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)
Docs
»
Code Documentation: Plot Helper
»
plot_real_vs_predicted_error
Edit on GitHub
plot_real_vs_predicted_error
¶
mastml.plot_helper.
plot_real_vs_predicted_error
(
y_true
,
savepath
,
model
,
data_test_type
)
[source]
¶
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v: Version_2.0
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