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
IMPROVE API Reference
Parameters
Preprocess
Train
Infer
Creating Model-Specific Parameters
Configuration Files
improvelib.utils
get_response_data
get_x_data
get_response_with_features
get_features_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
v0.1.0
v0.0.3
Branch Naming
IMPROVE
IMPROVE API Reference
Metrics Functions
View page source
Metrics Functions
The following functions used in improvelib and can be used as needed.
compute_metrics
mse
rmse
pearson
spearman
r_square
acc
bacc
kappa
f1
precision
recall
roc_auc
aupr
Version: latest
Versions
latest
v0.1.0
v0.0.3-beta