exhaufs 0.1

Exhaustive Feature Selection

ExhaustiveFS

Exhaustive feature selection for classification and survival analysis.

Table of Contents

Introduction
Requirements
Running ExhaustiveFS
Functions and classes
etc

Introduction

The main idea underlying ExhaustiveFS is the exhaustive search of feature subsets for constructing the most powerfull classification and survival regression models. Since computational complexity of such approach grows exponentially with respect to combination length, we first narrow down features list in order to make search practically feasible. Briefly, the following pipeline is implemented:

1. Feature pre-selection: select fixed number of features for the next steps.
2. Feature selection: select n features for exhaustive search.
3. Exhaustive search: iterate through all possible k-element feature subsets and fit classification/regression models.

Values of n and k actually define running time of the pipeline (there are C_n^k feature subsets). For example, iterating through all 8-gene signatures composed of n = 20 genes is possible (see example breast cancer data below), while search for over n = 1000 genes will never end even on the most powerful supercomputer.

Input data can consist from different batches (datasets), and each dataset should be labeled by one of the following types:

1. Training set: samples from training datasets will be used for tuning classification/regression models. At least one such dataset is required; if multiple given, the union will be used.
2. Filtration set: all tuned models will be first evaluated on training and filtration sets. If specified thresholds for accuracy are reached, model will be evaluated on validation (test) sets. The use of filtration sets is optional.
3. Validation (test) set: performance of models which passed filtration thresholds are then evaluated on validation sets. At least one such dataset is required; if multiple given, model will be evaluated on all test sets independently.

TODO: add flowchart.

Requirements

List of requirements

• scipy
• scikit-learn
• numpy
• pandas
• lifelines
• scikit-survival
• xgboost

You can install them via:
pip3 install -r requirements.txt

Running ExhaustiveFS

Step 1: data preparation

Before running the tool, you should prepare three csv tables containing actual data, its annotation and n \ k grid. Both for classification and survival analysis data table should contain numerical values associated with samples (rows) and features (columns):

Example

	Feature 1	Feature 2
Sample 1	17.17	365.1
Sample 2	56.99	123.9
...
Sample 98	22.22	123.4
Sample 99	23.23	567.8
...
Sample 511	10.82	665.8
Sample 512	11.11	200.2

Sample annotation table formats are different for classification and survival analysis. For classification it should contain binary indicator of sample class (e.g., 1 for recurrent tumor and 0 for non-recurrent), dataset (batch) label and dataset type (Training/Filtration/Validation).
It is important that Class = 1 represents "Positives" and Class = 0 are "negatives", otherwise accuracy scores may be calculated incorrectly.
Note that annotation should be present for each sample listed in the data table in the same order:

Example

	Class	Dataset	Dataset type
Sample 1	1	GSE3494	Training
Sample 2	0	GSE3494	Training
...
Sample 98	0	GSE12093	Filtration
Sample 99	0	GSE12093	Filtration
...
Sample 511	1	GSE1456	Validation
Sample 512	1	GSE1456	Validation

For survival analysis, annotation table should contain binary event indicator and time to event:

Example

	Event	Time to event	Dataset	Dataset type
Sample 1	1	100.1	GSE3494	Training
Sample 2	0	500.2	GSE3494	Training
...
Sample 98	0	623.9	GSE12093	Filtration
Sample 99	0	717.1	GSE12093	Filtration
...
Sample 511	1	40.5	GSE1456	Validation
Sample 512	1	66.7	GSE1456	Validation

Table with n and k grid for exhaustive feature selection:
n is a number of selected features, k is a length of each features subset.

If you are not sure what values for n k to use, see Step 3: defining a n, k grid

Example

n	k
100	1
100	2
...	...
20	5
20	10
20	15

TODO: add real example to examples/ and write about it here.

Step 2: creating configuration file

Configuration file is a json file containing all customizable parameters for the model (classification and survival analysis)

Available parameters

🔴!NOTE! - All paths to files / directories should be relative to the configuration file directory

• data_path Path to csv table of the data.

• annotation_path Path to csv table of the data annotation.

• n_k_path Path to a n/k grid file.

• output_dir Path to directory for output files. If not exist, it will be created.

• feature_pre_selector
  TODO: add link and table of possible choices below
  Name of feature pre-selection function from ./core/feature_pre_selectors.py.

• feature_pre_selector_kwargs
  Object/Dictionary of keyword arguments for feature pre-selector function.

• feature_selector
  TODO: add link and table of possible choices below
  Name of feature selection function from ./core/feature_selectors.py.

• feature_selector_kwargs
  TODO: add link and table of possible choices below
  Object/Dictionary of keyword arguments for feature selector function.

• preprocessor Name of class for data preprocessing from sklearn.preprocessing.

• preprocessor_kwargs Object/Dictionary of keyword arguments for preprocessor class initialization.

• model
  TODO: add link and table of possible choices below
  Name of class for classification / survival analysis from ./core/classifiers.py.

• model_kwargs Object/Dictionary of keyword arguments for model initialization.

• model_CV_ranges Object/Dictionary defining model parameters which should be cross-validated. Keys are parameter names, values are lists for grid search.

• model_CV_folds Number of folds for K-Folds cross-validation.

• limit_feature_subsets If true, limit the number of processed feature subsets.

• n_feature_subsets Number of processed feature subsets.

• shuffle_feature_subsets If true, processed feature subsets are selected randomly instead of alphabetical order.

• max_n Maximal number of selected features.

• max_estimated_time Maximal estimated pipeline running time.

• scoring_functions List with names for scoring functions (from accuracy_scores.py) which will be calculated for each classifier.

• main_scoring_function Key from scoring_functions dict defining the "main" scoring function which will be optimized during cross-validation and will be used for classifier filtering.

• main_scoring_threshold A number defining threshold for classifier filtering: classifiers with score below this threshold on training/filtration sets will not be further evaluated.

  • n_processes Number of processes / threads to run on.

• random_state Random seed (set to an arbitrary integer for reproducibility).

• verbose If true, print running time for each pair of n, k.

Step 3: defining a n, k grid

To estimate running time of the exhaustive pipeline and define adequate n / k values you can run:

python3 running_time_estimator.py <config_file> <max_k> <max_estimated_time> <n_feature_subsets> <search_max_n> <is_regressor>

where

• config_file is the path to json configuration file.
• max_k is the maximal length of each features subset.
• max_estimated_time is the maximal estimated time (in hours) of single running of the exhaustive pipeline.
• n_feature_subsets is the number of feature subsets processed by the exhaustive pipeline (100 is usually enough).
• search_max_n is 1 if you need to find the maximal number of selected features for which estimated run time of the exhaustive pipeline is less than max_estimated_time, and 0 otherwise.
• is_regressor is 1 if you the estimation is for the regression.

Above script calculates maximum possible values n / k for each k=1...max_n such that pipeline running time for each pair (n, k) is less then max_estimated_time

Step 4: running the exhaustive pipeline

When input data, configuration file and n, k grid are ready, the exhaustive pipeline could be executed -

• Classifiers:

python3 build_classifiers.py <config_file>

• Regressions:

python3 build_regressors.py <config_file>

This will generate multiple files in the specified output folder:

• models.csv: this file contains all models (classifiers or regressors) which passed the filtration together with their quality metrics.
• summary_n_k.csv: for each pair of n, k three numbers are given: number of models which passed the filtration, number of models which showed reliable performance (i.e., passed quality thresholds) on the validation set and their ratio (in %). Low percentage of validation-reliable models together with high number of filtration-reliable models is usually associated with overfitting.
• summary_features.csv: for each feature percentage of models carrying this feature is listed (models which passed the filtration are considered).

Step 5: generating report for a single model

To get detailed report on the specific model (== specific set of features):

• Create configuration file (use ./examples/make_(classifier | regressor)_summary/config.json as template) and set following key parameters:
  • data_path - path to dataset used for search of classifiers (relative to directory with configuration file);
  • "annotation_path" - path to annotation file (relative to directory with configuration file);
  • output_dir - path to output directory for detailed report (relative to directory with configuration file);
  • features_subset - set of features belonging to the classifier of interest;
• • For classifier run python3 make_classifier_summary.py <config_file>
  • For regressor run python3 make_regressor_summary.py <config_file>
• Check the detailed report in output_dir

Functions ans classes

• Feature pre-selectors

  • from_file

    Pre-select features from a given file

    name: from_file
    kwargs:

    {
      "sep": " "
    }
    

• Feature selectors

  • t_test

    Select n features with the lowest p-values according to t-test

    name: t_test
    kwargs:

    {
      "datasets": ["Training", "Filtration"]
    }
    

  • spearman_correlation

    Select n features with the highest correlation with target label

    name: spearman_correlation
    kwargs:

    {
      "datasets": ["Training", "Filtration"]
    }
    

  • from_file

    Select first n features from a given file

    name: spearman_correlation
    kwargs:

    {
      "sep": " "
    }
    

  • median

    Select n features with the highest median value
    name: median
    kwargs:

    {}
    

  Regression specific selectors:

  • cox_concordance

    Select n features with the highest concordance index on one-factor Cox regression.

    name: cox_concordance
    kwargs:

    {
      "datasets": ["Training", "Filtration"]
    }
    

  • cox_dynamic_auc

    Select n features with the highest time-dependent auc on one-factor Cox regression.

    name: cox_dynamic_auc
    kwargs:

    {
      "year": 3, // time at which to calculate auc
      "datasets": ["Training", "Filtration"]
    }
    

  • cox_hazard_ratio

    Select n features with the highest hazard ratio on one-factor Cox regression.

    name: cox_hazard_ratio
    kwargs:

    {
      "datasets": ["Training", "Filtration"]
    }
    

  • cox_likelihood

    Select n features with the highest log-likelihood on one-factor Cox regression.

    name: cox_likelihood
    kwargs:

    {
      "datasets": ["Training", "Filtration"]
    }
    

• Classifiers

  • SVC
  • KNeighborsClassifier
  • RandomForestClassifier
  • XGBClassifier

  Accuracy scores

  • TPR
  • FPR
  • TNR
  • min_TPR_TNR
• Regressors

  • CoxRegression

  Accuracy scores

  • concordance_index
  • dynamic_auc
  • hazard_ratio
  • logrank

etc

Breast and colorectal cancer microarray datasets: OneDrive.