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app1_2.py
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app1_2.py
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### Data
import pandas as pd
import pickle
### Graphing
import plotly.graph_objects as go
### Dash
import dash
import dash_core_components as dcc
import dash_html_components as html
import dash_bootstrap_components as dbc
from dash.dependencies import Output, Input
## Navbar
from nav import Navbar
import numpy as np
import plotly.express as px
import seaborn as sns
from plotly.subplots import make_subplots
nav = Navbar()
# header = html.H3(
# 'Reliability of Feature Importance'
# )
# accs=pd.read_csv('feature_impo_accs_reg.csv')
# accs['test_acc']=np.exp(-accs['test_acc'])
# accs=accs[accs.model!='LASSO']
# accs['test_acc']=(accs['test_acc']-min(accs['test_acc']))/(max(accs['test_acc'])-min(accs['test_acc']))
df = pd.read_csv("feature_impo_reg.csv")
#msee =[1/float(i) for i in df['Accuracy']]
msee =np.array([float(i) for i in df['MSE']])
df['Accuracy']= np.exp(-msee)
cross = pd.read_csv('cross_reg.csv')
cross_pred=pd.read_csv('fi_cross_pred_reg.csv')
# cross_pred['Consistency']=1-(cross_pred['value']-min(cross_pred['value']))/max(cross_pred['value'])-min(cross_pred['value'])
cross_pred['Consistency']=np.exp(-cross_pred['MSE'])
puris=pd.read_csv('feature_impo_pur_reg.csv')
criteria_options = df['criteria'].unique().tolist()
k_options =df['K'].unique().tolist()
method_category_options = ['Selected','Model Specific','Model Agnostic','All']
plot_summary_options = {'heatmap':'Consistency heatmap across methods',
'line':'Consistency across data sets',
'bump':'Bump plot of the most consistent methods across data sets',
'dot':'Consistency/predictive accuracy vs. methods',
'fit':'Scatter plots of interpretation consistency, predictive consistency, and preditvie accuracy',
# 'cor': 'Correlation between consistency and predictive error (MSE)'
}
plot_raw_options = {'scatter_raw':'Consistency vs. number of features for all data sets',
'line_raw':'Consistency vs. predictive accuracy for all data sets',
'acc_raw':'Predictive accuracy for all data sets',
'heatmap_raw':'Consistency heatmap across methods for all data sets'}
markers_choice = {'LASSO':'0',
'SVM':'0',
'Ridge':'0',
'Tree':'0',
'XGB':'0' ,
'RF':'0' ,
'MLP':'0',
'deepLIFT (MLP)':"square",
'Integrated Gradients (MLP)':"square",
'Epsilon-LRP (MLP)':"square",
'Saliency Maps (MLP)':"square",
'Guided Backpropagation (MLP)':"square",
'Occlusion (MLP)' :"x",
'Permutation (Ridge)':"x",
'Permutation (RF)':"x" ,
'Permutation (XGB)':"x",
'Permutation (MLP)':"x",
'Shapley Value (Ridge)':"x" ,
'Shapley Value (RF)':"x",
'Shapley Value (XGB)': "x",
'Shapley Value (MLP)':"x",
}
# palette = {
# 'LASSO':"purple",
# 'Ridge': 'indigo',
# 'SVM':"firebrick",
# 'Tree':'deeppink',
# 'RF':"magenta",
# 'XGB':"violet",
# 'MLP':'green',
# 'deepLIFT (MLP)':"powderblue",
# 'Integrated Gradients (MLP)':'cornflowerblue',
# 'Epsilon-LRP (MLP)':"cyan",
# 'Guided Backpropagation (MLP)':"limegreen",
# 'Saliency Maps (MLP)':"green",
# 'Occlusion (MLP)' :'greenyellow' ,
# 'permutation (Ridge)':'tomato',
# 'permutation (RF)':"gold",
# 'permutation (XGB)':"olive",
# 'permutation (MLP)':"orange",
# 'Shapley Value (Ridge)':'chocolate',
# 'Shapley Value (RF)': "yellow",
# 'Shapley Value (XGB)': "darkkhaki",
# 'Shapley Value (MLP)':'peru',
# }
# palette = {
# ## purple
# 'Ridge': 'indigo',
# 'LASSO':"purple",
# 'SVM':"firebrick",
# 'Tree':'deeppink',
# 'RF':"magenta",
# 'XGB':"violet",
# ## green
# 'MLP':"green",
# 'Epsilon-LRP (MLP)':"green",
# 'Guided Backpropagation (MLP)':"greenyellow",
# 'deepLIFT (MLP)':"darkcyan",
# 'Integrated Gradients (MLP)':'medianseagreen',
# 'Saliency Maps (MLP)':"olivedrab",
# 'Occlusion (MLP)' :'limegreen' ,
# ## orange scheme
# 'Permutation (Ridge)':'tomato',
# 'Permutation (RF)':"gold",
# 'Permutation (XGB)':"peru",
# 'Permutation (MLP)':"yellow",
# ## blue
# 'Shapley Value (Ridge)':'powderblue',
# 'Shapley Value (RF)': "cornflowerblue",
# 'Shapley Value (XGB)': "skyblue",
# 'Shapley Value (MLP)':'slateblue',
# }
palette = {
## purple
'SVM':"deeppink",
'LASSO':"tomato",
'Ridge': 'indigo',
'Permutation (Ridge)':'purple',
'Shapley Value (Ridge)':'firebrick',
'Tree':'skyblue',
## blue
'RF':"slateblue",
'Permutation (RF)':"darkturquoise",
'Shapley Value (RF)': "cornflowerblue",
'XGB':"violet",
'Permutation (XGB)':"peru",
'Shapley Value (XGB)': "magenta",
## green
'MLP':"teal",
'Epsilon-LRP (MLP)':"teal",
'Guided Backpropagation (MLP)':"olivedrab",
'Permutation (MLP)':"orange",
'Shapley Value (MLP)':'gold',
'deepLIFT (MLP)':"darkcyan",
'Integrated Gradients (MLP)':'seagreen',
'Saliency Maps (MLP)':"greenyellow",
'Occlusion (MLP)' :'limegreen' ,
}
line_choice = {'SVM':'solid',
'LASSO':'solid',
'Ridge':'solid',
'Tree':'dot',
'RF':'dot' ,
'XGB':'dot',
'MLP':'dash',
'deepLIFT (MLP)':'dash',
'Integrated Gradients (MLP)':'dash',
'Epsilon-LRP (MLP)':'dash',
'Permutation (MLP)':'dashdot',
'Shapley Value (MLP)':'dashdot',
'Guided Backpropagation (MLP)':"dash",
'Saliency Maps (MLP)':"dash",
'Occlusion (MLP)' :'dash' ,
'Permutation (Ridge)':'dashdot',
'Permutation (RF)':"dashdot",
'Shapley Value (Ridge)':'dashdot',
'Shapley Value (RF)': "dashdot",
'Shapley Value (XGB)': "dashdot",
'Permutation (XGB)':"dashdot",
}
palette_data = {
'News':"powderblue", #671
'Blog':"deepskyblue", #187.13
'Satellite': 'cornflowerblue' ,# 178
'Star':'slateblue',#55
'Communities' :"gold",#20 '
'Bike':"orange",
'CPU':'salmon',
'Wine':'lightseagreen',
'Music':'olivedrab',
'Residential':'hotpink', #3.6
'Tecator':'firebrick',#1.9
'Word':"indigo",#0.9'
'Riboflavin':"purple",
}
def sort(df,column1,sorter1,column2=None,sorter2=None):
df[column1] = df[column1].astype("category")
df[column1].cat.set_categories(sorter1, inplace=True)
if column2 is not None:
df[column2] = df[column2].astype("category")
df[column2].cat.set_categories(sorter2, inplace=True)
df=df.sort_values([column1,column2])
df[column2]=df[column2].astype("str")
else:
df=df.sort_values([column1])
df[column1]=df[column1].astype("str")
return df
df=sort(df,'data',list(palette_data.keys()),'method',list(palette.keys()))
meths = list(palette.keys())
datas = list(palette_data.keys())
def description_card():
"""
:return: A Div containing dashboard title & descriptions.
"""
return html.Div(
id="description-card",
children=[
# html.H1("Interpretable Machine Learning"),
html.Div(
id="intro",
children="Explore IML reliability.",
),
],
)
# plot_summary_options = ['heatmap','line','bump','dot','cor']
# plot_raw_options = ['scatter_raw','line_raw','heatmap_raw']
def generate_control_card():
"""
:return: A Div containing controls for graphs.
"""
return html.Div(
id="control-card",
children=[
#############################
####### upload new data
############################
html.P("Upload your reliability results"),
dcc.Upload(
id='upload-data',
children=html.Div([
'Drag and Drop or ',
html.A('Select Files')
]),
style={
'width': '100%',
'height': '60px',
'lineHeight': '60px',
'borderWidth': '1px',
'borderStyle': 'dashed',
'borderRadius': '5px',
'textAlign': 'center',
'margin': '10px'
},
# Allow multiple files to be uploaded
multiple=True
),
html.Div(id='new_options'),
html.Hr(),
html.P("Select IML questions"),
dcc.RadioItems(
id="qq",
options=[{"label": i, "value": j} for (j,i) in [('Q1','Q1:If we sample a different training set, are the interpretations similar?'), ('Q2','Q2: Do two IML methods generate similar interpretations on the same data?'),('Q3','Q3: Does higher accuracy lead to more consistent interpretations?')]],
value='Q1',
),
###############################
###############################
html.Hr(),
html.P("Select: Consistency Metric"),
dcc.RadioItems(
id="criteria-select",
options=[{"label": i, "value": i} for i in criteria_options],
value='RBO',
),
html.Hr(),
html.P("Select: Top K Features"),
dcc.Dropdown(
id="k-select",
options=[{"label": i, "value": i} for i in k_options],
value=10,
),
html.Hr(),
html.P("Select: Interpretability Method"),
dcc.Dropdown(
id="method-select",
options=[{"label": i, "value": i} for i in meths],
value=meths[1],
multi=True,
),
html.Hr(),
html.P("Select: Interpretability Method Category"),
dcc.RadioItems(
id="method-select_c",
options=[{"label": i, "value": i} for i in method_category_options],
value=method_category_options[0],
),
html.Hr(),
html.P("Select: Data Sets"),
dcc.Dropdown(
id="data-select",
options=[{"label": i, "value": i} for i in datas],
value=datas[:],
multi=True,
),
html.Br(),
html.Hr(),
#################################
########### select figures
#################################
# html.P("Select Summary Graphs you want to show"),
# dcc.Checklist(id="all_summary",
# options=[{"label": 'All', "value":'All_summary' }],value= ['All_summary']),
# dcc.Checklist(id="select_summary",
# options=[{"label": plot_summary_options[i], "value": i} for i in plot_summary_options],
# value=list(plot_summary_options.keys()),
# ),
# html.Hr(),
# html.P("Select Raw Graphs you want to show"),
# dcc.Checklist(id="all_raw",
# options=[{"label": 'All', "value":'All_raw' }],value= ['All_raw']),
# dcc.Checklist(id="select_raw",
# options=[{"label": plot_raw_options[i], "value": i} for i in plot_raw_options],
# value=list(plot_raw_options.keys()),
# ),
# html.Hr(),
# dbc.Button('Submit', id='submit-button',n_clicks=0, color="primary",className="me-1"),
# dbc.Button('Reset',id='reset-button',n_clicks=0, color="secondary",className="me-1"),
# html.Hr(),
],
)
# options = [{'label':x.replace(', Illinois', ''), 'value': x} for x in df.columns]
# dropdown = html.Div(dcc.Dropdown(
# id = 'pop_dropdown',
# options = options,
# value = 'Abingdon city, Illinois'
# ))
# output = html.Div(id = 'output',
# children = [],
# )
def App1_2():
layout = html.Div([
nav,
dbc.Container([
html.H1('Feature Importance (Regression)'),
dbc.Row([
dbc.Col([
# Left column
html.Div(
id="left-column",
children=[description_card(), generate_control_card()],
className="four columns",
),
],
width={"size": 3},
),
dbc.Col(children=[
html.Div(id='output-datatable'),
###### summary plots
html.Div(id='title_summary'),
html.Div(id='subtitle_summary'),
html.Div(id='show_heat2'),
html.Div(id='show_bump'),
html.Div(id='show_line'),
html.Div(id='show_heatmap'),
html.Div(id='show_fit'),
# html.Div(id='show_dot'),
# html.Div(id='show_cor'),
######### raw plots
html.Div(id='title_summary_raw'),
html.Div(id='show_line_raw'),
html.Div(id='show_scatter_raw'),
# html.Div(id='show_acc_raw'),
html.Div(id='show_heatmap_raw'),
],
width={"size": 7, "offset": 1},
)])])
])
return layout
def build_scatter_reg(data_sel, method_sel,
k_sel, criteria_sel
):
this_palette=dict((i,palette[i]) for i in method_sel)
this_markers_choice=dict((i,markers_choice[i]) for i in method_sel)
dff=df[(df.data.isin(data_sel))
&(df.method.isin(method_sel))
&(df.K ==k_sel)
&(df.criteria==criteria_sel)]
fig = px.scatter(dff, x="Accuracy", y="Consistency", color='method',
facet_col='data',
facet_col_wrap=3,
color_discrete_map=(palette),
symbol='method', symbol_map= this_markers_choice,
text = 'method',
category_orders={"method":list(this_palette.keys())},
labels=dict(Consistency=criteria_sel, method="Method",
Accuracy='Accuracy')
)
fig.update_layout(legend=dict(
orientation="h",
yanchor="bottom",
y=-.2,
xanchor="right",
x=1
))
fig.update_traces(
textposition='top right'
)
return fig
def build_bump_reg(data_sel, method_sel,
k_sel, criteria_sel,new_data=None
):
####### filter data
dff=df[(df.data.isin(data_sel))
&(df.method.isin(method_sel))
&(df.K ==k_sel)
&(df.criteria==criteria_sel)]
this_palette=dict((i,palette[i]) for i in method_sel)
this_markers_choice=dict((i,markers_choice[i]) for i in method_sel)
if new_data is not None:
new_data = pd.DataFrame(new_data)
neww = new_data[(new_data.K ==k_sel)
&(new_data.criteria==criteria_sel)]
dff = pd.concat([dff, neww],join='inner', ignore_index=True)
for mm in set(new_data['method']):
this_palette[mm]='black'
for mm in set(new_data['data']):
data_sel = data_sel+[mm]
##### bump plot
df_ave = dff.groupby(['method','K','criteria'],as_index=False).mean()
df_ave['data']='Average'
df_ave=df_ave[['data','method','K','criteria','Consistency','Accuracy']]
dff=pd.concat([dff,df_ave])
########################
rankk = dff.sort_values(['Consistency'],ascending=False).sort_values(['data','Consistency'],ascending=False)[['data','method']]
rankk['ranking'] = (rankk.groupby('data').cumcount()+1)
rankk=pd.merge(dff,rankk,how='left',on = ['data','method'])
top= rankk[rankk["data"] == 'Average'].nsmallest(len(set(rankk.method)), "ranking")
rankk['ranking']=[str(i) for i in rankk['ranking']]
fig = px.line(rankk,
x="data", y="ranking",
color='method',markers=True,
color_discrete_map=this_palette,
category_orders={"data":list(dff.data.unique()),
'ranking':[str(i) for i in range(1,len(set(rankk['ranking']))+1)]
},
labels=dict(data="Data",ranking='Rank',
Accuracy='Accuracy')
)
fig.update_layout(showlegend=False)
y_annotation = list(top['method'])[::-1]
intervals = list(top['ranking'])
for k in intervals:
fig.add_annotation(dict(font=dict(color="black",size=10),
#x=x_loc,
x=1,
y=str(k-1),
showarrow=False,
text=y_annotation[k-1],
textangle=0,
xanchor='left',
xref="paper",
yref="y"
))
if new_data is not None:
new_rankk = rankk[(rankk.data.isin(set(neww.data)))&(rankk.method.isin(set(neww.method)))]
fig.add_trace(
go.Scatter(
x=new_rankk['data'],
y=new_rankk['ranking'],
mode='markers',
marker=dict(
color='black',
size=15,
line=dict(
color='MediumPurple',
width=5
),
),
showlegend=False,
hoverinfo='none', )
)
fig.update_layout(margin=dict( r=150))
# fig.add_annotation(dict(font=dict(color="grey",size=12),
# x=-0.05, y=-0.1,
# text="Large N",
# xref='paper',
# yref='paper',
# showarrow=False))
# fig.add_annotation(dict(font=dict(color="grey",size=12),
# x=1.1, y=-0.1,
# text="Large P",
# xref='paper',
# yref='paper',
# showarrow=False))
fig.update_layout(margin=dict( r=150))
return fig
def build_acc_bar_reg(data_sel, method_sel,
k_sel, new_data=None):
this_palette=dict((i,palette[i]) for i in method_sel)
dff=df[(df.data.isin(data_sel))
&(df.method.isin(method_sel))
&(df.K ==k_sel)]
dff = dff.groupby(['method']).mean().reset_index()
fig = px.bar(dff, x='method', y='Accuracy',
range_y = [0,1],
color='method',text_auto='.3',
color_discrete_map=this_palette,
labels=dict(method="Method",
Accuracy='Accuracy'),
title="Predictive Accuracy"
)
fig.update_traces(textfont_size=12, textangle=0, textposition="outside", cliponaxis=False)
fig.update_layout(height=300,showlegend=False)
return fig
# def build_heat_summary_reg(data_sel,method_sel,
# k_sel, criteria_sel,new_data=None
# ):
# cross_ave = cross[cross.data.isin(data_sel)]
# cross_ave=cross_ave.groupby(['method1','method2','criteria','K'],as_index=False)['value'].mean()
# sub = cross_ave[(cross_ave['method1'].isin(method_sel))&(cross_ave['method2'].isin(method_sel))]
# sub = sub[(sub['K']==k_sel)&(sub['criteria']==criteria_sel)]
# sub = sub.pivot("method1", "method2", "value")
# sub = sub.fillna(0)+sub.fillna(0).T
# np.fill_diagonal(sub.values, 1)
# sub=round(sub.reindex(columns=method_sel).reindex(method_sel),3)
# fig = px.imshow(sub, text_auto=True,color_continuous_scale='Purp',
# origin='lower',
# labels=dict(x="Method", y="Method", color="Consistency"))
# fig.layout.coloraxis.showscale = False
# fig.update_xaxes(tickangle=45)
# return fig
def build_heat_summary_reg(data_sel, method_sel,
k_sel, criteria_sel,new_data=None):
cross_ave = cross[cross.data.isin(data_sel)]
cross_ave=cross_ave.groupby(['method1','method2','criteria','K'],as_index=False)['value'].mean()
sub = cross_ave[(cross_ave['method1'].isin(method_sel))&(cross_ave['method2'].isin(method_sel))]
sub = sub[(sub['K']==k_sel)&(sub['criteria']==criteria_sel)]
sub = sub.pivot("method1", "method2", "value")
sub = sub.fillna(0)+sub.fillna(0).T
np.fill_diagonal(sub.values, 1)
sub=round(sub.reindex(columns=method_sel).reindex(method_sel),3)
sub2 = cross_pred[(cross_pred.method1!='LASSO')&(cross_pred.method2!='LASSO')]
sub2=sub2.groupby(['method1','method2'],as_index=False)['Consistency'].mean()
sub2 = sub2.pivot("method1", "method2", "Consistency")
sub2 = sub2.fillna(0)+sub2.fillna(0).T
np.fill_diagonal(sub2.values, 1)
sub2=round(sub2.reindex(columns=['SVM','Ridge','Tree','RF','XGB','MLP']).reindex(['SVM','Ridge','Tree','RF','XGB','MLP']),3)
fig = make_subplots(rows=1, cols=2, column_widths=[0.6, 0.4],
horizontal_spacing=0.15,
vertical_spacing=0.05, subplot_titles=('Interpretation Consistency','Prediction Consistency'))
heat1 = px.imshow(sub, text_auto=True, aspect="auto",range_color=(0,1),
color_continuous_scale=[(0,'whitesmoke'),(0.33, sns.xkcd_rgb['light lavender']),(0.66, sns.xkcd_rgb['lavender']),(1,sns.xkcd_rgb['amethyst'])],
origin='lower',labels=dict(x="Method", y="Method", color="Consistency"))
heat2 = px.imshow( sub2, text_auto=True, aspect="auto",range_color=(0,1),
color_continuous_scale=[(0,'whitesmoke'),(0.33, sns.xkcd_rgb['light lavender']),(0.66, sns.xkcd_rgb['lavender']),(1,sns.xkcd_rgb['amethyst'])],
origin='lower', labels=dict(x="Method", y="Method", color="Consistency"))
heat2.layout.height = 500
heat2.layout.width = 500
for trace in heat1.data:
fig.add_trace(trace, 1, 1)
for trace in heat2.data:
fig.add_trace(trace, 1, 2)
fig.update_layout(
coloraxis=dict(colorscale=[(0,'whitesmoke'),(0.33, sns.xkcd_rgb['light lavender']),(0.66, sns.xkcd_rgb['lavender']),(1,sns.xkcd_rgb['amethyst'])],cmin=0,cmax=1,
showscale = False),)
fig.update_xaxes(tickangle=45)# for trace in bar1.data:
fig['layout']['yaxis2']['title']='Method'
fig['layout']['xaxis2']['title']='Method'
fig['layout']['yaxis']['title']='Method'
fig['layout']['xaxis']['title']='Method'
return fig
def build_heat_consis_reg(data_sel, method_sel,
k_sel, criteria_sel,new_data=None):
dff=df[(df.data.isin(data_sel))
&(df.method.isin(method_sel))
&(df.K ==k_sel)
&(df.criteria==criteria_sel)]
this_palette=dict((i,palette[i]) for i in method_sel)
this_line_choice=dict((i,line_choice[i]) for i in method_sel)
this_palette_data=dict((i,palette_data[i]) for i in data_sel)
###### input new data
if new_data is not None:
new_data = pd.DataFrame(new_data)
neww = new_data[(new_data.K ==k_sel)
&(new_data.criteria==criteria_sel)]
dff = pd.concat([dff, neww])
for mm in set(new_data['method']):
this_palette[mm]='black'
this_line_choice[mm]='solid'
method_sel=method_sel+[mm]
for mm in set(new_data['data']):
this_palette_data[mm]='black'
sub = dff.pivot("data", "method", "Consistency")
sub=round(sub,3)
sub= pd.DataFrame(sub, index=this_palette_data)
sub=sub[method_sel]
h = px.imshow(sub, text_auto=True, aspect="auto",range_color=(0,1),
color_continuous_scale=[(0, "whitesmoke"),(0.33,sns.xkcd_rgb["light teal"]),(0.66, sns.xkcd_rgb["tealish"]),(1, sns.xkcd_rgb["dark cyan"])],
origin='lower',labels=dict(x="Method", y="Data", color="Consistency"))
h.update_layout({
'plot_bgcolor':'rgba(0, 0, 0, 0)',
'paper_bgcolor': 'rgba(0, 0, 0, 0)',
})
h.layout.height = 500
h.layout.width = 1000
dff_ac = dff[["data", "model", "Accuracy"]].drop_duplicates()
sub2 = dff_ac.pivot("data", "model", "Accuracy")
sub2=round(sub2,3)
sub2= pd.DataFrame(sub2, index=this_palette_data)
sub2=sub2[['SVM','Ridge','Tree','RF','XGB','MLP']]
h2=px.imshow(sub2, text_auto=True, aspect="auto",
color_continuous_scale= [(0, "whitesmoke"),(0.33,sns.xkcd_rgb["light teal"]),(0.66, sns.xkcd_rgb["tealish"]),(1, sns.xkcd_rgb["dark cyan"])],
range_color=(0,1),
origin='lower',labels=dict(x="Method", y="Data", color="Consistency"))
h2.layout.height = 500
h2.layout.width = 700
fig= make_subplots(rows=1, cols=2, column_widths=[0.5, 0.5],
horizontal_spacing=0.15,
vertical_spacing=0.05, subplot_titles=('Interpretation Consistency','Prediction Accuracy'))
for trace in h.data:
fig.add_trace(trace, 1, 1)
for trace in h2.data:
fig.add_trace(trace, 1, 2)
fig.update_xaxes(tickangle=45)# for trace in bar1.data:
fig.update_layout(
coloraxis=dict(colorscale=[(0, "whitesmoke"),(0.33,sns.xkcd_rgb["light teal"]),(0.66, sns.xkcd_rgb["tealish"]),(1, sns.xkcd_rgb["dark cyan"])],cmin=0,cmax=1,
showscale = False),)
fig['layout']['yaxis2']['title']='Data'
fig['layout']['xaxis2']['title']='Method'
fig['layout']['yaxis']['title']='Data'
fig['layout']['xaxis']['title']='Method'
return fig
def build_line_reg(data_sel, method_sel,
k_sel, criteria_sel,new_data=None
):
dff=df[(df.data.isin(data_sel))
&(df.method.isin(method_sel))
&(df.K ==k_sel)
&(df.criteria==criteria_sel)]
method_sel = method_sel+['MLP']
this_palette=dict((i,palette[i]) for i in method_sel)
this_line_choice=dict((i,line_choice[i]) for i in method_sel)
this_palette_data=dict((i,palette_data[i]) for i in data_sel)
###### input new data
if new_data is not None:
new_data = pd.DataFrame(new_data)
new_data.replace({'normal':'Normal','laplace':'Laplace'})
neww = new_data[(new_data.K ==k_sel)
&(new_data.criteria==criteria_sel)]
dff = pd.concat([dff, neww],join='inner', ignore_index=True)
for mm in set(new_data['method']):
this_palette[mm]='black'
this_line_choice[mm]='dash'
for mm in set(new_data['data']):
this_palette_data[mm]='black'
fig1 = px.line(dff,x="data", y='Consistency',color = 'method',markers=True,
color_discrete_map=this_palette,
# line_dash = 'method',
# line_dash_map = this_line_choice,
labels=dict(Consistency=criteria_sel, data= "Data",
method="Method"),
# title=
)
if new_data is not None:
fig1.add_trace(
go.Scatter(
x=neww['data'],
y=neww['Consistency'],
mode='markers',
marker=dict(
color='black',
size=15,
line=dict(
color='MediumPurple',
width=5
),
),
showlegend=False,
hoverinfo='none',
)
)
fig2 = px.line(dff[dff.model!='LASSO'],x="data", y='Accuracy',color = 'model',markers=True,
color_discrete_map=this_palette,
# line_dash_map = this_line_choice,
labels={
"model": "Method",'data':'Data'
},
)
if new_data is not None:
fig2.add_trace(
go.Scatter(
x=neww['data'],
y=neww['Accuracy'],
mode='markers',
marker=dict(
color='black',
size=15,
line=dict(
color='MediumPurple',
width=5
),
),
showlegend=False,
hoverinfo='none',
)
)
for i in range(len(fig2['data'])):
if fig2['data'][i]['name']!='MLP':
fig2['data'][i]['showlegend']=False
fig= make_subplots(rows=1, cols=2, column_widths=[0.5, 0.5],
horizontal_spacing=0.15,
vertical_spacing=0.05, subplot_titles=('Interpretation Consistency','Prediction Accuracy'))
for trace in fig1.data:
fig.add_trace(trace, 1, 1)
for trace in fig2.data:
fig.add_trace(trace, 1, 2)
fig.update_traces(line=dict(width=3))
# fig.add_annotation(dict(font=dict(color="grey",size=12),
# x=-0.05, y=-0.1,
# text="Large N",
# xref='paper',
# yref='paper',
# showarrow=False))
# fig.add_annotation(dict(font=dict(color="grey",size=12),
# x=0.55, y=-0.1,
# text="Large P",
# xref='paper',
# yref='paper',
# showarrow=False))
fig.update_xaxes(categoryorder='array', categoryarray= datas)
fig.update_xaxes(tickangle=45)
fig['layout']['xaxis2']['title']='Data'
fig['layout']['yaxis2']['title']='Consistency'
fig['layout']['xaxis']['title']='Data'
fig['layout']['yaxis']['title']='Consistency'
return fig
# def build_fit_reg(data_sel, method_sel,
# k_sel, criteria_sel,new_data=None
# ):
# dff=df[(df.data.isin(data_sel))
# &(df.method.isin(method_sel))
# &(df.K ==k_sel)
# &(df.criteria==criteria_sel)]
# this_palette=dict((i,palette[i]) for i in method_sel)
# this_markers_choice=dict((i,markers_choice[i]) for i in method_sel)
# this_palette_data=dict((i,palette_data[i]) for i in data_sel)
# ###### input new data
# if new_data is not None:
# new_data = pd.DataFrame(new_data)
# neww = new_data[(new_data.K ==k_sel)
# &(new_data.criteria==criteria_sel)]
# dff = pd.concat([dff, neww])
# for mm in set(new_data['method']):
# this_palette[mm]='black'
# this_markers_choice[mm]='star'
# fig1 = px.scatter(dff, x="Consistency", y="MSE", color='data',
# trendline="ols",
# color_discrete_map=this_palette_data,
# category_orders={"Data":list(this_palette_data.keys())},
# labels=dict(Consistency='Consistency', data="Data"),
# custom_data=['data','method'],
# )
# fig1.update_traces(
# hovertemplate="<br>".join([
# "Data: %{customdata[0]}",
# "Method: %{customdata[1]}",
# "Accuracy: %{y}",
# "Consistency: %{x}",
# ]))
# fig1.update_traces(line=dict(width=3),marker = dict(size=10),opacity=0.9)
# region_lst = []
# for trace in fig1["data"]:
# trace["name"] = trace["name"].split(",")[0]
# if trace["name"] not in region_lst and trace["marker"]['symbol'] == 'circle':
# trace["showlegend"] = True
# region_lst.append(trace["name"])
# else:
# trace["showlegend"] = False
# model = px.get_trendline_results(fig1)
# for i in range(0,len(fig1['data']),2):
# fig1["data"][i+1]['customdata'] = fig1["data"][i]['customdata']
# order = np.argsort(fig1["data"][i]['x'])
# fig1["data"][i]['x'] = fig1["data"][i]['x'][order]
# fig1["data"][i]['y'] = fig1["data"][i]['y'][order]
# results = model.iloc[i//2]["px_fit_results"]
# alpha = results.params[0]
# beta = results.params[1]
# p_beta = results.pvalues[1]
# r_squared = results.rsquared
# line1 = 'y = ' + str(round(alpha, 4)) + ' + ' + str(round(beta, 4))+'x'
# line2 = 'p-value = ' + '{:.5f}'.format(p_beta)
# line3 = 'R^2 = ' + str(round(r_squared, 3))
# # summary = line1 + '<br>' + line2 + '<br>' + line3
# fitted = np.repeat([[line1,line2,line3]], len(fig1["data"][i+1]['x']), axis=0)
# fig1["data"][i+1]['customdata']=np.column_stack((fig1["data"][i+1]['customdata'],fitted))
# fig1["data"][i+1]['hovertemplate'] = 'Data: %{customdata[0]}<br>Method: %{customdata[1]}<br> %{customdata[2]} <br> %{customdata[3]}<br> %{customdata[4]}'
# if new_data is not None:
# fig1.add_trace(
# go.Scatter(
# x=neww['MSE'],
# y=neww['Consistency'],
# mode='markers',
# marker=dict(
# color=[this_palette[i] for i in neww['method']],
# symbol=[this_markers_choice[i] for i in neww['method']],
# size=20
# ),
# showlegend=False,
# hoverinfo='none'
# )
# )
# fig2 = px.scatter(dff, x="Consistency", y="MSE", color='method',
# trendline="ols",
# color_discrete_map=this_palette_data,
# category_orders={"Method":list(this_palette.keys())},
# labels=dict(Consistency='Consistency', method="Method"),
# custom_data=['data','method'],
# )
# fig2.update_traces(
# hovertemplate="<br>".join([
# "Data: %{customdata[0]}",
# "Method: %{customdata[1]}",
# "Accuracy: %{y}",
# "Consistency: %{x}",
# ]))
# fig2.update_traces(line=dict(width=3),marker = dict(size=10),opacity=0.9)
# region_lst2 = []
# for trace in fig2["data"]:
# trace["name"] = trace["name"].split(",")[0]
# if trace["name"] not in region_lst2 and trace["marker"]['symbol'] == 'circle':
# trace["showlegend"] = True
# region_lst2.append(trace["name"])
# else:
# trace["showlegend"] = False
# model2 = px.get_trendline_results(fig2)
# for i in range(0,len(fig2['data']),2):
# fig2["data"][i+1]['customdata'] = fig2["data"][i]['customdata']
# order = np.argsort(fig2["data"][i]['x'])
# fig2["data"][i]['x'] = fig2["data"][i]['x'][order]
# fig2["data"][i]['y'] = fig2["data"][i]['y'][order]
# results2 = model2.iloc[i//2]["px_fit_results"]
# alpha = results2.params[0]
# beta = results2.params[1]
# p_beta = results2.pvalues[1]
# r_squared = results2.rsquared
# line1 = 'y = ' + str(round(alpha, 4)) + ' + ' + str(round(beta, 4))+'x'
# line2 = 'p-value = ' + '{:.5f}'.format(p_beta)
# line3 = 'R^2 = ' + str(round(r_squared, 3))