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| import plotly.express as px | ||
| from plotly.subplots import make_subplots | ||
| import plotly.graph_objects as go | ||
| import pandas as pd | ||
| import matplotlib.pyplot as plt | ||
| import plotly.io as pio | ||
| pio.templates.default = "simple_white" | ||
|
|
||
| data = [['Baseline', '10%', 'linear', .664, .032], | ||
| ['Supervised', '10%', 'fine-tune', .773, .0345], | ||
| ['Supervised (Full)', '10%', 'fine-tune', .889, .024], | ||
| ['Baseline', '100%', 'linear', .803, .043], | ||
| ['Supervised', '100%', 'fine-tune', .930, .025], | ||
| ['Supervised (Full)', '100%', 'fine-tune', .929, .021], | ||
|
|
||
| ['Spectrogram', '10%', 'fine-tune', .756, .0275], | ||
| ['Spectrogram + Split', '10%', 'fine-tune', .787, .037], | ||
| ['Frequency Only', '10%', 'fine-tune', .792, .0265], | ||
| ['Split', '10%', 'fine-tune', .797, .032], | ||
| ['Time Only', '10%', 'fine-tune', .857, .0285], | ||
|
|
||
| ['Spectrogram', '100%', 'fine-tune', .920, .027], | ||
| ['Spectrogram + Split', '100%', 'fine-tune', .924, .027], | ||
| ['Frequency Only', '100%', 'fine-tune', .925, .025], | ||
| ['Split', '100%', 'fine-tune', .927, .0255], | ||
| ['Time Only', '100%', 'fine-tune', .927, .025], | ||
|
|
||
| ['Spectrogram', '10%', 'linear', .660, .0375], | ||
| ['Frequency Only', '10%', 'linear', .666, .0165], | ||
| ['Spectrogram + Split', '10%', 'linear', .752, .038], | ||
| ['Split', '10%', 'linear', .744, .0335], | ||
| ['Time Only', '10%', 'linear', .808, .033], | ||
|
|
||
| ['Spectrogram', '100%', 'linear', .766, .043], | ||
| ['Frequency Only', '100%', 'linear', .782, .041], | ||
| ['Spectrogram + Split', '100%', 'linear', .795, .0385], | ||
| ['Split', '100%', 'linear', .807, .0435], | ||
| ['Time Only', '100%', 'linear', .874, .032], | ||
| ] | ||
|
|
||
| fig = make_subplots(cols=2) | ||
| metrics_df = pd.DataFrame(data, columns=['Runs', 'data', 'type', 'AUC', 'ci_95']) | ||
| data = metrics_df[metrics_df['type'] == 'linear'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Baseline' in name) else f'rgb({250 - 10 * i},0,0)' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Baseline' in name) else f'rgb({200 - 10 * i},0,0)' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=1, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=1, marker=dict(color=color100)) | ||
| data = metrics_df[metrics_df['type'] == 'fine-tune'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Supervised' in name) else f'rgb({250 - 10 * i},0,0)' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Supervised' in name) else f'rgb({200 - 10 * i},0,0)' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=2, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=2, marker=dict(color=color100)) | ||
| fig.update_yaxes(title_text="AUC", range=[0, 1], row=1, col=1) | ||
| fig.update_yaxes(title_text="", range=[0, 1], row=1, col=2) | ||
| fig.update_xaxes(title_text='Linear Evaluation', row=1, col=1) | ||
| fig.update_xaxes(title_text='Fine-Tune Evaluation', row=1, col=2) | ||
| #fig.show() | ||
| fig.write_image('output/heart.png', width=1500, height=800) | ||
|
|
||
| data = [['Baseline', '10%', 'linear', .512, .026], | ||
| ['Supervised', '10%', 'fine-tune', .628, .044], | ||
| ['Supervised (Full)', '10%', 'fine-tune', .687, .047], | ||
| ['Baseline', '100%', 'linear', .516, .048], | ||
| ['Supervised', '100%', 'fine-tune', .69, .059], | ||
| ['Supervised (Full)', '100%', 'fine-tune', .71, .0595], | ||
|
|
||
| ['Split', '10%', 'fine-tune', .562, .054], | ||
| ['Time Only', '10%', 'fine-tune', .627, .0565], | ||
| ['Spectrogram + Split', '10%', 'fine-tune', .562, .0505], | ||
| ['Frequency Only', '10%', 'fine-tune', .618, .0515], | ||
| ['Spectrogram', '10%', 'fine-tune', .633, .0575], | ||
|
|
||
| ['Split', '100%', 'fine-tune', .584, .063], | ||
| ['Time Only', '100%', 'fine-tune', .627, .0595], | ||
| ['Spectrogram + Split', '100%', 'fine-tune', .65, .066], | ||
| ['Frequency Only', '100%', 'fine-tune', .671, .0595], | ||
| ['Spectrogram', '100%', 'fine-tune', .691, .065], | ||
|
|
||
| ['Split', '10%', 'linear', .558, .028], | ||
| ['Spectrogram + Split', '10%', 'linear', .533, .035], | ||
| ['Time Only', '10%', 'linear', .643, .0485], | ||
| ['Frequency Only', '10%', 'linear', .649, .0415], | ||
| ['Spectrogram', '10%', 'linear', .652, .0535], | ||
|
|
||
| ['Split', '100%', 'linear', .552, .052], | ||
| ['Spectrogram + Split', '100%', 'linear', .609, .0595], | ||
| ['Time Only', '100%', 'linear', .654, .06], | ||
| ['Frequency Only', '100%', 'linear', .656, .058], | ||
| ['Spectrogram', '100%', 'linear', .659, .058], | ||
|
|
||
| ] | ||
|
|
||
| fig = make_subplots(cols=2) | ||
| metrics_df = pd.DataFrame(data, columns=['Runs', 'data', 'type', 'AUC', 'ci_95']) | ||
| data = metrics_df[metrics_df['type'] == 'linear'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Baseline' in name) else f'rgb(0,0,{250 - 10 * i})' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Baseline' in name) else f'rgb(0,0,{180 - 10 * i})' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=1, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=1, marker=dict(color=color100)) | ||
| data = metrics_df[metrics_df['type'] == 'fine-tune'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Supervised' in name) else f'rgb(0,0,{250 - 10 * i})' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Supervised' in name) else f'rgb(0,0,{180 - 10 * i})' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=2, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=2, marker=dict(color=color100)) | ||
| fig.update_yaxes(title_text="AUC", range=[0, 1], row=1, col=1) | ||
| fig.update_yaxes(title_text="", range=[0, 1], row=1, col=2) | ||
| fig.update_xaxes(title_text='Linear Evaluation', row=1, col=1) | ||
| fig.update_xaxes(title_text='Fine-Tune Evaluation', row=1, col=2) | ||
| #fig.show() | ||
| fig.write_image('output/lung.png', width=1500, height=800) | ||
|
|
||
| data = [ | ||
| ['Pos. Sim. Age', '10%', 'fine-tune', .665, .055], | ||
| ['Pos. Dif. Loc.', '10%', 'fine-tune', .681, .0595], | ||
| ['Pos. Same Loc.', '10%', 'fine-tune', .678, .057], | ||
| ['Pos. Same Loc./Neg. Same Loc.', '10%', 'fine-tune', .732, .052], | ||
| ['Neg. Sim. Sex', '10%', 'fine-tune', .754, .0515], | ||
| ['Neg. Sim. Age', '10%', 'fine-tune', .782, .0465], | ||
| ['Neg. Sim. Age + Sex', '10%', 'fine-tune', .822, .036], | ||
|
|
||
| ['Pos. Sim. Age', '100%', 'fine-tune', .695, .057], | ||
| ['Pos. Dif. Loc.', '100%', 'fine-tune', .702, .0605], | ||
| ['Pos. Same Loc.', '100%', 'fine-tune', .692, .0615], | ||
| ['Pos. Same Loc./Neg. Same Loc.', '100%', 'fine-tune', .768, .0505], | ||
| ['Neg. Sim. Sex', '100%', 'fine-tune', .765, .0565], | ||
| ['Neg. Sim. Age', '100%', 'fine-tune', .785, .052], | ||
| ['Neg. Sim. Age + Sex', '100%', 'fine-tune', .842, .0365], | ||
|
|
||
| ['Pos. Sim. Age', '10%', 'linear', .663, .0515], | ||
| ['Pos. Dif. Loc.', '10%', 'linear', .681, .049], | ||
| ['Pos. Same Loc.', '10%', 'linear', .690, .049], | ||
| ['Pos. Same Loc./Neg. Same Loc.', '10%', 'linear', .695, .048], | ||
| ['Neg. Sim. Sex', '10%', 'linear', .723, .0475], | ||
| ['Neg. Sim. Age', '10%', 'linear', .788, .0475], | ||
| ['Neg. Sim. Age + Sex', '10%', 'linear', .854, .0295], | ||
|
|
||
| ['Pos. Sim. Age', '100%', 'linear', .674, .054], | ||
| ['Pos. Dif. Loc.', '100%', 'linear', .689, .0555], | ||
| ['Pos. Same Loc.', '100%', 'linear', .700, .058], | ||
| ['Pos. Same Loc./Neg. Same Loc.', '100%', 'linear', .745, .0525], | ||
| ['Neg. Sim. Sex', '100%', 'linear', .748, .056], | ||
| ['Neg. Sim. Age', '100%', 'linear', .773, .051], | ||
| ['Neg. Sim. Age + Sex', '100%', 'linear', .863, .028], | ||
| ] | ||
|
|
||
| fig = make_subplots(cols=2) | ||
| metrics_df = pd.DataFrame(data, columns=['Runs', 'data', 'type', 'AUC', 'ci_95']) | ||
| data = metrics_df[metrics_df['type'] == 'linear'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Baseline' in name) else f'rgb({120 - 10 * i},0,{190 - 10 * i})' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Baseline' in name) else f'rgb({90 - 10 * i},0,{160 - 10 * i})' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=1, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=1, marker=dict(color=color100)) | ||
| data = metrics_df[metrics_df['type'] == 'fine-tune'] | ||
| data10 = data[data['data'] == "10%"] | ||
| data100 = data[data['data'] == "100%"] | ||
| color10 = ['rgb(150,150,150)' if ('Supervised' in name) else f'rgb({120 - 10 * i},0,{190 - 10 * i})' for i, name | ||
| in enumerate(data10['Runs'])] | ||
| color100 = ['rgb(120,120,120)' if ('Supervised' in name) else f'rgb({90 - 10 * i},0,{160 - 10 * i})' for i, name | ||
| in enumerate(data100['Runs'])] | ||
| fig.add_bar(x=data10['Runs'], y=data10['AUC'], error_y=dict(type='data', array=data10['ci_95']), row=1, col=2, marker=dict(color=color10)) | ||
| fig.add_bar(x=data100['Runs'], y=data100['AUC'], error_y=dict(type='data', array=data100['ci_95']), row=1, col=2, marker=dict(color=color100)) | ||
| fig.update_yaxes(title_text="AUC", range=[0, 1], row=1, col=1) | ||
| fig.update_yaxes(title_text="", range=[0, 1], row=1, col=2) | ||
| fig.update_xaxes(title_text='Linear Evaluation', row=1, col=1) | ||
| fig.update_xaxes(title_text='Fine-Tune Evaluation', row=1, col=2) | ||
| #fig.show() | ||
| fig.write_image('output/demographics.png', width=1500, height=800) |
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