add autoanomalydetector

Author: ppalmes

AutoAI/src/AutoAI.jl

@@ -40,4 +40,8 @@ include("automlflowregression.jl")
 using .AutoMLFlowRegressions
 export mlfregdriver
 
+include("autoanomalydetector.jl")
+using .AutoAnomalyDetectors
+export autoaddriver
+
 end # module AutoAI

AutoAI/src/autoanomalydetector.jl

@@ -0,0 +1,92 @@
+module AutoAnomalyDetectors
+# classification search blocks
+
+
+using Distributed
+using AutoMLPipeline
+using DataFrames: DataFrame, nrow, rename!
+using AutoMLPipeline: score
+using Random
+using Statistics
+using ..AbsTypes
+using ..Utils
+using ..CaretAnomalyDetectors
+import ..CaretAnomalyDetectors.caretadlearner_dict
+using ..SKAnomalyDetectors
+
+import ..AbsTypes: fit, fit!, transform, transform!
+export fit, fit!, transform, transform!
+export AutoAnomalyDetector, autoaddriver
+
+# define customized type
+mutable struct AutoAnomalyDetector <: Workflow
+  name::String
+  model::Dict{Symbol,Any}
+
+  function AutoAnomalyDetector(args=Dict())
+    default_args = Dict(
+      :name => "autoad",
+      :votepercent => 0.3,
+      :impl_args => Dict()
+    )
+    cargs = nested_dict_merge(default_args, args)
+    cargs[:name] = cargs[:name] * "_" * randstring(3)
+    new(cargs[:name], cargs)
+  end
+end
+
+function fit!(autodt::AutoAnomalyDetector, X::DataFrame, Y::Vector)
+  return nothing
+end
+
+function fit(clfb::AutoAnomalyDetector, X::DataFrame, Y::Vector)
+  return nothing
+end
+
+function transform!(autodt::AutoAnomalyDetector, X::DataFrame)
+  # detect anomalies using caret
+  dfres1 = DataFrame()
+  for learner in keys(caretadlearner_dict)
+    model = CaretAnomalyDetector(learner)
+    res = fit_transform!(model, X)
+    mname = string(learner)
+    dfres1 = hcat(dfres1, DataFrame(mname => res; makeunique=true))
+  end
+
+  #detect anomalies using scikitlearn
+  iso = SKAnomalyDetector("IsolationForest")
+  eli = SKAnomalyDetector("EllipticEnvelope")
+  osvm = SKAnomalyDetector("OneClassSVM")
+  lcl = SKAnomalyDetector("LocalOutlierFactor")
+  isores = fit_transform!(iso, X)
+  elires = fit_transform!(eli, X)
+  osvmres = fit_transform!(osvm, X)
+  lclres = fit_transform!(lcl, X)
+  dfres2 = DataFrame(iso=isores, eli=elires, osvm=osvmres, lcl=lclres)
+
+  # combine results and get mean anomaly for each row
+  mdf = hcat(dfres1, dfres2)
+  mdfm = hcat(mdf, DataFrame(admean=mean.(eachrow(mdf))))
+  # filter anomalies based on mean cut-off
+  # cutoff = autodt.model[:votepercent]
+  dfad = DataFrame()
+  for cutoff in 0.1:0.1:1.0
+    ndx = map(x -> x >= cutoff, mdfm.admean)
+    dfad = hcat(dfad, DataFrame(n=ndx); makeunique=true)
+  end
+  names = map(x -> string(x), 0.1:0.1:1.0)
+  rename!(dfad, names)
+  return dfad
+end
+
+function transform(autodt::AutoAnomalyDetector, X::DataFrame)
+end
+
+function autoaddriver()
+  autoaddt = AutoAnomalyDetector(Dict(:votepercent => 0.1))
+  X = vcat(5 * cos.(-10:10), sin.(-30:30), 3 * cos.(-10:10), 2 * tan.(-10:10), sin.(-30:30)) |> x -> DataFrame([x], :auto)
+  fit_transform!(autoaddt, X)
+end
+
+
+end