calculsIA fournis un modele de regression qui fonctionne, il faut appeler clf.predict() avec une liste de 4 acteurs et ça marche

calculsIA.py

@@ -0,0 +1,42 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn as sk
+
+#dfRatingsTropGrand = pd.read_csv("processedData/actorsRatingsPerMovie.tsv",sep='\t')
+#tconst	ratings	actorNames	averageRatingMovie
+
+#dfRatings = dfRatingsTropGrand[dfRatingsTropGrand['ratings'].apply(lambda x: len(eval(x)) >= 4)]
+
+#dfRatings.to_csv("processedData/actorsRatingsPerMovieGoodToUse.tsv", index=False, sep="\t")
+dfRatings = pd.read_csv("processedData/actorsRatingsPerMovieGoodToUse.tsv", sep="\t")
+
+#listMovies = dfRatings.head(1000)['tconst'].values
+listMovies = dfRatings['tconst'].values
+
+listRatingsA = []
+listRatingsM = []
+datas = []
+nbDiese = 0
+for i in range(len(listMovies)):
+	print(i/len(listMovies)*100,"%", end="\r")
+	film = listMovies[i]
+	bob = (dfRatings.averageRatingMovie.loc[dfRatings.tconst == film].values[0],eval(dfRatings.ratings.loc[dfRatings.tconst == film].values[0]))
+	listRatingsA.append(bob[1][:4])
+	listRatingsM.append(bob[0])
+print("")
+
+from sklearn.model_selection import train_test_split
+
+x=listRatingsA
+y=listRatingsM
+
+xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.3)
+
+xtrain = np.array(xtrain)
+from sklearn.linear_model import LinearRegression
+
+lnrg = LinearRegression()
+
+#clf = lnrg.fit(xtrain,ytrain)
+clf = lnrg.fit(x,y)

getRatingsActeur.py

@@ -93,10 +93,14 @@ def data_manipulation(DataIsLoaded = None):
         'actorNames': grouped['primaryName'],
         'averageRatingMovie': grouped['averageRating']  # Ajoute 'averageRating' comme 'averageRatingMovie'
     })
-
+    
     actorsRatingsPerMovie.to_csv("processedData/actorsRatingsPerMovie.tsv", index=False, sep="\t")
 
-    
+
+    moviesWhereNbActorsAtLeastFour = actorsRatingsPerMovie[actorsRatingsPerMovie['ratings'].apply(lambda x: len(eval(x)) >= 4)]
+
+    moviesWhereNbActorsAtLeastFour.to_csv("processedData/actorsRatingsPerMovieGoodToUse.tsv", index=False, sep="\t")
+
 
     # actorsRatingsPerMovie = pd.DataFrame(list(allActorRatingsMovies.items()), columns=['tconst', 'ratings'])
     # actorsRatingsPerMovie.to_csv("actorsRatingsPerMovie.tsv", index=False, sep="\t")