90 | diPLSlib — diPLSlib 2.5.0 documentation
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25 |
26 | Index — diPLSlib 2.5.0 documentation
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24 | diPLSlib.utils subpackage — diPLSlib 2.5.0 documentation
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127 | `;
128 |
129 | // Inject the generated flyout into the body HTML element.
130 | document.body.insertAdjacentHTML("beforeend", flyout);
131 |
132 | // Trigger the Read the Docs Addons Search modal when clicking on the "Search docs" input from inside the flyout.
133 | document
134 | .querySelector("#flyout-search-form")
135 | .addEventListener("focusin", () => {
136 | const event = new CustomEvent("readthedocs-search-show");
137 | document.dispatchEvent(event);
138 | });
139 | })
140 | }
141 |
142 | if (themeLanguageSelector || themeVersionSelector) {
143 | function onSelectorSwitch(event) {
144 | const option = event.target.selectedIndex;
145 | const item = event.target.options[option];
146 | window.location.href = item.dataset.url;
147 | }
148 |
149 | document.addEventListener("readthedocs-addons-data-ready", function (event) {
150 | const config = event.detail.data();
151 |
152 | const versionSwitch = document.querySelector(
153 | "div.switch-menus > div.version-switch",
154 | );
155 | if (themeVersionSelector) {
156 | let versions = config.versions.active;
157 | if (config.versions.current.hidden || config.versions.current.type === "external") {
158 | versions.unshift(config.versions.current);
159 | }
160 | const versionSelect = `
161 |
174 | `;
175 |
176 | versionSwitch.innerHTML = versionSelect;
177 | versionSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
178 | }
179 |
180 | const languageSwitch = document.querySelector(
181 | "div.switch-menus > div.language-switch",
182 | );
183 |
184 | if (themeLanguageSelector) {
185 | if (config.projects.translations.length) {
186 | // Add the current language to the options on the selector
187 | let languages = config.projects.translations.concat(
188 | config.projects.current,
189 | );
190 | languages = languages.sort((a, b) =>
191 | a.language.name.localeCompare(b.language.name),
192 | );
193 |
194 | const languageSelect = `
195 |
208 | `;
209 |
210 | languageSwitch.innerHTML = languageSelect;
211 | languageSwitch.firstElementChild.addEventListener("change", onSelectorSwitch);
212 | }
213 | else {
214 | languageSwitch.remove();
215 | }
216 | }
217 | });
218 | }
219 |
220 | document.addEventListener("readthedocs-addons-data-ready", function (event) {
221 | // Trigger the Read the Docs Addons Search modal when clicking on "Search docs" input from the topnav.
222 | document
223 | .querySelector("[role='search'] input")
224 | .addEventListener("focusin", () => {
225 | const event = new CustomEvent("readthedocs-search-show");
226 | document.dispatchEvent(event);
227 | });
228 | });
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92 | ${renderLanguages(config)}
93 | ${renderVersions(config)}
94 | ${renderDownloads(config)}
95 |
122 | 123 | Hosted by Read the Docs 124 | 125 |
126 | -
96 |
- On Read the Docs 97 |
- 98 | Project Home 99 | 100 |
- 101 | Builds 102 | 103 |
- 104 | Downloads 105 | 106 |
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108 |
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- 110 | 119 | 120 |
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133 | diPLSlib
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129 | -
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- diPLSlib package
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- diPLSlib.functions module
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79 |
convex_relaxation()
80 | dipals()
81 | edpls()
82 | kdapls()
83 | transfer_laplacian()
84 |
86 | - diPLSlib.models module
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87 |
DIPLS-
88 |
DIPLS.fit()
89 | DIPLS.predict()
90 | DIPLS.set_fit_request()
91 | DIPLS.set_score_request()
92 |
94 | EDPLS-
95 |
EDPLS.fit()
96 | EDPLS.predict()
97 | EDPLS.set_predict_request()
98 | EDPLS.set_score_request()
99 |
101 | GCTPLS-
102 |
GCTPLS.fit()
103 | GCTPLS.set_fit_request()
104 | GCTPLS.set_score_request()
105 |
107 | KDAPLS-
108 |
KDAPLS.fit()
109 | KDAPLS.predict()
110 | KDAPLS.set_fit_request()
111 | KDAPLS.set_score_request()
112 |
114 |
116 | - diPLSlib.utils subpackage
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117 |
- diPLSlib.utils subpackage
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118 |
- diPLSlib.utils.misc module 119 |
- Module contents 120 |
122 |
124 | - diPLSlib.utils subpackage
- Module contents 125 |
127 | - diPLSlib.functions module
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550 | .sig.c .s, .sig.c .sc,
551 | .sig.cpp .s, .sig.cpp .sc {
552 | color: #067D17;
553 | }
554 |
555 |
556 | /* -- other body styles ----------------------------------------------------- */
557 |
558 | ol.arabic {
559 | list-style: decimal;
560 | }
561 |
562 | ol.loweralpha {
563 | list-style: lower-alpha;
564 | }
565 |
566 | ol.upperalpha {
567 | list-style: upper-alpha;
568 | }
569 |
570 | ol.lowerroman {
571 | list-style: lower-roman;
572 | }
573 |
574 | ol.upperroman {
575 | list-style: upper-roman;
576 | }
577 |
578 | :not(li) > ol > li:first-child > :first-child,
579 | :not(li) > ul > li:first-child > :first-child {
580 | margin-top: 0px;
581 | }
582 |
583 | :not(li) > ol > li:last-child > :last-child,
584 | :not(li) > ul > li:last-child > :last-child {
585 | margin-bottom: 0px;
586 | }
587 |
588 | ol.simple ol p,
589 | ol.simple ul p,
590 | ul.simple ol p,
591 | ul.simple ul p {
592 | margin-top: 0;
593 | }
594 |
595 | ol.simple > li:not(:first-child) > p,
596 | ul.simple > li:not(:first-child) > p {
597 | margin-top: 0;
598 | }
599 |
600 | ol.simple p,
601 | ul.simple p {
602 | margin-bottom: 0;
603 | }
604 |
605 | aside.footnote > span,
606 | div.citation > span {
607 | float: left;
608 | }
609 | aside.footnote > span:last-of-type,
610 | div.citation > span:last-of-type {
611 | padding-right: 0.5em;
612 | }
613 | aside.footnote > p {
614 | margin-left: 2em;
615 | }
616 | div.citation > p {
617 | margin-left: 4em;
618 | }
619 | aside.footnote > p:last-of-type,
620 | div.citation > p:last-of-type {
621 | margin-bottom: 0em;
622 | }
623 | aside.footnote > p:last-of-type:after,
624 | div.citation > p:last-of-type:after {
625 | content: "";
626 | clear: both;
627 | }
628 |
629 | dl.field-list {
630 | display: grid;
631 | grid-template-columns: fit-content(30%) auto;
632 | }
633 |
634 | dl.field-list > dt {
635 | font-weight: bold;
636 | word-break: break-word;
637 | padding-left: 0.5em;
638 | padding-right: 5px;
639 | }
640 |
641 | dl.field-list > dd {
642 | padding-left: 0.5em;
643 | margin-top: 0em;
644 | margin-left: 0em;
645 | margin-bottom: 0em;
646 | }
647 |
648 | dl {
649 | margin-bottom: 15px;
650 | }
651 |
652 | dd > :first-child {
653 | margin-top: 0px;
654 | }
655 |
656 | dd ul, dd table {
657 | margin-bottom: 10px;
658 | }
659 |
660 | dd {
661 | margin-top: 3px;
662 | margin-bottom: 10px;
663 | margin-left: 30px;
664 | }
665 |
666 | .sig dd {
667 | margin-top: 0px;
668 | margin-bottom: 0px;
669 | }
670 |
671 | .sig dl {
672 | margin-top: 0px;
673 | margin-bottom: 0px;
674 | }
675 |
676 | dl > dd:last-child,
677 | dl > dd:last-child > :last-child {
678 | margin-bottom: 0;
679 | }
680 |
681 | dt:target, span.highlighted {
682 | background-color: #fbe54e;
683 | }
684 |
685 | rect.highlighted {
686 | fill: #fbe54e;
687 | }
688 |
689 | dl.glossary dt {
690 | font-weight: bold;
691 | font-size: 1.1em;
692 | }
693 |
694 | .versionmodified {
695 | font-style: italic;
696 | }
697 |
698 | .system-message {
699 | background-color: #fda;
700 | padding: 5px;
701 | border: 3px solid red;
702 | }
703 |
704 | .footnote:target {
705 | background-color: #ffa;
706 | }
707 |
708 | .line-block {
709 | display: block;
710 | margin-top: 1em;
711 | margin-bottom: 1em;
712 | }
713 |
714 | .line-block .line-block {
715 | margin-top: 0;
716 | margin-bottom: 0;
717 | margin-left: 1.5em;
718 | }
719 |
720 | .guilabel, .menuselection {
721 | font-family: sans-serif;
722 | }
723 |
724 | .accelerator {
725 | text-decoration: underline;
726 | }
727 |
728 | .classifier {
729 | font-style: oblique;
730 | }
731 |
732 | .classifier:before {
733 | font-style: normal;
734 | margin: 0 0.5em;
735 | content: ":";
736 | display: inline-block;
737 | }
738 |
739 | abbr, acronym {
740 | border-bottom: dotted 1px;
741 | cursor: help;
742 | }
743 |
744 | .translated {
745 | background-color: rgba(207, 255, 207, 0.2)
746 | }
747 |
748 | .untranslated {
749 | background-color: rgba(255, 207, 207, 0.2)
750 | }
751 |
752 | /* -- code displays --------------------------------------------------------- */
753 |
754 | pre {
755 | overflow: auto;
756 | overflow-y: hidden; /* fixes display issues on Chrome browsers */
757 | }
758 |
759 | pre, div[class*="highlight-"] {
760 | clear: both;
761 | }
762 |
763 | span.pre {
764 | -moz-hyphens: none;
765 | -ms-hyphens: none;
766 | -webkit-hyphens: none;
767 | hyphens: none;
768 | white-space: nowrap;
769 | }
770 |
771 | div[class*="highlight-"] {
772 | margin: 1em 0;
773 | }
774 |
775 | td.linenos pre {
776 | border: 0;
777 | background-color: transparent;
778 | color: #aaa;
779 | }
780 |
781 | table.highlighttable {
782 | display: block;
783 | }
784 |
785 | table.highlighttable tbody {
786 | display: block;
787 | }
788 |
789 | table.highlighttable tr {
790 | display: flex;
791 | }
792 |
793 | table.highlighttable td {
794 | margin: 0;
795 | padding: 0;
796 | }
797 |
798 | table.highlighttable td.linenos {
799 | padding-right: 0.5em;
800 | }
801 |
802 | table.highlighttable td.code {
803 | flex: 1;
804 | overflow: hidden;
805 | }
806 |
807 | .highlight .hll {
808 | display: block;
809 | }
810 |
811 | div.highlight pre,
812 | table.highlighttable pre {
813 | margin: 0;
814 | }
815 |
816 | div.code-block-caption + div {
817 | margin-top: 0;
818 | }
819 |
820 | div.code-block-caption {
821 | margin-top: 1em;
822 | padding: 2px 5px;
823 | font-size: small;
824 | }
825 |
826 | div.code-block-caption code {
827 | background-color: transparent;
828 | }
829 |
830 | table.highlighttable td.linenos,
831 | span.linenos,
832 | div.highlight span.gp { /* gp: Generic.Prompt */
833 | user-select: none;
834 | -webkit-user-select: text; /* Safari fallback only */
835 | -webkit-user-select: none; /* Chrome/Safari */
836 | -moz-user-select: none; /* Firefox */
837 | -ms-user-select: none; /* IE10+ */
838 | }
839 |
840 | div.code-block-caption span.caption-number {
841 | padding: 0.1em 0.3em;
842 | font-style: italic;
843 | }
844 |
845 | div.code-block-caption span.caption-text {
846 | }
847 |
848 | div.literal-block-wrapper {
849 | margin: 1em 0;
850 | }
851 |
852 | code.xref, a code {
853 | background-color: transparent;
854 | font-weight: bold;
855 | }
856 |
857 | h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
858 | background-color: transparent;
859 | }
860 |
861 | .viewcode-link {
862 | float: right;
863 | }
864 |
865 | .viewcode-back {
866 | float: right;
867 | font-family: sans-serif;
868 | }
869 |
870 | div.viewcode-block:target {
871 | margin: -1px -10px;
872 | padding: 0 10px;
873 | }
874 |
875 | /* -- math display ---------------------------------------------------------- */
876 |
877 | img.math {
878 | vertical-align: middle;
879 | }
880 |
881 | div.body div.math p {
882 | text-align: center;
883 | }
884 |
885 | span.eqno {
886 | float: right;
887 | }
888 |
889 | span.eqno a.headerlink {
890 | position: absolute;
891 | z-index: 1;
892 | }
893 |
894 | div.math:hover a.headerlink {
895 | visibility: visible;
896 | }
897 |
898 | /* -- printout stylesheet --------------------------------------------------- */
899 |
900 | @media print {
901 | div.document,
902 | div.documentwrapper,
903 | div.bodywrapper {
904 | margin: 0 !important;
905 | width: 100%;
906 | }
907 |
908 | div.sphinxsidebar,
909 | div.related,
910 | div.footer,
911 | #top-link {
912 | display: none;
913 | }
914 | }
--------------------------------------------------------------------------------
/doc/_build/html/diPLSlib.utils.html:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
57 |
306 |
58 |
67 |
305 |
68 |
290 |
304 |
69 |
70 |
71 |
99 |
100 |
146 |
147 |
158 |
159 |
174 |
175 |
186 |
187 |
194 |
195 |
206 |
207 |
227 |
228 |
241 |
242 |
249 |
250 |
277 |
278 |
285 |
286 |
287 |
288 |
289 | Index
72 | 73 |
74 | C
75 | | D
76 | | E
77 | | F
78 | | G
79 | | H
80 | | K
81 | | M
82 | | P
83 | | R
84 | | S
85 | | T
86 |
87 |
88 | C
89 || 94 | |
|
98 |
D
101 |
|
122 | 145 | |
E
148 |
|
153 |
|
157 |
F
160 |
|
173 |
G
176 |
|
181 |
|
185 |
H
188 |
|
193 |
K
196 |
|
201 |
|
205 |
M
208 |
|
226 |
P
229 |
|
240 |
R
243 |
|
248 |
S
251 |
|
264 |
|
276 |
T
279 |
|
284 |
27 |
65 |
66 |
70 |
71 |
259 |
260 |
265 |
266 |
267 |
--------------------------------------------------------------------------------
/doc/_build/html/_static/searchtools.js:
--------------------------------------------------------------------------------
1 | /*
2 | * Sphinx JavaScript utilities for the full-text search.
3 | */
4 | "use strict";
5 |
6 | /**
7 | * Simple result scoring code.
8 | */
9 | if (typeof Scorer === "undefined") {
10 | var Scorer = {
11 | // Implement the following function to further tweak the score for each result
12 | // The function takes a result array [docname, title, anchor, descr, score, filename]
13 | // and returns the new score.
14 | /*
15 | score: result => {
16 | const [docname, title, anchor, descr, score, filename, kind] = result
17 | return score
18 | },
19 | */
20 |
21 | // query matches the full name of an object
22 | objNameMatch: 11,
23 | // or matches in the last dotted part of the object name
24 | objPartialMatch: 6,
25 | // Additive scores depending on the priority of the object
26 | objPrio: {
27 | 0: 15, // used to be importantResults
28 | 1: 5, // used to be objectResults
29 | 2: -5, // used to be unimportantResults
30 | },
31 | // Used when the priority is not in the mapping.
32 | objPrioDefault: 0,
33 |
34 | // query found in title
35 | title: 15,
36 | partialTitle: 7,
37 | // query found in terms
38 | term: 5,
39 | partialTerm: 2,
40 | };
41 | }
42 |
43 | // Global search result kind enum, used by themes to style search results.
44 | class SearchResultKind {
45 | static get index() { return "index"; }
46 | static get object() { return "object"; }
47 | static get text() { return "text"; }
48 | static get title() { return "title"; }
49 | }
50 |
51 | const _removeChildren = (element) => {
52 | while (element && element.lastChild) element.removeChild(element.lastChild);
53 | };
54 |
55 | /**
56 | * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
57 | */
58 | const _escapeRegExp = (string) =>
59 | string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
60 |
61 | const _displayItem = (item, searchTerms, highlightTerms) => {
62 | const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
63 | const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
64 | const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
65 | const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
66 | const contentRoot = document.documentElement.dataset.content_root;
67 |
68 | const [docName, title, anchor, descr, score, _filename, kind] = item;
69 |
70 | let listItem = document.createElement("li");
71 | // Add a class representing the item's type:
72 | // can be used by a theme's CSS selector for styling
73 | // See SearchResultKind for the class names.
74 | listItem.classList.add(`kind-${kind}`);
75 | let requestUrl;
76 | let linkUrl;
77 | if (docBuilder === "dirhtml") {
78 | // dirhtml builder
79 | let dirname = docName + "/";
80 | if (dirname.match(/\/index\/$/))
81 | dirname = dirname.substring(0, dirname.length - 6);
82 | else if (dirname === "index/") dirname = "";
83 | requestUrl = contentRoot + dirname;
84 | linkUrl = requestUrl;
85 | } else {
86 | // normal html builders
87 | requestUrl = contentRoot + docName + docFileSuffix;
88 | linkUrl = docName + docLinkSuffix;
89 | }
90 | let linkEl = listItem.appendChild(document.createElement("a"));
91 | linkEl.href = linkUrl + anchor;
92 | linkEl.dataset.score = score;
93 | linkEl.innerHTML = title;
94 | if (descr) {
95 | listItem.appendChild(document.createElement("span")).innerHTML =
96 | " (" + descr + ")";
97 | // highlight search terms in the description
98 | if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
99 | highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
100 | }
101 | else if (showSearchSummary)
102 | fetch(requestUrl)
103 | .then((responseData) => responseData.text())
104 | .then((data) => {
105 | if (data)
106 | listItem.appendChild(
107 | Search.makeSearchSummary(data, searchTerms, anchor)
108 | );
109 | // highlight search terms in the summary
110 | if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
111 | highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
112 | });
113 | Search.output.appendChild(listItem);
114 | };
115 | const _finishSearch = (resultCount) => {
116 | Search.stopPulse();
117 | Search.title.innerText = _("Search Results");
118 | if (!resultCount)
119 | Search.status.innerText = Documentation.gettext(
120 | "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
121 | );
122 | else
123 | Search.status.innerText = Documentation.ngettext(
124 | "Search finished, found one page matching the search query.",
125 | "Search finished, found ${resultCount} pages matching the search query.",
126 | resultCount,
127 | ).replace('${resultCount}', resultCount);
128 | };
129 | const _displayNextItem = (
130 | results,
131 | resultCount,
132 | searchTerms,
133 | highlightTerms,
134 | ) => {
135 | // results left, load the summary and display it
136 | // this is intended to be dynamic (don't sub resultsCount)
137 | if (results.length) {
138 | _displayItem(results.pop(), searchTerms, highlightTerms);
139 | setTimeout(
140 | () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
141 | 5
142 | );
143 | }
144 | // search finished, update title and status message
145 | else _finishSearch(resultCount);
146 | };
147 | // Helper function used by query() to order search results.
148 | // Each input is an array of [docname, title, anchor, descr, score, filename, kind].
149 | // Order the results by score (in opposite order of appearance, since the
150 | // `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
151 | const _orderResultsByScoreThenName = (a, b) => {
152 | const leftScore = a[4];
153 | const rightScore = b[4];
154 | if (leftScore === rightScore) {
155 | // same score: sort alphabetically
156 | const leftTitle = a[1].toLowerCase();
157 | const rightTitle = b[1].toLowerCase();
158 | if (leftTitle === rightTitle) return 0;
159 | return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
160 | }
161 | return leftScore > rightScore ? 1 : -1;
162 | };
163 |
164 | /**
165 | * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
166 | * custom function per language.
167 | *
168 | * The regular expression works by splitting the string on consecutive characters
169 | * that are not Unicode letters, numbers, underscores, or emoji characters.
170 | * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
171 | */
172 | if (typeof splitQuery === "undefined") {
173 | var splitQuery = (query) => query
174 | .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
175 | .filter(term => term) // remove remaining empty strings
176 | }
177 |
178 | /**
179 | * Search Module
180 | */
181 | const Search = {
182 | _index: null,
183 | _queued_query: null,
184 | _pulse_status: -1,
185 |
186 | htmlToText: (htmlString, anchor) => {
187 | const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
188 | for (const removalQuery of [".headerlink", "script", "style"]) {
189 | htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
190 | }
191 | if (anchor) {
192 | const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
193 | if (anchorContent) return anchorContent.textContent;
194 |
195 | console.warn(
196 | `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
197 | );
198 | }
199 |
200 | // if anchor not specified or not found, fall back to main content
201 | const docContent = htmlElement.querySelector('[role="main"]');
202 | if (docContent) return docContent.textContent;
203 |
204 | console.warn(
205 | "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
206 | );
207 | return "";
208 | },
209 |
210 | init: () => {
211 | const query = new URLSearchParams(window.location.search).get("q");
212 | document
213 | .querySelectorAll('input[name="q"]')
214 | .forEach((el) => (el.value = query));
215 | if (query) Search.performSearch(query);
216 | },
217 |
218 | loadIndex: (url) =>
219 | (document.body.appendChild(document.createElement("script")).src = url),
220 |
221 | setIndex: (index) => {
222 | Search._index = index;
223 | if (Search._queued_query !== null) {
224 | const query = Search._queued_query;
225 | Search._queued_query = null;
226 | Search.query(query);
227 | }
228 | },
229 |
230 | hasIndex: () => Search._index !== null,
231 |
232 | deferQuery: (query) => (Search._queued_query = query),
233 |
234 | stopPulse: () => (Search._pulse_status = -1),
235 |
236 | startPulse: () => {
237 | if (Search._pulse_status >= 0) return;
238 |
239 | const pulse = () => {
240 | Search._pulse_status = (Search._pulse_status + 1) % 4;
241 | Search.dots.innerText = ".".repeat(Search._pulse_status);
242 | if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
243 | };
244 | pulse();
245 | },
246 |
247 | /**
248 | * perform a search for something (or wait until index is loaded)
249 | */
250 | performSearch: (query) => {
251 | // create the required interface elements
252 | const searchText = document.createElement("h2");
253 | searchText.textContent = _("Searching");
254 | const searchSummary = document.createElement("p");
255 | searchSummary.classList.add("search-summary");
256 | searchSummary.innerText = "";
257 | const searchList = document.createElement("ul");
258 | searchList.setAttribute("role", "list");
259 | searchList.classList.add("search");
260 |
261 | const out = document.getElementById("search-results");
262 | Search.title = out.appendChild(searchText);
263 | Search.dots = Search.title.appendChild(document.createElement("span"));
264 | Search.status = out.appendChild(searchSummary);
265 | Search.output = out.appendChild(searchList);
266 |
267 | const searchProgress = document.getElementById("search-progress");
268 | // Some themes don't use the search progress node
269 | if (searchProgress) {
270 | searchProgress.innerText = _("Preparing search...");
271 | }
272 | Search.startPulse();
273 |
274 | // index already loaded, the browser was quick!
275 | if (Search.hasIndex()) Search.query(query);
276 | else Search.deferQuery(query);
277 | },
278 |
279 | _parseQuery: (query) => {
280 | // stem the search terms and add them to the correct list
281 | const stemmer = new Stemmer();
282 | const searchTerms = new Set();
283 | const excludedTerms = new Set();
284 | const highlightTerms = new Set();
285 | const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
286 | splitQuery(query.trim()).forEach((queryTerm) => {
287 | const queryTermLower = queryTerm.toLowerCase();
288 |
289 | // maybe skip this "word"
290 | // stopwords array is from language_data.js
291 | if (
292 | stopwords.indexOf(queryTermLower) !== -1 ||
293 | queryTerm.match(/^\d+$/)
294 | )
295 | return;
296 |
297 | // stem the word
298 | let word = stemmer.stemWord(queryTermLower);
299 | // select the correct list
300 | if (word[0] === "-") excludedTerms.add(word.substr(1));
301 | else {
302 | searchTerms.add(word);
303 | highlightTerms.add(queryTermLower);
304 | }
305 | });
306 |
307 | if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
308 | localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
309 | }
310 |
311 | // console.debug("SEARCH: searching for:");
312 | // console.info("required: ", [...searchTerms]);
313 | // console.info("excluded: ", [...excludedTerms]);
314 |
315 | return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
316 | },
317 |
318 | /**
319 | * execute search (requires search index to be loaded)
320 | */
321 | _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
322 | const filenames = Search._index.filenames;
323 | const docNames = Search._index.docnames;
324 | const titles = Search._index.titles;
325 | const allTitles = Search._index.alltitles;
326 | const indexEntries = Search._index.indexentries;
327 |
328 | // Collect multiple result groups to be sorted separately and then ordered.
329 | // Each is an array of [docname, title, anchor, descr, score, filename, kind].
330 | const normalResults = [];
331 | const nonMainIndexResults = [];
332 |
333 | _removeChildren(document.getElementById("search-progress"));
334 |
335 | const queryLower = query.toLowerCase().trim();
336 | for (const [title, foundTitles] of Object.entries(allTitles)) {
337 | if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
338 | for (const [file, id] of foundTitles) {
339 | const score = Math.round(Scorer.title * queryLower.length / title.length);
340 | const boost = titles[file] === title ? 1 : 0; // add a boost for document titles
341 | normalResults.push([
342 | docNames[file],
343 | titles[file] !== title ? `${titles[file]} > ${title}` : title,
344 | id !== null ? "#" + id : "",
345 | null,
346 | score + boost,
347 | filenames[file],
348 | SearchResultKind.title,
349 | ]);
350 | }
351 | }
352 | }
353 |
354 | // search for explicit entries in index directives
355 | for (const [entry, foundEntries] of Object.entries(indexEntries)) {
356 | if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
357 | for (const [file, id, isMain] of foundEntries) {
358 | const score = Math.round(100 * queryLower.length / entry.length);
359 | const result = [
360 | docNames[file],
361 | titles[file],
362 | id ? "#" + id : "",
363 | null,
364 | score,
365 | filenames[file],
366 | SearchResultKind.index,
367 | ];
368 | if (isMain) {
369 | normalResults.push(result);
370 | } else {
371 | nonMainIndexResults.push(result);
372 | }
373 | }
374 | }
375 | }
376 |
377 | // lookup as object
378 | objectTerms.forEach((term) =>
379 | normalResults.push(...Search.performObjectSearch(term, objectTerms))
380 | );
381 |
382 | // lookup as search terms in fulltext
383 | normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
384 |
385 | // let the scorer override scores with a custom scoring function
386 | if (Scorer.score) {
387 | normalResults.forEach((item) => (item[4] = Scorer.score(item)));
388 | nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
389 | }
390 |
391 | // Sort each group of results by score and then alphabetically by name.
392 | normalResults.sort(_orderResultsByScoreThenName);
393 | nonMainIndexResults.sort(_orderResultsByScoreThenName);
394 |
395 | // Combine the result groups in (reverse) order.
396 | // Non-main index entries are typically arbitrary cross-references,
397 | // so display them after other results.
398 | let results = [...nonMainIndexResults, ...normalResults];
399 |
400 | // remove duplicate search results
401 | // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
402 | let seen = new Set();
403 | results = results.reverse().reduce((acc, result) => {
404 | let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
405 | if (!seen.has(resultStr)) {
406 | acc.push(result);
407 | seen.add(resultStr);
408 | }
409 | return acc;
410 | }, []);
411 |
412 | return results.reverse();
413 | },
414 |
415 | query: (query) => {
416 | const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
417 | const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
418 |
419 | // for debugging
420 | //Search.lastresults = results.slice(); // a copy
421 | // console.info("search results:", Search.lastresults);
422 |
423 | // print the results
424 | _displayNextItem(results, results.length, searchTerms, highlightTerms);
425 | },
426 |
427 | /**
428 | * search for object names
429 | */
430 | performObjectSearch: (object, objectTerms) => {
431 | const filenames = Search._index.filenames;
432 | const docNames = Search._index.docnames;
433 | const objects = Search._index.objects;
434 | const objNames = Search._index.objnames;
435 | const titles = Search._index.titles;
436 |
437 | const results = [];
438 |
439 | const objectSearchCallback = (prefix, match) => {
440 | const name = match[4]
441 | const fullname = (prefix ? prefix + "." : "") + name;
442 | const fullnameLower = fullname.toLowerCase();
443 | if (fullnameLower.indexOf(object) < 0) return;
444 |
445 | let score = 0;
446 | const parts = fullnameLower.split(".");
447 |
448 | // check for different match types: exact matches of full name or
449 | // "last name" (i.e. last dotted part)
450 | if (fullnameLower === object || parts.slice(-1)[0] === object)
451 | score += Scorer.objNameMatch;
452 | else if (parts.slice(-1)[0].indexOf(object) > -1)
453 | score += Scorer.objPartialMatch; // matches in last name
454 |
455 | const objName = objNames[match[1]][2];
456 | const title = titles[match[0]];
457 |
458 | // If more than one term searched for, we require other words to be
459 | // found in the name/title/description
460 | const otherTerms = new Set(objectTerms);
461 | otherTerms.delete(object);
462 | if (otherTerms.size > 0) {
463 | const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
464 | if (
465 | [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
466 | )
467 | return;
468 | }
469 |
470 | let anchor = match[3];
471 | if (anchor === "") anchor = fullname;
472 | else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
473 |
474 | const descr = objName + _(", in ") + title;
475 |
476 | // add custom score for some objects according to scorer
477 | if (Scorer.objPrio.hasOwnProperty(match[2]))
478 | score += Scorer.objPrio[match[2]];
479 | else score += Scorer.objPrioDefault;
480 |
481 | results.push([
482 | docNames[match[0]],
483 | fullname,
484 | "#" + anchor,
485 | descr,
486 | score,
487 | filenames[match[0]],
488 | SearchResultKind.object,
489 | ]);
490 | };
491 | Object.keys(objects).forEach((prefix) =>
492 | objects[prefix].forEach((array) =>
493 | objectSearchCallback(prefix, array)
494 | )
495 | );
496 | return results;
497 | },
498 |
499 | /**
500 | * search for full-text terms in the index
501 | */
502 | performTermsSearch: (searchTerms, excludedTerms) => {
503 | // prepare search
504 | const terms = Search._index.terms;
505 | const titleTerms = Search._index.titleterms;
506 | const filenames = Search._index.filenames;
507 | const docNames = Search._index.docnames;
508 | const titles = Search._index.titles;
509 |
510 | const scoreMap = new Map();
511 | const fileMap = new Map();
512 |
513 | // perform the search on the required terms
514 | searchTerms.forEach((word) => {
515 | const files = [];
516 | const arr = [
517 | { files: terms[word], score: Scorer.term },
518 | { files: titleTerms[word], score: Scorer.title },
519 | ];
520 | // add support for partial matches
521 | if (word.length > 2) {
522 | const escapedWord = _escapeRegExp(word);
523 | if (!terms.hasOwnProperty(word)) {
524 | Object.keys(terms).forEach((term) => {
525 | if (term.match(escapedWord))
526 | arr.push({ files: terms[term], score: Scorer.partialTerm });
527 | });
528 | }
529 | if (!titleTerms.hasOwnProperty(word)) {
530 | Object.keys(titleTerms).forEach((term) => {
531 | if (term.match(escapedWord))
532 | arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
533 | });
534 | }
535 | }
536 |
537 | // no match but word was a required one
538 | if (arr.every((record) => record.files === undefined)) return;
539 |
540 | // found search word in contents
541 | arr.forEach((record) => {
542 | if (record.files === undefined) return;
543 |
544 | let recordFiles = record.files;
545 | if (recordFiles.length === undefined) recordFiles = [recordFiles];
546 | files.push(...recordFiles);
547 |
548 | // set score for the word in each file
549 | recordFiles.forEach((file) => {
550 | if (!scoreMap.has(file)) scoreMap.set(file, {});
551 | scoreMap.get(file)[word] = record.score;
552 | });
553 | });
554 |
555 | // create the mapping
556 | files.forEach((file) => {
557 | if (!fileMap.has(file)) fileMap.set(file, [word]);
558 | else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
559 | });
560 | });
561 |
562 | // now check if the files don't contain excluded terms
563 | const results = [];
564 | for (const [file, wordList] of fileMap) {
565 | // check if all requirements are matched
566 |
567 | // as search terms with length < 3 are discarded
568 | const filteredTermCount = [...searchTerms].filter(
569 | (term) => term.length > 2
570 | ).length;
571 | if (
572 | wordList.length !== searchTerms.size &&
573 | wordList.length !== filteredTermCount
574 | )
575 | continue;
576 |
577 | // ensure that none of the excluded terms is in the search result
578 | if (
579 | [...excludedTerms].some(
580 | (term) =>
581 | terms[term] === file ||
582 | titleTerms[term] === file ||
583 | (terms[term] || []).includes(file) ||
584 | (titleTerms[term] || []).includes(file)
585 | )
586 | )
587 | break;
588 |
589 | // select one (max) score for the file.
590 | const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
591 | // add result to the result list
592 | results.push([
593 | docNames[file],
594 | titles[file],
595 | "",
596 | null,
597 | score,
598 | filenames[file],
599 | SearchResultKind.text,
600 | ]);
601 | }
602 | return results;
603 | },
604 |
605 | /**
606 | * helper function to return a node containing the
607 | * search summary for a given text. keywords is a list
608 | * of stemmed words.
609 | */
610 | makeSearchSummary: (htmlText, keywords, anchor) => {
611 | const text = Search.htmlToText(htmlText, anchor);
612 | if (text === "") return null;
613 |
614 | const textLower = text.toLowerCase();
615 | const actualStartPosition = [...keywords]
616 | .map((k) => textLower.indexOf(k.toLowerCase()))
617 | .filter((i) => i > -1)
618 | .slice(-1)[0];
619 | const startWithContext = Math.max(actualStartPosition - 120, 0);
620 |
621 | const top = startWithContext === 0 ? "" : "...";
622 | const tail = startWithContext + 240 < text.length ? "..." : "";
623 |
624 | let summary = document.createElement("p");
625 | summary.classList.add("context");
626 | summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
627 |
628 | return summary;
629 | },
630 | };
631 |
632 | _ready(Search.init);
633 |
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72 |
258 |
73 |
84 |
257 |
85 |
240 |
256 |
86 |
87 |
88 |
90 |
232 |
233 |
235 |
236 |
237 |
238 |
239 | diPLSlib.utils subpackage
89 |diPLSlib.utils.misc module
91 |Some helper functions for diPLSlib
92 |-
93 |
- 94 | diPLSlib.utils.misc.calibrateAnalyticGaussianMechanism(epsilon, delta, GS, tol=1e-12)[source] 95 |
Calibrate a Gaussian perturbation for differential privacy using the analytic Gaussian mechanism of [Balle and Wang, ICML’18]
96 |-
97 |
- Parameters: 98 |
-
99 |
- epsilonfloat
Privacy parameter epsilon
100 |
101 | - deltafloat
Desired privacy failure probability
102 |
103 | - GSfloat
Upper bound on the L2-sensitivity of the function to which the mechanism is applied
104 |
105 | - tolfloat
Error tolerance for binary search
106 |
107 |
109 | - Returns: 110 |
-
111 |
- sigmafloat
Standard deviation of Gaussian noise needed to achieve (epsilon,delta)-DP under global sensitivity GS
112 |
113 |
115 |
References
117 |-
118 |
Balle, B., & Wang, Y. X. (2018, July). Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising. In International Conference on Machine Learning (pp. 394-403). PMLR.
119 |
Examples
121 |126 |125 |>>> from diPLSlib.utils.misc import calibrateAnalyticGaussianMechanism 122 | >>> calibrateAnalyticGaussianMechanism(1.0, 1e-5, 1.0) 123 | 3.730631634944469 124 |
-
129 |
- 130 | diPLSlib.utils.misc.gengaus(length, mu, sigma, mag, noise=0)[source] 131 |
Generate a Gaussian spectrum-like signal with optional random noise.
132 |-
133 |
- Parameters: 134 |
-
135 |
- lengthint
Length of the generated signal.
136 |
137 | - mufloat
Mean of the Gaussian function.
138 |
139 | - sigmafloat
Standard deviation of the Gaussian function.
140 |
141 | - magfloat
Magnitude of the Gaussian signal.
142 |
143 | - noisefloat, optional (default=0)
Standard deviation of the Gaussian noise to be added to the signal.
144 |
145 |
147 | - Returns: 148 |
-
149 |
- signalndarray of shape (length,)
The generated Gaussian signal with noise.
150 |
151 |
153 |
Examples
155 |161 |160 |>>> from diPLSlib.utils.misc import gengaus 156 | >>> import numpy as np 157 | >>> import scipy.stats 158 | >>> signal = gengaus(100, 50, 10, 5, noise=0.1) 159 |
-
164 |
- 165 | diPLSlib.utils.misc.hellipse(X, alpha=0.05)[source] 166 |
Compute the 95% confidence interval ellipse for a 2D scatter plot.
167 |-
168 |
- Parameters: 169 |
-
170 |
- Xndarray of shape (n_samples, 2)
Matrix of data points.
171 |
172 | - alphafloat, optional (default=0.05)
Significance level for the confidence interval.
173 |
174 |
176 | - Returns: 177 |
-
178 |
- elndarray of shape (2, 100)
Coordinates of the ellipse’s points. To plot, use plt.plot(el[0, :], el[1, :]).
179 |
180 |
182 |
Examples
184 |197 |196 |>>> import matplotlib.pyplot as plt 185 | >>> import numpy as np 186 | >>> from diPLSlib.utils.misc import hellipse 187 | >>> X = np.random.random((100, 2)) 188 | >>> el = hellipse(X) 189 | >>> plt.scatter(X[:,0], X[:,1], label='Data points') 190 | <matplotlib.collections.PathCollection object at ...> 191 | >>> plt.plot(el[0,:], el[1,:], label='95% Confidence Ellipse') 192 | [<matplotlib.lines.Line2D object at ...>] 193 | >>> plt.legend() 194 | <matplotlib.legend.Legend object at ...> 195 |
-
200 |
- 201 | diPLSlib.utils.misc.rmse(y, yhat)[source] 202 |
Compute the Root Mean Squared Error (RMSE) between two arrays.
203 |-
204 |
- Parameters: 205 |
-
206 |
- yndarray of shape (n_samples,)
True values.
207 |
208 | - yhatndarray of shape (n_samples,)
Predicted values.
209 |
210 |
212 | - Returns: 213 |
-
214 |
- errorndarray of shape (n_samples,)
The RMSE between y and yhat.
215 |
216 |
218 |
Examples
220 |229 |228 |>>> import numpy as np 221 | >>> from diPLSlib.utils.misc import rmse 222 | >>> x = np.array([1, 2, 3]) 223 | >>> y = np.array([2, 3, 4]) 224 | >>> error = rmse(x, y) 225 | >>> print(error) 226 | 1.0 227 |