From 85cfc543c5af133ace7feabe109038488e63317d Mon Sep 17 00:00:00 2001
From: Filipe Braida <filipebraida@gmail.com>
Date: Sun, 19 Nov 2017 23:26:18 -0200
Subject: [PATCH 1/2] Rename package PLS to PLSRegressor

---
 .travis.yml                     |  6 ++--
 LICENSE.md                      |  2 +-
 README.md                       | 50 ++++++++++++++++-----------------
 appveyor.yml                    |  4 +--
 experiments/curve.jl            |  6 ++--
 experiments/housing.jl          | 12 ++++----
 src/{PLS.jl => PLSRegressor.jl} |  2 +-
 src/method.jl                   |  2 +-
 test/kpls_test.jl               | 40 +++++++++++++-------------
 test/pls1_test.jl               | 50 ++++++++++++++++-----------------
 test/pls2_test.jl               | 42 +++++++++++++--------------
 test/runtests.jl                |  2 +-
 test/utils_test.jl              | 30 ++++++++++----------
 13 files changed, 124 insertions(+), 124 deletions(-)
 rename src/{PLS.jl => PLSRegressor.jl} (92%)

diff --git a/.travis.yml b/.travis.yml
index cf7213b..c95a6e2 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -27,9 +27,9 @@ addons:
 
 ## uncomment the following lines to override the default test script
 #script:
-#  - julia -e 'Pkg.clone(pwd()); Pkg.build("PLS"); Pkg.test("PLS"; coverage=true)'
+#  - julia -e 'Pkg.clone(pwd()); Pkg.build("PLSRegressor"); Pkg.test("PLSRegressor"; coverage=true)'
 after_success:
   # push coverage results to Coveralls
-  - julia -e 'cd(Pkg.dir("PLS")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
+  - julia -e 'cd(Pkg.dir("PLSRegressor")); Pkg.add("Coverage"); using Coverage; Coveralls.submit(Coveralls.process_folder())'
   # push coverage results to Codecov
-  - julia -e 'cd(Pkg.dir("PLS")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'
+  - julia -e 'cd(Pkg.dir("PLSRegressor")); Pkg.add("Coverage"); using Coverage; Codecov.submit(Codecov.process_folder())'
diff --git a/LICENSE.md b/LICENSE.md
index 0f12ade..480994f 100644
--- a/LICENSE.md
+++ b/LICENSE.md
@@ -1,4 +1,4 @@
-The PLS.jl package is licensed under the MIT "Expat" License:
+The PLSRegressor.jl package is licensed under the MIT "Expat" License:
 
 > Copyright (c) 2017: Leandro Alvim.
 >
diff --git a/README.md b/README.md
index 98da09f..d436a42 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-PLS.jl
+PLSRegressor.jl
 ======
 
 A Partial Least Squares Regressor package. Contains PLS1, PLS2 and Kernel PLS2 NIPALS algorithms.
@@ -9,41 +9,41 @@ Can be used mainly for regression. However, for classification task, binarizing
 |:-------------------------------:|:-----------------------------------------:|
 | [![][pkg-0.6-img]][pkg-0.6-url] | [![][travis-img]][travis-url] [![][codecov-img]][codecov-url] |
 
-[travis-img]: https://travis-ci.org/lalvim/PLS.jl.svg?branch=master
-[travis-url]: https://travis-ci.org/lalvim/PLS.jl
+[travis-img]: https://travis-ci.org/lalvim/PLSRegressor.jl.svg?branch=master
+[travis-url]: https://travis-ci.org/lalvim/PLSRegressor.jl
 
-[codecov-img]: http://codecov.io/github/lalvim/PLS.jl/coverage.svg?branch=master
-[codecov-url]: http://codecov.io/github/lalvim/PLS.jl?branch=master
+[codecov-img]: http://codecov.io/github/lalvim/PLSRegressor.jl/coverage.svg?branch=master
+[codecov-url]: http://codecov.io/github/lalvim/PLSRegressor.jl?branch=master
 
-[issues-url]: https://github.com/lalvim/PLS.jl/issues
+[issues-url]: https://github.com/lalvim/PLSRegressor.jl/issues
 
-[pkg-0.6-img]: http://pkg.julialang.org/badges/PLS_0.6.svg
-[pkg-0.6-url]: http://pkg.julialang.org/?pkg=PLS&ver=0.6
-[pkg-0.7-img]: http://pkg.julialang.org/badges/PLS_0.7.svg
-[pkg-0.7-url]: http://pkg.julialang.org/?pkg=PLS&ver=0.7
+[pkg-0.6-img]: http://pkg.julialang.org/badges/PLSRegressor_0.6.svg
+[pkg-0.6-url]: http://pkg.julialang.org/?pkg=PLSRegressor&ver=0.6
+[pkg-0.7-img]: http://pkg.julialang.org/badges/PLSRegressor_0.7.svg
+[pkg-0.7-url]: http://pkg.julialang.org/?pkg=PLSRegressor&ver=0.7
 
 Install
 =======
 
-    Pkg.add("PLS")
+    Pkg.add("PLSRegressor")
 
 Using
 =====
 
-    using PLS
+    using PLSRegressor
 
 Examples
 ========
 
-    using PLS
+    using PLSRegressor
 
     # learning a single target
     X_train        = [1 2; 2 4; 4 6.0]
     Y_train        = [4; 6; 8.0]
     X_test         = [6 8; 8 10; 10 12.0]
 
-    model          = PLS.fit(X_train,Y_train,nfactors=2)
-    Y_test         = PLS.predict(model,X_test)
+    model          = PLSRegressor.fit(X_train,Y_train,nfactors=2)
+    Y_test         = PLSRegressor.predict(model,X_test)
 
     print("[PLS1] mae error : $(mean(abs.(Y_test .- Y_pred)))")
 
@@ -53,23 +53,23 @@ Examples
     Y_train        = [2 4;4 6;6 8.0]
     X_test         = [6 8; 8 10; 10 12.0]
 
-    model          = PLS.fit(X_train,Y_train,nfactors=2)
-    Y_test         = PLS.predict(model,X_test)
+    model          = PLSRegressor.fit(X_train,Y_train,nfactors=2)
+    Y_test         = PLSRegressor.predict(model,X_test)
 
     print("[PLS2] mae error : $(mean(abs.(Y_test .- Y_pred)))")
 
     # nonlinear learning with multiple targets
-    model          = PLS.fit(X_train,Y_train,nfactors=2,kernel="rbf",width=0.1)
-    Y_test         = PLS.predict(model,X_test)
+    model          = PLSRegressor.fit(X_train,Y_train,nfactors=2,kernel="rbf",width=0.1)
+    Y_test         = PLSRegressor.predict(model,X_test)
 
     print("[KPLS] mae error : $(mean(abs.(Y_test .- Y_pred)))")
 
 
     # if you want to save your model
-    PLS.save(model,filename="/tmp/pls_model.jld")
+    PLSRegressor.save(model,filename="/tmp/pls_model.jld")
 
     # if you want to load back your model
-    model = PLS.load(filename="/tmp/pls_model.jld")
+    model = PLSRegressor.load(filename="/tmp/pls_model.jld")
 
 
 What is Implemented
@@ -87,7 +87,7 @@ What is Upcoming
 Method Description
 =======
 
-* PLS.fit - learns from input data and its related single target
+* PLSRegressor.fit - learns from input data and its related single target
     * X::Matrix{:<AbstractFloat} - A matrix that columns are the features and rows are the samples
     * Y::Vector{:<AbstractFloat} - A vector with float values.
     * nfactors::Int = 10 - The number of latent variables to explain the data.
@@ -96,8 +96,8 @@ Method Description
     * kernel::AbstractString = "rbf" - use a non linear kernel.
     * width::AbstractFloat   = 1.0 - If you want to z-score columns. Recommended if not z-scored yet.
 
-* PLS.transform - predicts using the learnt model extracted from fit.
-    * model::PLS.Model - A PLS model learnt from fit.
+* PLSRegressor.transform - predicts using the learnt model extracted from fit.
+    * model::PLSRegressor.Model - A PLS model learnt from fit.
     * X::Matrix{:<AbstractFloat} - A matrix that columns are the features and rows are the samples.
     * copydata::Bool = true - If you want to use the same input matrix or a copy.
 
@@ -123,5 +123,5 @@ regression. Chemometrics and Intelligent Laboratory Systems, 18: 251–
 License
 =======
 
-The PLS.jl is free software: you can redistribute it and/or modify it under the terms of the MIT "Expat"
+The PLSRegressor.jl is free software: you can redistribute it and/or modify it under the terms of the MIT "Expat"
 License. A copy of this license is provided in ``LICENSE.md``
diff --git a/appveyor.yml b/appveyor.yml
index 0cab5ab..1a05f7f 100644
--- a/appveyor.yml
+++ b/appveyor.yml
@@ -41,7 +41,7 @@ build_script:
 # Need to convert from shallow to complete for Pkg.clone to work
   - IF EXIST .git\shallow (git fetch --unshallow)
   - C:\projects\julia\bin\julia -e "versioninfo();
-      Pkg.clone(pwd(), \"PLS\"); Pkg.build(\"PLS\")"
+      Pkg.clone(pwd(), \"PLSRegressor\"); Pkg.build(\"PLSRegressor\")"
 
 test_script:
-  - C:\projects\julia\bin\julia -e "Pkg.test(\"PLS\")"
+  - C:\projects\julia\bin\julia -e "Pkg.test(\"PLSRegressor\")"
diff --git a/experiments/curve.jl b/experiments/curve.jl
index 9efe738..ff58d2a 100644
--- a/experiments/curve.jl
+++ b/experiments/curve.jl
@@ -4,7 +4,7 @@
 
 ### Example extracted from a python KPLS implementation: https://github.com/jhumphry/regressions/blob/master/examples/kpls_example.py
 
-using PLS
+using PLSRegressor
 using Gadfly
 
 srand(1)
@@ -25,8 +25,8 @@ global best_g = 10
 for g in [1,2],
     w in linspace(0.01,3,10)
     print(".")
-    model      = PLS.fit(X,Y,centralize=true,nfactors=g,kernel="rbf",width=w)
-    Y_pred     = PLS.predict(model,X)
+    model      = PLSRegressor.fit(X,Y,centralize=true,nfactors=g,kernel="rbf",width=w)
+    Y_pred     = PLSRegressor.predict(model,X)
     mae = mean(abs.(Y .- Y_pred))
     if mae < min_mae
        min_mae = mae
diff --git a/experiments/housing.jl b/experiments/housing.jl
index 9063fd3..3740bc9 100644
--- a/experiments/housing.jl
+++ b/experiments/housing.jl
@@ -1,7 +1,7 @@
 using MultivariateStats
-using PLS
+using PLSRegressor
 
-const defdir = PLS.dir("datasets")
+const defdir = PLSRegressor.dir("datasets")
 
 function gethousingdata(dir, filename)
     url = "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data"
@@ -38,8 +38,8 @@ end
 
 (xtrn, ytrn, xtst, ytst) = loaddata()
 
-model    = PLS.fit(xtrn, ytrn, nfactors = 3)
-pred     = PLS.predict(model, xtst)
+model    = PLSRegressor.fit(xtrn, ytrn, nfactors = 3)
+pred     = PLSRegressor.predict(model, xtst)
 
 
 println("[PLS] mae error :", mean(abs.(ytst .- pred)))
@@ -51,5 +51,5 @@ yp = xtst * a + b
 println("[LLS] mae error :",mean(abs.(ytst .- yp)))
 
 ### if you want to save or load model use this
-#PLS.save(model,filename="/tmp/pls_model.jld",modelname="pls_model")
-#model = PLS.load(filename="/tmp/pls_model.jld",modelname="pls_model")
+#PLSRegressor.save(model,filename="/tmp/pls_model.jld",modelname="pls_model")
+#model = PLSRegressor.load(filename="/tmp/pls_model.jld",modelname="pls_model")
diff --git a/src/PLS.jl b/src/PLSRegressor.jl
similarity index 92%
rename from src/PLS.jl
rename to src/PLSRegressor.jl
index 0651d7f..35b409d 100644
--- a/src/PLS.jl
+++ b/src/PLSRegressor.jl
@@ -1,5 +1,5 @@
 # Partial Least Squares (PLS1 and PLS2 NIPALS version)
-module PLS
+module PLSRegressor
 
 using JLD
 
diff --git a/src/method.jl b/src/method.jl
index 1349d4d..28d0f6b 100644
--- a/src/method.jl
+++ b/src/method.jl
@@ -57,7 +57,7 @@ end
 
 
 """
-    transform(model::PLS.Model; X::Matrix{:<AbstractFloat}; copydata::Bool=true)
+    transform(model::PLSRegressor.Model; X::Matrix{:<AbstractFloat}; copydata::Bool=true)
 
 A Partial Least Squares predictor.
 
diff --git a/test/kpls_test.jl b/test/kpls_test.jl
index a2466e5..2b7c3c1 100644
--- a/test/kpls_test.jl
+++ b/test/kpls_test.jl
@@ -13,8 +13,8 @@
         z_noisy  = z_pure + noise
         X        = collect(x_values)
         Y        = z_noisy #z_pure
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred   = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred   = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-2
 
     end
@@ -24,14 +24,14 @@
 
         X        = [1 2; 2 4; 4.0 6]
         Y        = [-2; -4; -6.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
         X        = [1 2; 2 4; 4.0 6]
         Y        = [2; 4; 6.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
     end
@@ -41,14 +41,14 @@
 
         X        = [1; 2; 3.0]
         Y        = [1 1; 2 2; 3 3.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
         X        = [1; 2; 3.0]
         Y        = [1 -1; 2 -2; 3 -3.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
         @testset "Linear Prediction Tests " begin
@@ -56,14 +56,14 @@
 
         X        = [1 2; 2 4; 4 6.0]
         Y        = [4 2;6 4;8 6.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
         X           = [1 -2; 2 -4; 4 -6.0]
         Y           = [-4 -2;-6 -4;-8 -6.0]
-        model    = PLS.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
-    	Y_pred     = PLS.predict(model,X)
+        model    = PLSRegressor.fit(X,Y,nfactors=1,kernel="rbf",width=0.01)
+    	Y_pred     = PLSRegressor.predict(model,X)
         @test mean(abs.(Y .- Y_pred)) < 1e-6
 
 
@@ -82,14 +82,14 @@ end;
 	Xtr        = [1 -2; 2 -4; 4.0 -6]
 	Ytr        = [-2; -4; -6.0]
 	Xt         = [6 -8; 8 -10; 10.0 -12]
-	model1    = PLS.fit(Xtr,Ytr,nfactors=1,kernel="rbf",width=0.01)
-	pred1     = PLS.predict(model1,Xt)
+	model1    = PLSRegressor.fit(Xtr,Ytr,nfactors=1,kernel="rbf",width=0.01)
+	pred1     = PLSRegressor.predict(model1,Xt)
 
-	PLS.save(model1)
-	model2    = PLS.load()
+	PLSRegressor.save(model1)
+	model2    = PLSRegressor.load()
 
-	pred2     = PLS.predict(model2,Xt)
-    rm(PLS.MODEL_FILENAME)
+	pred2     = PLSRegressor.predict(model2,Xt)
+    rm(PLSRegressor.MODEL_FILENAME)
 	@test all(pred1 .== pred2)
 
 
diff --git a/test/pls1_test.jl b/test/pls1_test.jl
index 36eb10e..6af8f59 100644
--- a/test/pls1_test.jl
+++ b/test/pls1_test.jl
@@ -5,14 +5,14 @@
 	Xtr        = [1 -2; 2 -4; 4.0 -6]
 	Ytr        = [-2; -4; -6.0]
 	Xt         = [6 -8; 8 -10; 10.0 -12]
-	model1    = PLS.fit(Xtr,Ytr,nfactors=2)
-	pred1     = PLS.predict(model1,Xt)
+	model1    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+	pred1     = PLSRegressor.predict(model1,Xt)
 
-	PLS.save(model1)
-	model2    = PLS.load()
+	PLSRegressor.save(model1)
+	model2    = PLSRegressor.load()
 
-	pred2     = PLS.predict(model2,Xt)
-    rm(PLS.MODEL_FILENAME)
+	pred2     = PLSRegressor.predict(model2,Xt)
+    rm(PLSRegressor.MODEL_FILENAME)
 	@test all(pred1 .== pred2)
 
 
@@ -25,8 +25,8 @@ end
 
 		X        = [1; 2; 3.0][:,:]
 		Y        = [1; 2; 3.0]
-		model    = PLS.fit(X,Y,nfactors=1)
-		pred     = PLS.predict(model,X)
+		model    = PLSRegressor.fit(X,Y,nfactors=1)
+		pred     = PLSRegressor.predict(model,X)
 		@test isequal(round.(pred),[1; 2; 3.0])
 
 	end
@@ -36,7 +36,7 @@ end
 
 		X        = [1 3;2 1;3 2.0]
 		Y        = [1; 1; 1.0]
-		try PLS.fit(X,Y,nfactors=2) catch @test true end
+		try PLSRegressor.fit(X,Y,nfactors=2) catch @test true end
 
 	end
 
@@ -45,14 +45,14 @@ end
 
 		X        = [1 2; 2 4; 4.0 6]
 		Y        = [2; 4; 6.0]
-		model    = PLS.fit(X,Y,nfactors=2)
-		pred     = PLS.predict(model,X)
+		model    = PLSRegressor.fit(X,Y,nfactors=2)
+		pred     = PLSRegressor.predict(model,X)
 		@test isequal(round.(pred),[2; 4; 6.0])
 
 		X           = [1 -2; 2 -4; 4.0 -6]
 		Y           = [-2; -4; -6.0]
-		model       = PLS.fit(X,Y,nfactors=2)
-		pred        = PLS.predict(model,X)
+		model       = PLSRegressor.fit(X,Y,nfactors=2)
+		pred        = PLSRegressor.predict(model,X)
 		@test isequal(round.(pred),[-2; -4; -6.0])
 
 	end
@@ -63,16 +63,16 @@ end
 		Xtr        = [1 2; 2 4; 4.0 6]
 		Ytr        = [2; 4; 6.0]
 		Xt         = [6 8; 8 10; 10.0 12] # same sample
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8; 10; 12.0])
 
 		Xtr        = [1 2; 2 4.0; 4.0 6; 6 8]
 		Ytr        = [2; 4; 6.0; 8]
 		Xt         = [1 2; 2 4.0] # a subsample
 
-		model    = PLS.fit(Xtr,Ytr,nfactors=2,centralize=true)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2,centralize=true)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[2; 4])
 
 	end
@@ -89,16 +89,16 @@ end;
 		Xtr        = [1 2; 2 4; 4.0 6]
 		Ytr        = [2; 4; 6.0]
 		Xt         = [6 8; 8 10; 10.0 12]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8; 10; 12.0])
 
 
 		Xtr        = [1 2; 2 4; 4.0 6]
 		Ytr        = [4; 6; 8.0]
 		Xt         = [6 8; 8 10; 10.0 12]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[10; 12; 14.0])
 
 
@@ -111,16 +111,16 @@ end;
 		Xtr        = [1 -2; 2 -4; 4.0 -6]
 		Ytr        = [-2; -4; -6.0]
 		Xt         = [6 -8; 8 -10; 10.0 -12]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[-8; -10; -12.0])
 
 
 		Xtr        = [1 -2; 2 -4; 4.0 -6]
 		Ytr        = [-4; -6; -8.0]
 		Xt         = [6 -8; 8 -10; 10.0 -12]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[-10; -12; -14.0])
 
 	end
diff --git a/test/pls2_test.jl b/test/pls2_test.jl
index 7288ab3..3b0e477 100644
--- a/test/pls2_test.jl
+++ b/test/pls2_test.jl
@@ -6,14 +6,14 @@
 	Xtr        = [1 2;2 4;3 6;6 12;7 14.0]
 	Ytr        = [2 2;4 4;6 6;12 12;14 14.0]
 	Xt         = [4 8;5 10.0]
-	model1    = PLS.fit(Xtr,Ytr,nfactors=2)
-	pred1     = PLS.predict(model1,Xt)
+	model1    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+	pred1     = PLSRegressor.predict(model1,Xt)
 
-	PLS.save(model1)
-	model2    = PLS.load()
+	PLSRegressor.save(model1)
+	model2    = PLSRegressor.load()
 
-	pred2     = PLS.predict(model2,Xt)
-    rm(PLS.MODEL_FILENAME)
+	pred2     = PLSRegressor.predict(model2,Xt)
+    rm(PLSRegressor.MODEL_FILENAME)
 	@test all(pred1 .== pred2)
 
 
@@ -25,8 +25,8 @@ end
 
 		X        = [1; 2; 3.0]
 		Y        = [1 1; 2 2; 3 3.0]
-		model    = PLS.fit(X,Y,nfactors=1)
-		pred     = PLS.predict(model,X)
+		model    = PLSRegressor.fit(X,Y,nfactors=1)
+		pred     = PLSRegressor.predict(model,X)
 
 		@test isequal(round.(pred),[1 1; 2 2; 3 3.0])
 
@@ -36,7 +36,7 @@ end
 
 		X        = [1 3;2 1;3 2.0]
 		Y        = [1 1; 1 1; 1 1.0]
-		try PLS.fit(X,Y,nfactors=2) catch @test true end
+		try PLSRegressor.fit(X,Y,nfactors=2) catch @test true end
 
 	end
 
@@ -45,14 +45,14 @@ end
 
 		X        = [1 2; 2 4; 4 6.0]
 		Y        = [4 2;6 4;8 6.0]
-		model    = PLS.fit(X,Y,nfactors=2)
-		pred     = PLS.predict(model,X)
+		model    = PLSRegressor.fit(X,Y,nfactors=2)
+		pred     = PLSRegressor.predict(model,X)
 		@test isequal(round.(pred),[4 2;6 4;8 6.0])
 
 		X           = [1 -2; 2 -4; 4 -6.0]
 		Y           = [-4 -2;-6 -4;-8 -6.0]
-		model       = PLS.fit(X,Y,nfactors=2)
-		pred        = PLS.predict(model,X)
+		model       = PLSRegressor.fit(X,Y,nfactors=2)
+		pred        = PLSRegressor.predict(model,X)
 		@test isequal(round.(pred),[-4 -2;-6 -4;-8 -6.0])
 
 
@@ -70,16 +70,16 @@ end
 		Xtr        = [1 2;2 4;3 6;6 12;7 14.0]
 		Ytr        = [2 2;4 4;6 6;12 12;14 14.0]
 		Xt         = [4 8;5 10.0]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8 8;10 10.0])
 
 
 		Xtr        = [1 2;2 4;3 6;6 12;7 14.0]
 		Ytr        = [2 4;4 6;6 8;12 14;14 16.0]
 		Xt         = [4 8;5 10.0]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8 10;10 12.0])
 
 
@@ -91,16 +91,16 @@ end
 		Xtr        = [1 -2;2 -4;3 -6;6 -12;7 -14.0]
 		Ytr        = [2 -2;4 -4;6 -6;12 -12;14 -14.0]
 		Xt         = [4 -8;5 -10.0]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8 -8;10 -10.0])
 
 
 		Xtr        = [1 -2;2 -4;3 -6;6 -12;7 -14.0]
 		Ytr        = [2 -4;4 -6;6 -8;12 -14;14 -16.0]
 		Xt         = [4 -8;5 -10.0]
-		model    = PLS.fit(Xtr,Ytr,nfactors=2)
-		pred     = PLS.predict(model,Xt)
+		model    = PLSRegressor.fit(Xtr,Ytr,nfactors=2)
+		pred     = PLSRegressor.predict(model,Xt)
 		@test isequal(round.(pred),[8 -10;10 -12.0])
 
 
diff --git a/test/runtests.jl b/test/runtests.jl
index 86a105f..faf19cf 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -1,4 +1,4 @@
-using PLS
+using PLSRegressor
 using Base.Test
 
 include("./pls1_test.jl")
diff --git a/test/utils_test.jl b/test/utils_test.jl
index 93fd6fd..9dee815 100644
--- a/test/utils_test.jl
+++ b/test/utils_test.jl
@@ -4,20 +4,20 @@
 
     @testset "check constant columns" begin
 
-		try PLS.check_constant_cols(Matrix([1.0 1;1 2;1 3])) catch @test true end
-		try PLS.check_constant_cols(Matrix([1.0;1;1])) catch @test true end
-		try PLS.check_constant_cols(Matrix([1.0 2 3])) catch @test true end
-		try PLS.check_constant_cols([1.0; 1; 1]) catch @test true end
+		try PLSRegressor.check_constant_cols(Matrix([1.0 1;1 2;1 3])) catch @test true end
+		try PLSRegressor.check_constant_cols(Matrix([1.0;1;1])) catch @test true end
+		try PLSRegressor.check_constant_cols(Matrix([1.0 2 3])) catch @test true end
+		try PLSRegressor.check_constant_cols([1.0; 1; 1]) catch @test true end
 
-		@test PLS.check_constant_cols([1.0 1;2 2;3 3])
-		@test PLS.check_constant_cols([1.0;2;3])
+		@test PLSRegressor.check_constant_cols([1.0 1;2 2;3 3])
+		@test PLSRegressor.check_constant_cols([1.0;2;3])
 
 	end
 
 	@testset "centralize" begin
 
 		X        = reshape([1; 2; 3.0],(3,1))
-		X        = PLS.centralize_data(X,mean(X,1),std(X,1))
+		X        = PLSRegressor.centralize_data(X,mean(X,1),std(X,1))
 		@test all(X .== [-1,0,1.0])
 
 	end
@@ -26,26 +26,26 @@
 
 		Xo        = [1; 2; 3.0][:,:]
 		Xn        = [-1,0,1.0][:,:]
-		Xn        = PLS.decentralize_data(Xn,mean(Xo,1),std(Xo,1))
+		Xn        = PLSRegressor.decentralize_data(Xn,mean(Xo,1),std(Xo,1))
 		@test all(Xn .== [1; 2; 3.0])
 
 	end
 
 	@testset "checkdata" begin
 
-         try PLS.check_params(2,1,"linear") catch @test true end
-		 try PLS.check_params(-1,2,"linear") catch @test true end
-		 try PLS.check_params(1,2,"x") catch @test true end
+         try PLSRegressor.check_params(2,1,"linear") catch @test true end
+		 try PLSRegressor.check_params(-1,2,"linear") catch @test true end
+		 try PLSRegressor.check_params(1,2,"x") catch @test true end
 
-		 @test PLS.check_params(1,2,"linear")
+		 @test PLSRegressor.check_params(1,2,"linear")
 
 	end
 
 	@testset "checkparams" begin
 
-		 try PLS.check_data(Matrix{Float64}(0,0), 0) catch @test true end
-		 try PLS.check_data(Matrix{Float64}(1,1), 10) catch @test true end
-		 @test PLS.check_data(Matrix{Float64}(1,1), 1)
+		 try PLSRegressor.check_data(Matrix{Float64}(0,0), 0) catch @test true end
+		 try PLSRegressor.check_data(Matrix{Float64}(1,1), 10) catch @test true end
+		 @test PLSRegressor.check_data(Matrix{Float64}(1,1), 1)
 
 	end
 

From 5de064abfb452284cdbf3a1165392b4e9822fc66 Mon Sep 17 00:00:00 2001
From: Filipe Braida <filipebraida@gmail.com>
Date: Sun, 19 Nov 2017 23:29:15 -0200
Subject: [PATCH 2/2] Remove travis to 0.7 version

---
 .travis.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index c95a6e2..7244f96 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,7 +5,6 @@ os:
   - osx
 julia:
   - 0.6
-  - nightly
 notifications:
   email: false
 git: