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results-cv.txt
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Evaluation of:
models: ['mv_linear' 'mv_svm' 'mv_nn' 'simple_cnn' 'linear' 'meansvm']
datasets: ['GTZAN' 'columbia-train']
prepr: ['RhythmData' 'MIRData' 'SpectroData']
...loaded results from file
--------
|RESULTS:|
--------
highest cv_accuracy: cv_acc cv_acc_nc cv_acc_pc cv_f1 data_name \
356 1.0 1.0 1.0 1.0 GTZAN
hyper_params is_normalized \
356 {"C": 58.315789473684205, "gamma": 0.041753189... 1
... param_gamma param_linvar prepr_name test_acc test_acc_nc \
356 ... NaN False RhythmData 0.975 0.95
test_acc_pc test_f1
356 1.0 0.97561
[1 rows x 16 columns]
===============================
Best run per dataset cv_acc:
GTZAN: mv_svm on RhythmData with 1.0 (normalized, mean)
cv_acc 1
cv_acc_nc 1
cv_acc_pc 1
cv_f1 1
data_name GTZAN
hyper_params {"C": 58.315789473684205, "gamma": 0.041753189...
is_normalized 1
model_name mv_svm
param_c NaN
param_gamma NaN
param_linvar False
prepr_name RhythmData
test_acc 0.975
test_acc_nc 0.95
test_acc_pc 1
test_f1 0.97561
Name: 356, dtype: object
columbia-train: mv_svm on RhythmData with 1.0 (normalized, mean)
cv_acc 1
cv_acc_nc 1
cv_acc_pc 1
cv_f1 1
data_name columbia-train
hyper_params {"C": 6.2105263157894735, "gamma": 0.018873918...
is_normalized 1
model_name mv_svm
param_c NaN
param_gamma NaN
param_linvar False
prepr_name RhythmData
test_acc 1
test_acc_nc 1
test_acc_pc 1
test_f1 1
Name: 1311, dtype: object
===============================
Best run per dataset test_acc:
GTZAN: mv_linear on RhythmData with 1.0 (normalized, mean)
cv_acc 0.859077
cv_acc_nc 0.944444
cv_acc_pc 0.801555
cv_f1 0.837731
data_name GTZAN
hyper_params NaN
is_normalized 1
model_name mv_linear
param_c NaN
param_gamma NaN
param_linvar False
prepr_name RhythmData
test_acc 1
test_acc_nc 1
test_acc_pc 1
test_f1 1
Name: 0, dtype: object
columbia-train: mv_linear on RhythmData with 1.0 (normalized, mean)
cv_acc 0.975
cv_acc_nc 1
cv_acc_pc 0.952564
cv_f1 0.974638
data_name columbia-train
hyper_params NaN
is_normalized 1
model_name mv_linear
param_c NaN
param_gamma NaN
param_linvar False
prepr_name RhythmData
test_acc 1
test_acc_nc 1
test_acc_pc 1
test_f1 1
Name: 1256, dtype: object
Perfect Setups (cv_acc=test_acc=1) data_name prepr_name model_name \
1311 columbia-train RhythmData mv_svm
1343 columbia-train RhythmData mv_svm
1369 columbia-train RhythmData mv_svm
1372 columbia-train RhythmData mv_svm
1518 columbia-train RhythmData mv_svm
1609 columbia-train RhythmData mv_svm
1640 columbia-train RhythmData mv_svm
1848 columbia-train RhythmData mv_svm
1850 columbia-train RhythmData mv_svm
1864 columbia-train RhythmData mv_nn
1867 columbia-train RhythmData mv_nn
1868 columbia-train RhythmData mv_nn
1869 columbia-train RhythmData mv_nn
1870 columbia-train RhythmData mv_nn
11047 columbia-train RhythmData mv_svm
11079 columbia-train RhythmData mv_svm
11105 columbia-train RhythmData mv_svm
11108 columbia-train RhythmData mv_svm
11254 columbia-train RhythmData mv_svm
11345 columbia-train RhythmData mv_svm
11376 columbia-train RhythmData mv_svm
11584 columbia-train RhythmData mv_svm
11586 columbia-train RhythmData mv_svm
11600 columbia-train RhythmData mv_nn
11603 columbia-train RhythmData mv_nn
11604 columbia-train RhythmData mv_nn
11605 columbia-train RhythmData mv_nn
11606 columbia-train RhythmData mv_nn
20807 columbia-train RhythmData mv_svm
20839 columbia-train RhythmData mv_svm
20865 columbia-train RhythmData mv_svm
20868 columbia-train RhythmData mv_svm
21014 columbia-train RhythmData mv_svm
21105 columbia-train RhythmData mv_svm
21136 columbia-train RhythmData mv_svm
21344 columbia-train RhythmData mv_svm
21346 columbia-train RhythmData mv_svm
21360 columbia-train RhythmData mv_nn
21363 columbia-train RhythmData mv_nn
21364 columbia-train RhythmData mv_nn
21365 columbia-train RhythmData mv_nn
21366 columbia-train RhythmData mv_nn
30567 columbia-train RhythmData mv_svm
30599 columbia-train RhythmData mv_svm
30625 columbia-train RhythmData mv_svm
30628 columbia-train RhythmData mv_svm
30774 columbia-train RhythmData mv_svm
30865 columbia-train RhythmData mv_svm
30896 columbia-train RhythmData mv_svm
31104 columbia-train RhythmData mv_svm
31106 columbia-train RhythmData mv_svm
31120 columbia-train RhythmData mv_nn
31123 columbia-train RhythmData mv_nn
31124 columbia-train RhythmData mv_nn
31125 columbia-train RhythmData mv_nn
31126 columbia-train RhythmData mv_nn
hyper_params
1311 {"C": 6.2105263157894735, "gamma": 0.018873918...
1343 {"C": 11.421052631578947, "gamma": 0.092367085...
1369 {"C": 16.63157894736842, "gamma": 0.0038566204...
1372 {"C": 16.63157894736842, "gamma": 0.0417531893...
1518 {"C": 42.68421052631579, "gamma": 0.0017433288...
1609 {"C": 58.315789473684205, "gamma": 0.003856620...
1640 {"C": 63.526315789473685, "gamma": 0.008531678...
1848 {"C": 100.0, "gamma": 0.0017433288221999908}
1850 {"C": 100.0, "gamma": 0.008531678524172814}
1864 {"hidden_neurons": [50, 50, 50], "dropout": 0.25}
1867 {"hidden_neurons": [100], "dropout": 0.25}
1868 {"hidden_neurons": [100], "dropout": 0.5}
1869 {"hidden_neurons": [100, 100], "dropout": 0.0}
1870 {"hidden_neurons": [100, 100], "dropout": 0.25}
11047 {"C": 6.2105263157894735, "gamma": 0.018873918...
11079 {"C": 11.421052631578947, "gamma": 0.092367085...
11105 {"C": 16.63157894736842, "gamma": 0.0038566204...
11108 {"C": 16.63157894736842, "gamma": 0.0417531893...
11254 {"C": 42.68421052631579, "gamma": 0.0017433288...
11345 {"C": 58.315789473684205, "gamma": 0.003856620...
11376 {"C": 63.526315789473685, "gamma": 0.008531678...
11584 {"C": 100.0, "gamma": 0.0017433288221999908}
11586 {"C": 100.0, "gamma": 0.008531678524172814}
11600 {"hidden_neurons": [50, 50, 50], "dropout": 0.25}
11603 {"hidden_neurons": [100], "dropout": 0.25}
11604 {"hidden_neurons": [100], "dropout": 0.5}
11605 {"hidden_neurons": [100, 100], "dropout": 0.0}
11606 {"hidden_neurons": [100, 100], "dropout": 0.25}
20807 {"C": 6.2105263157894735, "gamma": 0.018873918...
20839 {"C": 11.421052631578947, "gamma": 0.092367085...
20865 {"C": 16.63157894736842, "gamma": 0.0038566204...
20868 {"C": 16.63157894736842, "gamma": 0.0417531893...
21014 {"C": 42.68421052631579, "gamma": 0.0017433288...
21105 {"C": 58.315789473684205, "gamma": 0.003856620...
21136 {"C": 63.526315789473685, "gamma": 0.008531678...
21344 {"C": 100.0, "gamma": 0.0017433288221999908}
21346 {"C": 100.0, "gamma": 0.008531678524172814}
21360 {"hidden_neurons": [50, 50, 50], "dropout": 0.25}
21363 {"hidden_neurons": [100], "dropout": 0.25}
21364 {"hidden_neurons": [100], "dropout": 0.5}
21365 {"hidden_neurons": [100, 100], "dropout": 0.0}
21366 {"hidden_neurons": [100, 100], "dropout": 0.25}
30567 {"C": 6.2105263157894735, "gamma": 0.018873918...
30599 {"C": 11.421052631578947, "gamma": 0.092367085...
30625 {"C": 16.63157894736842, "gamma": 0.0038566204...
30628 {"C": 16.63157894736842, "gamma": 0.0417531893...
30774 {"C": 42.68421052631579, "gamma": 0.0017433288...
30865 {"C": 58.315789473684205, "gamma": 0.003856620...
30896 {"C": 63.526315789473685, "gamma": 0.008531678...
31104 {"C": 100.0, "gamma": 0.0017433288221999908}
31106 {"C": 100.0, "gamma": 0.008531678524172814}
31120 {"hidden_neurons": [50, 50, 50], "dropout": 0.25}
31123 {"hidden_neurons": [100], "dropout": 0.25}
31124 {"hidden_neurons": [100], "dropout": 0.5}
31125 {"hidden_neurons": [100, 100], "dropout": 0.0}
31126 {"hidden_neurons": [100, 100], "dropout": 0.25}
****************************
****************************
Best model per feature set
==========================
data_name GTZAN
prepr_name MIRData
Name: 893, dtype: object
->
model_name mv_svm
param_linvar False
cv_acc 0.984308
test_acc 0.875
Name: 893, dtype: object
----
data_name GTZAN
prepr_name RhythmData
Name: 356, dtype: object
->
model_name mv_svm
param_linvar False
cv_acc 1
test_acc 0.975
Name: 356, dtype: object
----
data_name GTZAN
prepr_name SpectroData
Name: 3720, dtype: object
->
model_name simple_cnn
param_linvar True
cv_acc 0.944923
test_acc 0.925
Name: 3720, dtype: object
----
data_name columbia-train
prepr_name MIRData
Name: 2179, dtype: object
->
model_name mv_svm
param_linvar False
cv_acc 1
test_acc 0.9
Name: 2179, dtype: object
----
data_name columbia-train
prepr_name RhythmData
Name: 1311, dtype: object
->
model_name mv_svm
param_linvar False
cv_acc 1
test_acc 1
Name: 1311, dtype: object
----
data_name columbia-train
prepr_name SpectroData
Name: 5532, dtype: object
->
model_name simple_cnn
param_linvar True
cv_acc 0.991667
test_acc 0.95
Name: 5532, dtype: object
----
==========================
****************************
****************************
Best feature set for each model
==========================
data_name GTZAN
model_name linear
param_linvar False
Name: 3119, dtype: object
->
prepr_name MIRData
cv_acc 0.906154
test_acc 0.8
Name: 3119, dtype: object
----
data_name GTZAN
model_name linear
param_linvar True
Name: 2514, dtype: object
->
prepr_name RhythmData
cv_acc 0.899385
test_acc 0.975
Name: 2514, dtype: object
----
data_name GTZAN
model_name meansvm
param_linvar False
Name: 2663, dtype: object
->
prepr_name RhythmData
cv_acc 0.976308
test_acc 0.975
Name: 2663, dtype: object
----
data_name GTZAN
model_name mv_linear
param_linvar False
Name: 628, dtype: object
->
prepr_name MIRData
cv_acc 0.866769
test_acc 0.95
Name: 628, dtype: object
----
data_name GTZAN
model_name mv_nn
param_linvar False
Name: 625, dtype: object
->
prepr_name RhythmData
cv_acc 1
test_acc 0.975
Name: 625, dtype: object
----
data_name GTZAN
model_name mv_svm
param_linvar False
Name: 356, dtype: object
->
prepr_name RhythmData
cv_acc 1
test_acc 0.975
Name: 356, dtype: object
----
data_name GTZAN
model_name simple_cnn
param_linvar False
Name: 2513, dtype: object
->
prepr_name RhythmData
cv_acc 0.945231
test_acc 0.975
Name: 2513, dtype: object
----
data_name GTZAN
model_name simple_cnn
param_linvar True
Name: 3116, dtype: object
->
prepr_name MIRData
cv_acc 0.968615
test_acc 0.95
Name: 3116, dtype: object
----
data_name columbia-train
model_name linear
param_linvar False
Name: 5535, dtype: object
->
prepr_name SpectroData
cv_acc 0.966667
test_acc 0.95
Name: 5535, dtype: object
----
data_name columbia-train
model_name linear
param_linvar True
Name: 4326, dtype: object
->
prepr_name RhythmData
cv_acc 0.966667
test_acc 0.975
Name: 4326, dtype: object
----
data_name columbia-train
model_name meansvm
param_linvar False
Name: 4476, dtype: object
->
prepr_name RhythmData
cv_acc 1
test_acc 0.975
Name: 4476, dtype: object
----
data_name columbia-train
model_name mv_linear
param_linvar False
Name: 1256, dtype: object
->
prepr_name RhythmData
cv_acc 0.975
test_acc 1
Name: 1256, dtype: object
----
data_name columbia-train
model_name mv_nn
param_linvar False
Name: 1857, dtype: object
->
prepr_name RhythmData
cv_acc 1
test_acc 0.975
Name: 1857, dtype: object
----
data_name columbia-train
model_name mv_svm
param_linvar False
Name: 1311, dtype: object
->
prepr_name RhythmData
cv_acc 1
test_acc 1
Name: 1311, dtype: object
----
data_name columbia-train
model_name simple_cnn
param_linvar False
Name: 4325, dtype: object
->
prepr_name RhythmData
cv_acc 0.991667
test_acc 1
Name: 4325, dtype: object
----
data_name columbia-train
model_name simple_cnn
param_linvar True
Name: 4928, dtype: object
->
prepr_name MIRData
cv_acc 1
test_acc 0.95
Name: 4928, dtype: object
----
==========================
****************************
****************************
Linear seperability for each preprocessing
data_name GTZAN
prepr_name RhythmData
cv_acc 0.890769
test_acc 0.975
Name: 2515, dtype: object
--------
data_name GTZAN
prepr_name MIRData
cv_acc 0.906154
test_acc 0.8
Name: 3119, dtype: object
--------
data_name GTZAN
prepr_name SpectroData
cv_acc 0.82
test_acc 0.75
Name: 3723, dtype: object
--------
data_name columbia-train
prepr_name RhythmData
cv_acc 0.958333
test_acc 0.975
Name: 4327, dtype: object
--------
data_name columbia-train
prepr_name MIRData
cv_acc 0.883333
test_acc 0.95
Name: 4931, dtype: object
--------
data_name columbia-train
prepr_name SpectroData
cv_acc 0.966667
test_acc 0.95
Name: 5535, dtype: object
--------
data_name GTZAN
prepr_name RhythmData
cv_acc 0.890769
test_acc 0.975
Name: 12275, dtype: object
--------
data_name GTZAN
prepr_name MIRData
cv_acc 0.906154
test_acc 0.8
Name: 12879, dtype: object
--------
data_name GTZAN
prepr_name SpectroData
cv_acc 0.82
test_acc 0.75
Name: 13483, dtype: object
--------
data_name columbia-train
prepr_name RhythmData
cv_acc 0.958333
test_acc 0.975
Name: 14087, dtype: object
--------
data_name columbia-train
prepr_name MIRData
cv_acc 0.883333
test_acc 0.95
Name: 14691, dtype: object
--------
data_name columbia-train
prepr_name SpectroData
cv_acc 0.966667
test_acc 0.95
Name: 15295, dtype: object
--------
data_name GTZAN
prepr_name RhythmData
cv_acc 0.890769
test_acc 0.975
Name: 22035, dtype: object
--------
data_name GTZAN
prepr_name MIRData
cv_acc 0.906154
test_acc 0.8
Name: 22639, dtype: object
--------
data_name GTZAN
prepr_name SpectroData
cv_acc 0.82
test_acc 0.75
Name: 23243, dtype: object
--------
data_name columbia-train
prepr_name RhythmData
cv_acc 0.958333
test_acc 0.975
Name: 23847, dtype: object
--------
data_name columbia-train
prepr_name MIRData
cv_acc 0.883333
test_acc 0.95
Name: 24451, dtype: object
--------
data_name columbia-train
prepr_name SpectroData
cv_acc 0.966667
test_acc 0.95
Name: 25055, dtype: object
--------
****************************
****************************
Benefit of using linvar for each preprocessing and model
data_name GTZAN
prepr_name MIRData
model_name linear
Name: 3118, dtype: object
cv_acc benefit: -0.007384603096888709
test_acc benefit: 0.02499999999999991
--------
data_name GTZAN
prepr_name MIRData
model_name simple_cnn
Name: 3116, dtype: object
cv_acc benefit: 0.04000001720281754
test_acc benefit: 0.0
--------
data_name GTZAN
prepr_name RhythmData
model_name linear
Name: 2514, dtype: object
cv_acc benefit: 0.008615407907045736
test_acc benefit: 0.0
--------
data_name GTZAN
prepr_name RhythmData
model_name simple_cnn
Name: 2512, dtype: object
cv_acc benefit: 0.0006153956559987739
test_acc benefit: 0.0
--------
data_name GTZAN
prepr_name SpectroData
model_name linear
Name: 3722, dtype: object
cv_acc benefit: 0.07107693022948036
test_acc benefit: 0.09999999999999998
--------
data_name GTZAN
prepr_name SpectroData
model_name simple_cnn
Name: 3720, dtype: object
cv_acc benefit: 0.007384620006267939
test_acc benefit: -0.02499999999999991
--------
data_name columbia-train
prepr_name MIRData
model_name linear
Name: 4930, dtype: object
cv_acc benefit: 0.04166666269302377
test_acc benefit: -0.04999999999999993
--------
data_name columbia-train
prepr_name MIRData
model_name simple_cnn
Name: 4928, dtype: object
cv_acc benefit: 0.016666662693023637
test_acc benefit: 0.0
--------
data_name columbia-train
prepr_name RhythmData
model_name linear
Name: 4326, dtype: object
cv_acc benefit: 0.008333341280619577
test_acc benefit: 0.0
--------
data_name columbia-train
prepr_name RhythmData
model_name simple_cnn
Name: 4324, dtype: object
cv_acc benefit: -0.02499999602635683
test_acc benefit: -0.025000000000000022
--------
data_name columbia-train
prepr_name SpectroData
model_name linear
Name: 5534, dtype: object
cv_acc benefit: -0.008333329359690222
test_acc benefit: -0.02499999999999991
--------
data_name columbia-train
prepr_name SpectroData
model_name simple_cnn
Name: 5532, dtype: object
cv_acc benefit: 0.025000003973643214
test_acc benefit: 0.0
--------