IMPROVE

Contents:

  • What is the IMPROVE project?
    • Background
    • Future Directions
    • Acknowledgments
    • Publications
    • Contact Us
  • Installation
  • Quickstart
  • User Guide
    • Running a Single Model
    • Curating New Models
      • Curating Steps
      • Requirements for Curated Models
      • Step-by-Step Example
        • Preprocessing Script Example
        • Training Script Example
        • Inference Script Example
        • Configuration Example
      • Templates
        • Preprocessing
        • Training
        • Inference
        • Model Parameters
        • Config File
        • Readme
        • Download Scripts
    • Using Workflows
      • CSA
        • Brute-Force CSA
        • Scaling CSA
        • Swarm CSA
        • Post-process CSA Results
      • HPO
        • DeepHyper HPO
      • LCA
        • Generate LCA Splits
        • Brute-Force LCA
        • Swarm LCA
        • Post-process LCA Results
    • Using Non-Benchmark Data
  • Applications
    • Drug Response Prediction
      • Problem Formulation
      • Benchmark Data
      • Models
        • LGBM
        • XGBoost-DRP
        • RandomForest-DRP
        • DeepCDR
        • GraphDRP
        • HiDRA
        • tCNNS
        • Uno
        • DeepTTC
        • DualGCN
        • IGTD
        • PaccMann-MCA
        • PathDSP
    • Synergy
      • Problem Formulation
      • Benchmark Data
      • Models
        • Matchmaker
        • DeepDDS
        • RandomForest-Synergy
        • XGBoost-Synergy
  • IMPROVE API Reference
    • Parameters
      • Preprocess
      • Train
      • Infer
      • Creating Model-Specific Parameters
      • Configuration Files
    • improvelib.utils
      • get_y_data
      • get_x_data
      • get_y_data_with_features
      • get_y_data_in_response
      • determine_transform
      • transform_data
      • build_ml_data_file_name
      • save_stage_ydf
      • build_model_path
      • store_predictions_df
      • compute_performance_scores
      • get_common_samples
      • get_common_elements
      • Timer
    • improvelib.metrics
      • compute_metrics
      • mse
      • rmse
      • pearson
      • spearman
      • r_square
      • acc
      • bacc
      • kappa
      • f1
      • precision
      • recall
      • roc_auc
      • aupr
  • Developer Guide
    • Creating a Workflow
    • Creating an Application
  • Release Notes
    • v1.0.0
    • v0.1.0
    • v0.0.3
    • Branch Naming
  • Frequently Asked Questions
IMPROVE
  • IMPROVE API Reference
  • Utility Functions
  • View page source

Utility Functions

The following functions are part of improvelib and should be used to standardize aspects of the workflow.

  • get_y_data
  • get_x_data
  • get_y_data_with_features
  • get_y_data_in_response
  • determine_transform
  • transform_data
  • build_ml_data_file_name
  • save_stage_ydf
  • build_model_path
  • store_predictions_df
  • compute_performance_scores
  • get_common_samples
  • get_common_elements
  • Timer
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