diff --git a/.gitignore b/.gitignore index 290f701c2d..d3e3fb84af 100644 --- a/.gitignore +++ b/.gitignore @@ -100,10 +100,11 @@ botorch/qmc/sobol.c* # Sphinx documentation sphinx/build/ -# Docusaurus +# Docusaurus and diagnostic tools website/build/ website/i18n/ website/node_modules/ +node_modules # Tutorials docs/overview/tutorials/*/*.mdx diff --git a/src/beanmachine/ppl/__init__.py b/src/beanmachine/ppl/__init__.py index 60527548f9..9b57abced2 100644 --- a/src/beanmachine/ppl/__init__.py +++ b/src/beanmachine/ppl/__init__.py @@ -8,6 +8,7 @@ from . import experimental from .diagnostics import Diagnostics from .diagnostics.common_statistics import effective_sample_size, r_hat, split_r_hat +from .diagnostics.tools import viz from .inference import ( CompositionalInference, empirical, @@ -58,4 +59,5 @@ "random_variable", "simulate", "split_r_hat", + "viz", ] diff --git a/src/beanmachine/ppl/diagnostics/tools/__init__.py b/src/beanmachine/ppl/diagnostics/tools/__init__.py new file mode 100644 index 0000000000..3e58d695f5 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/__init__.py @@ -0,0 +1,22 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +# flake8: noqa + +"""Visual diagnostic tools for Bean Machine models.""" + +import sys +from pathlib import Path + + +if sys.version_info >= (3, 8): + from typing import TypedDict +else: + from typing_extensions import TypedDict + + +TOOLS_DIR = Path(__file__).parent.resolve() +JS_DIR = TOOLS_DIR.joinpath("js") +JS_DIST_DIR = JS_DIR.joinpath("dist") diff --git a/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js new file mode 100644 index 0000000000..351d8b36f8 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js @@ -0,0 +1,75 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +const OFF = 0; +const WARNING = 1; +const ERROR = 2; + +module.exports = { + root: true, + env: { + browser: true, + commonjs: true, + jest: true, + node: true, + }, + parser: '@typescript-eslint/parser', + parserOptions: { + allowImportExportEverywhere: true, + }, + extends: ['airbnb', 'prettier', 'plugin:import/typescript'], + plugins: ['prefer-arrow'], + rules: { + // Allow more than 1 class per file. + 'max-classes-per-file': ['error', {ignoreExpressions: true, max: 2}], + // Allow snake_case. + camelcase: [ + OFF, + { + properties: 'never', + ignoreDestructuring: true, + ignoreImports: true, + ignoreGlobals: true, + }, + ], + 'no-underscore-dangle': OFF, + // Arrow function rules. + 'prefer-arrow/prefer-arrow-functions': [ + ERROR, + { + disallowPrototype: true, + singleReturnOnly: false, + classPropertiesAllowed: false, + }, + ], + 'prefer-arrow-callback': [ERROR, {allowNamedFunctions: true}], + 'arrow-parens': [ERROR, 'always'], + 'arrow-body-style': [ERROR, 'always'], + 'func-style': [ERROR, 'declaration', {allowArrowFunctions: true}], + 'react/function-component-definition': [ + ERROR, + { + namedComponents: 'arrow-function', + unnamedComponents: 'arrow-function', + }, + ], + // Ignore the global require, since some required packages are BrowserOnly. + 'global-require': 0, + // We reassign several parameter objects since Bokeh is just updating values in the + // them. + 'no-param-reassign': 0, + // Ignore certain webpack alias because it can't be resolved + 'import/no-unresolved': [ + ERROR, + {ignore: ['^@theme', '^@docusaurus', '^@generated', '^@bokeh']}, + ], + 'import/extensions': OFF, + 'object-shorthand': [ERROR, 'never'], + 'prefer-destructuring': [WARNING, {object: true, array: true}], + 'no-nested-ternary': 0, + }, +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc b/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc new file mode 100644 index 0000000000..a9334cae01 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc @@ -0,0 +1,8 @@ +{ + "arrowParens": "always", + "bracketSpacing": false, + "printWidth": 88, + "proseWrap": "never", + "singleQuote": true, + "trailingComma": "all" +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/package.json b/src/beanmachine/ppl/diagnostics/tools/js/package.json new file mode 100644 index 0000000000..38e7770cb5 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/package.json @@ -0,0 +1,55 @@ +{ + "name": "visual-diagnostic-tools", + "version": "0.1.0", + "description": "", + "license": "MIT", + "keywords": [], + "repository": {}, + "scripts": { + "build": "webpack" + }, + "dependencies": { + "@bokeh/bokehjs": "^2.4.3", + "fast-kde": "^0.2.1" + }, + "devDependencies": { + "@types/node": "^18.0.4", + "@typescript-eslint/eslint-plugin": "^5.30.5", + "@typescript-eslint/parser": "^5.30.5", + "eslint": "^8.19.0", + "eslint-config-airbnb": "^19.0.4", + "eslint-config-prettier": "^8.5.0", + "eslint-plugin-import": "^2.26.0", + "eslint-plugin-jsx-a11y": "^6.5.1", + "eslint-plugin-prefer-arrow": "^1.2.3", + "eslint-plugin-react": "^7.28.0", + "eslint-plugin-react-hooks": "^4.3.0", + "prettier": "^2.7.1", + "ts-loader": "^9.3.1", + "ts-node": "^10.9.1", + "typescript": "^4.7.4", + "webpack": "^5.74.0", + "webpack-cli": "^4.10.0" + }, + "overrides": { + "cwise": "$cwise", + "minimist": "$minimist", + "quote-stream": "$quote-stream", + "static-eval": "$static-eval", + "static-module": "$static-module", + "typedarray-pool": "$typedarray-pool" + }, + "peerDependencies": { + "@types/cwise": "^1.0.4", + "@types/minimist": "^1.2.2", + "@types/static-eval": "^0.2.31", + "@types/typedarray-pool": "^1.1.2", + "buffer": "^6.0.3", + "cwise": "^1.0.10", + "minimist": "^1.2.6", + "quote-stream": "^1.0.2", + "static-eval": "2.1.0", + "static-module": "^3.0.4", + "typedarray-pool": "^1.2.0" + } +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts new file mode 100644 index 0000000000..e6c002fbd6 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts @@ -0,0 +1,190 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import {Axis} from '@bokehjs/models/axes/axis'; +import {cumulativeSum} from '../stats/array'; +import {scaleToOne} from '../stats/dataTransformation'; +import { + interval as hdiInterval, + data as hdiData, +} from '../stats/highestDensityInterval'; +import {oneD} from '../stats/marginal'; +import {mean as computeMean} from '../stats/pointStatistic'; +import {interpolatePoints} from '../stats/utils'; +import * as interfaces from './interfaces'; + +// Define the names of the figures used for this Bokeh application. +const figureNames = ['marginal', 'cumulative']; + +/** + * Update the given Bokeh Axis object with the new label string. You must use this + * method to update axis strings using TypeScript, otherwise the ts compiler will throw + * a type check error. + * + * @param {Axis} axis - The Bokeh Axis object needing a new label. + * @param {string | null} label - The new label for the Bokeh Axis object. + */ +export const updateAxisLabel = (axis: Axis, label: string | null): void => { + // Type check requirement. + if ('axis_label' in axis) { + axis.axis_label = label; + } +}; + +/** + * Compute the following statistics for the given random variable data + * + * - lower bound for the highest density interval calculated from the marginalX; + * - mean of the rawData; + * - upper bound for the highest density interval calculated from the marginalY. + * + * @param {number[]} rawData - Raw random variable data from the model. + * @param {number[]} marginalX - The support of the Kernel Density Estimate of the + * random variable. + * @param {number[]} marginalY - The Kernel Density Estimate of the random variable. + * @param {number | null} [hdiProb=null] - The highest density interval probability + * value. If the default value is not overwritten, then the default HDI probability + * is 0.89. See Statistical Rethinking by McElreath for a description as to why this + * value is the default. + * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text + * shown in the figure for the point statistics. + * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the + * x-axis. + * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the + * y-axis + * @returns {interfaces.LabelsData} Object containing the computed stats. + */ +export const computeStats = ( + rawData: number[], + marginalX: number[], + marginalY: number[], + hdiProb: number | null = null, + text_align: string[] = ['right', 'center', 'left'], + x_offset: number[] = [-5, 0, 5], + y_offset: number[] = [0, 10, 0], +): interfaces.LabelsData => { + // Set the default value to 0.89 if no default value has been given. + const hdiProbability = hdiProb ?? 0.89; + + // Compute the point statistics for the KDE, and create labels to display them in the + // figures. + const mean = computeMean(marginalX); + const hdiBounds = hdiInterval(rawData, hdiProbability); + const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound]; + const y = interpolatePoints({x: marginalX, y: marginalY, points: x}); + const text = [ + `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`, + `Mean: ${mean.toFixed(3)}`, + `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`, + ]; + + return { + x: x, + y: y, + text: text, + text_align: text_align, + x_offset: x_offset, + y_offset: y_offset, + }; +}; + +/** + * Compute data for the one-dimensional marginal diagnostic tool. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the Kernel Density Estimate (KDE). + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {interfaces.Data} The marginal distribution and cumulative + * distribution calculated from the given random variable data. Point statistics are + * also calculated. + */ +export const computeData = ( + data: number[], + bwFactor: number, + hdiProbability: number, +): interfaces.Data => { + const output = {} as interfaces.Data; + for (let i = 0; i < figureNames.length; i += 1) { + const figureName = figureNames[i]; + output[figureName] = {} as interfaces.GlyphData; + + // Compute the one-dimensional KDE and its cumulative distribution. + const distribution = oneD(data, bwFactor); + switch (figureName) { + case 'cumulative': + distribution.y = scaleToOne(cumulativeSum(distribution.y)); + break; + default: + break; + } + + // Compute the point statistics for the given data. + const stats = computeStats(data, distribution.x, distribution.y, hdiProbability); + + output[figureName] = { + distribution: distribution, + hdi: hdiData(data, distribution.x, distribution.y, hdiProbability), + stats: {x: stats.x, y: stats.y, text: stats.text}, + labels: stats, + }; + } + return output; +}; + +/** + * Callback used to update the Bokeh application in the notebook. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {string} rvName - The name of the random variable from the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the kernel density estimate. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the + * application. + * @param {interfaces.Figures} figures - Bokeh figures shown in the application. + * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs. + * @returns {number} We display the value of the bandwidth used for computing the Kernel + * Density Estimate in a div, and must return that value here in order to update the + * value displayed to the user. + */ +export const update = ( + data: number[], + rvName: string, + bwFactor: number, + hdiProbability: number, + sources: interfaces.Sources, + figures: interfaces.Figures, + tooltips: interfaces.Tooltips, +): number => { + const computedData = computeData(data, bwFactor, hdiProbability); + for (let i = 0; i < figureNames.length; i += 1) { + // Update all sources with new data calculated above. + const figureName = figureNames[i]; + sources[figureName].distribution.data = { + x: computedData[figureName].distribution.x, + y: computedData[figureName].distribution.y, + }; + sources[figureName].hdi.data = { + base: computedData[figureName].hdi.base, + lower: computedData[figureName].hdi.lower, + upper: computedData[figureName].hdi.upper, + }; + sources[figureName].stats.data = computedData[figureName].stats; + sources[figureName].labels.data = computedData[figureName].labels; + + // Update the axes labels. + updateAxisLabel(figures[figureName].below[0], rvName); + + // Update the tooltips. + tooltips[figureName].stats.tooltips = [['', '@text']]; + tooltips[figureName].distribution.tooltips = [[rvName, '@x']]; + } + return computedData.marginal.distribution.bandwidth; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts new file mode 100644 index 0000000000..2669193a60 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts @@ -0,0 +1,12 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import * as marginal1d from './callbacks'; + +// The CustomJS methods used by Bokeh require us to make the JavaScript available in the +// browser, which is done by defining it below. +(window as any).marginal1d = marginal1d; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts new file mode 100644 index 0000000000..fe8aede9e5 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts @@ -0,0 +1,97 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import {Plot} from '@bokehjs/models/plots/plot'; +import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; +import {HoverTool} from '@bokehjs/models/tools/inspectors/hover_tool'; + +// NOTE: In the corresponding Python typing files for the diagnostic tool, we define +// similar types using a TypedDict object. TypeScript allows us to maintain +// semantic information about the key names and their types in the same object and +// still have the ability to loop over objects as long as we have the +// [key: string]: any; indicator in the interface definition. This boils down to a +// Python type of Dict[Any, Any], which again loses all type information about the +// object we are defining. We are mirroring what is done in Python here, so we +// keep the semantic information here at the expense of losing type information +// similarly to what is done in Python. + +export interface DistributionData { + [key: string]: any; + x: number[]; + y: number[]; + bandwidth: number; +} + +export interface HDIData { + [key: string]: any; + base: number[]; + lower: number[]; + upper: number[]; +} + +export interface StatsData { + [key: string]: any; + x: number[]; + y: number[]; + text: string[]; +} + +export interface LabelsData { + [key: string]: any; + x: number[]; + y: number[]; + text: string[]; + text_align: string[]; + x_offset: number[]; + y_offset: number[]; +} + +export interface GlyphData { + [key: string]: any; + distribution: DistributionData; + hdi: HDIData; + stats: StatsData; + labels: LabelsData; +} + +export interface Data { + [key: string]: any; + marginal: GlyphData; + cumulative: GlyphData; +} + +export interface Source { + [key: string]: any; + distribution: ColumnDataSource; + hdi: ColumnDataSource; + stats: ColumnDataSource; + labels: ColumnDataSource; +} + +export interface Sources { + [key: string]: any; + marginal: Source; + cumulative: Source; +} + +export interface Figures { + [key: string]: any; + marginal: Plot; + cumulative: Plot; +} + +export interface Tooltip { + [key: string]: any; + distribution: HoverTool; + stats: HoverTool; +} + +export interface Tooltips { + [key: string]: any; + marginal: Tooltip; + cumulative: Tooltip; +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts new file mode 100644 index 0000000000..d67b2e7dbf --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts @@ -0,0 +1,41 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +/** + * Cumulative sum of the given data. + * + * @param {number[]} data - Any array of data. + * @returns {number[]} The cumulative sum of the given data. + */ +export const cumulativeSum = (data: number[]): number[] => { + // eslint-disable-next-line arrow-body-style + const cumulativeSumMap = ((sum: number) => (value: number) => { + // eslint-disable-next-line no-return-assign + return (sum += value); + })(0); + + const cSum = data.map(cumulativeSumMap); + const normalizationFactor = Math.max(...cSum); + const cumSum = []; + for (let i = 0; i < cSum.length; i += 1) { + cumSum.push(cSum[i] / normalizationFactor); + } + return cumSum; +}; + +/** + * Numerically sort the given array of numbers. + * + * @param {number[]} data - The array of numbers to sort. + * @returns {number[]} The sorted array of numbers. + */ +export const numericalSort = (data: number[]): number[] => { + const dataCopy = data.slice(0); + return dataCopy.sort((a, b) => { + return a < b ? -1 : a > b ? 1 : 0; + }); +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts new file mode 100644 index 0000000000..a8ecb46596 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts @@ -0,0 +1,34 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +/** + * Scale the given array of numbers by the given scaleFactor. Note that this method + * divides values in the given array by the scaleFactor. + * + * @param {number[]} data - An array of numbers that needs to be scaled. + * @param {number} scaleFactor - The value to divide all array values by. + * @returns {number[]} The scaled array. + */ +export const scaleBy = (data: number[], scaleFactor: number): number[] => { + const scaledData = data.map((datum) => { + return datum / scaleFactor; + }); + return scaledData; +}; + +/** + * Scale the given array to a maximum value of 1. This method uses the scaleBy method, + * and finds the maximum value of the given array to use as the scaleFactor of the + * scaleBy method. + * + * @param {number[]} data - Array of data to scale to one. + * @returns {number[]} The data scaled to one. + */ +export const scaleToOne = (data: number[]): number[] => { + const scaleFactor = Math.max(...data); + return scaleBy(data, scaleFactor); +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts new file mode 100644 index 0000000000..e0356eb0b2 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts @@ -0,0 +1,96 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import {numericalSort} from './array'; + +export interface HdiInterval { + lowerBound: number; + upperBound: number; + lowerBoundIndex: number; + upperBoundIndex: number; +} + +export interface HdiData { + base: number[]; + lower: number[]; + upper: number[]; + lowerBound: number; + upperBound: number; +} + +/** + * Find the lower and upper bound of the Highest Density Interval (HDI) for the given + * data. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of + * the given data, as well as the indices of the lower and upper bound for the + * sorted raw data. + */ +export const interval = (data: number[], hdiProbability: number): HdiInterval => { + const N = data.length; + const sortedData = numericalSort(data); + const stopIndex = Math.floor(hdiProbability * N); + const startIndex = N - stopIndex; + const leftData = sortedData.slice(stopIndex); + const rightData = sortedData.slice(0, startIndex); + const hdi = []; + for (let i = 0; i < leftData.length; i += 1) { + hdi.push(leftData[i] - rightData[i]); + } + const lowerIndex = hdi.indexOf(Math.min(...hdi)); + const upperIndex = lowerIndex + stopIndex; + return { + lowerBound: sortedData[lowerIndex], + upperBound: sortedData[upperIndex], + lowerBoundIndex: lowerIndex, + upperBoundIndex: upperIndex, + }; +}; + +/** + * Constructs x and y arrays from the HDI bounds using the raw random variable data. It + * also returns the actual HDI bounds of the random variable data. + * + * @param {number[]} rvData - Raw random variable data from the model. + * @param {number[]} marginalX - The support of the Kernel Density Estimate of the + * random variable. + * @param {number[]} marginalY - The Kernel Density Estimate of the random variable. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {HdiData} The x array for the HDI data is the "lower" key, while the y array + * for the HDI data is the "upper" key and the "base" key is the support along the + * axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object, + * which is the annotation type we will use when rendering the HDI intervals. + */ +export const data = ( + rvData: number[], + marginalX: number[], + marginalY: number[], + hdiProbability: number, +): HdiData => { + const hdi = interval(rvData, hdiProbability); + const base = []; + const upper = []; + for (let i = 0; i < marginalX.length; i += 1) { + if (marginalX[i] <= hdi.upperBound && marginalX[i] >= hdi.lowerBound) { + base.push(marginalX[i]); + upper.push(marginalY[i]); + } + } + // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is + // an array of zeros since we will fill the Band all the way to the lower axis. + return { + base: base, + lower: new Array(base.length).fill(0.0), + upper: upper, + lowerBound: hdi.lowerBound, + upperBound: hdi.upperBound, + }; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts new file mode 100644 index 0000000000..fed70ee34c --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts @@ -0,0 +1,49 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +import {density1d} from 'fast-kde/src/density1d'; +import {scaleToOne} from './dataTransformation'; + +/** + * Calculate the one-dimensional Kernel Density Estimate. This method uses the "scott" + * estimate for the bandwidth used when calculating the marginal. This bandwidth + * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ. + * + * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605–610. + * doi:10.1093/biomet/66.3.605. + * + * @param {number[]} data - The raw random variable data of the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the Kernel Density Estimate (KDE). + * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data, + * along with the numerical value of the bandwidth used to calculate the KDE. + */ +// eslint-disable-next-line import/prefer-default-export +export const oneD = ( + data: number[], + bwFactor: number, +): {x: number[]; y: number[]; bandwidth: number} => { + // Calculate the 1D KDE. + const kde1d = density1d(data, { + adjust: bwFactor, + pad: 3, + bins: 512, + }); + // Extract the data points from the 1D KDE calculation. + const points: {x: number; y: number}[] = Array.from(kde1d); + const x = []; + const y = []; + for (let i = 0; i < points.length; i += 1) { + x.push(points[i].x); + y.push(points[i].y); + } + return { + x: x, + y: scaleToOne(y), + bandwidth: kde1d.bandwidth(), + }; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts new file mode 100644 index 0000000000..3ee47b9876 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts @@ -0,0 +1,29 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +/** + * Syntactic sugar for summing an array of numbers. + * + * @param {number[]} data - The array of data. + * @returns {number} The sum of the array of data. + */ +export const sum = (data: number[]): number => { + return data.reduce((previousValue, currentValue) => { + return previousValue + currentValue; + }); +}; + +/** + * Calculate the mean of the given array of data. + * + * @param {number[]} data - The array of data. + * @returns {number} The mean of the given data. + */ +export const mean = (data: number[]): number => { + const dataSum = sum(data); + return dataSum / data.length; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts new file mode 100644 index 0000000000..fd8049989f --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts @@ -0,0 +1,84 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +const left_edge_index = (point: number, intervals: number[]) => { + const len = intervals.length; + if (point < intervals[0]) { + return -1; + } + if (point > intervals[len - 1]) { + return intervals.length; + } + let leftEdgeIndex = 0; + let rightEdgeIndex = len - 1; + while (rightEdgeIndex - leftEdgeIndex !== 1) { + const indexOfNumberToCompare = + leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2); + if (point >= intervals[indexOfNumberToCompare]) + leftEdgeIndex = indexOfNumberToCompare; + else rightEdgeIndex = indexOfNumberToCompare; + } + return leftEdgeIndex; +}; + +const lerp = (x: number, x0: number, y0: number, x1: number, y1: number): number => { + const slope = (y1 - y0) / (x1 - x0); + let res = slope * (x - x0) + y0; + if (!Number.isFinite(res)) { + res = slope * (x - x1) + y1; + if (!Number.isFinite(res) && y0 === y1) res = y0; + } + return res; +}; + +const interpolate = (points: number[], x: number[], y: number[]): number[] => { + // Implementation ported from np.interp + const n = points.length; + const results = new Array(n); + for (let i = 0; i < n; i += 1) { + const point = points[i]; + if (Number.isNaN(point)) { + results[i] = point; + // eslint-disable-next-line no-continue + continue; + } + const index = left_edge_index(point, x); + if (index === -1) { + // eslint-disable-next-line prefer-destructuring + results[i] = y[0]; + } else if (index === x.length) { + results[i] = y[y.length - 1]; + } else if (index === x.length - 1 || x[index] === point) { + results[i] = y[index]; + } else { + const x0 = x[index]; + const y0 = y[index]; + const x1 = x[index + 1]; + const y1 = y[index + 1]; + results[i] = lerp(point, x0, y0, x1, y1); + } + } + return results; +}; + +// eslint-disable-next-line import/prefer-default-export +export const interpolatePoints = ({ + x, + y, + points, +}: { + x: number[]; + y: number[]; + points: number[]; +}): number[] => { + const output = []; + for (let i = 0; i < points.length; i += 1) { + const interpolatedPoint = Array.from(interpolate([points[i]], x, y)); + output.push(interpolatedPoint[0]); + } + return output; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts new file mode 100644 index 0000000000..fb89503344 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts @@ -0,0 +1,8 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +declare module 'fast-kde/*'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json b/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json new file mode 100644 index 0000000000..37de8788f8 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json @@ -0,0 +1,48 @@ +{ + "compilerOptions": { + "allowJs": true, + "alwaysStrict": true, + "baseUrl": ".", + "declaration": true, + "esModuleInterop": true, + "experimentalDecorators": true, + "importHelpers": false, + "lib": ["dom", "dom.iterable", "es2020"], + "module": "ES2020", + "moduleResolution": "node", + "noEmitOnError": false, + "noErrorTruncation": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "outDir": "./dist/lib", + "paths": { + "@bokehjs/*": [ + "node_modules/@bokeh/bokehjs/build/js/lib/*", + "node_modules/@bokeh/bokehjs/build/js/types/*" + ], + "compute-histogram/*": ["node_modules/compute-histogram/*"], + "fast-kde/*": ["node_modules/fast-kde/*"], + "ndarray/*": ["node_modules/ndarray/*"], + "ndarray-fft/*": [ + "node_modules/ndarray-fft/*", + "bokeh_extensions/types/ndarray-fft.d.ts" + ] + }, + "resolveJsonModule": true, + "skipLibCheck": true, + "sourceMap": true, + "strictBindCallApply": false, + "strictFunctionTypes": false, + "strictNullChecks": true, + "strictPropertyInitialization": false, + "target": "es2020" + }, + "include": [ + "./**/*.ts", + "./**/*.d.ts", + "./node_modules/@bokeh/bokehjs/build/js/types/api/main.d.ts" + ] +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js new file mode 100644 index 0000000000..cbe79da94d --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js @@ -0,0 +1,36 @@ +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ + +const path = require('path'); + +module.exports = { + entry: { + marginal1d: './src/marginal1d/index.ts', + }, + output: { + filename: '[name].js', + path: path.resolve(__dirname, 'dist'), + }, + module: { + rules: [{test: /\.ts$/, use: 'ts-loader', exclude: /node_modules/}], + }, + target: 'web', + mode: 'production', + resolve: { + extensions: ['.ts'], + modules: ['./stats', './interfaces', './types', './node_modules'], + alias: { + 'fast-kde/src/density1d': path.resolve( + __dirname, + 'node_modules/fast-kde/src/density1d.js', + ), + }, + }, + optimization: { + minimize: false, + }, +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock new file mode 100644 index 0000000000..5ece43a2c8 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock @@ -0,0 +1,2653 @@ +# THIS IS AN AUTOGENERATED FILE. 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a/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py new file mode 100644 index 0000000000..7bec24cb17 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py new file mode 100644 index 0000000000..47b073f082 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Marginal 1D diagnostic tool for a Bean Machine model.""" + +from typing import TypeVar + +from beanmachine.ppl.diagnostics.tools.marginal1d import utils +from beanmachine.ppl.diagnostics.tools.utils.diagnostic_tool_base import ( + DiagnosticToolBaseClass, +) +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from bokeh.models import Model +from bokeh.models.callbacks import CustomJS + + +T = TypeVar("T", bound="Marginal1d") + + +class Marginal1d(DiagnosticToolBaseClass): + """ + Marginal 1D diagnostic tool. + + Args: + mcs (MonteCarloSamples): The return object from running a Bean Machine model. + + Attributes: + data (Dict[str, List[List[float]]]): JSON serializable representation of the + given `mcs` object. + rv_names (List[str]): The list of random variables string names for the given + model. + num_chains (int): The number of chains of the model. + num_draws (int): The number of draws of the model for each chain. + palette (List[str]): A list of color values used for the glyphs in the figures. + The colors are specifically chosen from the Colorblind palette defined in + Bokeh. + tool_js (str):The JavaScript callbacks needed to render the Bokeh tool + independently from a Python server. + """ + + def __init__(self: T, mcs: MonteCarloSamples) -> None: + super(Marginal1d, self).__init__(mcs) + + def create_document(self: T) -> Model: + # Initialize widget values using Python. + rv_name = self.rv_names[0] + bw_factor = 1.0 + bandwidth = 1.0 + + # NOTE: We are going to use Python and Bokeh to render the tool in the notebook + # output cell, however, we WILL NOT use Python to calculate any of the + # statistics displayed in the tool. We do this so we can make the BROWSER + # run all the calculations based on user interactions. If we did not + # employ this strategy, then the initial display a user would receive + # would be calculated by Python, and any subsequent updates would be + # calculated by JavaScript. The side-effect of having two backends + # calculate data could cause the figures to flicker, which would not be a + # good end user experience. + # + # Bokeh 3.0 is implementing an "on load" feature, which would nullify this + # requirement, and until that version is released, we have to employ this + # work-around. + + # Create empty Bokeh sources using Python. + sources = utils.create_sources() + + # Create empty figures for the tool using Python. + figures = utils.create_figures(rv_name=rv_name) + + # Create empty glyphs and attach them to the figures using Python. + glyphs = utils.create_glyphs() + utils.add_glyphs(sources=sources, figures=figures, glyphs=glyphs) + + # Create empty annotations and attach them to the figures using Python. + annotations = utils.create_annotations(sources=sources) + utils.add_annotations(figures=figures, annotations=annotations) + + # Create empty tool tips and attach them to the figures using Python. + tooltips = utils.create_tooltips(figures=figures, rv_name=rv_name) + utils.add_tooltips(figures=figures, tooltips=tooltips) + + # Create the widgets for the tool using Python. + widgets = utils.create_widgets( + rv_names=self.rv_names, + rv_name=rv_name, + bandwidth=bandwidth, + bw_factor=bw_factor, + ) + + # Create the view of the tool and serialize it into HTML using static resources + # from Bokeh. Embedding the tool in this manner prevents external CDN calls for + # JavaScript resources, and prevents the user from having to know where the + # Bokeh server is. + tool_view = utils.create_view(figures=figures, widgets=widgets) + + # Create callbacks for the tool using JavaScript. + callback_js = f""" + const rvName = widgets.rv_select.value; + const rvData = data[rvName].flat(); + let bw = 0.0; + // Remove the CSS classes that dim the tool output on initial load. + const toolTab = toolView.tabs[0]; + const toolChildren = toolTab.child.children; + const dimmedComponent = toolChildren[1]; + dimmedComponent.css_classes = []; + try {{ + bw = marginal1d.update( + rvData, + rvName, + bwFactor, + hdiProbability, + sources, + figures, + tooltips, + ); + }} catch (error) {{ + {self.tool_js} + bw = marginal1d.update( + rvData, + rvName, + bwFactor, + hdiProbability, + sources, + figures, + tooltips, + ); + }} + """ + + # Each widget requires the following dictionary for the CustomJS method. Notice + # that the callback_js object above uses the names defined as keys in the below + # object with values defined by the Python objects. + callback_arguments = { + "data": self.data, + "widgets": widgets, + "sources": sources, + "figures": figures, + "tooltips": tooltips, + "toolView": tool_view, + } + + # Each widget requires slightly different JS, except for the sliders. + rv_select_js = f""" + console.log(toolView); + const bwFactor = 1.0; + const hdiProbability = 0.89; + widgets.bw_factor_slider.value = bwFactor; + widgets.hdi_slider.value = 100 * hdiProbability; + {callback_js}; + widgets.bw_div.text = `Bandwidth: ${{bwFactor * bw}}`; + figures.marginal.reset.emit(); + """ + slider_js = f""" + const bwFactor = widgets.bw_factor_slider.value; + const hdiProbability = widgets.hdi_slider.value / 100; + {callback_js}; + widgets.bw_div.text = `Bandwidth: ${{bwFactor * bw}}`; + """ + rv_select_callback = CustomJS(args=callback_arguments, code=rv_select_js) + slider_callback = CustomJS(args=callback_arguments, code=slider_js) + + # Tell Python to use the JavaScript. + widgets["rv_select"].js_on_change("value", rv_select_callback) + widgets["bw_factor_slider"].js_on_change("value", slider_callback) + widgets["hdi_slider"].js_on_change("value", slider_callback) + + return tool_view diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py new file mode 100644 index 0000000000..881cd308e4 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py @@ -0,0 +1,142 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Marginal 1D diagnostic tool types for a Bean Machine model.""" + +from typing import Any, Dict, List, Union + +from beanmachine.ppl.diagnostics.tools import TypedDict + +from bokeh.models.annotations import Band, LabelSet +from bokeh.models.glyphs import Circle, Line +from bokeh.models.sources import ColumnDataSource +from bokeh.models.tools import HoverTool +from bokeh.models.widgets.inputs import Select +from bokeh.models.widgets.markups import Div +from bokeh.models.widgets.sliders import Slider +from bokeh.plotting.figure import Figure + + +# NOTE: These are the types pyre gives us when using `reveal_type(...)` on the outputs +# of the methods. +StatsAndLabelsData = Dict[str, Dict[str, Any]] +HDIData = Dict[str, Any] +Data = Dict[Any, Any] +Sources = Dict[Any, Any] +Figures = Dict[Any, Any] +Glyphs = Dict[Any, Any] +Annotations = Dict[Any, Any] +Tooltips = Dict[Any, Any] +Widgets = Dict[str, Union[Div, Select, Slider]] + + +# NOTE: TypedDict objects are for reference only. Due to the way pyre accesses keys in +# dictionaries, and how NumPy casts arrays when using tolist(), we are unable to +# use them, but they provide semantic information for the different types. We must +# ignore a lot of lines due to the issue discussed here +# https://pyre-check.org/docs/errors/#13-uninitialized-attribute. + + +class _DistributionData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + bandwidth: float + + +class _HDIData(TypedDict): # pyre-ignore + base: List[float] + lower: List[float] + upper: List[float] + + +class _StatsData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + text: List[str] + + +class _LabelsData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + text: List[str] + text_align: List[str] + x_offset: List[int] + y_offset: List[int] + + +class _GlyphData(TypedDict): # pyre-ignore + distribtution: _DistributionData + hdi: _HDIData + stats: _StatsData + labels: _LabelsData + + +class _Data(TypedDict): # pyre-ignore + marginal: _GlyphData + cumulative: _GlyphData + + +class _Source(TypedDict): # pyre-ignore + distribution: ColumnDataSource + hdi: ColumnDataSource + stats: ColumnDataSource + labels: ColumnDataSource + + +class _Sources(TypedDict): # pyre-ignore + marginal: _Source + cumulative: _Source + + +class _Figures(TypedDict): # pyre-ignore + marginal: Figure + cumulative: Figure + + +class _DistributionGlyph(TypedDict): # pyre-ignore + glyph: Line + hover_glyph: Line + + +class _StatsGlyph(TypedDict): # pyre-ignore + glyph: Circle + hover_glyph: Circle + + +class _FigureGlyphs(TypedDict): # pyre-ignore + distribution: _DistributionGlyph + stats: _StatsGlyph + + +class _Glyphs(TypedDict): # pyre-ignore + marginal: _FigureGlyphs + cumulative: _FigureGlyphs + + +class _FigureAnnotations(TypedDict): # pyre-ignore + hdi: Band + labels: LabelSet + + +class _Annotations(TypedDict): # pyre-ignore + marginal: _FigureAnnotations + cumulative: _FigureAnnotations + + +class _Tooltip(TypedDict): # pyre-ignore + distribution: HoverTool + stats: HoverTool + + +class _Tooltips(TypedDict): # pyre-ignore + marginal: _Tooltip + cumulative: _Tooltip + + +class _Widgets(TypedDict): # pyre-ignore + rv_select: Select + bw_factor_slider: Slider + bw_div: Div + hdi_slider: Slider diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py new file mode 100644 index 0000000000..6ef4dfebf7 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -0,0 +1,469 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Methods used to generate the diagnostic tool.""" + +from typing import List + +import numpy as np +from beanmachine.ppl.diagnostics.tools.marginal1d import typing +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from bokeh.models.annotations import Band, LabelSet +from bokeh.models.glyphs import Circle, Line +from bokeh.models.layouts import Column, Row +from bokeh.models.sources import ColumnDataSource +from bokeh.models.tools import HoverTool +from bokeh.models.widgets.inputs import Select +from bokeh.models.widgets.markups import Div +from bokeh.models.widgets.panels import Panel, Tabs +from bokeh.models.widgets.sliders import Slider +from bokeh.plotting.figure import figure + + +PLOT_WIDTH = 500 +PLOT_HEIGHT = 500 +FIGURE_NAMES = ["marginal", "cumulative"] +# Define what the empty data object looks like in order to make the browser handle all +# computations. +EMPTY_DATA = { + "marginal": { + "distribution": {"x": [], "y": [], "bandwidth": np.NaN}, + "hdi": {"base": [], "lower": [], "upper": []}, + "stats": {"x": [], "y": [], "text": []}, + "labels": { + "x": [], + "y": [], + "text": [], + "text_align": [], + "x_offset": [], + "y_offset": [], + }, + }, + "cumulative": { + "distribution": {"x": [], "y": [], "bandwidth": np.NaN}, + "hdi": {"base": [], "lower": [], "upper": []}, + "stats": {"x": [], "y": [], "text": []}, + "labels": { + "x": [], + "y": [], + "text": [], + "text_align": [], + "x_offset": [], + "y_offset": [], + }, + }, +} +SIZING = { + "sizing_mode": "scale_both", + "max_height": PLOT_HEIGHT + 250, # drop down menus and tabs + "max_width": 2 * PLOT_WIDTH + 30, # tool bars +} + + +def create_sources() -> typing.Sources: + """Create Bokeh sources from the given data that will be bound to glyphs. + + Returns + ------- + typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + """ + output = {} + for figure_name, figure_data in EMPTY_DATA.items(): + output[figure_name] = {} + for glyph_name, glyph_data in figure_data.items(): + if "bandwidth" in list(glyph_data.keys()): + glyph_data.pop("bandwidth") + output[figure_name][glyph_name] = ColumnDataSource(data=glyph_data) + return output + + +def create_figures(rv_name: str) -> typing.Figures: + """Create the Bokeh figures used for the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + + Returns + ------- + typing.Figures + A dictionary of Bokeh Figure objects. + """ + output = {} + for figure_name in FIGURE_NAMES: + fig = figure( + max_width=PLOT_WIDTH, + max_height=PLOT_HEIGHT, + outline_line_color="black", + title=f"{figure_name} distribution", + x_axis_label=rv_name, + y_axis_label=None, + sizing_mode="scale_both", + ) + fig.yaxis.visible = False + plotting_utils.style_figure(fig) + output[figure_name] = fig + output[FIGURE_NAMES[0]].x_range = output[FIGURE_NAMES[1]].x_range + output[FIGURE_NAMES[0]].y_range = output[FIGURE_NAMES[1]].y_range + return output + + +def create_glyphs() -> typing.Glyphs: + """Create the glyphs used for the figures of the tool. + + Returns + ------- + typing.Glyphs + A dictionary of Bokeh Glyphs objects. + """ + palette = plotting_utils.choose_palette(num_colors=2) + output = {} + for figure_name, figure_data in EMPTY_DATA.items(): + output[figure_name] = {} + for glyph_name, _ in figure_data.items(): + if glyph_name in ["distribution", "stats"]: + if glyph_name == "distribution": + output[figure_name][glyph_name] = { + "glyph": Line( + x="x", + y="y", + line_color=palette[0], + line_alpha=0.7, + line_width=2.0, + name=f"{figure_name}DistributionGlyph", + ), + "hover_glyph": Line( + x="x", + y="y", + line_color=palette[1], + line_alpha=1.0, + line_width=2.0, + name=f"{figure_name}DistributionHoverGlyph", + ), + } + if glyph_name == "stats": + output[figure_name][glyph_name] = { + "glyph": Circle( + x="x", + y="y", + size=10, + fill_color=palette[0], + line_color="white", + fill_alpha=1.0, + name=f"{figure_name}StatsGlyph", + ), + "hover_glyph": Circle( + x="x", + y="y", + size=10, + fill_color=palette[1], + line_color="black", + fill_alpha=1.0, + name=f"{figure_name}StatsHoverGlyph", + ), + } + return output + + +def add_glyphs( + figures: typing.Figures, + glyphs: typing.Glyphs, + sources: typing.Sources, +) -> None: + """Bind source data to glyphs and add the glyphs to the given figures. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + glyphs : typing.Glyphs + A dictionary of Bokeh Glyphs objects. + sources : typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + + Returns + ------- + None + Adds data bound glyphs to the given figures directly. + """ + for figure_name, figure_glyphs in glyphs.items(): + fig = figures[figure_name] + figure_sources = sources[figure_name] + for glyph_name, glyphs in figure_glyphs.items(): + glyph_source = figure_sources[glyph_name] + fig.add_glyph( + source_or_glyph=glyph_source, + glyph=glyphs["glyph"], + hover_glyph=glyphs["hover_glyph"], + name=glyphs["glyph"].name, + ) + + +def create_annotations(sources: typing.Sources) -> typing.Annotations: + """Create any annotations for the figures of the tool. + + Parameters + ---------- + source : typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + + Returns + ------- + typing.Annotations + A dictionary of Bokeh Annotation objects. + """ + palette = plotting_utils.choose_palette(num_colors=1) + output = {} + for figure_name, figure_sources in sources.items(): + output[figure_name] = {} + for glyph_name, glyph_source in figure_sources.items(): + if glyph_name == "hdi": + output[figure_name][glyph_name] = Band( + base="base", + lower="lower", + upper="upper", + source=glyph_source, + level="underlay", + fill_color=palette[0], + fill_alpha=0.2, + line_width=1.0, + line_color="white", + name=f"{figure_name}HdiAnnotation", + ) + elif glyph_name == "labels": + output[figure_name][glyph_name] = LabelSet( + x="x", + y="y", + text="text", + x_offset="x_offset", + y_offset="y_offset", + text_align="text_align", + source=glyph_source, + background_fill_color="white", + background_fill_alpha=0.8, + name=f"{figure_name}LabelAnnotation", + ) + return output + + +def add_annotations(figures: typing.Figures, annotations: typing.Annotations) -> None: + """Add the given annotations to the given figures of the tool. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + annotations : typing.Annotations + A dictionary of Bokeh Annotation objects. + + Returns + ------- + None + Adds annotations directly to the given figures. + """ + for figure_name, annotation_sources in annotations.items(): + fig = figures[figure_name] + for _, annotation in annotation_sources.items(): + fig.add_layout(annotation) + + +def create_tooltips(rv_name: str, figures: typing.Figures) -> typing.Tooltips: + """Create hover tools for the glyphs used in the figures of the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + typing.Tooltips + A dictionary of Bokeh HoverTools objects. + """ + output = {} + for figure_name, fig in figures.items(): + output[figure_name] = { + "distribution": HoverTool( + renderers=plotting_utils.filter_renderers( + figure=fig, + search="DistributionGlyph", + glyph_type="GlyphRenderer", + substring=True, + ), + tooltips=[(rv_name, "@x")], + ), + "stats": HoverTool( + renderers=plotting_utils.filter_renderers( + figure=fig, + search="StatsGlyph", + glyph_type="GlyphRenderer", + substring=True, + ), + tooltips=[("", "@text")], + ), + } + return output + + +def add_tooltips(figures: typing.Figures, tooltips: typing.Tooltips) -> None: + """Add the given tools to the figures. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + tooltips : typing.Tooltips + A dictionary of Bokeh HoverTools objects. + + Returns + ------- + None + Adds the tooltips directly to the given figures. + """ + for figure_name, figure_tooltips in tooltips.items(): + fig = figures[figure_name] + for _, tooltip in figure_tooltips.items(): + fig.add_tools(tooltip) + + +def create_widgets( + rv_name: str, + rv_names: List[str], + bw_factor: float, + bandwidth: float, +) -> typing.Widgets: + """Create the widgets used in the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + rv_names : List[str] + A list of all available random variable names. + bw_factor : float + Multiplicative factor used when calculating the kernel density estimate. + bandwidth : float + The bandwidth used to calculate the KDE. + + Returns + ------- + typing.Widgets + A dictionary of Bokeh widget objects. + """ + return { + "rv_select": Select(value=rv_name, options=rv_names, title="Query"), + "bw_factor_slider": Slider( + title="Bandwidth factor", + start=0.01, + end=2.00, + value=1.00, + step=0.01, + ), + "bw_div": Div(text=f"Bandwidth: {bw_factor * bandwidth}"), + "hdi_slider": Slider(start=1, end=99, step=1, value=89, title="HDI"), + } + + +def help_page() -> Div: + """Help tab for the tool. + + Returns + ------- + Div + Bokeh Div widget containing the help tab information. + """ + text = """ +

+ Highest density interval +

+

+ The highest density interval region is not equal tailed like a typical + equal tailed interval of 2.5%. Thus it will include the mode(s) of the + posterior distribution. +

+

+ There is nothing particularly specific about having a default HDI of 89%. + If fact, the only remarkable thing about defaulting to 89% is that it is + the highest prime number that does not exceed the unstable 95% threshold. + See the link to McElreath's book below for further discussion. +

+ + """ + return Div(text=text, disable_math=False, min_width=PLOT_WIDTH) + + +def create_figure_grid(figures: typing.Figures) -> Row: + """Layout the given figures in a grid, and make one toolbar. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + Row + A Bokeh layout object. + """ + toolbar = plotting_utils.create_toolbar(figures=list(figures.values())) + return Row(children=[*list(figures.values()), toolbar], css_classes=["bk-loading"]) + + +def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: + """Create the tool view. + + Parameters + ---------- + widgets : typing.Widgets + A dictionary of Bokeh widget objects. + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + Tabs + Bokeh Tabs objects. + """ + help_panel = Panel(child=help_page(), title="Help", name="helpPanel") + fig_child = Column( + children=[ + create_figure_grid(figures), + widgets["bw_factor_slider"], + widgets["bw_div"], + widgets["hdi_slider"], + ], + css_classes=["bm-tool-loading", "arcs"], + ) + fig_child.update_from_json(SIZING) + tool_child = Column(children=[widgets["rv_select"], fig_child]) + tool_child.update_from_json(SIZING) + tool_panel = Panel( + child=tool_child, + title="Marginal 1D", + name="toolPanel", + ) + tabs = Tabs(tabs=[tool_panel, help_panel]) + tabs.update_from_json(SIZING) + return tabs diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py b/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py new file mode 100644 index 0000000000..7bec24cb17 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py new file mode 100644 index 0000000000..e4e23f6bc6 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py @@ -0,0 +1,169 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Accessor definition for extending Bean Machine `MonteCarloSamples` objects. + +These methods are heavily influenced by the implementations by pandas and xarray. + +- `pandas`: https://github.com/pandas-dev/pandas/blob/main/pandas/core/accessor.py +- `xarray`: https://github.com/pydata/xarray/blob/main/xarray/core/extensions.py +""" + +import contextlib +import warnings +from typing import Callable, TypeVar + +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + + +T = TypeVar("T", bound="CachedAccessor") + + +class CachedAccessor: + """ + A descriptor for caching accessors. + + Args: + name (str): Namespace for the accessor. + accessor (object): Class that defines the extension methods. + + Attributes: + _name (str): Namespace for the accessor. + _accessor (object): Class that defines the extension methods. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. + """ + + def __init__(self: T, name: str, accessor: object) -> None: + self._name = name + self._accessor = accessor + + def __get__(self: T, obj: object, cls: object) -> object: + """ + Method to retrieve the accessor namespace. + + Args: + obj (object): Object that the accessor is attached to. + cls (object): Needed for registering the accessor. + + Returns: + object: The accessor object. + """ + # Accessing an attribute of the class. + if obj is None: + return self._accessor + + try: + cache = obj._cache # type: ignore + except AttributeError: + cache = obj._cache = {} + + try: + return cache[self._name] + except KeyError: + contextlib.suppress(KeyError) + + try: + accessor_obj = self._accessor(obj) # type: ignore + except Exception as error: + msg = f"error initializing {self._name!r} accessor." + raise RuntimeError(msg) from error + + cache[self._name] = accessor_obj + return accessor_obj # noqa: R504 (unnecessary variable assignment) + + +def _register_accessor(name: str, cls: object) -> Callable: + """ + Method used for registering an accessor to a given object. + + Args: + name (str): The name for the accessor. + cls (object): The object the accessor should be attached to. + + Returns: + Callable: A decorator for creating accessors. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. + """ + + def decorator(accessor: object) -> object: + if hasattr(cls, name): + warnings.warn( + f"registration of accessor {repr(accessor)} under name " + f"{repr(name)} for type {repr(cls)} is overriding a preexisting " + f"attribute with the same name.", + UserWarning, + stacklevel=2, + ) + setattr(cls, name, CachedAccessor(name, accessor)) + return accessor + + return decorator + + +def register_mcs_accessor(name: str) -> Callable: + """ + Register an accessor object for `MonteCarloSamples` objects. + + Args: + name (str): The name for the accessor. + + Returns: + Callable: A decorator for creating the `MonteCarloSamples` accessor. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. + + Example: + >>> from typing import Dict, List, TypeVar + >>> + >>> import beanmachine.ppl as bm + >>> import numpy as np + >>> import torch.distributions as dist + >>> from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + >>> from beanmachine.ppl.diagnostics.tools.utils import accessor + >>> from torch import tensor + >>> + >>> T = TypeVar("T", bound="MagicAccessor") + >>> + >>> @bm.random_variable + >>> def alpha(): + >>> return dist.Normal(0, 1) + >>> + >>> @bm.random_variable + >>> def beta(): + >>> return dist.Normal(0, 1) + >>> + >>> @accessor.register_mcs_accessor("magic") + >>> class MagicAccessor: + >>> def __init__(self: T, mcs: MonteCarloSamples) -> None: + >>> self.mcs = mcs + >>> def show_me(self: T) -> Dict[str, List[List[float]]]: + >>> # Return a JSON serializable object from a MonteCarloSamples object. + >>> return dict( + >>> sorted( + >>> { + >>> str(key): value.tolist() + >>> for key, value in self.mcs.items() + >>> }.items(), + >>> key=lambda item: item[0], + >>> ), + >>> ) + >>> + >>> chain_results = { + >>> beta(): tensor([4, 3], [2, 1]), + >>> alpha(): tensor([[1, 2], [3, 4]]), + >>> } + >>> samples = MonteCarloSamples(chain_results=chain_results) + >>> samples.magic.show_me() + {'alpha()': [[1, 2], [3, 4]], 'beta()': [[4, 3], [2, 1]]} + """ + return _register_accessor(name, MonteCarloSamples) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py new file mode 100644 index 0000000000..d423bd06b2 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py @@ -0,0 +1,162 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Base class for diagnostic tools of a Bean Machine model.""" + +import re +from abc import ABC, abstractmethod +from typing import Any, Mapping, TypeVar + +from beanmachine.ppl.diagnostics.tools import JS_DIST_DIR +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from beanmachine.ppl.diagnostics.tools.utils.model_serializers import serialize_bm +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from bokeh.embed import file_html, json_item +from bokeh.models import Model +from bokeh.resources import INLINE + + +T = TypeVar("T", bound="DiagnosticToolBaseClass") + + +class DiagnosticToolBaseClass(ABC): + """ + Base class for visual diagnostic tools. + + Args: + mcs (MonteCarloSamples): The return object from running a Bean Machine model. + + Attributes: + data (Dict[str, List[List[float]]]): JSON serializable representation of the + given `mcs` object. + rv_names (List[str]): The list of random variables string names for the given + model. + num_chains (int): The number of chains of the model. + num_draws (int): The number of draws of the model for each chain. + palette (List[str]): A list of color values used for the glyphs in the figures. + The colors are specifically chosen from the Colorblind palette defined in + Bokeh. + tool_js (str):The JavaScript callbacks needed to render the Bokeh tool + independently from a Python server. + """ + + @abstractmethod + def __init__(self: T, mcs: MonteCarloSamples) -> None: + self.data = serialize_bm(mcs) + self.rv_names = ["Select a random variable..."] + list(self.data.keys()) + self.num_chains = mcs.num_chains + self.num_draws = mcs.get_num_samples() + self.palette = plotting_utils.choose_palette(self.num_chains) + self.tool_js = self.load_tool_js() + + def load_tool_js(self: T) -> str: + """ + Load the JavaScript for the diagnostic tool. + + Tools must be built by `yarn` in order for this method to find the appropriate + file. If no file is found, then the tools will not function, and an error will + be shown to the user. + + Returns: + str: A string containing all the JavaScript necessary to run the tool in a + notebook. + + Raises: + FileNotFoundError: Raised if the diagnostic tool has not been built by + `yarn` prior to its use. + """ + name = self.__class__.__name__ + name_tokens = re.findall(r"[A-Z][^A-Z]*", name) + name = "_".join(name_tokens) + path = JS_DIST_DIR.joinpath(f"{name.lower()}.js") + with path.open() as f: + tool_js = f.read() + return tool_js + + def show(self: T) -> None: + """ + Show the diagnostic tool in the notebook. + + This method uses IPython's `display` and `HTML` methods in order to display the + diagnostic tool in a notebook. The Bokeh `Model` object returned by the + `create_document` method is converted to HTML using Bokeh's `file_html` method. + The advantage of encapsulating the tool in this manner is that it allows all the + JavaScript needed to render the tool to exist in the output cell of the + notebook. Doing so will allow the Bokeh Application to render in Google's Colab + or Meta's Bento notebooks, which do not allow calls to third party JavaScript to + be loaded and executed. The disadvantage is that it embeds duplicate JavaScript + if more than one tool is used in a notebook. + """ + # import Ipython only when we need to render the plot, so that we don't + # need to have jupyter notebook as one of our dependencies + from IPython.display import display, HTML # pyre-ignore[21] + + doc = self.create_document() + html = file_html(doc, resources=INLINE, template=self.html_template()) + display(HTML(html)) + + def html_template(self: T) -> str: + """ + HTML template object used to inject CSS styles for Bokeh Applications. + + We inject CSS into the output for the diagnostic tools because we need users to + interact with the tool before it renders any statistics. The reason for this is + due to the lack of a callback between Bokeh and BokehJS for an "on load" event. + The necessary callback for an "on load" event is being worked on for the Bokeh + 3.0 release. Until Bokeh 3.0 is released, this is a work-around that makes the + user interact with the tool before any rendering occurs. + + Returns: + str: Template for injecting CSS in the HTML returned by `create_document`. + """ + return """ + {% block postamble %} + + {% endblock %} + """ + + @abstractmethod + def create_document(self: T) -> Model: + """To be implemented by the inheriting class.""" + ... + + def _tool_json(self: T) -> Mapping[Any, Any]: + """ + Debugging method used primarily when creating a new diagnostic tool. + + Returns: + Dict[Any, Any]: Creates a JSON serializable object using Bokeh's `json_item` + method and the output from `create_document`. + """ + doc = self.create_document() + json_data = json_item(doc) + return json_data diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py new file mode 100644 index 0000000000..109b588ea0 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py @@ -0,0 +1,30 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Collection of serializers for the diagnostics tool use.""" + +from typing import Dict, List + +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + + +def serialize_bm(samples: MonteCarloSamples) -> Dict[str, List[List[float]]]: + """ + Convert Bean Machine models to a JSON serializable object. + + Args: + samples (MonteCarloSamples): Output of a model from Bean Machine. + + Returns + Dict[str, List[List[float]]]: The JSON serializable object for use in the + diagnostics tools. + """ + model = dict( + sorted( + {str(key): value.tolist() for key, value in samples.items()}.items(), + key=lambda item: item[0], + ), + ) + return model diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py new file mode 100644 index 0000000000..82b48f2a1f --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py @@ -0,0 +1,148 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Plotting utilities for the diagnostic tools.""" + +from typing import List + +from bokeh.core.property.nullable import Nullable +from bokeh.core.property.primitive import Null +from bokeh.core.property.wrappers import PropertyValueList +from bokeh.models.layouts import Column, Row +from bokeh.models.renderers import GlyphRenderer +from bokeh.models.tools import ProxyToolbar, ResetTool, SaveTool, ToolbarBox +from bokeh.palettes import Colorblind +from bokeh.plotting.figure import Figure + + +def style_figure(figure: Figure) -> None: + """ + Style the given Bokeh `figure`. + + Args: + figure (Figure): A Bokeh `Figure` object. + + Returns: + None: Styles the given figure without copying. + """ + figure.grid.grid_line_alpha = 0.3 + figure.grid.grid_line_color = "grey" + figure.grid.grid_line_width = 0.3 + figure.xaxis.minor_tick_line_color = "grey" + figure.yaxis.minor_tick_line_color = "grey" + + +def choose_palette(num_colors: int) -> List[str]: + """ + Determine which palette from Bokeh's Colorblind to use. + + Args: + num_colors (int): The number of colors to use for the palette. + + Returns: + List[str]: A list of colors to be used as the palette for a figure. + """ + palette_indices = [key for key in Colorblind.keys() if num_colors <= key] + if not palette_indices: + palette_index = max(Colorblind.keys()) + else: + palette_index = min(palette_indices) + return Colorblind[palette_index] + + +def create_toolbar(figures: List[Figure]) -> ToolbarBox: + """ + Create a single toolbar for the given list of figures. + + This method ignores all `HoverTool` entries in the final toolbar object. The + rational for ignoring them is to prevent the final toolbar from having too much + visual clutter. + + Args: + figures (List[Figure]): A list of Bokeh `Figure` objects that all have their own + toolbars that will be merged into one. + + Returns: + ToolbarBox: The merged toolbar. + """ + toolbars = [] + for figure in figures: + toolbars.append(figure.toolbar) + figure.toolbar_location = Nullable(Null)._default + tools = [] + for toolbar in toolbars: + tools.extend(toolbar.tools) + tools = [tool for tool in tools if type(tool).__name__ != "HoverTool"] + if len(tools) == 0: + tools = [SaveTool(), ResetTool()] + return ToolbarBox( + toolbar=ProxyToolbar(toolbars=toolbars, tools=tools), + toolbar_location="right", + ) + + +def create_figure_grid(figures: List[Figure], num_figure_columns: int) -> Row: + """ + Similar to Bokeh's `grid_plot` method, except we merge toolbars in this method. + + Args: + figures (List[Figure]): A list of Bokeh `Figure` objects. + num_figure_columns (int): The number of columns for the grid. + + Returns: + Row: Returns a single Bokeh `Row` object that contains all the given figures. + """ + toolbar = create_toolbar(figures) + figure_rows = [] + while len(figures): + figs = figures[:num_figure_columns] + for i, fig in enumerate(figs): + if i != 0: + fig.yaxis.axis_label = None + figure_rows.append(figs) + for fig in figs: + figures.pop(figures.index(fig)) + for i, figure_row in enumerate(figure_rows): + if i != len(figure_rows) - 1: + for fig in figure_row: + fig.xaxis.axis_label = None + figure_layout = [] + for i in range(len(figure_rows)): + figure_layout.append(Row(children=figure_rows[i])) + return Row(children=[Column(children=figure_layout), toolbar]) + + +def filter_renderers( + figure: Figure, + search: str, + glyph_type: str = "GlyphRenderer", + substring: bool = False, +) -> List[GlyphRenderer]: + """ + Find renderers in the given figure using the `search` string. + + Filters renderers from the given figure based on renderer names that match the given + search parameters. + + Args: + figure (Figure): A Bokeh `Figure` object. + search (str): A string to filter renderer names with. + glyph_type (:obj:`str`, optional): The type of renderer to search for in the + figure. Default is `GlyphRenderer`. + substring (:obj:`bool`, optional): Flag to indicate if the given `search` string + should be used as a substring search. Default is `False`. + + Returns: + List[GlyphRenderer]: A list of renderers that match the search parameters. + """ + output = [] + renderers = PropertyValueList(figure.renderers) + for renderer in renderers: + if renderer.name is not None and type(renderer).__name__ == glyph_type: + if substring and search in renderer.name: + output.append(renderer) + if not substring and renderer.name == search: + output.append(renderer) + return output diff --git a/src/beanmachine/ppl/diagnostics/tools/viz.py b/src/beanmachine/ppl/diagnostics/tools/viz.py new file mode 100644 index 0000000000..eb3e627c55 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/viz.py @@ -0,0 +1,55 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Visual diagnostics tools for Bean Machine models.""" +from __future__ import annotations + +from functools import wraps +from typing import Callable, TypeVar + +from beanmachine.ppl.diagnostics.tools.utils import accessor +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from typing_extensions import ParamSpec + +P = ParamSpec("P") +R = TypeVar("R") + + +def _requires_dev_packages(f: Callable[P, R]) -> Callable[P, R]: + """A utility decorator that allow us to lazily imports the plotting modules + and throw a useful error message when the required dependencies are not + installed.""" + + @wraps(f) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: + try: + return f(*args, **kwargs) + except ModuleNotFoundError as e: + # The diagnostic tools uses packages that are not part of the core + # BM dependency, so we need to prompt users to manually install + # those + raise ModuleNotFoundError( + "Dev packages are required for the diagnostic widgets, which " + "can be installed with `pip install 'beanmachine[dev]'" + ) from e + + return wrapper + + +@accessor.register_mcs_accessor("diagnostics") +class DiagnosticsTools: + """Accessor object for the visual diagnostics tools.""" + + def __init__(self: DiagnosticsTools, mcs: MonteCarloSamples) -> None: + """Initialize.""" + self.mcs = mcs + self.idata = self.mcs.to_inference_data() + + @_requires_dev_packages + def marginal1d(self: DiagnosticsTools) -> None: + """Marginal 1D tool.""" + from beanmachine.ppl.diagnostics.tools.marginal1d.tool import Marginal1d + + Marginal1d(self.mcs).show()