autoNeuro

Pipeline to run the experiments with tabular data for classification tasks (Major Depressive Disorder vs Healthy Control, Schisophrenia vs Healthy Control).

Pipeline consist of several steps

1. GridSearchBase - search for the best ML method with best feature selection algorithm from (XGBClassifier, SVC, RandomForestClassifier(), "lr" : LogisticRegression() ) and feature selection methods (SelectKBest, RandomForestClassifier (select top important features), LogisticRegression (select top important features) ).It is located in core/gridcv.py module. You can configure the parameters for grid search algorithm for each model in GRID_CONFIG_MODELS in core/constants.py module.
2. ExperimentsInfo - calculate the most important features for the best methods, build roc curves and other metrics, save important features as DataFrame to EXPERIMENTS_PATH (experiments.py module). It is located in core/metrics.py module.
3. FeaturesStats - calculate the distributions and post-hoc t-test for the the most important features calculated in the previos step. It is located in core/stats.py

How to run

You can run code using experiments.py or see examples with jupyter notebooks in experiments package.

  X - pd.DataFrame with numeric data
  y - pd.Series with target values
  experiment_name - name of experiment (results are saved in directiry with this name) 

  best_result,best_f1 =run(X,y, experiment_name=file.stem, repeats =10, topN=1,scaling=False)