VulnerabilityExtractionMethods/testplot.py at master · ref3t/VulnerabilityExtractionMethods · GitHub

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###F1
import plotly.express as px
import pandas as pd

# Create sample data
data = {
    'knn': [95,77,72,67,62],
    'nb': [84,59,46,43,37],
    'rf': [99,93,91,88,84],
    'dt': [99,94,91,87,84],
    'nn': [98,92,89,85,82]
}

df = pd.DataFrame(data)

# Create the box plot
fig = px.box(df, title='Box Plot F1')
fig.update_xaxes(title_text='Classifiers')
fig.update_yaxes(title_text='Scores')
# fig.update_layout(boxmode='overlay', boxgroupgap=.1)
# Display the plot
fig.show()


########################### 222222222222222222
import plotly.express as px
import pandas as pd

# Create sample data
data = {
    '2': [95,84,99,99,98],
    '3': [77,59,93,94,92],
    '4': [72,46,91,91,89],
    '5': [67,43,88,87,85],
    '6': [62,37,84,84,82]
}

df = pd.DataFrame(data)

# Create the box plot
fig = px.box(df, title='Box Plot F1 of classifiers')
fig.update_xaxes(title_text='# of classes')
fig.update_yaxes(title_text='Scores')
# fig.update_layout(boxmode='overlay', boxgroupgap=.1)
# Display the plot
fig.show()
#######################################################3333333333333333333333


# import plotly.graph_objects as go
# import pandas as pd

# # Create sample data
# data = {
#     'Method': ['Method 1', 'Method 2', 'Method 3', 'Method 4', 'Method 5'],
#     'Precision': [[95,77,74,70,66], [85,61,51,46,40], [99,93,93,90,87], [99,94,91,87,85], [98,92,90,87,83]],
#     'Recall': [[95,77,72,67,62], [86,61,50,47,41], [99,93,91,89,84], [99,94,91,87,84], [99,92,89,85,82]],
#     'F1': [[95,77,72,67,62], [95,77,72,67,62], [84,59,46,43,37], [99,93,91,88,84], [98,92,89,85,82]],
#     'Accuracy': [[95,80,75,72,68], [95,80,75,72,68], [84,61,48,45,39], [99,95,92,90,87], [99,93,91,88,86]]
# }

# df = pd.DataFrame(data)

# # Create the box plots
# fig = go.Figure()

# metrics = ['Precision', 'Recall', 'F1', 'Accuracy']

# for metric in metrics:
#     fig.add_trace(go.Box(y=df[metric], x=df['Method'], name=metric, boxpoints='all', jitter=0.5))

# fig.update_layout(
#     title='Performance Comparison of Methods for Each Metric',
#     xaxis=dict(title='Method'),
#     yaxis=dict(title='Score'),
#     boxmode='group'
# )

# # Display the plot
# fig.show()


import plotly.express as px
import pandas as pd

# sample data
data = {'Classifiers': ['knn']*5 + ['nb']*5 + ['rf']*5+ ['dt']*5+ ['nn']*5,
        'Precision': [95,77,74,70,66,85,61,51,46,40,99,93,93,90,87,99,94,91,87,85,98,92,90,87,83],
        'Recall':    [95,77,72,67,62,86,61,50,47,41,99,93,91,89,84,99,94,91,87,84,99,92,89,85,82],
        'F1':        [95,77,72,67,62,84,59,46,43,37,99,93,91,88,84,99,94,91,87,84,98,92,89,85,82],
        'Accuracy':  [95,80,75,72,68,84,61,48,45,39,99,94,93,91,88,99,95,92,90,87,99,93,91,88,86]}
df = pd.DataFrame(data)

# create boxplot
fig = px.box(df.melt(id_vars='Classifiers'), x='Classifiers', y='value', color='variable')

# show plot
fig.show()