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0px 1px 1px 0px var(--jp-shadow-penumbra-color),
0px 1px 3px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
0px 1px 5px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
0px 1px 10px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
0px 1px 18px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
0px 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
0px 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
0px 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
0px 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
0px 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-400);
--jp-border-color1: var(--md-grey-400);
--jp-border-color2: var(--md-grey-300);
--jp-border-color3: var(--md-grey-200);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
"Segoe UI Symbol";
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(0, 0, 0, 1);
--jp-ui-font-color1: rgba(0, 0, 0, 0.87);
--jp-ui-font-color2: rgba(0, 0, 0, 0.54);
--jp-ui-font-color3: rgba(0, 0, 0, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color1: rgba(255, 255, 255, 1);
--jp-ui-inverse-font-color2: rgba(255, 255, 255, 0.7);
--jp-ui-inverse-font-color3: rgba(255, 255, 255, 0.5);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(0, 0, 0, 1);
--jp-content-font-color1: rgba(0, 0, 0, 0.87);
--jp-content-font-color2: rgba(0, 0, 0, 0.54);
--jp-content-font-color3: rgba(0, 0, 0, 0.38);
--jp-content-link-color: var(--md-blue-700);
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
"Segoe UI Symbol";
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: white;
--jp-layout-color1: white;
--jp-layout-color2: var(--md-grey-200);
--jp-layout-color3: var(--md-grey-400);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: #111111;
--jp-inverse-layout-color1: var(--md-grey-900);
--jp-inverse-layout-color2: var(--md-grey-800);
--jp-inverse-layout-color3: var(--md-grey-700);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-900);
--jp-brand-color1: var(--md-blue-700);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-900);
--jp-accent-color1: var(--md-green-700);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-900);
--jp-warn-color1: var(--md-orange-700);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-900);
--jp-error-color1: var(--md-red-700);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-900);
--jp-success-color1: var(--md-green-700);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-900);
--jp-info-color1: var(--md-cyan-700);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--md-grey-100);
--jp-cell-editor-border-color: var(--md-grey-300);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0px;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 0.5;
--jp-cell-prompt-not-active-font-color: var(--md-grey-700);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: var(--md-blue-50);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: #fdd;
--jp-rendermime-table-row-background: var(--md-grey-100);
--jp-rendermime-table-row-hover-background: var(--md-light-blue-50);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.25);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color1);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.24);
--jp-toolbar-header-margin: 4px 4px 0px 4px;
--jp-toolbar-active-background: var(--md-grey-300);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color1);
--jp-input-hover-background: var(--jp-layout-color1);
--jp-input-background: var(--md-grey-100);
--jp-input-border-color: var(--jp-border-color1);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: #d9d9d9;
--jp-editor-selected-focused-background: #d7d4f0;
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: #008000;
--jp-mirror-editor-atom-color: #88f;
--jp-mirror-editor-number-color: #080;
--jp-mirror-editor-def-color: #00f;
--jp-mirror-editor-variable-color: var(--md-grey-900);
--jp-mirror-editor-variable-2-color: #05a;
--jp-mirror-editor-variable-3-color: #085;
--jp-mirror-editor-punctuation-color: #05a;
--jp-mirror-editor-property-color: #05a;
--jp-mirror-editor-operator-color: #aa22ff;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ba2121;
--jp-mirror-editor-string-2-color: #708;
--jp-mirror-editor-meta-color: #aa22ff;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: #008000;
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: #170;
--jp-mirror-editor-attribute-color: #00c;
--jp-mirror-editor-header-color: blue;
--jp-mirror-editor-quote-color: #090;
--jp-mirror-editor-link-color: #00c;
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/* Vega extension styles */
--jp-vega-background: white;
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.5;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(245, 200, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
}
[data-md-color-scheme="slate"] .jupyter-wrapper {
/* Elevation
*
* We style box-shadows using Material Design's idea of elevation. These particular numbers are taken from here:
*
* https://github.com/material-components/material-components-web
* https://material-components-web.appspot.com/elevation.html
*/
/* The dark theme shadows need a bit of work, but this will probably also require work on the core layout
* colors used in the theme as well. */
--jp-shadow-base-lightness: 32;
--jp-shadow-umbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.2
);
--jp-shadow-penumbra-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.14
);
--jp-shadow-ambient-color: rgba(
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
var(--jp-shadow-base-lightness),
0.12
);
--jp-elevation-z0: none;
--jp-elevation-z1: 0px 2px 1px -1px var(--jp-shadow-umbra-color),
0px 1px 1px 0px var(--jp-shadow-penumbra-color),
0px 1px 3px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z2: 0px 3px 1px -2px var(--jp-shadow-umbra-color),
0px 2px 2px 0px var(--jp-shadow-penumbra-color),
0px 1px 5px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z4: 0px 2px 4px -1px var(--jp-shadow-umbra-color),
0px 4px 5px 0px var(--jp-shadow-penumbra-color),
0px 1px 10px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z6: 0px 3px 5px -1px var(--jp-shadow-umbra-color),
0px 6px 10px 0px var(--jp-shadow-penumbra-color),
0px 1px 18px 0px var(--jp-shadow-ambient-color);
--jp-elevation-z8: 0px 5px 5px -3px var(--jp-shadow-umbra-color),
0px 8px 10px 1px var(--jp-shadow-penumbra-color),
0px 3px 14px 2px var(--jp-shadow-ambient-color);
--jp-elevation-z12: 0px 7px 8px -4px var(--jp-shadow-umbra-color),
0px 12px 17px 2px var(--jp-shadow-penumbra-color),
0px 5px 22px 4px var(--jp-shadow-ambient-color);
--jp-elevation-z16: 0px 8px 10px -5px var(--jp-shadow-umbra-color),
0px 16px 24px 2px var(--jp-shadow-penumbra-color),
0px 6px 30px 5px var(--jp-shadow-ambient-color);
--jp-elevation-z20: 0px 10px 13px -6px var(--jp-shadow-umbra-color),
0px 20px 31px 3px var(--jp-shadow-penumbra-color),
0px 8px 38px 7px var(--jp-shadow-ambient-color);
--jp-elevation-z24: 0px 11px 15px -7px var(--jp-shadow-umbra-color),
0px 24px 38px 3px var(--jp-shadow-penumbra-color),
0px 9px 46px 8px var(--jp-shadow-ambient-color);
/* Borders
*
* The following variables, specify the visual styling of borders in JupyterLab.
*/
--jp-border-width: 1px;
--jp-border-color0: var(--md-grey-700);
--jp-border-color1: var(--md-grey-700);
--jp-border-color2: var(--md-grey-800);
--jp-border-color3: var(--md-grey-900);
--jp-border-radius: 2px;
/* UI Fonts
*
* The UI font CSS variables are used for the typography all of the JupyterLab
* user interface elements that are not directly user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-ui-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-ui-font-scale-factor: 1.2;
--jp-ui-font-size0: 0.83333em;
--jp-ui-font-size1: 13px; /* Base font size */
--jp-ui-font-size2: 1.2em;
--jp-ui-font-size3: 1.44em;
--jp-ui-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
"Segoe UI Symbol";
/*
* Use these font colors against the corresponding main layout colors.
* In a light theme, these go from dark to light.
*/
/* Defaults use Material Design specification */
--jp-ui-font-color0: rgba(255, 255, 255, 1);
--jp-ui-font-color1: rgba(255, 255, 255, 0.87);
--jp-ui-font-color2: rgba(255, 255, 255, 0.54);
--jp-ui-font-color3: rgba(255, 255, 255, 0.38);
/*
* Use these against the brand/accent/warn/error colors.
* These will typically go from light to darker, in both a dark and light theme.
*/
--jp-ui-inverse-font-color0: rgba(0, 0, 0, 1);
--jp-ui-inverse-font-color1: rgba(0, 0, 0, 0.8);
--jp-ui-inverse-font-color2: rgba(0, 0, 0, 0.5);
--jp-ui-inverse-font-color3: rgba(0, 0, 0, 0.3);
/* Content Fonts
*
* Content font variables are used for typography of user generated content.
*
* The font sizing here is done assuming that the body font size of --jp-content-font-size1
* is applied to a parent element. When children elements, such as headings, are sized
* in em all things will be computed relative to that body size.
*/
--jp-content-line-height: 1.6;
--jp-content-font-scale-factor: 1.2;
--jp-content-font-size0: 0.83333em;
--jp-content-font-size1: 14px; /* Base font size */
--jp-content-font-size2: 1.2em;
--jp-content-font-size3: 1.44em;
--jp-content-font-size4: 1.728em;
--jp-content-font-size5: 2.0736em;
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-content-presentation-font-size1: 17px;
--jp-content-heading-line-height: 1;
--jp-content-heading-margin-top: 1.2em;
--jp-content-heading-margin-bottom: 0.8em;
--jp-content-heading-font-weight: 500;
/* Defaults use Material Design specification */
--jp-content-font-color0: rgba(255, 255, 255, 1);
--jp-content-font-color1: rgba(255, 255, 255, 1);
--jp-content-font-color2: rgba(255, 255, 255, 0.7);
--jp-content-font-color3: rgba(255, 255, 255, 0.5);
--jp-content-link-color: var(--md-blue-300);
--jp-content-font-family: -apple-system, BlinkMacSystemFont, "Segoe UI",
Helvetica, Arial, sans-serif, "Apple Color Emoji", "Segoe UI Emoji",
"Segoe UI Symbol";
/*
* Code Fonts
*
* Code font variables are used for typography of code and other monospaces content.
*/
--jp-code-font-size: 13px;
--jp-code-line-height: 1.3077; /* 17px for 13px base */
--jp-code-padding: 5px; /* 5px for 13px base, codemirror highlighting needs integer px value */
--jp-code-font-family-default: Menlo, Consolas, "DejaVu Sans Mono",
monospace;
--jp-code-font-family: var(--jp-code-font-family-default);
/* This gives a magnification of about 125% in presentation mode over normal. */
--jp-code-presentation-font-size: 16px;
/* may need to tweak cursor width if you change font size */
--jp-code-cursor-width0: 1.4px;
--jp-code-cursor-width1: 2px;
--jp-code-cursor-width2: 4px;
/* Layout
*
* The following are the main layout colors use in JupyterLab. In a light
* theme these would go from light to dark.
*/
--jp-layout-color0: #111111;
--jp-layout-color1: var(--md-grey-900);
--jp-layout-color2: var(--md-grey-800);
--jp-layout-color3: var(--md-grey-700);
--jp-layout-color4: var(--md-grey-600);
/* Inverse Layout
*
* The following are the inverse layout colors use in JupyterLab. In a light
* theme these would go from dark to light.
*/
--jp-inverse-layout-color0: white;
--jp-inverse-layout-color1: white;
--jp-inverse-layout-color2: var(--md-grey-200);
--jp-inverse-layout-color3: var(--md-grey-400);
--jp-inverse-layout-color4: var(--md-grey-600);
/* Brand/accent */
--jp-brand-color0: var(--md-blue-700);
--jp-brand-color1: var(--md-blue-500);
--jp-brand-color2: var(--md-blue-300);
--jp-brand-color3: var(--md-blue-100);
--jp-brand-color4: var(--md-blue-50);
--jp-accent-color0: var(--md-green-700);
--jp-accent-color1: var(--md-green-500);
--jp-accent-color2: var(--md-green-300);
--jp-accent-color3: var(--md-green-100);
/* State colors (warn, error, success, info) */
--jp-warn-color0: var(--md-orange-700);
--jp-warn-color1: var(--md-orange-500);
--jp-warn-color2: var(--md-orange-300);
--jp-warn-color3: var(--md-orange-100);
--jp-error-color0: var(--md-red-700);
--jp-error-color1: var(--md-red-500);
--jp-error-color2: var(--md-red-300);
--jp-error-color3: var(--md-red-100);
--jp-success-color0: var(--md-green-700);
--jp-success-color1: var(--md-green-500);
--jp-success-color2: var(--md-green-300);
--jp-success-color3: var(--md-green-100);
--jp-info-color0: var(--md-cyan-700);
--jp-info-color1: var(--md-cyan-500);
--jp-info-color2: var(--md-cyan-300);
--jp-info-color3: var(--md-cyan-100);
/* Cell specific styles */
--jp-cell-padding: 5px;
--jp-cell-collapser-width: 8px;
--jp-cell-collapser-min-height: 20px;
--jp-cell-collapser-not-active-hover-opacity: 0.6;
--jp-cell-editor-background: var(--jp-layout-color1);
--jp-cell-editor-border-color: var(--md-grey-700);
--jp-cell-editor-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-cell-editor-active-background: var(--jp-layout-color0);
--jp-cell-editor-active-border-color: var(--jp-brand-color1);
--jp-cell-prompt-width: 64px;
--jp-cell-prompt-font-family: var(--jp-code-font-family-default);
--jp-cell-prompt-letter-spacing: 0px;
--jp-cell-prompt-opacity: 1;
--jp-cell-prompt-not-active-opacity: 1;
--jp-cell-prompt-not-active-font-color: var(--md-grey-300);
/* A custom blend of MD grey and blue 600
* See https://meyerweb.com/eric/tools/color-blend/#546E7A:1E88E5:5:hex */
--jp-cell-inprompt-font-color: #307fc1;
/* A custom blend of MD grey and orange 600
* https://meyerweb.com/eric/tools/color-blend/#546E7A:F4511E:5:hex */
--jp-cell-outprompt-font-color: #bf5b3d;
/* Notebook specific styles */
--jp-notebook-padding: 10px;
--jp-notebook-select-background: var(--jp-layout-color1);
--jp-notebook-multiselected-color: rgba(33, 150, 243, 0.24);
/* The scroll padding is calculated to fill enough space at the bottom of the
notebook to show one single-line cell (with appropriate padding) at the top
when the notebook is scrolled all the way to the bottom. We also subtract one
pixel so that no scrollbar appears if we have just one single-line cell in the
notebook. This padding is to enable a 'scroll past end' feature in a notebook.
*/
--jp-notebook-scroll-padding: calc(
100% - var(--jp-code-font-size) * var(--jp-code-line-height) -
var(--jp-code-padding) - var(--jp-cell-padding) - 1px
);
/* Rendermime styles */
--jp-rendermime-error-background: rgba(244, 67, 54, 0.28);
--jp-rendermime-table-row-background: var(--md-grey-900);
--jp-rendermime-table-row-hover-background: rgba(3, 169, 244, 0.2);
/* Dialog specific styles */
--jp-dialog-background: rgba(0, 0, 0, 0.6);
/* Console specific styles */
--jp-console-padding: 10px;
/* Toolbar specific styles */
--jp-toolbar-border-color: var(--jp-border-color2);
--jp-toolbar-micro-height: 8px;
--jp-toolbar-background: var(--jp-layout-color1);
--jp-toolbar-box-shadow: 0px 0px 2px 0px rgba(0, 0, 0, 0.8);
--jp-toolbar-header-margin: 4px 4px 0px 4px;
--jp-toolbar-active-background: var(--jp-layout-color0);
/* Statusbar specific styles */
--jp-statusbar-height: 24px;
/* Input field styles */
--jp-input-box-shadow: inset 0 0 2px var(--md-blue-300);
--jp-input-active-background: var(--jp-layout-color0);
--jp-input-hover-background: var(--jp-layout-color2);
--jp-input-background: var(--md-grey-800);
--jp-input-border-color: var(--jp-border-color1);
--jp-input-active-border-color: var(--jp-brand-color1);
--jp-input-active-box-shadow-color: rgba(19, 124, 189, 0.3);
/* General editor styles */
--jp-editor-selected-background: var(--jp-layout-color2);
--jp-editor-selected-focused-background: rgba(33, 150, 243, 0.24);
--jp-editor-cursor-color: var(--jp-ui-font-color0);
/* Code mirror specific styles */
--jp-mirror-editor-keyword-color: var(--md-green-500);
--jp-mirror-editor-atom-color: var(--md-blue-300);
--jp-mirror-editor-number-color: var(--md-green-400);
--jp-mirror-editor-def-color: var(--md-blue-600);
--jp-mirror-editor-variable-color: var(--md-grey-300);
--jp-mirror-editor-variable-2-color: var(--md-blue-400);
--jp-mirror-editor-variable-3-color: var(--md-green-600);
--jp-mirror-editor-punctuation-color: var(--md-blue-400);
--jp-mirror-editor-property-color: var(--md-blue-400);
--jp-mirror-editor-operator-color: #aa22ff;
--jp-mirror-editor-comment-color: #408080;
--jp-mirror-editor-string-color: #ff7070;
--jp-mirror-editor-string-2-color: var(--md-purple-300);
--jp-mirror-editor-meta-color: #aa22ff;
--jp-mirror-editor-qualifier-color: #555;
--jp-mirror-editor-builtin-color: var(--md-green-600);
--jp-mirror-editor-bracket-color: #997;
--jp-mirror-editor-tag-color: var(--md-green-700);
--jp-mirror-editor-attribute-color: var(--md-blue-700);
--jp-mirror-editor-header-color: var(--md-blue-500);
--jp-mirror-editor-quote-color: var(--md-green-300);
--jp-mirror-editor-link-color: var(--md-blue-700);
--jp-mirror-editor-error-color: #f00;
--jp-mirror-editor-hr-color: #999;
/* Vega extension styles */
--jp-vega-background: var(--md-grey-400);
/* Sidebar-related styles */
--jp-sidebar-min-width: 250px;
/* Search-related styles */
--jp-search-toggle-off-opacity: 0.6;
--jp-search-toggle-hover-opacity: 0.8;
--jp-search-toggle-on-opacity: 1;
--jp-search-selected-match-background-color: rgb(255, 225, 0);
--jp-search-selected-match-color: black;
--jp-search-unselected-match-background-color: var(
--jp-inverse-layout-color0
);
--jp-search-unselected-match-color: var(--jp-ui-inverse-font-color0);
/* scrollbar related styles. Supports every browser except Edge. */
/* colors based on JetBrain's Darcula theme */
--jp-scrollbar-background-color: #3f4244;
--jp-scrollbar-thumb-color: 88, 96, 97; /* need to specify thumb color as an RGB triplet */
--jp-scrollbar-endpad: 3px; /* the minimum gap between the thumb and the ends of a scrollbar */
/* hacks for setting the thumb shape. These do nothing in Firefox */
--jp-scrollbar-thumb-margin: 3.5px; /* the space in between the sides of the thumb and the track */
--jp-scrollbar-thumb-radius: 9px; /* set to a large-ish value for rounded endcaps on the thumb */
/* Icon colors that work well with light or dark backgrounds */
--jp-icon-contrast-color0: var(--md-purple-600);
--jp-icon-contrast-color1: var(--md-green-600);
--jp-icon-contrast-color2: var(--md-pink-600);
--jp-icon-contrast-color3: var(--md-blue-600);
}
:root{--md-red-50: #ffebee;--md-red-100: #ffcdd2;--md-red-200: #ef9a9a;--md-red-300: #e57373;--md-red-400: #ef5350;--md-red-500: #f44336;--md-red-600: #e53935;--md-red-700: #d32f2f;--md-red-800: #c62828;--md-red-900: #b71c1c;--md-red-A100: #ff8a80;--md-red-A200: #ff5252;--md-red-A400: #ff1744;--md-red-A700: #d50000;--md-pink-50: #fce4ec;--md-pink-100: #f8bbd0;--md-pink-200: #f48fb1;--md-pink-300: #f06292;--md-pink-400: #ec407a;--md-pink-500: #e91e63;--md-pink-600: #d81b60;--md-pink-700: #c2185b;--md-pink-800: #ad1457;--md-pink-900: #880e4f;--md-pink-A100: #ff80ab;--md-pink-A200: #ff4081;--md-pink-A400: #f50057;--md-pink-A700: #c51162;--md-purple-50: #f3e5f5;--md-purple-100: #e1bee7;--md-purple-200: #ce93d8;--md-purple-300: #ba68c8;--md-purple-400: #ab47bc;--md-purple-500: #9c27b0;--md-purple-600: #8e24aa;--md-purple-700: #7b1fa2;--md-purple-800: #6a1b9a;--md-purple-900: #4a148c;--md-purple-A100: #ea80fc;--md-purple-A200: #e040fb;--md-purple-A400: #d500f9;--md-purple-A700: #aa00ff;--md-deep-purple-50: #ede7f6;--md-deep-purple-100: #d1c4e9;--md-deep-purple-200: #b39ddb;--md-deep-purple-300: #9575cd;--md-deep-purple-400: #7e57c2;--md-deep-purple-500: #673ab7;--md-deep-purple-600: #5e35b1;--md-deep-purple-700: #512da8;--md-deep-purple-800: #4527a0;--md-deep-purple-900: #311b92;--md-deep-purple-A100: #b388ff;--md-deep-purple-A200: #7c4dff;--md-deep-purple-A400: #651fff;--md-deep-purple-A700: #6200ea;--md-indigo-50: #e8eaf6;--md-indigo-100: #c5cae9;--md-indigo-200: #9fa8da;--md-indigo-300: #7986cb;--md-indigo-400: #5c6bc0;--md-indigo-500: #3f51b5;--md-indigo-600: #3949ab;--md-indigo-700: #303f9f;--md-indigo-800: #283593;--md-indigo-900: #1a237e;--md-indigo-A100: #8c9eff;--md-indigo-A200: #536dfe;--md-indigo-A400: #3d5afe;--md-indigo-A700: #304ffe;--md-blue-50: #e3f2fd;--md-blue-100: #bbdefb;--md-blue-200: #90caf9;--md-blue-300: #64b5f6;--md-blue-400: #42a5f5;--md-blue-500: #2196f3;--md-blue-600: #1e88e5;--md-blue-700: #1976d2;--md-blue-800: #1565c0;--md-blue-900: #0d47a1;--md-blue-A100: #82b1ff;--md-blue-A200: #448aff;--md-blue-A400: #2979ff;--md-blue-A700: #2962ff;--md-light-blue-50: #e1f5fe;--md-light-blue-100: #b3e5fc;--md-light-blue-200: #81d4fa;--md-light-blue-300: #4fc3f7;--md-light-blue-400: #29b6f6;--md-light-blue-500: #03a9f4;--md-light-blue-600: #039be5;--md-light-blue-700: #0288d1;--md-light-blue-800: #0277bd;--md-light-blue-900: #01579b;--md-light-blue-A100: #80d8ff;--md-light-blue-A200: #40c4ff;--md-light-blue-A400: #00b0ff;--md-light-blue-A700: #0091ea;--md-cyan-50: #e0f7fa;--md-cyan-100: #b2ebf2;--md-cyan-200: #80deea;--md-cyan-300: #4dd0e1;--md-cyan-400: #26c6da;--md-cyan-500: #00bcd4;--md-cyan-600: #00acc1;--md-cyan-700: #0097a7;--md-cyan-800: #00838f;--md-cyan-900: #006064;--md-cyan-A100: #84ffff;--md-cyan-A200: #18ffff;--md-cyan-A400: #00e5ff;--md-cyan-A700: #00b8d4;--md-teal-50: #e0f2f1;--md-teal-100: #b2dfdb;--md-teal-200: #80cbc4;--md-teal-300: #4db6ac;--md-teal-400: #26a69a;--md-teal-500: #009688;--md-teal-600: #00897b;--md-teal-700: #00796b;--md-teal-800: #00695c;--md-teal-900: #004d40;--md-teal-A100: #a7ffeb;--md-teal-A200: #64ffda;--md-teal-A400: #1de9b6;--md-teal-A700: #00bfa5;--md-green-50: #e8f5e9;--md-green-100: #c8e6c9;--md-green-200: #a5d6a7;--md-green-300: #81c784;--md-green-400: #66bb6a;--md-green-500: #4caf50;--md-green-600: #43a047;--md-green-700: #388e3c;--md-green-800: #2e7d32;--md-green-900: #1b5e20;--md-green-A100: #b9f6ca;--md-green-A200: #69f0ae;--md-green-A400: #00e676;--md-green-A700: #00c853;--md-light-green-50: #f1f8e9;--md-light-green-100: #dcedc8;--md-light-green-200: #c5e1a5;--md-light-green-300: #aed581;--md-light-green-400: #9ccc65;--md-light-green-500: #8bc34a;--md-light-green-600: #7cb342;--md-light-green-700: #689f38;--md-light-green-800: #558b2f;--md-light-green-900: #33691e;--md-light-green-A100: #ccff90;--md-light-green-A200: #b2ff59;--md-light-green-A400: #76ff03;--md-light-green-A700: #64dd17;--md-lime-50: #f9fbe7;--md-lime-100: #f0f4c3;--md-lime-200: #e6ee9c;--md-lime-300: #dce775;--md-lime-400: #d4e157;--md-lime-500: #cddc39;--md-lime-600: #c0ca33;--md-lime-700: #afb42b;--md-lime-800: #9e9d24;--md-lime-900: #827717;--md-lime-A100: #f4ff81;--md-lime-A200: #eeff41;--md-lime-A400: #c6ff00;--md-lime-A700: #aeea00;--md-yellow-50: #fffde7;--md-yellow-100: #fff9c4;--md-yellow-200: #fff59d;--md-yellow-300: #fff176;--md-yellow-400: #ffee58;--md-yellow-500: #ffeb3b;--md-yellow-600: #fdd835;--md-yellow-700: #fbc02d;--md-yellow-800: #f9a825;--md-yellow-900: #f57f17;--md-yellow-A100: #ffff8d;--md-yellow-A200: #ffff00;--md-yellow-A400: #ffea00;--md-yellow-A700: #ffd600;--md-amber-50: #fff8e1;--md-amber-100: #ffecb3;--md-amber-200: #ffe082;--md-amber-300: #ffd54f;--md-amber-400: #ffca28;--md-amber-500: #ffc107;--md-amber-600: #ffb300;--md-amber-700: #ffa000;--md-amber-800: #ff8f00;--md-amber-900: #ff6f00;--md-amber-A100: #ffe57f;--md-amber-A200: #ffd740;--md-amber-A400: #ffc400;--md-amber-A700: #ffab00;--md-orange-50: #fff3e0;--md-orange-100: #ffe0b2;--md-orange-200: #ffcc80;--md-orange-300: #ffb74d;--md-orange-400: #ffa726;--md-orange-500: #ff9800;--md-orange-600: #fb8c00;--md-orange-700: #f57c00;--md-orange-800: #ef6c00;--md-orange-900: #e65100;--md-orange-A100: #ffd180;--md-orange-A200: #ffab40;--md-orange-A400: #ff9100;--md-orange-A700: #ff6d00;--md-deep-orange-50: #fbe9e7;--md-deep-orange-100: #ffccbc;--md-deep-orange-200: #ffab91;--md-deep-orange-300: #ff8a65;--md-deep-orange-400: #ff7043;--md-deep-orange-500: #ff5722;--md-deep-orange-600: #f4511e;--md-deep-orange-700: #e64a19;--md-deep-orange-800: #d84315;--md-deep-orange-900: #bf360c;--md-deep-orange-A100: #ff9e80;--md-deep-orange-A200: #ff6e40;--md-deep-orange-A400: #ff3d00;--md-deep-orange-A700: #dd2c00;--md-brown-50: #efebe9;--md-brown-100: #d7ccc8;--md-brown-200: #bcaaa4;--md-brown-300: #a1887f;--md-brown-400: #8d6e63;--md-brown-500: #795548;--md-brown-600: #6d4c41;--md-brown-700: #5d4037;--md-brown-800: #4e342e;--md-brown-900: #3e2723;--md-grey-50: #fafafa;--md-grey-100: #f5f5f5;--md-grey-200: #eeeeee;--md-grey-300: #e0e0e0;--md-grey-400: #bdbdbd;--md-grey-500: #9e9e9e;--md-grey-600: #757575;--md-grey-700: #616161;--md-grey-800: #424242;--md-grey-900: #212121;--md-blue-grey-50: #eceff1;--md-blue-grey-100: #cfd8dc;--md-blue-grey-200: #b0bec5;--md-blue-grey-300: #90a4ae;--md-blue-grey-400: #78909c;--md-blue-grey-500: #607d8b;--md-blue-grey-600: #546e7a;--md-blue-grey-700: #455a64;--md-blue-grey-800: #37474f;--md-blue-grey-900: #263238}.jupyter-wrapper{/*!
Copyright 2015-present Palantir Technologies, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0.
*//*!
Copyright 2017-present Palantir Technologies, Inc. All rights reserved.
Licensed under the Apache License, Version 2.0.
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.CodeMirror-vscrollbar::-webkit-scrollbar-thumb{background:rgba(var(--jp-scrollbar-thumb-color), 0.5);border:var(--jp-scrollbar-thumb-margin) solid rgba(0,0,0,0);background-clip:content-box;border-radius:var(--jp-scrollbar-thumb-radius)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-hscrollbar::-webkit-scrollbar-track:horizontal{border-left:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0);border-right:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0)}.jupyter-wrapper [data-jp-theme-scrollbars=true] .CodeMirror-vscrollbar::-webkit-scrollbar-track:vertical{border-top:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0);border-bottom:var(--jp-scrollbar-endpad) solid rgba(0,0,0,0)}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal]{min-height:16px;max-height:16px;min-width:45px;border-top:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical]{min-width:16px;max-width:16px;min-height:45px;border-left:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar-button{background-color:#f0f0f0;background-position:center center;min-height:15px;max-height:15px;min-width:15px;max-width:15px}.jupyter-wrapper .lm-ScrollBar-button:hover{background-color:#dadada}.jupyter-wrapper .lm-ScrollBar-button.lm-mod-active{background-color:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-track{background:#f0f0f0}.jupyter-wrapper .lm-ScrollBar-thumb{background:#cdcdcd}.jupyter-wrapper .lm-ScrollBar-thumb:hover{background:#bababa}.jupyter-wrapper .lm-ScrollBar-thumb.lm-mod-active{background:#a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] .lm-ScrollBar-thumb{height:100%;min-width:15px;border-left:1px solid #a0a0a0;border-right:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=vertical] .lm-ScrollBar-thumb{width:100%;min-height:15px;border-top:1px solid #a0a0a0;border-bottom:1px solid #a0a0a0}.jupyter-wrapper .lm-ScrollBar[data-orientation=horizontal] 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<h1 id="Applying-Interoperable-Machine-Learning-in-StreamPipes">Applying Interoperable Machine Learning in StreamPipes<a class="anchor-link" href="#Applying-Interoperable-Machine-Learning-in-StreamPipes"></a></h1><p>The last tutorial (<a href="../4-using-online-machine-learning-on-a-streampipes-data-stream">Using Online Machine Learning on a StreamPipes data stream</a>) demonstrated how patterns in streaming data can be learned online. In contrast, this tutorial demonstrates how one can apply a pre-trained machine learning (ML) model to a StreamPipes data stream making use of <a href="https://onnx.ai/">ONNX</a>. We will show how StreamPipes can be used for both: extracting historical data for training purposes and using model inference on live data with a pre-trained model.</p>
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<h2 id="Preparation">Preparation<a class="anchor-link" href="#Preparation"></a></h2><p>The following lines configure the client and establish a connection to the StreamPipes instance. If you're not familiar with it or anything is unclear, please have a look at our <a href="../1-introduction-to-streampipes-python-client">first tutorial</a>.</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">git</span><span class="o">+</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">github</span><span class="o">.</span><span class="n">com</span><span class="o">/</span><span class="n">apache</span><span class="o">/</span><span class="n">streampipes</span><span class="o">.</span><span class="n">git</span><span class="c1">#subdirectory=streampipes-client-python</span>
<span class="o">%</span><span class="n">pip</span> <span class="n">install</span> <span class="n">scikit</span><span class="o">-</span><span class="n">learn</span><span class="o">==</span><span class="mf">1.4.0</span> <span class="n">skl2onnx</span><span class="o">==</span><span class="mf">1.16.0</span> <span class="n">onnxruntime</span><span class="o">==</span><span class="mf">1.17.1</span> <span class="n">matplotlib</span><span class="o">==</span><span class="mf">3.8.3</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-1">%pip install git+https://github.com/apache/streampipes.git#subdirectory=streampipes-client-python
%pip install scikit-learn==1.4.0 skl2onnx==1.16.0 onnxruntime==1.17.1 matplotlib==3.8.3</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">streampipes.client</span> <span class="kn">import</span> <span class="n">StreamPipesClient</span>
<span class="kn">from</span> <span class="nn">streampipes.client.config</span> <span class="kn">import</span> <span class="n">StreamPipesClientConfig</span>
<span class="kn">from</span> <span class="nn">streampipes.client.credential_provider</span> <span class="kn">import</span> <span class="n">StreamPipesApiKeyCredentials</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"BROKER-HOST"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"localhost"</span>
<span class="n">os</span><span class="o">.</span><span class="n">environ</span><span class="p">[</span><span class="s2">"KAFKA-PORT"</span><span class="p">]</span> <span class="o">=</span> <span class="s2">"9094"</span> <span class="c1"># When using Kafka as message broker</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">StreamPipesClientConfig</span><span class="p">(</span>
<span class="n">credential_provider</span><span class="o">=</span><span class="n">StreamPipesApiKeyCredentials</span><span class="p">(</span>
<span class="n">username</span><span class="o">=</span><span class="s2">"admin@streampipes.apache.org"</span><span class="p">,</span>
<span class="n">api_key</span><span class="o">=</span><span class="s2">"TOKEN"</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">host_address</span><span class="o">=</span><span class="s2">"localhost"</span><span class="p">,</span>
<span class="n">https_disabled</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">port</span><span class="o">=</span><span class="mi">80</span>
<span class="p">)</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">StreamPipesClient</span><span class="p">(</span><span class="n">client_config</span><span class="o">=</span><span class="n">config</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-2">import os
from streampipes.client import StreamPipesClient
from streampipes.client.config import StreamPipesClientConfig
from streampipes.client.credential_provider import StreamPipesApiKeyCredentials
os.environ["BROKER-HOST"] = "localhost"
os.environ["KAFKA-PORT"] = "9094" # When using Kafka as message broker
config = StreamPipesClientConfig(
credential_provider=StreamPipesApiKeyCredentials(
username="admin@streampipes.apache.org",
api_key="TOKEN",
),
host_address="localhost",
https_disabled=True,
port=80
)
client = StreamPipesClient(client_config=config)</div>
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<pre>2024-03-26 10:21:38,538 - streampipes.client.client - [INFO] - [client.py:198] [_set_up_logging] - Logging successfully initialized with logging level INFO.
2024-03-26 10:21:38,632 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.
2024-03-26 10:21:38,634 - streampipes.client.client - [INFO] - [client.py:171] [_get_server_version] - The StreamPipes version was successfully retrieved from the backend: 0.95.0. By means of that, authentication via the provided credentials is also tested successfully.
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<p>The main objective of this tutorial is to demonstrate how to make predictions with an existing and pre-trained ML model using a StreamPipes function and ONNX. Therefore, you can skip the following sections on use case and model training if you already have an existing ONNX model and are only interested in applying it using StreamPipes.</p>
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<h2 id="Machine-Learning-Use-Case">Machine Learning Use Case<a class="anchor-link" href="#Machine-Learning-Use-Case"></a></h2><p>In this tutorial, we will use data generated by the <a href="https://streampipes.apache.org/docs/pe/org.apache.streampipes.connect.iiot.adapters.simulator.machine/">Machine Data Simulator</a> adapter. More specifically, we will focus on the <code>flowrate</code> data, which consists of various sensor values coming from a water pipe system. Our goal is keep an eye on the parameter <code>volume_flow</code>, which represents the current volume flow in cubic meters/second. For this parameter, we want to detect anomalies that could indicate problems such as leaks, blockages, etc.</p>
<p>To get the concerned data, we simply need to create an instance of the machine data simulator and persist the data in the data lake:</p>
<p><img alt="tutorial-preparation" src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-preparation.gif"/></p>
<p>If you choose to perform the model training step yourself, you will need to wait approximately 15 minutes for enough data to be available for model training. If you want to speed this up, you can configure a lower wait time when creating the adapter. Please be aware that this also influences the inference scenario.</p>
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<h2 id="Model-Training-with-Historic-Data">Model Training with Historic Data<a class="anchor-link" href="#Model-Training-with-Historic-Data"></a></h2><p>As said above, the aim of our model is to detect anomalies of the <code>volume_flow</code> parameter. For this task, we will use <a href="https://en.wikipedia.org/wiki/Isolation_forest">Isolation Forests</a>.
Please note that the focus of the tutorial is not on training the model, so please be patient even though the training is very simplified and lacks important preparation steps such as standardization.</p>
<p>As a first step, lets query the <code>flowrate</code> data from the StreamPipes data lake and extract the values of <code>volume_flow</code> as a feature:</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="n">flowrate_df</span> <span class="o">=</span> <span class="n">client</span><span class="o">.</span><span class="n">dataLakeMeasureApi</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"flow-rate"</span><span class="p">)</span><span class="o">.</span><span class="n">to_pandas</span><span class="p">()</span>
<span class="n">X</span> <span class="o">=</span> <span class="n">flowrate_df</span><span class="p">[</span><span class="s2">"volume_flow"</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-3">flowrate_df = client.dataLakeMeasureApi.get("flow-rate").to_pandas()
X = flowrate_df["volume_flow"].values.reshape(-1, 1).astype("float32")</div>
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<pre>2024-03-26 10:21:48,582 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.
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<p>Let's fit the model to the data:</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">IsolationForest</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">IsolationForest</span><span class="p">(</span><span class="n">contamination</span><span class="o">=</span><span class="mf">0.01</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-4">from sklearn.ensemble import IsolationForest
model = IsolationForest(contamination=0.01)
model.fit(X)</div>
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<style>#sk-container-id-3 {
/* Definition of color scheme common for light and dark mode */
--sklearn-color-text: black;
--sklearn-color-line: gray;
/* Definition of color scheme for unfitted estimators */
--sklearn-color-unfitted-level-0: #fff5e6;
--sklearn-color-unfitted-level-1: #f6e4d2;
--sklearn-color-unfitted-level-2: #ffe0b3;
--sklearn-color-unfitted-level-3: chocolate;
/* Definition of color scheme for fitted estimators */
--sklearn-color-fitted-level-0: #f0f8ff;
--sklearn-color-fitted-level-1: #d4ebff;
--sklearn-color-fitted-level-2: #b3dbfd;
--sklearn-color-fitted-level-3: cornflowerblue;
/* Specific color for light theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
--sklearn-color-icon: #696969;
@media (prefers-color-scheme: dark) {
/* Redefinition of color scheme for dark theme */
--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
--sklearn-color-icon: #878787;
}
}
#sk-container-id-3 {
color: var(--sklearn-color-text);
}
#sk-container-id-3 pre {
padding: 0;
}
#sk-container-id-3 input.sk-hidden--visually {
border: 0;
clip: rect(1px 1px 1px 1px);
clip: rect(1px, 1px, 1px, 1px);
height: 1px;
margin: -1px;
overflow: hidden;
padding: 0;
position: absolute;
width: 1px;
}
#sk-container-id-3 div.sk-dashed-wrapped {
border: 1px dashed var(--sklearn-color-line);
margin: 0 0.4em 0.5em 0.4em;
box-sizing: border-box;
padding-bottom: 0.4em;
background-color: var(--sklearn-color-background);
}
#sk-container-id-3 div.sk-container {
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
but bootstrap.min.css set `[hidden] { display: none !important; }`
so we also need the `!important` here to be able to override the
default hidden behavior on the sphinx rendered scikit-learn.org.
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
display: inline-block !important;
position: relative;
}
#sk-container-id-3 div.sk-text-repr-fallback {
display: none;
}
div.sk-parallel-item,
div.sk-serial,
div.sk-item {
/* draw centered vertical line to link estimators */
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
background-size: 2px 100%;
background-repeat: no-repeat;
background-position: center center;
}
/* Parallel-specific style estimator block */
#sk-container-id-3 div.sk-parallel-item::after {
content: "";
width: 100%;
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
flex-grow: 1;
}
#sk-container-id-3 div.sk-parallel {
display: flex;
align-items: stretch;
justify-content: center;
background-color: var(--sklearn-color-background);
position: relative;
}
#sk-container-id-3 div.sk-parallel-item {
display: flex;
flex-direction: column;
}
#sk-container-id-3 div.sk-parallel-item:first-child::after {
align-self: flex-end;
width: 50%;
}
#sk-container-id-3 div.sk-parallel-item:last-child::after {
align-self: flex-start;
width: 50%;
}
#sk-container-id-3 div.sk-parallel-item:only-child::after {
width: 0;
}
/* Serial-specific style estimator block */
#sk-container-id-3 div.sk-serial {
display: flex;
flex-direction: column;
align-items: center;
background-color: var(--sklearn-color-background);
padding-right: 1em;
padding-left: 1em;
}
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*/
/* Pipeline and ColumnTransformer style (default) */
#sk-container-id-3 div.sk-toggleable {
/* Default theme specific background. It is overwritten whether we have a
specific estimator or a Pipeline/ColumnTransformer */
background-color: var(--sklearn-color-background);
}
/* Toggleable label */
#sk-container-id-3 label.sk-toggleable__label {
cursor: pointer;
display: block;
width: 100%;
margin-bottom: 0;
padding: 0.5em;
box-sizing: border-box;
text-align: center;
}
#sk-container-id-3 label.sk-toggleable__label-arrow:before {
/* Arrow on the left of the label */
content: "▸";
float: left;
margin-right: 0.25em;
color: var(--sklearn-color-icon);
}
#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {
color: var(--sklearn-color-text);
}
/* Toggleable content - dropdown */
#sk-container-id-3 div.sk-toggleable__content {
max-height: 0;
max-width: 0;
overflow: hidden;
text-align: left;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-3 div.sk-toggleable__content.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-3 div.sk-toggleable__content pre {
margin: 0.2em;
border-radius: 0.25em;
color: var(--sklearn-color-text);
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-3 div.sk-toggleable__content.fitted pre {
/* unfitted */
background-color: var(--sklearn-color-fitted-level-0);
}
#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {
/* Expand drop-down */
max-height: 200px;
max-width: 100%;
overflow: auto;
}
#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
content: "▾";
}
/* Pipeline/ColumnTransformer-specific style */
#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator-specific style */
/* Colorize estimator box */
#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
#sk-container-id-3 div.sk-label label.sk-toggleable__label,
#sk-container-id-3 div.sk-label label {
/* The background is the default theme color */
color: var(--sklearn-color-text-on-default-background);
}
/* On hover, darken the color of the background */
#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-unfitted-level-2);
}
/* Label box, darken color on hover, fitted */
#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
color: var(--sklearn-color-text);
background-color: var(--sklearn-color-fitted-level-2);
}
/* Estimator label */
#sk-container-id-3 div.sk-label label {
font-family: monospace;
font-weight: bold;
display: inline-block;
line-height: 1.2em;
}
#sk-container-id-3 div.sk-label-container {
text-align: center;
}
/* Estimator-specific */
#sk-container-id-3 div.sk-estimator {
font-family: monospace;
border: 1px dotted var(--sklearn-color-border-box);
border-radius: 0.25em;
box-sizing: border-box;
margin-bottom: 0.5em;
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-0);
}
#sk-container-id-3 div.sk-estimator.fitted {
/* fitted */
background-color: var(--sklearn-color-fitted-level-0);
}
/* on hover */
#sk-container-id-3 div.sk-estimator:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-2);
}
#sk-container-id-3 div.sk-estimator.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-2);
}
/* Specification for estimator info (e.g. "i" and "?") */
/* Common style for "i" and "?" */
.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {
float: right;
font-size: smaller;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1em;
height: 1em;
width: 1em;
text-decoration: none !important;
margin-left: 1ex;
/* unfitted */
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
color: var(--sklearn-color-unfitted-level-1);
}
.sk-estimator-doc-link.fitted,
a:link.sk-estimator-doc-link.fitted,
a:visited.sk-estimator-doc-link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover,
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {
display: none;
z-index: 9999;
position: relative;
font-weight: normal;
right: .2ex;
padding: .5ex;
margin: .5ex;
width: min-content;
min-width: 20ex;
max-width: 50ex;
color: var(--sklearn-color-text);
box-shadow: 2pt 2pt 4pt #999;
/* unfitted */
background: var(--sklearn-color-unfitted-level-0);
border: .5pt solid var(--sklearn-color-unfitted-level-3);
}
.sk-estimator-doc-link.fitted span {
/* fitted */
background: var(--sklearn-color-fitted-level-0);
border: var(--sklearn-color-fitted-level-3);
}
.sk-estimator-doc-link:hover span {
display: block;
}
/* "?"-specific style due to the `<a>` HTML tag */
#sk-container-id-3 a.estimator_doc_link {
float: right;
font-size: 1rem;
line-height: 1em;
font-family: monospace;
background-color: var(--sklearn-color-background);
border-radius: 1rem;
height: 1rem;
width: 1rem;
text-decoration: none;
/* unfitted */
color: var(--sklearn-color-unfitted-level-1);
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
}
#sk-container-id-3 a.estimator_doc_link.fitted {
/* fitted */
border: var(--sklearn-color-fitted-level-1) 1pt solid;
color: var(--sklearn-color-fitted-level-1);
}
/* On hover */
#sk-container-id-3 a.estimator_doc_link:hover {
/* unfitted */
background-color: var(--sklearn-color-unfitted-level-3);
color: var(--sklearn-color-background);
text-decoration: none;
}
#sk-container-id-3 a.estimator_doc_link.fitted:hover {
/* fitted */
background-color: var(--sklearn-color-fitted-level-3);
}
</style><div class="sk-top-container" id="sk-container-id-3"><div class="sk-text-repr-fallback"><pre>IsolationForest(contamination=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br/>On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden=""><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input checked="" class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox"/><label class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted" for="sk-estimator-id-3">  IsolationForest<a class="sk-estimator-doc-link fitted" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.IsolationForest.html" rel="noreferrer" target="_blank">?<span>Documentation for IsolationForest</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>IsolationForest(contamination=0.01)</pre></div> </div></div></div></div>
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<p>The <code>contamination</code> parameter models the proportion of outliers in the data. See the <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html">scikit-learn documentation</a> for more information.</p>
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<p>Before we convert the model to an ONNX representation, let's do a quick visual analysis of the model results:</p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [15]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">flowrate_df</span><span class="p">[</span><span class="s2">"anomaly"</span><span class="p">]</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X</span><span class="p">)</span>
<span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
<span class="n">anomalies</span> <span class="o">=</span> <span class="n">flowrate_df</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">flowrate_df</span><span class="p">[</span><span class="s2">"anomaly"</span><span class="p">]</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="p">[</span><span class="s2">"volume_flow"</span><span class="p">]]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">flowrate_df</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">flowrate_df</span><span class="p">[</span><span class="s1">'volume_flow'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'black'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'volume_flow'</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">anomalies</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">anomalies</span><span class="p">[</span><span class="s1">'volume_flow'</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="s1">'red'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">'Anomaly'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-5">import matplotlib.pyplot as plt
flowrate_df["anomaly"] = model.predict(X)
fig, ax = plt.subplots(figsize=(10, 6))
anomalies = flowrate_df.loc[flowrate_df["anomaly"] == -1, ["volume_flow"]]
ax.plot(flowrate_df.index, flowrate_df['volume_flow'], color='black', label='volume_flow')
ax.scatter(anomalies.index, anomalies['volume_flow'], color='red', label='Anomaly')
plt.legend()
plt.show()</div>
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<p>Okay, that looks quite reasonable so lets covert the model to an ONNX representation so that we can make use of it later.</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">onnxconverter_common</span> <span class="kn">import</span> <span class="n">FloatTensorType</span>
<span class="kn">from</span> <span class="nn">skl2onnx</span> <span class="kn">import</span> <span class="n">to_onnx</span>
<span class="n">model_onnx</span> <span class="o">=</span> <span class="n">to_onnx</span><span class="p">(</span>
<span class="n">model</span><span class="p">,</span>
<span class="n">initial_types</span><span class="o">=</span><span class="p">[(</span><span class="s1">'input'</span><span class="p">,</span> <span class="n">FloatTensorType</span><span class="p">([</span><span class="kc">None</span><span class="p">,</span> <span class="n">X</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]]))],</span>
<span class="n">target_opset</span><span class="o">=</span><span class="p">{</span><span class="s1">'ai.onnx.ml'</span><span class="p">:</span> <span class="mi">3</span><span class="p">,</span> <span class="s1">'ai.onnx'</span><span class="p">:</span> <span class="mi">15</span><span class="p">,</span> <span class="s1">''</span><span class="p">:</span> <span class="mi">15</span><span class="p">}</span>
<span class="p">)</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"isolation_forest.onnx"</span><span class="p">,</span> <span class="s2">"wb"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">f</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">model_onnx</span><span class="o">.</span><span class="n">SerializeToString</span><span class="p">())</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-6">from onnxconverter_common import FloatTensorType
from skl2onnx import to_onnx
model_onnx = to_onnx(
model,
initial_types=[('input', FloatTensorType([None, X.shape[1]]))],
target_opset={'ai.onnx.ml': 3, 'ai.onnx': 15, '': 15}
)
with open("isolation_forest.onnx", "wb") as f:
f.write(model_onnx.SerializeToString())</div>
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<h2 id="Model-Inference-with-Live-Data">Model Inference with Live Data<a class="anchor-link" href="#Model-Inference-with-Live-Data"></a></h2><p>Utilizing a pre-trained model within StreamPipes becomes seamless with the ONNX interoperability standard, enabling effortless application of your existing model on live data streams.</p>
<p>Interacting with live data from StreamPipes is facilitated through StreamPipes functions. Below, we'll create a Python StreamPipes function that leverages an ONNX model to generate predictions for each incoming event, making the results accessible as a data stream within StreamPipes for subsequent steps.</p>
<p>So let's create an <code>ONNXFunction</code> that is capable of applying a model in ONNX representation to a StreamPipes data stream. If you'd like to read more details about how functions are defined, refer to <a href="../3-getting-live-data-from-the-streampipes-data-stream">our third tutorial</a>.</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">onnxruntime</span> <span class="k">as</span> <span class="nn">rt</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.broker.broker_handler</span> <span class="kn">import</span> <span class="n">get_broker_description</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.streampipes_function</span> <span class="kn">import</span> <span class="n">StreamPipesFunction</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.data_stream_generator</span> <span class="kn">import</span> <span class="n">create_data_stream</span><span class="p">,</span> <span class="n">RuntimeType</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.utils.function_context</span> <span class="kn">import</span> <span class="n">FunctionContext</span>
<span class="kn">from</span> <span class="nn">streampipes.model.resource</span> <span class="kn">import</span> <span class="n">FunctionDefinition</span><span class="p">,</span> <span class="n">DataStream</span>
<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">List</span>
<span class="k">class</span> <span class="nc">ONNXFunction</span><span class="p">(</span><span class="n">StreamPipesFunction</span><span class="p">):</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_names</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">input_stream</span><span class="p">:</span> <span class="n">DataStream</span><span class="p">):</span>
<span class="n">output_stream</span> <span class="o">=</span> <span class="n">create_data_stream</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"flowrate-prediction"</span><span class="p">,</span>
<span class="n">attributes</span><span class="o">=</span><span class="p">{</span>
<span class="s2">"is_anomaly"</span><span class="p">:</span> <span class="n">RuntimeType</span><span class="o">.</span><span class="n">BOOLEAN</span><span class="o">.</span><span class="n">value</span>
<span class="p">},</span>
<span class="n">broker</span><span class="o">=</span><span class="n">get_broker_description</span><span class="p">(</span><span class="n">input_stream</span><span class="p">)</span>
<span class="p">)</span>
<span class="n">function_definition</span> <span class="o">=</span> <span class="n">FunctionDefinition</span><span class="p">(</span>
<span class="n">consumed_streams</span><span class="o">=</span><span class="p">[</span><span class="n">input_stream</span><span class="o">.</span><span class="n">element_id</span><span class="p">]</span>
<span class="p">)</span><span class="o">.</span><span class="n">add_output_data_stream</span><span class="p">(</span><span class="n">output_stream</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">feature_names</span> <span class="o">=</span> <span class="n">feature_names</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_name</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_name</span> <span class="o">=</span> <span class="kc">None</span>
<span class="bp">self</span><span class="o">.</span><span class="n">session</span> <span class="o">=</span> <span class="kc">None</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">function_definition</span><span class="o">=</span><span class="n">function_definition</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">onServiceStarted</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">context</span><span class="p">:</span> <span class="n">FunctionContext</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">session</span> <span class="o">=</span> <span class="n">rt</span><span class="o">.</span><span class="n">InferenceSession</span><span class="p">(</span>
<span class="n">path_or_bytes</span><span class="o">=</span><span class="s2">"isolation_forest.onnx"</span><span class="p">,</span>
<span class="n">providers</span><span class="o">=</span><span class="n">rt</span><span class="o">.</span><span class="n">get_available_providers</span><span class="p">(),</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">input_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">get_inputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="bp">self</span><span class="o">.</span><span class="n">output_name</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">get_outputs</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span>
<span class="k">def</span> <span class="nf">onEvent</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">],</span> <span class="n">streamId</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">feature_vector</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_names</span><span class="p">:</span>
<span class="n">feature_vector</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">event</span><span class="p">[</span><span class="n">feature</span><span class="p">])</span>
<span class="n">prediction</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">session</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
<span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">output_name</span><span class="p">],</span>
<span class="p">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_name</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">feature_vector</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">"float32"</span><span class="p">)}</span>
<span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">output</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"is_anomaly"</span><span class="p">:</span> <span class="nb">int</span><span class="p">(</span><span class="n">prediction</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="o">-</span><span class="mi">1</span>
<span class="p">}</span>
<span class="bp">self</span><span class="o">.</span><span class="n">add_output</span><span class="p">(</span>
<span class="n">stream_id</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">function_definition</span><span class="o">.</span><span class="n">get_output_stream_ids</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span>
<span class="n">event</span><span class="o">=</span><span class="n">output</span>
<span class="p">)</span>
<span class="k">def</span> <span class="nf">onServiceStopped</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">pass</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-7">import numpy as np
import onnxruntime as rt
from streampipes.functions.broker.broker_handler import get_broker_description
from streampipes.functions.streampipes_function import StreamPipesFunction
from streampipes.functions.utils.data_stream_generator import create_data_stream, RuntimeType
from streampipes.functions.utils.function_context import FunctionContext
from streampipes.model.resource import FunctionDefinition, DataStream
from typing import Dict, Any, List
class ONNXFunction(StreamPipesFunction):
def __init__(self, feature_names: list[str], input_stream: DataStream):
output_stream = create_data_stream(
name="flowrate-prediction",
attributes={
"is_anomaly": RuntimeType.BOOLEAN.value
},
broker=get_broker_description(input_stream)
)
function_definition = FunctionDefinition(
consumed_streams=[input_stream.element_id]
).add_output_data_stream(output_stream)
self.feature_names = feature_names
self.input_name = None
self.output_name = None
self.session = None
super().__init__(function_definition=function_definition)
def onServiceStarted(self, context: FunctionContext) -&gt; None:
self.session = rt.InferenceSession(
path_or_bytes="isolation_forest.onnx",
providers=rt.get_available_providers(),
)
self.input_name = self.session.get_inputs()[0].name
self.output_name = self.session.get_outputs()[0].name
def onEvent(self, event: Dict[str, Any], streamId: str) -&gt; None:
feature_vector = []
for feature in self.feature_names:
feature_vector.append(event[feature])
prediction = self.session.run(
[self.output_name],
{self.input_name: np.expand_dims(np.array(feature_vector), axis=0).astype("float32")}
)[0]
output = {
"is_anomaly": int(prediction[0]) == -1
}
self.add_output(
stream_id=self.function_definition.get_output_stream_ids()[0],
event=output
)
def onServiceStopped(self) -&gt; None:
pass</div>
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<p>Let's dive a little deeper into the different parts of the function</p>
<ul>
<li><p><strong><code>__init__</code></strong>: First, we need to take care about the data stream that is required to send the predictions from our function to StreamPipes. Thus, we create a dedicated output data stream which we need to provide with the attributes our event will consist of (a timestamp attribute is always added automatically). This output data stream needs to be registered at the function definition which is to be passed to the parent class. Lastly, we need to define some instance variables that are mainly required for the ONNX runtime.</p>
</li>
<li><p><strong><code>onServiceStarted</code></strong>: Here we prepare the ONNX runtime session by creating an <code>InferenceSession</code> and retrieving the corresponding configuration parameters.</p>
</li>
<li><p><strong><code>onEvent</code></strong>: Following the parameter names specified by <code>self.feature_names</code>, we extract all feature values from the current event. Subsequently, the corresponding feature vector is transmitted to the ONNX runtime session. The resulting prediction is then converted into our output event, where a value of <code>-1</code> signifies an anomaly. Finally, the generated output event is forwarded to StreamPipes.</p>
</li>
</ul>
<p>Having the function code in place, we can start the function with the following:</p>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">streampipes.functions.registration</span> <span class="kn">import</span> <span class="n">Registration</span>
<span class="kn">from</span> <span class="nn">streampipes.functions.function_handler</span> <span class="kn">import</span> <span class="n">FunctionHandler</span>
<span class="n">stream</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">stream</span>
<span class="k">for</span> <span class="n">stream</span>
<span class="ow">in</span> <span class="n">client</span><span class="o">.</span><span class="n">dataStreamApi</span><span class="o">.</span><span class="n">all</span><span class="p">()</span>
<span class="k">if</span> <span class="n">stream</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">"flow-rate"</span>
<span class="p">][</span><span class="mi">0</span><span class="p">]</span>
<span class="n">function</span> <span class="o">=</span> <span class="n">ONNXFunction</span><span class="p">(</span>
<span class="n">feature_names</span><span class="o">=</span><span class="p">[</span><span class="s2">"volume_flow"</span><span class="p">],</span>
<span class="n">input_stream</span><span class="o">=</span><span class="n">stream</span>
<span class="p">)</span>
<span class="n">registration</span> <span class="o">=</span> <span class="n">Registration</span><span class="p">()</span>
<span class="n">registration</span><span class="o">.</span><span class="n">register</span><span class="p">(</span><span class="n">function</span><span class="p">)</span>
<span class="n">function_handler</span> <span class="o">=</span> <span class="n">FunctionHandler</span><span class="p">(</span><span class="n">registration</span><span class="p">,</span> <span class="n">client</span><span class="p">)</span>
<span class="n">function_handler</span><span class="o">.</span><span class="n">initializeFunctions</span><span class="p">()</span>
</pre></div>
<div class="clipboard-copy-txt" id="cell-8">from streampipes.functions.registration import Registration
from streampipes.functions.function_handler import FunctionHandler
stream = [
stream
for stream
in client.dataStreamApi.all()
if stream.name == "flow-rate"
][0]
function = ONNXFunction(
feature_names=["volume_flow"],
input_stream=stream
)
registration = Registration()
registration.register(function)
function_handler = FunctionHandler(registration, client)
function_handler.initializeFunctions()</div>
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<pre>2024-03-26 12:39:50,443 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.
2024-03-26 12:39:50,502 - streampipes.functions.function_handler - [INFO] - [function_handler.py:76] [initializeFunctions] - The data stream could not be created.
2024-03-26 12:39:50,503 - streampipes.functions.function_handler - [INFO] - [function_handler.py:78] [initializeFunctions] - This is due to the fact that this data stream already exists. Continuing with the existing data stream.
2024-03-26 12:39:50,503 - streampipes.functions.function_handler - [INFO] - [function_handler.py:84] [initializeFunctions] - Using output data stream 'sp:spdatastream:flowrate-prediction' for function '7c06fa31-9534-4f91-9c50-b7a3607ec3dc'
2024-03-26 12:39:50,548 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:164] [_make_request] - Successfully retrieved all resources.
2024-03-26 12:39:50,549 - streampipes.functions.function_handler - [INFO] - [function_handler.py:100] [initializeFunctions] - Using KafkaConsumer for ONNXFunction
</pre>
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<p>We can now access the live values of the prediction in the StreamPipes UI, e.g., in the pipeline editor.</p>
<p><img alt="prediction-data-stream" src="https://raw.githubusercontent.com/apache/streampipes/dev/streampipes-client-python/docs/img/tutorial-prediction-data-stream.png"/></p>
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<p>That's already it. We hope this tutorial serves as an illustration how ML models can be utilized in StreamPipes with the help of ONNX.</p>
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<p>How do you like this tutorial? We hope you like it and would love to receive some feedback from you. Just go to our GitHub discussion page and let us know your impression. We'll read and react to them all, we promise!</p>
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