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iMML

  • Overview
    • Key features
    • Installation
    • Usage
    • Free software
    • Contribute
    • Help us grow
  • Installation
    • Instructions
      • Using pip
      • Using uv
      • Manually
      • Dependencies
    • Optional dependencies
    • OS Requirements
    • Testing
    • Documenting
  • Algorithm selection guide
    • How to install an additional module
    • How to install extra dependencies
      • Extra dependencies when using "matlab" module
      • Extra dependencies when using "r" module
  • Tutorials
    • Statistics and interaction structure of a multi-modal dataset
      • Step 1: Import required libraries
      • Step 2: Create or load a multi-modal dataset
      • Step 3: Summarize the dataset
      • Step 4: Compute PID statistics (Redundancy, Uniqueness, Synergy)
      • Step 5: Visualize the PID as a Venn-like diagram
      • Step 6: Interpreting PID results
      • Step 7: Working with more than two modalities
      • Conclusion
    • Modality-wise missing data simulation (Amputation)
      • Step 1: Import required libraries
      • Step 2: Load the dataset
      • Step 3: Simulate missing data
      • Step 4: Compare amputation mechanisms
      • Step 5: Vary the missingness rate
      • Conclusion
    • Clustering a multi-modal dataset
      • Step 1: Import required libraries
      • Step 2: Load the dataset
      • Step 3: Clustering
      • Step 4: Simulate missing data (Amputation)
      • Step 5: Clustering with missing data
      • Step 6: Benchmarking
      • Summary of results
      • Conclusion
    • Retrieval on a vision–language dataset (flickr30k)
      • Step 0: Prerequisites
      • Step 1: Import required libraries
      • Step 2: Prepare the dataset
      • Step 3: Simulate missing modalities
      • Step 4: Generate the memory bank
      • Step 5: Retrieve
      • Step 6: Visualize the retrieved instances
      • Summary of results
      • Conclusion
    • Impute incomplete modality- and feature-wise multi-modal data
      • Step 1: Import required libraries
      • Step 2: Load the dataset
      • Step 3: Impute missing data
      • Step 4: Benchmark imputation accuracy
      • Summary of results
      • Conclusion
    • Classify an incomplete vision–language dataset (Oxford‑IIIT Pets) with deep learning
      • Step 0: Prerequisites
      • Step 1: Import required libraries
      • Step 2: Prepare the dataset
      • Step 3: Simulate missing modalities
      • Step 4: Generate the prompts using a retriever
      • Step 5: Training the model
      • Step 6: Advanced Usage: Track Metrics During Training
      • Step 7: Evaluation
      • Summary of results
      • Conclusion
    • Dimensionality reduction: Feature extraction and feature selection
      • Step 0: Prerequisites
      • Step 1: Import required libraries
      • Step 2: Define plotting functions
      • Step 3: Load the dataset
      • Step 4: Apply feature selection and feature extraction
      • Step 6: Analyzing an incomplete multi-modal dataset
      • Summary of results
      • Conclusion
  • API Reference
    • Ampute
      • Amputer
        • Amputer
          • Amputer.fit()
          • Amputer.transform()
          • Amputer.set_fit_request()
          • Amputer.set_transform_request()
      • Remove Modalities
        • RemoveMods
        • remove_mods
    • Classify
      • Missing Modalities in Multimodal healthcare data (M3Care)
        • M3Care
          • M3Care.training_step()
          • M3Care.validation_step()
          • M3Care.test_step()
          • M3Care.predict_step()
          • M3Care.configure_optimizers()
      • MUtual-conSistEnt graph contrastive learning (MUSE)
        • MUSE
          • MUSE.training_step()
          • MUSE.validation_step()
          • MUSE.test_step()
          • MUSE.predict_step()
          • MUSE.configure_optimizers()
      • Retrieval-AuGmented dynamic Prompt Tuning (RAGPT)
        • RAGPT
          • RAGPT.training_step()
          • RAGPT.validation_step()
          • RAGPT.test_step()
          • RAGPT.predict_step()
          • RAGPT.configure_optimizers()
    • Cluster
      • Doubly Aligned Incomplete Multi-view Clustering (DAIMC)
        • DAIMC
          • DAIMC.labels_
          • DAIMC.embedding_
          • DAIMC.U_
          • DAIMC.B_
          • DAIMC.fit()
          • DAIMC.fit_predict()
          • DAIMC.set_fit_request()
      • Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC)
        • EEIMVC
          • EEIMVC.labels_
          • EEIMVC.embedding_
          • EEIMVC.WP_
          • EEIMVC.HP_
          • EEIMVC.beta_
          • EEIMVC.loss_
          • EEIMVC.n_iter_
          • EEIMVC.fit()
          • EEIMVC.fit_predict()
          • EEIMVC.set_fit_request()
      • Incomplete Multiview Spectral Clustering With Adaptive Graph Learning (IMSCAGL)
        • IMSCAGL
          • IMSCAGL.labels_
          • IMSCAGL.embedding_
          • IMSCAGL.fit()
          • IMSCAGL.fit_predict()
          • IMSCAGL.set_fit_request()
      • Self-representation Subspace Clustering for Incomplete Multi-view Data (IMSR)
        • IMSR
          • IMSR.labels_
          • IMSR.embedding_
          • IMSR.loss_
          • IMSR.n_iter_
          • IMSR.fit()
          • IMSR.fit_predict()
          • IMSR.set_fit_request()
      • Integrate Any Omics (IntegrAO)
        • IntegrAO
          • IntegrAO.embedding_
          • IntegrAO.cluster_model_
          • IntegrAO.fused_networks_
          • IntegrAO.training_step()
          • IntegrAO.validation_step()
          • IntegrAO.test_step()
          • IntegrAO.predict_step()
          • IntegrAO.configure_optimizers()
      • Late Fusion Incomplete Multi-View Clustering (LF-IMVC)
        • LFIMVC
          • LFIMVC.labels_
          • LFIMVC.embedding_
          • LFIMVC.WP_
          • LFIMVC.HP_
          • LFIMVC.loss_
          • LFIMVC.n_iter_
          • LFIMVC.fit()
          • LFIMVC.fit_predict()
          • LFIMVC.set_fit_request()
      • Multiple Kernel K-Means with Incomplete Kernels (MKKM-IK)
        • MKKMIK
          • MKKMIK.labels_
          • MKKMIK.embedding_
          • MKKMIK.gamma_
          • MKKMIK.KA_
          • MKKMIK.loss_
          • MKKMIK.n_iter_
          • MKKMIK.fit()
          • MKKMIK.fit_predict()
          • MKKMIK.set_fit_request()
      • Multi Omic Clustering by Non-Exhaustive Types (MONET)
        • MONET
          • MONET.labels_
          • MONET.glob_var_
          • MONET.total_weight_
          • MONET.mod_graphs_
          • MONET.mod_views_
          • MONET.n_clusters_
          • MONET.fit()
          • MONET.fit_predict()
          • MONET.set_fit_request()
      • Multi-Reconstruction Graph Convolutional Network (MRGCN)
        • MRGCN
          • MRGCN.kmeans_
          • MRGCN.configure_optimizers()
          • MRGCN.training_step()
          • MRGCN.validation_step()
          • MRGCN.test_step()
          • MRGCN.predict_step()
          • MRGCN.on_fit_end()
      • NEighborhood based Multi-Omics clustering (NEMO)
        • NEMO
          • NEMO.labels_
          • NEMO.embedding_
          • NEMO.n_clusters_
          • NEMO.num_neighbors_
          • NEMO.affinity_matrix_
          • NEMO.fit()
          • NEMO.fit_predict()
          • NEMO.set_fit_request()
      • Online Multi-View Clustering (OMVC)
        • OMVC
          • OMVC.labels_
          • OMVC.embedding_
          • OMVC.U_
          • OMVC.V_
          • OMVC.loss_
          • OMVC.n_iter_
          • OMVC.fit()
          • OMVC.fit_predict()
          • OMVC.set_fit_request()
      • One-Pass Incomplete Multi-View Clustering (OPIMC)
        • OPIMC
          • OPIMC.labels_
          • OPIMC.embedding_
          • OPIMC.fit()
          • OPIMC.fit_predict()
          • OPIMC.set_fit_request()
      • One-Stage Incomplete Multi-View Clustering via Late Fusion (OS-LF-IMVC)
        • OSLFIMVC
          • OSLFIMVC.labels_
          • OSLFIMVC.embedding_
          • OSLFIMVC.WP_
          • OSLFIMVC.C_
          • OSLFIMVC.beta_
          • OSLFIMVC.loss_
          • OSLFIMVC.n_iter_
          • OSLFIMVC.fit()
          • OSLFIMVC.fit_predict()
          • OSLFIMVC.set_fit_request()
      • Projective Incomplete Multi-View Clustering (PIMVC)
        • PIMVC
          • PIMVC.labels_
          • PIMVC.embedding_
          • PIMVC.loss_
          • PIMVC.n_iter_
          • PIMVC.fit()
          • PIMVC.fit_predict()
          • PIMVC.set_fit_request()
      • Scalable Incomplete Multiview Clustering with Adaptive Data Completion (SIMC-ADC)
        • SIMCADC
          • SIMCADC.labels_
          • SIMCADC.embedding_
          • SIMCADC.V_
          • SIMCADC.A_
          • SIMCADC.Z_
          • SIMCADC.loss_
          • SIMCADC.n_iter_
          • SIMCADC.fit()
          • SIMCADC.fit_predict()
          • SIMCADC.set_fit_request()
      • Subtyping Tool for Multi-Omic Data (SUMO)
        • SUMO
          • SUMO.labels_
          • SUMO.embedding_
          • SUMO.graph_
          • SUMO.nmf_
          • SUMO.similarity_
          • SUMO.cophenet_list_
          • SUMO.pac_list_
          • SUMO.fit()
          • SUMO.fit_predict()
          • SUMO.set_fit_request()
    • Decomposition
      • Data Fusion by Matrix Factorization (DFMF)
        • DFMF
          • DFMF.fuser_
          • DFMF.transformer_
          • DFMF.t_
          • DFMF.ts_
      • Joint Non-Negative Matrix Factorization (JNMF)
        • JNMF
          • JNMF.H_
          • JNMF.V_
          • JNMF.reconstruction_err_
          • JNMF.observed_reconstruction_err_
          • JNMF.missing_reconstruction_err_
          • JNMF.relchange_
      • Multi-Omics Factor Analysis (MOFA)
        • MOFA
          • MOFA.mofa_
          • MOFA.factors_
          • MOFA.weights_
    • Explore
      • Get summary
        • get_summary
      • Get number of modalities
        • get_n_mods
      • Get number of samples by modalities
        • get_n_samples_by_mod
      • Get complete samples
        • get_com_samples
      • Get incomplete samples
        • get_incom_samples
      • Get samples
        • get_samples
      • Get samples by modality
        • get_samples_by_mod
      • Get missing samples by modality
        • get_missing_samples_by_mod
      • Get number of complete samples
        • get_n_com_samples
      • Get number of incomplete samples
        • get_n_incom_samples
      • Get percentage of complete samples
        • get_pct_com_samples
      • Get percentage of incomplete samples
        • get_pct_incom_samples
    • Feature selection
      • JNMF Feature Selector
        • JNMFFeatureSelector
          • JNMFFeatureSelector.selected_features_
          • JNMFFeatureSelector.weights_
          • JNMFFeatureSelector.fit()
          • JNMFFeatureSelector.transform()
          • JNMFFeatureSelector.fit_transform()
          • JNMFFeatureSelector.set_fit_request()
          • JNMFFeatureSelector.set_transform_request()
    • Impute
      • DFMF Imputer
        • DFMFImputer
          • DFMFImputer.fit_transform()
          • DFMFImputer.set_fit_request()
          • DFMFImputer.set_transform_request()
      • MOFA Imputer
        • MOFAImputer
          • MOFAImputer.fit_transform()
          • MOFAImputer.set_fit_request()
          • MOFAImputer.set_transform_request()
      • JNMF Imputer
        • JNMFImputer
          • JNMFImputer.transform()
          • JNMFImputer.fit_transform()
          • JNMFImputer.set_fit_request()
          • JNMFImputer.set_transform_request()
      • Missing Modality Indicator
        • MissingModIndicator
        • get_missing_mod_indicator
      • Observed Modality Indicator
        • ObservedModIndicator
        • get_observed_mod_indicator
      • Simple Modality Imputer
        • SimpleModImputer
          • SimpleModImputer.features_mod_mean_list_
          • SimpleModImputer.fit()
          • SimpleModImputer.transform()
          • SimpleModImputer.set_fit_request()
          • SimpleModImputer.set_transform_request()
    • Load
      • M3Care Dataset Loader
        • M3CareDataset
      • MRGCN Dataset Loader
        • MRGCNDataset
      • MUSE Dataset Loader
        • MUSEDataset
      • RAGPT Dataset Loader
        • RAGPTDataset
    • Preprocessing
      • Drop Modality
        • DropMod
        • drop_mod
      • Concatenate Modalities
        • ConcatenateMods
        • concatenate_mods
      • Single Modality
        • SingleMod
        • single_mod
      • Add Missing Modalities
        • AddMissingMods
        • add_missing_mods
      • Sort Data
        • SortData
        • sort_data
      • Multi-Modal Transformer
        • MultiModTransformer
          • MultiModTransformer.transformer_list_
          • MultiModTransformer.same_transformer_
          • MultiModTransformer.fit()
          • MultiModTransformer.transform()
          • MultiModTransformer.set_fit_request()
          • MultiModTransformer.set_transform_request()
      • Normalizer NaN
        • NormalizerNaN
          • NormalizerNaN.fit()
          • NormalizerNaN.transform()
      • Select Complete Samples
        • SelectCompleteSamples
        • select_complete_samples
      • Select Incomplete Samples
        • SelectIncompleteSamples
        • select_incomplete_samples
    • Retrieve
      • Multi-Channel Retriever (MCR)
        • MCR
          • MCR.memory_bank_
          • MCR.fit()
          • MCR.predict()
          • MCR.fit_predict()
          • MCR.transform()
          • MCR.fit_transform()
    • Statistics
      • PID
        • pid
    • Utils
      • Convert dataset format
        • convert_dataset_format
      • Check Xs
        • check_Xs
    • Visualize
      • Plot PID
        • plot_pid
      • Plot missing modality
        • plot_missing_modality

Development

  • Contributing to iMML
    • Submitting a bug report or a feature request
      • How to make a good bug report
    • Contributing code
      • Pull Request Checklist
    • Guidelines
      • Coding Guidelines
      • Docstring Guidelines
    • API of iMML Objects
      • Estimators
        • Instantiation
        • Fitting
        • Transformers
        • Predictors
      • Deep Learning
        • Dataset defition
        • Estimator defition
        • Training
  • Project roadmap
    • Guiding principles
    • Thematic tracks
      • Expanding the multi-modal learning field
      • Algorithm diversity
      • Performance and scalability
      • Interoperability and compatibility
      • Documentation, tutorials, and examples
      • Community and governance
    • How to get involved
    • Notes
  • Changelog
    • Version 0.2.0
      • imml.ampute
      • imml.classify
      • imml.impute
      • imml.load
      • imml.utils
    • Version 0.1.1
      • .github/workflows/ci_test.yml
    • Version 0.1.0
  • License

Links

  • GitHub
  • PyPI
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