def plot_average_rocs(self, filename, roc_pickles, boundary=0.1):
"""
Average several ROC curves from different models and plot
them together in the same figure.
:filename: name of the file to save the plot
:boundary: upper False Positive limit for the roc plot
:roc_pickles: list of pz file names containing several rocs each
:returns: None. It saves the roc plot in a png file with the
specified filename
"""
fps = np.linspace(0.0, 1.0, 10000)
plt.figure(figsize=(18.5, 10.5))
linestyles = ['k-', 'k--', 'k-.', 'k:', 'k.',
'k*', 'k^', 'ko', 'k+', 'kx']
for f, style in zip(roc_pickles, linestyles[:len(roc_pickles)]):
avg_roc, std_roc = eval.average_roc(pz.load(f), fps)
plt.plot(avg_roc[1], avg_roc[0], style)
plt.legend(roc_pickles, 'lower right', shadow=True)
plt.xlabel('False Positive Rate')
plt.xlim((0.0, boundary))
plt.ylabel('True Positive Rate')
plt.ylim((0.0, 1.0))
plt.title("Average ROCs")
plt.grid(True)
plt.savefig(filename, format='png')
def plot_average_rocs(self, filename, roc_pickles, boundary=0.1):
"""
Average several ROC curves from different models and plot
them together in the same figure.
:filename: name of the file to save the plot
:boundary: upper False Positive limit for the roc plot
:roc_pickles: list of pz file names containing several rocs each
:returns: None. It saves the roc plot in a png file with the
specified filename
"""
fps = np.linspace(0.0, 1.0, 10000)
plt.figure(figsize=(18.5, 10.5))
linestyles = [
'k-', 'k--', 'k-.', 'k:', 'k.', 'k*', 'k^', 'ko', 'k+', 'kx'
]
for f, style in zip(roc_pickles, linestyles[:len(roc_pickles)]):
avg_roc, std_roc = eval.average_roc(pz.load(f), fps)
plt.plot(avg_roc[1], avg_roc[0], style)
plt.legend(roc_pickles, 'lower right', shadow=True)
plt.xlabel('False Positive Rate')
plt.xlim((0.0, boundary))
plt.ylabel('True Positive Rate')
plt.ylim((0.0, 1.0))
plt.title("Average ROCs")
plt.grid(True)
plt.savefig(filename, format='png')
def plot_average_roc(self, filename, boundary=0.1):
"""
Plot an average roc curve up to boundary using the rocs object
of the the Analysis object. It can be called after
run_linear_experiment.
:filename: name of the file to save the roc plot
:boundary: upper False Positive limit for the roc plot
:returns: None. It saves the roc plot in a png file with
the specified filename
"""
fps = np.linspace(0.0, 1.0, 10000)
(avg_roc, std_roc) = eval.average_roc(self.rocs, fps)
std0 = std_roc[1]
std1 = avg_roc[0] + std_roc[0]
std2 = avg_roc[0] - std_roc[0]
plt.plot(avg_roc[1], avg_roc[0], 'k-',
std0, std1, 'k--', std0, std2, 'k--')
plt.legend(('Average ROC', 'StdDev ROC'), 'lower right', shadow=True)
plt.xlabel('False Positive Rate')
plt.xlim((0.0, boundary))
plt.ylabel('True Positive Rate')
plt.ylim((0.0, 1.0))
plt.title("Average ROC")
plt.grid(True)
plt.savefig(filename, format='png')
def plot_average_roc(self, filename, boundary=0.1):
"""
Plot an average roc curve up to boundary using the rocs object
of the the Analysis object. It can be called after
run_linear_experiment.
:filename: name of the file to save the roc plot
:boundary: upper False Positive limit for the roc plot
:returns: None. It saves the roc plot in a png file with
the specified filename
"""
fps = np.linspace(0.0, 1.0, 10000)
(avg_roc, std_roc) = eval.average_roc(self.rocs, fps)
std0 = std_roc[1]
std1 = avg_roc[0] + std_roc[0]
std2 = avg_roc[0] - std_roc[0]
plt.plot(avg_roc[1], avg_roc[0], 'k-', std0, std1, 'k--', std0, std2,
'k--')
plt.legend(('Average ROC', 'StdDev ROC'), 'lower right', shadow=True)
plt.xlabel('False Positive Rate')
plt.xlim((0.0, boundary))
plt.ylabel('True Positive Rate')
plt.ylim((0.0, 1.0))
plt.title("Average ROC")
plt.grid(True)
plt.savefig(filename, format='png')