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105 lines
4.0 KiB
105 lines
4.0 KiB
[boxplot]
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definition = untable _x field_name value\
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| stats min exactperc25 median exactperc75 max by field_name\
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| untable field_name calculations value\
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| xyseries calculations field_name value\
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| eval calculations = rtrim(calculations, "(value)")
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[regressionstatistics(2)]
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args = a, p
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definition = rename "$a$" as _actual, "$p$" as _predicted | eventstats avg(_actual) as _avgActual | eval _actualMinusAvg = _actual - _avgActual, _residual = _actual - _predicted | stats sumsq(_actualMinusAvg) as _sumsqActualMinusAvg, sumsq(_residual) as _sumsqResidual, count(_residual) as _sampleCount | eval _sumsqActualMinusAvg=round(_sumsqActualMinusAvg,6), _sumsqResidual = round(_sumsqResidual, 6) | eval rSquared = if(_sumsqActualMinusAvg == 0, "NaN", round(1 - _sumsqResidual / _sumsqActualMinusAvg, 4)), RMSE = round(sqrt(_sumsqResidual / _sampleCount), 2) | table rSquared RMSE
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[confusionmatrix(2)]
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args = a, p
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definition = rename "$a$" as actual, "$p$" as predicted\
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| stats count by actual predicted\
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| appendpipe [ eval predicted=actual, count=0 ]\
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| stats sum(count) as count by actual predicted\
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| xyseries actual predicted count\
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| rename * as "Predicted *"\
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| rename "Predicted $a$" as "Actual $a$"\
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| fillnull value=0
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iseval = 0
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[classificationreport(2)]
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args = a, p
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definition = rename "$a$" as actual, "$p$" as predicted\
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| stats count by actual predicted\
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| xyseries actual predicted count\
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| fillnull\
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| untable actual predicted count\
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| stats sum(eval(if(actual == predicted, count, 0))) as t sum(eval(if(actual != predicted, count, 0))) as f by actual predicted\
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| eventstats sum(eval(if(actual==predicted, t, 0))) as tp sum(eval(if(actual!=predicted, f, 0))) as fn by actual\
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| eventstats sum(eval(if(actual!=predicted, f, 0))) as fp by predicted\
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| eval count=if(actual==predicted,t,f), fp=if(actual==predicted, fp, 0), fn=if(actual==predicted, fn,0), tp=if(actual==predicted, tp, 0)\
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| stats sum(count) as count sum(t) as t sum(f) as f sum(tp) as tp sum(fn) as fn sum(fp) as fp by actual\
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| eventstats sum(count) as total\
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| eval precision=tp/(tp+fp)\
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| eval recall=tp/(tp+fn)\
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| eval f1=2*precision*recall/(precision+recall)\
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| eval accuracy=t/count\
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| fillnull\
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| appendpipe [ stats sum(count) as count sum(eval(accuracy*count/total)) as accuracy sum(eval(precision*count/total)) as precision sum(eval(recall*count/total)) as recall sum(eval(f1*count/total)) as f1 | eval actual="Weighted Average" ]\
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| rename actual as class\
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| table class accuracy precision recall f1 count
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iseval = 0
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[classificationstatistics(2)]
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args = a, p
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definition = `classificationreport("$a$", "$p$")`\
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| tail 1\
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| eval precision=round(precision, 2), recall=round(recall, 2), accuracy=round(accuracy, 2), f1=round(f1, 2)
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iseval = 0
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[modvizpredict(6)]
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args= v, a, f, h, p, ci
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definition = predict "$v$" as prediction algorithm=$a$ future_timespan=$f$ holdback=$h$ $p$ lower$ci$=lower$ci$ upper$ci$=upper$ci$ | eval _ft=$f$, _hb=$h$, _vars="$v$", _ci=$ci$
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[forecastviz(4)]
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args = ft, hb, v, ci
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definition = eval _ft=$ft$, _hb=$hb$, _vars="$v$", _ci=$ci$
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[smartforecastviz(1)]
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args = v1
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definition = eval _vars="$v1$"
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[smartforecastviz(2)]
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args = v1, v2
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definition = eval _vars=mvappend("$v1$", "$v2$")
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[smartforecastviz(3)]
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args = v1, v2, v3
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definition = eval _vars=mvappend("$v1$", "$v2$", "$v3$")
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[smartforecastviz(4)]
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args = v1, v2, v3, v4
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definition = eval _vars=mvappend("$v1$", "$v2$", "$v3$", "$v4$")
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[smartforecastviz(5)]
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args = v1, v2, v3, v4, v5
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definition = eval _vars=mvappend("$v1$", "$v2$", "$v3$", "$v4$", "$v5$")
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[histogram(2)]
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args = var, bins
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definition = bin "$var$" bins=$bins$ | stats count by "$var$" | makecontinuous "$var$" | fillnull count
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[splitby(1)]
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args = s
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definition = eval _split_by="$s$"
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[splitby(2)]
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args = s1, s2
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definition = eval _split_by=mvappend("$s1$", "$s2$")
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[splitby(3)]
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args = s1, s2, s3
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definition = eval _split_by=mvappend("$s1$", "$s2$", "$s3$")
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[splitby(4)]
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args = s1, s2, s3, s4
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definition = eval _split_by=mvappend("$s1$", "$s2$", "$s3$", "$s4$")
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[splitby(5)]
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args = s1, s2, s3, s4, s5
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definition = eval _split_by=mvappend("$s1$", "$s2$", "$s3$", "$s4$", "$s5$")
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