WIP: Breaking changes

Many breaking changes:

• Use a less biometric-related name for functions (setup aliases)
• Reduce package-namespace cluttering by moving elements to submodules
• Improve confidence interval and add credible region analysis support, documentation, plotting support and paired system comparison. Limited testing is available for the time being
• Reduce to a bare minimal the amount of stuff that needs to numba-fied. Everything else can be just proper Python code.
• Pin matplotlib to 3.4.x to avoid issues with multi-page PDF output with 3.5.0

bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb

+%% Cell type:code id: tags:
+``` python
+from scipy.stats import beta
+import matplotlib.pyplot as plt
+import numpy
+import random
+
+#Precision
+
+#System 1
+TP1 = 10
+FP1 = 10
+# System 2
+TP2 = 3
+FP2 = 2
+
+nbsamples = 10000 # Sample size, higher is better
+lbd = 0.5 # lambda
+%% Cell type:code id: tags:
+``` python
+
+x = numpy.linspace(0.01, 0.99, nbsamples)
+pdf1 = beta.pdf(x, TP1 + lbd, FP1 + lbd)
+pdf2 = beta.pdf(x, TP2 + lbd, FP2 + lbd)
+plt.plot(x, pdf1, label='TP = {}, FP = {}'.format(TP1, FP1))
+plt.plot(x, pdf2, label='TP = {}, FP = {}'.format(TP2, FP2))
+plt.title('Beta distributions')
+plt.legend()
+%% Output
+    <matplotlib.legend.Legend at 0x7f65a3d3c828>
+
+%% Cell type:code id: tags:
+``` python
+def randombeta(nbsamples, shape1, shape2):
+    betageneration = numpy.empty(nbsamples)
+    for i in range(nbsamples):
+        betageneration[i] = random.betavariate(shape1, shape2)
+    return betageneration
+
+p1 = randombeta(nbsamples, TP1 + lbd, FP1 + lbd)
+p2 = randombeta(nbsamples, TP2 + lbd, FP2 + lbd)
+print('Empirical probability that system 2 is better than system 1 = {}'.format(numpy.count_nonzero(p2 > p1) / nbsamples))
+%% Output
+    Empirical probability that system 2 is better than system 1 = 0.6548
+%% Cell type:code id: tags:
+``` python
+plt.hist(p2, bins=25)
+plt.title("Distribution (empirical) for System 2 Precision")
+plt.xlabel("Precision")
+plt.ylabel("Count")
+
+%% Output
+    Text(0, 0.5, 'Count')
+
+%% Cell type:code id: tags:
+``` python
+# F1-score
+
+def randomgamma(nbsamples, shape, scale):
+    gammageneration = numpy.empty(nbsamples)
+    for i in range(nbsamples):
+        gammageneration[i] = random.gammavariate(shape, scale)
+    return gammageneration
+
+FN1 = 0
+FN2 = 7
+# Sampling Gamma distributions to produce F-score samples
+U1 = randomgamma(nbsamples, shape=TP1+lbd, scale=2)
+V1 = randomgamma(nbsamples, FP1+FN1+2*lbd, scale=1)
+F1scores1 = U1/(U1+V1)
+U2 = randomgamma(nbsamples, TP2+lbd, scale=2)
+V2 = randomgamma(nbsamples, FP2+FN2+2*lbd, scale=1)
+F1scores2 = U2/(U2+V2)
+
+fig, axs = plt.subplots(1, 2,figsize=(15,5))
+fig.suptitle('Empirical distribution for F1 scores')
+axs[0].hist(F1scores1, bins=25)
+axs[0].set_title('System 1')
+axs[0].set(xlabel='F1 score')
+axs[1].hist(F1scores2, bins=25)
+axs[1].set_title('System 2')
+axs[1].set(xlabel='F1 score', ylabel='count')
+
+# plt.hist(F1scores1, bins=25)
+# plt.title("Distribution (empirical) for System 2 Precision")
+# plt.xlabel("Precision")
+# plt.ylabel("Count")
+
+# plt.hist(F1scores2, bins=25)
+# plt.title("Distribution (empirical) for System 1 Precision")
+# plt.xlabel("Precision")
+# plt.ylabel("Count")
+
+print('Empirical probability that system 1 is better than system 2 in F1 score = {}'
+      .format(numpy.count_nonzero(F1scores1 > F1scores2) / nbsamples))
+%% Output
+    Empirical probability that system 1 is better than system 2 in F1 score = 0.9245
+