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ppalmesppalmes
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add timeseries mlflow tracking.
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3 files changed

+204
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‎AutoAD/src/AutoAD.jl‎

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@@ -46,4 +46,9 @@ include("carettspredictor.jl")
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using .CaretTSPredictors
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export CaretTSPredictor, carettsdriver
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include("automlflowtsprediction.jl")
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using .AutoMLFlowTSPredictions
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export AutoMLFlowTSPrediction
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export mlftsdriver
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end # module AutoAD

‎AutoAD/src/automlflowanomalydetection.jl‎

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@@ -66,7 +66,7 @@ function (obj::AutoMLFlowAnomalyDetection)(; args...)
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return obj
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end
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function fit!(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame, Y::Vector)
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function fit!(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame, Y::Vector=[])::Nothing
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# start experiment run
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setupautofit!(mlfad)
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# automate anomaly detection
@@ -79,9 +79,10 @@ function fit!(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame, Y::Vector)
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MLF.log_metric("votepercent", autoad.model[:votepercent])
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# log artifacts and end experiment run
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logmlartifact(mlfad)
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return nothing
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end
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function fit(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame, Y::Vector)
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function fit(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame, Y::Vector=[])::Nothing
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mlfcopy = deepcopy(mlfad)
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fit!(mlfcopy, X, Y)
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return mlfcopy
@@ -142,9 +143,9 @@ function transform(mlfad::AutoMLFlowAnomalyDetection, X::DataFrame)
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end
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function mlfaddriver()
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url = "http://localhost:8081"
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url = "http://mlflow.home"
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url = "http://mlflow.isiath.duckdns.org:8082"
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url = "http://localhost:8081"
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X = CSV.read("./data/node_cpu_ratio_rate_5m_1d_1m.csv",DataFrame;header=false)
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‎AutoAD/src/automlflowtsprediction.jl‎

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@@ -0,0 +1,195 @@
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module AutoMLFlowTSPredictions
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using PDFmerger: append_pdf!
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using Plots
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using Statistics
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using Serialization
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import PythonCall
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const PYC = PythonCall
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using CSV
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using DataFrames: DataFrame
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using Random
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using ..AbsTypes
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using ..Utils
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using ..CaretTSPredictors
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import ..AbsTypes: fit, fit!, transform, transform!
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export fit, fit!, transform, transform!
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export mlftsdriver, AutoMLFlowTSPrediction
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const MLF = PYC.pynew()
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const REQ = PYC.pynew()
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function __init__()
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PYC.pycopy!(MLF, PYC.pyimport("mlflow"))
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PYC.pycopy!(REQ, PYC.pyimport("requests"))
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end
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include("./mlflowutils.jl")
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mutable struct AutoMLFlowTSPrediction <: Workflow
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name::String
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model::Dict{Symbol,Any}
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function AutoMLFlowTSPrediction(args=Dict())
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default_args = Dict(
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:name => "AutoTSPredictions",
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:projectname => "AutoTSPredictions",
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:url => "http://localhost:8080",
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:description => "Automated Timeseries Prediction",
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:projecttype => "tsprediction",
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:artifact_name => "AutoTSPredictionModel.bin",
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:impl_args => Dict(
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:name => "autots",
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:learner=>"auto",
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:forecast_horizon=>10
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)
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)
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cargs = nested_dict_merge(default_args, args)
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initmlflowcargs!(cargs)
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cargs[:automodel] = CaretTSPredictor(cargs[:impl_args])
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new(cargs[:name], cargs)
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end
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end
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function AutoMLFlowTSPrediction(name::String, args::Dict)
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AutoMLFlowTSPrediction(Dict(:name => name, args...))
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end
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function AutoMLFlowTSPrediction(name::String; args...)
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AutoMLFlowTSPrediction(Dict(Dict(pairs(args))...))
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end
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function (obj::AutoMLFlowTSPrediction)(; args...)
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model = obj.model
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cargs = nested_dict_merge(model, Dict(pairs(args)))
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obj.model = cargs
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return obj
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end
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function fit!(mlfas::AutoMLFlowTSPrediction, X::DataFrame, Y::Vector=[])::Nothing
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# start experiment run
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setupautofit!(mlfas)
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# automate prediction
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autots = mlfas.model[:automodel]
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tsoutput = fit_transform!(autots, X, Y)
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# save model in memory
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mlfas.model[:automodel] = autots
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# log info to mlflow
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MLF.log_param("TSOutput", tsoutput)
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MLF.log_metric("ForecastHorizon", autots.model[:forecast_horizon])
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# log artifacts and end experiment run
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logmlartifact(mlfas)
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return nothing
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end
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function fit(mlfas::AutoMLFlowTSPrediction, X::DataFrame, Y::Vector=[])::Nothing
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mlfcopy = deepcopy(mlfas)
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fit!(mlfcopy, X, Y)
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return mlfcopy
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end
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function plottroutput(mlfas::AutoMLFlowTSPrediction, Y::Union{Vector,DataFrame})
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data = Y
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votepercent = mlfas.model[:automodel].model[:votepercent]
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tmpdir = tempdir()
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println(tmpdir)
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artifact_plot = joinpath(tmpdir, "plots.pdf")
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artifact_allplots = joinpath(tmpdir, "allplots.pdf")
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rm(artifact_allplots, force=true)
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if votepercent == 0.0
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for ndx in 0.1:0.1:1.0
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strndx = string(ndx)
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coldata = data[:, strndx]
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ndx = findall(x -> x == true, coldata)
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Plots.plot(data[:,1], label="tsdata", title="TS Prediction")
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xlabel!("X")
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ylabel!("Y")
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plp = scatter!(ndx, data[:,1][ndx], label="prediction")
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savefig(plp, artifact_plot)
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append_pdf!(artifact_allplots, artifact_plot, cleanup=true)
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end
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else
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strndx = string(votepercent)
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coldata = data[:, strndx]
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ndx = findall(x -> x == true, coldata)
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Plots.plot(data[:,1], label="tsdata", title="TS Prediction")
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xlabel!("X")
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ylabel!("Y")
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scatter!(ndx, data[:,1][ndx], label="prediction")
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savefig(artifact_allplots)
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end
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MLF.log_artifact(artifact_allplots)
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end
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function transform!(mlfas::AutoMLFlowTSPrediction, X::DataFrame)
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# start experiment run
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Y = autotransform!(mlfas, X)
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# create plots and save them as mlfow artifacts
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# plottroutput(mlfas, Y)
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# end run
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MLF.end_run()
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return Y
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end
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function transform(mlfas::AutoMLFlowTSPrediction, X::DataFrame)
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mlfasc = deepcopy(mlfas)
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return transform!(mlfasc, X)
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end
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function mlftsdriver()
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url = "http://mlflow.home"
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url = "http://mlflow.isiath.duckdns.org:8082"
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url = "http://localhost:8081"
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X = CSV.read("./data/node_cpu_ratio_rate_5m_1d_1m.csv",DataFrame;header=false)
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#X = vcat(5 * cos.(-10:10), sin.(-30:30), 3 * cos.(-10:10), 2 * tan.(-10:10), sin.(-30:30)) |> x -> DataFrame([x], :auto)
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# test all voting percent
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mlfas = AutoMLFlowTSPrediction(Dict(:url => url))
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fit!(mlfas, X)
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#println(Yc |> x -> first(x, 5))
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# # test specific votepercent
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# mlvad = AutoMLFlowTSPrediction(Dict(:url => url, :impl_args => Dict(:votepercent => 0.3)))
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# Yc = fit_transform!(mlvad, X)
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# println(Yc |> x -> first(x, 5))
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#
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# # override default votepercent
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# mlfas = AutoMLFlowTSPrediction(Dict(:url => url))
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# mlfas.model[:automodel](; votepercent=0.5)
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# Yc = fit_transform!(mlfas, X)
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# println(Yc |> x -> first(x, 5))
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#
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#
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# mlfas = AutoMLFlowTSPrediction(Dict(:url => url))
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# mlfas.model[:automodel](; votepercent=0.0)
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# Yc = fit_transform!(mlfas, X)
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# println(Yc |> x -> first(x, 5))
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#
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# ## test prediction using exisiting trained model from artifacts
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# #### alternative 1 to use trained model for transform
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# mlvad = AutoMLFlowTSPrediction(Dict(:url => url))
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# Yc = fit_transform!(mlvad, X)
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# run_id = mlvad.model[:run_id]
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# newmlad = AutoMLFlowTSPrediction(Dict(:run_id => run_id, :url => url, :impl_args => Dict(:votepercent => 0.5)))
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# newmlad.model[:automodel](; votepercent=0.2)
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# Yn = transform!(newmlad, X)
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# println(Yn |> x -> first(x, 5))
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#
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# ## alternative 2 to use trained model for transform
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# mlvad = AutoMLFlowTSPrediction(Dict(:url => url))
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# Yc = fit_transform!(mlvad, X)
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# run_id = mlvad.model[:run_id]
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# votepercent = 0.3
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# newmlad = AutoMLFlowTSPrediction(Dict(:url => url))
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# newmlad(; run_id)
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# newmlad.model[:automodel](; votepercent)
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# Yn = transform!(newmlad, X)
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# println(Yn |> x -> first(x, 5))
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return nothing
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end
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end

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