'use strict'; /*jshint expr: true*/ const chai = require('chai'); const sinon = require('sinon'); const fs = require('fs-extra'); const path = require('path'); const luxon = require('luxon'); const expect = require('chai').expect; const { RepetereModel, } = require('../../../../lib/models/model_class'); const MS = require('modelscript/build/modelscript.cjs'); // const validMongoId = '5b1eca428d021f08885edbf5'; chai.use(require('sinon-chai')); chai.use(require('chai-as-promised')); describe('models', function() { this.timeout(60000); describe('model_class Neural Network', () => { describe('Pre calculated trainning values', () => { const trainningData = [ { input_1: 10, input_2: 20, input_3: 30, output_1: 100, output_2: 200, }, ].reduce((result, datum) => { for (let i = 1; i <= 10; i++){ result.push({ input_1: 10 + (i * 10), input_2: 20 + (i * 10), input_3: 30 + (i * 10), output_1: 100 + (i * 10), output_2: 200 + (i * 10), }); } return result; }, []); const independentVariables = [ // 'Tickets', 'input_1', 'input_2', 'input_3', 'previous_input_1', 'previous2_input_1', ]; const dependentVariables = [ 'output_1', 'output_2', ]; it('should calculate next value function values', async () => { const trainingTest = new RepetereModel({ trainningData: trainningData, x_independent_features: independentVariables, y_dependent_labels: dependentVariables, next_value_functions: [ { variable_name: 'previous_input_1', function_body: 'return state.sumPreviousRows({ property:"input_1", rows:1, })', }, { variable_name: 'previous2_input_1', function_body: 'return state.sumPreviousRows({ property:"input_1", rows:2, })', }, ], }, { use_tensorflow_cplusplus: false, }); const trainedModel = await trainingTest.trainModel({ cross_validate_trainning_data: false, use_next_value_functions_for_training_data: true, }); expect(trainedModel.DataSet.data[ 2 ].previous_input_1).to.eql(trainedModel.DataSet.data[ 1 ].input_1); expect(trainedModel.DataSet.data[ 2 ].previous2_input_1).to.eql(trainedModel.DataSet.data[ 1 ].input_1 + trainedModel.DataSet.data[ 0 ].input_1); }); it('should filter trainning data', async () => { const trainingTest = new RepetereModel({ trainningData: trainningData, x_independent_features: independentVariables, y_dependent_labels: dependentVariables, trainning_data_filter_function: ` return datum.previous2_input_1>0 && datum.previous2_input_1<80`, next_value_functions: [ { variable_name: 'previous_input_1', function_body: 'return state.sumPreviousRows({ property:"input_1", rows:1, })', }, { variable_name: 'previous2_input_1', function_body: 'return state.sumPreviousRows({ property:"input_1", rows:2, })', }, ], }, { use_tensorflow_cplusplus: false, }); const trainedModel = await trainingTest.trainModel({ cross_validate_trainning_data: false, use_next_value_functions_for_training_data: true, }); expect(trainedModel.DataSet.data.filter(d => d.previous2_input_1 <= 0)).to.have.lengthOf(0); expect(trainedModel.DataSet.data.filter(d => d.previous2_input_1 >= 80)).to.have.lengthOf(0); }); }); describe('Multiple Regression Timeseries Predictions', () => { it('should forecast the number of passengers', async () => { const csvPath = path.join(__dirname, '../../../mock/tensorflowcsv/airline-trips-sales.csv'); const airline_prediction_inputs = [ { Month: '1960-01', Flights: 47, Stops: 4, Tickets: 417, }, { Month: '1960-02', Flights: 31, Stops: 3, Tickets: 391, }, { Month: '1960-03', Flights: 49, Stops: 4, Tickets: 419, }, { Month: '1960-04', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-05', Flights: 42, Stops: 4, Tickets: 472, }, { Month: '1960-06', Flights: 55, Stops: 5, Tickets: 535, }, { Month: '1960-07', Flights: 62, Stops: 6, Tickets: 622, },/// { Month: '1960-08', Flights: 66, Stops: 6, Tickets: 606, }, { Month: '1960-09', Flights: 58, Stops: 5, Tickets: 508, }, { Month: '1960-10', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-11', Flights: 30, Stops: 3, Tickets: 390, }, { Month: '1960-12', Flights: 42, Stops: 4, Tickets: 432, }, { Month: '1961-01', Flights: 47, Stops: 4, Tickets: 427, }, { Month: '1961-02', Flights: 41, Stops: 4, Tickets: 401, }, { Month: '1961-03', Flights: 49, Stops: 4, Tickets: 429, }, ]; const independentVariables = [ // 'Tickets', 'Flights', 'Stops', ]; const dependentVariables = [ 'Passengers', ]; const airlineColumns = [].concat(independentVariables, dependentVariables); const airlinetrainning_feature_column_options = airlineColumns .reduce((result, val) => { result[ val ] = ['scale', 'standard', ]; return result; }, {}); const airlineData = await MS.csv.loadCSV(csvPath); const timeseriesTestEnvParameters = { modelDocument: { model_configuration: { model_type: 'ai-timeseries-regression-forecast', // model_type:'ai-classification', model_category: 'timeseries', }, }, trainning_options: { fit: { epochs: 100, batchSize: 1, }, // stateful: true, // features: 2, // lookBack: 3, }, trainningData: airlineData, trainning_feature_column_options: airlinetrainning_feature_column_options, x_independent_features: independentVariables, y_dependent_labels: dependentVariables, // y_raw_dependent_labels:rawDependentVariables, prediction_timeseries_date_feature: 'Month', // prediction_timeseries_start_date: '1961-01', // prediction_timeseries_start_date: '1960-08', prediction_timeseries_start_date: '1960-01', // prediction_timeseries_end_date: '1960-12', prediction_timeseries_end_date: '1961-03', // prediction_timeseries_end_date: '1962-12', next_value_functions: [ { variable_name: 'previous_3_stops', function_body: 'return state.sumPreviousRows({ property:"Stops", rows:3, })', }, ], }; const timeseriesModelTest = new RepetereModel(timeseriesTestEnvParameters, { cross_validate_trainning_data: false, use_tensorflow_cplusplus: false, }); const predictions = await timeseriesModelTest.predictModel({ reevaluate: true, cross_validate_trainning_data: false, // fixedModel:false, prediction_inputs:airline_prediction_inputs, }); // console.log('predictions',predictions) expect(predictions).to.be.an('array'); expect(predictions).to.have.lengthOf(15); expect(predictions[0].previous_3_stops).to.eql(11); return (predictions); }); }); describe('Multiple Variable LSTM Timeseries Predictions', () => { it('should forecast the number of passengers', async () => { const csvPath = path.join(__dirname, '../../../mock/tensorflowcsv/airline-trips-sales.csv'); const airline_prediction_inputs = [ { Month: '1960-01', Flights: 47, Stops: 4, Tickets: 417, }, { Month: '1960-02', Flights: 31, Stops: 3, Tickets: 391, }, { Month: '1960-03', Flights: 49, Stops: 4, Tickets: 419, }, { Month: '1960-04', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-05', Flights: 42, Stops: 4, Tickets: 472, }, { Month: '1960-06', Flights: 55, Stops: 5, Tickets: 535, }, { Month: '1960-07', Flights: 62, Stops: 6, Tickets: 622, },/// { Month: '1960-08', Flights: 66, Stops: 6, Tickets: 606, }, { Month: '1960-09', Flights: 58, Stops: 5, Tickets: 508, }, { Month: '1960-10', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-11', Flights: 30, Stops: 3, Tickets: 390, }, { Month: '1960-12', Flights: 42, Stops: 4, Tickets: 432, }, { Month: '1961-01', Flights: 47, Stops: 4, Tickets: 427, }, { Month: '1961-02', Flights: 41, Stops: 4, Tickets: 401, }, { Month: '1961-03', Flights: 49, Stops: 4, Tickets: 429, }, ]; const independentVariables = [ // 'Tickets', 'Flights', 'Stops', ]; const dependentVariables = [ 'Passengers', ]; const airlineColumns = [].concat(independentVariables, dependentVariables); const airlinetrainning_feature_column_options = airlineColumns .reduce((result, val) => { result[ val ] = ['scale', 'standard', ]; return result; }, {}); const airlineData = await MS.csv.loadCSV(csvPath); const timeseriesTestEnvParameters = { modelDocument: { model_configuration: { model_type: 'ai-forecast', // model_type:'ai-classification', model_category: 'timeseries', }, }, trainning_options: { fit: { epochs: 50, batchSize: 1, }, // stateful: true, // features: 2, // lookBack: 3, }, trainningData: airlineData, trainning_feature_column_options: airlinetrainning_feature_column_options, x_independent_features: independentVariables, y_dependent_labels: dependentVariables, // y_raw_dependent_labels:rawDependentVariables, prediction_timeseries_date_feature: 'Month', // prediction_timeseries_start_date: '1961-01', // prediction_timeseries_start_date: '1960-08', prediction_timeseries_start_date: '1960-01', // prediction_timeseries_end_date: '1960-12', prediction_timeseries_end_date: '1961-03', // prediction_timeseries_end_date: '1962-12', next_value_functions: [ { variable_name: 'previous_3_stops', function_body: 'return state.sumPreviousRows({ property:"Stops", rows:3, })', }, ], }; const timeseriesModelTest = new RepetereModel(timeseriesTestEnvParameters, { cross_validate_trainning_data: false, use_tensorflow_cplusplus: false, }); const predictions = await timeseriesModelTest.predictModel({ reevaluate: true, cross_validate_trainning_data: false, // fixedModel:false, prediction_inputs:airline_prediction_inputs, }); // console.log('predictions',predictions) expect(predictions).to.be.an('array'); expect(predictions).to.have.lengthOf(15); expect(predictions[0].previous_3_stops).to.eql(11); return (predictions); }); }); describe('Single Value Timeseries Predictions', () => { it('should forecast the number of passengers', async () => { const csvPath = path.join(__dirname, '../../../mock/tensorflowcsv/airline-trips-sales.csv'); const airline_prediction_inputs = [ { Month: '1960-01', Flights: 47, Stops: 4, Tickets: 417, }, { Month: '1960-02', Flights: 31, Stops: 3, Tickets: 391, }, { Month: '1960-03', Flights: 49, Stops: 4, Tickets: 419, }, { Month: '1960-04', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-05', Flights: 42, Stops: 4, Tickets: 472, }, { Month: '1960-06', Flights: 55, Stops: 5, Tickets: 535, }, { Month: '1960-07', Flights: 62, Stops: 6, Tickets: 622, },/// { Month: '1960-08', Flights: 66, Stops: 6, Tickets: 606, }, { Month: '1960-09', Flights: 58, Stops: 5, Tickets: 508, }, { Month: '1960-10', Flights: 41, Stops: 4, Tickets: 461, }, { Month: '1960-11', Flights: 30, Stops: 3, Tickets: 390, }, { Month: '1960-12', Flights: 42, Stops: 4, Tickets: 432, }, { Month: '1961-01', Flights: 47, Stops: 4, Tickets: 427, }, { Month: '1961-02', Flights: 41, Stops: 4, Tickets: 401, }, { Month: '1961-03', Flights: 49, Stops: 4, Tickets: 429, }, ]; const independentVariables = [ 'Passengers', ]; const dependentVariables = [ 'Passengers', ]; const airlineColumns = [].concat(independentVariables, dependentVariables); const airlinetrainning_feature_column_options = airlineColumns .reduce((result, val) => { result[ val ] = ['scale', 'standard', ]; return result; }, {}); const airlineData = await MS.csv.loadCSV(csvPath); // console.log(airlineData.slice(100)); const timeseriesTestEnvParameters = { modelDocument: { model_configuration: { model_type: 'ai-fast-forecast', // model_type:'ai-classification', model_category: 'timeseries', }, }, trainning_options: { fit: { epochs: 50, batchSize: 1, }, stateful: true, // lookBack: 3, }, trainningData: airlineData, trainning_feature_column_options: airlinetrainning_feature_column_options, x_independent_features: independentVariables, y_dependent_labels: dependentVariables, prediction_timeseries_date_feature: 'Month', prediction_timeseries_start_date:'1960-01', prediction_timeseries_end_date:'1962-12', // y_raw_dependent_labels:rawDependentVariables, }; const timeseriesModelTest = new RepetereModel(timeseriesTestEnvParameters, { cross_validate_trainning_data: false, use_tensorflow_cplusplus: false, }); const predictions = await timeseriesModelTest.predictModel({ reevaluate: true, cross_validate_trainning_data: false, fixedModel:false, prediction_inputs:airline_prediction_inputs, }); expect(predictions).to.be.an('array'); expect(predictions).to.have.lengthOf(36); return (predictions); }); }); describe('Multi-Variate Linear Regression', () => { it('should predict boston housing prices', async () => { const independentVariables = [ 'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'LSTAT', 'B', ]; const dependentVariables = [ 'MEDV', ]; const bostonColumns = [].concat(independentVariables, dependentVariables); const bosonttrainning_feature_column_options = bostonColumns .reduce((result, val) => { result[ val ] = ['scale', 'standard', ]; return result; }, {}); const csvPath = path.join(__dirname, '../../../mock/tensorflowcsv/boston_housing_data.csv'); // console.log({ csvPath }); // console.log({ bosonttrainning_feature_column_options }); const bostonhousingData = await MS.csv.loadCSV(csvPath); const regressionTestEnvParameters = { modelDocument: { model_configuration: { model_type:'ai-regression', model_category:'regression', }, }, trainning_options: { fit: { epochs: 100, batchSize: 5, }, }, trainningData: bostonhousingData, trainning_feature_column_options: bosonttrainning_feature_column_options, x_independent_features:independentVariables, y_dependent_labels:dependentVariables, }; const regressionModelTest = new RepetereModel(regressionTestEnvParameters, { cross_validate_trainning_data: true, use_tensorflow_cplusplus: false, }); // await regressionModelTest.trainModel(); // const modelEvaluation = await regressionModelTest.evaluateModel(); const ranModel = await regressionModelTest.runModel({ reevaluate: true, }); // console.log(ranModel.evaluation.MEDV); expect(ranModel.evaluation.MEDV).to.be.an('object'); expect(ranModel.evaluation.MEDV.rSquared).to.be.greaterThan(0.8); expect(ranModel.evaluation.MEDV.adjustedRSquared).to.be.greaterThan(0.8); expect(ranModel.evaluation.MEDV.standardError).to.be.lessThan(10); expect(ranModel.evaluation.MEDV.actuals.length).to.eql(ranModel.evaluation.MEDV.estimates.length); return (true); }); }); describe('Deep Learning Classification', () => { it('should classify iris flows', async () => { const independentVariables = [ 'sepal_length_cm', 'sepal_width_cm', 'petal_length_cm', 'petal_width_cm', ]; const dependentVariables = [ 'plant_Iris-setosa', 'plant_Iris-versicolor', 'plant_Iris-virginica', ]; const rawDependentVariables = [ 'plant', ]; const flowertrainning_feature_column_options = { plant: 'onehot', }; const csvPath = path.join(__dirname, '../../../mock/tensorflowcsv/iris_data.csv'); const irisData = await MS.csv.loadCSV(csvPath); const classificationTestEnvParameters = { modelDocument: { model_configuration: { model_type:'ai-classification', // model_type:'ai-classification', model_category:'classification', }, }, trainning_options: { fit: { epochs: 100, batchSize: 5, }, }, trainningData: irisData, trainning_feature_column_options: flowertrainning_feature_column_options, x_independent_features:independentVariables, y_dependent_labels:dependentVariables, y_raw_dependent_labels:rawDependentVariables, }; const classificationModelTest = new RepetereModel(classificationTestEnvParameters, { use_tensorflow_cplusplus: false, cross_validate_trainning_data: true, }); // await classificationModelTest.trainModel(); // const modelEvaluation = await classificationModelTest.evaluateModel(); const ranModel = await classificationModelTest.runModel({ reevaluate: true, cross_validate_trainning_data: true, }); // console.log(ranModel.evaluation.plant); expect(ranModel.evaluation.plant).to.be.an('object'); expect(ranModel.evaluation.plant.accuracy).to.be.greaterThan(0.8); expect(ranModel.evaluation.plant.actuals.length).to.eql(ranModel.evaluation.plant.estimates.length); return (true); }); }); }); });