Feature - Field Correlation

Collapse Tensorflow Coupon Figure

Correlation of Coupon Fields and Corresponding Features

Correlation of Coupon Fields and Corresponding Features

Collapse Pytorch Nested Cylinder Figure

Correlation of Nested Cylinder Fields and Corresponding Features

Correlation of Nested Cylinder Fields and Corresponding Features


Code Documentation

Generates a heatmap plot of the 2D auto-cross-correlation-coefficients across hydrodynamic fields, averaged across multiple samples

  • Can plot all features (-T All) or some selected features (-T # #)

  • Can plot all fields (-F All) or some selected features (-F str str)

Fixed key (-XK) specifies what subset of data to consider

  • ‘None’ can be passed to consider any input with no restrictions

  • For coupon data, fixed keys must be in the form ‘tpl###’ or ‘idx#####’

  • For nested cylinder data, fixed keys must be in the form ‘id####’ or ‘idx#####’

Exports correlation coeffients as a pandas-readable .csv

Samples can be preselected and listed in a .txt file (-FL filepath) OR
Number of samples can be specified and a random selection satisfying the fixed key requirement will be made (-FL MAKE -NS #)

Input Line for TF Coupon Models: python feature_field_corr.py -P tensorflow -E coupon -M ../examples/tf_coupon/trained_pRad2TePla_model.h5 -IF pRad -ID ../examples/tf_coupon/data/ -DF ../examples/tf_coupon/coupon_design_file.csv -L activation_15 -T All -NM ft01 -F All -S ../examples/tf_coupon/figures/

Input Line for PYT Nested Cylinder Models: python feature_field_corr.py -P pytorch -E nestedcylinder -M ../examples/pyt_nestedcyl/trained_rho2PTW_model.pth -IF rho -ID ../examples/pyt_nestedcyl/data/ -DF ../examples/pyt_nestedcyl/nestedcyl_design_file.csv -L interp_module.interpActivations.10 -T All -NM ft01 -F All -S ../examples/pyt_nestedcyl/figures/

Arguments

Generates a matrix of the scalar 2D cross-correlation-coefficient between selected features and selected fields across a given number of randomly chosen samples

usage: python feature_field_corr.py [-h] [--PACKAGE] [--EXPERIMENT] [--MODEL]
                                    [--INPUT_FIELD] [--INPUT_DIR]
                                    [--FILE_LIST] [--DESIGN_FILE]
                                    [--PRINT_LAYERS] [--PRINT_FEATURES]
                                    [--PRINT_FIELDS] [--PRINT_KEYS]
                                    [--PRINT_SAMPLES] [--LAYER]
                                    [--FEATURES  [...]] [--MAT_NORM]
                                    [--FIELDS  [...]] [--FIXED_KEY]
                                    [--NUM_SAMPLES] [--SAVE_FIG]

Named Arguments

--PACKAGE, -P

Possible choices: tensorflow, pytorch

Which python package was used to create the model

Default: “tensorflow”

--EXPERIMENT, -E

Possible choices: coupon, nestedcylinder

Which experiment the model was trained on

Default: “coupon”

--MODEL, -M

Model file

Default: “../examples/tf_coupon/trained_pRad2TePla_model.h5”

--INPUT_FIELD, -IF

The radiographic/hydrodynamic field the model is trained on

Default: “pRad”

--INPUT_DIR, -ID

Directory path where all of the .npz files are stored

Default: “../examples/tf_coupon/data/”

--FILE_LIST, -FL

The .txt file containing a list of .npz file paths; use “MAKE” to generate a file list given an input directory (passed with -ID) and a number of samples (passed with -NS).

Default: “MAKE”

--DESIGN_FILE, -DF

The .csv file with master design study parameters

Default: “../examples/tf_coupon/coupon_design_file.csv”

--PRINT_LAYERS, -PL

Prints list of layer names in a model (passed with -M) and quits program

Default: False

--PRINT_FEATURES, -PT

Prints number of features extracted by a layer (passed with -L) and quits program

Default: False

--PRINT_FIELDS, -PF

Prints list of hydrodynamic/radiographic fields present in a given .npz file (passed with -IN) and quits program

Default: False

--PRINT_KEYS, -PK

Prints list of choices for the fixed key avialable in a given input dirrectory (passed with -ID) and quits program

Default: False

--PRINT_SAMPLES, -PS

Prints number of samples in a directory (passed with -ID) matching a fixed key (passed with -XK) and quits program

Default: False

--LAYER, -L

Name of model layer that features will be extracted from

Default: “None”

--FEATURES, -T

List of features to include; “Grid” plots all features in one figure using subplots; “All” plots all features each in a new figure; A list of integers can be passed to plot those features each in a new figure. Integer convention starts at 1.

Default: [‘All’]

--MAT_NORM, -NM

Possible choices: ft01, all01, none

How the extracted features will be normalized, resulting in a scaled matrix; “ft01” normalizes by the min and max of each feature separately; “all01” normalizes by the min and max of all extracted features; “none” does not normalize features.

Default: “ft01”

--FIELDS, -F

List of fields to be included; pass “none” to use an all-zero field; pass “All” to use all valid fields.

Default: [‘rho’, ‘eqps’, ‘eqps_rate’, ‘eff_stress’, ‘porosity’]

--FIXED_KEY, -XK

The identifying string for some subset of all data samples; pass “None” to consider all samples

Default: “None”

--NUM_SAMPLES, -NS

Number of samples to use; pass “All” to use all samples in a given input dirrectory (passed with -ID)

Default: “All”

--SAVE_FIG, -S

Directory to save the outputs to.

Default: “../examples/tf_coupon/figures/”