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Commit cda79c35 authored by Antonio MORAIS's avatar Antonio MORAIS
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Added Goutte implementation port

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bob/measure/.ipynb_checkpoints/goutte_port-checkpoint.ipynb 0 → 100644
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%% 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
bob/measure/goutte_port.ipynb 0 → 100644
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