def calcular_similitud(self, pair_key, lines):
"""
Se suman los componentes de cada par con todos los usuarios que
calificaron los items X y Y del par, luego se calcula la similitud
de Pearson. Esta es normalizada entre [0,1] por el sort numerico
"""
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y in lines:
sum_xx += item_x * item_x
sum_yy += item_y * item_y
sum_xy += item_x * item_y
sum_x += item_x
sum_y += item_y
n += 1
corr_sim = correlation(n, sum_xy, sum_x,
sum_y, sum_xx, sum_yy)
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, sum_y,
sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
jaccard_sim = 0.0
yield (item_xname, item_yname), (corr_sim, cos_sim,
reg_corr_sim, jaccard_sim, n)
def Cal_similarity(self,itemids, ratings): ''' Many pairs of movie and ratings For example (1,2) (5,0,4.5) (2,3) (3.1,3,3) (3,4) (2.0,3.0) (4,2) (1.0,2.3) ... ''' sum_xx,sum_yy,sum_xy,sum_x,sum_y,sumx_y,n=0.0,0.0,0.0,0.0,0.0,0.0,0 item_pairs,rate=itemids,ratings itemx,itemy=item_pairs tempx=[] tempy=[] for x,y in rate: sumx_y+=(x-y)*(x-y) sum_xx+=x*x sum_yy+=y*y sum_xy=x*y sum_x+=x sum_y+=y tempx.append(x) tempy.append(y) n+=1 tempx=np.array(tempx) tempy=np.array(tempy) tempx-=np.mean(tempx) tempy-=np.mean(tempy) tempx=tempx*tempx tempy=tempy*tempy sumx2,sumy2=sum(tempx),sum(tempy) temp=0.0 T=tempx*tempy for i in range(len(T)): temp+=T[i] # corr_sim=correaltion(n,sum_xy,sum_x,sum_y,sum_xx,sum_yy) # reg_corr_sim=regularized_correlation(n,sum_xy,sum_x,sum_y,sum_xx,sum_yy,PRIOR_COUNT,PRIOR_CORRELATION) cos_sim=cosine(sum_xy,sqrt(sum_xx),sqrt(sum_yy)) pdis=pearsondistance(temp,sqrt(sumx2),sqrt(sumy2)) # jac=jaccard(count,n,n) edis=EuclideanDistance(n,sumx_y) if n>20: yield (itemx,itemy),(cos_sim,edis,pdis,n)
def Cal_similarity(self, itemids, ratings):
'''
Many pairs of movie and ratings
For example
(1,2) (5,0,4.5)
(2,3) (3.1,3,3)
(3,4) (2.0,3.0)
(4,2) (1.0,2.3)
...
'''
sum_xx, sum_yy, sum_xy, sum_x, sum_y, sumx_y, n = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0
item_pairs, rate = itemids, ratings
itemx, itemy = item_pairs
tempx = []
tempy = []
for x, y in rate:
sumx_y += (x - y) * (x - y)
sum_xx += x * x
sum_yy += y * y
sum_xy = x * y
sum_x += x
sum_y += y
tempx.append(x)
tempy.append(y)
n += 1
tempx = np.array(tempx)
tempy = np.array(tempy)
tempx -= np.mean(tempx)
tempy -= np.mean(tempy)
tempx = tempx * tempx
tempy = tempy * tempy
sumx2, sumy2 = sum(tempx), sum(tempy)
temp = 0.0
T = tempx * tempy
for i in range(len(T)):
temp += T[i]
# corr_sim=correaltion(n,sum_xy,sum_x,sum_y,sum_xx,sum_yy)
# reg_corr_sim=regularized_correlation(n,sum_xy,sum_x,sum_y,sum_xx,sum_yy,PRIOR_COUNT,PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
pdis = pearsondistance(temp, sqrt(sumx2), sqrt(sumy2))
# jac=jaccard(count,n,n)
edis = EuclideanDistance(n, sumx_y)
if n > 20:
yield (itemx, itemy), (cos_sim, edis, pdis, n)
def calculate_similarity(self, pair_key, lines):
'''
Sum components of each corating pair across all users who rated both
item x and item y, then calculate pairwise pearson similarity and
corating counts. The similarities are normalized to the [0,1] scale
because we do a numerical sort.
19,21 0.4,2
21,19 0.4,2
19,70 0.6,1
70,19 0.6,1
21,70 0.1,1
70,21 0.1,1
'''
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y in lines:
sum_xx += item_x * item_x
sum_yy += item_y * item_y
sum_xy += item_x * item_y
sum_y += item_y
sum_x += item_x
n += 1
corr_sim = correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy)
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
jaccard_sim = 0.0
yield (item_xname, item_yname), (corr_sim, \
cos_sim, reg_corr_sim, jaccard_sim, n)
def calculate_similarity(self, pair_key, lines):
'''
Sum components of each corating pair across all users who rated both
item x and item y, then calculate pairwise pearson similarity and
corating counts. The similarities are normalized to the [0,1] scale
because we do a numerical sort.
19,21 0.4,2
21,19 0.4,2
19,70 0.6,1
70,19 0.6,1
21,70 0.1,1
70,21 0.1,1
'''
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y in lines:
sum_xx += item_x * item_x
sum_yy += item_y * item_y
sum_xy += item_x * item_y
sum_y += item_y
sum_x += item_x
n += 1
corr_sim = correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy)
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
jaccard_sim = 0.0
yield (item_xname, item_yname), (corr_sim, \
cos_sim, reg_corr_sim, jaccard_sim, n)
def calculate_similarity(self, pair_key, lines):
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y in lines:
sum_xx += item_x * item_x
sum_yy += item_y * item_y
sum_xy += item_x * item_y
sum_y += item_y
sum_x += item_x
n += 1
corr_sim = correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy)
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, \
sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
jaccard_sim = 0.0
yield (item_xname, item_yname), (corr_sim, \
cos_sim, reg_corr_sim, jaccard_sim, n)
def calculate_similarity(self, pair_key, lines):
sum_xx, sum_xy, sum_yy, sum_x, sum_y, n = (0.0, 0.0, 0.0, 0.0, 0.0, 0)
n_x, n_y = 0, 0
item_pair, co_ratings = pair_key, lines
item_xname, item_yname = item_pair
for item_x, item_y, nx_count, ny_count in lines:
sum_xx += item_x * item_x
sum_yy += item_y * item_y
sum_xy += item_x * item_y
sum_y += item_y
sum_x += item_x
n += 1
n_x = int(ny_count)
n_y = int(nx_count)
corr_sim = correlation(n, sum_xy, sum_x, sum_y, sum_xx, sum_yy)
reg_corr_sim = regularized_correlation(n, sum_xy, sum_x, sum_y, sum_xx, sum_yy, PRIOR_COUNT, PRIOR_CORRELATION)
cos_sim = cosine(sum_xy, sqrt(sum_xx), sqrt(sum_yy))
jaccard_sim = jaccard(n, n_x, n_y)
yield (item_xname, item_yname), (corr_sim, cos_sim, reg_corr_sim, jaccard_sim, n)
