machine_learning

Schizophrenia

Repository for analysis of schizophrenia dataset

This projects needs python 3 and the dependencies specified in environment.yml.

To use the classical machine learning pipeline a configuration file needs to be created as a JSON file (details below). To run the analysis execute: python analysis/classical_ml/classical_ml.py cfg_file_path.

The configuration file describes how the analysis will be run. It should be on a JSON format with the following structure:

Attribute	Value	Remarks
read_mode	String that specifies the type of data to be used	Value should be one of the following: BIDS, table, h5
paths	JSON Object with specification of important paths	The paths that can be described here are: data: path to BIDS folder table: path to table with data h5:{training:path_tr, holdout:path_ho}: paths to h5 files atlas: path to atlas for region based analysis
images	JSON Object with the description for data loading and preprocessing	The possible keys (default value) are: transform: list with functions that will be applied to each image target_transform_map (transform labels to a range of integers) : name of an existing function n_samples (null): Number of samples of the whole dataset to use, uses the entire dataset if null. use_holdout (True): Flag that determines if the data should be split in training test sets. split_seed (0) seed to do the train-test split test_size (null): Fraction of the dataset to use as holdout set. See sklearn.model_selection.train_test_split. label ("label"): Column associated to the data points label in a tabular columns (null): Array with the list of columns to use when read_mode is table . If none, all columns but the label are used. stratify_cols (["label"]) List of columns use to stratify the train test split. mean_normalize (False) Flag to standarize the data minmax_normalize (False) Flag for min-max normalization mask_label (null): Name of a region to extract, ignored if null. use_atlas (False): Flag to use an atlas as a dimensionality reduction. atlas_strategy ('mean'): Aggregation function if use_atlas is true.
training	JSON Object with the description for data training	The possible keys (default value) are: n_splits (3): Number of folds for cross validation trials (4): Number of independent experiments to perform scorers (['balanced_accuracy']) list of scoring metrics to evaluate the model retrain_metric (balanced_accuracy) Metric from scoring to be used to retrain the model return_train_score (False): Computes and return the train scores in the GridSearchCV models List to specify the models to be used, where each element corresponds to one independent model to analyse. Each element should be a list of strings with the names of estimators to be chained. E.g. [['PCA', 'SVC'], ['DummyClassifier']] means that 2 models will be tested, first a pipeline that performs PCA followed by a support vector classifier, and the second model is just a dummy classifier parameters_lst: Array of JSON objects with as many elements as models were specified. The key-values for ach element should correspond to the name of a hyperparameter mapped to a list of the values to be tried. E.g. [{"PCA__n_components":[5, 20], {"SVC__C:[1, 10]"}, {"DummyClassifier__strategy":["most_frequent"]}]
holdout	JSON Object with the description for inference in holdout set (optional)	The possible keys (default value) are: scorers (['balanced_accuracy']) list of scoring metrics to evaluate the model best_metric (balanced_accuracy) Metric from training.scorers to be used to select a model model_selection (all_models): strategy to select a model. See model_selection plotting (True): Flag to determine if the results should be saved as a graph voting: Groups multiple selected models into an ensemble through a voting system. Works when model_selection is all_models chosen_estimator: See model_selection chosen_trial: See model_selection
	model_selection	It can have one of the following values: all_models: Reports mean and variance across trials for all estimators best_model: Selects best overall model best_model_type: Selects the estimator that performed best in average according to best_metric and retrieves the models for every trial best_model_type_random: as best_model_type but selects a random model from all the trials random_model: A random model is selected Selects a model from a random trial for all estimators specific_model: Selects the models from chosen_estimator for the trial chosen_trial