Options being explored

[amritbhanu]

Transformations

• StandardScaler
• MinMaxScaler
• MaxAbsScaler
• RobustScaler(quantile_range=(a, b))
  • a,b=_randint(0,50),_randint(51,100)
• KernelCenterer
• QuantileTransformer(n_quantiles=a, output_distribution=c, subsample=b)
  • a, b = _randint(100, 1000), _randint(1000, 1e5)
  • c=_randchoice(['normal','uniform'])
• Normalizer(norm=a)
  • a = _randchoice(['l1', 'l2','max'])
• Binarizer(threshold=a)
  • a=_randuniform(0,100)

Learners

• DecisionTreeClassifier(criterion=b, splitter=c, min_samples_split=a)
  • a=_randuniform(0.0,1.0)
  • b=_randchoice(['gini','entropy'])
  • c=_randchoice(['best','random'])
• RandomForestClassifier(n_estimators=a,criterion=b,min_samples_split=c)
  • a = _randint(50, 150)
  • b = _randchoice(['gini', 'entropy'])
  • c = _randuniform(0.0, 1.0)
• LogisticRegression(penalty=a, tol=b, C=float(c), solver='liblinear')
  • a=_randchoice(['l1','l2'])
  • b=_randuniform(0.0,0.1)
  • c=_randint(1,500)
• MultinomialNB(alpha=a)
  • a=_randuniform(0.0,0.1)
• KNeighborsClassifier(n_neighbors=a, weights=b, p=d, metric=c)
  • a = _randint(2, 25)
  • b = _randchoice(['uniform', 'distance'])
  • c = _randchoice(['minkowski','chebyshev'])
  • if c=='minkowski': d=_randint(1,15) else: d=2

[timm]

make this into table4 of https://arxiv.org/pdf/1805.00336.pdf

[timm]

convert everything to tile charts (using stats.py) which gives you the statistical results

[timm]

need popt20

[timm]

termination graphs (x-axis = #evaluation), y-axis = improvement (each iteration iqr)

[timm]

redo all the results from both FFT papers

[timm]

consider the problem of tabu search across a wide range.