refresh model

recommendation-api/learner.py

@@ -1,25 +1,6 @@
 from fastai.collab import load_learner
 from fastai.tabular.all import *
 
-def create_params(size):
-    return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))
-
-class DotProductBias(Module):
-    def __init__(self, n_users, n_items, n_factors, y_range=(0, 1.5)):
-        super().__init__()
-        self.user_factors = create_params([n_users, n_factors])
-        self.user_bias = create_params([n_users])
-        self.item_factors = create_params([n_items, n_factors])
-        self.item_bias = create_params([n_items])
-        self.y_range = y_range
-
-    def forward(self, x):
-        users = self.user_factors[x[:, 0]]
-        items = self.item_factors[x[:, 1]]
-        res = (users * items).sum(dim=1)
-        res += self.user_bias[x[:, 0]] + self.item_bias[x[:, 1]]
-        return sigmoid_range(res, *self.y_range)
-
 def custom_accuracy(prediction, target):    
     # set all predictions above 0.95 as true positive (correct prediction)
     prediction = torch.where(prediction > 0.95, torch.tensor(1.0), prediction)

recommendation-api/model.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:469699967d2f8722c960d94ca566bd4401837cc76a9b60de70fe1141a15a87f2
-size 10578681
+oid sha256:e3ac0f088fb42f998622c2dbb6f9f060fa7fb9cfd0de1e66f876fa9aa95228ce
+size 9239534

recommendation-api/recommender.py

@@ -5,6 +5,7 @@ from tracks import get_unlistened_tracks_for_user, predictions_to_tracks
 
 def get_recommendations_for_user(learn: Learner, user_id: str, limit: int = 5):
     not_listened_tracks = get_unlistened_tracks_for_user(user_id)
+    print(len(not_listened_tracks))
 
     # Get predictions for the tracks user hasn't listened yet
     input_dataframe = pd.DataFrame({'user_id': [user_id] * len(not_listened_tracks), 'entry': not_listened_tracks})
@@ -15,6 +16,8 @@ def get_recommendations_for_user(learn: Learner, user_id: str, limit: int = 5):
     tracks_with_predictions = list(zip(not_listened_tracks, predictions[0].numpy()))
     tracks_with_predictions.sort(key=lambda x: x[1], reverse=True)
 
+    print(tracks_with_predictions[:limit])
+
     # Pick n and return as full tracks
     recommendations = predictions_to_tracks(tracks_with_predictions[:limit])