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Improve similarities performance #448

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Improve similarities performance #448

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40 changes: 24 additions & 16 deletions surprise/similarities.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -61,13 +61,15 @@ def cosine(int n_x, yr, int min_support):
# the similarity matrix
cdef double [:, ::1] sim = np.zeros((n_x, n_x), np.double)

cdef int xi, xj, y
cdef int xi, xj, y, i
cdef double ri, rj
cdef int min_sprt = min_support

for y, y_ratings in yr.items():
for xi, ri in y_ratings:
for xj, rj in y_ratings:
sorted_yr = { y : sorted(y_ratings, key = lambda x: x[0]) for y, y_ratings in yr.items() }

for y, y_ratings in sorted_yr.items():
for i, (xi, ri) in enumerate(y_ratings):
for xj, rj in y_ratings[i + 1:]:
freq[xi, xj] += 1
prods[xi, xj] += ri * rj
sqi[xi, xj] += ri**2
Expand Down Expand Up @@ -128,13 +130,15 @@ def msd(int n_x, yr, int min_support):
# the similarity matrix
cdef double [:, ::1] sim = np.zeros((n_x, n_x), np.double)

cdef int xi, xj
cdef int xi, xj, i
cdef double ri, rj
cdef int min_sprt = min_support

for y, y_ratings in yr.items():
for xi, ri in y_ratings:
for xj, rj in y_ratings:
sorted_yr = { y : sorted(y_ratings, key = lambda x: x[0]) for y, y_ratings in yr.items() }

for y, y_ratings in sorted_yr.items():
for i, (xi, ri) in enumerate(y_ratings):
for xj, rj in y_ratings[i + 1:]:
sq_diff[xi, xj] += (ri - rj)**2
freq[xi, xj] += 1

Expand Down Expand Up @@ -200,13 +204,15 @@ def pearson(int n_x, yr, int min_support):
# the similarity matrix
cdef double [:, ::1] sim = np.zeros((n_x, n_x), np.double)

cdef int xi, xj, y, n
cdef int xi, xj, y, n, i
cdef double ri, rj, num, denum
cdef int min_sprt = min_support

for y, y_ratings in yr.items():
for xi, ri in y_ratings:
for xj, rj in y_ratings:
sorted_yr = { y : sorted(y_ratings, key = lambda x: x[0]) for y, y_ratings in yr.items() }

for y, y_ratings in sorted_yr.items():
for i, (xi, ri) in enumerate(y_ratings):
for xj, rj in y_ratings[i + 1:]:
prods[xi, xj] += ri * rj
freq[xi, xj] += 1
sqi[xi, xj] += ri**2
Expand Down Expand Up @@ -296,7 +302,7 @@ def pearson_baseline(
# the similarity matrix
cdef double [:, ::1] sim = np.zeros((n_x, n_x), np.double)

cdef int y, xi, xj
cdef int y, xi, xj, i
cdef double ri, rj, diff_i, diff_j, partial_bias
cdef int min_sprt = min_support
cdef double global_mean_ = global_mean
Expand All @@ -305,10 +311,12 @@ def pearson_baseline(
# is 1, so that's OK.
min_sprt = max(2, min_sprt)

for y, y_ratings in yr.items():
sorted_yr = { y : sorted(y_ratings, key = lambda x: x[0]) for y, y_ratings in yr.items() }

for y, y_ratings in sorted_yr.items():
partial_bias = global_mean_ + y_biases[y]
for xi, ri in y_ratings:
for xj, rj in y_ratings:
for i, (xi, ri) in enumerate(y_ratings):
for xj, rj in y_ratings[i + 1:]:
freq[xi, xj] += 1
diff_i = (ri - (partial_bias + x_biases[xi]))
diff_j = (rj - (partial_bias + x_biases[xj]))
Expand Down