Merge remote-tracking branch 'remotes/upstream/master'

tidy up a bit
Conflicts:
	config.py
	utils/ImplicitFeedbackFunctions.py
	utils/Model.py
	utils/SVDModel.py

.gitignore

@@ -38,6 +38,11 @@ Data/
 *.Rout
 *.Rhistory
 
+#################
+## Idea
+#################
+*.idea
+
 #################
 ## Eclipse
 #################
@@ -169,7 +174,6 @@ publish/
 *.pubxml
 
 # NuGet Packages Directory
-## TODO: If you have NuGet Package Restore enabled, uncomment the next line
 #packages/
 
 # Windows Azure Build Output

Documentation/Documentation.md

@@ -1,7 +1,7 @@
 Hybrid Movie Recommendation System Documentation
 ==============================================
 
-This program is a hybrid recommendation system. This documentation is written for those who wish to modify the program in some manner or join the effect. 
+This program is a hybrid recommendation system. This documentation is written for those who wish to modify the program in some manner or join the effort. 
 
 Program Structure
 ----------------------------------------------

Hybrid/basicEnsembles.R → Hybrid/ensembles.R

@@ -8,6 +8,7 @@ RMSEPath  = args[6]
 model.type= args[7]
 input1      = args[8]
 
+library(Metrics)
 
 dataTrain = read.csv(trainPath, sep="\t")
 dataCV    = read.csv(CVPath,    sep="\t")
@@ -17,10 +18,7 @@ if(model.type=="OLS"){
   ## Ordinary Least Squares
   library(ipred)
   fit      = lm(y~0 + .,data=dataTrain)
-  errFit   = errorest(y~0+.,data=dataTrain,model=lm)
   summary(fit)
-  print(errFit)
-  error = errFit$error
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
@@ -30,10 +28,7 @@ if(model.type=="OLSI"){
   library(ipred)
   formula = paste("y~0 + (.)^",input1,sep="")
   fit      = lm(y~0 + (.)^2,data=dataTrain)
-  errFit   = errorest(y~0+(.)^2,data=dataTrain,model=lm)
   summary(fit)
-  print(errFit)
-  error = errFit$error
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
@@ -44,16 +39,9 @@ if(model.type=="RR"){
   library(ridge)
   input1 = as.numeric(input1)
   fit = linearRidge(y~0+.,data=dataTrain,nPCs=input1)
-  ridgeModel = function(formula, data) {
-    mod <- linearRidge(formula, data=data,nPCs=input1)
-    function(newdata) predict(mod, newdata)
-  }
-  errFit   = errorest(y~0+.,data=dataTrain,model=ridgeModel)
   print(fit)
   print("Ridge lambdas")
   print(fit$lambda)
-  print(errFit)
-  error = errFit$error
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
@@ -64,10 +52,7 @@ if(model.type=="Lasso"){
   y = data.matrix(dataTrain$y)
   drops = c("y")
   x = data.matrix(dataTrain[,!(names(dataTrain) %in% drops)])
-  errFit = cv.glmnet(x,y)
-  print(errFit)
-  error = sqrt(mean(errFit$cvm))
-  fit = glmnet(x,y)
+  fit = cv.glmnet(x,y)
   dataCVMat = data.matrix(dataCV[,!(names(dataCV) %in% drops)])
   CVPredictions = predict(fit,dataCVMat)
   TestPredictions= predict(fit,as.matrix(dataTest))
@@ -77,10 +62,7 @@ if(model.type=="BRT"){
   library(ipred)
   ## Bagged Regression Trees
   fit      = bagging(y~0+.,data=dataTrain)
-  errFit   = errorest(y~0+.,data=dataTrain,model=bagging)
   print(fit)
-  print(errFit)
-  error = errFit$error
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
@@ -93,34 +75,19 @@ if(model.type=="BMAR"){
   drops = c("y")
   x = dataTrain[,!(names(dataTrain) %in% drops)]
   fit      =  bicreg(x, y)
-  errBicReg = function(formula,data){
-    y = data$y
-    drops = c("y")
-    x = data[,!(names(data) %in% drops)]
-    bicreg(x,y)
-  }
   summary(fit)
-  errFit   = errorest(y~0+.,data=dataTrain,model=errBicReg)
-  print(errFit)
-  error = errFit$error
   cvp = predict(fit,dataCV)
   tp  = predict(fit,dataTest)
   CVPredictions   = unlist(cvp[1])
-  testPreidctions = unlist(tp[1] )
+  TestPredictions = unlist(tp[1] )
 }
 
 
 if(model.type=="RFR"){
   library(randomForest)
   library(ipred)
   ## Random Forest
-  fit      = randomForest(y ~0+., data=dataTrain,importance=TRUE, sampsize=1000, ntree=100)
-  randFor = function(formula,data){
-    randomForest(y ~0+., data=data,importance=TRUE, sampsize=1000, ntree=100)
-  }
-  errFit   = errorest(y~0+.,data=dataTrain,model=randFor)
-  print(errFit)
-  error = errFit$error
+  fit      = randomForest(y ~0+., data=dataTrain,importance=TRUE, ntree=100)
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
@@ -130,7 +97,7 @@ if(model.type=="CIRF"){
   ## Not Working
   library(party)
   library(languageR)
-  fit <- cforest(y ~ 0 + ., data = dataTrain) 
+  fit <- cforest(y ~ 0 + ., data = dataTrain)
 }
 
 if(model.type=="GBRT"){
@@ -143,13 +110,12 @@ if(model.type=="GBRT"){
   print(cvm)
   mstop(cvm)
   fit <- blackboost(y ~ 0+., data = dataTrain,control = boost_control(mstop = mstop(cvm)))
-  error = min(cvm)
   CVPredictions = predict(fit,dataCV)
   TestPredictions= predict(fit,dataTest)
 }
 
-
-
+error=rmse(dataCV$y,CVPredictions)
+print(error)
 
 write(CVPredictions, file = predCV, ncolumns=1)
 write(TestPredictions, file = predTest, ncolumns=1)

Hybrid/hybrid.py

@@ -26,7 +26,7 @@ def setupHybridTrial(hybridOriginalPath,strTrial,modelBootPath,CVPredictionPaths
     bootCV = modelBootPath  +   \
                         'CV' + '_t' + strTrial
     buildTrainingMatrixFromPredictions(bootCV,hybridOriginal,
-                        CVPredictionPaths,grabCSVColumnFunc)
+                        CVPredictionPaths,grabCSVColumnFunc,2)
     buildPredictorMatrixFromPredictions(testPredictionPaths,
                         grabCSVColumnFunc,hybridPredict)
     bootsplitFunc(hybridOriginal,hybridOriginal + '_tmp',
@@ -42,13 +42,13 @@ def setupHybridTrial(hybridOriginalPath,strTrial,modelBootPath,CVPredictionPaths
                 hybridOriginalPath + 
                 'test_t'    + strTrial,True)
 
-def buildTrainingMatrixFromPredictions(fullSet,outputPath,predictorPaths,grabCSVColumnFunc):
+def buildTrainingMatrixFromPredictions(fullSet,outputPath,predictorPaths,grabCSVColumnFunc,masterColumn):
 #-------------------------------------------------
 # Takes in the prediction of various models on CV data
 # Through CVPredictionPaths array
 # Generates a txt file that is a matrix for training Hybrid
 #-------------------------------------------------
-    predictionArrays = [grabCSVColumnFunc(fullSet,2)]
+    predictionArrays = [grabCSVColumnFunc(fullSet,masterColumn)]
     for predictPath in predictorPaths:
         predictionArrays.append(grabCSVColumnFunc(predictPath,2))
     toWrite = []

Hybrid/synthesize.py

@@ -1,14 +1,17 @@
 import hybrid
 from SynthModel import SynthModel
-def setupSynthesize(utils,CVPredictionPaths,testPredictionPaths,configModel,trials,modelList,mproc,processes):
+def setupSynthesize(utils,CVPredictionPaths,testPredictionPaths,split,random,configModel,trials,modelList,mproc,processes):
     processes = []
     for trial in range(0,trials):
         strTrial = str(trial)
         p = mproc.Process(target=synthSetupTrial,
                         args=(utils.SYNTH_ORIGINAL_PATH,strTrial,
                         utils.HYBRID_BOOT_PATH,
+                        utils.SYNTH_BOOT_PATH,
                         CVPredictionPaths[trial],
                         testPredictionPaths[trial],
+                        split,random,
+                        utils.bootsplit,
                         utils.grabCSVColumn,
                         hybrid.buildTrainingMatrixFromPredictions,
                         hybrid.buildPredictorMatrixFromPredictions,
@@ -22,17 +25,22 @@ def setupSynthesize(utils,CVPredictionPaths,testPredictionPaths,configModel,tria
         p.join()
 
 
-def synthSetupTrial(synthOriginalPath,strTrial,hybridBootPath,CVPredictionPaths,testPredictionPaths,grabCSVColumnFunc,buildTrainingMatrixFromPredictionsFunc,buildPredictorMatrixFromPredictionsFunc,addHeaderFunc): 
+def synthSetupTrial(synthOriginalPath,strTrial,hybridBootPath,synthBootPath,CVPredictionPaths,testPredictionPaths,split,random,bootsplitFunc,grabCSVColumnFunc,buildTrainingMatrixFromPredictionsFunc,buildPredictorMatrixFromPredictionsFunc,addHeaderFunc):
         synthOriginal = synthOriginalPath \
                     + 'train_t' + strTrial
         synthPredict  = synthOriginalPath \
                     + 'test_t'  + strTrial
-
-        buildTrainingMatrixFromPredictionsFunc(hybridBootPath + 
-                    'CV_t' + strTrial + '_tmp', synthOriginal + '_tmp',
-                    CVPredictionPaths,grabCSVColumnFunc)
+        bootCV = hybridBootPath + 'CV_t' + strTrial + '_tmp'
+        buildTrainingMatrixFromPredictionsFunc(bootCV,synthOriginal,
+                    CVPredictionPaths,grabCSVColumnFunc,0)
         buildPredictorMatrixFromPredictionsFunc(testPredictionPaths,
                     grabCSVColumnFunc,synthPredict + '_tmp')
-        addHeaderFunc(synthOriginal + '_tmp', synthOriginal,False)
-        addHeaderFunc(synthPredict  + '_tmp', synthPredict,True)
-
+        bootsplitFunc(synthOriginal,synthOriginal + '_tmp',
+                      synthBootPath + 'train_t' + strTrial + '_tmp',
+                      synthBootPath + 'CV_t'    + strTrial + '_tmp',
+                      split,random)
+        addHeaderFunc(synthBootPath + 'train_t' + strTrial + '_tmp',
+                      synthBootPath + 'train_t' + strTrial ,False)
+        addHeaderFunc(synthBootPath + 'CV_t'    + strTrial + '_tmp',
+                      synthBootPath + 'CV_t'    + strTrial ,False)
+        addHeaderFunc(synthPredict + '_tmp', synthPredict,True)

HybridMovieRecommendationSystem.iml

@@ -0,0 +1,12 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager" inherit-compiler-output="true">
+    <exclude-output />
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$" isTestSource="false" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
+

LICENSE

@@ -0,0 +1,23 @@
+The MIT License (MIT)
+
+Copyright (c) 2013 Christopher Rackauckas
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of
+this software and associated documentation files (the "Software"), to deal in
+the Software without restriction, including without limitation the rights to
+use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
+the Software, and to permit persons to whom the Software is furnished to do so,
+subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
+FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
+COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
+IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
+CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+
+

PostProcess/post.py

@@ -16,7 +16,7 @@ def postProcess(os,utils, DE_EFFECT,trials,userMovieRating,RMSEPaths):
 
     winner = pickWinner(trials,RMSEPaths)
     print("Best trial: " + str(winner[0]))
-    print("Best Synth Boot/K-Fold RMSE:  " + str(winner[1]))
+    print("Best Synth CV-RMSE:  " + str(winner[1]))
     trialOutput = utils.TRIAL_OUTPUT_PATH + 't' + str(winner[0])
     os.system('cp ' + trialOutput + ' ' + utils.OUTPUT_PATH)
 
@@ -57,4 +57,4 @@ def pickWinner(trials,RMSEPaths):
             bestTrial = i
             bestRMSE  = RMSE
 
-    return(bestTrial,bestRMSE)
+    return bestTrial,bestRMSE