fix deps in Project.toml

Signed-off-by: Paulito Palmes, PhD <ppalmes@gmail.com>
IBM · Apr 7, 2023 · 26d5cd1 · 26d5cd1 · ppalmes · Apr 7, 2023
2 parents 8229595 + fe0206a
commit 26d5cd1
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diff --git a/Project.toml b/Project.toml
@@ -1,20 +1,21 @@
 name = "TSML"
 uuid = "198dc43e-9e51-5cd7-9d40-d9794d335912"
 authors = ["Paulito Palmes <ppalmes@gmail.com>"]
-version = "2.7.4"
+version = "2.7.5"
 
 [deps]
 AMLPipelineBase = "e3c3008a-8869-4d53-9f34-c96f99c8a2b6"
 ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
 Impute = "f7bf1975-0170-51b9-8c5f-a992d46b9575"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLDataUtils = "cc2ba9b6-d476-5e6d-8eaf-a92d5412d41d"
 MultivariateStats = "6f286f6a-111f-5878-ab1e-185364afe411"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
@@ -29,7 +30,6 @@ Impute = "0.5, 0.6"
 MLDataUtils = "0.2, 0.3, 0.4, 0.5"
 MultivariateStats = "0.5, 0.6, 0.7, 0.8, 0.9, 0.10"
 Plots = "1.38"
-RecipesBase = "1.1"
 StatsBase = "0.29, 0.30, 0.31, 0.32, 0.33"
 julia = "1"
 

diff --git a/README.md b/README.md
@@ -1,15 +1,22 @@
-<div align="center"> <img
-src="https://ibm.github.io/TSML.jl/tsmllogo/tsmllogo13.png"
-alt="TSML Logo" width="250"></img> </div>
+<div align="center"> 
 
-![Overall Stats](https://github-readme-stats.vercel.app/api?username=ppalmes&count_private=true&show_icons=true&hide=contribs)
+![Visitor](https://visitor-badge.laobi.icu/badge?page_id=ppalmes.TSML.jl)
+
+<img
+src="https://ibm.github.io/TSML.jl/tsmllogo/tsmllogo13.png"
+alt="TSML Logo" width="250">
+</img> 
 
 [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/7094/badge)](https://bestpractices.coreinfrastructure.org/projects/7094)
 
+![Overall Stats](https://github-readme-stats.vercel.app/api?username=ppalmes&count_private=true&show_icons=true&hide=contribs)
+
 | **Documentation** | **Build Status** | **Help** |
 |:---:|:---:|:---:|
 | [![][docs-dev-img]][docs-dev-url] [![][docs-stable-img]][docs-stable-url] | [![][travis-img]][travis-url] | [![][slack-img]][slack-url] [![][gitter-img]][gitter-url] |
 
+</div>
+
 #### Stargazers over time
 [![Stargazers over time](https://starchart.cc/IBM/TSML.jl.svg)](https://starchart.cc/IBM/TSML.jl)
 
@@ -141,7 +148,7 @@ chkoutlier   = Outliernicer(Dict(:dateinterval => Dates.Hour(1))) # normalize ou
 
 - ##### Pipeline to load csv data
 ```julia
-pipexpr = @pipeline csvread
+pipexpr = csvread
 data    = fit_transform!(pipexpr)
 first(data,5)
 
@@ -158,7 +165,7 @@ first(data,5)
 
 - ##### Pipeline to aggregate and check statistics
 ```julia
-pipexpr = @pipeline csvread |> aggregate |> chkstats
+pipexpr = csvread |> aggregate |> chkstats
 stats   = fit_transform!(pipexpr)
 
 1×26 DataFrame. Omitted printing of 19 columns
@@ -171,7 +178,7 @@ Note: `fit_transform!` is equivalent to calling in sequence `fit!` and `transfor
 
  - ##### Pipeline to aggregate, impute, and check stats
 ```julia
-pipexpr = @pipeline csvread |> aggregate |> impute |> chkstats
+pipexpr = csvread |> aggregate |> impute |> chkstats
 stats2  = fit_transform!(pipexpr)
 
 1×26 DataFrame. Omitted printing of 19 columns
@@ -183,7 +190,7 @@ stats2  = fit_transform!(pipexpr)
 
 - ##### Pipeline to aggregate, impute, and normalize monotonic data
 ```julia
-pipexpr = @pipeline csvread |> aggregate |> impute |> normtonic  
+pipexpr = csvread |> aggregate |> impute |> normtonic  
 fit_transform!(pipexpr)
 
 8761×2 DataFrame
@@ -224,7 +231,7 @@ ts      = DataFrame(Date=datets,Value=valuets)
 args     = Dict(:ahead => 24,:size => 24,:stride => 5)
 datemtr  = Dateifier(args)
 valuemtr = Matrifier(args)
-ppl      = @pipeline datemtr + valuemtr
+ppl      = datemtr + valuemtr
 dateval  = fit_transform!(ppl,ts)
 first(dateval,5)
 

diff --git a/docs/notebooks/JuliaCon2019.jl.ipynb b/docs/notebooks/JuliaCon2019.jl.ipynb
@@ -304,7 +304,7 @@
     }
    ],
    "source": [
-    "pltr=Plotter(Dict(:interactive => true))\n",
+    "pltr=Plotter(Dict(:pdfoutput => true))\n",
     "\n",
     "mypipeline = pltr\n",
     "\n",

diff --git a/docs/notebooks/StaticPlotting.jl.ipynb b/docs/notebooks/StaticPlotting.jl.ipynb
diff --git a/docs/src/man/dateproc.md b/docs/src/man/dateproc.md
@@ -79,7 +79,7 @@ Another way is to use the symbolic pipeline to
 describe the transformation and concatenation in
 just one line of expression.
 ```@example dateifier
-ppl = @pipeline dtr + mtr
+ppl = dtr + mtr
 features = fit_transform!(ppl,X)
 nothing #hide
 ```

diff --git a/docs/src/tutorial/monotonic_plotting.md b/docs/src/tutorial/monotonic_plotting.md
@@ -17,11 +17,12 @@ We can use the `Plotter` filter to visualize the generated data.
 using TSML
 
 Random.seed!(123)
-pltr = Plotter(Dict(:interactive => false,:pdfoutput => false))
+pltr = Plotter(Dict(:pdfoutput => false))
 mdates = DateTime(2017,12,1,1):Dates.Hour(1):DateTime(2017,12,31,10) |> collect
 mvals = rand(length(mdates)) |> cumsum
 df =  DataFrame(Date=mdates ,Value = mvals)
-fit_transform!(pltr,df)
+fit_transform!(pltr,df);
+nothing #hide
 ```
 
 Now that we have a monotonic data, let's use the `Monotonicer` to normalize and plot the result:
@@ -31,9 +32,10 @@ using TSML
 
 mono = Monotonicer(Dict())
 
-pipeline = @pipeline mono |> pltr
+pipeline = mono |> pltr
 
-res=fit_transform!(pipeline,df)
+res=fit_transform!(pipeline,df);
+nothing #hide
 ```
 
 ## Real Data Example
@@ -61,10 +63,10 @@ monofilecsv = CSVDateValReader(Dict(:filename=>monofile,:dateformat=>"dd/mm/yyyy
 dailymonofilecsv = CSVDateValReader(Dict(:filename=>dailymonofile,:dateformat=>"dd/mm/yyyy HH:MM"))
 
 valgator = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))
-valnner = DateValNNer(Dict(:dateinterval=>Dates.Hour(1)))
+valnner = DateValLinearImputer(Dict(:dateinterval=>Dates.Hour(1)))
 stfier = Statifier(Dict(:processmissing=>true))
 mono = Monotonicer(Dict())
-pltr = Plotter(Dict(:interactive => false))
+pltr = Plotter(Dict(:pdfoutput => false))
 nothing #hide
 ```
 
@@ -75,16 +77,18 @@ Let's test by feeding the regular time series type to the pipeline. We expect th
 
 - Pipeline with `Monotonicer`: regular time series
 ```@example mono
-pipeline = @pipeline regularfilecsv |> valgator |> valnner |> mono |> pltr
+pipeline = regularfilecsv |> valgator |> valnner |> mono |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 - Pipeline without `Monotonicer`: regular time series
 ```@example mono
-pipeline = @pipeline regularfilecsv |> valgator |> valnner |> pltr
+pipeline = regularfilecsv |> valgator |> valnner |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 Notice that the plots are the same with or without the `Monotonicer` instance.
@@ -94,16 +98,18 @@ Let's now feed the same pipeline with a monotonic csv data.
 
 - Pipeline without `Monotonicer`: monotonic time series
 ```@example mono
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> pltr
+pipeline = monofilecsv |> valgator |> valnner |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 - Pipeline with `Monotonicer`: monotonic time series
 ```@example mono
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> mono |> pltr
+pipeline = monofilecsv |> valgator |> valnner |> mono |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 Notice that without the `Monotonicer` instance, the data is monotonic. Applying
@@ -118,17 +124,19 @@ We can use the `Outliernicer` filter to remove outliers. Let's apply this filter
 using TSML: Outliernicer
 outliernicer = Outliernicer(Dict(:dateinterval=>Dates.Hour(1)));
 
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> mono |>  outliernicer |> pltr
-fit_transform!(pipeline)
+pipeline = monofilecsv |> valgator |> valnner |> mono |>  outliernicer |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 ## Daily Monotonic TS Processing
 Lastly, let's feed the daily monotonic data using similar pipeline and examine its plot.
 
 - Pipeline without `Monotonicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 This plot is characterized by monotonically increasing trend but resets to certain baseline value 
@@ -138,8 +146,9 @@ the correct normalization.
 
 - Pipeline with `Monotonicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> mono |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> mono |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 While the `Monotonicer` filter is able to transform the data into a regular time series,
@@ -149,8 +158,9 @@ Let's remove the outliers by applying the `Outliernicer` filter and examine the
 
 - Pipeline with `Monotonicer` and `Outliernicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> mono |> outliernicer |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> mono |> outliernicer |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 The `Outliernicer` filter effectively removed the outliers as shown in the plot.
diff --git a/docs/src/tutorial/monotonic_plotting_pdf.md → docs/src/tutorial/monotonic_plotting_pdf.md1 b/docs/src/tutorial/monotonic_plotting_pdf.md → docs/src/tutorial/monotonic_plotting_pdf.md1
@@ -17,11 +17,12 @@ We can use the `Plotter` filter to visualize the generated data.
 using TSML
 
 Random.seed!(123)
-pltr = Plotter(Dict(:interactive => false,:pdfoutput => true))
+pltr = Plotter(Dict(:pdfoutput => true))
 mdates = DateTime(2017,12,1,1):Dates.Hour(1):DateTime(2017,12,31,10) |> collect
 mvals = rand(length(mdates)) |> cumsum
 df =  DataFrame(Date=mdates ,Value = mvals)
-fit_transform!(pltr,df)
+fit_transform!(pltr,df);
+nothing #hide
 ```
 
 Now that we have a monotonic data, let's use the `Monotonicer` to normalize and plot the result:
@@ -31,9 +32,10 @@ using TSML
 
 mono = Monotonicer(Dict())
 
-pipeline = @pipeline mono |> pltr
+pipeline = mono |> pltr
 
 res=fit_transform!(pipeline,df)
+nothing #hide
 ```
 
 ## Real Data Example
@@ -61,10 +63,10 @@ monofilecsv = CSVDateValReader(Dict(:filename=>monofile,:dateformat=>"dd/mm/yyyy
 dailymonofilecsv = CSVDateValReader(Dict(:filename=>dailymonofile,:dateformat=>"dd/mm/yyyy HH:MM"))
 
 valgator = DateValgator(Dict(:dateinterval=>Dates.Hour(1)))
-valnner = DateValNNer(Dict(:dateinterval=>Dates.Hour(1)))
+valnner = DateValLinearImputer(Dict(:dateinterval=>Dates.Hour(1)))
 stfier = Statifier(Dict(:processmissing=>true))
 mono = Monotonicer(Dict())
-pltr = Plotter(Dict(:interactive => false))
+pltr = Plotter(Dict(:pdfoutput => true))
 nothing #hide
 ```
 
@@ -75,16 +77,18 @@ Let's test by feeding the regular time series type to the pipeline. We expect th
 
 - Pipeline with `Monotonicer`: regular time series
 ```@example mono
-pipeline = @pipeline regularfilecsv |> valgator |> valnner |> mono |> pltr
+pipeline = regularfilecsv |> valgator |> valnner |> mono |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 - Pipeline without `Monotonicer`: regular time series
 ```@example mono
-pipeline = @pipeline regularfilecsv |> valgator |> valnner |> pltr
+pipeline = regularfilecsv |> valgator |> valnner |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 Notice that the plots are the same with or without the `Monotonicer` instance.
@@ -94,16 +98,18 @@ Let's now feed the same pipeline with a monotonic csv data.
 
 - Pipeline without `Monotonicer`: monotonic time series
 ```@example mono
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> pltr
+pipeline = monofilecsv |> valgator |> valnner |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 - Pipeline with `Monotonicer`: monotonic time series
 ```@example mono
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> mono |> pltr
+pipeline = monofilecsv |> valgator |> valnner |> mono |> pltr
 
-fit_transform!(pipeline)
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 Notice that without the `Monotonicer` instance, the data is monotonic. Applying
@@ -118,17 +124,19 @@ We can use the `Outliernicer` filter to remove outliers. Let's apply this filter
 using TSML: Outliernicer
 outliernicer = Outliernicer(Dict(:dateinterval=>Dates.Hour(1)));
 
-pipeline = @pipeline monofilecsv |> valgator |> valnner |> mono |>  outliernicer |> pltr
-fit_transform!(pipeline)
+pipeline = monofilecsv |> valgator |> valnner |> mono |>  outliernicer |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 ## Daily Monotonic TS Processing
 Lastly, let's feed the daily monotonic data using similar pipeline and examine its plot.
 
 - Pipeline without `Monotonicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 This plot is characterized by monotonically increasing trend but resets to certain baseline value 
@@ -138,8 +146,9 @@ the correct normalization.
 
 - Pipeline with `Monotonicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> mono |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> mono |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 While the `Monotonicer` filter is able to transform the data into a regular time series,
@@ -149,8 +158,9 @@ Let's remove the outliers by applying the `Outliernicer` filter and examine the
 
 - Pipeline with `Monotonicer` and `Outliernicer`: daily monotonic time series
 ```@example mono
-pipeline = @pipeline dailymonofilecsv |> valgator |> valnner |> mono |> outliernicer |> pltr
-fit_transform!(pipeline)
+pipeline = dailymonofilecsv |> valgator |> valnner |> mono |> outliernicer |> pltr
+fit_transform!(pipeline);
+nothing #hide
 ```
 
 The `Outliernicer` filter effectively removed the outliers as shown in the plot.