from __future__ import (absolute_import, division, print_function,
unicode_literals)
import pandas as pd
from surprise import NormalPredictor
from surprise import Dataset
from surprise import Reader
# Dummy algo
algo = NormalPredictor()
# Creation of the dataframe. Column names are irrelevant.
ratings_dict = {
'itemID': [1, 1, 1, 2, 2],
'userID': [9, 32, 2, 45, 'user_foo'],
'rating': [3, 2, 4, 3, 1]
}
df = pd.DataFrame(ratings_dict)
# A reader is still needed but only the rating_scale param is requiered.
reader = Reader(rating_scale=(1, 5))
# The columns must correspond to user id, item id and ratings (in that order).
data = Dataset.load_from_df(df[['userID', 'itemID', 'rating']], reader)
data.split(2) # data can now be used normally
for trainset, testset in data.folds():
algo.train(trainset)
algo.test(testset)
def compute_recommendations(user_id, prediction_table,
numeric_prediction_table):
algo = 'Normal Predictor'
algorithm = NormalPredictor()
# add_pageview(user_id=user_id, item_id=None, page="Model Predictions", activity_type="Initialize Predictions - " + algo, rating=None) #pageview
engine = create_engine(config.DB_URI, echo=True)
session = scoped_session(
sessionmaker(bind=engine, autocommit=False, autoflush=False))
#reading in the database
df_ratings = pd.read_sql('SELECT * FROM ratings;', con=engine)
df_ratings = df_ratings[['user_id', 'item_id', 'rating']]
df_ratings = df_ratings.dropna()
df_ratings = df_ratings.drop_duplicates()
df_ratings2 = pd.read_csv('data/ratings.csv', low_memory=False)
df_ratings2 = df_ratings2.rename(columns={'movie_id': 'item_id'})
df_ratings2 = df_ratings2[['user_id', 'item_id', 'rating']]
df_ratings2 = df_ratings2.dropna()
df_ratings2 = df_ratings2.drop_duplicates()
df_ratings = pd.concat([df_ratings, df_ratings2], axis=0)
reader = Reader(line_format='user item rating',
sep=',',
rating_scale=(1, 10))
data = Dataset.load_from_df(df_ratings, reader=reader)
trainset = data.build_full_trainset()
# algorithm = eval(algo + "()")# set the algorithm...............................................
algorithm.train(trainset)
items = pd.read_sql('SELECT distinct id FROM items;', con=engine)
df_user_items = df_ratings.loc[df_ratings['user_id'] == user_id]
total_items = items.id.unique()
user_items = df_user_items.item_id.unique()
# user_id = str(user_id)
prediction_items = [x for x in total_items if x not in user_items]
predictions = pd.DataFrame(columns=['user_id', 'item_id', 'prediction'])
predicted_ratings = []
for i in prediction_items:
a = user_id
b = i
est = algorithm.predict(a, b)
predicted_ratings.append(est[3])
predictions['item_id'] = prediction_items
predictions['user_id'] = pd.Series(
[user_id for x in range(len(predictions.index))],
index=predictions.index)
predictions['prediction'] = predicted_ratings
predictions = predictions.sort_values('prediction', ascending=False)
test_prediction = predictions
predictions = predictions.head(n=10)
cols = [
'pred_1', 'pred_2', 'pred_3', 'pred_4', 'pred_5', 'pred_6', 'pred_7',
'pred_8', 'pred_9', 'pred_10'
]
df_pred = predictions[['item_id']].T
df_pred.columns = cols
df_pred['id'] = user_id
df_pred = df_pred[[
'id', 'pred_1', 'pred_2', 'pred_3', 'pred_4', 'pred_5', 'pred_6',
'pred_7', 'pred_8', 'pred_9', 'pred_10'
]]
df_pred['id'] = df_pred['id'].astype(int)
df_pred.to_sql(prediction_table, engine, if_exists='append',
index=False) #if_exists='append'
session.commit()
df_num_ratings = test_prediction
df_num_ratings = df_num_ratings.head(n=20)
df_num_ratings['algorithm'] = algo
df_num_ratings.rename(columns={'prediction': 'predicted_rating'},
inplace=True)
df_num_ratings.to_sql('numeric_predictions',
engine,
if_exists='append',
index=False) #if_exists='append'
session.commit()
predcols = [
'num_1', 'num_2', 'num_3', 'num_4', 'num_5', 'num_6', 'num_7', 'num_8',
'num_9', 'num_10'
]
df_num_ratings_transpose = predictions[['prediction']].T
df_num_ratings_transpose.columns = predcols
df_num_ratings_transpose['id'] = user_id
df_num_ratings_transpose = df_num_ratings_transpose[[
'id', 'num_1', 'num_2', 'num_3', 'num_4', 'num_5', 'num_6', 'num_7',
'num_8', 'num_9', 'num_10'
]]
df_num_ratings_transpose['id'] = df_num_ratings_transpose['id'].astype(int)
df_num_ratings_transpose.to_sql(numeric_prediction_table,
engine,
if_exists='append',
index=False) #if_exists='append'
session.commit()
# test_set = pickle.load(f)
#with open('/Shared/bdagroup7/download/training_set.dat', "rb") as f:
# training_set = pickle.load(f)
# Learning options
sim_options = {'name': 'cosine', 'min_support': 50, 'user_based': True}
bsl_options = {'method': 'sgd', 'learning_rate': .0005}
# Algorithms (only select one)
#algo = SVD()
#algo = KNNBasic(k=10, min_k=8, sim_options=sim_options)
#algo = KNNWithMeans(k=15, min_k=5, sim_options=sim_options)
#algo = CoClustering()
#algo = SVDpp()
algo = NormalPredictor()
algo.train(training_set)
predictions = algo.test(test_set)
with open('/Shared/bdagroup7/download/predictions_normal.dat', "wb") as f:
pickle.dump(predictions, f)
# TODO: Ensemble
rmse = accuracy.rmse(predictions, verbose=True)
print("RMSE is: ")
print(rmse)
