===== MAIN: learn based on training data =====
=== START program1: ./run learn ../dataset2/train
../dataset2/train
Number of TrainExamples=82793 , Number of TrainFeatures=5934 , MaxTrainFeaturesId=5934
Positive examples=42376
Negative examples=40417
*************************
iterations=25
maxF1=0.677117 TP=42376 FP=40414 FN=0 TN=3
WallTime=52.36 sec
=== END program1: ./run learn ../dataset2/train --- OK [53s]
===== 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 [6s]
=== START program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out
../program0/evalTrain.in
Number of TestExamples=82793 , Number of TestFeatures=5934 , MaxTestFeaturesId=5934
F1=0.999982 TP=82790 FN=3 FP=0 TN=0
Detailed Evaluation
---------------------------------------
Accuracy =99.9964
Precision =100
Recall =99.9964
F(1) =99.9982
BEP (PR break even point)=0
Area Under ROC Curve =-nan
Area Under PR Curve =100
Average Precision =100
WallTime=39.83 sec
=== END program1: ./run predict ../program0/evalTrain.in ../program0/evalTrain.out --- OK [41s]
=== START program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out
=== END program4: ./run evaluate ../dataset2/train ../program0/evalTrain.out --- OK [17s]
===== MAIN: predict/evaluate on test data =====
=== START program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in
=== END program3: ./run stripLabels ../dataset2/test ../program0/evalTest.in --- OK [2s]
=== START program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out
../program0/evalTest.in
Number of TestExamples=35483 , Number of TestFeatures=5934 , MaxTestFeaturesId=5934
F1=1 TP=35483 FN=0 FP=0 TN=0
Detailed Evaluation
---------------------------------------
Accuracy =100
Precision =100
Recall =100
F(1) =100
BEP (PR break even point)=0
Area Under ROC Curve =-nan
Area Under PR Curve =99.9972
Average Precision =100
WallTime=17.63 sec
=== END program1: ./run predict ../program0/evalTest.in ../program0/evalTest.out --- OK [18s]
=== START program4: ./run evaluate ../dataset2/test ../program0/evalTest.out
=== END program4: ./run evaluate ../dataset2/test ../program0/evalTest.out --- OK [7s]
real 2m24.853s
user 2m11.588s
sys 0m11.013s
Run specification
supervised-learning: Main entry for supervised learning for training and testing a program on a dataset.
(learner:Program) MoPe: Modified Perceptron for binary classification.
(dataset:Dataset) neut_1.-.01: Task: text sentiment analysis.
Features: text term/freq counts, along with other real-valued features.
Labels: binary sentiment score.
(stripper:Program[Strip]) binary-utils: Validates and inspects a dataset in BinaryClassification format.
(evaluator:Program[Evaluate]) classification-evaluator: Evaluates predictions of classification datasets (discrete outputs).
When you generate a run, you can set a time limit for the run (no more than 24 hours). After that point, we will terminate the program.
Your program can use 1.5GB of memory. More information here.
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.
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