recs

engine.py

@@ -4,45 +4,45 @@ FS = FuzzySystem()
 
 # Define fuzzy sets for the variable
 # RELEASE_YEAR
-release_year_newer = TriangleFuzzySet(-68, -68, 0, term="newer")
-release_year_similar = TriangleFuzzySet(-68, 0, 68, term="similar")
-release_year_older = TriangleFuzzySet(0, 68, 68, term="older")
+release_year_newer = TriangleFuzzySet(-20, -20, 0, term="newer")
+release_year_similar = TriangleFuzzySet(-20, 0, 20, term="similar")
+release_year_older = TriangleFuzzySet(0, 20, 20, term="older")
 
 FS.add_linguistic_variable("RELEASE_YEAR",
                            LinguisticVariable([release_year_newer, release_year_similar, release_year_older],
                                               universe_of_discourse=[-136, 136]))
 
 # RUNTIME
-runtime_shorter = TriangleFuzzySet(-238, -238, 0, term="shorter")
-runtime_similar = TriangleFuzzySet(-238, 0, 238, term="similar")
-runtime_longer = TriangleFuzzySet(0, 238, 238, term="longer")
+runtime_shorter = TriangleFuzzySet(-90, -90, 0, term="shorter")
+runtime_similar = TriangleFuzzySet(-90, 0, 90, term="similar")
+runtime_longer = TriangleFuzzySet(0, 90, 90, term="longer")
 
 FS.add_linguistic_variable("RUNTIME", LinguisticVariable([runtime_shorter, runtime_similar, runtime_longer],
                                                          universe_of_discourse=[-476, 476]))
 
 # SEASONS
-seasons_less = TriangleFuzzySet(-42, -42, 0, term="less")
-seasons_similar = TriangleFuzzySet(-42, 0, 42, term="similar")
-seasons_more = TriangleFuzzySet(0, 42, 42, term="more")
+seasons_less = TriangleFuzzySet(-5, -5, 0, term="less")
+seasons_similar = TriangleFuzzySet(-5, 0, 5, term="similar")
+seasons_more = TriangleFuzzySet(0, 5, 5, term="more")
 
 FS.add_linguistic_variable("SEASONS", LinguisticVariable([seasons_less, seasons_similar, seasons_more],
                                                          universe_of_discourse=[-84, 84]))
 
 # GENRES
-genres_different = TriangleFuzzySet(-100, -100, 0, term="different")
-genres_similar = TriangleFuzzySet(-100, 0, 100, term="similar")
-genres_same = TriangleFuzzySet(0, 100, 100, term="same")
+genres_different = TriangleFuzzySet(0, 0, 0.5, term="different")
+genres_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
+genres_same = TriangleFuzzySet(0.5, 1, 1, term="same")
 
 FS.add_linguistic_variable("GENRES", LinguisticVariable([genres_different, genres_similar, genres_same],
-                                                        universe_of_discourse=[-200, 200]))
+                                                        universe_of_discourse=[0, 1]))
 
 # EMOTIONS
-emotions_different = TriangleFuzzySet(-4, -4, 0, term="different")
-emotions_similar = TriangleFuzzySet(-4, 0, 4, term="similar")
-emotions_same = TriangleFuzzySet(0, 4, 4, term="same")
+emotions_different = TriangleFuzzySet(0, 0, 0.5, term="different")
+emotions_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
+emotions_same = TriangleFuzzySet(0.5, 1, 1, term="same")
 
 FS.add_linguistic_variable("EMOTIONS", LinguisticVariable([emotions_different, emotions_similar, emotions_same],
-                                                          universe_of_discourse=[-8, 8]))
+                                                          universe_of_discourse=[0, 1]))
 
 # RECOMMENDATION
 low_recommendation = TriangleFuzzySet(0, 0, 50, term="low_recommendation")

main.py

@@ -18,6 +18,32 @@ app = FastAPI()
 data = pd.DataFrame()
 
 
+def inference(first_id: str, second_id: str):
+    first = data.loc[first_id]
+    second = data.loc[second_id]
+
+    year_diff = int(first['release_year'] - second['release_year'])
+    FS.set_variable('RELEASE_YEAR', year_diff)
+
+    runtime_diff = int(first['runtime'] - second['runtime'])
+    FS.set_variable('RUNTIME', runtime_diff)
+
+    if not (np.isnan(first['seasons']) or np.isnan(second['seasons'])):
+        season_diff = int(first['seasons'] - second['seasons'])
+        FS.set_variable('SEASONS', season_diff)
+    else:
+        FS.set_variable('SEASONS', 0)
+
+    genre_diff = 1 - cosine(first['genres'], second['genres'])
+    FS.set_variable('GENRES', genre_diff)
+
+    emotion_diff = 1 - cosine(first['emotions'], second['emotions'])
+    FS.set_variable('EMOTIONS', emotion_diff)
+
+    return FS.inference(['RECOMMENDATION'])
+
+
+
 @app.on_event('startup')
 async def startup_event():
     global data
@@ -40,22 +66,23 @@ def rec_score(first_id: str, second_id: str):
     except KeyError:
         return {'error': f'{second_id} is not a valid id'}
 
-    year_diff = int(first['release_year'] - second['release_year'])
-    FS.set_variable('RELEASE_YEAR', year_diff)
+    return inference(first_id, second_id)
 
-    runtime_diff = int(first['runtime'] - second['runtime'])
-    FS.set_variable('RUNTIME', runtime_diff)
 
-    if not (np.isnan(first['seasons']) or np.isnan(second['seasons'])):
-        season_diff = int(first['seasons'] - second['seasons'])
-        FS.set_variable('SEASONS', season_diff)
-    else:
-        FS.set_variable('SEASONS', 0)
+@app.get('/recs/{production_id}')
+async def recs(production_id: str, count: int | None):
+    try:
+        first = data.loc[production_id]
+    except KeyError:
+        return {'error': f'{production_id} is not a valid id'}
 
-    genre_diff = 1 - cosine(first['genres'], second['genres'])
-    FS.set_variable('GENRES', genre_diff)
+    scores = []
 
-    emotion_diff = 1 - cosine(first['emotions'], second['emotions'])
-    FS.set_variable('EMOTIONS', emotion_diff)
+    for index, row in data.iterrows():
+        if str(index) == production_id:
+            continue
+        scores.append((index, inference(production_id, str(index))['RECOMMENDATION']))
 
-    return FS.inference(['RECOMMENDATION'])
+    scores = [idx[0] for idx in sorted(scores, key=lambda x: x[1], reverse=True)[:count]]
+
+    return list(scores)