def update_recommendation(user_id):
sim_matrix = pd.read_csv("data/sim_matrix.csv")
# Create a placeholder items for closes neighbours to an item
#Specifying how many neighbours to return
no__neighbours = 7
data_neighbours = pd.DataFrame(index=sim_matrix.columns,
columns=[range(no__neighbours)])
# Loop through our similarity dataframe and fill in neighbouring item names
for i in range(0,len(sim_matrix.columns)):
data_neighbours.iloc[i,:no__neighbours] = \
sim_matrix.iloc[0:,i].order(ascending=False)[:no__neighbours].index
data_neighbours = data_neighbours.drop(0, 1)
# --- Start User Based Recommendations --- #
# Helper function to get similarity scores
def getScore(history, similarities):
return sum(history*similarities.values)/sum(similarities)
#addratings #test user = 101
user_used = user_id
users_ratings = pd.DataFrame(list(UserRating.objects.filter(user=user_used).values()))
ratings = pd.DataFrame(list(Rating.objects.all().values()))
ratings.columns = ['average', 'content_type_id', 'count',
'rating_id', 'object_id', 'total']
all_data = users_ratings.merge(ratings, on = 'rating_id', how = 'outer')
all_data['user_id'] = user_used
all_data_r = all_data.pivot(index='user_id', columns='object_id',
values='score')
# fill NANs with 0's
all_data_r = all_data_r.fillna(0)
data = all_data_r
# Create a place holder matrix for similarities, and fill in the user name column
data_sims = pd.DataFrame(index=[1],
columns=all_data_r.columns)
#end ratings
#user = data_sims.index[0]
#Loop through all rows, skip the user column, and fill with similarity scores
for j in range(0,len(data_sims.columns)):
product = data_sims.columns[j]
if data.iloc[0,j] > 0:
data_sims.iloc[0,j] = 0
else:
product_top_names = data_neighbours.loc[str(product)]
product_top_sims = sim_matrix.ix[product].drop(['object_id',str(product)]) \
.order(ascending=False)[0:no__neighbours-1]
user_purchases = data.ix[user_used,product_top_names]
#data_sims.iloc[0,j] = getScore(user_purchases,product_top_sims)
data_sims.iloc[0,j] = getScore(user_purchases,product_top_sims)
## subjects to recommend
subs_to_rec = 3
column_names = flatten(['user_id', map(str, range(1,subs_to_rec+1) ) ])
# Get the top-3 subjects
data_recommend = pd.DataFrame(index=data_sims.index,
columns = column_names)
data_recommend['user_id'] = user_used
#data_recommend.ix[0:,0] = data_sims.ix[:,0]
# Instead of top song scores, we want to see IDs
data_recommend.iloc[0,1:subs_to_rec+1] = \
data_sims.iloc[0].order(ascending=False).iloc[0:subs_to_rec].index.transpose()
# Update Recommendation
Recommendation.objects.filter(user = user_used).delete()
new_rec = Recommendation(user_id = user_used)
new_rec.save()
for i in range(1, subs_to_rec+1):
new_rec.Subjects.add(Subject.objects.get(id =data_recommend.iloc[0,i] ) )
#new_rec.Subjects.add(Subject.objects.filter(id__in=data_recommend.iloc[0,1:subs_to_rec+1]))
# Translate IDs to Names
#data_recommend_fn = pd.melt(data_recommend, id_vars=['user_id'])
#
#subjects_names = pd.DataFrame(index=data_sims.index,
# columns=['1','2','3'])
#
#for i in range(0,len(subjects_names)):
# subjects_names.iloc
## Print a sample
#print data_recommend
#
#data_recommend
#end
# Get the top-3 subjects
data_recommend = pd.DataFrame(index=data_sims.index,
columns = column_names)
data_recommend['user_id'] = user_used
#data_recommend.ix[0:,0] = data_sims.ix[:,0]
# Instead of top song scores, we want to see IDs
data_recommend.iloc[0,1:subs_to_rec+1] = \
data_sims.iloc[0].order(ascending=False).iloc[0:subs_to_rec].index.transpose()
# Update Recommendation
Recommendation.objects.filter(user = user_used).delete()
new_rec = Recommendation(user_id = user_used)
new_rec.save()
for i in range(1, subs_to_rec+1):
new_rec.Subjects.add(Subject.objects.get(id =data_recommend.iloc[0,i] ) )
#new_rec.Subjects.add(Subject.objects.filter(id__in=data_recommend.iloc[0,1:subs_to_rec+1]))
# Translate IDs to Names
#data_recommend_fn = pd.melt(data_recommend, id_vars=['user_id'])
#
#subjects_names = pd.DataFrame(index=data_sims.index,
# columns=['1','2','3'])
