import ensembleNN2
import trainAndTestModels
import pandas
from imblearn.combine import SMOTETomek
from imblearn.combine import SMOTEENN
from imblearn.under_sampling import TomekLinks
from imblearn.under_sampling import RandomUnderSampler
from imblearn.under_sampling import ClusterCentroids
from imblearn.under_sampling import EditedNearestNeighbours
from imblearn.over_sampling import SMOTE
df = pandas.read_csv('osu18_cerenkov_feat_mat.tsv', sep='\t')
testRuns = []
nreps = 5
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=None,
    nreps=nreps)
testRuns.append({'name': 'xgboost_8020', 'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=
    RandomUnderSampler(), nreps=nreps)
testRuns.append({'name': 'xgboost_8020_eqClasses', 'accuracy': acc, 'AUROC':
    auc})
k = 10
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold'.format(k), 'accuracy': acc,
    'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_eqClasses'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=None,
    batchSize=150, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble', 'accuracy': acc, 'AUROC': auc})
batchSize = 150
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble_eqClasses', 'accuracy': acc,
    'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=None,
    batchSize=150, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
with open('testLog.csv', 'w') as wf:
    wf.write('model,accuracy,AUROC\n')
    for run in testRuns:
        wf.write(str(run['name']) + ',' + str(run['accuracy']) + ',' + str(
            run['AUROC']) + '\n')

2f4006d07fa9e045bca864f4d94c34ae5addd8f5
<mask token>

<mask token>
testRuns.append({'name': 'xgboost_8020', 'accuracy': acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'xgboost_8020_eqClasses', 'accuracy': acc, 'AUROC':
    auc})
<mask token>
testRuns.append({'name': 'xgboost_{}Fold'.format(k), 'accuracy': acc,
    'AUROC': auc})
<mask token>
testRuns.append({'name': 'xgboost_{}Fold_eqClasses'.format(k), 'accuracy':
    acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble'.format(k),
    'accuracy': acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'NN_8020Ensemble', 'accuracy': acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'NN_8020Ensemble_eqClasses', 'accuracy': acc,
    'AUROC': auc})
<mask token>
testRuns.append({'name': 'NN_{}Fold_8020Ensemble'.format(k), 'accuracy':
    acc, 'AUROC': auc})
<mask token>
testRuns.append({'name': 'NN_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
with open('testLog.csv', 'w') as wf:
    wf.write('model,accuracy,AUROC\n')
    for run in testRuns:
        wf.write(str(run['name']) + ',' + str(run['accuracy']) + ',' + str(
            run['AUROC']) + '\n')

<mask token>
df = pandas.read_csv('osu18_cerenkov_feat_mat.tsv', sep='\t')
testRuns = []
nreps = 5
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=None,
    nreps=nreps)
testRuns.append({'name': 'xgboost_8020', 'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=
    RandomUnderSampler(), nreps=nreps)
testRuns.append({'name': 'xgboost_8020_eqClasses', 'accuracy': acc, 'AUROC':
    auc})
k = 10
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold'.format(k), 'accuracy': acc,
    'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_eqClasses'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=None,
    batchSize=150, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble', 'accuracy': acc, 'AUROC': auc})
batchSize = 150
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble_eqClasses', 'accuracy': acc,
    'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=None,
    batchSize=150, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
with open('testLog.csv', 'w') as wf:
    wf.write('model,accuracy,AUROC\n')
    for run in testRuns:
        wf.write(str(run['name']) + ',' + str(run['accuracy']) + ',' + str(
            run['AUROC']) + '\n')

import ensembleNN2
import trainAndTestModels
import pandas
from imblearn.combine import SMOTETomek
from imblearn.combine import SMOTEENN
from imblearn.under_sampling import TomekLinks
from imblearn.under_sampling import RandomUnderSampler
from imblearn.under_sampling import ClusterCentroids
from imblearn.under_sampling import EditedNearestNeighbours
from imblearn.over_sampling import SMOTE
df = pandas.read_csv('osu18_cerenkov_feat_mat.tsv', sep='\t')
testRuns = []
nreps = 5
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=None,
    nreps=nreps)
testRuns.append({'name': 'xgboost_8020', 'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoost(df, test_split=0.2, sampler=
    RandomUnderSampler(), nreps=nreps)
testRuns.append({'name': 'xgboost_8020_eqClasses', 'accuracy': acc, 'AUROC':
    auc})
k = 10
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold'.format(k), 'accuracy': acc,
    'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.0, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_eqClasses'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=None,
    k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = trainAndTestModels.xgBoostKFold(df, test_split=0.2, sampler=
    RandomUnderSampler(), k=k, nreps=nreps)
testRuns.append({'name': 'xgboost_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=None,
    batchSize=150, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble', 'accuracy': acc, 'AUROC': auc})
batchSize = 150
auc, acc = ensembleNN2.feedforward(df, test_split=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, num_models=nreps)
testRuns.append({'name': 'NN_8020Ensemble_eqClasses', 'accuracy': acc,
    'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=None,
    batchSize=150, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble'.format(k), 'accuracy':
    acc, 'AUROC': auc})
auc, acc = ensembleNN2.feedforwardKFold(df, test_size=0.2, sampler=
    RandomUnderSampler(), batchSize=batchSize, k=k, nreps=nreps)
testRuns.append({'name': 'NN_{}Fold_8020Ensemble_eqClasses'.format(k),
    'accuracy': acc, 'AUROC': auc})
with open('testLog.csv', 'w') as wf:
    wf.write('model,accuracy,AUROC\n')
    for run in testRuns:
        wf.write(str(run['name']) + ',' + str(run['accuracy']) + ',' + str(
            run['AUROC']) + '\n')
