qbiocode package

QBioCode: Quantum Machine Learning for Biological Data Analysis

QBioCode is a comprehensive Python package for quantum machine learning (QML) research and applications in biological data analysis. It provides tools for data generation, classical and quantum machine learning algorithms, evaluation metrics, and visualization utilities.

Main Modules

• learning: Classical and quantum machine learning algorithms

• embeddings: Feature embedding and encoding methods

• evaluation: Model, dataset and graph-complexity evaluation tools

• data_generation: Synthetic dataset generators

• visualization: Result visualization and correlation analysis

• utils: Helper functions and utilities

• apps: Command-line applications (QProfiler, QSage, QuVINE)

Quick Start

>>> from qbiocode import compute_rf, generate_data
>>> # Generate synthetic data
>>> generate_data(type_of_data='circles', save_path='data/circles')
>>> # Train a random forest model
>>> results = compute_rf(X_train, y_train, X_test, y_test)
>>> # Use QProfiler programmatically
>>> from qbiocode.apps.qprofiler import qprofiler
>>> qprofiler.main(config)

Submodules:

• qbiocode.version module

Subpackages:

• qbiocode.apps package
• qbiocode.data_generation package
• qbiocode.embeddings package
• qbiocode.evaluation package
• qbiocode.learning package
• qbiocode.utils package
• qbiocode.visualization package

Summary

__all__ Functions:

checkpoint_restart	Identify completed datasets from a previous run to enable checkpoint restart.
compute_dt	This function generates a model using a Decision Tree (DT) Classifier method as implemented in scikit-learn.
compute_dt_opt	This function also generates a model using a Decision Tree (DT) Classifier method as implemented in scikit-learn.
compute_lr	This function generates a model using a Logistic Regression (LR) method as implemented in scikit-learn.
compute_lr_opt	This function also generates a model using a Logistic Regression (LR) method as implemented in scikit-learn.
compute_mlp	This function generates a model using a Multi-layer Perceptron (mlp), a neural network, method as implemented in scikit-learn.
compute_mlp_opt	This function also generates a model using a Multi-layer Perceptron (mlp), a neural network, as implemented in scikit-learn (https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html).
compute_nb	This function generates a model using a Gaussian Naive Bayes (NB) Classifier method as implemented in scikit-learn.
compute_nb_opt	This function generates a model using a Gaussian Naive Bayes (NB) Classifier method as implemented in scikit-learn.
compute_pqk	This function generates quantum circuits, computes projections of the data onto these circuits, and evaluates the performance of classical machine learning models on the projected data.
compute_qnn	This function computes a Quantum Neural Network (QNN) model on the provided training data and evaluates it on the test data.
compute_qsvc	This function computes a quantum support vector classifier (QSVC) using the Qiskit Machine Learning library.
compute_results_correlation	This function takes in as input a Pandas Dataframe containing the results and data evaluations for a given dataset.
compute_rf	This function generates a model using a Random Forest (RF) Classifier method as implemented in scikit-learn.
compute_rf_opt	This function also generates a model using a Random Forest (RF) Classifier method as implemented in scikit-learn.
compute_svc	This function generates a model using a Support Vector Classifier (SVC) method as implemented in scikit-learn.
compute_svc_opt	This function generates a model using a Support Vector Classifier (SVC) method as implemented in scikit-learn.
compute_vqc	This function computes a Variational Quantum Classifier (VQC) using the Qiskit Machine Learning library.
compute_xgb	This function generates a model using an Extreme Gradient Boositing (xgb) Classifier method as implemented in xgboost.
compute_xgb_opt	This function generates a model using an Extreme Gradient Boositing (xgb) Classifier method as implemented in xgboost.
evaluate	This function evaluates a dataset and returns a transposed summary DataFrame with various statistical measures, derived from the dataset.
evaluate_graph	Summarize a graph’s complexity as a one-row DataFrame.
feature_encoding	Encode categorical features using various encoding strategies.
generate_circles_datasets	Generate multiple concentric circles datasets with varying parameters.
generate_classification_datasets	Generate multiple high-dimensional classification datasets with varying parameters.
generate_data	Generate synthetic datasets for machine learning benchmarking.
generate_moons_datasets	Generate multiple two-moons datasets with varying parameters.
generate_s_curve_datasets	Generate multiple 3D S-curve datasets with varying parameters.
generate_spheres_datasets	Generate multiple concentric n-dimensional spheres datasets with varying parameters.
generate_spirals_datasets	Generate multiple n-dimensional spiral datasets with varying parameters.
generate_swiss_roll_datasets	Generate multiple 3D Swiss roll datasets with varying parameters.
get_embeddings	Apply an embedding to the training and test datasets.
is_transductive	Return True if embedding is fit on the combined train and test rows.
model_run	This function runs the ML methods, with or without a grid search, as specified in the config.yaml file.
modeleval	Evaluates the model performance using accuracy, F1 score, and AUC.
plot_results_correlation	Plot publication-quality correlation figures from a correlations_df.
pqk	This function generates quantum circuits, computes projections of the data onto these circuits.
publication_style	Return a copy of PUBLICATION_STYLE for use as a matplotlib style.
qml_winner	This function finds data sets where QML was beneficial (higher F1 scores than CML) and create new .csv files with the relevant evaluation and performance for these specific datasets, for further analysis.
scale_train_test	Scale train and test with a single scaler fit on the training set only.
scaler_fn	Apply scaling transformation to input data.
tutorial_data_dirs	Return the directories tutorial_data_path() searches, in order.
tutorial_data_path	Return the absolute path of a tutorial fixture.

__all__ Data:

• QUVINE_HEADLINE_METHODS

• QUVINE_METHODS

• SKLEARN_METHODS

Reference