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Grouping in warp #498

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Grouping in warp #498

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1e234d7
Grouping in WARP loss
jotsif Oct 17, 2019
6ea2aad
Add shuffling of groups when creating group map
jotsif Oct 18, 2019
d033097
Blacked the files
jotsif Nov 13, 2019
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110 changes: 100 additions & 10 deletions lightfm/_lightfm_fast.pyx.template
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@ from libc.stdlib cimport free, malloc

{openmp_import}


ctypedef float flt

# Allow sequential code blocks in a parallel setting.
Expand Down Expand Up @@ -81,7 +80,8 @@ cdef int rand_r(unsigned int * seed) nogil:
return temper(seed[0]) / 2


cdef int sample_range(int min_val, int max_val, unsigned int *seed) nogil:
cdef int sample_range(int min_val, int max_val,
unsigned int *seed) nogil:

cdef int val_range

Expand All @@ -90,6 +90,17 @@ cdef int sample_range(int min_val, int max_val, unsigned int *seed) nogil:
return min_val + (rand_r(seed) % val_range)


cdef int *shuffle_rows(int *sh_rows, int rows, unsigned int *seed) nogil:
cdef int a, j, tmp
for a in range(rows):
sh_rows[a] = a
for a in range(rows - 1):
j = sample_range(a, rows, seed)
tmp = sh_rows[j]
sh_rows[j] = sh_rows[a]
sh_rows[a] = tmp


cdef int int_min(int x, int y) nogil:

if x < y:
Expand All @@ -110,6 +121,10 @@ cdef struct Pair:
int idx
flt val

cdef struct GroupData:
int size
int offset


cdef int reverse_pair_compare(const_void *a, const_void *b) nogil:

Expand Down Expand Up @@ -284,6 +299,39 @@ cdef inline int in_positives(int item_id, int user_id, CSRMatrix interactions) n
return 1


cdef inline int* gen_item_group_map(int *item_group_map,
CSRMatrix item_groups,
unsigned int *seed) nogil:
cdef int group_row_start, group_row_end, item_id, r, i, sr
cdef int *randomised_rows

randomised_rows = <int *>malloc(sizeof(int) * item_groups.rows)

shuffle_rows(randomised_rows, item_groups.rows, seed)

for r in {range_block}(item_groups.rows):
sr = randomised_rows[r]
group_row_start = item_groups.get_row_start(sr)
group_row_end = item_groups.get_row_end(sr)
for i in {range_block}(group_row_end - group_row_start):
item_id = item_groups.indices[group_row_start + i]
item_group_map[item_id] = sr

free(randomised_rows)

return item_group_map


cdef inline GroupData *get_group_size(GroupData *g, int group, CSRMatrix item_groups) nogil:
cdef int group_row_start, group_row_end

group_row_start = item_groups.get_row_start(group)
group_row_end = item_groups.get_row_end(group)
# get size and offset of group
g.size = group_row_end - group_row_start
g.offset = group_row_start


cdef inline void compute_representation(CSRMatrix features,
flt[:, ::1] feature_embeddings,
flt[::1] feature_biases,
Expand Down Expand Up @@ -608,7 +656,9 @@ cdef void warp_update(double loss,
avg_learning_rate += update_features(item_features, lightfm.item_features,
lightfm.item_feature_gradients,
lightfm.item_feature_momentum,
i, positive_item_start_index, positive_item_stop_index,
i,
positive_item_start_index,
positive_item_stop_index,
-loss * user_component,
lightfm.adadelta,
lightfm.learning_rate,
Expand All @@ -618,7 +668,9 @@ cdef void warp_update(double loss,
avg_learning_rate += update_features(item_features, lightfm.item_features,
lightfm.item_feature_gradients,
lightfm.item_feature_momentum,
i, negative_item_start_index, negative_item_stop_index,
i,
negative_item_start_index,
negative_item_stop_index,
loss * user_component,
lightfm.adadelta,
lightfm.learning_rate,
Expand All @@ -629,17 +681,17 @@ cdef void warp_update(double loss,
lightfm.user_feature_gradients,
lightfm.user_feature_momentum,
i, user_start_index, user_stop_index,
loss * (negative_item_component -
positive_item_component),
loss * (negative_item_component
- positive_item_component),
lightfm.adadelta,
lightfm.learning_rate,
user_alpha,
lightfm.rho,
lightfm.eps)

avg_learning_rate /= ((lightfm.no_components + 1) * (user_stop_index - user_start_index)
+ (lightfm.no_components + 1) *
(positive_item_stop_index - positive_item_start_index)
+ (lightfm.no_components + 1)
* (positive_item_stop_index - positive_item_start_index)
+ (lightfm.no_components + 1)
* (negative_item_stop_index - negative_item_start_index))

Expand Down Expand Up @@ -784,6 +836,7 @@ def fit_logistic(CSRMatrix item_features,
def fit_warp(CSRMatrix item_features,
CSRMatrix user_features,
CSRMatrix interactions,
CSRMatrix item_groups,
int[::1] user_ids,
int[::1] item_ids,
flt[::1] Y,
Expand All @@ -801,12 +854,16 @@ def fit_warp(CSRMatrix item_features,

cdef int i, no_examples, user_id, positive_item_id, gamma
cdef int negative_item_id, sampled, row
cdef int group, rand_n
cdef double positive_prediction, negative_prediction
cdef double loss, MAX_LOSS
cdef GroupData *group_data
cdef bint use_groups
cdef flt weight
cdef flt *user_repr
cdef flt *pos_it_repr
cdef flt *neg_it_repr
cdef int *item_group_map
cdef unsigned int[::1] random_states

random_states = random_state.randint(0,
Expand All @@ -816,8 +873,15 @@ def fit_warp(CSRMatrix item_features,
no_examples = Y.shape[0]
MAX_LOSS = 10.0

use_groups = item_groups.nnz > 0

{nogil_block}

if use_groups:
group_data = <GroupData *>malloc(sizeof(GroupData) * no_examples)
item_group_map = <int *>malloc(sizeof(int) * item_groups.cols)
gen_item_group_map(item_group_map, item_groups, &random_states[0])

user_repr = <flt *>malloc(sizeof(flt) * (lightfm.no_components + 1))
pos_it_repr = <flt *>malloc(sizeof(flt) * (lightfm.no_components + 1))
neg_it_repr = <flt *>malloc(sizeof(flt) * (lightfm.no_components + 1))
Expand All @@ -828,6 +892,9 @@ def fit_warp(CSRMatrix item_features,
user_id = user_ids[row]
positive_item_id = item_ids[row]

if use_groups:
group = item_group_map[positive_item_id]
get_group_size(&group_data[i], group, item_groups)
if not Y[row] > 0:
continue

Expand Down Expand Up @@ -857,8 +924,13 @@ def fit_warp(CSRMatrix item_features,
while sampled < lightfm.max_sampled:

sampled = sampled + 1
negative_item_id = (rand_r(&random_states[{thread_num}])
% item_features.rows)
if use_groups:
rand_n = (rand_r(&random_states[{thread_num}])
% group_data[i].size)
negative_item_id = item_groups.indices[group_data[i].offset + rand_n]
else:
negative_item_id = (rand_r(&random_states[{thread_num}])
% item_features.rows)

compute_representation(item_features,
lightfm.item_features,
Expand Down Expand Up @@ -906,6 +978,9 @@ def fit_warp(CSRMatrix item_features,
free(user_repr)
free(pos_it_repr)
free(neg_it_repr)
if use_groups:
free(item_group_map)
free(group_data)

regularize(lightfm,
item_alpha,
Expand Down Expand Up @@ -1383,3 +1458,18 @@ def __test_in_positives(int row, int col, CSRMatrix mat):
return True
else:
return False


def test_item_group_map(int n, CSRMatrix mat, random_state):
cdef unsigned int[::1] random_states
cdef int *item_group_map

random_states = random_state.randint(0,
np.iinfo(np.int32).max,
size=1).astype(np.uint32)
item_group_map = <int *>malloc(sizeof(int) * mat.cols)

gen_item_group_map(item_group_map, mat, &random_states[0])
val = item_group_map[n]
free(item_group_map)
return val
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