def plot(self, resultDict=None, show=False):
if resultDict == None:
f=open(os.path.join(self.outFolder, self.outFile))
resultDict= pickle.load(f); f.close()
if type(resultDict.keys()[0])==str:
#meaning we are dealing with many alg results
pass
elif type(resultDict.keys()[0])==tuple:
#plotting legend plot
param_set = resultDict.keys()[0]
try:
print self.classNames
except AttributeError:
self.classNames = resultDict[param_set].keys()
f=p.figure(figsize=(17,17))
ax=f.add_subplot(111)
for morpho in resultDict[param_set]:
r = resultDict[param_set][morpho]
colors = makeColorRamp(17, hex_output=True)
ax.errorbar(np.mean(r[1]), np.mean(r[0]), xerr = np.std(r[1]), yerr=np.std(r[0]),
color =colors[self.classNames.index(morpho)], fmt = markers[methods.index(self.algo)],
label = '{} {}'.format(self.algo, morpho))
ax.legend()
f.suptitle("Algorithm {}".format(self.algo))
p.savefig(os.path.join(self.outFolder, 'legend {}.png'.format(self.algo)))
#plotting result plot, one for all classes and one for each morphological class
plots = {morphological_class : p.subplots(2, sharex=True) for morphological_class in self.classNames}
f=p.figure(figsize=(17,17))
ax=f.add_subplot(121)
for param_set in resultDict:
param_dict=dict(param_set)
for morpho in resultDict[param_set]:
r = resultDict[param_set][morpho]
ax.errorbar(np.mean(r[1]), np.mean(r[0]), xerr = np.std(r[1]), yerr=np.std(r[0]),
color =colors[self.classNames.index(morpho)], fmt = markers[methods.index(self.algo)],
label = '{} {}'.format(self.algo, morpho))
plots[morpho][1][0].errorbar(np.mean(r[1]), np.mean(r[0]), xerr = np.std(r[1]), yerr=np.std(r[0]),
color =colors[whiten_param.index(param_dict['whiten'])], fmt = markers[pca_param.index(param_dict['pca'])],
label = 'W{} PCA {}'.format(param_dict['whiten'],param_dict["pca"]))
plots[morpho][1][0].grid(True)
plots[morpho][1][0].set_xlim(0,1)
plots[morpho][1][0].set_ylim(0,1)
plots[morpho][1][0].set_xlabel("Nb of class elements in cluster/cluster size")
plots[morpho][1][0].set_ylabel("Nb of class elements in cluster/class size")
#plots[morpho][1][0].legend()
ax.grid(True)
ax.set_xlim(0,1)
ax.set_ylim(0,1)
ax.set_xlabel("Nb of class elements in cluster/cluster size")
ax.set_ylabel("Nb of class elements in cluster/class size")
ax=f.add_subplot(122)
for param_set in resultDict:
param_dict=dict(param_set)
for morpho in resultDict[param_set]:
r = resultDict[param_set][morpho]
ax.errorbar(np.mean(r[3]), np.mean(r[2]), xerr = np.std(r[3]), yerr=np.std(r[2]),
color =colors[self.classNames.index(morpho)], fmt = markers[methods.index(self.algo)],
label = '{} {}'.format(self.algo, morpho))
plots[morpho][1][1].errorbar(np.mean(r[3]), np.mean(r[2]), xerr = np.std(r[3]), yerr=np.std(r[2]),
color =colors[whiten_param.index(param_dict['whiten'])], fmt = markers[pca_param.index(param_dict['pca'])],
label = 'W{} PCA {}'.format(param_dict['whiten'],param_dict["pca"]))
plots[morpho][1][1].grid(True)
plots[morpho][1][1].set_xlim(0,1)
plots[morpho][1][1].set_ylim(0,1)
plots[morpho][1][1].set_xlabel("Entropy of cluster which is the one with the most elements from this class")
plots[morpho][1][1].set_ylabel("Entropy of class with regard to the clustering")
ax.grid(True)
ax.set_xlim(0,1)
ax.set_ylim(0,1)
ax.set_xlabel("Entropy of cluster which is the one with the most elements from this class")
ax.set_ylabel("Entropy of class with regard to the clustering")
f.suptitle("Algorithm {}".format(self.algo))
for morpho in plots:
plots[morpho][0].suptitle("Algorithm {}, class {}".format(self.algo, morpho))
plots[morpho][0].savefig(os.path.join(self.outFolder, 'figure {} {}.png'.format(morpho, self.algo)))
if show:
p.show()
else:
p.savefig(os.path.join(self.outFolder, 'figure {}.png'.format(self.algo)))
def plotMoy(outFolder='../resultData/nucleus', show=False):
f=p.figure(figsize=(24,17))
for i, algo in enumerate(["gaussianmixture", 'mini-batchk-means', 'kernelk-means','spectralclustering']):
file_=open(os.path.join(outFolder, outFile.format(algo)), 'r')
resultDict = pickle.load(file_); file_.close()
perClass = []; perCluster = []
for parameter_set in resultDict:
currLperClass=[]
currLperCluster = []
for morphoClass in resultDict[parameter_set]:
currLperClass.append(np.mean(resultDict[parameter_set][morphoClass][0]))
currLperCluster.append(np.mean(resultDict[parameter_set][morphoClass][1]))
perClass.append(currLperClass)
perCluster.append(currLperCluster)
ax=f.add_subplot(2,2,i)
for k in range(len(perClass)):
ax.errorbar(np.mean(perCluster[k]), np.mean(perClass[k]), xerr = np.std(perCluster[k]), yerr=np.std(perClass[k]),
fmt = markers[methods.index(algo)],
label = '{}'.format(algo))
ax.grid(True)
ax.set_xlim(0,1)
ax.set_ylim(0,1)
ax.set_title("Algorithm {}, on average on all morphological classes".format(algo))
ax.set_xlabel("Nb of class elements in cluster/cluster size")
ax.set_ylabel("Nb of class elements in cluster/class size")
if show:
p.show()
else:
p.savefig(os.path.join(outFolder, 'figure_MOY.png'.format(algo)))
f=p.figure(figsize=(24,17))
for i, algo in enumerate(["gaussianmixture", 'mini-batchk-means', 'kernelk-means','spectralclustering']):
file_=open(os.path.join(outFolder, outFile.format(algo)), 'r')
resultDict = pickle.load(file_); file_.close()
perClass = []; perCluster = []
for parameter_set in resultDict:
currLperClass=[]
currLperCluster = []
for morphoClass in resultDict[parameter_set]:
currLperClass.append(np.mean(resultDict[parameter_set][morphoClass][2]))
currLperCluster.append(np.mean(resultDict[parameter_set][morphoClass][3]))
perClass.append(currLperClass)
perCluster.append(currLperCluster)
ax=f.add_subplot(2,2,i)
for k in range(len(perClass)):
ax.errorbar(np.mean(perCluster[k]), np.mean(perClass[k]), xerr = np.std(perCluster[k]), yerr=np.std(perClass[k]),
fmt = markers[methods.index(algo)],
label = '{}'.format(algo))
ax.grid(True)
ax.set_xlim(0,1)
ax.set_ylim(0,1)
ax.set_title("Algorithm {}, on average on all morphological classes".format(algo))
ax.set_xlabel("Cluster entropy")
ax.set_ylabel("Class entropy")
if show:
p.show()
else:
p.savefig(os.path.join(outFolder, 'figure_ENT_MOY.png'.format(algo)))
