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Angelos Chatzimparmpas 5 years ago
parent 2bd14c6470
commit 5279b5b147
8 changed files with 358 additions and 1090 deletions
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  1. 1
      cachedir/joblib/run/randomSearch/27a2cfe3ccd1c8af73f4e7bc6238df2a/metadata.json
  2. BIN
      cachedir/joblib/run/randomSearch/32ca191ced3e3ea351d4805ae2631119/output.pkl
  3. 753
      frontend/src/components/AlgorithmsController.vue
  4. 672
      frontend/src/components/History.vue
  5. 6
      frontend/src/components/Main.vue
  6. 12
      frontend/src/components/Predictions.vue
  7. 2
      frontend/src/components/ValidationController.vue
  8. 2
      run.py

1
cachedir/joblib/run/randomSearch/27a2cfe3ccd1c8af73f4e7bc6238df2a/metadata.json
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@ -0,0 +1 @@
{"duration": 62.07993197441101, "input_args": {"XData": " Fbs Slope Trestbps Exang Thalach Age Chol Sex Oldpeak Restecg Cp Ca Thal\n0 1 0 145 0 150 63 233 1 2.3 0 3 0 1\n1 0 0 130 0 187 37 250 1 3.5 1 2 0 2\n2 0 2 130 0 172 41 204 0 1.4 0 1 0 2\n3 0 2 120 0 178 56 236 1 0.8 1 1 0 2\n4 0 2 120 1 163 57 354 0 0.6 1 0 0 2\n.. ... ... ... ... ... ... ... ... ... ... .. .. ...\n298 0 1 140 1 123 57 241 0 0.2 1 0 0 3\n299 0 1 110 0 132 45 264 1 1.2 1 3 0 3\n300 1 1 144 0 141 68 193 1 3.4 1 0 2 3\n301 0 1 130 1 115 57 131 1 1.2 1 0 1 3\n302 0 1 130 0 174 57 236 0 0.0 0 1 1 2\n\n[303 rows x 13 columns]", "yData": "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]", "clf": "KNeighborsClassifier(algorithm='ball_tree', metric='chebyshev', n_neighbors=61,\n weights='distance')", "params": "{'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99], 'metric': ['chebyshev', 'manhattan', 'euclidean', 'minkowski'], 'algorithm': ['brute', 'kd_tree', 'ball_tree'], 'weights': ['uniform', 'distance']}", "eachAlgor": "'KNN'", "AlgorithmsIDsEnd": "700"}}

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cachedir/joblib/run/randomSearch/32ca191ced3e3ea351d4805ae2631119/output.pkl
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753
frontend/src/components/AlgorithmsController.vue
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@ -1,5 +1,8 @@
<template>
<div id="Bees" class="chart-wrapper" style="min-height: 357px;"></div>
<div id="containerForAllAlg">
<div id="Bees" class="chart-wrapper" style="min-height: 357px;"></div>
<div id="MainPlot"></div>
</div>
</template>
<script>
@ -27,6 +30,8 @@ export default {
reset () {
var svg = d3.select("#Bees");
svg.selectAll("*").remove();
var svg = d3.select("#MainPlot");
svg.selectAll("*").remove();
this.PerF = []
this.PerFCM = []
this.storedEnsem = []
@ -37,6 +42,8 @@ export default {
BeesFun () {
var svg = d3.select("#Bees");
svg.selectAll("*").remove();
var svg = d3.select("#MainPlot");
svg.selectAll("*").remove();
var chart1
var data = []
@ -121,16 +128,101 @@ export default {
}
}
chart1 = makeDistroChart({
data:data,
xName:'Algorithm',
yName:'value',
axisLabels: {xAxis: 'Algorithhm', yAxis: '# Performance (%) #'},
selector:"#Bees",
constrainExtremes:true});
chart1.renderDataPlots({showPlot:true,plotType:'beeswarm',showBeanLines:false, colors:null});
chart1.renderNotchBoxes({showNotchBox:false});
chart1.renderViolinPlot({showViolinPlot:false});
var widthChr = 589;
var heightChr = 330;
let svgAlg = d3v5
.select("#MainPlot")
.append("svg")
.attr("height", heightChr)
.attr("width", widthChr);
let sectors = Array.from(new Set(data.map((d) => d.Algorithm)));
let xCoords = sectors.map((d, i) => 95 + i * 108);
let xScale = d3v5.scaleOrdinal().domain(sectors).range(xCoords);
let yScale = d3v5
.scaleLinear()
.domain(d3v5.extent(data.map((d) => d.value)))
.range([heightChr-30, 30]);
var colorsF = d3.scale.ordinal().range(['#ff7f00','#fdbf6f','#fb9a99','#b15928','#a6cee3'])
svgAlg
.selectAll(".circ")
.data(data)
.enter()
.append("circle")
.attr("class", "circ")
.attr("stroke", "black")
.attr("fill", function (d) {
if (d.sw) { return "#000000" }
else { return colorsF(d.Algorithm) }
})
.attr("r", (d) => d.size)
.attr("cx", (d) => xScale(d.Algorithm))
.attr("cy", (d) => yScale(d.value));
let simulation = d3v5
.forceSimulation(data)
.force(
"x",
d3v5
.forceX((d) => {
return xScale(d.Algorithm);
})
.strength(1)
)
.force(
"y",
d3v5
.forceY(function (d) {
return yScale(d.value);
})
.strength(1)
)
.force(
"collide",
d3v5.forceCollide((d) => {
return d.size;
})
)
.alphaDecay(0)
.alpha(1)
//.on("tick", tick);
function tick() {
d3v5.selectAll(".circ")
.attr("cx", (d) => {
return d.x;
})
.attr("cy", (d) => d.y);
}
function first() {
simulation.alphaDecay(0.1);
second()
setTimeout(function () {
tick();
}, 5000);
}
function second() {
chart1 = makeDistroChart({
data:data,
xName:'Algorithm',
yName:'value',
axisLabels: {xAxis: 'Algorithhm', yAxis: '# Performance (%) #'},
selector:"#Bees",
constrainExtremes:true});
//chart1.renderDataPlots({showPlot:true,plotType:'beeswarm',showBeanLines:false, colors:null});
}
first()
/**
* Creates a box plot, violin plot, and or notched box plot
* @param settings Configuration options for the base plot
@ -509,552 +601,6 @@ export default {
chart.update();
}();
/**
* Render a violin plot on the current chart
* @param options
* @param [options.showViolinPlot=true] True or False, show the violin plot
* @param [options.resolution=100 default]
* @param [options.bandwidth=10 default] May need higher bandwidth for larger data sets
* @param [options.width=50] The max percent of the group rangeBand that the violin can be
* @param [options.interpolation=''] How to render the violin
* @param [options.clamp=0 default]
* 0 = keep data within chart min and max, clamp once data = 0. May extend beyond data set min and max
* 1 = clamp at min and max of data set. Possibly no tails
* -1 = extend chart axis to make room for data to interpolate to 0. May extend axis and data set min and max
* @param [options.colors=chart default] The color mapping for the violin plot
* @returns {*} The chart object
*/
chart.renderViolinPlot = function (options) {
chart.violinPlots = {};
var defaultOptions = {
show: true,
showViolinPlot: true,
resolution: 100,
bandwidth: 20,
width: 50,
interpolation: 'cardinal',
clamp: 1,
colors: chart.colorFunct,
_yDomainVP: null // If the Violin plot is set to close all violin plots, it may need to extend the domain, that extended domain is stored here
};
chart.violinPlots.options = shallowCopy(defaultOptions);
for (var option in options) {
chart.violinPlots.options[option] = options[option]
}
var vOpts = chart.violinPlots.options;
// Create violin plot objects
for (var cName in chart.groupObjs) {
chart.groupObjs[cName].violin = {};
chart.groupObjs[cName].violin.objs = {};
}
/**
* Take a new set of options and redraw the violin
* @param updateOptions
*/
chart.violinPlots.change = function (updateOptions) {
if (updateOptions) {
for (var key in updateOptions) {
vOpts[key] = updateOptions[key]
}
}
for (var cName in chart.groupObjs) {
chart.groupObjs[cName].violin.objs.g.remove()
}
chart.violinPlots.prepareViolin();
chart.violinPlots.update();
};
chart.violinPlots.reset = function () {
chart.violinPlots.change(defaultOptions)
};
chart.violinPlots.show = function (opts) {
if (opts !== undefined) {
opts.show = true;
if (opts.reset) {
chart.violinPlots.reset()
}
} else {
opts = {show: true};
}
chart.violinPlots.change(opts);
};
chart.violinPlots.hide = function (opts) {
if (opts !== undefined) {
opts.show = false;
if (opts.reset) {
chart.violinPlots.reset()
}
} else {
opts = {show: false};
}
chart.violinPlots.change(opts);
};
/**
* Update the violin obj values
*/
chart.violinPlots.update = function () {
var cName, cViolinPlot;
for (cName in chart.groupObjs) {
cViolinPlot = chart.groupObjs[cName].violin;
// Build the violins sideways, so use the yScale for the xScale and make a new yScale
var xVScale = chart.yScale.copy();
// Create the Kernel Density Estimator Function
cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution));
cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values);
var interpolateMax = chart.groupObjs[cName].metrics.max,
interpolateMin = chart.groupObjs[cName].metrics.min;
if (vOpts.clamp == 0 || vOpts.clamp == -1) { //
// When clamp is 0, calculate the min and max that is needed to bring the violin plot to a point
// interpolateMax = the Minimum value greater than the max where y = 0
interpolateMax = d3.min(cViolinPlot.kdedata.filter(function (d) {
return (d.x > chart.groupObjs[cName].metrics.max && d.y == 0)
}), function (d) {
return d.x;
});
// interpolateMin = the Maximum value less than the min where y = 0
interpolateMin = d3.max(cViolinPlot.kdedata.filter(function (d) {
return (d.x < chart.groupObjs[cName].metrics.min && d.y == 0)
}), function (d) {
return d.x;
});
// If clamp is -1 we need to extend the axises so that the violins come to a point
if (vOpts.clamp == -1) {
kdeTester = eKernelTest(eKernel(vOpts.bandwidth), chart.groupObjs[cName].values);
if (!interpolateMax) {
var interMaxY = kdeTester(chart.groupObjs[cName].metrics.max);
var interMaxX = chart.groupObjs[cName].metrics.max;
var count = 25; // Arbitrary limit to make sure we don't get an infinite loop
while (count > 0 && interMaxY != 0) {
interMaxY = kdeTester(interMaxX);
interMaxX += 1;
count -= 1;
}
interpolateMax = interMaxX;
}
if (!interpolateMin) {
var interMinY = kdeTester(chart.groupObjs[cName].metrics.min);
var interMinX = chart.groupObjs[cName].metrics.min;
var count = 25; // Arbitrary limit to make sure we don't get an infinite loop
while (count > 0 && interMinY != 0) {
interMinY = kdeTester(interMinX);
interMinX -= 1;
count -= 1;
}
interpolateMin = interMinX;
}
}
// Check to see if the new values are outside the existing chart range
// If they are assign them to the master _yDomainVP
if (!vOpts._yDomainVP) vOpts._yDomainVP = chart.range.slice(0);
if (interpolateMin && interpolateMin < vOpts._yDomainVP[0]) {
vOpts._yDomainVP[0] = interpolateMin;
}
if (interpolateMax && interpolateMax > vOpts._yDomainVP[1]) {
vOpts._yDomainVP[1] = interpolateMax;
}
}
if (vOpts.showViolinPlot) {
chart.update();
xVScale = chart.yScale.copy();
// Need to recalculate the KDE because the xVScale changed
cViolinPlot.kde = kernelDensityEstimator(eKernel(vOpts.bandwidth), xVScale.ticks(vOpts.resolution));
cViolinPlot.kdedata = cViolinPlot.kde(chart.groupObjs[cName].values);
}
cViolinPlot.kdedata = cViolinPlot.kdedata
.filter(function (d) {
return (!interpolateMin || d.x >= interpolateMin)
})
.filter(function (d) {
return (!interpolateMax || d.x <= interpolateMax)
});
}
for (cName in chart.groupObjs) {
cViolinPlot = chart.groupObjs[cName].violin;
// Get the violin width
var objBounds = getObjWidth(vOpts.width, cName);
var width = (objBounds.right - objBounds.left) / 2;
var yVScale = d3.scale.linear()
.range([width, 0])
.domain([0, d3.max(cViolinPlot.kdedata, function (d) {return d.y;})])
.clamp(true);
var area = d3.svg.area()
.interpolate(vOpts.interpolation)
.x(function (d) {return xVScale(d.x);})
.y0(width)
.y1(function (d) {return yVScale(d.y);});
var line = d3.svg.line()
.interpolate(vOpts.interpolation)
.x(function (d) {return xVScale(d.x);})
.y(function (d) {return yVScale(d.y)});
if (cViolinPlot.objs.left.area) {
cViolinPlot.objs.left.area
.datum(cViolinPlot.kdedata)
.attr("d", area);
cViolinPlot.objs.left.line
.datum(cViolinPlot.kdedata)
.attr("d", line);
cViolinPlot.objs.right.area
.datum(cViolinPlot.kdedata)
.attr("d", area);
cViolinPlot.objs.right.line
.datum(cViolinPlot.kdedata)
.attr("d", line);
}
// Rotate the violins
cViolinPlot.objs.left.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.left + ") scale(1,-1)");
cViolinPlot.objs.right.g.attr("transform", "rotate(90,0,0) translate(0,-" + objBounds.right + ")");
}
};
/**
* Create the svg elements for the violin plot
*/
chart.violinPlots.prepareViolin = function () {
var cName, cViolinPlot;
if (vOpts.colors) {
chart.violinPlots.color = getColorFunct(vOpts.colors);
} else {
chart.violinPlots.color = chart.colorFunct
}
if (vOpts.show == false) {return}
for (cName in chart.groupObjs) {
cViolinPlot = chart.groupObjs[cName].violin;
cViolinPlot.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "violin-plot");
cViolinPlot.objs.left = {area: null, line: null, g: null};
cViolinPlot.objs.right = {area: null, line: null, g: null};
cViolinPlot.objs.left.g = cViolinPlot.objs.g.append("g");
cViolinPlot.objs.right.g = cViolinPlot.objs.g.append("g");
if (vOpts.showViolinPlot !== false) {
//Area
cViolinPlot.objs.left.area = cViolinPlot.objs.left.g.append("path")
.attr("class", "area")
.style("fill", chart.violinPlots.color(cName));
cViolinPlot.objs.right.area = cViolinPlot.objs.right.g.append("path")
.attr("class", "area")
.style("fill", chart.violinPlots.color(cName));
//Lines
cViolinPlot.objs.left.line = cViolinPlot.objs.left.g.append("path")
.attr("class", "line")
.attr("fill", 'none')
.style("stroke", chart.violinPlots.color(cName));
cViolinPlot.objs.right.line = cViolinPlot.objs.right.g.append("path")
.attr("class", "line")
.attr("fill", 'none')
.style("stroke", chart.violinPlots.color(cName));
}
}
};
function kernelDensityEstimator(kernel, x) {
return function (sample) {
return x.map(function (x) {
return {x:x, y:d3.mean(sample, function (v) {return kernel(x - v);})};
});
};
}
function eKernel(scale) {
return function (u) {
return Math.abs(u /= scale) <= 1 ? .75 * (1 - u * u) / scale : 0;
};
}
// Used to find the roots for adjusting violin axis
// Given an array, find the value for a single point, even if it is not in the domain
function eKernelTest(kernel, array) {
return function (testX) {
return d3.mean(array, function (v) {return kernel(testX - v);})
}
}
chart.violinPlots.prepareViolin();
d3.select(window).on('resize.' + chart.selector + '.violinPlot', chart.violinPlots.update);
chart.violinPlots.update();
return chart;
};
/**
* Render a notched box on the current chart
* @param options
* @param [options.show=true] Toggle the whole plot on and off
* @param [options.showNotchBox=true] Show the notch box
* @param [options.showLines=false] Show lines at the confidence intervals
* @param [options.boxWidth=35] The width of the widest part of the box
* @param [options.medianWidth=20] The width of the narrowist part of the box
* @param [options.lineWidth=50] The width of the confidence interval lines
* @param [options.notchStyle=null] null=traditional style, 'box' cuts out the whole notch in right angles
* @param [options.colors=chart default] The color mapping for the notch boxes
* @returns {*} The chart object
*/
chart.renderNotchBoxes = function (options) {
chart.notchBoxes = {};
//Defaults
var defaultOptions = {
show: true,
showNotchBox: true,
showLines: false,
boxWidth: 35,
medianWidth: 20,
lineWidth: 50,
notchStyle: null,
colors: null
};
chart.notchBoxes.options = shallowCopy(defaultOptions);
for (var option in options) {
chart.notchBoxes.options[option] = options[option]
}
var nOpts = chart.notchBoxes.options;
//Create notch objects
for (var cName in chart.groupObjs) {
chart.groupObjs[cName].notchBox = {};
chart.groupObjs[cName].notchBox.objs = {};
}
/**
* Makes the svg path string for a notched box
* @param cNotch Current notch box object
* @param notchBounds objBound object
* @returns {string} A string in the proper format for a svg polygon
*/
function makeNotchBox(cNotch, notchBounds) {
var scaledValues = [];
if (nOpts.notchStyle == 'box') {
scaledValues = [
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.medianLeft, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)],
[notchBounds.medianLeft, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.medianRight, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.medianRight, chart.yScale(cNotch.metrics.median)],
[notchBounds.medianRight, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)]
];
} else {
scaledValues = [
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile1)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.medianLeft, chart.yScale(cNotch.metrics.median)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.boxLeft, chart.yScale(cNotch.metrics.quartile3)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile3)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.upperNotch)],
[notchBounds.medianRight, chart.yScale(cNotch.metrics.median)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.lowerNotch)],
[notchBounds.boxRight, chart.yScale(cNotch.metrics.quartile1)]
];
}
return scaledValues.map(function (d) {
return [d[0], d[1]].join(",");
}).join(" ");
}
/**
* Calculate the confidence intervals
*/
!function calcNotches() {
var cNotch, modifier;
for (var cName in chart.groupObjs) {
cNotch = chart.groupObjs[cName];
modifier = (1.57 * (cNotch.metrics.iqr / Math.sqrt(cNotch.values.length)));
cNotch.metrics.upperNotch = cNotch.metrics.median + modifier;
cNotch.metrics.lowerNotch = cNotch.metrics.median - modifier;
}
}();
/**
* Take a new set of options and redraw the notch boxes
* @param updateOptions
*/
chart.notchBoxes.change = function (updateOptions) {
if (updateOptions) {
for (var key in updateOptions) {
nOpts[key] = updateOptions[key]
}
}
for (var cName in chart.groupObjs) {
chart.groupObjs[cName].notchBox.objs.g.remove()
}
chart.notchBoxes.prepareNotchBoxes();
chart.notchBoxes.update();
};
chart.notchBoxes.reset = function () {
chart.notchBoxes.change(defaultOptions)
};
chart.notchBoxes.show = function (opts) {
if (opts !== undefined) {
opts.show = true;
if (opts.reset) {
chart.notchBoxes.reset()
}
} else {
opts = {show: true};
}
chart.notchBoxes.change(opts)
};
chart.notchBoxes.hide = function (opts) {
if (opts !== undefined) {
opts.show = false;
if (opts.reset) {
chart.notchBoxes.reset()
}
} else {
opts = {show: false};
}
chart.notchBoxes.change(opts)
};
/**
* Update the notch box obj values
*/
chart.notchBoxes.update = function () {
var cName, cGroup;
for (cName in chart.groupObjs) {
cGroup = chart.groupObjs[cName];
// Get the box size
var boxBounds = getObjWidth(nOpts.boxWidth, cName);
var medianBounds = getObjWidth(nOpts.medianWidth, cName);
var notchBounds = {
boxLeft: boxBounds.left,
boxRight: boxBounds.right,
middle: boxBounds.middle,
medianLeft: medianBounds.left,
medianRight: medianBounds.right
};
// Notch Box
if (cGroup.notchBox.objs.notch) {
cGroup.notchBox.objs.notch
.attr("points", makeNotchBox(cGroup, notchBounds));
}
if (cGroup.notchBox.objs.upperLine) {
var lineBounds = null;
if (nOpts.lineWidth) {
lineBounds = getObjWidth(nOpts.lineWidth, cName)
} else {
lineBounds = objBounds
}
var confidenceLines = {
upper: chart.yScale(cGroup.metrics.upperNotch),
lower: chart.yScale(cGroup.metrics.lowerNotch)
};
cGroup.notchBox.objs.upperLine
.attr("x1", lineBounds.left)
.attr("x2", lineBounds.right)
.attr('y1', confidenceLines.upper)
.attr("y2", confidenceLines.upper);
cGroup.notchBox.objs.lowerLine
.attr("x1", lineBounds.left)
.attr("x2", lineBounds.right)
.attr('y1', confidenceLines.lower)
.attr("y2", confidenceLines.lower);
}
}
};
/**
* Create the svg elements for the notch boxes
*/
chart.notchBoxes.prepareNotchBoxes = function () {
var cName, cNotch;
if (nOpts && nOpts.colors) {
chart.notchBoxes.colorFunct = getColorFunct(nOpts.colors);
} else {
chart.notchBoxes.colorFunct = chart.colorFunct
}
if (nOpts.show == false) {
return
}
for (cName in chart.groupObjs) {
cNotch = chart.groupObjs[cName].notchBox;
cNotch.objs.g = chart.groupObjs[cName].g.append("g").attr("class", "notch-plot");
// Plot Box (default show)
if (nOpts.showNotchBox) {
cNotch.objs.notch = cNotch.objs.g.append("polygon")
.attr("class", "notch")
.style("fill", chart.notchBoxes.colorFunct(cName))
.style("stroke", chart.notchBoxes.colorFunct(cName));
//A stroke is added to the notch with the group color, it is
// hidden by default and can be shown through css with stroke-width
}
//Plot Confidence Lines (default hide)
if (nOpts.showLines) {
cNotch.objs.upperLine = cNotch.objs.g.append("line")
.attr("class", "upper confidence line")
.style("stroke", chart.notchBoxes.colorFunct(cName));
cNotch.objs.lowerLine = cNotch.objs.g.append("line")
.attr("class", "lower confidence line")
.style("stroke", chart.notchBoxes.colorFunct(cName));
}
}
};
chart.notchBoxes.prepareNotchBoxes();
d3.select(window).on('resize.' + chart.selector + '.notchBox', chart.notchBoxes.update);
chart.notchBoxes.update();
return chart;
};
/**
* Render a raw data in various forms
* @param options
@ -1171,17 +717,16 @@ export default {
if (cPlot.objs.points) {
if (dOpts.plotType == 'beeswarm') {
var swarmBounds = getObjWidth(100, cName);
console.log(swarmBounds)
var yPtScale = chart.yScale.copy()
.range([Math.floor(chart.yScale.range()[0] / dOpts.pointSize), 0])
//.interpolate(d3.interpolateRound)
.domain(chart.yScale.domain());
var maxWidth = Math.ceil(chart.xScale.rangeBand() / dOpts.pointSize);
console.log(maxWidth)
var ptsObj = {};
var cYBucket = null;
// Bucket points
// // Bucket points
for (var pt = 0; pt < cGroup.values.length; pt++) {
cYBucket = yPtScale(cGroup.values[pt]);
if (ptsObj.hasOwnProperty(cYBucket) !== true) {
@ -1191,7 +736,7 @@ export default {
.attr("cx", swarmBounds.middle)
.attr("cy", yPtScale(cGroup.values[pt]) * dOpts.pointSize));
}
// Plot buckets
// // Plot buckets
var rightMax = Math.min(swarmBounds.right - dOpts.pointSize);
for (var row in ptsObj) {
var leftMin = swarmBounds.left + (Math.max((maxWidth - ptsObj[row].length) / 2, 0) * dOpts.pointSize);
@ -1201,35 +746,8 @@ export default {
col++
}
}
// FIX THAT!
// let simulation = d3v5.forceSimulation(chart.data)
// .force("x", d3v5.forceX((d) => {
// return chart.xScale(d.Algorithm);
// }).strength(0.2))
// .force("y", d3v5.forceY((d) => {
// return chart.yScale(d.value);
// }).strength(1))
// .force("collide", d3v5.forceCollide((d) => {
// return d.size;
// }))
// .alphaDecay(0)
// .alpha(0.3)
// .on("tick", tick);
// function tick() {
// d3v5.selectAll(".CirclePoint")
// .attr("cx", (d) => d.x)
// .attr("cy", (d) => d.y);
// }
// let init_decay = setTimeout(function () {
// console.log("start alpha decay");
// simulation.alphaDecay(0.1);
// }, 3000); // start decay after 3 seconds
}
}
}
@ -1251,36 +769,6 @@ export default {
return
}
// Metrics lines
chart.dataPlots.objs.g = chart.objs.g.append("g").attr("class", "metrics-lines");
if (dOpts.showLines && dOpts.showLines.length > 0) {
chart.dataPlots.objs.lines = {};
var cMetric;
for (var line in dOpts.showLines) {
cMetric = dOpts.showLines[line];
chart.dataPlots.objs.lines[cMetric] = {};
chart.dataPlots.objs.lines[cMetric].values = [];
for (var cGroup in chart.groupObjs) {
chart.dataPlots.objs.lines[cMetric].values.push({
x: cGroup,
y: chart.groupObjs[cGroup].metrics[cMetric]
})
}
chart.dataPlots.objs.lines[cMetric].line = d3.svg.line()
.interpolate("cardinal")
.y(function (d) {
return chart.yScale(d.y)
});
chart.dataPlots.objs.lines[cMetric].g = chart.dataPlots.objs.g.append("path")
.attr("class", "line " + cMetric)
.attr("data-metric", cMetric)
.style("fill", 'none')
.style("stroke", chart.colorFunct(cMetric));
}
}
for (cName in chart.groupObjs) {
cPlot = chart.groupObjs[cName].dataPlots;
@ -1292,7 +780,7 @@ export default {
cPlot.objs.points.g = cPlot.objs.g.append("g").attr("class", "points-plot");
for (var pt = 0; pt < chart.groupObjs[cName].values.length; pt++) {
cPlot.objs.points.pts.push(cPlot.objs.points.g.append("circle")
.attr("class", "CirclePoint")
.attr("class", function () { return "CirclePoint" })
.attr('r', function () {
var dataLoc = data.filter( i => cName.includes( i.Algorithm ) );
return dataLoc[pt].size;
@ -1304,12 +792,12 @@ export default {
}));
}
}
}
}
};
chart.dataPlots.preparePlots();
chart.dataPlots.preparePlots();
d3.select(window).on('resize.' + chart.selector + '.dataPlot', chart.dataPlots.update);
chart.dataPlots.update();
return chart;
@ -1453,4 +941,19 @@ export default {
@media (max-width:500px){
.chart-options p {display: block;}
}
#containerForAllAlg {
height: 100px;
position: relative;
}
#MainPlot {
width: 100%;
height: 100%;
position: absolute;
top: 0;
left: 0;
}
#MainPlot {
z-index: 10;
}
</style>

672
frontend/src/components/History.vue
Unescape View File

@ -317,13 +317,6 @@ export default {
var numberofModels = 6
var units = "Models";
var initialModels = this.RandomSearLoc * 5
var months = [{month:"RandSear",value:initialModels,loss:null},
{month:"Crossover_S1",value:250,loss:null},
{month:"Mutate_S1",value:250,loss:null},
{month:"Crossover_S2",value:125,loss:null},
{month:"Mutate_S2",value:125,loss:null},
{month:"Crossover_S2_Prime'",value:125,loss:null},
{month:"Mutate_S2_Prime",value:125,loss:null}];
//this is the svg canvas attributes: (not buidlign abything just seeting up varaibels)
var margin = {top: 0, right: 40, bottom: 0, left: 40}, //comma is the equivalent of var :
width = 1250 - margin.left - margin.right,
@ -336,23 +329,6 @@ export default {
format = function(d) { return formatNumber(d) + " " + units; }
var color = d3.scale.category20b()
var startingAxis = this.RandomSearLoc
var axisScale = d3.scale.linear()
.domain([startingAxis*6,0])
.range([0, height]);
//Create the Axis
var yAxis = d3.svg.axis()
.scale(axisScale)
.orient("left")
.ticks(10);
// var lossScale = d3.scale.linear()
// .domain([.95,1,1.05])
// .range(["red","black","green"]);
// append the svg canvas to the page
var svg = d3.select("#SankeyInter").append("svg") //will select the id of cahrt from index.html ln:135 --> # looks for the id= from html
.attr("width", width + margin.left + margin.right)
@ -369,124 +345,91 @@ export default {
var path = sankey.link(); //sankey.link() is something happening in sankey.js
// svg.selectAll("text.values")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return formatNumber(d.value)})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10+15) + ")";});
// svg.selectAll("text.loss")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return d.loss})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("fill",function(d){ return lossScale(d.loss)})
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10-5) + ")";});
// svg.selectAll("text.months")
// .data(months)
// .enter()
// .append("text")
// .attr("class", "innerText")
// .text(function(d){return d.month})
// .attr("x",function(d,i){return i*89-margin.left-10})
// .attr("y",20)
// .attr("transform",
// "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + margin.bottom + ")");
// load the data
var graph = {
"nodes":[
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3","dh":height/numberofModels},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928","dh":height/numberofModels},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99","dh":height/numberofModels},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f","dh":height/numberofModels},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00","dh":height/numberofModels},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff","dh":height/numberofModels},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":18,"month":"Crossover_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":19,"month":"Crossover_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":20,"month":"Crossover_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":21,"month":"Crossover_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":22,"month":"Crossover_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Mutate S2 (M)","node":23,"month":"Crossover_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":24,"month":"Mutate_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":25,"month":"Mutate_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":26,"month":"Mutate_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":27,"month":"Mutate_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":28,"month":"Mutate_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Crossover S2 (M)","node":29,"month":"Mutate_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":30,"month":"Crossover_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":31,"month":"Crossover_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":32,"month":"Crossover_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":33,"month":"Crossover_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":34,"month":"Crossover_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Mutate S2 (C)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":36,"month":"Mutate_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":37,"month":"Mutate_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":38,"month":"Mutate_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":39,"month":"Mutate_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":40,"month":"Mutate_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Crossover S2 (C)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3",valueN:this.RandomSearLoc},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928",valueN:this.RandomSearLoc},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99",valueN:this.RandomSearLoc},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f",valueN:this.RandomSearLoc},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00",valueN:this.RandomSearLoc},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff",valueN:this.RandomSearLoc},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3",valueN:this.RandomSearLoc/2},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928",valueN:this.RandomSearLoc/2},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99",valueN:this.RandomSearLoc/2},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f",valueN:this.RandomSearLoc/2},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00",valueN:this.RandomSearLoc/2},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff",valueN:this.RandomSearLoc/2},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3",valueN:this.RandomSearLoc/2},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928",valueN:this.RandomSearLoc/2},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99",valueN:this.RandomSearLoc/2},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f",valueN:this.RandomSearLoc/2},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00",valueN:this.RandomSearLoc/2},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff",valueN:this.RandomSearLoc/2},
{"name":"GradB","node":18,"month":"Crossover_S2","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":19,"month":"Crossover_S2","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":20,"month":"Crossover_S2","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":21,"month":"Crossover_S2","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":22,"month":"Crossover_S2","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Mutate S2 (M)","node":23,"month":"Crossover_S2","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":24,"month":"Mutate_S2","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":25,"month":"Mutate_S2","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":26,"month":"Mutate_S2","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":27,"month":"Mutate_S2","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":28,"month":"Mutate_S2","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Crossover S2 (M)","node":29,"month":"Mutate_S2","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":30,"month":"Crossover_S2_Prime","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":31,"month":"Crossover_S2_Prime","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":32,"month":"Crossover_S2_Prime","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":33,"month":"Crossover_S2_Prime","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":34,"month":"Crossover_S2_Prime","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Mutate S2 (C)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":36,"month":"Mutate_S2_Prime","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":37,"month":"Mutate_S2_Prime","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":38,"month":"Mutate_S2_Prime","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":39,"month":"Mutate_S2_Prime","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":40,"month":"Mutate_S2_Prime","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Crossover S2 (C)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff",valueN:this.RandomSearLoc/4},
],
"links":[
{"source":5,"target":11,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[6]+valuesLoc[7]+valuesLoc[8]+valuesLoc[9]+valuesLoc[10]))},
{"source":5,"target":17,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[12]+valuesLoc[13]+valuesLoc[14]+valuesLoc[15]+valuesLoc[16]))},
{"source":0,"target":6,"value":valuesLoc[6],"dh":height/(numberofModels*localStep)*(valuesLoc[6]/50)},
{"source":0,"target":12,"value":valuesLoc[12],"dh":height/(numberofModels*localStep)*(valuesLoc[12]/50)},
{"source":1,"target":7,"value":valuesLoc[7],"dh":height/(numberofModels*localStep)*(valuesLoc[7]/50)},
{"source":1,"target":13,"value":valuesLoc[13],"dh":height/(numberofModels*localStep)*(valuesLoc[13]/50)},
{"source":2,"target":8,"value":valuesLoc[8],"dh":height/(numberofModels*localStep)*(valuesLoc[8]/50)},
{"source":2,"target":14,"value":valuesLoc[14],"dh":height/(numberofModels*localStep)*(valuesLoc[14]/50)},
{"source":3,"target":9,"value":valuesLoc[9],"dh":height/(numberofModels*localStep)*(valuesLoc[9]/50)},
{"source":3,"target":15,"value":valuesLoc[15],"dh":height/(numberofModels*localStep)*(valuesLoc[15]/50)},
{"source":4,"target":10,"value":valuesLoc[10],"dh":height/(numberofModels*localStep)*(valuesLoc[10]/50)},
{"source":4,"target":16,"value":valuesLoc[16],"dh":height/(numberofModels*localStep)*(valuesLoc[16]/50)},
{"source":11,"target":23,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[18]+valuesLoc[19]+valuesLoc[20]+valuesLoc[21]+valuesLoc[22]))},
{"source":11,"target":35,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[30]+valuesLoc[31]+valuesLoc[32]+valuesLoc[33]+valuesLoc[34]))},
{"source":6,"target":18,"value":valuesLoc[18],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[18]/25)},
{"source":6,"target":24,"value":valuesLoc[24],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[24]/25)},
{"source":7,"target":19,"value":valuesLoc[19],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[19]/25)},
{"source":7,"target":25,"value":valuesLoc[25],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[25]/25)},
{"source":8,"target":20,"value":valuesLoc[20],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[20]/25)},
{"source":8,"target":26,"value":valuesLoc[26],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[26]/25)},
{"source":9,"target":21,"value":valuesLoc[21],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[21]/25)},
{"source":9,"target":27,"value":valuesLoc[27],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[27]/25)},
{"source":10,"target":22,"value":valuesLoc[22],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[22]/25)},
{"source":10,"target":28,"value":valuesLoc[28],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[28]/25)},
{"source":17,"target":29,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[24]+valuesLoc[25]+valuesLoc[26]+valuesLoc[27]+valuesLoc[28]))},
{"source":17,"target":41,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[36]+valuesLoc[37]+valuesLoc[38]+valuesLoc[39]+valuesLoc[40]))},
{"source":12,"target":30,"value":valuesLoc[30],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[30]/25)},
{"source":12,"target":36,"value":valuesLoc[36],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[36]/25)},
{"source":13,"target":31,"value":valuesLoc[31],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[31]/25)},
{"source":13,"target":37,"value":valuesLoc[37],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[37]/25)},
{"source":14,"target":32,"value":valuesLoc[32],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[32]/25)},
{"source":14,"target":38,"value":valuesLoc[38],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[38]/25)},
{"source":15,"target":33,"value":valuesLoc[33],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[33]/25)},
{"source":15,"target":39,"value":valuesLoc[39],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[39]/25)},
{"source":16,"target":34,"value":valuesLoc[34],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[34]/25)},
{"source":16,"target":40,"value":valuesLoc[40],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[40]/25)},
{"source":5,"target":11,"value":0},
{"source":5,"target":17,"value":0},
{"source":0,"target":6,"value":valuesLoc[6]},
{"source":0,"target":12,"value":valuesLoc[12]},
{"source":1,"target":7,"value":valuesLoc[7]},
{"source":1,"target":13,"value":valuesLoc[13]},
{"source":2,"target":8,"value":valuesLoc[8]},
{"source":2,"target":14,"value":valuesLoc[14]},
{"source":3,"target":9,"value":valuesLoc[9]},
{"source":3,"target":15,"value":valuesLoc[15]},
{"source":4,"target":10,"value":valuesLoc[10]},
{"source":4,"target":16,"value":valuesLoc[16]},
{"source":11,"target":23,"value":0},
{"source":11,"target":35,"value":0},
{"source":6,"target":18,"value":valuesLoc[18]},
{"source":6,"target":24,"value":valuesLoc[24]},
{"source":7,"target":19,"value":valuesLoc[19]},
{"source":7,"target":25,"value":valuesLoc[25]},
{"source":8,"target":20,"value":valuesLoc[20]},
{"source":8,"target":26,"value":valuesLoc[26]},
{"source":9,"target":21,"value":valuesLoc[21]},
{"source":9,"target":27,"value":valuesLoc[27]},
{"source":10,"target":22,"value":valuesLoc[22]},
{"source":10,"target":28,"value":valuesLoc[28]},
{"source":17,"target":29,"value":0},
{"source":17,"target":41,"value":0},
{"source":12,"target":30,"value":valuesLoc[30]},
{"source":12,"target":36,"value":valuesLoc[36]},
{"source":13,"target":31,"value":valuesLoc[31]},
{"source":13,"target":37,"value":valuesLoc[37]},
{"source":14,"target":32,"value":valuesLoc[32]},
{"source":14,"target":38,"value":valuesLoc[38]},
{"source":15,"target":33,"value":valuesLoc[33]},
{"source":15,"target":39,"value":valuesLoc[39]},
{"source":16,"target":34,"value":valuesLoc[34]},
{"source":16,"target":40,"value":valuesLoc[40]},
]}
sankey.nodes(graph.nodes)
@ -578,8 +521,8 @@ export default {
return "#808080"
}
})
.style("stroke-width", function(d) { return Math.max(.5, d.dh); }) //setting the stroke length by the data . d.dh is defined in sankey.js
.sort(function(a, b) { return b.dh - a.dh; })
.style("stroke-width", function(d) { return Math.max(.5, d.dy); }) //setting the stroke length by the data . d.dy is defined in sankey.js
.sort(function(a, b) { return b.dy - a.dy; })
.on("mouseover",linkmouseover)
.on("mouseout",linkmouseout);
@ -596,15 +539,10 @@ export default {
.attr("transform", function(d) {
return "translate(" + d.x + "," + d.y + ")";
});
//.call(d3.behavior.drag() <---------- THIS IS THE DRAG THING TO REMOVE!!
//.origin(function(d) { return d; })
// .on("dragstart", function() { //<-------- THIS IS MOUSEOVER DRAG CAPABILITIES .on(mousemove) called pointer events, look it up!
// this.parentNode.appendChild(this); })
// .on("drag", dragmove);
// add the rectangles for the nodes
node.append("rect")
.attr("height", function(d) { return d.dh; })
.attr("height", function(d) { return d.dy; })
.attr("width", sankey.nodeWidth( ))
.style("fill", function(d) { return d.color; }) //matches name with the colors here! inside the replace is some sort of regex
// .style("stroke",function(d) { return d3.rgb(d.color).darker(1); }) //line around the box formatting
@ -617,11 +555,11 @@ export default {
.attr("x", -6)
.attr("y", function(d) {
if (d.node <= 5) {
return d.dh - 81;
return d.dy - 81;
} else if (d.node <= 17) {
return d.dh - 41;
return d.dy - 41;
} else {
return d.dh - 21;
return d.dy - 21;
}
})
.attr("dy", ".35em")
@ -641,15 +579,6 @@ export default {
.filter(function(d) { return d.x < width / 2; })//positioning left or right of node
.attr("x", 6 + sankey.nodeWidth())
.attr("text-anchor", "start");
// // the function for moving the nodes
// function dragmove(d) {
// d3.select(this).attr("transform",
// "translate(" + d.x + "," + (
// d.y = Math.max(0, Math.min(height/2 - d.dy, d3.event.y))
// ) + ")");
// sankey.relayout();
// link.attr("d", path);
// }
var status=null;
@ -696,12 +625,6 @@ export default {
.attr("transform",
"translate(" + -45 + "," + 0 + ") scale(1,-1) translate(" + 0 + "," + -(height) + ") rotate(-90 150 150)");
//y axis
// svg.append("g")
// .call(yAxis)
// .attr("class", "axis")
// .attr("transform",
// "translate(" + -45 + "," + 0 + ") scale(1,-1) translate(" + 0 + "," + -(height) + ")");
},
computePerformanceDiff () {
@ -865,13 +788,6 @@ export default {
var numberofModels = 6
var units = "Models";
var initialModels = this.RandomSearLoc * 5
var months = [{month:"RandSear",value:initialModels,loss:null},
{month:"Crossover_S1",value:250,loss:null},
{month:"Mutate_S1",value:250,loss:null},
{month:"Crossover_S2",value:125,loss:null},
{month:"Mutate_S2",value:125,loss:null},
{month:"Crossover_S2_Prime'",value:125,loss:null},
{month:"Mutate_S2_Prime",value:125,loss:null}];
//this is the svg canvas attributes: (not buidlign abything just seeting up varaibels)
var margin = {top: 0, right: 40, bottom: 0, left: 40}, //comma is the equivalent of var :
width = 1230 - margin.left - margin.right,
@ -885,15 +801,15 @@ export default {
var color = d3.scale.category20b()
var startingAxis = this.RandomSearLoc
var axisScale = d3.scale.linear()
.domain([startingAxis*6,0])
.range([0, height]);
// var axisScale = d3.scale.linear()
// .domain([startingAxis*6,0])
// .range([0, height]);
//Create the Axis
var yAxis = d3.svg.axis()
.scale(axisScale)
.orient("left")
.ticks(10);
// //Create the Axis
// var yAxis = d3.svg.axis()
// .scale(axisScale)
// .orient("left")
// .ticks(10);
@ -917,124 +833,90 @@ export default {
var path = sankey.link(); //sankey.link() is something happening in sankey.js
// svg.selectAll("text.values")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return formatNumber(d.value)})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10+15) + ")";});
// svg.selectAll("text.loss")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return d.loss})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("fill",function(d){ return lossScale(d.loss)})
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10-5) + ")";});
// svg.selectAll("text.months")
// .data(months)
// .enter()
// .append("text")
// .attr("class", "innerText")
// .text(function(d){return d.month})
// .attr("x",function(d,i){return i*89-margin.left-10})
// .attr("y",20)
// .attr("transform",
// "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + margin.bottom + ")");
// load the data
var graph = {
"nodes":[
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3","dh":height/numberofModels},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928","dh":height/numberofModels},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99","dh":height/numberofModels},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f","dh":height/numberofModels},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00","dh":height/numberofModels},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff","dh":height/numberofModels},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":18,"month":"Crossover_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":19,"month":"Crossover_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":20,"month":"Crossover_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":21,"month":"Crossover_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":22,"month":"Crossover_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Mutate S2 (M)","node":23,"month":"Crossover_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":24,"month":"Mutate_S2","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":25,"month":"Mutate_S2","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":26,"month":"Mutate_S2","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":27,"month":"Mutate_S2","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":28,"month":"Mutate_S2","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Crossover S2 (M)","node":29,"month":"Mutate_S2","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":30,"month":"Crossover_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":31,"month":"Crossover_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":32,"month":"Crossover_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":33,"month":"Crossover_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":34,"month":"Crossover_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Mutate S2 (C)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
{"name":"GradB","node":36,"month":"Mutate_S2_Prime","color":"#a6cee3","dh":height/(numberofModels*(localStep*2))},
{"name":"RF","node":37,"month":"Mutate_S2_Prime","color":"#b15928","dh":height/(numberofModels*(localStep*2))},
{"name":"MLP","node":38,"month":"Mutate_S2_Prime","color":"#fb9a99","dh":height/(numberofModels*(localStep*2))},
{"name":"LR","node":39,"month":"Mutate_S2_Prime","color":"#fdbf6f","dh":height/(numberofModels*(localStep*2))},
{"name":"KNN","node":40,"month":"Mutate_S2_Prime","color":"#ff7f00","dh":height/(numberofModels*(localStep*2))},
{"name":"Crossover S2 (C)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff","dh":height/(numberofModels*(localStep*2))},
"nodes":[
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3",valueN:this.RandomSearLoc},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928",valueN:this.RandomSearLoc},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99",valueN:this.RandomSearLoc},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f",valueN:this.RandomSearLoc},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00",valueN:this.RandomSearLoc},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff",valueN:this.RandomSearLoc},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3",valueN:this.RandomSearLoc/2},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928",valueN:this.RandomSearLoc/2},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99",valueN:this.RandomSearLoc/2},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f",valueN:this.RandomSearLoc/2},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00",valueN:this.RandomSearLoc/2},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff",valueN:this.RandomSearLoc/2},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3",valueN:this.RandomSearLoc/2},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928",valueN:this.RandomSearLoc/2},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99",valueN:this.RandomSearLoc/2},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f",valueN:this.RandomSearLoc/2},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00",valueN:this.RandomSearLoc/2},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff",valueN:this.RandomSearLoc/2},
{"name":"GradB","node":18,"month":"Crossover_S2","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":19,"month":"Crossover_S2","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":20,"month":"Crossover_S2","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":21,"month":"Crossover_S2","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":22,"month":"Crossover_S2","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Mutate S2 (M)","node":23,"month":"Crossover_S2","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":24,"month":"Mutate_S2","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":25,"month":"Mutate_S2","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":26,"month":"Mutate_S2","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":27,"month":"Mutate_S2","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":28,"month":"Mutate_S2","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Crossover S2 (M)","node":29,"month":"Mutate_S2","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":30,"month":"Crossover_S2_Prime","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":31,"month":"Crossover_S2_Prime","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":32,"month":"Crossover_S2_Prime","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":33,"month":"Crossover_S2_Prime","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":34,"month":"Crossover_S2_Prime","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Mutate S2 (C)","node":35,"month":"Crossover_S2_Prime","color":"#ffffff",valueN:this.RandomSearLoc/4},
{"name":"GradB","node":36,"month":"Mutate_S2_Prime","color":"#a6cee3",valueN:this.RandomSearLoc/4},
{"name":"RF","node":37,"month":"Mutate_S2_Prime","color":"#b15928",valueN:this.RandomSearLoc/4},
{"name":"MLP","node":38,"month":"Mutate_S2_Prime","color":"#fb9a99",valueN:this.RandomSearLoc/4},
{"name":"LR","node":39,"month":"Mutate_S2_Prime","color":"#fdbf6f",valueN:this.RandomSearLoc/4},
{"name":"KNN","node":40,"month":"Mutate_S2_Prime","color":"#ff7f00",valueN:this.RandomSearLoc/4},
{"name":"Crossover S2 (C)","node":41,"month":"Mutate_S2_Prime","color":"#ffffff",valueN:this.RandomSearLoc/4},
],
"links":[
{"source":5,"target":11,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[6]+valuesLoc[7]+valuesLoc[8]+valuesLoc[9]+valuesLoc[10]))},
{"source":5,"target":17,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[12]+valuesLoc[13]+valuesLoc[14]+valuesLoc[15]+valuesLoc[16]))},
{"source":0,"target":6,"value":valuesLoc[6],"dh":height/(numberofModels*localStep)*(valuesLoc[6]/50)},
{"source":0,"target":12,"value":valuesLoc[12],"dh":height/(numberofModels*localStep)*(valuesLoc[12]/50)},
{"source":1,"target":7,"value":valuesLoc[7],"dh":height/(numberofModels*localStep)*(valuesLoc[7]/50)},
{"source":1,"target":13,"value":valuesLoc[13],"dh":height/(numberofModels*localStep)*(valuesLoc[13]/50)},
{"source":2,"target":8,"value":valuesLoc[8],"dh":height/(numberofModels*localStep)*(valuesLoc[8]/50)},
{"source":2,"target":14,"value":valuesLoc[14],"dh":height/(numberofModels*localStep)*(valuesLoc[14]/50)},
{"source":3,"target":9,"value":valuesLoc[9],"dh":height/(numberofModels*localStep)*(valuesLoc[9]/50)},
{"source":3,"target":15,"value":valuesLoc[15],"dh":height/(numberofModels*localStep)*(valuesLoc[15]/50)},
{"source":4,"target":10,"value":valuesLoc[10],"dh":height/(numberofModels*localStep)*(valuesLoc[10]/50)},
{"source":4,"target":16,"value":valuesLoc[16],"dh":height/(numberofModels*localStep)*(valuesLoc[16]/50)},
{"source":11,"target":23,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[18]+valuesLoc[19]+valuesLoc[20]+valuesLoc[21]+valuesLoc[22]))},
{"source":11,"target":35,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[30]+valuesLoc[31]+valuesLoc[32]+valuesLoc[33]+valuesLoc[34]))},
{"source":6,"target":18,"value":valuesLoc[18],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[18]/25)},
{"source":6,"target":24,"value":valuesLoc[24],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[24]/25)},
{"source":7,"target":19,"value":valuesLoc[19],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[19]/25)},
{"source":7,"target":25,"value":valuesLoc[25],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[25]/25)},
{"source":8,"target":20,"value":valuesLoc[20],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[20]/25)},
{"source":8,"target":26,"value":valuesLoc[26],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[26]/25)},
{"source":9,"target":21,"value":valuesLoc[21],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[21]/25)},
{"source":9,"target":27,"value":valuesLoc[27],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[27]/25)},
{"source":10,"target":22,"value":valuesLoc[22],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[22]/25)},
{"source":10,"target":28,"value":valuesLoc[28],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[28]/25)},
{"source":17,"target":29,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[24]+valuesLoc[25]+valuesLoc[26]+valuesLoc[27]+valuesLoc[28]))},
{"source":17,"target":41,"value":25,"dh":height/(numberofModels*(localStep*2))*(125/(valuesLoc[36]+valuesLoc[37]+valuesLoc[38]+valuesLoc[39]+valuesLoc[40]))},
{"source":12,"target":30,"value":valuesLoc[30],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[30]/25)},
{"source":12,"target":36,"value":valuesLoc[36],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[36]/25)},
{"source":13,"target":31,"value":valuesLoc[31],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[31]/25)},
{"source":13,"target":37,"value":valuesLoc[37],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[37]/25)},
{"source":14,"target":32,"value":valuesLoc[32],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[32]/25)},
{"source":14,"target":38,"value":valuesLoc[38],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[38]/25)},
{"source":15,"target":33,"value":valuesLoc[33],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[33]/25)},
{"source":15,"target":39,"value":valuesLoc[39],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[39]/25)},
{"source":16,"target":34,"value":valuesLoc[34],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[34]/25)},
{"source":16,"target":40,"value":valuesLoc[40],"dh":height/(numberofModels*(localStep*2))*(valuesLoc[40]/25)},
{"source":5,"target":11,"value":0},
{"source":5,"target":17,"value":0},
{"source":0,"target":6,"value":valuesLoc[6]},
{"source":0,"target":12,"value":valuesLoc[12]},
{"source":1,"target":7,"value":valuesLoc[7]},
{"source":1,"target":13,"value":valuesLoc[13]},
{"source":2,"target":8,"value":valuesLoc[8]},
{"source":2,"target":14,"value":valuesLoc[14]},
{"source":3,"target":9,"value":valuesLoc[9]},
{"source":3,"target":15,"value":valuesLoc[15]},
{"source":4,"target":10,"value":valuesLoc[10]},
{"source":4,"target":16,"value":valuesLoc[16]},
{"source":11,"target":23,"value":0},
{"source":11,"target":35,"value":0},
{"source":6,"target":18,"value":valuesLoc[18]},
{"source":6,"target":24,"value":valuesLoc[24]},
{"source":7,"target":19,"value":valuesLoc[19]},
{"source":7,"target":25,"value":valuesLoc[25]},
{"source":8,"target":20,"value":valuesLoc[20]},
{"source":8,"target":26,"value":valuesLoc[26]},
{"source":9,"target":21,"value":valuesLoc[21]},
{"source":9,"target":27,"value":valuesLoc[27]},
{"source":10,"target":22,"value":valuesLoc[22]},
{"source":10,"target":28,"value":valuesLoc[28]},
{"source":17,"target":29,"value":0},
{"source":17,"target":41,"value":0},
{"source":12,"target":30,"value":valuesLoc[30]},
{"source":12,"target":36,"value":valuesLoc[36]},
{"source":13,"target":31,"value":valuesLoc[31]},
{"source":13,"target":37,"value":valuesLoc[37]},
{"source":14,"target":32,"value":valuesLoc[32]},
{"source":14,"target":38,"value":valuesLoc[38]},
{"source":15,"target":33,"value":valuesLoc[33]},
{"source":15,"target":39,"value":valuesLoc[39]},
{"source":16,"target":34,"value":valuesLoc[34]},
{"source":16,"target":40,"value":valuesLoc[40]},
]}
sankey.nodes(graph.nodes)
@ -1084,8 +966,8 @@ export default {
return "#808080"
}
})
.style("stroke-width", function(d) { return Math.max(.5, d.dh); }) //setting the stroke length by the data . d.dh is defined in sankey.js
.sort(function(a, b) { return b.dh - a.dh; })
.style("stroke-width", function(d) { return Math.max(.5, d.dy); }) //setting the stroke length by the data . d.dy is defined in sankey.js
.sort(function(a, b) { return b.dy - a.dy; })
.on("mouseover",linkmouseover)
.on("mouseout",linkmouseout);
@ -1110,7 +992,7 @@ export default {
// add the rectangles for the nodes
node.append("rect")
.attr("height", function(d) { return d.dh; })
.attr("height", function(d) { return d.dy; })
.attr("width", sankey.nodeWidth( ))
.style("fill", function(d) { return d.color; }) //matches name with the colors here! inside the replace is some sort of regex
// .style("stroke",function(d) { return d3.rgb(d.color).darker(1); }) //line around the box formatting
@ -1134,7 +1016,7 @@ export default {
$("input[type='number']").change( function(d) {
valuesLoc[d.target.id] = parseInt(d.target.value)
EventBus.$emit('changeValues2', valuesLoc)
EventBus.$emit('changeValues2Run', valuesLoc)
});
}
// .append("title")
@ -1146,11 +1028,11 @@ export default {
.attr("x", -6)
.attr("y", function(d) {
if (d.node <= 5) {
return d.dh - 81;
return d.dy - 81;
} else if (d.node <= 17) {
return d.dh - 41;
return d.dy - 41;
} else {
return d.dh - 21;
return d.dy - 21;
}
})
.attr("dy", ".35em")
@ -1235,13 +1117,11 @@ export default {
},
SankeyView () {
var valuesLoc = this.values
var valuesLocSt2 = this.valuesStage2
var localStep = 2
var numberofModels = 6
var units = "Models";
var initialModels = this.RandomSearLoc * 5
var months = [{month:"RandSear",value:initialModels,loss:null},
{month:"Crossover_S1",value:250,loss:null},
{month:"Mutate_S1",value:250,loss:null}];
//this is the svg canvas attributes: (not buidlign abything just seeting up varaibels)
var margin = {top: 0, right: 40, bottom: 0, left: 40}, //comma is the equivalent of var :
width = 1230 - margin.left - margin.right,
@ -1287,76 +1167,41 @@ export default {
var path = sankey.link(); //sankey.link() is something happening in sankey.js
// svg.selectAll("text.values")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return formatNumber(d.value)})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10+15) + ")";});
// svg.selectAll("text.loss")
// .data(months)
// .enter()
// .append("text")
// .text(function(d){return d.loss})
// .attr("class", "innerText")
// .attr("x",function(d,i){return i*89-margin.left-5})
// .attr("y",20)
// .attr("fill",function(d){ return lossScale(d.loss)})
// .attr("transform", function(d){
// return "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + -(d.value/10-5) + ")";});
// svg.selectAll("text.months")
// .data(months)
// .enter()
// .append("text")
// .attr("class", "innerText")
// .text(function(d){return d.month})
// .attr("x",function(d,i){return i*89-margin.left-10})
// .attr("y",20)
// .attr("transform",
// "translate(" + margin.left + "," + margin.top + ") scale(1,-1) translate(" + 0 + "," + margin.bottom + ")");
// load the data
var graph = {
"nodes":[
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3","dh":height/numberofModels},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928","dh":height/numberofModels},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99","dh":height/numberofModels},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f","dh":height/numberofModels},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00","dh":height/numberofModels},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff","dh":height/numberofModels},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3","dh":height/(numberofModels*localStep)},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928","dh":height/(numberofModels*localStep)},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99","dh":height/(numberofModels*localStep)},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f","dh":height/(numberofModels*localStep)},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00","dh":height/(numberofModels*localStep)},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff","dh":height/(numberofModels*localStep)},
{"name":"GradB","node":0,"month":"RandSear","color":"#a6cee3", valueN: this.RandomSearLoc},
{"name":"RF","node":1,"month":"RandSear","color":"#b15928", valueN: this.RandomSearLoc},
{"name":"MLP","node":2,"month":"RandSear","color":"#fb9a99", valueN: this.RandomSearLoc},
{"name":"LR","node":3,"month":"RandSear","color":"#fdbf6f", valueN: this.RandomSearLoc},
{"name":"KNN","node":4,"month":"RandSear","color":"#ff7f00", valueN: this.RandomSearLoc},
{"name":"Random search","node":5,"month":"RandSear","color":"#ffffff", valueN: this.RandomSearLoc},
{"name":"GradB","node":6,"month":"Crossover_S1","color":"#a6cee3", valueN: this.RandomSearLoc/2},
{"name":"RF","node":7,"month":"Crossover_S1","color":"#b15928", valueN: this.RandomSearLoc/2},
{"name":"MLP","node":8,"month":"Crossover_S1","color":"#fb9a99", valueN: this.RandomSearLoc/2},
{"name":"LR","node":9,"month":"Crossover_S1","color":"#fdbf6f", valueN: this.RandomSearLoc/2},
{"name":"KNN","node":10,"month":"Crossover_S1","color":"#ff7f00", valueN: this.RandomSearLoc/2},
{"name":"Mutate (M) S1","node":11,"month":"Crossover_S1","color":"#ffffff", valueN: this.RandomSearLoc/2},
{"name":"GradB","node":12,"month":"Mutate_S1","color":"#a6cee3", valueN: this.RandomSearLoc/2},
{"name":"RF","node":13,"month":"Mutate_S1","color":"#b15928", valueN: this.RandomSearLoc/2},
{"name":"MLP","node":14,"month":"Mutate_S1","color":"#fb9a99", valueN: this.RandomSearLoc/2},
{"name":"LR","node":15,"month":"Mutate_S1","color":"#fdbf6f", valueN: this.RandomSearLoc/2},
{"name":"KNN","node":16,"month":"Mutate_S1","color":"#ff7f00", valueN: this.RandomSearLoc/2},
{"name":"Crossover (C) S1","node":17,"month":"Mutate_S1","color":"#ffffff", valueN: this.RandomSearLoc/2},
],
"links":[
{"source":5,"target":11,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[6]+valuesLoc[7]+valuesLoc[8]+valuesLoc[9]+valuesLoc[10]))},
{"source":5,"target":17,"value":50,"dh":height/(numberofModels*localStep)*(250/(valuesLoc[12]+valuesLoc[13]+valuesLoc[14]+valuesLoc[15]+valuesLoc[16]))},
{"source":0,"target":6,"value":valuesLoc[6],"dh":height/(numberofModels*localStep)*(valuesLoc[6]/50)},
{"source":0,"target":12,"value":valuesLoc[12],"dh":height/(numberofModels*localStep)*(valuesLoc[12]/50)},
{"source":1,"target":7,"value":valuesLoc[7],"dh":height/(numberofModels*localStep)*(valuesLoc[7]/50)},
{"source":1,"target":13,"value":valuesLoc[13],"dh":height/(numberofModels*localStep)*(valuesLoc[13]/50)},
{"source":2,"target":8,"value":valuesLoc[8],"dh":height/(numberofModels*localStep)*(valuesLoc[8]/50)},
{"source":2,"target":14,"value":valuesLoc[14],"dh":height/(numberofModels*localStep)*(valuesLoc[14]/50)},
{"source":3,"target":9,"value":valuesLoc[9],"dh":height/(numberofModels*localStep)*(valuesLoc[9]/50)},
{"source":3,"target":15,"value":valuesLoc[15],"dh":height/(numberofModels*localStep)*(valuesLoc[15]/50)},
{"source":4,"target":10,"value":valuesLoc[10],"dh":height/(numberofModels*localStep)*(valuesLoc[10]/50)},
{"source":4,"target":16,"value":valuesLoc[16],"dh":height/(numberofModels*localStep)*(valuesLoc[16]/50)},
{"source":5,"target":11,"value":0},
{"source":5,"target":17,"value":0},
{"source":0,"target":6,"value":valuesLoc[6]},
{"source":0,"target":12,"value":valuesLoc[12]},
{"source":1,"target":7,"value":valuesLoc[7]},
{"source":1,"target":13,"value":valuesLoc[13]},
{"source":2,"target":8,"value":valuesLoc[8]},
{"source":2,"target":14,"value":valuesLoc[14]},
{"source":3,"target":9,"value":valuesLoc[9]},
{"source":3,"target":15,"value":valuesLoc[15]},
{"source":4,"target":10,"value":valuesLoc[10]},
{"source":4,"target":16,"value":valuesLoc[16]},
]}
sankey.nodes(graph.nodes)
@ -1376,8 +1221,8 @@ export default {
return "#808080"
}
})
.style("stroke-width", function(d) { return Math.max(.5, d.dh); }) //setting the stroke length by the data . d.dh is defined in sankey.js
.sort(function(a, b) { return b.dh - a.dh; })
.style("stroke-width", function(d) { return Math.max(.5, d.dy); }) //setting the stroke length by the data . d.dy is defined in sankey.js
.sort(function(a, b) { return b.dy - a.dy; })
.on("mouseover",linkmouseover)
.on("mouseout",linkmouseout);
@ -1402,7 +1247,7 @@ export default {
// add the rectangles for the nodes
node.append("rect")
.attr("height", function(d) { return d.dh; })
.attr("height", function(d) { return d.dy; })
.attr("width", sankey.nodeWidth( ))
.style("fill", function(d) { return d.color; }) //matches name with the colors here! inside the replace is some sort of regex
// .style("stroke",function(d) { return d3.rgb(d.color).darker(1); }) //line around the box formatting
@ -1426,7 +1271,9 @@ export default {
$("input[type='number']").change( function(d) {
valuesLoc[d.target.id] = parseInt(d.target.value)
valuesLocSt2[d.target.id] = parseInt(d.target.value)
EventBus.$emit('changeValues', valuesLoc)
EventBus.$emit('changeValues2', valuesLocSt2)
});
}
// .append("title")
@ -1438,9 +1285,9 @@ export default {
.attr("x", -6)
.attr("y", function(d) {
if (d.node <= 5) {
return d.dh - 81;
return d.dy - 81;
} else {
return d.dh - 41;
return d.dy - 41;
}
})
.attr("dy", ".35em")
@ -1456,16 +1303,6 @@ export default {
.attr("x", 6 + sankey.nodeWidth())
.attr("text-anchor", "start");
// // the function for moving the nodes
// function dragmove(d) {
// d3.select(this).attr("transform",
// "translate(" + d.x + "," + (
// d.y = Math.max(0, Math.min(height/2 - d.dy, d3.event.y))
// ) + ")");
// sankey.relayout();
// link.attr("d", path);
// }
var status=null;
function nodemouseover(d){
@ -1511,79 +1348,8 @@ export default {
.attr("transform",
"translate(" + -45 + "," + 0 + ") scale(1,-1) translate(" + 0 + "," + -(height) + ") rotate(-90 150 150)");
//y axis
// svg.append("g")
// .call(yAxis)
// .attr("class", "axis")
// .attr("transform",
// "translate(" + -45 + "," + 0 + ") scale(1,-1) translate(" + 0 + "," + -(height) + ")");
},
// LegendStable() {
// var viewerWidth = this.responsiveWidthHeight[0]*7
// var viewerHeight = this.responsiveWidthHeight[1]*1.6
// var viewerPosTop = viewerWidth * 0.01;
// var cellSizeHeat = 20
// var legendElementWidth = cellSizeHeat * 3;
// // http://bl.ocks.org/mbostock/5577023
// var colors = colorbrewer.PRGn[this.classesNumber];
// var svgLeg = d3.select("#LegendHeat");
// svgLeg.selectAll("*").remove();
// var svgLeg = d3.select("#LegendHeat").append("svg")
// .attr("width", viewerWidth/2)
// .attr("height", viewerHeight*0.10)
// .style("margin-top", "12px")
// var legend = svgLeg.append('g')
// .attr("class", "legend")
// .attr("transform", "translate(0,0)")
// .selectAll(".legendElement")
// .data([1.00, 0.75, 0.50, 0.25, 0.00, 0.25, 0.50, 0.75, 1.00])
// .enter().append("g")
// .attr("class", "legendElement");
// legend.append("svg:rect")
// .attr("x", viewerPosTop)
// .attr("y", function(d, i) {
// return (legendElementWidth * i) + 35;
// })
// .attr("class", "cellLegend bordered")
// .attr("width", legendElementWidth)
// .attr("height", cellSizeHeat / 2)
// .style("fill", function(d, i) {
// console.log(colors[i])
// return colors[i];
// });
// legend.append("text")
// .attr("class", "mono legendElement")
// .text(function(d, i) {
// console.log(i)
// if (i < 4) {
// return "-" + (d * 100) + "%";
// } else if (i > 4) {
// return "+" + (d * 100) + "%";
// } else {
// return "" + (d * 100) + "%";
// }
// })
// .attr("x", function(d, i) {
// if (i > 4) {
// return (legendElementWidth * i) + 45;
// } else if (i == 4) {
// return (legendElementWidth * i) + 55;
// } else {
// return (legendElementWidth * i) + 40;
// }
// })
// .attr("y", (viewerPosTop + cellSizeHeat) + 5);
// svgLeg.append("text").attr("x", 220).attr("y", 32).text("Difference in PD (solution space vs. ensemble").style("font-size", "16px").attr("alignment-baseline","top").attr('transform', 'translate(0,10) rotate(90)')
// }
},
mounted() {
//EventBus.$on('emittedEventCallingSankeyLegend', this.LegendStable)
@ -1591,11 +1357,11 @@ export default {
EventBus.$on('emittedEventCallingSankeyStage2', this.SankeyViewStage2)
EventBus.$on('emittedEventCallingSankeyStage3', this.SankeyViewStage3)
EventBus.$on('changeValues', data => { this.values = data })
EventBus.$on('changeValues', data => { this.values = data; })
EventBus.$on('changeValues', this.SankeyView )
EventBus.$on('changeValues2', data => { this.valuesStage2 = data})
EventBus.$on('changeValues2', this.SankeyViewStage2)
EventBus.$on('changeValues2Run', this.SankeyViewStage2)
EventBus.$on('SendtheChangeinRangePos', data => { this.RandomSearLoc = data })

6
frontend/src/components/Main.vue
Unescape View File

@ -374,7 +374,6 @@ export default Vue.extend({
EventBus.$emit('emittedEventCallingScatterPlot', this.OverviewResults)
EventBus.$emit('emittedEventCallingSankey')
//EventBus.$emit('emittedEventCallingSankeyLegend')
EventBus.$emit('callAlgorithhms')
EventBus.$emit('callValidationData', this.OverviewResults)
EventBus.$emit('callValidation')
EventBus.$emit('emittedEventCallingGrid', this.OverviewResults)
@ -383,6 +382,7 @@ export default Vue.extend({
EventBus.$emit('LegendPredict')
this.storeBothEnsCM[0] = this.OverviewResults
this.firstTimeExec = false
EventBus.$emit('callAlgorithhms')
} else {
var Performance = JSON.parse(this.OverviewResults[1])
EventBus.$emit('SendStoredEnsembleHist', this.storeEnsemble)
@ -399,7 +399,6 @@ export default Vue.extend({
EventBus.$emit('hideCrossMut')
}
this.storeBothEnsCM[1] = this.OverviewResults
EventBus.$emit('callAlgorithhms')
this.getFinalResults()
EventBus.$emit('emittedEventCallingGrid', this.OverviewResults)
EventBus.$emit('SendSelectedPointsToServerEvent', this.PredictSelEnsem)
@ -407,6 +406,7 @@ export default Vue.extend({
EventBus.$emit('callValidationData', this.OverviewResults)
EventBus.$emit('callValidation')
EventBus.$emit('LegendPredictEnsem')
EventBus.$emit('callAlgorithhms')
}
})
.catch(error => {
@ -1130,7 +1130,7 @@ export default Vue.extend({
EventBus.$on('factorsChanged', data => { this.basicValuesFact = data })
EventBus.$on('changeValues', data => { this.CMNumberofModelsOFFICIAL = data })
EventBus.$on('changeValues2', data => { this.CMNumberofModelsOFFICIALS2 = data })
EventBus.$on('changeValues2Run', data => { this.CMNumberofModelsOFFICIALS2 = data })
//Prevent double click to search for a word.
document.addEventListener('mousedown', function (event) {

12
frontend/src/components/Predictions.vue
Unescape View File

@ -195,7 +195,7 @@ export default {
var t = d3.timer(function(elapsed) {
draw();
if (elapsed > 2000) t.stop();
if (elapsed > 2500) t.stop();
}); // start a timer that runs the draw function for 500 ms (this needs to be higher than the transition in the databind function)
@ -240,7 +240,7 @@ export default {
.transition()
.attr('width', 0)
.attr('height', 0)
.remove();
.remove();
} // databind()
@ -438,7 +438,7 @@ export default {
var t = d3.timer(function(elapsed) {
draw();
if (elapsed > 2000) t.stop();
if (elapsed > 2500) t.stop();
}); // start a timer that runs the draw function for 500 ms (this needs to be higher than the transition in the databind function)
@ -545,11 +545,11 @@ export default {
var heightText = 180
svgLegGl.append("line")
.attr("x1", 614)
.attr("x1", 613)
.attr("y1", 0)
.attr("x2", 614)
.attr("x2", 613)
.attr("y2", heightText+30)
.style("stroke-width", 1)
.style("stroke-width", 2)
.style("stroke", "black")
.style("fill", "none");

2
frontend/src/components/ValidationController.vue
Unescape View File

@ -155,7 +155,7 @@ export default {
yName:'value',
axisLabels: {xAxis: 'Algorithm', yAxis: '# Performance (%) #'},
selector:"#violin",
constrainExtremes:true});
constrainExtremes:false});
chart2.renderBoxPlot({showBox:false});
chart2.renderDataPlots({showBeanLines:true,beanWidth:15,showPlot:false,showLines:activeLines});
chart2.renderNotchBoxes({showNotchBox:false});

2
run.py
Unescape View File

@ -806,8 +806,6 @@ def PreprocessingPredEnsemble():
numberIDRF = []
numberIDGradB = []
print(EnsembleActive)
for el in EnsembleActive:
match = re.match(r"([a-z]+)([0-9]+)", el, re.I)
if match:

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