class MetricGrapher():
def __init__(self, paths, params, Y, GO):
self.params = params
self.paths = paths
#self.paths.save_file(__file__)
# Set path ans params
# managers
self.Y = Y
# Set predictions array
self.GO = GO
# Set label manager
if self.Y.shape[0] != self.GO.data.shape[0]:
raise ValueError(
'Must have same number of predictions and labels.')
self.thresholds = np.arange(
self.params.get('thresholdmin-Metrics', 0.00),
self.params.get('thresholdmax-Metrics', 1.01),
self.params.get('thresholdincrement-Metrics', 0.01))
# Which thresholds to threshold Y with.
self.log = None
# A log to log the metrics
# for each function
# Initialized later when
# all metrics are known.
self.metrics = []
# A list of metrics objects
self.axies = ['None', 'Units']
# X, Y Axies to put on plot
# Keep axies[0] as None to
# use thresholds
def new_metric(self, ymetfunc, xmetfunc=None, label=None):
'''
Creates a new metric, sets function.
'''
m = Metric(self.paths, self.params, self.Y, self.GO, self.thresholds)
if xmetfunc == None:
m.xaxis = Metric.Function('None')
else:
m.xaxis = xmetfunc
m.yaxis = ymetfunc
m.label = label
self.metrics.append(m)
#return m
def initialize_metrics(self):
'''
Called after all desired metrics are initalized.
'''
names = [
'AUC ' + m.yaxis.name + ' ' + m.xaxis.name for m in self.metrics
]
self.log = Log(self.paths, cols=['function', *names])
def threshold(self, guess):
'''
Returns this guess array, thresholded
by each value in thresholds.
'''
mn = guess.min()
rng = guess.max() - mn
# Vars mn and rng for rescaleing
# thresholds according to guess,
# makes thresholds be compairable
# to guess.
thresholds = (self.thresholds * rng) + mn
# Rescale thresholds for this guess.
return ((thresholds[:, np.newaxis] - guess) <= 0).astype(
np.int8), thresholds
# Makes Array of thresholdsXguesses, with threshold minus
# guesses at each point, then returns 1 where threshold was
# less than guess.
def make_graphs(self, show=False):
for f in self.GO.CancerFunctions:
index = self.GO.FunctionOrder.index(f)
annotation = self.GO.get_function(f)
# Get annotation for this protein
thresholded, thresholds = self.threshold(self.Y.T[index])
aucs = []
pl.figure()
pl.title(f)
pl.xlim(0, 1)
pl.ylim(0, 1)
pl.xlabel(self.axies[0])
pl.ylabel(self.axies[1])
for m in self.metrics:
x, y = m.get(annotation, thresholded)
if m.xaxis.name == 'None':
x = thresholds
aucs.append(auc(x, y))
if not m.label == None:
name = m.label
else:
name = m.yaxis.name + ' vs. ' + m.xaxis.name
pl.plot(x, y, label=name) #, marker='o')
#print('plotting')
pl.legend()
f = f[:2] + '-' + f[3:]
if show:
pl.show()
else:
pl.savefig(self.paths.join(self.paths.output, f + '.pdf'))
pl.close()
self.log.append([f, *aucs])
