Note: this page autoupdates while a run is in progress
On ./run predict <in> <out>, your program (../program1) did not write to <out>.
Usage: "run learn trainingFileName" OR "run predict testFileName predictionFileName"
===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
number of users : 5000number of movies : 3952 "start optimization"
Error: cannot allocate vector of size 357.7 Mb
=== END program1: ./run learn ../dataset2/train --- OK [837s]
===== MAIN: predict/evaluate on train data =====
=== START program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in
=== END program3: ./run stripLabels ../dataset2/train ../program0/evalTrain.in --- OK [2s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Calls: load -> readChar
In addition: Warning message:
In readChar(con, 5L, useBytes = TRUE) :
cannot open compressed file 'itemMean.Rdata', probable reason 'No such file or directory'
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [3s]
Failed: On ./run predict <in> <out>, your program (../program1) did not write to <out>.
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) kerenl-center_svd_em-r: Center and shrink data. Initial guesses are the calculate item centers. These guesses are refined using a EM approach.
(dataset:Dataset) movielens1m: 1M MovieLens movie ratings dataset from http://www.grouplens.org/.
The original dataset contains 1,000,209 anonymous ratings (1-5 stars) of approximately 3,900 movies made by 6,040 MovieLens users who joined MovieLens in 2000.
This sub-dataset includes the ratings of 5,000 randomly selected users. A single rating from each user was withheld to form the test set.
See included README.txt for more information.
(stripper:Program[Strip]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
(evaluator:Program[Evaluate]) collaborativefiltering-utils: Validates, inspects, and evaluates a dataset in CollaborativeFiltering format.
message: On ./run predict , your program (../program1) did not write to .
Go to the page for the run and look at the log file for signs of the responsible error.
You can also download the run and run it locally on your machine (a README file should
be included in the download which provides more information).
We said that a run was simply a program/dataset pair, but that's not the full story.
A run actually includes other helper programs such as the evaluation program and
various programs for reductions (e.g., one-versus-all, hyperparameter tuning).
More formally, a run is a given by a run specification,
which can be found on the page for any run.
A run specification is a tree where each internal node represents a program
and its children represents the arguments to be passed into its constructor.
For example, the one-versus-all program takes your binary classification program
as a constructor argument and behaves like a multiclass classification program.