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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]>
Addition on Apr 29 2024 Made it compatible with Bokeh 3.4.1 replacing "circle" with "scatter"
Addition on Feb 24 2024
Replaced the randon number in the file names with a time string.
Addition on Feb 21 2024
Added ability to make score plots with a gradient color
of nbins based on a numerical value in the classids
Addition on Jan 18 2024 Added flag to score_scatter to include model scores in plot
replaced phi.unique -> np.unique
Updated call to maptplotlib colormap to keep it compatible
Addition on Sep 26 2023 All plots are now viewable offline (e.g. in airplane mode)
Addition on May 1 2023 corrected description of mb_vip
Addition on Apr 25 2023 added markersize to score_scatter
Addition on Apr 23 2023 also added the text_alpha flag to loadings map for PCA models
Addition on Apr 22 2023 added tooltips to contribution plots and VIP
implemented multiple columns in score scatter (yay!)
Addition on Apr 17 2023 added tpls to the supported models in all loadings, vip, r2pv
and score_scatter plots
Addition on Apr 15 2023, made all loadings, vip, r2pv and score_scatter compatible with
lpls and jrpls models
Addition on April 9 2023, added legends and pan tools to r2pv (yay!)
Addition on April 8 2023, fixed predvsobs to take MB data
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
from datetime import datetime
#import matplotlib.cm as cm
import matplotlib
def timestr():
now=datetime.now()
return now.strftime("%Y%m%d%H%M%S")
def r2pv(mvm_obj,*,plotwidth=600,plotheight=400,addtitle='',material=False,zspace=False):
"""
R2 per variable plots
by Salvador Garcia-Munoz
mvm_obj: A model created with phi.pca or phi.pls
"""
mvmobj=mvm_obj.copy()
A= mvmobj['T'].shape[1]
yaxlbl='X'
if (mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls'):
if ((mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls')) and not(isinstance(material, bool) ):
mvmobj['r2xpv']=mvmobj['r2xpvi'][mvmobj['materials'].index(material)]
mvmobj['varidX']=mvmobj['varidXi'][mvmobj['materials'].index(material) ]
elif (mvmobj['type']=='tpls') and zspace :
mvmobj['r2xpv']=mvmobj['r2zpv']
mvmobj['varidX']=mvmobj['varidZ']
yaxlbl='Z'
else:
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())))
rnd_num=timestr()
output_file("r2xypv_"+rnd_num+".html",title="R2"+ yaxlbl+ "YPV",mode='inline')
#colormap =cm.get_cmap("rainbow")
colormap = matplotlib.colormaps['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="R2"+ yaxlbl+" Per Variable "+addtitle,
tools="save,box_zoom,xpan,hover,reset", tooltips="$name @XVar: @$name",width=plotwidth,height=plotheight)
v=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 = 'R2'+ yaxlbl
px.xaxis.major_label_orientation = 45
legend = Legend(items=[(x, [v[i]]) for i, x in enumerate(lv_labels)], location=(0, 0))
px.add_layout(legend, 'right')
py = figure(x_range=YVar, height=plotheight, title="R2Y Per Variable "+addtitle,
tools="save,box_zoom,xpan,hover,reset", tooltips="$name @YVar: @$name",width=plotwidth)
v=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
legend = Legend(items=[(x, [v[i]]) for i, x in enumerate(lv_labels)], location=(0, 0))
py.add_layout(legend, 'right')
show(column(px,py))
else:
#rnd_num=str(int(np.round(1000*np.random.random_sample())))
rnd_num=timestr()
output_file("r2xpv_"+rnd_num+".html",title='R2XPV',mode='inline')
#colormap =cm.get_cmap("rainbow")
colormap = matplotlib.colormaps['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 "+addtitle,
tools="save,box_zoom,xpan,hover,reset", tooltips="$name @XVar: @$name",width=plotwidth,height=plotheight)
v=p.vbar_stack(lv_labels, x='XVar', width=0.9,color=bokeh_palette,source=r2pvX_dict)
legend = Legend(items=[(x, [v[i]]) for i, x in enumerate(lv_labels)], location=(0, 0))
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
p.add_layout(legend, 'right')
show(p)
return
def loadings(mvm_obj,*,plotwidth=600,xgrid=False,addtitle='',material=False,zspace=False):
"""
Column plots of loadings
by Salvador Garcia-Munoz
mvm_obj: A model created with phi.pca or phi.pls
"""
mvmobj=mvm_obj.copy()
space_lbl='X'
A= mvmobj['T'].shape[1]
if (mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls'):
loading_lbl='S*'
if (mvmobj['type']=='lpls'):
mvmobj['Ws']=mvmobj['Ss']
if isinstance(material, bool) and not(zspace):
mvmobj['Ws']=mvmobj['Ss']
if ((mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls') ) and not(isinstance(material, bool) ):
mvmobj['Ws']=mvmobj['Ssi'][mvmobj['materials'].index(material)]
mvmobj['varidX']=mvmobj['varidXi'][mvmobj['materials'].index(material) ]
elif (mvmobj['type']=='tpls') and zspace :
mvmobj['varidX']=mvmobj['varidZ']
loading_lbl='Wz*'
space_lbl='Z'
else:
num_varX=mvmobj['P'].shape[0]
loading_lbl='W*'
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())))
rnd_num=timestr()
output_file("Loadings "+space_lbl+" Space_"+rnd_num+".html",title=space_lbl+' Loadings PLS',mode='inline')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title=space_lbl+" Space Loadings "+lv_labels[i]+addtitle,
tools=TOOLS,tooltips=TOOLTIPS,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 = loading_lbl+' ['+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())))
rnd_num=timestr()
output_file("Loadings Y Space_"+rnd_num+".html",title='Y Loadings PLS',mode='inline')
for i in list(np.arange(A)):
p = figure(x_range=YVar, title="Y Space Loadings "+lv_labels[i]+addtitle,
tools="save,box_zoom,pan,reset",tooltips=TOOLTIPS,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())))
rnd_num=timestr()
output_file("Loadings X Space_"+rnd_num+".html",title='X Loadings PCA',mode='inline')
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]+addtitle,
tools=TOOLS,tooltips=TOOLTIPS,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(mvm_obj,dims,*,plotwidth=600,addtitle='',material=False,zspace=False,textalpha=0.75):
"""
Scatter plot overlaying X and Y loadings
by Salvador Garcia-Munoz
mvm_obj: 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]
"""
mvmobj=mvm_obj.copy()
A= mvmobj['T'].shape[1]
if (mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls'):
if (mvmobj['type']=='lpls'):
mvmobj['Ws']=mvmobj['Ss']
if isinstance(material, bool) and not(zspace):
mvmobj['Ws']=mvmobj['Ss']
if ((mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls')) and not(isinstance(material, bool) ):
mvmobj['Ws']=mvmobj['Ssi'][mvmobj['materials'].index(material)]
mvmobj['varidX']=mvmobj['varidXi'][mvmobj['materials'].index(material) ]
elif (mvmobj['type']=='tpls') and zspace :
mvmobj['varidX']=mvmobj['varidZ']
else:
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())))
rnd_num=timestr()
output_file("Loadings Map"+rnd_num+".html",title='Loadings Map',mode='inline')
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, $y)"),
("Variable:","@names")
]
source1 = ColumnDataSource(data=dict(x=x_ws, y=y_ws,names=XVar))
source2 = ColumnDataSource(data=dict(x=x_q, y=y_q,names=YVar))
p = figure(tools=TOOLS, tooltips=TOOLTIPS,width=plotwidth, title="Loadings Map LV["+str(dims[0])+"] - LV["+str(dims[1])+"] "+addtitle,
x_range=(-1.5,1.5),y_range=(-1.5,1.5))
#p.circle('x', 'y', source=source1,size=10,color='darkblue')
#p.circle('x', 'y', source=source2,size=10,color='red')
p.scatter(x='x',y= 'y', source=source1,size=10,color='darkblue')
p.scatter('x', 'y', source=source2,size=10,color='red')
p.xaxis.axis_label = lv_labels [dims[0]-1]
p.yaxis.axis_label = lv_labels [dims[1]-1]
labelsX = LabelSet(x='x', y='y', text='names',
level='glyph',x_offset=5, y_offset=5,
source=source1,text_color='darkgray',
text_alpha=textalpha )
labelsY = LabelSet(x='x', y='y', text='names',
level='glyph',x_offset=5, y_offset=5,
source=source2,text_color='darkgray',
text_alpha=textalpha )
p.add_layout(labelsX)
p.add_layout(labelsY)
vline = Span(location=0, dimension='height', line_color='black', line_width=2)
# Horizontal line
hline = Span(location=0, dimension='width', line_color='black', line_width=2)
p.renderers.extend([vline, hline])
show(p)
else:
lv_prefix='PC #'
lv_labels = []
for a in list(np.arange(A)+1):
lv_labels.append(lv_prefix+str(a))
if 'varidX' in mvmobj:
XVar=mvmobj['varidX']
else:
XVar = []
for n in list(np.arange(num_varX)+1):
XVar.append('XVar #'+str(n))
#rnd_num=str(int(np.round(1000*np.random.random_sample())))
rnd_num=timestr()
output_file("Loadings Map"+rnd_num+".html",title='Loadings Map',mode='inline')
x_p = mvmobj['P'][:,dims[0]-1]
y_p = mvmobj['P'][:,dims[1]-1]
TOOLS = "save,wheel_zoom,box_zoom,pan,reset,box_select,lasso_select"
TOOLTIPS = [
("index", "$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,width=plotwidth, title="Loadings Map PC["+str(dims[0])+"] - PC["+str(dims[1])+"] "+addtitle, x_range=(-1.5,1.5),y_range=(-1.5,1.5))
#p.circle('x', 'y', source=source1,size=10,color='darkblue')
p.scatter(x='x',y='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,
text_color='darkgray',text_alpha=textalpha)
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(mvm_obj,*,plotwidth=600,xgrid=False,addtitle='',material=False,zspace=False):
"""
Column plots of loadings weighted by r2x/r2y correspondingly
by Salvador Garcia-Munoz
mvm_obj: A model created with phi.pca or phi.pls
"""
mvmobj=mvm_obj.copy()
A= mvmobj['T'].shape[1]
space_lbl='X'
if (mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls'):
loading_lbl='S*'
if (mvmobj['type']=='lpls'):
mvmobj['Ws']=mvmobj['Ss']
if isinstance(material, bool) and not(zspace):
mvmobj['Ws']=mvmobj['Ss']
if ((mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls')) and not(isinstance(material, bool) ):
mvmobj['Ws']=mvmobj['Ssi'][mvmobj['materials'].index(material)]
mvmobj['varidX']=mvmobj['varidXi'][mvmobj['materials'].index(material) ]
mvmobj['r2xpv']=mvmobj['r2xpvi'][mvmobj['materials'].index(material) ]
elif (mvmobj['type']=='tpls') and zspace:
mvmobj['varidX']=mvmobj['varidZ']
mvmobj['r2xpv']=mvmobj['r2zpv']
loading_lbl='Wz*'
space_lbl='Z'
else:
num_varX=mvmobj['P'].shape[0]
loading_lbl='W*'
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())))
rnd_num=timestr()
output_file("Loadings "+space_lbl+" Space_"+rnd_num+".html",title=space_lbl+' Weighted Loadings PLS',mode='inline')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title=space_lbl+" Space Weighted Loadings "+lv_labels[i]+addtitle,
tools=TOOLS,tooltips=TOOLTIPS,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 = loading_lbl+' x R2'+space_lbl+' ['+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())))
rnd_num=timestr()
output_file("Loadings Y Space_"+rnd_num+".html",title='Y Weighted Loadings PLS',mode='inline')
for i in list(np.arange(A)):
p = figure(x_range=YVar, title="Y Space Weighted Loadings "+lv_labels[i]+addtitle,
tools=TOOLS,tooltips=TOOLTIPS,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 x R2Y ['+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())))
rnd_num=timestr()
output_file("Loadings X Space_"+rnd_num+".html",title='X Weighted Loadings PCA',mode='inline')
for i in list(np.arange(A)):
p = figure(x_range=XVar, title="X Space Weighted Loadings "+lv_labels[i]+addtitle,
tools=TOOLS,tooltips=TOOLTIPS,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 x R2X['+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(mvm_obj,*,plotwidth=600,material=False,zspace=False,addtitle=''):
"""
Very Important to the Projection (VIP) plot
by Salvador Garcia-Munoz
mvm_obj: A model created with phi.pls
"""
mvmobj=mvm_obj.copy()
if 'Q' in mvmobj:
if (mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls'):
if (mvmobj['type']=='lpls'):
mvmobj['Ws']=mvmobj['Ss']
if isinstance(material, bool) and not(zspace):
mvmobj['Ws']=mvmobj['Ss']
if ((mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls')) and not(isinstance(material, bool) ):
mvmobj['Ws']=mvmobj['Ssi'][mvmobj['materials'].index(material)]
mvmobj['varidX']=mvmobj['varidXi'][mvmobj['materials'].index(material) ]
elif (mvmobj['type']=='tpls') and zspace:
mvmobj['varidX']=mvmobj['varidZ']
else:
num_varX=mvmobj['P'].shape[0]
#rnd_num=str(int(np.round(1000*np.random.random_sample())))
rnd_num=timestr()
output_file("VIP_"+rnd_num+".html",title='VIP Coefficient',mode='inline')
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])
TOOLTIPS = [
("Variable","@names")
]
p = figure(x_range=sorted_XVar, title="VIP "+addtitle,
tools="save,box_zoom,pan,reset",tooltips=TOOLTIPS,width=plotwidth)
source1 = ColumnDataSource(data=dict(x_=sorted_XVar, y_=vip.tolist(),names=sorted_XVar))
#p.vbar(x=sorted_XVar, top=vip.tolist(), width=0.5)
p.vbar(x='x_', top='y_', source=source1,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 _create_classid_(df,column,*,nbins=5):
'''
Internal routine to create a CLASSID dataframe from values in a column
'''
hist,bin_edges=np.histogram(df[column].values[np.logical_not(np.isnan(df[column].values))],bins=nbins )
range_list=[]
for i,e in enumerate(bin_edges[:-1]):
range_list.append(str(np.round(bin_edges[i],3))+' to '+ str(np.round(bin_edges[i+1],3)))
#range_list.append('NaN')
bin_edges_=bin_edges.copy()
bin_edges_[-1]=bin_edges_[-1]+0.1
membership_=np.digitize(df[column].values,bin_edges_)
membership=[]
for m in membership_:
membership.append(range_list[m-1])
classid_df=df[df.columns[0]].to_frame()
classid_df.insert(1,column,membership)
return classid_df
def score_scatter(mvm_obj,xydim,*,CLASSID=False,colorby=False,Xnew=False,
add_ci=False,add_labels=False,add_legend=True,legend_cols=1,
addtitle='',plotwidth=600,plotheight=600,
rscores=False,material=False,marker_size=7,nbins=False,include_model=False):
'''
Score scatter plot
by Salvador Garcia-Munoz
mvm_obj : 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
add_legend : When = True will add a legend with classid labels
legend_cols: Number of columns for legend
addtitle : Additional text to be added to title
plotwidth : If omitted, width is 600
plotheight : If omitted, height is 600
rscores : Plot scores for all material space in lpls|jrpls|tpls
material : Label for specific material to plot scores for in lpls|jrpls|tpls
'''
if not(isinstance(nbins, bool)):
if colorby in CLASSID.columns.to_list():
classid_by_var = _create_classid_(CLASSID,colorby,nbins=nbins)
CLASSID = classid_by_var.copy()
mvmobj=mvm_obj.copy()
if ((mvmobj['type']=='lpls') or (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls')) and (not(isinstance(Xnew,bool))):
Xnew=False
print('score scatter does not take Xnew for jrpls or lpls for now')
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']
if not(rscores):
if (mvmobj['type']=='lpls'):
ObsID_=mvmobj['obsidR']
if (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls') :
ObsID_=mvmobj['obsidRi'][0]
else:
if (mvmobj['type']=='lpls'):
ObsID_=mvmobj['obsidX']
T_matrix=mvmobj['Rscores']
if (mvmobj['type']=='jrpls') or (mvmobj['type']=='tpls') :
if isinstance(material,bool):
allobsids=[y for x in mvmobj['obsidXi'] for y in x]
ObsID_=allobsids
clssid_obs=[]
clssid_class=[]
for i,R_ in enumerate(mvmobj['Rscores']):
clssid_obs.extend(mvmobj['obsidXi'][i])
clssid_class.extend([mvmobj['materials'][i]]*len( mvmobj['obsidXi'][i]))
if i==0:
allrscores=R_
else:
allrscores=np.vstack((allrscores,R_))
classid=pd.DataFrame(clssid_class,columns=['material'])
classid.insert(0,'obs',clssid_obs)
CLASSID=classid
colorby='material'
T_matrix=allrscores
else:
ObsID_ = mvmobj['obsidXi'][mvmobj['materials'].index(material) ]
T_matrix = mvmobj['Rscores'][mvmobj['materials'].index(material) ]
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']
if include_model:
if 'obsidX' in mvmobj:
ObsID__=mvmobj['obsidX'].copy()
else:
ObsID__ = []
for n in list(np.arange(mvmobj['T'].shape[0])+1):
ObsID__.append('Model Obs #'+str(n))
T_matrix_=mvmobj['T'].copy()
if isinstance(CLASSID,bool): #If there are no classids I need to create one
source=(['Model']*T_matrix_.shape[0])
source.extend(['New']*T_matrix.shape[0])
ObsID__.extend(ObsID_)
CLASSID=pd.DataFrame.from_dict( {'ObsID':ObsID__,'_Source_':source })
colorby='_Source_'
else: #IF there are I need to augment it
source=['Model']*T_matrix_.shape[0]
CLASSID_=pd.DataFrame.from_dict( {CLASSID.columns[0]:ObsID__,colorby:source })
ObsID__.extend(ObsID_)
CLASSID = pd.concat([CLASSID_,CLASSID])
ObsID_=ObsID__.copy()
T_matrix=np.vstack((T_matrix_,T_matrix ))
ObsNum_=[]
for n in list(range(1,len(ObsID_)+1)):
ObsNum_.append(str(n))
if isinstance(CLASSID,bool): # No CLASSIDS
#rnd_num=str(int(np.round(1000*np.random.random_sample())))
rnd_num=timestr()
output_file("Score_Scatter_"+rnd_num+".html",title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']',mode='inline')
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,width=plotwidth,height=plotheight, title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ '] '+addtitle)
#p.circle('x', 'y', source=source,size=marker_size)
p.scatter('x', 'y', source=source,size=marker_size)
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)
p.add_layout(labelsX)
if not(rscores):
p.xaxis.axis_label = 't ['+str(xydim[0])+']'
p.yaxis.axis_label = 't ['+str(xydim[1])+']'
else:
p.xaxis.axis_label = 'r ['+str(xydim[0])+']'
p.yaxis.axis_label = 'r ['+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_=phi.unique(CLASSID,colorby)
A=len(Classes_)
colormap = matplotlib.colormaps['rainbow']
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
#Test code to overwrite "Model" Category with light Cyan
if Classes_[0]=='Model':
color_mapping=colormap(np.linspace(0,1,different_colors-1),1,True)
color_mapping=np.vstack((np.array([225,225,225,255]),color_mapping))
if not(isinstance(nbins, bool)):
if colorby in CLASSID.columns.to_list():
#Classes_=phi.unique(CLASSID,colorby)
Classes_=np.unique(CLASSID[colorby]).tolist()
item_o=[]
for item in Classes_:
item_o.append(float(item[:item.find(' ')]))
idx=idx=np.argsort(item_o)
Classes_o=[]
for i in idx:
Classes_o.append(Classes_[i])
Classes_=Classes_o.copy()
#print(Classes_)
A=len(Classes_)
#colormap =cm.get_cmap("rainbow")
colormap = matplotlib.colormaps['viridis']
different_colors=A
color_mapping=colormap(np.linspace(0,1,different_colors),1,True)
if Classes_[0]=='Model':
color_mapping=colormap(np.linspace(0,1,different_colors-1),1,True)
color_mapping=np.vstack((np.array([225,225,225,255]),color_mapping))
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())))
rnd_num=timestr()
output_file("Score_Scatter_"+rnd_num+".html",title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ ']',mode='inline')
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")
]
# if not(isinstance(nbins, bool)):
# if colorby in CLASSID.columns.to_list():
# classid_=list(classid_by_var[colorby])
# else:
classid_=list(CLASSID[colorby])
legend_it = []
p = figure(tools=TOOLS, tooltips=TOOLTIPS,toolbar_location="above",width=plotwidth,height=plotheight,title='Score Scatter t['+str(xydim[0])+'] - t['+str(xydim[1])+ '] '+addtitle)
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_,size=marker_size)
c = p.scatter('x','y',source=source,color=color_,size=marker_size)
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_,size=marker_size)
p.scatter('x','y',source=source,color=color_,size=marker_size)
if add_labels:
labelsX = LabelSet(x='x', y='y', text='ObsID', level='glyph',x_offset=5, y_offset=5, source=source)
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')
if not(rscores):
p.xaxis.axis_label = 't ['+str(xydim[0])+']'
p.yaxis.axis_label = 't ['+str(xydim[1])+']'
else:
p.xaxis.axis_label = 'r ['+str(xydim[0])+']'
p.yaxis.axis_label = 'r ['+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_cols=1
ipc=[np.round(len(legend_it)/legend_cols)]*legend_cols
ipc[-1]=len(legend_it)-sum(ipc[:-1])
pastit=0
for it in ipc:
leg_ = Legend(
items=legend_it[int(0+pastit):int(pastit+it)])
#location=(0,15+pastit*5))
pastit+=it
p.add_layout(leg_, 'right')
leg_.click_policy="hide"
#legend = Legend(items=legend_it, location='top_right')
#p.add_layout(legend, 'right')
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