clean code adreco

algo/adreco.py

@@ -36,6 +36,29 @@ def movieDbToDf():
 
     return df
 
+def userDbToDf():
+    '''
+    This function convert a movie DataBase from mongoDB into a pandas DataFrame
+    '''
+    #load DB
+    client = MongoClient("mongodb://group3:GJF6cQqM4RLxBfNb@cs2022.lmichelin.fr:27017/group3?ssl=true")
+    db = client.group3
+    collection = db.users
+
+    #projection on useful data
+    cursor = collection.find({},{"_id":1, "liked_movies": 1, "update":1})
+    df=pd.DataFrame(list(cursor))
+
+    return df
+
+def loadRecDB():
+
+    #load DB
+    client = MongoClient("mongodb://group3:GJF6cQqM4RLxBfNb@cs2022.lmichelin.fr:27017/group3?ssl=true")
+    db = client.group3
+    collection = db['recommendations']
+    return collection
+
 def preFiltering(df,percent=90):
     '''
     This function removes movies who do not have enough votes to be evaluated
@@ -103,7 +126,6 @@ def index_from_id(df,id):
     '''
     return df[df['_id']==id].index.values[0]
 
-
 def recommendations(original_title, df, number_of_recommendations):
     
     #prefilter the dataframe
@@ -130,6 +152,10 @@ def recommendations(original_title, df, number_of_recommendations):
     return df['original_title'].iloc[recommendations_indices]
 
 def formatingFeatures(df_row):
+    """
+    This function creates a new column "features" in the df 
+    used to calculate similarities between users_profiles et movies
+    """
     g = []
     genres = []
     k=[]
@@ -150,21 +176,6 @@ def formatingFeatures(df_row):
 
     return ' '.join([genres]*w_genres)+' '+' '.join([keywords]*w_keywords)+' '+' '.join([str(df_row['main_actor'])]*w_actor)+' '+' '.join([str(df_row['director'])]*w_director)+' '+' '.join([str(df_row['release_date'])]*w_release_date)
 
-def userDbToDf():
-    '''
-    This function convert a movie DataBase from mongoDB into a pandas DataFrame
-    '''
-    #load DB
-    client = MongoClient("mongodb://group3:GJF6cQqM4RLxBfNb@cs2022.lmichelin.fr:27017/group3?ssl=true")
-    db = client.group3
-    collection = db.users
-
-    #projection on useful data
-    cursor = collection.find({},{"_id":1, "liked_movies": 1, "update":1})
-    df=pd.DataFrame(list(cursor))
-
-    return df
-
 def user_profile( user_index, moviesdf, usersdf, vectMatrix ):
     """
     This function creates a user profile based on the likef movies of the user 
@@ -200,15 +211,11 @@ def user_profile( user_index, moviesdf, usersdf, vectMatrix ):
     else:
         return [i for i in range(100)]
 
-def loadRecDB():
-
-    #load DB
-    client = MongoClient("mongodb://group3:GJF6cQqM4RLxBfNb@cs2022.lmichelin.fr:27017/group3?ssl=true")
-    db = client.group3
-    collection = db['recommendations']
-    return collection
-
 def updateDB():
+    """
+    This function update the recommandation DB based on the likes of thes users
+    """
+
 
     #loadDB
     moviesdf = movieDbToDf()