File size: 10,653 Bytes
39b4c8c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 |
import pandas as pd
import numpy as np
import os
import re
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import scipy.stats as ss
import scikit_posthocs as sp
from .stattests import friedman_aligned_ranks_test as ft
import Orange
class StatAnalysis():
""" Statistics analysis for multiple datasets and multiple VHR methods"""
def __init__(self, filepath='default'):
if os.path.isdir(filepath):
self.multidataset = True
self.path = filepath + "/"
self.datasetsList = os.listdir(filepath)
elif os.path.isfile(filepath):
self.multidataset = False
self.datasetsList = [filepath]
self.path = ""
else:
raise("Error: filepath is wrong!")
# -- get common methods
self.__getMethods()
self.metricSort = {'MAE':'min','RMSE':'min','CC':'max','PCC':'max'}
self.scale = {'MAE':'log','RMSE':'log','CC':'linear','PCC':'linear'}
def FriedmanTest(self, methods=None, metric='MAE'):
# -- Method(s)
if methods == None:
methods = self.methods
else:
if set(methods) <= set(self.methods):
raise("Some method is wrong!")
else:
self.methods = methods
# -- set metric
self.metric = metric
self.mag = self.metricSort[metric]
# -- get data from dataset(s)
# return Y = mat(n-datasets,k-methods)
if self.multidataset:
Y = self.__getData()
else:
Y = self.__getDataMono()
self.ndataset = Y.shape[0]
# -- Friedman test
t,p,ranks,piv = ft(Y)
self.avranks = list(np.divide(ranks, self.ndataset))
return t,p,ranks,piv,self.ndataset
def SignificancePlot(self, methods=None, metric='MAE'):
# -- Method(s)
if methods == None:
methods = self.methods
else:
if set(methods) <= set(self.methods):
raise("Some method is wrong!")
else:
self.methods = methods
# -- set metric
self.metric = metric
self.mag = self.metricSort[metric]
# -- get data from dataset(s)
if self.multidataset:
Y = self.__getData()
else:
Y = self.__getDataMono()
# -- Significance plot, a heatmap of p values
methodNames = [x.upper() for x in self.methods]
Ypd = pd.DataFrame(Y, columns=methodNames)
ph = sp.posthoc_nemenyi_friedman(Ypd)
cmap = ['1', '#fb6a4a', '#08306b', '#4292c6', '#c6dbef']
heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5',
'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.85, 0.35, 0.04, 0.3]}
plt.figure(figsize=(5,4))
sp.sign_plot(ph, cbar=True, **heatmap_args)
plt.title('p-vals')
fname = 'SP_' + self.metric + '.pdf'
plt.savefig(fname)
plt.show()
def computeCD(self, avranks=None, numDatasets=None, alpha='0.05', display=True):
"""
Returns critical difference for Nemenyi or Bonferroni-Dunn test according
to given alpha (either alpha=”0.05” or alpha=”0.1”) for average ranks and
number of tested datasets N. Test can be either “nemenyi” for for Nemenyi
two tailed test or “bonferroni-dunn” for Bonferroni-Dunn test.
See Orange package docs.
"""
if not numDatasets:
numDatasets = self.ndataset
if not avranks:
avranks = self.avranks
cd = Orange.evaluation.compute_CD(avranks, numDatasets, alpha=alpha) #tested on 30 datasets
if self.mag == 'min':
reverse = True
else:
reverse = False
methodNames = [x.upper() for x in self.methods]
if display:
Orange.evaluation.graph_ranks(avranks, methodNames, cd=cd, width=6, textspace=1.5, reverse=reverse)
name = 'CD Diagram (metric: ' + self.metric +')'
plt.title(name)
fname = 'CD_' + self.metric + '.pdf'
plt.savefig(fname)
plt.show()
return cd
def displayBoxPlot(self, methods=None, metric='MAE', scale=None, title=True):
# -- Method(s)
if methods == None:
methods = self.methods
else:
if set(methods) <= set(self.methods):
raise("Some method is wrong!")
else:
self.methods = methods
# -- set metric
self.metric = metric
self.mag = self.metricSort[metric]
if scale == None:
scale = self.scale[metric]
# -- get data from dataset(s)
if self.multidataset:
Y = self.__getData()
else:
Y = self.__getDataMono()
# -- display box plot
self.boxPlot(methods, metric, Y, scale=scale, title=title)
def boxPlot(self, methods, metric, Y, scale, title):
# Y = mat(n-datasets,k-methods)
k = len(methods)
if not (k == Y.shape[1]):
raise("error!")
offset = 50
fig = go.Figure()
methodNames = [x.upper() for x in self.methods]
for i in range(k):
yd = Y[:,i]
name = methodNames[i]
# -- set color for box
if metric == 'MAE' or metric == 'RMSE':
med = np.median(yd)
col = str(min(200,5*int(med)+offset))
if metric == 'CC' or metric == 'PCC':
med = 1-np.abs(np.median(yd))
col = str(int(200*med)+offset)
# -- add box
fig.add_trace(go.Box(
y=yd,
name=name,
boxpoints='all',
jitter=.7,
#whiskerwidth=0.2,
fillcolor="rgba("+col+","+col+","+col+",0.5)",
line_color="rgba(0,0,255,0.5)",
marker_size=2,
line_width=2)
)
gwidth = np.max(Y)/10
if title:
tit = "Metric: " + metric
top = 40
else:
tit=''
top = 10
fig.update_layout(
title=tit,
yaxis_type=scale,
xaxis_type="category",
yaxis=dict(
autorange=True,
showgrid=True,
zeroline=True,
#dtick=gwidth,
gridcolor='rgb(255,255,255)',
gridwidth=.1,
zerolinewidth=2,
titlefont=dict(size=30)
),
font=dict(
family="monospace",
size=16,
color='rgb(20,20,20)'
),
margin=dict(
l=20,
r=10,
b=20,
t=top,
),
paper_bgcolor='rgb(250, 250, 250)',
plot_bgcolor='rgb(243, 243, 243)',
showlegend=False
)
fig.show()
def saveStatsData(self, methods=None, metric='MAE', outfilename='statsData.csv'):
Y = self.getStatsData(methods=methods, metric=metric, printTable=False)
np.savetxt(outfilename, Y)
def getStatsData(self, methods=None, metric='MAE', printTable=True):
# -- Method(s)
if methods == None:
methods = self.methods
else:
if set(methods) <= set(self.methods):
raise("Some method is wrong!")
else:
self.methods = methods
# -- set metric
self.metric = metric
self.mag = self.metricSort[metric]
# -- get data from dataset(s)
# return Y = mat(n-datasets,k-methods)
if self.multidataset:
Y = self.__getData()
else:
Y = self.__getDataMono()
# -- add median and IQR
I = ss.iqr(Y,axis=0)
M = np.median(Y,axis=0)
Y = np.vstack((Y,M))
Y = np.vstack((Y,I))
if printTable:
methodNames = [x.upper() for x in self.methods]
dataseNames = self.datasetNames
dataseNames.append('Median')
dataseNames.append('IQR')
df = pd.DataFrame(Y, columns=methodNames, index=dataseNames)
display(df)
return Y
def __getDataMono(self):
mag = self.mag
metric = self.metric
methods = self.methods
frame = self.dataFrame[0]
# -- loop on methods
Y = []
for method in methods:
vals = frame[frame['method'] == method][metric]
if mag == 'min':
data = [v[np.argmin(v)] for v in vals]
else:
data = [v[np.argmax(v)] for v in vals]
Y.append(data)
return np.array(Y).T
def __getData(self):
mag = self.mag
metric = self.metric
methods = self.methods
# -- loop on datasets
Y = []
for frame in self.dataFrame:
# -- loop on methods
y = []
for method in methods:
vals = frame[frame['method'] == method][metric]
if mag == 'min':
data = [v[np.argmin(v)] for v in vals]
else:
data = [v[np.argmax(v)] for v in vals]
y.append(data)
y = np.array(y)
Y.append(np.mean(y,axis=1))
return np.array(Y)
def __getMethods(self):
mets = []
dataFrame = []
N = len(self.datasetsList)
# -- load dataframes
self.datasetNames = []
for file in self.datasetsList:
filename = self.path + file
self.datasetNames.append(file)
data = pd.read_hdf(filename)
mets.append(set(list(data['method'])))
dataFrame.append(data)
# -- method names intersection among datasets
methods = set(mets[0])
if N > 1:
for m in range(1,N-1):
methods.intersection(mets[m])
methods = list(methods)
methods.sort()
self.methods = methods
self.dataFrame = dataFrame
|