scalr

data_ingestion_pipeline

This file is a class for data ingestion into the pipeline.

class scalr.data_ingestion_pipeline.DataIngestionPipeline(data_config: dict, dirpath: str = '.')

Bases: object

Class for Data Ingestion into the pipeline

generate_mappings()

A function to generate an Integer mapping to and from target columns.

generate_train_val_test_split()

A function to split data into train, validation and test sets.

get_updated_config()

This function returns updated configs.

preprocess_data()

A function to apply preprocessing on data splits.

eval_and_analysis_pipeline

This file contains an implementation of model evaluation and performs downstream analysis tasks.

class scalr.eval_and_analysis_pipeline.EvalAndAnalysisPipeline(analysis_config, dirpath, device)

Bases: object

Class for evaluation and analysis of the trained model.

build_dataloaders()

A function to build dataloader for train, validation, and test data.

evaluation_and_classification_report()

A function to evaluate the trained model and generate classification report on test data.

gene_analysis()

A function to perform analysis on trained model to get top genes and biomarkers.

get_updated_config() → dict

A function to return updated configs.

load_data_and_targets_from_config(data_config: dict)

A function to load data and targets from data config.

Parameters:

data_config – Data config.

perform_downstream_anlaysis()

A function to perform all downstream analysis tasks on model and data.

set_data_and_targets(train_data: AnnData | AnnCollection, val_data: AnnData | AnnCollection, test_data: AnnData | AnnCollection, target: str | list[str], mappings: dict)

A function to set data when you don’t use data directly from config, but rather by other sources like feature subsetting, etc.

Parameters:

• train_data (Union[AnnData, AnnCollection]) – Training data.

• val_data (Union[AnnData, AnnCollection]) – Validation data.

• target (Union[str, list[str]]) – Target columns name(s).

• mappings (dict) – Mapping of a column value to ids eg. mappings[column_name][label2id] = {A: 1, B:2, …}.

feature_extraction_pipeline

This file contains the implementation of feature subsetting, model training followed by top feature extraction.

class scalr.feature_extraction_pipeline.FeatureExtractionPipeline(feature_selection_config, dirpath, device)

Bases: object

feature_scoring() → DataFrame

A function to generate scores of each feature for each class using a scorer and chunked models.

feature_subsetted_model_training() → list[Module]

This function train models on subsetted data containing feature_subsetsize genes.

get_updated_config() → dict

This function returns updated configs.

load_data_and_targets_from_config(data_config: dict)

A function to load data and targets from data config.

Parameters:

data_config – Data config.

set_data_and_targets(train_data: AnnData | AnnCollection, val_data: AnnData | AnnCollection, target: str | list[str], mappings: dict)

A function to set data when you don’t use data directly from config, but rather by other sources like feature subsetting, etc.

Parameters:

• train_data (Union[AnnData, AnnCollection]) – Training data.

• val_data (Union[AnnData, AnnCollection]) – Validation data.

• target (Union[str, list[str]]) – Target columns name(s).

• mappings (dict) – Mapping of a column value to ids eg. mappings[column_name][label2id] = {A: 1, B:2, …}.

set_model(models: list[Module])

A function to set the trained model for downstream feature tasks.

set_score_matrix(score_matrix: DataFrame)

A function to set score_matrix for feature extraction.

top_feature_extraction() → list[str] | dict

A function to get top features using Selector.

write_top_features_subset_data(data_config: dict) → dict

A function to write top features subset data onto disk and return updated data_config.

Parameters:

data_config – Data config.

model_training_pipeline

This file contains an implementation for the model training pipeline.

class scalr.model_training_pipeline.ModelTrainingPipeline(model_config: dict, train_config: dict, dirpath: str | None = None, device: str = 'cpu')

Bases: object

Class for Model training pipeline.

build_model_training_artifacts()

This function configures the model, optimizer, and loss function required for model training.

build_optimizer(opt_config: dict | None = None)

A function to build optimizer.

Parameters:

opt_config (dict) – Optimizer config.

get_updated_config()

This function returns updated configs.

load_data_and_targets_from_config(data_config: dict)

A function to load data and targets from data config.

Parameters:

data_config – Data config.

set_data_and_targets(train_data: AnnData | AnnCollection, val_data: AnnData | AnnCollection, target: str | list[str], mappings: dict)

A function to set data when you don’t use data directly from config, but rather by other sources like feature subsetting, etc.

Parameters:

• train_data (Union[AnnData, AnnCollection]) – Training data.

• val_data (Union[AnnData, AnnCollection]) – Validation data.

• target (Union[str, list[str]]) – Target columns name(s).

• mappings (dict) – Mapping of a column value to ids eg. mappings[column_name][label2id] = {A: 1, B:2, …}.

train()

This function trains the model.