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import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import plotly.graph_objects as go
from plotly.subplots import make_subplots
rangeLabel_col = ["MW_range", "PSA_range", "NRB_range", "HBA_range", "HBD_range", "LogP_range"]
superscript_map = {
0: "\u2070",
1: "\u00B9",
2: "\u00B2",
3: "\u00B3",
4: "\u2074",
5: "\u2075",
6: "\u2076",
7: "\u2077",
8: "\u2078",
9: "\u2079"
}
def draw_plot(result_path:str, save_path:str):
## -- predict plot save -- ##
# df_plot = pd.read_csv(os.path.join("../", result_path))
df_plot = pd.read_csv(result_path)
labels, predict = df_plot['Clint'], df_plot['predict']
x_ax = list(range(len(labels)))
fig = make_subplots(rows=2, cols=1)
fig.add_trace(go.Scatter(x=x_ax, y=labels,
mode='lines',
name='labels',
legendgroup="1"), row=1, col=1)
fig.add_trace(go.Scatter(x=x_ax, y=predict,
mode='lines',
name='predicted',
legendgroup="1"), row=1, col=1)
## -- Compare model result Scatter plot -- ##
model = LinearRegression().fit(np.array(labels).reshape(-1,1), (np.array(predict)))
y_hat = model.predict(np.array(labels).reshape(-1,1))
fig.add_trace(go.Scatter(x=labels, y=predict,
mode='markers',
legendgroup="2"), row=2, col=1)
fig.add_trace(go.Scatter(x=labels, y=y_hat,
mode="lines",
name="trend line",
line=dict(shape="linear", color="red", width=2, dash="dot"),
legendgroup="2"), row=2, col=1)
ideal_list = list(range(-1, int(max(labels)) + 3))
ideal_x, ideal_y = ideal_list, ideal_list
ideal_x_rev = ideal_x[::-1]
ideal_y_uper,ideal_y_lower = [val+1 for val in ideal_y], [val-1 for val in ideal_y]
ideal_y_lower = ideal_y_lower[::-1]
ideal_y_uper_dot,ideal_y_lower_dot = [val+0.5 for val in ideal_y], [val-0.5 for val in ideal_y]
ideal_y_lower_dot = ideal_y_lower_dot[::-1]
fig.add_trace(go.Scatter(x=ideal_x, y=ideal_y,
mode="lines",
name="ideal",
line=dict(shape="linear", color="black", width=2, dash="dot"),
legendgroup="2"), row=2, col=1)
fig.add_trace(go.Scatter(x=ideal_x + ideal_x_rev, y=ideal_y_uper + ideal_y_lower,
mode="lines",
name="margin 1",
line=dict(shape="linear", color='rgba(231,107,243,0.8)', width=2, dash="dashdot"),
legendgroup="2"), row=2, col=1)
fig.add_trace(go.Scatter(x=ideal_x + ideal_x_rev, y=ideal_y_uper_dot + ideal_y_lower_dot,
mode="lines",
name="margin 2",
line=dict(shape="linear", color='rgba(0,176,246,0.8)', width=2, dash="dash"),
legendgroup="2"), row=2, col=1)
fig.update_layout(
height=800,
width=800,
title_text="Compare Observed Clint to Prediction results",
xaxis1_title = 'Data Length',
yaxis1_title = 'Clint_logScale',
xaxis2_title = 'Observed',
yaxis2_title = 'Predicted',
legend_tracegroupgap = 320,
xaxis2_range=[-0.5, int(max(labels)) + 1],
yaxis2_range=[-0.5, int(max(labels)) + 1],
)
# fig.write_image(os.path.join("../", save_path))
fig.write_image(save_path)
def draw_boundaryplot(df_plot:pd.DataFrame, save_path:str, df_duplData:pd.DataFrame = None):
labels, predict = np.expm1(df_plot["Clint"]), np.expm1(df_plot["predict"])
labels, predict = np.log10(labels), np.log10(predict)
# labels, predict = df_plot["Clint"], df_plot["predict"]
variance_list =[abs(data - labels[idx])/predict.mean() for idx, data in enumerate(predict)]
df_plot["variance"] = variance_list
variance_boundary = 0.5
in_labels, in_predict = df_plot[df_plot["variance"] <= variance_boundary]['Clint'], df_plot[df_plot["variance"] <= variance_boundary]['predict']
out_labels, out_predict = df_plot[df_plot["variance"] > variance_boundary]['Clint'], df_plot[df_plot["variance"] > variance_boundary]['predict']
x_extended = np.linspace(df_plot['Clint'].min() - 1, df_plot['Clint'].max() + 1, 200)
grad, bias = np.polyfit(labels, predict, 1)
yhat = grad* x_extended + bias
in_grad, in_bias = np.polyfit(in_labels, in_predict, 1)
in_yhat = in_grad* x_extended + in_bias
## -- y축: 예측치, x축: 관측치 -- ##
scatter = go.Scatter(x=labels, y=predict,
mode='markers',
name="Predict_point")
out_scatter = go.Scatter(x=out_labels, y=out_predict,
mode='markers',
name="out_bound",
)
trend_line = go.Scatter(x=x_extended, y=yhat,
mode="lines",
name="trend line",
line=dict(shape="linear", color="red", width=2, dash="dot"))
in_trend_line = go.Scatter(x=x_extended, y=in_yhat,
mode="lines",
name="in-variance trend line",
line=dict(shape="linear", color="olive", width=2, dash="dot"))
## -- log scale된 데이터의 Ideal 기울기와 fold 기울기 -- ##
ideal_list = list(range(-1, int(max(labels)) + 3))
ideal_x, ideal_y = ideal_list, ideal_list
ideal_line = go.Scatter(x=ideal_x, y=ideal_y,
mode="lines",
name="ideal",
line=dict(shape="linear", color="black", width=2))
## -- 2-fold 기울기 : 관측치 대비 예측치가 /2 ~ *2인 범위
fold2_top = [x + np.log10(2.0) for x in ideal_list]
fold2_bottom = [x - np.log10(2.0) for x in ideal_list]
fold2_topline = go.Scatter(x=ideal_list, y=fold2_top,
mode="lines",
name="2-fold",
line=dict(shape="linear", color="black", width=2, dash="dash"))
fold2_bottomline = go.Scatter(x=ideal_list, y=fold2_bottom,
mode="lines",
name="2-fold",
showlegend =False,
line=dict(shape="linear", color="black", width=2, dash="dash"))
fold5_top = [x + np.log10(5.0) for x in ideal_list]
fold5_bottom = [x - np.log10(5.0) for x in ideal_list]
fold5_topline = go.Scatter(x=ideal_x, y=fold5_top,
mode="lines",
name="5-fold",
line=dict(shape="linear", color="black", width=2, dash="dot"))
fold5_bottomline = go.Scatter(x=ideal_x, y=fold5_bottom,
mode="lines",
name="5-fold",
showlegend =False,
line=dict(shape="linear", color="black", width=2, dash="dot"))
fig = go.Figure(data=[scatter, out_scatter, trend_line, in_trend_line, ideal_line,
fold2_topline, fold2_bottomline, fold5_topline, fold5_bottomline])
axis_map = [f'10{superscript_map[0]}', f'10{superscript_map[1]}', f'10{superscript_map[2]}', f'10{superscript_map[3]}']
# x축 제목과 폰트 설정
fig.update_xaxes(title_text='Observed Clint', title_font=dict(size=18))
# y축 제목과 폰트 설정
fig.update_yaxes(title_text='Predicted Clint', title_font=dict(size=18))
fig.update_layout(
height=800,
width=900,
title_text="",
legend=dict(font=dict(size=16)),
legend_tracegroupgap = 320,
xaxis_range=[0.0, int(max(labels)) + 1],
yaxis_range=[0.0, int(max(labels)) + 1],
plot_bgcolor='white', # Set the background color to white
xaxis=dict(
showgrid=True,
gridcolor='lightgrey',
tickfont=dict(size=18),
tickvals = [0.0, 1.0, 2.0, 3.0],
ticktext = axis_map,
gridwidth=1,
griddash='dash', # Set the x-axis grid lines to light grey dashed
linecolor='black', # Set x-axis line color to black
linewidth=2, # Set x-axis line width
),
yaxis=dict(
showgrid=True,
gridcolor='lightgrey',
gridwidth=1,
tickfont=dict(size=18),
tickvals = [0.0, 1.0, 2.0, 3.0],
ticktext = axis_map,
griddash='dash', # Set the y-axis grid lines to light grey dashed
linecolor='black', # Set y-axis line color to black
linewidth=2, # Set y-axis line width
)
)
fig.write_image(save_path)
return grad, in_grad
def draw_boxplot(result_path:str, save_path:str):
## -- predict plot save -- ##
x_axisName = ["molecular weight", "PSA", "Number of Rotatable Bonds",
"Number of Hydrogen-Bond Acceptors", "Number of Hydrogen-Bond Donors", "LogP"]
df_plot = pd.read_csv(result_path)
y_labels, y_predict = df_plot['Clint'], df_plot['predict']
fig = make_subplots(rows=3, cols=2)
for idx, axisName in enumerate(x_axisName):
x_labels, x_predict = df_plot[rangeLabel_col[idx]], df_plot[rangeLabel_col[idx]]
row_numb, col_numb = (idx // 2) + 1, (idx % 2) + 1
fig.add_trace(go.Box(x=x_labels, y=y_labels,
name="Label",
jitter=0.3,
pointpos=-1.8,
boxpoints='all', # represent all points
marker_color='rgb(7,40,89)',
line_color='rgb(7,40,89)',
legendgroup="1"), row=row_numb, col=col_numb)
fig.add_trace(go.Scatter(x=x_predict, y=y_predict,
name="Prediction",
jitter=0.3,
pointpos=-1.8,
boxpoints='all', # represent all points
marker_color='rgb(7,40,89)',
line_color='rgb(7,40,89)',
legendgroup="1"), row=row_numb, col=col_numb)
fig.update_xaxes(title_text=x_axisName[idx], row=row_numb, col=col_numb)
fig.update_yaxes(title_text='Intrinsic CL(Log Scale)', row=row_numb, col=col_numb)
fig.update_layout(
height=800,
width=800,
title_text="Compare Observed Clint to Prediction results",
legend_tracegroupgap = 320,
)
# fig.write_image(os.path.join("../", save_path))
fig.write_image(save_path) |