def cluster(data, kwargs):
# Set algorithm
cluster_u = kwargs.get('user_cluster')
cluster_i = kwargs.get('item_cluster')
n_epochs = kwargs.get('maxiter')
# Set algorithm
algo = CoClustering(n_cltr_u = cluster_u[0], n_cltr_i = cluster_i[0],
n_epochs = n_epochs , random_state = kwargs['random_seed'] )
# Train the algorithm on the data, and predict ratings for the testset
algo.fit(data)
prediction = np.zeros([10000,1000])
for row in range(10000):
for col in range(1000):
prediction[row,col] = algo.predict(str(row+1),str(col+1)).est
return prediction
def computeCoClustering(data, test_np):
"""Compute the co-clustering method and return the predictions on the test
The method has the following parameter:
- Number of user clusters : 2
- Number of item clusters : 19
- Number of epochs: 30
data : data frame which represent the train set
test_np : data frame on which the prediction will be returned
return : test_np with a column of prediction named 'cocluster_rating'"""
trainset, test = dataTrainSurprise(data, test_np)
cocltr_algo = CoClustering(n_cltr_u=2, n_cltr_i=19, n_epochs = 30).fit(trainset)
test['cocluster_rating'] = test[['user_id', 'movie_id']] \
.apply(lambda row: cocltr_algo.predict(row['user_id'], row['movie_id'])[3], axis=1)
return test
def co_clustering(train, test, ids, Xtest, Xids):
"""
Co-clustering algorithm, users and items assigned to clusters and co_clusters
Argument : train, the trainset
test, the testset
ids, unknown ratings
Xtest, predicted ratings for testset, to be used for final blending
Xids, predicted ratings for unknown ratings, to be used for final blending
"""
print('Co-clustering')
algo = CoClustering(n_cltr_u=1, n_cltr_i=1, n_epochs=50, random_state=15)
#Train algorithm on training set
algo.fit(train)
#Predict on train and compute RMSE
predictions = algo.test(train.build_testset())
print(' Training RMSE: ', accuracy.rmse(predictions, verbose=False))
#Predict on test and compute RMSE
predictions = algo.test(test)
rmse = accuracy.rmse(predictions, verbose=False)
print(' Test RMSE: ', rmse)
preds_test = np.zeros(len(predictions))
for j, pred in enumerate(predictions):
preds_test[j] = pred.est
#Predict unknown ratings
preds_ids = []
for i in range(len(ids[0])):
pred = algo.predict(str(ids[0][i]), str(ids[1][i]))
preds_ids.append(pred.est)
Xtest.append(preds_test)
Xids.append(preds_ids)
return rmse, Xtest, Xids, preds_test, preds_ids
# Use the famous SVD algorithm.
algo = CoClustering()
# Run 5-fold cross-validation and print results.
#cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)
algo.fit(trainset)
'''
predictions = algo.test(testset)
print(predictions)
'''
'''
uid = str(0) # raw user id (as in the ratings file). They are **strings**!
iid = str(35546) # raw item id (as in the ratings file). They are **strings**!
# get a prediction for specific users and items.
pred = algo.predict(uid, iid, verbose=True)
print(pred[3])
'''
testset = open("../test1.csv", "r")
result = open("./result/result_CoClustering.txt", "w")
full_result = open("./full_result/result_CoClustering.txt", "w")
for line in testset:
temp = line.split(",")
pred = algo.predict(temp[0], temp[1], verbose=True)
score = round(pred[3])
#print()
result.write(str(score) + "\n")
full_result.write(str(pred[3]) + "\n")
duplicates = ratings.duplicated()
duplicates = duplicates[duplicates == True]
columnsTitles=["user_id","book_id","rating"]
ratings = ratings.reindex(columns=columnsTitles)
reader = Reader(rating_scale=(1, 5))
data = Dataset.load_from_df(ratings[['user_id', 'book_id', 'rating']], reader)
algo = CoClustering()
start = time.time()
cross_validate(algo, data, measures=['RMSE'], cv=10, verbose=True)
cv_time = str(datetime.timedelta(seconds=int(time.time() - start)))
pred = algo.predict(7563, 1, r_ui=4, verbose=True)
from collections import defaultdict
def get_top_k(predictions, k):
'''Return a top_k dicts where keys are user ids and values are lists of
tuples [(item id, rating estimation) ...].
Takes in a list of predictions as returned by the test method.
'''
# First map the predictions to each user.
top_k = defaultdict(list)
for uid, iid, true_r, est, _ in predictions:
top_k[uid].append((iid, est))
class Surprise():
def train(self, algo='SVD', like=True, test='cv', local=False):
if local:
csv_path = os.path.join(os.path.dirname(__file__),
"data/preprocessed")
self.recipes = pd.read_csv(f"{csv_path}/recipe_pp.csv")
self.reviews = pd.read_csv(f"{csv_path}/review_pp.csv")
else:
self.recipes = storage.import_file('data/preprocessed',
'recipe_pp.csv')
self.reviews = storage.import_file('data/preprocessed',
'review_pp.csv')
if like:
self.target = 'liked'
self.s_min = 0
self.s_max = 1
else:
self.target = 'rating'
self.s_min = 1
self.s_max = 5
reader = Reader(rating_scale=(self.s_min, self.s_max))
self.relevant_data = self.reviews[[
'user_id', 'recipe_id', self.target
]]
model_data = Dataset.load_from_df(self.relevant_data, reader)
# Algos
if 'NormalPredictor':
self.algorithm = NormalPredictor()
elif 'BaselineOnly':
self.algorithm = BaselineOnly()
elif 'KNNBasic':
self.algorithm = KNNBasic()
elif 'KNNWithMeans':
self.algorithm = KNNWithMeans()
elif 'KNNWithZScore':
self.algorithm = KNNWithZScore()
elif 'KNNBaseline':
self.algorithm = KNNBaseline()
elif 'SVD':
params = {
'n_epochs': 20,
'n_factors': 100,
'lr_all': 0.002,
'reg_all': 0.02
}
self.algorithm = SVD(params) # Tuned with svd_grid
elif 'SVDpp':
self.algorithm = SVDpp()
elif 'NMF':
self.algorithm = NMF()
elif 'SlopeOne':
self.algorithm = SlopeOne()
elif 'CoClustering':
self.algorithm = CoClustering()
if test == 'cv':
cv_results = cross_validate(self.algorithm,
model_data,
measures=['RMSE', 'MAE'],
cv=5,
verbose=True)
rmse = np.round(cv_results['test_rmse'].mean(), 3)
mae = np.round(cv_results['test_mae'].mean(), 3)
train_data = model_data.build_full_trainset()
self.algorithm.fit(train_data)
elif test == 'svd_grid':
param_grid = {
'n_epochs': [10, 20],
'n_factors': [100, 200],
'lr_all': [0.001, 0.002],
'reg_all': [0.01, 0.02]
}
train_data = model_data.build_full_trainset()
gs = GridSearchCV(SVD, param_grid, measures=['rmse', 'mae'], cv=3)
gs.fit(model_data)
rmse = gs.best_score['rmse']
mae = gs.best_score['mae']
print(gs.best_params['rmse'], gs.best_params['mae'])
self.algorithm = gs.best_estimator['rmse']
train_data = model_data.build_full_trainset()
self.algorithm.fit(train_data)
else:
train, test = train_test_split(model_data,
test_size=0.3,
random_state=42)
self.algorithm.fit(train)
predictions = self.algorithm.test(test)
rmse = np.round(accuracy.rmse(predictions), 3)
mae = np.round(accuracy.mae(predictions), 3)
return rmse, mae
def predict(self, user_id):
inputs = self.relevant_data[self.relevant_data['user_id'] == user_id] \
.merge(self.recipes, on="recipe_id", how="left")[['recipe_id', 'name', self.target]]
display(inputs)
user_recipes = self.relevant_data[self.relevant_data['user_id'] ==
user_id].recipe_id.unique()
recipe_list = self.relevant_data[
self.relevant_data['user_id'] != user_id].recipe_id.unique()
predictions = [
self.algorithm.predict(user_id, rec) for rec in recipe_list
if rec not in list(user_recipes)
]
pdf = pd.DataFrame(predictions,
columns=[
'user_id', 'recipe_id', self.target,
f'rec_{self.target}', 'details'
])
pdf = pdf.drop(columns=[self.target, 'details'])
pdf = pdf.sort_values(f'rec_{self.target}', ascending=False)
rec_target = pdf[f'rec_{self.target}']
pdf['rec_score'] = (rec_target - self.s_min) / (self.s_max -
self.s_min)
outputs = pdf.merge(self.recipes, on="recipe_id", how="left")[[
'recipe_id', 'name', f'rec_{self.target}', 'rec_score'
]]
display(outputs.head(10))
return outputs
# 'n_epochs': [10,20,30,40,50,60,70,80,90,100]}
# Evaluate the model with 5-fold cross validation
#data.split(5)
#grid_search = GridSearch(CoClustering, param_grid, measures=['RMSE'])
#grid_search.evaluate(data)
#print ("after grid_search.evaluate(data)")
#print_perf(perf)
#results_df = pd.DataFrame.from_dict(grid_search.cv_results)
#print(results_df) """
# create a co-clustering algorithm
algo = CoClustering(n_cltr_u=3, n_cltr_i=3, n_epochs=100)
algo.train(trainset)
# use the trained algorithm to predict ratings for every user in the test set
f = open('testOutput.csv', 'w')
f.write("test_id,rating\n")
for i in range(len(dftest)):
prediction = algo.predict(dftest.at[i, 'user_id'],
dftest.at[i, 'business_id'],
r_ui=4,
verbose=True)
predRating = prediction.est
f.write(str(i) + "," + str(predRating) + '\n')
f.close()