api
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engine.py
56
engine.py
@ -2,64 +2,72 @@ from simpful import *
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FS = FuzzySystem()
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# Define fuzzy sets for the variable # Define fuzzy sets for the variable
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# Define fuzzy sets for the variable
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# RELEASE_YEAR
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release_year_newer = TriangleFuzzySet(-68, -68, 0, term="newer")
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release_year_similar = TriangleFuzzySet(-68, 0, 68, term="similar")
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release_year_older = TriangleFuzzySet(0, 68, 68, term="older")
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FS.add_linguistic_variable("RELEASE_YEAR", LinguisticVariable( [release_year_newer, release_year_similar, release_year_older], universe_of_discourse=[-136, 136] ))
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FS.add_linguistic_variable("RELEASE_YEAR",
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LinguisticVariable([release_year_newer, release_year_similar, release_year_older],
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universe_of_discourse=[-136, 136]))
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# RUNTIME
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runtime_shorter = TriangleFuzzySet(-238, -238, 0, term="shorter")
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runtime_similar = TriangleFuzzySet(-238, 0, 238, term="similar")
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runtime_longer = TriangleFuzzySet(0, 238, 238, term="longer")
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FS.add_linguistic_variable("RUNTIME", LinguisticVariable( [runtime_shorter, runtime_similar, runtime_longer], universe_of_discourse=[-476, 476] ))
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FS.add_linguistic_variable("RUNTIME", LinguisticVariable([runtime_shorter, runtime_similar, runtime_longer],
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universe_of_discourse=[-476, 476]))
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# SEASONS
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seasons_less = TriangleFuzzySet(-42, -42, 0, term="less")
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seasons_similar = TriangleFuzzySet(-42, 0, 42, term="similar")
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seasons_more = TriangleFuzzySet(0, 42, 42, term="more")
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FS.add_linguistic_variable("SEASONS", LinguisticVariable( [seasons_less, seasons_similar, seasons_more], universe_of_discourse=[-84, 84] ))
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FS.add_linguistic_variable("SEASONS", LinguisticVariable([seasons_less, seasons_similar, seasons_more],
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universe_of_discourse=[-84, 84]))
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# GENRES
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genres_different = TriangleFuzzySet(-100, -100, 0, term="different")
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genres_similar = TriangleFuzzySet(-100, 0, 100, term="similar")
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genres_same = TriangleFuzzySet(0, 100, 100, term="same")
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FS.add_linguistic_variable("GENRES", LinguisticVariable( [genres_different, genres_similar, genres_same], universe_of_discourse=[-200, 200] ))
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FS.add_linguistic_variable("GENRES", LinguisticVariable([genres_different, genres_similar, genres_same],
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universe_of_discourse=[-200, 200]))
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# EMOTIONS
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emotions_different = TriangleFuzzySet(-4, -4, 0, term="different")
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emotions_similar = TriangleFuzzySet(-4, 0, 4, term="similar")
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emotions_same = TriangleFuzzySet(0, 4, 4, term="same")
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FS.add_linguistic_variable("EMOTIONS", LinguisticVariable( [emotions_different, emotions_similar, emotions_same], universe_of_discourse=[-8, 8] ))
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FS.add_linguistic_variable("EMOTIONS", LinguisticVariable([emotions_different, emotions_similar, emotions_same],
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universe_of_discourse=[-8, 8]))
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# RECOMENDATION
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low_recomendation = TriangleFuzzySet(0, 0, 50, term="low_recomendation")
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medium_recomendation = TriangleFuzzySet(0, 50, 100, term="medium_recomendation")
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high_recomendation = TriangleFuzzySet(0, 100, 100, term="high_recomendation")
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FS.add_linguistic_variable("RECOMENDATION", LinguisticVariable( [low_recomendation, medium_recomendation, high_recomendation], universe_of_discourse=[0, 200] ))
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# RECOMMENDATION
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low_recommendation = TriangleFuzzySet(0, 0, 50, term="low_recommendation")
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medium_recommendation = TriangleFuzzySet(0, 50, 100, term="medium_recommendation")
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high_recommendation = TriangleFuzzySet(0, 100, 100, term="high_recommendation")
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FS.add_linguistic_variable("RECOMMENDATION",
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LinguisticVariable([low_recommendation, medium_recommendation, high_recommendation],
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universe_of_discourse=[0, 200]))
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# RULES
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RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMENDATION IS low_recomendation)"
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RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMENDATION IS low_recomendation)"
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RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMENDATION IS medium_recomendation)"
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RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMENDATION IS medium_recomendation)"
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RULE5 = "IF (RELEASE_YEAR IS similar) AND (RUNTIME IS similar) AND (SEASONS IS similar) AND (EMOTIONS IS same) AND (GENRES IS same) THEN (RECOMENDATION IS high_recomendation)"
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RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMMENDATION IS low_recommendation)"
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RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMMENDATION IS low_recommendation)"
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RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMMENDATION IS medium_recommendation)"
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RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMMENDATION IS medium_recommendation)"
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RULE5 = "IF (RELEASE_YEAR IS similar) AND (RUNTIME IS similar) AND (SEASONS IS similar) AND (EMOTIONS IS same) AND (GENRES IS same) THEN (RECOMMENDATION IS high_recommendation)"
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# Z regułami trzeba eksperymentować, można porównywać ze scorem dla sprawdzania skuteczności
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FS.add_rules([RULE1, RULE2, RULE3, RULE4, RULE5])
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FS.set_variable("RELEASE_YEAR", -12.0)
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FS.set_variable("RUNTIME", -10.0)
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FS.set_variable("SEASONS", -2.0)
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FS.set_variable("GENRES", 50.0)
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FS.set_variable("EMOTIONS", 1.0)
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# FS.set_variable("RELEASE_YEAR", -12.0)
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# FS.set_variable("RUNTIME", -10.0)
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# FS.set_variable("SEASONS", -2.0)
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# FS.set_variable("GENRES", 50.0)
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# FS.set_variable("EMOTIONS", 1.0)
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#
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# print(FS.inference(["RECOMMENDATION"]))
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print(FS.inference(["RECOMENDATION"]))
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FS.produce_figure(outputfile='visualize_terms.pdf')
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# FS.produce_figure(outputfile='visualize_terms.pdf')
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61
main.py
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61
main.py
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@ -0,0 +1,61 @@
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"""
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!pip install scikit-learn
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!pip install pandas
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!pip install fastapi
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!pip install "uvicorn[standard]"
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!uvicorn main:app --reload
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"""
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import numpy as np
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import pandas as pd
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from fastapi import FastAPI
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from scipy.spatial.distance import cosine
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from sklearn.preprocessing import MultiLabelBinarizer
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from engine import FS
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app = FastAPI()
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data = pd.DataFrame()
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@app.on_event('startup')
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async def startup_event():
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global data
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data = pd.read_csv('processed_data.csv', index_col='id', converters={'genres': pd.eval})
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all_genres = data.genres.explode().unique()
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mlb = MultiLabelBinarizer()
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mlb.fit([all_genres])
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data['genres'] = data['genres'].apply(lambda x: mlb.transform([x])[0])
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data['emotions'] = data[['Happy', 'Angry', 'Surprise', 'Sad', 'Fear']].values.tolist()
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@app.get('/score/{first_id}/{second_id}')
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def rec_score(first_id: str, second_id: str):
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try:
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first = data.loc[first_id]
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except KeyError:
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return {'error': f'{first_id} is not a valid id'}
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try:
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second = data.loc[second_id]
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except KeyError:
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return {'error': f'{second_id} is not a valid id'}
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year_diff = int(first['release_year'] - second['release_year'])
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FS.set_variable('RELEASE_YEAR', year_diff)
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runtime_diff = int(first['runtime'] - second['runtime'])
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FS.set_variable('RUNTIME', runtime_diff)
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if not (np.isnan(first['seasons']) or np.isnan(second['seasons'])):
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season_diff = int(first['seasons'] - second['seasons'])
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FS.set_variable('SEASONS', season_diff)
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else:
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FS.set_variable('SEASONS', 0)
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genre_diff = 1 - cosine(first['genres'], second['genres'])
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FS.set_variable('GENRES', genre_diff)
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emotion_diff = 1 - cosine(first['emotions'], second['emotions'])
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FS.set_variable('EMOTIONS', emotion_diff)
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return {'score': FS.inference(['RECOMMENDATION'])}
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