features: null
transpose: true

selector_grid_search: true
selector_grid_search_params:
  cv:
    splitter: StratifiedShuffleSplit
    n_splits: 5
    test_size: 0.1
  iid: false
  scoring: roc_auc

preprocess_steps:
  # apply log transformation
  - log_transform:
      name: LogTransform
      type: transformer
      enabled: true
      params:
        base: 2
        pseudo_count: 1
  # method to scale features across samples
  - scale_features:
      name: StandardScaler
      type: scaler
      enabled: true
      params:
        with_mean: true

# all possible selectors
feature_selector_params:
  DiffExp_TTest:
    name: DiffExpFilter
    type: selector
    params:
      # rscript_path: /usr/bin/Rscript
      score_type: neglogp
      method: ttest
  MaxFeatures_RandomForest:
    name: MaxFeatures
    type: selector
    params:
      classifier: RandomForestClassifier
      grid_search: true
      grid_search_params:
        param_grid:
          n_estimators: [25, 50, 75]
          max_depth: [3, 4, 5]
  MaxFeatures_LogRegL2:
    name: MaxFeatures
    type: selector
    params:
      classifier: LogisticRegression
      # parameters for the classifier used for feature selection
      classifier_params:
        penalty: l2
        solver: liblinear
      # grid search for hyper-parameters for the classifier
      grid_search: true
      grid_search_params:
        param_grid:
          C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
  MaxFeatures_LogRegL1:
    name: MaxFeatures
    type: selector
    params:
      classifier: LogisticRegression
      # parameters for the classifier used for feature selection
      classifier_params:
        penalty: l1
        solver: liblinear
      # grid search for hyper-parameters for the classifier
      grid_search: true
      grid_search_params:
        param_grid:
          C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
  MaxFeatures_ElasticNet:
    name: MaxFeatures
    type: selector
    params:
      classifier: SGDClassifier
      classifier_params:
        penalty: elasticnet
        max_iter: 100
        tol: 0.001
      grid_search: true
      grid_search_params:
        param_grid:
          alpha: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
          l1_ratio: [0.15, 0.30, 0.45, 0.60, 0.75, 0.90]
  SIS:
    name: SIS
    type: selector
    params:
      # rscript_path: /usr/bin/Rscript
      sis_params:
        family: binomial
        tune: bic
        penalty: lasso
        varISIS: cons
  ReliefF:
    name: ReliefF
    type: selector
    params:
      n_jobs: 1
  SURF:
    name: SURF
    type: selector
    params:
      n_jobs: 1
  MultiSURF:
    name: MultiSURF
    type: selector
    params:
      n_jobs: 1
  RandomSubset_RandomForest:
    name: RandomSubsetSelector
    type: selector
    params:
      subset_size: 50
      n_subsets: 20
      classifier: RandomForestClassifier
      grid_search: true
      grid_search_params:
        param_grid:
          n_estimators: [25, 50, 75]
          max_depth: [3, 4, 5]
  RandomSubset_LogRegL2:
    name: RandomSubsetSelector
    type: selector
    params:
      subset_size: 50
      n_subsets: 20
      classifier: LogisticRegression
      # parameters for the classifier used for feature selection
      classifier_params:
        penalty: l2
        solver: liblinear
      # grid search for hyper-parameters for the classifier
      grid_search: true
      grid_search_params:
        param_grid:
          C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
  RandomSubset_LogRegL1:
    name: RandomSubsetSelector
    type: selector
    params:
      subset_size: 50
      n_subsets: 20
      classifier: LogisticRegression
      # parameters for the classifier used for feature selection
      classifier_params:
        penalty: l1
        solver: liblinear
      # grid search for hyper-parameters for the classifier
      grid_search: true
      grid_search_params:
        param_grid:
          C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]

# template for grid_search_params in classifiers
classifier_grid_search_params:
  cv:
    splitter: StratifiedShuffleSplit
    n_splits: 5
    test_size: 0.1
  iid: false
  scoring: roc_auc

classifier_params:
  LogRegL2:
    classifier: LogisticRegression
    # parameters for the classifier used for feature selection
    classifier_params:
      penalty: l2
      solver: liblinear
    # grid search for hyper-parameters for the classifier
    grid_search: true
    grid_search_params:
      param_grid:
        C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
  RandomForest:
    classifier: RandomForestClassifier
    grid_search: true
    grid_search_params:
      param_grid:
        n_estimators: [25, 50, 75]
        max_depth: [3, 4, 5]
  RBFSVM:
    classifier: SVC
    classifier_params:
      kernel: rbf
      gamma: scale
    grid_search: true
    grid_search_params:
      param_grid:
        C: [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000, 100000]
  DecisionTree:
    classifier: DecisionTreeClassifier
    grid_search: true
    grid_search_params:
      param_grid:
        max_depth: [2, 3, 4, 5, 6, 7, 8]
  MLP:
    classifier: MLPClassifier
    classifier_params:
      activation: relu
      solver: adam
      max_iter: 40
    grid_search: true
    grid_search_params:
      param_grid:
        hidden_layer_sizes: [[50], [100], [150], [200], [250], [300]]

# cross-validation parameters for performance evaluation
cv_params:
  splitter: StratifiedShuffleSplit
  # number of train-test splits for cross-validation
  n_splits: 5
  # number or proportion of samples to use as test set
  test_size: 0.1
  # scoring metric for performance evaluation
  scoring: roc_auc
# method for computing sample weight
#  balanced: compute sample weight from data such that classes are balanced
sample_weight: balanced

# list of feature selector names
feature_selectors: [DiffExp_TTest, MaxFeatures_RandomForest, MaxFeatures_LogRegL2, MaxFeatures_LogRegL1,
  MaxFeatures_ElasticNet, SIS, ReliefF, SURF, MultiSURF, RandomSubset_RandomForest,
  RandomSubset_LogRegL2, RandomSubset_LogRegL1]
# list of classifier names
classifiers: [LogRegL2, RandomForest, RBFSVM, DecisionTree, MLP]