Feature Sensitivity

Collapse Tensorflow Coupon Figure

Standard Deviation of Features Extracted from the Tensorflow Coupon Network on idx001100 Data

Standard Deviation of Features Extracted from the Tensorflow Coupon Network on idx001100 Data

Collapse Pytorch Nested Cylinder Figure

Standard Deviation of Features Extracted from the Pytorch Nested Cylinder Network on idx00112 Data

Standard Deviation of Features Extracted from the Pytorch Nested Cylinder Network on idx00112 Data


Code Documentation

Plots the average and standard deviation of features extracted from a set of related inputs

  • Averages each pixel across multiple inputs

Plots average and standard deviations in seperate figures

  • Can plot all features from a layer on the same plot (-T Grid)

  • Can plot all features from a layer on their own plots (-T All)

  • Can plot some features from a layer on their own plots (-T # #)

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#####’

Saves all averages and standard deviations to a .npz file.

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_sensitivity.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 Grid -NM ft01 -XK idx00110 -S ../examples/tf_coupon/figures/

Input Line for PYT Nested Cylinder Models: python feature_sensitivity.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 Grid -NM ft01 -XK idx00112 -S ../examples/pyt_nestedcyl/figures/

Arguments

Plots the average and standard deviation of features extracted from a set of related inputs

usage: python feature_sensitivity.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]
                                     [--FIXED_KEY] [--NUM_SAMPLES] [--ALPHA1]
                                     [--ALPHA2] [--COLOR1] [--COLOR2]
                                     [--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: [‘Grid’]

--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”

--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”

--ALPHA1, -A1

Opacity of colormap at value 0

Default: 0.25

--ALPHA2, -A2

Opacity of colormap at value 1

Default: 1.0

--COLOR1, -C1

Possible choices: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgreen, darkgrey, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, green, greenyellow, grey, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgreen, lightgrey, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, rebeccapurple, red, rosybrown, royalblue, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen

Color of colormap at value 0; Choose from matplotlib CSS4 color list.

Default: “yellow”

--COLOR2, -C2

Possible choices: aliceblue, antiquewhite, aqua, aquamarine, azure, beige, bisque, black, blanchedalmond, blue, blueviolet, brown, burlywood, cadetblue, chartreuse, chocolate, coral, cornflowerblue, cornsilk, crimson, cyan, darkblue, darkcyan, darkgoldenrod, darkgray, darkgreen, darkgrey, darkkhaki, darkmagenta, darkolivegreen, darkorange, darkorchid, darkred, darksalmon, darkseagreen, darkslateblue, darkslategray, darkslategrey, darkturquoise, darkviolet, deeppink, deepskyblue, dimgray, dimgrey, dodgerblue, firebrick, floralwhite, forestgreen, fuchsia, gainsboro, ghostwhite, gold, goldenrod, gray, green, greenyellow, grey, honeydew, hotpink, indianred, indigo, ivory, khaki, lavender, lavenderblush, lawngreen, lemonchiffon, lightblue, lightcoral, lightcyan, lightgoldenrodyellow, lightgray, lightgreen, lightgrey, lightpink, lightsalmon, lightseagreen, lightskyblue, lightslategray, lightslategrey, lightsteelblue, lightyellow, lime, limegreen, linen, magenta, maroon, mediumaquamarine, mediumblue, mediumorchid, mediumpurple, mediumseagreen, mediumslateblue, mediumspringgreen, mediumturquoise, mediumvioletred, midnightblue, mintcream, mistyrose, moccasin, navajowhite, navy, oldlace, olive, olivedrab, orange, orangered, orchid, palegoldenrod, palegreen, paleturquoise, palevioletred, papayawhip, peachpuff, peru, pink, plum, powderblue, purple, rebeccapurple, red, rosybrown, royalblue, saddlebrown, salmon, sandybrown, seagreen, seashell, sienna, silver, skyblue, slateblue, slategray, slategrey, snow, springgreen, steelblue, tan, teal, thistle, tomato, turquoise, violet, wheat, white, whitesmoke, yellow, yellowgreen

Color of colormap at value 1; Choose from matplotlib CSS4 color list.

Default: “red”

--SAVE_FIG, -S

Directory to save the outputs to.

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