[mlpack-git] (jenkins-conf) master: Add a benchmark config file for mlpack to be run for every commit. (8910f4f)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Tue Mar 17 16:14:44 EDT 2015
Repository : https://github.com/mlpack/jenkins-conf
On branch : master
Link : https://github.com/mlpack/jenkins-conf/compare/e15fa63f457cac24586f43608cded46b5075aef1...8910f4faf94205f525f7db091029a3215b40a269
>---------------------------------------------------------------
commit 8910f4faf94205f525f7db091029a3215b40a269
Author: Marcus Edel <marcus.edel at fu-berlin.de>
Date: Tue Mar 17 21:14:37 2015 +0100
Add a benchmark config file for mlpack to be run for every commit.
>---------------------------------------------------------------
8910f4faf94205f525f7db091029a3215b40a269
...{daily-benchmark.yaml => commit-benchmark.yaml} | 279 ++++++++-------------
1 file changed, 108 insertions(+), 171 deletions(-)
diff --git a/benchmarks/daily-benchmark.yaml b/benchmarks/commit-benchmark.yaml
similarity index 63%
copy from benchmarks/daily-benchmark.yaml
copy to benchmarks/commit-benchmark.yaml
index 422d9ec..1ef9715 100644
--- a/benchmarks/daily-benchmark.yaml
+++ b/benchmarks/commit-benchmark.yaml
@@ -3,135 +3,103 @@ library: general
settings:
# A short timeout, since we want to run this nightly.
timeout: 1000
- database: 'reports/benchmark-daily.db'
+ database: 'reports/commit.db'
# These shouldn't matter with the newer js interface.
keepReports: 20
bootstrap: 10
libraries: ['mlpack'] # Only mlpack in this configuration.
version: ['trunk']
+ irc: ['#mlpack', 'benchmark', 'irc.freenode.net'] # channel, nickname, server
---
library: mlpack
methods:
ALLKNN:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['neighbor_search', 'neighbor_search_impl', 'neighbor_search_rules',
+ 'nearest_neighbor_rules_impl', 'ns_traversal_info', 'unmap',
+ 'furthest_neighbor_sort', 'nearest_neighbor_sort']
script: methods/mlpack/allknn.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --single'
-
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --single --cover_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --cover_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --single --r_tree'
-
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --r_tree'
-
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --random_basis --seed 42'
-
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --naive'
-
ALLKFN:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['neighbor_search', 'neighbor_search_impl', 'neighbor_search_rules',
+ 'nearest_neighbor_rules_impl', 'ns_traversal_info', 'unmap',
+ 'furthest_neighbor_sort', 'nearest_neighbor_sort']
script: methods/mlpack/allkfn.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --single'
-
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --single --cover_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --cover_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ options: '-k 3 --cover_tree'
+
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --single --r_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --r_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
- options: '-k 3 --naive'
-
ALLKRANN:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['neighbor_search', 'neighbor_search_impl', 'neighbor_search_rules',
+ 'nearest_neighbor_rules_impl', 'ns_traversal_info', 'unmap',
+ 'furthest_neighbor_sort', 'nearest_neighbor_sort']
script: methods/mlpack/allkrann.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --alpha 0.9'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --alpha 0.9 --tau 10'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --sample_at_leaves'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --sample_at_leaves --single'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --first_leaf_exact'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --first_leaf_exact --single'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --cover_tree'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --cover_tree --single'
DecisionStump:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/decision_stump.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/iris_train.csv', 'datasets/iris_test.csv'],
['datasets/optdigits_train.csv', 'datasets/optdigits_test.csv'] ]
@@ -141,10 +109,11 @@ methods:
options: '-b 20'
DET:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['dtree']
script: methods/mlpack/det.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/iris.csv', 'datasets/cloud.csv',
['datasets/diabetes_X.csv', 'datasets/diabetes_y.csv'] ]
@@ -154,57 +123,51 @@ methods:
options: '-f 0'
EMST:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['dtb', 'edge_pair', 'union_find']
script: methods/mlpack/emst.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '--naive'
- - files: ['datasets/cloud.csv', 'datasets/isolet.csv',
- 'datasets/covtype.csv', 'datasets/corel-histogram.csv']
+ - files: ['datasets/covtype.csv', 'datasets/corel-histogram.csv']
options: '--leaf_size 20'
FastMKS:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/fastmks.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel linear'
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel linear --single'
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel gaussian'
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel gaussian --single'
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel polynomial --degree 2'
- - files: ['datasets/optdigits.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv']
+ - files: ['datasets/optdigits.csv', 'datasets/cloud.csv']
options: '-k 3 --kernel polynomial --degree 2 --single'
GMM:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['diagonal_constraint', 'eigenvalue_ratio_constraint',
+ 'em_fit', 'no_constraint', 'positive_definite_constraint']
script: methods/mlpack/gmm.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/iris.csv', 'datasets/wine.csv']
options: '--gaussians 3 --seed 42'
@@ -237,10 +200,10 @@ methods:
options: '--gaussians 10 --seed 42 --trials 20 --refined_start'
HMMTRAIN:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/hmm_train.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/artificial_2DSignal.csv']
options: '--type gaussian --states 20 --seed 42'
@@ -249,37 +212,38 @@ methods:
options: '--type discrete --states 20 --seed 42'
HMMGENERATE:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/hmm_generate.py
format: [csv, txt, xml]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/artificial_2DSignal_hmm.xml', 'datasets/artificial_1DSignal_hmm.xml']
options: '--length 10000'
HMMLOGLIK:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/hmm_loglik.py
format: [csv, txt, xml]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/artificial_2DSignal.csv', 'datasets/artificial_2DSignal_hmm.xml'],
['datasets/artificial_1DSignal.csv', 'datasets/artificial_1DSignal_hmm.xml'] ]
HMMVITERBI:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/hmm_viterbi.py
format: [csv, txt, xml]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/artificial_2DSignal.csv', 'datasets/artificial_2DSignal_hmm.xml'],
['datasets/artificial_1DSignal.csv', 'datasets/artificial_1DSignal_hmm.xml'] ]
KPCA:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['naive_method', 'nystroem_method']
script: methods/mlpack/kernel_pca.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/circle_data.csv', 'datasets/abalone.csv']
options: '--kernel linear --new_dimensionality 2'
@@ -318,10 +282,11 @@ methods:
options: '--kernel hyptan --degree 2 --new_dimensionality 2 --nystroem_method --sampling kmeans'
KMEANS:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['max_variance_new_cluster', 'random_partition', 'refined_start', 'allow_empty_clusters']
script: methods/mlpack/kmeans.py
format: [csv, txt, arff]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/cloud.csv', 'datasets/cloud_centroids.csv'] ]
options: '--clusters 5'
@@ -336,10 +301,10 @@ methods:
options: '--clusters 6 --allow_empty_clusters'
LARS:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/lars.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/diabetes_X.csv', 'datasets/diabetes_y.csv'],
['datasets/madelon_X.csv', 'datasets/madelon_y.csv'] ]
@@ -358,19 +323,20 @@ methods:
options: '--lambda1 0.01 --lambda2 0.005 --use_cholesky'
LinearRegression:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/linear_regression.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/diabetes_X.csv'], ['datasets/cosExp_X.csv'],
['datasets/madelon_train.csv', 'datasets/madelon_test.csv'] ]
LocalCoordinateCoding:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['lcc']
script: methods/mlpack/local_coordinate_coding.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/pendigits.csv']
options: '--atoms 12 --seed 42'
@@ -382,77 +348,44 @@ methods:
options: '--atoms 12 --seed 42 --normalize --lambda 0.1'
LSH:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/lsh.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/wine.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv', 'datasets/covtype.csv']
+ - files: ['datasets/cloud.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --seed 42'
- - files: ['datasets/wine.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv', 'datasets/covtype.csv']
+ - files: ['datasets/cloud.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --seed 42 --bucket_size 50'
- - files: ['datasets/wine.csv', 'datasets/cloud.csv',
- 'datasets/corel-histogram.csv', 'datasets/covtype.csv']
+ - files: ['datasets/cloud.csv', 'datasets/corel-histogram.csv']
options: '-k 3 --seed 42 --tables 40'
- NBC:
- run: ['timing']
- script: methods/mlpack/nbc.py
- format: [csv, txt]
- iteration: 2
- datasets:
- - files: [ ['datasets/iris_train.csv', 'datasets/iris_test.csv'],
- ['datasets/transfusion_train.csv', 'datasets/transfusion_test.csv'],
- ['datasets/madelon_train.csv', 'datasets/madelon_test.csv'] ]
-
- - files: [ ['datasets/iris_train.csv', 'datasets/iris_test.csv'],
- ['datasets/transfusion_train.csv', 'datasets/transfusion_test.csv'],
- ['datasets/madelon_train.csv', 'datasets/madelon_test.csv'] ]
- options: '--incremental_variance'
-
- NCA:
- run: ['timing']
- script: methods/mlpack/nca.py
- format: [csv, txt]
- iteration: 2
- datasets:
- - files: ['datasets/iris_train.csv',
- ['datasets/diabetes_X.csv', 'datasets/diabetes_y.csv'],
- 'datasets/wine.csv', 'datasets/optdigits.csv']
- options: '--optimizer sgd --max_iteration 2000 --seed 42'
-
- - files: ['datasets/iris_train.csv',
- ['datasets/diabetes_X.csv', 'datasets/diabetes_y.csv'],
- 'datasets/wine.csv', 'datasets/optdigits.csv']
- options: '--optimizer lbfgs --max_iteration 2000 --seed 42'
-
NMF:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['amf', 'simple_residue_termination', 'simple_tolerance_termination',
+ 'validation_rmse_termination', 'incomplete_incremental_termination',
+ 'complete_incremental_termination', 'averge_init', 'random_acol_init',
+ 'random_init']
script: methods/mlpack/nmf.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- - files: ['datasets/ionosphere.csv', 'datasets/piano_magnitude_spectogram.csv',
- 'datasets/optdigits.csv', 'datasets/isolet.csv']
+ - files: ['datasets/isolet.csv']
options: '--rank 6 --seed 42 --update_rules multdist'
- - files: ['datasets/ionosphere.csv', 'datasets/piano_magnitude_spectogram.csv',
- 'datasets/optdigits.csv', 'datasets/isolet.csv']
+ - files: ['datasets/isolet.csv']
options: '--rank 6 --seed 42 --update_rules multdiv'
- - files: ['datasets/ionosphere.csv', 'datasets/piano_magnitude_spectogram.csv',
- 'datasets/optdigits.csv', 'datasets/isolet.csv']
+ - files: ['datasets/isolet.csv']
options: '--rank 6 --seed 42 --update_rules als'
PCA:
- run: ['timing']
+ run: ['timing', 'watch']
script: methods/mlpack/pca.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/iris.csv', 'datasets/wine.csv',
'datasets/cities.csv', 'datasets/diabetes_X.csv']
@@ -466,21 +399,23 @@ methods:
options: '--var_to_retain 0.7 --scale'
PERCEPTRON:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['simple_weight_update', 'random_init', 'zero_init']
script: methods/mlpack/perceptron.py
format: [csv, txt, arff]
- iteration: 2
+ iteration: 1
datasets:
- files: [ ['datasets/iris_train.csv', 'datasets/iris_test.csv', 'datasets/iris_labels.csv'],
['datasets/oilspill_train.csv', 'datasets/oilspill_test.csv', 'datasets/oilspill_labels.csv'],
['datasets/ecoli_train.csv', 'datasets/ecoli_test.csv', 'datasets/ecoli_labels.csv'] ]
- options: '--iteration 10000'
+ options: '--iteration 1000'
RANGESEARCH:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['range_search']
script: methods/mlpack/range_search.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/wine.csv', 'datasets/cloud.csv',
'datasets/corel-histogram.csv']
@@ -523,10 +458,12 @@ methods:
options: '--max 0.02 --min 0.01 --naive'
SparseCoding:
- run: ['timing']
+ run: ['timing', 'watch']
+ watch: ['data_dependent_random_initializer', 'nothing_initializer',
+ 'random_initializer', 'sparse_coding']
script: methods/mlpack/sparse_coding.py
format: [csv, txt]
- iteration: 2
+ iteration: 1
datasets:
- files: ['datasets/pendigits.csv']
options: '--atoms 12 --seed 42 --max_iteration 100'
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