@mindconnect/mindconnect-nodejs
Version:
NodeJS Library for MindSphere Connectivity - TypeScript SDK for MindSphere - MindSphere Command Line Interface - MindSphere Development Proxy
205 lines • 12.3 kB
JavaScript
;
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
Object.defineProperty(exports, "__esModule", { value: true });
const console_1 = require("console");
const fs = require("fs");
const _ = require("lodash");
const path = require("path");
const utils_1 = require("../../api/utils");
const command_utils_1 = require("./command-utils");
let color = (0, command_utils_1.getColor)("blue");
exports.default = (program) => {
program
.command("trend-prediction")
.alias("tp")
.option("-f, --file <timeseries>", `timeseries file`, `timeseries-sample.mdsp.json`)
.option("-m, --mode [train|predict|trainandpredict|list|read|delete]", `mode see ${color("@ Additional Documentation")}`, "list")
.option("-o, --output <output>", `output variables`)
.option("-i, --input <input>", `input variables (comma separated)`)
.option("-e, --modelid <modelid>", `modelid of the stored model for prediction`)
.option("-r, --predict <predict>", `regression parameters for prediction (comma separated)`)
.option("-c, --predictfile <predictfile>", `regression parameters for prediction as timeseries`)
.option("-d, --degree [degree]", "degree for linear / polynomial regression ", "1")
.option("-y, --retry <number>", "retry attempts before giving up", "3")
.option("-p, --passkey <passkey>", `passkey`)
.option("-v, --verbose", "verbose output")
.description(`${color("perform trend prediction (linear/polynomial) @")}`)
.action((options) => {
(() => __awaiter(void 0, void 0, void 0, function* () {
try {
checkRequiredParameters(options);
const sdk = (0, command_utils_1.getSdk)(options);
color = (0, command_utils_1.adjustColor)(color, options, true);
(0, command_utils_1.homeDirLog)(options.verbose, color);
(0, command_utils_1.proxyLog)(options.verbose, color);
const trendPrediction = sdk.GetTrendPredictionClient();
switch (options.mode) {
case "list":
yield listModels(trendPrediction, options);
break;
case "get":
const model = yield (0, utils_1.retry)(options.retry, () => trendPrediction.GetModel(options.modelid));
console.log(JSON.stringify(model, null, 2));
console.log(`Sucessfully retrieved model with ${color(options.modelid)} id.`);
break;
case "delete":
yield (0, utils_1.retry)(options.retry, () => trendPrediction.DeleteModel(options.modelid));
console.log(`Sucessfully deleted model with ${color(options.modelid)} id`);
break;
case "train":
{
const trainBody = getTrainBody(options);
const trainedModel = yield (0, utils_1.retry)(options.retry, () => trendPrediction.Train(trainBody));
(0, command_utils_1.verboseLog)(JSON.stringify(trainedModel, null, 2), options.verbose);
console.log(`Sucessfully trained model with ${color(trainedModel.id)} id.`);
}
break;
case "predict":
{
const predictionData = getpredictors(options);
const prediction = (yield (0, utils_1.retry)(options.retry, () => trendPrediction.Predict(Object.assign({ modelConfiguration: { modelId: options.modelid } }, predictionData))));
displayPrediction(options, prediction);
}
break;
case "trainandpredict":
{
const trainBody = getTrainBody(options);
const predictionData = getpredictors(options);
const prediction = (yield (0, utils_1.retry)(options.retry, () => trendPrediction.TrainAndPredict(Object.assign(Object.assign({}, trainBody), predictionData))));
displayPrediction(options, prediction);
}
break;
}
}
catch (err) {
(0, command_utils_1.errorLog)(err, options.verbose);
}
}))();
})
.on("--help", () => {
(0, console_1.log)("\n Examples:\n");
(0, console_1.log)(` mc trend-prediction --mode list \t\t\t\t lists all trend prediction models`);
(0, console_1.log)(` mc trend-prediction --mode get --modelid 12345..ef \t\t retrieves the trend prediction model from the mindsphere`);
(0, console_1.log)(` mc trend-prediction --mode delete --modelid 12345..ef \t deletes the trend prediction model from the mindsphere`);
(0, console_1.log)(` mc tp --mode trendandpredict \t\t\t\t training and prediction in one single step (see parameters below)`);
(0, console_1.log)(`\n mc tp --mode train -f data.json -i "temp,vibration" -o "quality" -d 2 \t\t trains quadratic fit function for f(temp, vibration) = quality `);
(0, console_1.log)(` mc tp --mode predict --modelid 12345..ef -i "temp,vibration" -o "quality" -p "30,0.01" predict the quality with temp=30, vibration=0.01 using trained model`);
(0, console_1.log)("\n Additional Documentation:\n");
(0, console_1.log)(` ${color("https://developer.mindsphere.io/apis/analytics-trendprediction/api-trendprediction-basics.html")}`);
(0, command_utils_1.serviceCredentialLog)(color);
});
};
function displayPrediction(options, prediction) {
options.predict && console.log(`${color("(")}${options.input}${color(") =>")} ${options.output}`);
const predictionValue = prediction[0].timeSeries[0][options.output];
options.predict && console.log(`${color("(")}${options.predict}${color(") =>")} ${predictionValue}`);
options.predictfile
? console.log(JSON.stringify(prediction, null, 2))
: (0, command_utils_1.verboseLog)(JSON.stringify(prediction, null, 2), options.verbose);
}
function getpredictors(options) {
const predictiondata = {
predictionData: [
{
variable: { entityId: "cli-trend-prediction", propertySetName: "cli-trend-prediction" },
timeSeries: [{ _time: new Date().toISOString() }],
},
],
};
if (options.predictfile) {
const predictorsPath = path.resolve(options.predictfile);
const predictorData = fs.readFileSync(predictorsPath);
const timeseries = JSON.parse(predictorData.toString());
predictiondata.predictionData[0].timeSeries = timeseries;
}
else {
const variables = `${options.input}`.split(",").map((x) => x.trim());
const predictors = `${options.predict}`.split(",").map((x) => x.trim());
for (let i = 0; i < variables.length; i++) {
const element = variables[i];
predictiondata.predictionData[0].timeSeries[0][element] = predictors[i];
}
}
return predictiondata;
}
function getTrainBody(options) {
const timeSeriesDataFile = path.resolve(options.file);
(0, command_utils_1.verboseLog)(`reading data from ${timeSeriesDataFile}`, options.verbose);
const buffer = fs.readFileSync(timeSeriesDataFile);
const data = JSON.parse(buffer.toString());
const variables = `${options.input}`.split(",").map((x) => x.trim());
const allvariables = [...variables, options.output, "_time"];
const result = _.map(data, (item) => _.pick(item, allvariables));
const params = {
modelConfiguration: { polynomialDegree: options.degree },
metadataConfiguration: {
outputVariable: {
entityId: "cli-trend-prediction",
propertySetName: "cli-trend-prediction",
propertyName: options.output,
},
inputVariables: [],
},
trainingData: [
{
variable: { entityId: "cli-trend-prediction", propertySetName: "cli-trend-prediction" },
timeSeries: result,
},
],
};
variables.forEach((item) => {
params.metadataConfiguration.inputVariables.push({
entityId: "cli-trend-prediction",
propertySetName: "cli-trend-prediction",
propertyName: item,
});
});
return params;
}
function listModels(trendPrediction, options) {
return __awaiter(this, void 0, void 0, function* () {
const result = (yield (0, utils_1.retry)(options.retry, () => trendPrediction.GetModels()));
console.log(`${color("id")} function creation date`);
result.forEach((element) => {
var _a, _b, _c, _d;
console.log(`${color(element.id)} ${color("(")}${(_b = (_a = element.metadataConfiguration) === null || _a === void 0 ? void 0 : _a.inputVariables) === null || _b === void 0 ? void 0 : _b.map((x) => x.propertyName).join(",")}${color(") =>")} ${(_d = (_c = element.metadataConfiguration) === null || _c === void 0 ? void 0 : _c.outputVariable) === null || _d === void 0 ? void 0 : _d.propertyName} ${element.creationDate}`);
});
});
}
function checkRequiredParameters(options) {
!options.mode &&
(0, command_utils_1.errorLog)("You have to provide the mode for the command. Run mc tp --help for full syntax and examples.", true);
!["train", "predict", "trainandpredict", "list", "get", "delete"].includes(options.mode) &&
(0, command_utils_1.errorLog)(`the mode must be either one of: ${color("train, predict or trainandpredict")} for trend prediction or ${color("list, get, delete")} for model management`, true);
options.mode === "get" && !options.modelid && (0, command_utils_1.errorLog)("you have to specify the id of the model (--modelid)", true);
options.mode === "delete" &&
!options.modelid &&
(0, command_utils_1.errorLog)("you have to specify the id of the model (--modelid)", true);
(options.mode === "train" || options.mode === "trainandpredict") &&
!options.file &&
(0, command_utils_1.errorLog)("you have to provide the file with timeseries data (--file)", true);
(options.mode === "train" || options.mode === "trainandpredict") &&
!options.degree &&
(0, command_utils_1.errorLog)("you have to provide the polynomial degree for the fit function (--degree)", true);
(options.mode === "train" || options.mode === "predict" || options.mode === "trainandpredict") &&
!options.input &&
(0, command_utils_1.errorLog)("you have to provide the input variables for the fit function (--input)", true);
(options.mode === "train" || options.mode === "predict" || options.mode === "trainandpredict") &&
!options.output &&
(0, command_utils_1.errorLog)("you have to provide the output variable for the fit function (--output)", true);
options.mode === "predict" &&
!options.modelid &&
(0, command_utils_1.errorLog)("you have to specify the id of the model (--modelid)", true);
(options.mode === "predict" || options.mode === "trainandpredict") &&
!(options.predict ? !options.predictfile : options.predictfile) &&
(0, command_utils_1.errorLog)("you have to provide the values of input variables (--predict) or (--predictfile) (but not both)", true);
}
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