data_to_me.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
features = Object.keys(data[0]).slice(18, 31)
// viewof selectedFeatures = Inputs.checkbox(features, {label: "Features", value: features.slice(0, 3)})
outcomes = Object.keys(data[0]).slice(0, 17)
selectedPredictions
selectedPredictionsIndices = selectedPredictions.map(e => outcomes.indexOf(e))
data = data_to_me
OUTPUT_OBJECTS = data.map(e => Object.fromEntries(Object.entries(e).slice(0,17)))
// INPUTS_OBJECTS.map(e => Object.values(e))
outputs_raw = OUTPUT_OBJECTS.map(e => Object.values(e))
outputs = tf.tensor(OUTPUT_OBJECTS.map(e => Object.values(e)))
INPUT_OBJECTS = data.map(e => Object.fromEntries(Object.entries(e).slice(18,31)))
// INPUT_OBJECTS = data.map(e => subset(e, selectedFeatures))
inputs_raw = INPUT_OBJECTS.map(e => Object.values(e))
inputs = tf.tensor(INPUT_OBJECTS.map(e => Object.values(e)))
inputs.shape
viewof activation1 = Inputs.radio(["sigmoid", "tanh"], {label: "First layter activation", value: "sigmoid"})
viewof activation2 = Inputs.radio(["sigmoid", "tanh"], {label: "First layter activation", value: "sigmoid"})
viewof learningRate1 = Inputs.range([0, 0.1], {label: "Learning Rate", step: 0.001, value: "0.005"})
viewof learningRate2 = Inputs.range([0, 0.1], {label: "Learning Rate", step: 0.001, value: "0.005"})
viewof epoch1 = Inputs.range([0, 30], {label: "Epoch", step: 1, value: "10"})
viewof epoch2 = Inputs.range([0, 30], {label: "Epoch", step: 1, value: "10"})
width = 1000
trainLossPlot1 = html`
<div id='lossPlot'></div>
`
trainLossPlot2 = html`
<div id='lossPlot2'></div>
`
// trainLossPlot = html`
// <div style=width:${width}; display: flex; flex-direction: row;'>
// <div id='lossPlot' style='display:flex; flex: 0.5;' />
// <div id='lossPlot2' style='display:flex; flex: 0.5;' />
// </div>
// `
loss1
loss2
loss1 = []
loss2 = []
selectedPredictions
// viewof selectedPredictions = Inputs.checkbox(outcomes, {label: "Outcomes", value: outcomes.slice(0, 3)})
viewof selectedPredictions = checkbox({
title: "Select Time Usage",
options: outcomes,
value: outcomes.slice(0, 3),
})
import {select, radio, slider, checkbox} from "@jashkenas/inputs"
mod2pred = model2.predict(tf.tensor([toPredData])).dataSync();
pred1_all_raw = model1.predict(tf.tensor([toPredData])).dataSync();
pred1_all = pred1_all_raw.map(e => e* 1440);
dataForPlot = dataplot()
JSON.stringify(inputs_raw).indexOf(JSON.stringify(toPredData))
// {
// var a = [ [1,2] , [1,2], [3,4] ];
// var b = [1,2];
// a = JSON.stringify(a);
// b = JSON.stringify(b);
// var c = a.indexOf(b);
// return c
// }
// inputs_raw[0]
viewof age = Inputs.range([15, 85], {label: "Age", step: 1, value: "45"})
viewof year = Inputs.select(new Map(yearList), {value: 19, label: "Year"})
viewof edu = Inputs.select(new Map(eduList), {value: 40, label: "Education"})
viewof gender = Inputs.select(new Map(genderList), {value: 2, label: "Gender"})
viewof race = Inputs.select(new Map(raceList), {value: 1, label: "Race"})
viewof salary = Inputs.select(new Map(incomeList), {value: 1, label: "Annual Salary"})
viewof metro = Inputs.select(new Map(metroList), {value: 1, label: "Metropolitan"})
viewof ageYoungestChild = Inputs.range([1, 18], {label: "Age of youngest child", step: 1, value: "1"})
viewof dayOfWeek = Inputs.range([1, 7], {label: "Day of week", step: 1, value: "1"})
viewof partner = Inputs.select(new Map(partnerList), {value: 1, label: "Partner"})
viewof employment = Inputs.select(new Map(employmentList), {value: 1, label: "Employment"})
viewof jobType = Inputs.select(new Map(jobtypeList), {value: 1, label: "Job Type"})
viewof numChildren = Inputs.range([0, 10], {label: "# of Chidren", step: 1, value: "3"})
viewof numberTicks = Inputs.range([0, 10], {label: "Number of Ticks", step: 1, value: "3"})
// toPredData = Array.from({length: 13}, () => Math.floor(Math.random() * 13));
parallelPlot = Plot.plot({
marginLeft: 260,
marginRight: 20,
height: selectedPredictions.length * 60,
x: {
axis: null
},
y: {
padding: 0.1,
domain: attribs,
label: "Time Usage Type",
tickPadding: 15,
},
color: {
type: "categorical",
scheme: schemec,
legend: true,
},
marks: [
Plot.ruleY(attribs, {
stroke: "black",
strokeWidth: 1
}),
Plot.line(parallelData, {
...xy,
z: "model",
stroke: (d) => d.datum.model,
strokeWidth: 2.9,
strokeOpacity: 0.9
}),
Plot.text(ticks, {
...xy,
text: "value",
fontSize: 12,
fill: "black",
stroke: "white",
strokeWidth: 3
})
]
})
tf = require("@tensorflow/tfjs@0.8.0/dist/tf.min.js")
import {vl} from "@vega/vega-lite-api"
tfvis = require('https://bundle.run/@tensorflow/tfjs-vis@0.1.1')
Plotly = require('plotly.js-dist')
// import {button} from "@jashkenas/inputs"
d3 = require("d3@6")
data
// dataForPlot = {
// predButton; // Wait for the start button to be clicked!
// for (let i = 0; i < 2; ++i) {
// let predData = [];
// let random_numbers = Array.from({length: features.length}, () => Math.floor(Math.random() * features.length));
// let predraw_all = model1.predict(tf.tensor([random_numbers])).dataSync()
// let predraw = selectedPredictionsIndices.map(i => predraw_all[i])
// let pred1 = predraw.map(e => Math.floor(e * 1000))
// const pred2 = pred1.map(e => e + 1);
// const pred3 = pred1.map(e => e + 2);
// let model1 = zip(selectedPredictions, pred1)
// let model2 = zip(selectedPredictions, pred2)
// let model3 = zip(selectedPredictions, pred3)
// // let model1 = Object.assign(selectedPredictions, pred1);
// // let model2 = Object.assign(selectedPredictions, pred2);
// // let model3 = Object.assign(selectedPredictions, pred3);
// model1['model'] = 1
// model2['model'] = 2
// model3['model'] = 3
// predData.push(model1);
// predData.push(model2);
// predData.push(model3);
// yield predData;
// }
// }
// dataForPlot = {
// predButton; // Wait for the start button to be clicked!
// for (let i = 0; i < 2; ++i) {
// let predData = [];
// const pred1_all = model1.predict(tf.tensor([toPredData])).dataSync();
// const pred2_all = model2.predict(tf.tensor([toPredData])).dataSync();
// let pred1 = selectedPredictionsIndices.map(i => pred1_all[i]);
// let pred2 = selectedPredictionsIndices.map(i => pred2_all[i]);
// let model1_data = zip(selectedPredictions, pred1);
// let model2_data = zip(selectedPredictions, pred2);
// model1_data['model'] = "Model 1";
// model2_data['model'] = "Model 2";
// predData.push(model1_data);
// predData.push(model2_data);
// yield predData;
// }
// }
One platform to build and deploy the best data apps
Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.