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pyphi_plots.py
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pyphi_plots.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Plots for pyPhi
@author: Sal Garcia <[email protected]> <[email protected]>
Release Nov 15 2021
* Added "xgrid" flag to all plots using bar plots (loadings, weighted loadings, contributions) to add the Xgrid lines to the plot
Release Jan 15, 2021
* Added mb_blockweights plot for MBPSL models
Release Date: March 30 2020
* Fixed small syntax change for Bohek to stay compatible
Release Date: Aug 22 2019
What was done:
* This header is now included to track high level changes
"""
import numpy as np
from bokeh.io import show, output_file
from bokeh.plotting import figure
from bokeh.layouts import column
from bokeh.models import ColumnDataSource,LabelSet,Span,Legend
import pyphi as phi
import pandas as pd
import matplotlib.cm as cm
def r2pv(mvmobj,*,plotwidth=600,plotheight=400):
"""
R2 per variable plots
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pca or phi.pls
"""
A= mvmobj['T'].shape[1]
num_varX=mvmobj['P'].shape[0]
if 'Q' in mvmobj:
is_pls=True
lv_prefix='LV #'
else:
is_pls=False
lv_prefix='PC #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
r2pvX_dict = {'XVar': mvmobj['varidX']}
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
r2pvX_dict = {'XVar': XVar}
for i in list(np.arange(A)):
r2pvX_dict.update({lv_labels[i] : mvmobj['r2xpv'][:,i].tolist()})
if 'Q' in mvmobj:
num_varY=mvmobj['Q'].shape[0]
if 'varidY' in mvmobj:
r2pvY_dict = {'YVar': mvmobj['varidY']}
YVar=mvmobj['varidY']
else:
YVar = []
for n in list(np.arange(num_varY)+1):
YVar.append('YVar #'+str(n))
r2pvY_dict = {'YVar': YVar}
for i in list(np.arange(A)):
r2pvY_dict.update({lv_labels[i] : mvmobj['r2ypv'][:,i].tolist()})
if is_pls:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("r2xypv_"+rnd_num+".html",title="R2XYPV")
colormap =cm.get_cmap("rainbow")
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
bokeh_palette=["#%02x%02x%02x" % (r, g, b) for r, g, b in color_mapping[:,0:3]]
px = figure(x_range=XVar, title="R2X Per Variable",
tools="save,box_zoom,hover,reset", tooltips="$name @XVar: @$name",plot_width=plotwidth,plot_height=plotheight)
px.vbar_stack(lv_labels, x='XVar', width=0.9,color=bokeh_palette,source=r2pvX_dict)
px.y_range.range_padding = 0.1
px.ygrid.grid_line_color = None
px.xgrid.grid_line_color = None
px.axis.minor_tick_line_color = None
px.outline_line_color = None
px.yaxis.axis_label = 'R2X'
px.xaxis.major_label_orientation = 45
py = figure(x_range=YVar, plot_height=plotheight, title="R2Y Per Variable",
tools="save,box_zoom,hover,reset", tooltips="$name @YVar: @$name",plot_width=plotwidth)
py.vbar_stack(lv_labels, x='YVar', width=0.9,color=bokeh_palette,source=r2pvY_dict)
py.y_range.range_padding = 0.1
py.ygrid.grid_line_color = None
py.axis.minor_tick_line_color = None
py.xgrid.grid_line_color = None
py.outline_line_color = None
py.yaxis.axis_label = 'R2Y'
py.xaxis.major_label_orientation = 45
show(column(px,py))
else:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("r2xpv_"+rnd_num+".html",title='R2XPV')
colormap =cm.get_cmap("rainbow")
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
bokeh_palette=["#%02x%02x%02x" % (r, g, b) for r, g, b in color_mapping[:,0:3]]
p = figure(x_range=XVar, title="R2X Per Variable",
tools="save,box_zoom,hover,reset", tooltips="$name @XVar: @$name",plot_width=plotwidth,plot_height=plotheight)
p.vbar_stack(lv_labels, x='XVar', width=0.9,color=bokeh_palette,source=r2pvX_dict)
p.y_range.range_padding = 0.1
p.ygrid.grid_line_color = None
p.axis.minor_tick_line_color = None
p.outline_line_color = None
p.yaxis.axis_label = 'R2X'
p.xaxis.major_label_orientation = 45
show(p)
return
def loadings(mvmobj,*,plotwidth=600,xgrid=False):
"""
Column plots of loadings
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pca or phi.pls
"""
A= mvmobj['T'].shape[1]
num_varX=mvmobj['P'].shape[0]
if 'Q' in mvmobj:
is_pls=True
lv_prefix='LV #'
else:
is_pls=False
lv_prefix='PC #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
X_loading_dict = {'XVar': mvmobj['varidX']}
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
X_loading_dict = {'XVar': XVar}
if 'Q' in mvmobj:
for i in list(np.arange(A)):
X_loading_dict.update({lv_labels[i] : mvmobj['Ws'][:,i].tolist()})
num_varY=mvmobj['Q'].shape[0]
if 'varidY' in mvmobj:
Q_dict = {'YVar': mvmobj['varidY']}
YVar=mvmobj['varidY']
else:
YVar = []
for n in list(np.arange(num_varY)+1):
YVar.append('YVar #'+str(n))
Q_dict = {'YVar': YVar}
for i in list(np.arange(A)):
Q_dict.update({lv_labels[i] : mvmobj['Q'][:,i].tolist()})
else:
for i in list(np.arange(A)):
X_loading_dict.update({lv_labels[i] : mvmobj['P'][:,i].tolist()})
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Variable:","@names")
]
if is_pls:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings X Space_"+rnd_num+".html",title='X Loadings PLS')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title="X Space Loadings "+lv_labels[i],
tools=TOOLS,tooltips=TOOLTIPS,plot_width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=XVar, y_=mvmobj['Ws'][:,i].tolist(),names=XVar))
#p.vbar(x=XVar, top=mvmobj['Ws'][:,i].tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
p.ygrid.grid_line_color = None
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'W* ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings Y Space_"+rnd_num+".html",title='Y Loadings PLS')
for i in list(np.arange(A)):
p = figure(x_range=YVar, title="Y Space Loadings "+lv_labels[i],
tools="save,box_zoom,pan,reset",tooltips=TOOLTIPS,plot_width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=YVar, y_=mvmobj['Q'][:,i].tolist(),names=YVar))
#p.vbar(x=YVar, top=mvmobj['Q'][:,i].tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
p.ygrid.grid_line_color = None
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'Q ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
else:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings X Space_"+rnd_num+".html",title='X Loadings PCA')
for i in list(np.arange(A)):
source1 = ColumnDataSource(data=dict(x_=XVar, y_=mvmobj['P'][:,i].tolist(),names=XVar))
p = figure(x_range=XVar, title="X Space Loadings "+lv_labels[i],
tools=TOOLS,tooltips=TOOLTIPS,plot_width=plotwidth)
#p.vbar(x=XVar, top=mvmobj['P'][:,i].tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'P ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
return
def loadings_map(mvmobj,dims,*,plotwidth=600):
"""
Scatter plot overlaying X and Y loadings
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pca or phi.pls
dims: what latent spaces to plot in x and y axes e.g. dims=[1,2]
"""
A= mvmobj['T'].shape[1]
num_varX=mvmobj['P'].shape[0]
if 'Q' in mvmobj:
lv_prefix='LV #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
num_varY=mvmobj['Q'].shape[0]
if 'varidY' in mvmobj:
YVar=mvmobj['varidY']
else:
YVar = []
for n in list(np.arange(num_varY)+1):
YVar.append('YVar #'+str(n))
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings Map"+rnd_num+".html",title='Loadings Map')
x_ws = mvmobj['Ws'][:,dims[0]-1]
x_ws = x_ws/np.max(np.abs(x_ws))
y_ws = mvmobj['Ws'][:,dims[1]-1]
y_ws = y_ws/np.max(np.abs(y_ws))
x_q = mvmobj['Q'][:,dims[0]-1]
x_q = x_q/np.max(np.abs(x_q))
y_q = mvmobj['Q'][:,dims[1]-1]
y_q = y_q/np.max(np.abs(y_q))
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("index", "$index"),
("(x,y)", "($x, $y)"),
("Variable:","@names")
]
source1 = ColumnDataSource(data=dict(x=x_ws, y=y_ws,names=XVar))
source2 = ColumnDataSource(data=dict(x=x_q, y=y_q,names=YVar))
p = figure(tools=TOOLS, tooltips=TOOLTIPS,plot_width=plotwidth, title="Loadings Map LV["+str(dims[0])+"] - LV["+str(dims[1])+"]",
x_range=(-1.5,1.5),y_range=(-1.5,1.5))
p.circle('x', 'y', source=source1,size=10,color='darkblue')
p.circle('x', 'y', source=source2,size=10,color='red')
p.xaxis.axis_label = lv_labels [dims[0]-1]
p.yaxis.axis_label = lv_labels [dims[1]-1]
labelsX = LabelSet(x='x', y='y', text='names', level='glyph',x_offset=5, y_offset=5, source=source1, render_mode='canvas',text_color='darkgray')
labelsY = LabelSet(x='x', y='y', text='names', level='glyph',x_offset=5, y_offset=5, source=source2, render_mode='canvas',text_color='darkgray')
p.add_layout(labelsX)
p.add_layout(labelsY)
vline = Span(location=0, dimension='height', line_color='black', line_width=2)
# Horizontal line
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([vline, hline])
show(p)
else:
lv_prefix='PC #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings Map"+rnd_num+".html",title='Loadings Map')
x_p = mvmobj['P'][:,dims[0]-1]
y_p = mvmobj['P'][:,dims[1]-1]
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("index", "$index"),
("(x,y)", "($x, $y)"),
("Variable:","@names")
]
source1 = ColumnDataSource(data=dict(x=x_p, y=y_p,names=XVar))
p = figure(tools=TOOLS, tooltips=TOOLTIPS,plot_width=plotwidth, title="Loadings Map PC["+str(dims[0])+"] - PC["+str(dims[1])+"]", x_range=(-1.5,1.5),y_range=(-1.5,1.5))
p.circle('x', 'y', source=source1,size=10,color='darkblue')
p.xaxis.axis_label = lv_labels [dims[0]-1]
p.yaxis.axis_label = lv_labels [dims[1]-1]
labelsX = LabelSet(x='x', y='y', text='names', level='glyph',x_offset=5, y_offset=5, source=source1, render_mode='canvas',text_color='darkgray')
p.add_layout(labelsX)
vline = Span(location=0, dimension='height', line_color='black', line_width=2)
# Horizontal line
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([vline, hline])
show(p)
return
def weighted_loadings(mvmobj,*,plotwidth=600,xgrid=False):
"""
Column plots of loadings weighted by r2x/r2y correspondingly
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pca or phi.pls
"""
A= mvmobj['T'].shape[1]
num_varX=mvmobj['P'].shape[0]
if 'Q' in mvmobj:
is_pls=True
lv_prefix='LV #'
else:
is_pls=False
lv_prefix='PC #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
X_loading_dict = {'XVar': mvmobj['varidX']}
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
X_loading_dict = {'XVar': XVar}
if 'Q' in mvmobj:
for i in list(np.arange(A)):
X_loading_dict.update({lv_labels[i] : mvmobj['Ws'][:,i].tolist()})
num_varY=mvmobj['Q'].shape[0]
if 'varidY' in mvmobj:
Q_dict = {'YVar': mvmobj['varidY']}
YVar=mvmobj['varidY']
else:
YVar = []
for n in list(np.arange(num_varY)+1):
YVar.append('YVar #'+str(n))
Q_dict = {'YVar': YVar}
for i in list(np.arange(A)):
Q_dict.update({lv_labels[i] : mvmobj['Q'][:,i].tolist()})
else:
for i in list(np.arange(A)):
X_loading_dict.update({lv_labels[i] : mvmobj['P'][:,i].tolist()})
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Variable:","@names")
]
if is_pls:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings X Space_"+rnd_num+".html",title='X Weighted Loadings PLS')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title="X Space Weighted Loadings "+lv_labels[i],
tools=TOOLS,tooltips=TOOLTIPS,plot_width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=XVar, y_=(mvmobj['r2xpv'][:,i] * mvmobj['Ws'][:,i]).tolist(),names=XVar))
#p.vbar(x=XVar, top=(mvmobj['r2xpv'][:,i] * mvmobj['Ws'][:,i]).tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
p.ygrid.grid_line_color = None
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'W* ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings Y Space_"+rnd_num+".html",title='Y Weighted Loadings PLS')
for i in list(np.arange(A)):
p = figure(x_range=YVar, title="Y Space Weighted Loadings "+lv_labels[i],
tools=TOOLS,tooltips=TOOLTIPS,plot_width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=YVar, y_=(mvmobj['r2ypv'][:,i] * mvmobj['Q'][:,i]).tolist(),names=YVar))
#p.vbar(x=YVar, top=(mvmobj['r2ypv'][:,i] * mvmobj['Q'][:,i]).tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
p.ygrid.grid_line_color = None
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'Q ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
else:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Loadings X Space_"+rnd_num+".html",title='X Weighted Loadings PCA')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title="X Space Weighted Loadings "+lv_labels[i],
tools=TOOLS,tooltips=TOOLTIPS,plot_width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=XVar, y_=(mvmobj['r2xpv'][:,i] * mvmobj['P'][:,i]).tolist(),names=XVar))
#p.vbar(x=XVar, top=(mvmobj['r2xpv'][:,i] * mvmobj['P'][:,i]).tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,width=0.5)
p.ygrid.grid_line_color = None
if xgrid:
p.xgrid.grid_line_color = 'lightgray'
else:
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'P ['+str(i+1)+']'
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([hline])
p.xaxis.major_label_orientation = 45
if i==0:
p_list=[p]
else:
p_list.append(p)
show(column(p_list))
return
def vip(mvmobj,*,plotwidth=600):
"""
Very Important to the Projection (VIP) plot
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pls
"""
if 'Q' in mvmobj:
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("VIP_"+rnd_num+".html",title='VIP Coefficient')
num_varX=mvmobj['P'].shape[0]
if 'varidX' in mvmobj:
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
vip=np.sum(np.abs(mvmobj['Ws'] * np.tile(mvmobj['r2y'],(mvmobj['Ws'].shape[0],1)) ),axis=1)
vip=np.reshape(vip,(len(vip),-1))
sort_indx=np.argsort(-vip,axis=0)
vip=vip[sort_indx]
sorted_XVar=[]
for i in sort_indx[:,0]:
sorted_XVar.append(XVar[i])
p = figure(x_range=sorted_XVar, title="VIP",
tools="save,box_zoom,pan,reset",plot_width=plotwidth)
p.vbar(x=sorted_XVar, top=vip.tolist(), width=0.5)
p.xgrid.grid_line_color = None
p.yaxis.axis_label = 'Very Important to the Projection'
p.xaxis.major_label_orientation = 45
show(p)
return
def score_scatter(mvmobj,xydim,*,CLASSID=False,colorby=False,Xnew=False,add_ci=False,add_labels=False,add_legend=True,plotwidth=600,plotheight=600):
'''
Score scatter plot
by Salvador Garcia-Munoz
mvmobj : PLS or PCA object from phyphi
xydim : LV to plot on x and y axes. eg [1,2] will plot t1 vs t2
CLASSID : Pandas DataFrame with CLASSIDS
colorby : Category (one of the CLASSIDS) to color by
Xnew : New data for which to make the score plot this routine evaluates and plots
add_ci : when = True will add confidence intervals
add_labels : When = True labels each point with Obs ID
plotwidth : If omitted, width is 600
'''
if isinstance(Xnew,bool):
if 'obsidX' in mvmobj:
ObsID_=mvmobj['obsidX']
else:
ObsID_ = []
for n in list(np.arange(mvmobj['T'].shape[0])+1):
ObsID_.append('Obs #'+str(n))
T_matrix=mvmobj['T']
else:
if isinstance(Xnew,np.ndarray):
X_=Xnew.copy()
ObsID_ = []
for n in list(np.arange(Xnew.shape[0])+1):
ObsID_.append('Obs #'+str(n))
elif isinstance(Xnew,pd.DataFrame):
X_=np.array(Xnew.values[:,1:]).astype(float)
ObsID_ = Xnew.values[:,0].astype(str)
ObsID_ = ObsID_.tolist()
if 'Q' in mvmobj:
xpred=phi.pls_pred(X_,mvmobj)
else:
xpred=phi.pca_pred(X_,mvmobj)
T_matrix=xpred['Tnew']
ObsNum_=[]
for n in list(range(1,len(ObsID_)+1)):
ObsNum_.append('Obs #'+str(n))
if isinstance(CLASSID,np.bool): # No CLASSIDS
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Score_Scatter_"+rnd_num+".html",title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']')
x_=T_matrix[:,[xydim[0]-1]]
y_=T_matrix[:,[xydim[1]-1]]
source = ColumnDataSource(data=dict(x=x_, y=y_,ObsID=ObsID_,ObsNum=ObsNum_))
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Obs #", "@ObsNum"),
("(x,y)", "($x, $y)"),
("Obs: ","@ObsID")
]
p = figure(tools=TOOLS, tooltips=TOOLTIPS,plot_width=plotwidth,plot_height=plotheight, title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']')
p.circle('x', 'y', source=source,size=7)
if add_ci:
T_aux1=mvmobj['T'][:,[xydim[0]-1]]
T_aux2=mvmobj['T'][:,[xydim[1]-1]]
T_aux = np.hstack((T_aux1,T_aux2))
st=(T_aux.T @ T_aux)/T_aux.shape[0]
[xd95,xd99,yd95p,yd95n,yd99p,yd99n]=phi.scores_conf_int_calc(st,mvmobj['T'].shape[0])
p.line(xd95,yd95p,line_color="gold",line_dash='dashed')
p.line(xd95,yd95n,line_color="gold",line_dash='dashed')
p.line(xd99,yd99p,line_color="red",line_dash='dashed')
p.line(xd99,yd99n,line_color="red",line_dash='dashed')
if add_labels:
labelsX = LabelSet(x='x', y='y', text='ObsID', level='glyph',x_offset=5, y_offset=5, source=source, render_mode='canvas')
p.add_layout(labelsX)
p.xaxis.axis_label = 't ['+str(xydim[0])+']'
p.yaxis.axis_label = 't ['+str(xydim[1])+']'
# Vertical line
vline = Span(location=0, dimension='height', line_color='black', line_width=2)
# Horizontal line
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([vline, hline])
show(p)
else: # YES CLASSIDS
Classes_=np.unique(CLASSID[colorby]).tolist()
A=len(Classes_)
colormap =cm.get_cmap("rainbow")
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
bokeh_palette=["#%02x%02x%02x" % (r, g, b) for r, g, b in color_mapping[:,0:3]]
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Score_Scatter_"+rnd_num+".html",title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']')
x_=T_matrix[:,[xydim[0]-1]]
y_=T_matrix[:,[xydim[1]-1]]
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Obs #", "@ObsNum"),
("(x,y)", "($x, $y)"),
("Obs: ","@ObsID"),
("Class:","@Class")
]
classid_=list(CLASSID[colorby])
legend_it = []
p = figure(tools=TOOLS, tooltips=TOOLTIPS,toolbar_location="above",plot_width=plotwidth,plot_height=plotheight,title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']')
for classid_in_turn in Classes_:
x_aux = []
y_aux = []
obsid_aux = []
obsnum_aux = []
classid_aux = []
for i in list(range(len(ObsID_))):
if classid_[i]==classid_in_turn:
x_aux.append(x_[i][0])
y_aux.append(y_[i][0])
obsid_aux.append(ObsID_[i])
obsnum_aux.append(ObsNum_[i])
classid_aux.append(classid_in_turn)
source = ColumnDataSource(data=dict(x=x_aux, y=y_aux,ObsID=obsid_aux,ObsNum=obsnum_aux, Class=classid_aux))
color_=bokeh_palette[Classes_.index(classid_in_turn)]
if add_legend:
c = p.circle('x','y',source=source,color=color_)
aux_=classid_in_turn
if isinstance(aux_,(float,int)):
aux_=str(aux_)
#legend_it.append((classid_in_turn, [c]))
legend_it.append((aux_, [c]))
else:
p.circle('x','y',source=source,color=color_)
if add_labels:
labelsX = LabelSet(x='x', y='y', text='ObsID', level='glyph',x_offset=5, y_offset=5, source=source, render_mode='canvas')
p.add_layout(labelsX)
if add_ci:
T_aux1=mvmobj['T'][:,[xydim[0]-1]]
T_aux2=mvmobj['T'][:,[xydim[1]-1]]
T_aux = np.hstack((T_aux1,T_aux2))
st=(T_aux.T @ T_aux)/T_aux.shape[0]
[xd95,xd99,yd95p,yd95n,yd99p,yd99n]=phi.scores_conf_int_calc(st,mvmobj['T'].shape[0])
p.line(xd95,yd95p,line_color="gold",line_dash='dashed')
p.line(xd95,yd95n,line_color="gold",line_dash='dashed')
p.line(xd99,yd99p,line_color="red",line_dash='dashed')
p.line(xd99,yd99n,line_color="red",line_dash='dashed')
p.xaxis.axis_label = 't ['+str(xydim[0])+']'
p.yaxis.axis_label = 't ['+str(xydim[1])+']'
# Vertical line
vline = Span(location=0, dimension='height', line_color='black', line_width=2)
# Horizontal line
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([vline, hline])
if add_legend:
legend = Legend(items=legend_it, location='top_right')
p.add_layout(legend, 'right')
legend.click_policy="hide"
show(p)
return
def score_line(mvmobj,dim,*,CLASSID=False,colorby=False,Xnew=False,add_ci=False,add_labels=False,add_legend=True,plotline=True,plotwidth=600,plotheight=600):
'''
Score scatter plot
by Salvador Garcia-Munoz
mvmobj : PLS or PCA object from phyphi
dim : LV to plot eg "1" will plot t1 vs observation #
CLASSID : Pandas DataFrame with CLASSIDS
colorby : Category (one of the CLASSIDS) to color by
Xnew : New data for which to make the score plot this routine evaluates and plots
add_ci : When = True will add confidence intervals
add_labels : When =True will display Obs ID per point
plotwidth : When Omitted is = 600
plotline : Adds a conecting line between dots [True by default]
'''
if not(isinstance(dim,list)):
if isinstance(dim, int):
dim=[dim]
if isinstance(Xnew,bool):
if 'obsidX' in mvmobj:
ObsID_=mvmobj['obsidX']
else:
ObsID_ = []
for n in list(np.arange(mvmobj['T'].shape[0])+1):
ObsID_.append('Obs #'+str(n))
T_matrix=mvmobj['T']
else:
if isinstance(Xnew,np.ndarray):
X_=Xnew.copy()
ObsID_ = []
for n in list(np.arange(Xnew.shape[0])+1):
ObsID_.append('Obs #'+str(n))
elif isinstance(Xnew,pd.DataFrame):
X_=np.array(Xnew.values[:,1:]).astype(float)
ObsID_ = Xnew.values[:,0].astype(str)
ObsID_ = ObsID_.tolist()
if 'Q' in mvmobj:
xpred=phi.pls_pred(X_,mvmobj)
else:
xpred=phi.pca_pred(X_,mvmobj)
T_matrix=xpred['Tnew']
ObsNum_=[]
for n in list(range(1,len(ObsID_)+1)):
ObsNum_.append('Obs #'+str(n))
if isinstance(CLASSID,np.bool): # No CLASSIDS
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Score_Line_"+rnd_num+".html",title='Score Line t['+str(dim[0])+ ']')
y_=T_matrix[:,[dim[0]-1]]
x_=list(range(1,y_.shape[0]+1))
source = ColumnDataSource(data=dict(x=x_, y=y_,ObsID=ObsID_,ObsNum=ObsNum_))
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Obs#", "@ObsNum"),
("(x,y)", "($x, $y)"),
("Obs: ","@ObsID")
]
p = figure(tools=TOOLS, tooltips=TOOLTIPS,plot_width=plotwidth,plot_height=plotheight, title='Score Line t['+str(dim[0])+']' )
p.circle('x', 'y', source=source,size=7)
if plotline:
p.line('x', 'y', source=source)
if add_ci:
lim95,lim99=phi.single_score_conf_int(mvmobj['T'][:,[dim[0]-1]])
p.line(x_, lim95,line_color="gold",line_dash='dashed')
p.line(x_,-lim95,line_color="gold",line_dash='dashed')
p.line(x_, lim99,line_color="red",line_dash='dashed')
p.line(x_,-lim99,line_color="red",line_dash='dashed')
if add_labels:
labelsX = LabelSet(x='x', y='y', text='ObsID', level='glyph',x_offset=5, y_offset=5, source=source, render_mode='canvas')
p.add_layout(labelsX)
p.xaxis.axis_label = 'Observation'
p.yaxis.axis_label = 't ['+str(dim[0])+']'
show(p)
else: # YES CLASSIDS
Classes_=np.unique(CLASSID[colorby]).tolist()
A=len(Classes_)
colormap =cm.get_cmap("rainbow")
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
bokeh_palette=["#%02x%02x%02x" % (r, g, b) for r, g, b in color_mapping[:,0:3]]
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Score_Line_"+rnd_num+".html",title='Score Line t['+str(dim[0])+ ']')
y_=T_matrix[:,[dim[0]-1]]
x_=list(range(1,y_.shape[0]+1))
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("Obs#", "@ObsNum"),
("(x,y)", "($x, $y)"),
("Obs: ","@ObsID"),
("Class:","@Class")
]
classid_=list(CLASSID[colorby])
legend_it = []
p = figure(tools=TOOLS, tooltips=TOOLTIPS,toolbar_location="above",plot_width=plotwidth,plot_height=plotheight, title='Score Line t['+str(dim[0])+ ']')
for classid_in_turn in Classes_:
x_aux=[]
y_aux=[]
obsid_aux=[]
classid_aux=[]
obsnum_aux=[]
for i in list(range(len(ObsID_))):
if classid_[i]==classid_in_turn:
x_aux.append(x_[i])
y_aux.append(y_[i][0])
obsid_aux.append(ObsID_[i])
obsnum_aux.append(ObsNum_[i])
classid_aux.append(classid_in_turn)
source = ColumnDataSource(data=dict(x=x_aux, y=y_aux,ObsID=obsid_aux,ObsNum=obsnum_aux,Class=classid_aux))
color_=bokeh_palette[Classes_.index(classid_in_turn)]
c=p.circle('x','y',source=source,color=color_)
if plotline:
c1=p.line('x','y',source=source,color=color_)
#added to allow numbers in classids
aux_=classid_in_turn
if isinstance(aux_,(float,int)):
aux_=str(aux_)
#
if add_legend and plotline:
# legend_it.append((classid_in_turn, [c,c1]))
legend_it.append((aux_, [c,c1]))
if add_legend and not(plotline):
# legend_it.append((classid_in_turn, [c]))
legend_it.append((aux_, [c]))
if add_labels:
labelsX = LabelSet(x='x', y='y', text='ObsID', level='glyph',x_offset=5, y_offset=5, source=source, render_mode='canvas')
p.add_layout(labelsX)
if add_ci:
lim95,lim99=phi.single_score_conf_int(mvmobj['T'][:,[dim[0]-1]])
p.line(x_, lim95,line_color="gold",line_dash='dashed')
p.line(x_,-lim95,line_color="gold",line_dash='dashed')
p.line(x_, lim99,line_color="red",line_dash='dashed')
p.line(x_,-lim99,line_color="red",line_dash='dashed')
p.xaxis.axis_label = 'Observation'
p.yaxis.axis_label = 't ['+str(dim[0])+']'
if add_legend:
legend = Legend(items=legend_it, location='top_right')
p.add_layout(legend, 'right')
legend.click_policy="hide"
show(p)
return
def diagnostics(mvmobj,*,Xnew=False,Ynew=False,score_plot_xydim=False,plotwidth=600,ht2_logscale=False,spe_logscale=False):
"""
Plot calculated Hotelling's T2 and SPE
by Salvador Garcia-Munoz
mvmobj: A model created with phi.pca or phi.pls
Xnew/Ynew: Data used to calculate diagnostics[numpy arrays or pandas dataframes]
optional:
score_plot_xydim: will add a score scatter plot at the bottom
if sent with a list of [dimx, dimy] where dimx/dimy
are integers and refer to the latent space to plot
in the x and y axes of the scatter plot. e.g. [1,2] will
add a t1-t2 plot
"""
if isinstance(score_plot_xydim,np.bool):
add_score_plot = False
else:
add_score_plot = True
if isinstance(Xnew,np.bool): #No Xnew was given need to plot all from model
if 'obsidX' in mvmobj:
ObsID_=mvmobj['obsidX']
else:
ObsID_ = []
for n in list(np.arange(mvmobj['T'].shape[0])+1):
ObsID_.append('Obs #'+str(n))
Obs_num = np.arange(mvmobj['T'].shape[0])+1
if add_score_plot and not(isinstance(score_plot_xydim,np.bool)):
t_x = mvmobj['T'][:,[score_plot_xydim[0]-1]]
t_y = mvmobj['T'][:,[score_plot_xydim[1]-1]]
else:
add_score_plot = False
t2_ = mvmobj['T2']
spex_ = mvmobj['speX']
if ht2_logscale:
t2_=np.log10(t2_)
if spe_logscale:
spex_= np.log10(spex_)
if not(add_score_plot):
if 'Q' in mvmobj:
spey_=1
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,t2=t2_,spex=spex_,spey=mvmobj['speY']))
else:
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,t2=t2_,spex=spex_))
else:
if 'Q' in mvmobj:
spey_=1
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,t2=t2_,spex=spex_,spey=mvmobj['speY'],tx=t_x,ty=t_y))
else:
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,t2=t2_,spex=spex_,tx=t_x,ty=t_y))
else: #Xnew was given
if isinstance(Xnew,np.ndarray):
ObsID_ = []
for n in list(np.arange(Xnew.shape[0])+1):
ObsID_.append('Obs #'+str(n))
elif isinstance(Xnew,pd.DataFrame):
X_=np.array(Xnew.values[:,1:]).astype(float)
ObsID_ = Xnew.values[:,0].astype(str)
ObsID_ = ObsID_.tolist()
if add_score_plot and not(isinstance(score_plot_xydim,np.bool)):
if 'Q' in mvmobj:
xpred=phi.pls_pred(X_,mvmobj)
else:
xpred=phi.pca_pred(X_,mvmobj)
T_matrix=xpred['Tnew']
t_x = T_matrix[:,[score_plot_xydim[0]-1]]
t_y = T_matrix[:,[score_plot_xydim[1]-1]]
else:
add_score_plot = False
t2_ = phi.hott2(mvmobj,Xnew=Xnew)
Obs_num = np.arange(t2_.shape[0])+1
if 'Q' in mvmobj and not(isinstance(Ynew,np.bool)):
spex_,spey_ = phi.spe(mvmobj,Xnew,Ynew=Ynew)
else:
spex_ = phi.spe(mvmobj,Xnew)
spey_ = False
if ht2_logscale:
t2_=np.log10(t2_)
if spe_logscale:
spex_= np.log10(spex_)
ObsNum_=[]
for n in list(range(1,len(ObsID_)+1)):
ObsNum_.append('Obs #'+str(n))
if not(add_score_plot):
if 'Q' in mvmobj and not(isinstance(Ynew,np.bool)):
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,ObsNum=ObsNum_,t2=t2_,spex=spex_,spey=spey_))
else:
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,ObsNum=ObsNum_,t2=t2_,spex=spex_))
else:
if 'Q' in mvmobj and not(isinstance(Ynew,np.bool)):
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,ObsNum=ObsNum_,t2=t2_,spex=spex_,spey=spey_,tx=t_x,ty=t_y))
else:
source = ColumnDataSource(data=dict(x=Obs_num, ObsID=ObsID_,ObsNum=ObsNum_,t2=t2_,spex=spex_,tx=t_x,ty=t_y))
TOOLS = "save,wheel_zoom,box_zoom,reset,lasso_select"
TOOLTIPS = [
("Obs #", "@x"),
("(x,y)", "($x, $y)"),
("Obs: ","@ObsID")
]
rnd_num=str(int(np.round(1000*np.random.random_sample())))
output_file("Diagnostics"+rnd_num+".html",title='Diagnostics')
p = figure(tools=TOOLS, tooltips=TOOLTIPS, plot_width=plotwidth, title="Hotelling's T2")
p.circle('x','t2',source=source)
if ht2_logscale:
p.line([0,Obs_num[-1]],[np.log10(mvmobj['T2_lim95']),np.log10(mvmobj['T2_lim95'])],line_color='gold')
p.line([0,Obs_num[-1]],[np.log10(mvmobj['T2_lim99']),np.log10(mvmobj['T2_lim99'])],line_color='red')
else:
p.line([0,Obs_num[-1]],[mvmobj['T2_lim95'],mvmobj['T2_lim95']],line_color='gold')
p.line([0,Obs_num[-1]],[mvmobj['T2_lim99'],mvmobj['T2_lim99']],line_color='red')
p.xaxis.axis_label = 'Observation sequence'
p.yaxis.axis_label = "HT2"
p_list=[p]
p = figure(tools=TOOLS, tooltips=TOOLTIPS, plot_width=plotwidth, title='SPE X')
p.circle('x','spex',source=source)
if spe_logscale:
p.line([0,Obs_num[-1]],[np.log10(mvmobj['speX_lim95']),np.log10(mvmobj['speX_lim95'])],line_color='gold')
p.line([0,Obs_num[-1]],[np.log10(mvmobj['speX_lim99']),np.log10(mvmobj['speX_lim99'])],line_color='red')
else:
p.line([0,Obs_num[-1]],[mvmobj['speX_lim95'],mvmobj['speX_lim95']],line_color='gold')
p.line([0,Obs_num[-1]],[mvmobj['speX_lim99'],mvmobj['speX_lim99']],line_color='red')