Anomaly Detection - Local Outlier Factor

Greg_Deckler — Tue, 16 May 2023 16:36:06 GMT

Demonstrates how to implement the Local Outlier Factor (LOF) anomaly detection algorithm in DAX.

LOF Measure = VAR __ID = MAX('Table'[ID]) VAR __Table0 = ALL('Table') VAR __Table = FILTER( GENERATE( SELECTCOLUMNS( __Table0,"ID1",[ID] ) , SELECTCOLUMNS( __Table0,"ID2",[ID] ) ), [ID1] <> [ID2] ) VAR __Table1 = ADDCOLUMNS( __Table, "ID3", IF([ID1] < [ID2], [ID1], [ID2]), "ID4", IF([ID1] > [ID2], [ID1], [ID2]) ) VAR __Table3 = ADDCOLUMNS( DISTINCT( SELECTCOLUMNS( __Table1, "ID1", [ID3], "ID2", [ID4] ) ), "MD", VAR __ID1 = [ID1] VAR __ID2 = [ID2] VAR __x1 = MAXX(FILTER(__Table0, [ID] = __ID1), [X]) VAR __y1 = MAXX(FILTER(__Table0, [ID] = __ID1), [Y]) VAR __x2 = MAXX(FILTER(__Table0, [ID] = __ID2), [X]) VAR __y2 = MAXX(FILTER(__Table0, [ID] = __ID2), [Y]) VAR __Result = ABS(__x1 - __x2) + ABS(__y1 - __y2) RETURN __Result ) VAR __Table4 = ADDCOLUMNS( ADDCOLUMNS( SELECTCOLUMNS( __Table0, "ID",[ID],"X",[X],"Y",[Y] ), "k-MD", VAR __ID = [ID] VAR __Table = FILTER(__Table3, [ID1] = __ID || [ID2] = __ID) VAR __kMD = MAXX(TOPN([k], __Table, [MD],ASC),[MD]) RETURN __kMD ), "K-neighborhood", VAR __kMD = [k-MD] VAR __ID = [ID] VAR __Table = FILTER('Table 2', [ID1] = __ID || [ID2] = __ID) VAR __Result = COUNTROWS( FILTER( __Table, [MD] <= __kMD ) ) RETURN __Result ) VAR __Table5 = ADDCOLUMNS( __Table4, "LRD", VAR __kMD = [k-MD] VAR __Kneighborhood = [K-neighborhood] VAR __ID = [ID] VAR __Table = FILTER('Table 2', ( [ID1] = __ID || [ID2] = __ID ) && [MD] <= __kMD ) VAR __Table1 = ADDCOLUMNS( __Table, "MaxMD", VAR __LookupValue = IF([ID1] = __ID, [ID2], [ID1]) VAR __LookupKMD = MAXX(FILTER(__Table4, [ID] = __LookupValue), [k-MD]) RETURN MAX( [MD], __LookupKMD) ) VAR __Result = DIVIDE( 1, DIVIDE( SUMX( __Table1, [MaxMD] ), __Kneighborhood )) RETURN __Result ) VAR __Table6 = ADDCOLUMNS( __Table5, "LOF", VAR __kMD = [k-MD] VAR __Kneighborhood = [K-neighborhood] VAR __LRD = [LRD] VAR __ID = [ID] VAR __Table = FILTER(__Table3, ( [ID1] = __ID || [ID2] = __ID ) && [MD] <= __kMD ) VAR __IDs = DISTINCT( FILTER( UNION( SELECTCOLUMNS( __Table, "ID", [ID1] ), SELECTCOLUMNS( __Table, "ID", [ID2] ) ), [ID] <> __ID ) ) VAR __Table1 = FILTER( __Table5, [ID] IN __IDs ) VAR __Result = DIVIDE( SUMX( __Table1, [LRD] ), __Kneighborhood ) * DIVIDE(1, __LRD) RETURN __Result ) VAR __Result = MAXX(FILTER(__Table6, [ID] = __ID), [LOF]) RETURN __Result

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Anomaly Detection - Local Outlier Factor