s444498/fuzzy-logic-movies
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engine update

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Mateusz Tylka 2023-01-25 14:39:25 +01:00
parent 62e91b264e
commit 08ee963d86
3 changed files with 60 additions and 57 deletions
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1
.gitignore vendored Normal file
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@ -0,0 +1 @@
__pycache__

32
engine.py
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@ -1,5 +1,7 @@
from simpful import *
def fuzzy_system(release_year_param, runtime_param, seasons_param, genres_param, emotions_param):
FS = FuzzySystem(show_banner=False)
# Define fuzzy sets for the variable
@ -52,22 +54,20 @@ FS.add_linguistic_variable("RECOMMENDATION",
LinguisticVariable([low_recommendation, medium_recommendation, high_recommendation],
universe_of_discourse=[0, 200]))
# RULES
RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMMENDATION IS low_recommendation)"
RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMMENDATION IS low_recommendation)"
RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMMENDATION IS medium_recommendation)"
RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMMENDATION IS medium_recommendation)"
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)"
# Z regułami trzeba eksperymentować, można porównywać ze scorem dla sprawdzania skuteczności
# # RULES
# RULE1 = "IF (RELEASE_YEAR IS older) AND (RUNTIME IS longer) AND (SEASONS IS more) THEN (RECOMMENDATION IS low_recommendation)"
# RULE2 = "IF (EMOTIONS IS different) AND (GENRES IS different) THEN (RECOMMENDATION IS low_recommendation)"
# RULE3 = "IF (RELEASE_YEAR IS newer) AND (RUNTIME IS similar) AND (SEASONS IS less) THEN (RECOMMENDATION IS medium_recommendation)"
# RULE4 = "IF (EMOTIONS IS similar) AND (GENRES IS similar) THEN (RECOMMENDATION IS medium_recommendation)"
# 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)"
# # Z regułami trzeba eksperymentować, można porównywać ze scorem dla sprawdzania skuteczności
# RULES
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)"
RULE2 = f"IF (NOT (EMOTIONS IS {emotions_param})) AND (NOT (GENRES IS {genres_param})) THEN (RECOMMENDATION IS low_recommendation)"
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)"
RULE4 = f"IF (EMOTIONS IS {emotions_param}) AND (GENRES IS {genres_param}) THEN (RECOMMENDATION IS medium_recommendation)"
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)"
FS.add_rules([RULE1, RULE2, RULE3, RULE4, RULE5])
# FS.set_variable("RELEASE_YEAR", -12.0)
# FS.set_variable("RUNTIME", -10.0)
# FS.set_variable("SEASONS", -2.0)
# FS.set_variable("GENRES", 50.0)
# FS.set_variable("EMOTIONS", 1.0)
#
# print(FS.inference(["RECOMMENDATION"]))
# FS.produce_figure(outputfile='visualize_terms.pdf')
return FS

4
main.py
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@ -16,7 +16,7 @@ from fastapi import FastAPI
from scipy.spatial.distance import cosine
from sklearn.preprocessing import MultiLabelBinarizer
from engine import FS
from engine import fuzzy_system
app = FastAPI()
data = pd.DataFrame()
@ -28,6 +28,8 @@ def inference(first: pandas.core.series.Series, second_id: str, df=None):
else:
second = data.loc[second_id]
FS = fuzzy_system(release_year_param='similar', runtime_param='similar', seasons_param='similar', genres_param='same', emotions_param='same')
year_diff = int(first['release_year'] - second['release_year'])
FS.set_variable('RELEASE_YEAR', year_diff)