MAterials Simulation Toolkit for Machine Learning (MAST-ML)
Version_2.0
  • Acknowledgements
  • Installing MAST-ML
    • Hardware and Data Requirements
      • Hardware
      • Data
    • Terminal installation (Linux or linux-like terminal on Mac)
      • Install Python3
        • Create a conda environment
        • Set up Juptyer notebooks
      • Install the MAST-ML package
        • Imports that don’t work
    • Windows installation
      • Install Python3
        • Create a conda environment
        • Set up the Spyder IDE and Jupyter notebooks
      • Install the MAST-ML package
        • Imports that don’t work
      • Windows 10 install: step-by-step guide (credit Joe Kern)
    • Startup
      • Locate the examples folder
      • Run the MASTML command
      • Check output
  • MAST-ML Input File
    • Input file sections
      • General Setup
      • Data Cleaning
      • Clustering
      • Feature Generation
      • Feature Normalization
      • Learning Curve
      • Feature Selection
      • Data Splits
      • Models
      • Misc Settings
  • MAST-ML overview slides
  • Running MAST-ML on Google Colab
  • MAST-ML tutorial
    • Introduction
    • Your first MAST-ML run
    • Cleaning input data
    • Feature generation and normalization
    • Training and evaluating your first model
    • Feature selection and learning curves
    • Hyperparameter optimization
    • Random leave-out versus leave-out-group cross-validation
    • Making predictions by importing a previously fit model
    • Predicting values for new, extrapolated data
  • Code Documentation: Metrics
    • mastml.metrics Module
      • Functions
        • adjusted_r2_score
        • check_and_fetch_names
        • r2_score_fitted
        • r2_score_noint
        • rmse_over_stdev
        • root_mean_squared_error
  • Code Documentation: Configuration file parser
    • mastml.conf_parser Module
      • Functions
        • fix_types
        • mybool
        • parse_conf_file
  • Code Documentation: Data cleaner
    • mastml.data_cleaner Module
      • Functions
        • columns_with_strings
        • flag_outliers
        • imputation
        • ppca
        • remove
      • Classes
        • PPCA
      • Class Inheritance Diagram
  • Code Documentation: Data loader
    • mastml.data_loader Module
      • Functions
        • load_data
  • Code Documentation: Learning curve
    • mastml.learning_curve Module
      • Functions
        • feature_learning_curve
        • sample_learning_curve
  • Code Documentation: Clusterers
    • mastml.legos.clusterers Module
  • Code Documentation: Data splitters
    • mastml.legos.data_splitters Module
      • Classes
        • Bootstrap
        • JustEachGroup
        • LeaveCloseCompositionsOut
        • LeaveOutPercent
        • NoSplit
        • SplittersUnion
      • Class Inheritance Diagram
  • Code Documentation: Utils
    • mastml.utils Module
      • Functions
        • activate_logging
        • log_header
        • nice_range
        • verbosalize_logger
      • Classes
        • BetweenFilter
        • ConfError
        • FileNotFoundError
        • FiletypeError
        • InvalidConfParameters
        • InvalidConfSection
        • InvalidConfSubSection
        • InvalidModel
        • InvalidValue
        • MastError
        • MissingColumnError
      • Class Inheritance Diagram
  • Code Documentation: MAST-ML Driver
    • mastml.mastml_driver Module
      • Functions
        • check_paths
        • get_commandline_args
        • main
        • mastml_run
  • Code Documentation: Plot Helper
    • mastml.plot_helper Module
      • Functions
        • get_divisor
        • get_histogram_bins
        • ipynb_maker
        • make_axis_same
        • make_error_plots
        • make_fig_ax
        • make_fig_ax_square
        • make_train_test_plots
        • nice_mean
        • nice_names
        • nice_range
        • nice_std
        • parse_error_data
        • plot_1d_heatmap
        • plot_2d_heatmap
        • plot_3d_heatmap
        • plot_average_cumulative_normalized_error
        • plot_average_normalized_error
        • plot_best_worst_per_point
        • plot_best_worst_split
        • plot_confusion_matrix
        • plot_cumulative_normalized_error
        • plot_keras_history
        • plot_learning_curve
        • plot_learning_curve_convergence
        • plot_metric_vs_group
        • plot_metric_vs_group_size
        • plot_normalized_error
        • plot_precision_recall_curve
        • plot_predicted_vs_true
        • plot_predicted_vs_true_bars
        • plot_real_vs_predicted_error
        • plot_residuals_histogram
        • plot_roc_curve
        • plot_scatter
        • plot_stats
        • plot_target_histogram
        • prediction_intervals
        • recursive_max
        • recursive_max_and_min
        • recursive_min
        • round_down
        • round_up
        • rounder
        • stat_to_string
        • trim_array
  • Code Documentation: HTML Helper
    • mastml.html_helper Module
      • Functions
        • is_test_image
        • is_train_image
        • make_html
        • make_image
        • make_link
        • show_combo
        • simple_section
  • Code Documentation: Feature Selectors
    • mastml.legos.feature_selectors Module
      • Functions
        • dataframify_new_column_names
        • dataframify_selector
        • fitify_just_use_values
      • Classes
        • EnsembleModelFeatureSelector
        • MASTMLFeatureSelector
        • PearsonSelector
      • Class Inheritance Diagram
  • Code Documentation: Feature Normalizers
    • mastml.legos.feature_normalizers Module
      • Functions
        • dataframify
      • Classes
        • MeanStdevScaler
      • Class Inheritance Diagram
  • Code Documentation: Randomizers
    • mastml.legos.randomizers Module
      • Classes
        • Randomizer
      • Class Inheritance Diagram
  • Code Documentation: Model Finder
    • mastml.legos.model_finder Module
      • Functions
        • check_models_mixed
        • find_model
      • Classes
        • AlwaysFive
        • EnsembleRegressor
        • KerasRegressor
        • ModelImport
        • RandomGuesser
      • Class Inheritance Diagram
  • Code Documentation: Utility Legos
    • mastml.legos.util_legos Module
      • Classes
        • DataFrameFeatureUnion
        • DoNothing
      • Class Inheritance Diagram
  • Code Documentation: Feature Generators
    • mastml.legos.feature_generators Module
      • Functions
        • clean_dataframe
      • Classes
        • ContainsElement
        • DataframeUtilities
        • Magpie
        • MagpieFeatureGeneration
        • MaterialsProject
        • MaterialsProjectFeatureGeneration
        • Matminer
        • NoGenerate
        • PolynomialFeatures
      • Class Inheritance Diagram
MAterials Simulation Toolkit for Machine Learning (MAST-ML)
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