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import { | ||
AucRocChart, | ||
ConfusionMatrixChart, | ||
TimeSeriesChart, | ||
} from "@/features/Train/types/trainTypes"; | ||
import dynamic from "next/dynamic"; | ||
import { Data, XAxisName, YAxisName } from "plotly.js"; | ||
const Plot = dynamic(() => import("react-plotly.js"), { ssr: false }); | ||
|
||
const LINE_CHART_COLORS = ["red", "blue", "green"]; | ||
|
||
const mapMetricToLinePlot = (metric: TimeSeriesChart) => { | ||
const data = []; | ||
for (let i = 0; i < metric.time_series.length; i++) { | ||
const time_series = metric.time_series[i]; | ||
data.push({ | ||
name: time_series.y_name, | ||
x: time_series.x_values, | ||
y: time_series.y_values, | ||
type: "scatter", | ||
mode: "markers", | ||
marker: { color: LINE_CHART_COLORS[i], size: 10 }, | ||
}); | ||
} | ||
return ( | ||
<Plot | ||
data={data as Data[]} | ||
layout={{ | ||
height: 350, | ||
width: 525, | ||
xaxis: { title: metric.time_series[0].x_name }, | ||
// yaxis: { title: "Y axis" }, | ||
title: metric.name, | ||
showlegend: true, | ||
paper_bgcolor: "rgba(0,0,0,0)", | ||
plot_bgcolor: "rgba(0,0,0,0)", | ||
}} | ||
config={{ responsive: true }} | ||
/> | ||
); | ||
}; | ||
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||
const mapMetricToAucRocPlot = (metric: AucRocChart) => { | ||
return ( | ||
<Plot | ||
data={[ | ||
{ | ||
name: "baseline", | ||
x: [0, 1], | ||
y: [0, 1], | ||
type: "scatter", | ||
marker: { color: "grey" }, | ||
line: { | ||
dash: "dash", | ||
}, | ||
}, | ||
...(metric.values.map((x) => ({ | ||
name: `(AUC: ${x[2]})`, | ||
x: x[0] as number[], | ||
y: x[1] as number[], | ||
type: "scatter", | ||
})) as Data[]), | ||
]} | ||
layout={{ | ||
height: 350, | ||
width: 525, | ||
xaxis: { title: "False Positive Rate" }, | ||
yaxis: { title: "True Positive Rate" }, | ||
title: "AUC/ROC Curves for your Deep Learning Model", | ||
showlegend: true, | ||
paper_bgcolor: "rgba(0,0,0,0)", | ||
plot_bgcolor: "rgba(0,0,0,0)", | ||
}} | ||
config={{ responsive: true }} | ||
/> | ||
); | ||
}; | ||
|
||
const mapMetricToConfusionMatrixPlot = (metric: ConfusionMatrixChart) => { | ||
<Plot | ||
data={[ | ||
{ | ||
z: metric.values, | ||
type: "heatmap", | ||
colorscale: [ | ||
[0, "#e6f6fe"], | ||
[1, "#003058"], | ||
], | ||
}, | ||
]} | ||
layout={{ | ||
height: 525, | ||
width: 525, | ||
title: "Confusion Matrix (Last Epoch)", | ||
xaxis: { | ||
title: "Predicted", | ||
}, | ||
yaxis: { | ||
title: "Actual", | ||
autorange: "reversed", | ||
}, | ||
showlegend: true, | ||
annotations: metric.values | ||
.map((row, i) => | ||
row.map((_, j) => ({ | ||
xref: "x1" as XAxisName, | ||
yref: "y1" as YAxisName, | ||
x: j, | ||
y: (i + metric.values.length - 1) % metric.values.length, | ||
text: metric.values[ | ||
(i + metric.values.length - 1) % metric.values.length | ||
][j].toString(), | ||
font: { | ||
color: | ||
metric.values[ | ||
(i + metric.values.length - 1) % metric.values.length | ||
][j] > 0 | ||
? "white" | ||
: "black", | ||
}, | ||
showarrow: false, | ||
})) | ||
) | ||
.flat(), | ||
paper_bgcolor: "rgba(0,0,0,0)", | ||
plot_bgcolor: "rgba(0,0,0,0)", | ||
}} | ||
/>; | ||
}; | ||
|
||
export { | ||
mapMetricToLinePlot, | ||
mapMetricToAucRocPlot, | ||
mapMetricToConfusionMatrixPlot, | ||
}; |
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DLP_EXECUTIONS_BUCKET_NAME = "dlp-executions" |
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