Skip to content

Eval use precomputed representations #4

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
JasonTam merged 8 commits into from
May 17, 2017
Merged

Eval use precomputed representations #4

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
7f85ca8
Fix link to Catalant blog post in readme.
maciejkula Mar 23, 2017
94ffd38
Merge pull request #163 from maciejkula/readme_link_fix
maciejkula Mar 23, 2017
a7666ba
Correctly convert feature dtypes in predict_rank.
maciejkula Apr 12, 2017
0c11c0a
Merge pull request #167 from maciejkula/features_dtype_conversion
maciejkula Apr 12, 2017
1326a35
Merge branch 'master' of github.com:lyst/lightfm
JasonTam Apr 20, 2017
2fd7787
Merge branch 'master' of github.com:JasonTam/lightfm
JasonTam Apr 20, 2017
181af04
option to pass in precomputed representations for prediction / evalua…
JasonTam Apr 20, 2017
48073bd
user/item features only necessary if represenations not given for pre…
JasonTam Apr 25, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ test_precision = precision_at_k(model, data['test'], k=5).mean()
1. [Learning to Rank Sketchfab Models with LightFM](http://blog.ethanrosenthal.com/2016/11/07/implicit-mf-part-2/)
2. [Metadata Embeddings for User and Item Cold-start Recommendations](http://building-babylon.net/2016/01/26/metadata-embeddings-for-user-and-item-cold-start-recommendations/)
3. [Recommendation Systems - Learn Python for Data Science](https://www.youtube.com/watch?v=9gBC9R-msAk)
4. [Using LightFM to Recommend Projects to Consultants] (https://medium.com/product-at-catalant-technologies/using-lightfm-to-recommend-projects-to-consultants-44084df7321c#.gu887ky51)
4. [Using LightFM to Recommend Projects to Consultants](https://medium.com/product-at-catalant-technologies/using-lightfm-to-recommend-projects-to-consultants-44084df7321c#.gu887ky51)

## How to cite
Please cite LightFM if it helps your research. You can use the following BibTeX entry:
Expand Down
102 changes: 62 additions & 40 deletions lightfm/_lightfm_fast.pyx.template
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1233,12 +1233,15 @@ def predict_lightfm(CSRMatrix item_features,
int[::1] item_ids,
double[::1] predictions,
FastLightFM lightfm,
int num_threads):
int num_threads,
bint use_precomputed,
flt[:, ::1] user_reprs,
flt[:, ::1] item_reprs):
"""
Generate predictions.
"""

cdef int i, no_examples
cdef int i, no_examples, factor_dim
cdef flt *user_repr
cdef flt *it_repr

Expand All @@ -1250,21 +1253,25 @@ def predict_lightfm(CSRMatrix item_features,
it_repr = <flt *>malloc(sizeof(flt) * (lightfm.no_components + 1))

for i in {range_block}(no_examples):

compute_representation(user_features,
lightfm.user_features,
lightfm.user_biases,
lightfm,
user_ids[i],
lightfm.user_scale,
user_repr)
compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_ids[i],
lightfm.item_scale,
it_repr)
if use_precomputed:
for factor_dim in {range_block}(lightfm.no_components + 1):
user_repr[factor_dim] = user_reprs[user_ids[i], factor_dim]
it_repr[factor_dim] = item_reprs[item_ids[i], factor_dim]
else:
compute_representation(user_features,
lightfm.user_features,
lightfm.user_biases,
lightfm,
user_ids[i],
lightfm.user_scale,
user_repr)
compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_ids[i],
lightfm.item_scale,
it_repr)

predictions[i] = compute_prediction_from_repr(user_repr,
it_repr,
Expand All @@ -1280,11 +1287,14 @@ def predict_ranks(CSRMatrix item_features,
CSRMatrix train_interactions,
flt[::1] ranks,
FastLightFM lightfm,
int num_threads):
int num_threads,
bint use_precomputed,
flt[:, ::1] user_reprs,
flt[:, ::1] item_reprs):
"""
"""

cdef int i, j, user_id, item_id, predictions_size, row_start, row_stop
cdef int i, j, user_id, item_id, predictions_size, row_start, row_stop, factor_dim
cdef flt *user_repr
cdef flt *it_repr
cdef flt *predictions
Expand Down Expand Up @@ -1315,27 +1325,35 @@ def predict_ranks(CSRMatrix item_features,
# No test interactions for this user
continue

compute_representation(user_features,
lightfm.user_features,
lightfm.user_biases,
lightfm,
user_id,
lightfm.user_scale,
user_repr)
if use_precomputed:
for factor_dim in {range_block}(lightfm.no_components + 1):
user_repr[factor_dim] = user_reprs[user_id, factor_dim]
else:
compute_representation(user_features,
lightfm.user_features,
lightfm.user_biases,
lightfm,
user_id,
lightfm.user_scale,
user_repr)

# Compute predictions for the items whose
# ranks we want to know
for i in range(row_stop - row_start):

item_id = test_interactions.indices[row_start + i]

compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_id,
lightfm.item_scale,
it_repr)
if use_precomputed:
for factor_dim in range(lightfm.no_components + 1):
it_repr[factor_dim] = item_reprs[item_id, factor_dim]
else:
compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_id,
lightfm.item_scale,
it_repr)

item_ids[i] = item_id
predictions[i] = compute_prediction_from_repr(user_repr,
Expand All @@ -1350,13 +1368,17 @@ def predict_ranks(CSRMatrix item_features,
# This depends on how we want to evaluate (so it's not necessary)
continue

compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_id,
lightfm.item_scale,
it_repr)
if use_precomputed:
for factor_dim in range(lightfm.no_components + 1):
it_repr[factor_dim] = item_reprs[item_id, factor_dim]
else:
compute_representation(item_features,
lightfm.item_features,
lightfm.item_biases,
lightfm,
item_id,
lightfm.item_scale,
it_repr)
prediction = compute_prediction_from_repr(user_repr,
it_repr,
lightfm.no_components)
Expand Down
Loading