engine update
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.gitignore
vendored
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.gitignore
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__pycache__
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engine.py
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engine.py
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from simpful import *
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FS = FuzzySystem(show_banner=False)
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def fuzzy_system(release_year_param, runtime_param, seasons_param, genres_param, emotions_param):
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# Define fuzzy sets for the variable
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# RELEASE_YEAR
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release_year_newer = TriangleFuzzySet(-20, -20, 0, term="newer")
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release_year_similar = TriangleFuzzySet(-20, 0, 20, term="similar")
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release_year_older = TriangleFuzzySet(0, 20, 20, term="older")
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FS = FuzzySystem(show_banner=False)
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FS.add_linguistic_variable("RELEASE_YEAR",
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# Define fuzzy sets for the variable
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# RELEASE_YEAR
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release_year_newer = TriangleFuzzySet(-20, -20, 0, term="newer")
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release_year_similar = TriangleFuzzySet(-20, 0, 20, term="similar")
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release_year_older = TriangleFuzzySet(0, 20, 20, term="older")
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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(-90, -90, 0, term="shorter")
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runtime_similar = TriangleFuzzySet(-90, 0, 90, term="similar")
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runtime_longer = TriangleFuzzySet(0, 90, 90, term="longer")
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# RUNTIME
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runtime_shorter = TriangleFuzzySet(-90, -90, 0, term="shorter")
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runtime_similar = TriangleFuzzySet(-90, 0, 90, term="similar")
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runtime_longer = TriangleFuzzySet(0, 90, 90, term="longer")
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FS.add_linguistic_variable("RUNTIME", LinguisticVariable([runtime_shorter, runtime_similar, runtime_longer],
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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(-5, -5, 0, term="less")
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seasons_similar = TriangleFuzzySet(-5, 0, 5, term="similar")
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seasons_more = TriangleFuzzySet(0, 5, 5, term="more")
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# SEASONS
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seasons_less = TriangleFuzzySet(-5, -5, 0, term="less")
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seasons_similar = TriangleFuzzySet(-5, 0, 5, term="similar")
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seasons_more = TriangleFuzzySet(0, 5, 5, term="more")
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FS.add_linguistic_variable("SEASONS", LinguisticVariable([seasons_less, seasons_similar, seasons_more],
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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(0, 0, 0.5, term="different")
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genres_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
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genres_same = TriangleFuzzySet(0.5, 1, 1, term="same")
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# GENRES
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genres_different = TriangleFuzzySet(0, 0, 0.5, term="different")
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genres_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
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genres_same = TriangleFuzzySet(0.5, 1, 1, term="same")
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FS.add_linguistic_variable("GENRES", LinguisticVariable([genres_different, genres_similar, genres_same],
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FS.add_linguistic_variable("GENRES", LinguisticVariable([genres_different, genres_similar, genres_same],
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universe_of_discourse=[0, 1]))
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# EMOTIONS
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emotions_different = TriangleFuzzySet(0, 0, 0.5, term="different")
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emotions_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
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emotions_same = TriangleFuzzySet(0.5, 1, 1, term="same")
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# EMOTIONS
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emotions_different = TriangleFuzzySet(0, 0, 0.5, term="different")
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emotions_similar = TriangleFuzzySet(0, 0.5, 1, term="similar")
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emotions_same = TriangleFuzzySet(0.5, 1, 1, term="same")
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FS.add_linguistic_variable("EMOTIONS", LinguisticVariable([emotions_different, emotions_similar, emotions_same],
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FS.add_linguistic_variable("EMOTIONS", LinguisticVariable([emotions_different, emotions_similar, emotions_same],
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universe_of_discourse=[0, 1]))
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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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# 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 (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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# # RULES
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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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# RULES
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RULE1 = f"IF (NOT (RELEASE_YEAR IS {release_year_param})) AND (NOT (RUNTIME IS {runtime_param})) AND (NOT (SEASONS IS {seasons_param})) THEN (RECOMMENDATION IS low_recommendation)"
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RULE2 = f"IF (NOT (EMOTIONS IS {emotions_param})) AND (NOT (GENRES IS {genres_param})) THEN (RECOMMENDATION IS low_recommendation)"
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RULE3 = f"IF (NOT (RELEASE_YEAR IS {release_year_param})) AND (RUNTIME IS {runtime_param}) AND (NOT (SEASONS IS {seasons_param})) THEN (RECOMMENDATION IS medium_recommendation)"
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RULE4 = f"IF (EMOTIONS IS {emotions_param}) AND (GENRES IS {genres_param}) THEN (RECOMMENDATION IS medium_recommendation)"
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RULE5 = f"IF (RELEASE_YEAR IS {release_year_param}) AND (RUNTIME IS {runtime_param}) AND (SEASONS IS {seasons_param}) AND (EMOTIONS IS {emotions_param}) AND (GENRES IS {genres_param}) THEN (RECOMMENDATION IS high_recommendation)"
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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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#
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# print(FS.inference(["RECOMMENDATION"]))
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# FS.produce_figure(outputfile='visualize_terms.pdf')
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return FS
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4
main.py
4
main.py
@ -16,7 +16,7 @@ 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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from engine import fuzzy_system
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app = FastAPI()
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data = pd.DataFrame()
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@ -28,6 +28,8 @@ def inference(first: pandas.core.series.Series, second_id: str, df=None):
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else:
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second = data.loc[second_id]
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FS = fuzzy_system(release_year_param='similar', runtime_param='similar', seasons_param='similar', genres_param='same', emotions_param='same')
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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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