import pandas as pd
import numpy as np
from scipy import stats
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
ROC = 1
PR = 2
def add_p_value_annotation(fig, array_columns, subplot=None, _format=dict(interline=0.03, text_height=1.03, color='black')):
''' Adds notations giving the p-value between two box plot data (t-test two-sided comparison)
Parameters:
----------
fig: figure
plotly boxplot figure
array_columns: np.array
array of which columns to compare
e.g.: [[0,1], [1,2]] compares column 0 with 1 and 1 with 2
subplot: None or int
specifies if the figures has subplots and what subplot to add the notation to
_format: dict
format characteristics for the lines
Returns:
-------
fig: figure
figure with the added notation
'''
# Specify in what y_range to plot for each pair of columns
y_range = np.zeros([len(array_columns), 2])
for i in range(len(array_columns)):
y_range[i] = [1.03+i*_format['interline'], 1.04+i*_format['interline']]
# Get values from figure
fig_dict = fig.to_dict()
# Get indices if working with subplots
if subplot:
if subplot == 1:
subplot_str = ''
else:
subplot_str =str(subplot)
indices = [] #Change the box index to the indices of the data for that subplot
for index, data in enumerate(fig_dict['data']):
#print(index, data['xaxis'], 'x' + subplot_str)
if data['xaxis'] == 'x' + subplot_str:
indices = np.append(indices, index)
indices = [int(i) for i in indices]
print((indices))
else:
subplot_str = ''
# Print the p-values
for index, column_pair in enumerate(array_columns):
if subplot:
data_pair = [indices[column_pair[0]], indices[column_pair[1]]]
else:
data_pair = column_pair
# Mare sure it is selecting the data and subplot you want
#print('0:', fig_dict['data'][data_pair[0]]['name'], fig_dict['data'][data_pair[0]]['xaxis'])
#print('1:', fig_dict['data'][data_pair[1]]['name'], fig_dict['data'][data_pair[1]]['xaxis'])
# Get the p-value
pvalue = stats.ttest_ind(
fig_dict['data'][data_pair[0]]['y'],
fig_dict['data'][data_pair[1]]['y'],
equal_var=False,
)[1]
if pvalue >= 0.05:
symbol = 'ns'
elif pvalue >= 0.01:
symbol = '*'
elif pvalue >= 0.001:
symbol = '**'
else:
symbol = '***'
# Vertical line
fig.add_shape(type="line",
xref="x"+subplot_str, yref="y"+subplot_str+" domain",
x0=column_pair[0], y0=y_range[index][0],
x1=column_pair[0], y1=y_range[index][1],
line=dict(color=_format['color'], width=1.5,)
)
# Horizontal line
fig.add_shape(type="line",
xref="x"+subplot_str, yref="y"+subplot_str+" domain",
x0=column_pair[0], y0=y_range[index][1],
x1=column_pair[1], y1=y_range[index][1],
line=dict(color=_format['color'], width=1.5,)
)
# Vertical line
fig.add_shape(type="line",
xref="x"+subplot_str, yref="y"+subplot_str+" domain",
x0=column_pair[1], y0=y_range[index][0],
x1=column_pair[1], y1=y_range[index][1],
line=dict(color=_format['color'], width=1.5,)
)
## add text at the correct x, y coordinates
## for bars, there is a direct mapping from the bar number to 0, 1, 2...
fig.add_annotation(dict(font=dict(color=_format['color'],size=14),
x=(column_pair[0] + column_pair[1])/2,
y=y_range[index][1]*_format['text_height'],
showarrow=False,
text=symbol,
textangle=0,
xref="x"+subplot_str,
yref="y"+subplot_str+" domain"
))
return fig
def box_plot(df):
fig = px.box(df, x = 'Task_name', y='test_auroc', color="Model")
fig.update_layout(plot_bgcolor="white")
fig.update_xaxes(linecolor='rgba(0,0,0,0.25)', gridcolor='rgba(0,0,0,0)',mirror=False)
fig.update_yaxes(linecolor='rgba(0,0,0,0.25)', gridcolor='rgba(0,0,0,0.07)',mirror=False)
fig.update_layout(title={'text': "ROC-AUC score distribution",
'font':{'size':40},
'y': 0.96,
'x': 0.5,
'xanchor': 'center',
'yanchor': 'top'},
xaxis_title={'text': "Datasets",
'font':{'size':30}},
yaxis_title={'text': "ROC-AUC",
'font':{'size':30}},
font=dict(family="Calibri, monospace",
size=17
))
fig = add_p_value_annotation(fig, [[0,7], [3,7], [6,7]], subplot=1)
fig.write_image('../figures/box_plot_integration.png', width=1.5*1200, height=0.75*1200, scale=2)
fig.show()
def go_box_plot(df, metric = ROC):
dataset_list = ['BIOSNAP', 'DAVIS', 'BindingDB']
model_list = ['LR', 'DNN', 'GNN-CPI', 'DeepDTI', 'DeepDTA', 'DeepConv-DTI', 'Moltrans', 'ours']
clr_list = ['red', 'orange', 'green', 'indianred', 'lightseagreen', 'goldenrod', 'magenta', 'blue']
if metric == ROC:
# fig_title = "ROC-AUC score distribution"
file_title = "boxplot_auroc.png"
select_metric = "test_auroc"
else:
# fig_title = "PR-AUC score distribution"
file_title = "boxplot_auprc.png"
select_metric = "test_auprc"
fig = make_subplots(rows=1, cols=3, subplot_titles=[c for c in dataset_list])
groups = df.groupby(df.Task_name)
Legand = True
for dataset_idx, dataset in enumerate(dataset_list):
df_modelgroup = groups.get_group(dataset)
model_groups = df_modelgroup.groupby(df_modelgroup.Model)
if dataset_idx != 0:
Legand = False
for model_idx, model in enumerate(model_list):
df_data = model_groups.get_group(model)
fig.append_trace(go.Box(y=df_data[select_metric],
name=model,
marker_color=clr_list[model_idx],
showlegend = Legand
),
row=1,
col=dataset_idx+1)
# fig.update_layout(title={'text': fig_title,
# 'font':{'size':25},
# 'y': 0.98,
# 'x': 0.46,
# 'xanchor': 'center',
# 'yanchor': 'top'})
# fig = add_p_value_annotation(fig, [[0,7], [3,7], [6,7]], subplot=1)
# fig = add_p_value_annotation(fig, [[0,7], [3,7], [6,7]], subplot=2)
# fig = add_p_value_annotation(fig, [[0,7], [3,7], [6,7]], subplot=3)
fig.write_image(f'../figures/{file_title}', width=1.5*1200, height=0.75*1200, scale=2)
fig.show()
if __name__ == '__main__':
df = pd.read_csv("../dataset/wandb_export_boxplotdata.csv")
box_plot(df)