def nmf_ratings_predicate(observed_ratings_df,
truth_ratings_df,
fold='0',
phase='eval'):
"""
nmf_ratings Predicates
"""
print("NMF predicates")
nmf_model = NMF()
reader = Reader(rating_scale=(0.2, 1))
train_dataset = Dataset.load_from_df(df=observed_ratings_df.reset_index(
).loc[:, ['userId', 'movieId', 'rating']],
reader=reader)
nmf_model.fit(train_dataset.build_full_trainset())
# make predictions
predictions = pd.DataFrame(index=truth_ratings_df.index,
columns=['rating'])
for row in truth_ratings_df.loc[:, ['rating']].iterrows():
uid = row[0][0]
iid = row[0][1]
predictions.loc[(uid, iid), 'rating'] = nmf_model.predict(uid, iid).est
write(predictions, 'nmf_rating_obs', fold, phase)
def nmf_algorithm() -> NMF:
user_input = input(
'Do you want to continue with the default parameters? Y/N')
if user_input.lower() == 'y':
return NMF()
else:
n_factors = int(input('Enter total number of factors: '))
n_epochs = int(input('Enter number of epochs: '))
return NMF(n_factors, n_epochs)
def nmf_compute_high_var_trim_rmse(k):
nmf = NMF(n_factors=k, random_state=42)
rmse = []
for trainset, testset in KFold(n_splits=10, random_state=42).split(R):
nmf.fit(trainset)
testset_trimmed = high_variance_trimming(testset, frequency, variance)
pred = nmf.test(testset_trimmed)
rmse.append(accuracy.rmse(pred, verbose=False))
print('k: %s | RMSE: %f' % (k, np.mean(rmse)))
return np.mean(rmse)
def nmf_compute_prec_rec(t):
precision, recall = [], []
for trainset, testset in KFold(n_splits=10, random_state=42).split(R):
nmf = NMF(n_factors=nmf_best_k, random_state=42)
nmf.fit(trainset)
trimmed_testset = trim_unpopular_user(testset, t, threshold)
pred = nmf.test(trimmed_testset)
precision_dict, recall_dict = calculate_precision_recall(
pred, t, threshold)
precision.append(np.mean([prec for prec in precision_dict.values()]))
recall.append(np.mean([rec for rec in recall_dict.values()]))
return np.mean(precision), np.mean(recall)
def Q23(col=0):
print('Chosen column is ' + str(col))
data = np.loadtxt('ml-latest-small/ratings.csv',
delimiter=',',
skiprows=1,
usecols=(0, 1, 2))
row_userId = data[:, :1].astype(int)
row_movieId = data[:, 1:2].astype(int)
row_rating = data[:, 2:3]
sortedId = np.sort(row_movieId.transpose()[0])
m = {}
idx = 0
last = None
for i in sortedId.tolist():
if i != last:
m[i] = idx
idx += 1
last = i
data = load_data()
model = NMF(n_factors=20)
trainset, testset = train_test_split(data, test_size=0.0001)
model.fit(trainset)
U = model.pu
V = model.qi
import csv
dict_ID_to_genre = {}
with open('ml-latest-small/movies.csv', 'rt') as csvfile:
reader = csv.reader(csvfile, delimiter=',', quotechar='|')
cnt = 0
for row in reader:
if cnt != 0:
dict_ID_to_genre[row[0]] = row[1:]
cnt += 1
dict_col_to_ID = {}
for key in m:
dict_col_to_ID[m[key]] = key
V_col = V[:, col]
V_col_sort_top10 = np.sort(V_col)[::-1][:10]
V_col_list = V_col.tolist()
for val in V_col_sort_top10:
ind = V_col_list.index(val)
m_id = dict_col_to_ID[ind]
genre = dict_ID_to_genre[str(m_id)]
print(genre[-1])
def NMF_trim_filter(ratings, dims, func, mv_dict):
reader = Reader(rating_scale=(0.0, 5.0))
data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']],
reader)
RMSE = np.empty([len(dims)])
MAE = np.empty([len(dims)])
min_RMSE = False
min_MAE = False
fac_num_RMSE = 0
fac_num_MAE = 0
kf = KFold(n_splits=10, random_state=42)
for k in range(len(dims)):
nmf = NMF(n_factors=dims[k], random_state=42)
test_rmse = np.array([])
test_mae = np.array([])
for trainset, testset in kf.split(data):
nmf.fit(trainset)
full_data = trainset.build_testset() + testset
func(mv_dict, testset)
pred = nmf.test(testset)
test_rmse = np.append(test_rmse, accuracy.rmse(pred,
verbose=False))
test_mae = np.append(test_mae, accuracy.mae(pred, verbose=False))
RMSE[k] = np.mean(test_rmse)
if ((not min_RMSE) or RMSE[k] < min_RMSE):
min_RMSE = RMSE[k]
fac_num_RMSE = dims[k]
MAE[k] = np.mean(test_mae)
if ((not min_MAE) or MAE[k] < min_MAE):
min_MAE = MAE[k]
fac_num_MAE = dims[k]
print('For k = %i :' % dims[k])
print('RMSE: ', RMSE[k])
print('MAE: ', MAE[k])
plt.plot(dims, RMSE)
plt.plot(dims, MAE)
plt.legend(['RMSE', 'MAE'])
plt.show()
print('Finishing Plotting...')
print('For RMSE:')
print('\t---Optimal number of latent factors is ', fac_num_RMSE)
print('\t---Minumun Average RMSE is ', min_RMSE)
print('\nFor MAE:')
print('\t---Optimal number of latent factors is ', fac_num_MAE)
print('\t---Minumun Average MAE is ', min_MAE)
def slot_select_algo_combobox(self):
self.algo_change_flag=True
self.algo_trained_flag=False
algo_name=self.select_algo_comboBox.currentText()
if algo_name=='SVD':
self.algo=SVD()
self.display_process_label.append('加载SVD模型...')
elif algo_name=='SVD++':
self.algo = SVDpp()
self.display_process_label.append('加载SVD++模型...')
elif algo_name == 'NMF':
self.algo = NMF()
self.display_process_label.append('加载NMF模型...')
elif algo_name == 'Slope One':
self.algo = SlopeOne()
self.display_process_label.append('加载Slope One模型...')
elif algo_name == 'k-NN':
self.algo = KNNBasic()
self.display_process_label.append('加载k-NN模型...')
elif algo_name == 'Centered k-NN':
self.algo = KNNWithMeans()
self.display_process_label.append('加载Centered k-NN模型...')
elif algo_name == 'k-NN Baseline':
self.algo = KNNBaseline()
self.display_process_label.append('加载k-NN Baseline模型...')
elif algo_name == 'Co-Clustering':
self.algo = CoClustering()
self.display_process_label.append('加载Co-Clustering模型...')
elif algo_name == 'Baseline':
self.algo = BaselineOnly()
self.display_process_label.append('加载Baseline模型...')
elif algo_name == 'Random':
self.algo = NormalPredictor()
self.display_process_label.append('加载Random模型...')
def Q15and22and29(qNum, bestK, thres=[2.5, 3, 3.5, 4]):
range = 5.0
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
data = load_data()
trainset, testset = train_test_split(data, test_size=0.1)
if qNum == 15:
model = KNNWithMeans(bestK, sim_options=sim_options)
elif qNum == 22:
model = NMF(n_factors=bestK)
else:
model = SVD(n_factors=bestK)
model.fit(trainset)
pred = model.test(testset)
for thrs in thres:
np_true = np.array([])
np_score = np.array([])
for u, i, t, p, d in pred:
if t >= thrs:
t = 1
else:
t = 0
np_true = np.append(np_true, t)
np_score = np.append(np_score, p / range)
title = 'Threshold ' + str(thrs)
plot_ROC(np_true, np_score, title=title)
def problem_17_rmse_mae_full_dataset():
x_axis = range(2, 52, 2)
dim = len(x_axis)
rmse_test_store = np.zeros(dim)
mae_test_store = np.zeros(dim)
for i in x_axis:
algo = NMF(i, verbose=False) # i = number of latent factors
result = cross_validate(algo,
data,
measures=['rmse', 'mae'],
cv=10,
verbose=True)
rmse_score = np.mean(result['test_rmse'])
mae_score = np.mean(result['test_mae'])
##################### Index to store values in rmse and mae ###################
ind = int(i / 2 - 1)
rmse_test_store[ind] = rmse_score
mae_test_store[ind] = mae_score
pd.DataFrame(rmse_test_store).to_csv("rmse_test_store_10.csv")
pd.DataFrame(mae_test_store).to_csv("mae_test_store_10.csv")
plotgraphs(x_axis, rmse_test_store, 'K', 'Mean rmse scores', 'Plot',
'q17_rmse.png')
plotgraphs(x_axis, mae_test_store, 'K', 'Mean Mae scores', 'Plot',
'q17_Mae.png')
def rank_predictions(model_name):
k_KNN = 22
k_NNMF = 20
k_MF = 26
if model_name == 'KNN':
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0
}
model = KNNWithMeans(k_KNN, sim_options=sim_options)
elif model_name == 'NNMF':
model = NMF(n_factors= k_NNMF)
else:
model = SVD(n_factors = k_MF)
precision_arr = []
recall_arr = []
for t in range (1,26):
kf = KFold(n_splits=10)
print(t)
p = []
r = []
for trainSet, testSet in kf.split(data):
model.fit(trainSet)
predictions = model.test(testSet)
precisions, recalls = precision_recall (predictions, t)
p.append(sum(prec for prec in precisions.values()) / len(precisions))
r.append(sum(rec for rec in recalls.values()) / len(recalls))
precision_arr.append(np.mean(np.array(p)))
recall_arr.append(np.mean(np.array(r)))
# precision vs t
plt.plot(list(range (1,26)), precision_arr)
plt.xlabel("Size")
plt.ylabel("Precision")
plt.title("The average precision plot using " + model_name)
plt.show()
# recall vs t
plt.plot(list(range (1,26)), recall_arr)
plt.xlabel("Size")
plt.ylabel("Recall")
plt.title("The average recall plot using MF " + model_name)
plt.show()
# precision vs recall
plt.plot(recall_arr, precision_arr)
plt.xlabel("Recall")
plt.ylabel("Precision")
plt.title("The average precision and recall plot using " + model_name)
plt.show()
return precision_arr, recall_arr
def __init__(self,category,save_name):
self.category=category
self.max_user=10000 #maximum number of user
self.price_dict={}
self.price_dict_temp={}
self.cate_dict={}
self.cate_dict_temp={}
self.top_value=15 # top x features in SVD
self.model=NMF()
self.topk=500 #maximum items in each category, finding the top k popular
self.max_price={}
self.save_path= os.path.join("..", "feature", save_name)
if not os.path.isfile(self.save_path):
self.load_data() #load raw data
#self.create_user_item_matrix()
self.create_ratings()
self.gen_new_price_dict()
self.save_data(self.save_path) #save the feature
else:
self.load(self.save_path) #load the feature
def train_nmf(data):
rmse = []
mae = []
sim_options = {'name': 'pearson'}
for k in range(2, 52, 2):
print("using k = %d" % k)
nmf = NMF(n_factors=k)
temp = cross_validate(nmf, data, measures=['RMSE', 'MAE'], cv=10)
rmse.append(np.mean(temp['test_rmse']))
mae.append(np.mean(temp['test_mae']))
print("k-fold validation finished!")
return (rmse, mae)
def Q19to21(qNum):
data = load_data()
kf = KFold(n_splits=10)
trimFun = {12: popularTrim, 13: unpopularTrim, 14: highVarTrim}
RMSE = []
for k in range(2, 20, 2):
nmf = NMF()
subRMSE = []
for trainSet, testSet in kf.split(data):
subsubRMSE = 0
nmf.fit(trainSet)
testSet = trimFun[qNum](testSet)
nTest = len(testSet)
print("test set size after trimming: %d", nTest)
predictions = nmf.test(testSet)
for p in predictions:
subsubRMSE += pow(p.est - p.r_ui, 2)
# average of all train-test splits of k-NN
RMSE.append(np.mean(subRMSE))
return RMSE
def train_trim_nmf(data, R):
kfold = KFold(n_splits=10)
rmse_list = [[], [], []]
for k in range(2, 52, 2):
print("using k = %d" % k)
p_rmse = []
u_rmse = []
hv_rmse = []
nmf = NMF(n_factors=k)
for trainset, testset in kfold.split(data):
nmf.fit(trainset)
(p_testset, u_testset, hv_testset) = trim(testset, R)
p_pred = nmf.test(p_testset)
u_pred = nmf.test(u_testset)
hv_pred = nmf.test(hv_testset)
p_rmse.append(accuracy.rmse(p_pred))
u_rmse.append(accuracy.rmse(u_pred))
hv_rmse.append(accuracy.rmse(hv_pred))
rmse_list[0].append(np.mean(p_rmse))
rmse_list[1].append(np.mean(u_rmse))
rmse_list[2].append(np.mean(hv_rmse))
print("NMF with trim is finished!!")
return rmse_list
def Q34():
rang = 5.0
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
data = load_data()
trainset, testset = train_test_split(data, test_size=0.1)
knn = KNNWithMeans(22, sim_options=sim_options)
nmf = NMF(n_factors=18)
svd = SVD(n_factors=8)
fp = {}
tp = {}
area = np.array([])
for model, key in zip([knn, nmf, svd], ['KNN', 'NNMF', 'SVD']):
model.fit(trainset)
pred = model.test(testset)
np_true = np.array([])
np_score = np.array([])
for _, _, t, p, _ in pred:
if t >= 3:
t = 1
else:
t = 0
np_true = np.append(np_true, t)
np_score = np.append(np_score, p / rang)
fpr, tpr, thresholds = roc_curve(np_true, np_score)
print(fpr.shape, tpr.shape)
roc_auc = auc(fpr, tpr)
fp[key] = fpr
tp[key] = tpr
area = np.append(area, roc_auc)
plt.figure()
lw = 2
for mod, f, t, roc_auc in zip(['KNN', 'NNMF', 'SVD'], fp, tp, area):
fpr = fp[f]
tpr = tp[t]
# label = mod+'ROC curve (area = '+str(roc_auc)+'0.2f)'
plt.plot(fpr,
tpr,
lw=lw,
label='%s ROC curve (area = %0.2f)' % (mod, roc_auc))
plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves')
plt.legend(loc="lower right")
plt.show()
plt.close()
def use_nmf():
start = time.time()
performance = []
data = Dataset.load_builtin('ml-100k')
trainset = data.build_full_trainset()
print('Using NMF')
algo_NMF = NMF()
algo_NMF.fit(trainset)
testset = trainset.build_anti_testset()
predictions_NMF = algo_NMF.test(testset)
accuracy_rmse = accuracy.rmse(predictions_NMF)
accuracy_mae = accuracy.mae(predictions_NMF)
performance.append(accuracy_rmse)
performance.append(accuracy_mae)
end = time.time()
performance.append(end - start)
return performance
def NMF_bin_pre(ratings, ts, nmf_fac, thrd):
reader = Reader(rating_scale=(0.0, 5.0))
data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']],
reader)
trainset, testset = train_test_split(data, test_size=ts)
algo = NMF(n_factors=nmf_fac, random_state=42)
algo.fit(trainset)
pre = algo.test(testset)
true_rating = np.empty(len(pre))
pred_rating = np.empty(len(pre))
for i in range(len(pre)):
true_rating[i] = pre[i][2]
pred_rating[i] = pre[i][3]
bi_rating = np.empty(len(pre))
one_idx = true_rating >= thrd
zero_idx = true_rating < thrd
bi_rating[one_idx] = 1.0
bi_rating[zero_idx] = 0.0
return bi_rating, pred_rating
def trim_performance(qNum,maxk=0):
pop, unpop, highVar = trimMovies()
if maxk == 0:
if 12 <= qNum <= 14:
maxk = 100
elif 19 <= qNum <= 21:
maxk = 50
trim_Model = {
12: (pop, 'KNNWithMeans'),
13: (unpop, 'KNNWithMeans'),
14: (highVar, 'KNNWithMeans'),
19: (pop, 'NMF'),
20: (unpop, 'NMF'),
21: (highVar, 'NMF'),
}
trimSet, modelName = trim_Model[qNum]
kf = KFold(n_splits=10)
RMSE = []
for k in range(2, maxk + 1, 2):
print('-' * 20 + 'k = ' + str(k) + ' ' + '-' * 20)
if modelName == 'KNNWithMeans':
model = KNNWithMeans(k=k, sim_options={'name': 'pearson'})
elif modelName == 'NMF':
model = NMF(n_factors=k)
subRMSE = []
temp = 1
for trainSet, testSet in kf.split(data):
model.fit(trainSet)
testSet = list(filter(lambda x: int(x[1]) in trimSet, testSet))
print("Split " + str(temp) + ": test set size after trimming: %d", len(testSet))
temp += 1
predictions = model.test(testSet)
subRMSE.append(accuracy.rmse(predictions, verbose=True))
RMSE.append(np.mean(subRMSE))
plt.figure()
plt.plot(list(range(2, maxk+1, 2)), RMSE)
plt.xlabel("k")
plt.ylabel("Average RMSE")
plt.title("Q"+str(qNum)+": Average RMSE Along k")
plt.show()
print(min(RMSE))
return min(RMSE)
def Q17():
data = load_data()
meanRMSE, meanMAE = [], []
start = time.time()
for k in range(16, 24, 2):
nmf = NMF(n_factors=k)
out = cross_validate(nmf, data, measures=['RMSE', 'MAE'], cv=10)
meanRMSE.append(np.mean(out['test_rmse']))
meanMAE.append(np.mean(out['test_mae']))
cv_time = str(datetime.timedelta(seconds=int(time.time() - start)))
print("Total time used for cross validation: " + cv_time)
k = list(range(16, 24, 2))
ys = [[meanRMSE, 'mean RMSE'], [meanMAE, 'mean MAE']]
make_plot(k, ys, 'Number of Latent Factors', 'ratings')
return meanRMSE, meanMAE
def plot_all_ROC():
rang = 5.0
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
trainset, testset = train_test_split(data, test_size=0.1)
knn = KNNWithMeans(22, sim_options=sim_options)
nmf = NMF(n_factors=18)
svd = SVD(n_factors=8)
fp = {}
tp = {}
area = np.array([])
for model, key in zip([knn, nmf, svd], ['KNN','NNMF','SVD']):
model.fit(trainset)
pred = model.test(testset)
np_true = np.array([])
np_score = np.array([])
for _, _, t, p, _ in pred:
if t >= 3:
t = 1
else:
t = 0
np_true = np.append(np_true, t)
np_score = np.append(np_score, p/rang)
fpr, tpr, thresholds = metrics.roc_curve(np_true, np_score)
print(fpr.shape, tpr.shape)
roc_auc = metrics.auc(fpr, tpr)
fp[key] = fpr
tp[key] = tpr
area = np.append(area, roc_auc)
plt.figure()
lw = 2
for mod, f, t, roc_auc in zip(['k-NN','NNMF','MF'], fp, tp, area):
fpr = fp[f]
tpr = tp[t]
plt.plot(fpr, tpr, lw=lw, label='%s'%mod)
plt.plot([0, 1], [0, 1], lw=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves')
plt.legend()
plt.show()
def grid_search(surprise_model):
if type(surprise_model()) == type(SVDpp()):
param_grid = {'n_factors':[20] , 'n_epochs':[20], 'lr_all':[0.005, 0.007, 0.05, 0.07, 0.5, 0.7, 1.0], 'reg_all':[0.02, 0.05, 0.2, 0.5]}
gs = GridSearchCV(surprise_model, param_grid, measures=['rmse', 'mae'], cv=3,n_jobs=-1,joblib_verbose=1,refit=True)
elif type(surprise_model()) == type(SVD()):
param_grid = {'n_epochs':[20], 'lr_all':[0.005, 0.007, 0.05, 0.07, 0.5, 0.7, 1.0], 'reg_all':[0.02, 0.05, 0.2, 0.5]}
gs = GridSearchCV(surprise_model, param_grid, measures=['rmse', 'mae'], cv=3,n_jobs=-1,joblib_verbose=1,refit=True)
elif type(surprise_model()) == type(NMF()):
param_grid = {'n_epochs':[20], 'reg_pu':[0.02, 0.04, 0.06, 0.08, 0.2], 'reg_qi':[0.02, 0.04, 0.06, 0.08, 0.2]}
gs = GridSearchCV(surprise_model, param_grid, measures=['rmse', 'mae'], cv=3,n_jobs=-1,joblib_verbose=1,refit=True)
elif type(surprise_model()) == type(BaselineOnly()):
param_grid = {'bsl_options': {'method': ['als', 'sgd'], 'reg': [1, 2], 'learning_rate': [0.005, 0.05, 0.5, 1.0]}}
gs = GridSearchCV(surprise_model, param_grid, measures=['rmse', 'mae'], cv=3,n_jobs=-1,joblib_verbose=1,refit=True)
return gs
def NMF_filter(ratings, dims):
reader = Reader(rating_scale=(0.0, 5.0))
data = Dataset.load_from_df(ratings[['userId', 'movieId', 'rating']],
reader)
RMSE = np.empty([len(dims)])
MAE = np.empty([len(dims)])
min_RMSE = False
min_MAE = False
fac_num_RMSE = 0
fac_num_MAE = 0
for k in range(len(dims)):
nmf = NMF(n_factors=dims[k], biased=False)
cv = cross_validate(algo=nmf,
data=data,
measures=['RMSE', 'MAE'],
cv=10,
verbose=True)
RMSE[k] = np.mean(cv['test_rmse'])
if ((not min_RMSE) or RMSE[k] < min_RMSE):
min_RMSE = RMSE[k]
fac_num_RMSE = dims[k]
MAE[k] = np.mean(cv['test_mae'])
if ((not min_MAE) or MAE[k] < min_MAE):
min_MAE = MAE[k]
fac_num_MAE = dims[k]
plt.plot(dims, RMSE)
plt.plot(dims, MAE)
plt.legend(['RMSE', 'MAE'])
plt.show()
print('Finishing Plotting...')
print('For RMSE:')
print('\t---Optimal number of latent factors is ', fac_num_RMSE)
print('\t---Minumun Average RMSE is ', min_RMSE)
print('\nFor MAE:')
print('\t---Optimal number of latent factors is ', fac_num_MAE)
print('\t---Minumun Average MAE is ', min_MAE)
def nmf(dataName, data, biased=True):
print('Start building NMF with ' + dataName + '!')
for i, k in enumerate(ks):
nmf = NMF(n_factors=k, biased=biased)
scores = cross_validate(nmf, data, cv=10)
mae[i] = scores['test_mae'].mean()
rmse[i] = scores['test_rmse'].mean()
print('k = ' + str(k) + ' finished!')
plt.figure()
plt.subplot(211)
plt.plot(ks, mae)
plt.xlabel('k')
plt.ylabel('mean absolute error')
plt.title('Mean absolute error vs. k of ' + dataName)
plt.subplot(212)
plt.plot(ks, rmse)
plt.xlabel('k')
plt.ylabel('root mean squared error')
plt.title('Root mean squared error vs. k of ' + dataName)
print('mae:')
print(mae)
print('rmse:')
print(rmse)
print('Finish building NMF with ' + dataName + '!')
def Q12To14And19To21And26To28(qNum, maxk=None):
data = load_data()
kf = KFold(n_splits=10)
if maxk is None:
if 12 <= qNum <= 14:
maxk = 100
elif 19 <= qNum <= 21:
maxk = 50
elif 26 <= qNum <= 28:
maxk = 50
pop, unpop, highVar = classifyMovies()
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
trimAndModel = {
12: (pop, 'KNNWithMeans'),
13: (unpop, 'KNNWithMeans'),
14: (highVar, 'KNNWithMeans'),
19: (pop, 'NMF'),
20: (unpop, 'NMF'),
21: (highVar, 'NMF'),
26: (pop, 'SVD'),
27: (unpop, 'SVD'),
28: (highVar, 'SVD')
}
RMSE = [] # RMSE for each k
for k in range(2, maxk + 1, 2): # inclusive
print('-' * 20 + ' k = ' + str(k) + ' ' + '-' * 20)
trimSet, modelName = trimAndModel[qNum]
if modelName == 'KNNWithMeans':
model = KNNWithMeans(k, sim_options=sim_options)
elif modelName == 'NMF':
model = NMF(n_factors=k)
else:
model = SVD(n_factors=k)
subRMSE = [] # RMSE for each k for each train-test split
iter = 1
for trainSet, testSet in kf.split(data):
subsubRMSE = 0
model.fit(trainSet)
testSet = list(filter(lambda x: x[1] in trimSet, testSet))
nTest = len(testSet)
print("Split " + str(iter) + ": test set size after trimming: %d",
nTest)
iter += 1
predictions = model.test(testSet)
for p in predictions:
subsubRMSE += pow(p.est - p.r_ui, 2)
# calculate RMSE of this train-test split
subRMSE.append(np.sqrt(subsubRMSE / nTest))
# average of all train-test splits of k-NN for this k
RMSE.append(np.mean(subRMSE))
# plotting
k = list(range(2, maxk + 1, 2))
ys = [[RMSE, 'RMSE']]
xTitle = 'Number of Neighbors' if qNum <= 14 else 'Number of latent factors'
make_plot(k, ys, xTitle, 'Error')
return RMSE
plt.savefig('plot/q22_nmf_roc_' + str(threshold) + '.png')
plt.clf()
if __name__ == "__main__":
threshold = [2.5, 3, 3.5, 4]
file_path = os.path.expanduser("ml-latest-small/ratings_new.csv")
reader = Reader(sep=',')
data = Dataset.load_from_file(file_path, reader=reader)
sim_options = {'name': 'pearson', 'user_based': True}
trainset, testset = train_test_split(data, test_size=0.1)
for th in threshold:
algo = NMF(n_factors=16)
algo.fit(trainset)
predictions = algo.test(testset)
y_true = []
y_estimate = []
for row in predictions:
if row[2] >= th:
y_true.append(1)
else:
y_true.append(0)
y_estimate.append(row[3])
plot_roc(y_true, y_estimate, th)
threshold = 3
file_path = os.path.expanduser("ml-latest-small/ratings_new.csv")
reader = Reader(sep=',')
data = Dataset.load_from_file(file_path, reader=reader)
sim_options = {'name': 'pearson',
'user_based': True
}
trainset, testset = train_test_split(data, test_size=0.1)
algo = KNNWithMeans(k=34, sim_options=sim_options)
algo.fit(trainset)
predictions1 = algo.test(testset)
algo = NMF(n_factors=16)
algo.fit(trainset)
predictions2 = algo.test(testset)
algo = SVD(n_factors=14)
algo.fit(trainset)
predictions3 = algo.test(testset)
y_true = []
y_estimate1 = []
y_estimate2 = []
y_estimate3 = []
for row in predictions1:
if row[2] >= threshold:
y_true.append(1)
print "Fold for" + str(t)
algo.fit(trainset)
# print testset
predictions = algo.test(testset)
Prec, Reca = metrics(predictions, t)
pr = pr + Prec
re = re + Reca
return pr / 10.0, re / 10.0
if __name__ == '__main__':
data = retrieve_data()
G_max = ret_mod_user_dict(data)
algo_NMF = NMF(NMF_no_of_LF, verbose=False)
algo_SVD = SVD(n_factors=MF_no_of_LF)
algo_KNN = KNNWithMeans(k=KNN_no_of_LF,
sim_options=sim_options,
verbose=False)
# Q36
Pr1 = []
Re1 = []
t = list(range(1, 26))
for l in t:
Precision, Recall = cross_val_(data, G_max, l, algo_KNN)
Pr1.append(Precision)
Re1.append(Recall)
plotgraphs(t, Pr1, "Number of Suggestions", "Precision",
def Q36To38(qNum):
print("problem ", qNum)
data = load_data()
sim_options = {
'name': 'pearson_baseline',
'shrinkage': 0 # no shrinkage
}
filter = {
36: 'KNNWithMeans',
37: 'NMF',
38: 'SVD',
}
k_KNNWithMeans = 30 # from Q11
k_NMF = 18 # from Q18
k_SVD = 8 # from Q25
modelName = filter[qNum]
if modelName == 'KNNWithMeans':
model = KNNWithMeans(k_KNNWithMeans, sim_options=sim_options)
elif modelName == 'NMF':
model = NMF(n_factors=k_NMF)
else:
model = SVD(n_factors=k_SVD)
# sweep t from 1 to 25
precision_arr = []
recall_arr = []
for t in range(1, 26):
kf = KFold(n_splits=10)
for trainSet, testSet in kf.split(data):
sub_precisions = 0.0
sub_recalls = 0.0
model.fit(trainSet)
predictions = model.test(testSet)
precisions, recalls = precision_recall(predictions, t)
print(sum(prec for prec in precisions.values()) / len(precisions))
sub_precisions += (sum(prec for prec in precisions.values()) /
len(precisions))
print(sum(rec for rec in recalls.values()) / len(recalls))
sub_recalls += (sum(rec
for rec in recalls.values()) / len(recalls))
precision_arr.append(np.mean(sub_precisions))
recall_arr.append(np.mean(sub_recalls))
t_list = list(range(1, 26))
ys = [[precision_arr, 'mean precisions'], [recall_arr, 'mean recalls']]
print("model name: ", modelName)
# make_plot(t_list, ys, 'recommended item size t','Precision')
# precision vs t
title_ = "precision vs t for: " + modelName
make_plot(t_list, [[precision_arr, 'mean precisions']],
'recommended item size t',
'Precision',
title=title_)
# recall vs t
title_ = "recall vs t for: " + modelName
make_plot(t_list, [[recall_arr, 'mean recalls']],
'recommended item size t',
'Recall',
title=title_)
# precision vs recall
title_ = "precision vs recall for: " + modelName
#make_plot([recall_arr, 'mean recalls'], [[precision_arr, 'mean precisions']], 'Recall','Precision', title = title_)
plt.plot(recall_arr, precision_arr, label=modelName)
xlabel = "recall"
ylabel = "precision"
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.legend()
plt.grid()
plt.title(title_)
plt.show()
return precision_arr, recall_arr
movie_rating_map = defaultdict(list)
for val in data:
movie_rating_map[val[1]].append(val[2])
high_var_data = [val for val in data if
len(movie_rating_map[val[1]]) >= 5 and np.var(movie_rating_map[val[1]]) >= 2.0]
return high_var_data
print("=====================Non-negative Matrix Factorization based filtering=============================================================")
print("Evaluating NNMF collaborative filtering based on #of latent factors vs RMSE and MAE errors on 10folds cross-validation")
k_range = range(2, 51, 2)
avg_rmse, avg_mae = [], []
for k in k_range:
algo = NMF(n_factors=k)
cv_result = cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=10, verbose=False)
avg_rmse.append(np.mean(cv_result['test_rmse']))
avg_mae.append(np.mean(cv_result['test_mae']))
plt.plot(k_range, avg_rmse, label="Average RMSE")
plt.plot(k_range, avg_mae, label="Average MAE")
plt.xlabel('Number of latent factors', fontsize=15)
plt.ylabel('Error', fontsize=15)
plt.legend()
plt.show()
print("=================================Optimal Number of Latent Factors=============================================================")
all_genres = set('|'.join(movies.genres).split('|'))
print('#of Genres - ', len(all_genres))
line_format='user item rating',
sep=',',
rating_scale=(1, 5),
skip_lines=0)
RS_data = Dataset.load_from_df(RS_ratings, RS_reader)
# Benchmark_Algorithm_Metric
benchmark = []
for algorithm in [
BaselineOnly(),
CoClustering(),
KNNBaseline(),
KNNBasic(),
KNNWithMeans(),
KNNWithZScore(),
NMF(),
NormalPredictor(),
SlopeOne(),
SVD(),
SVDpp()
]:
# Perform cross validation
results = cross_validate(algorithm,
RS_data,
measures=['rmse', 'mae', 'mse', 'fcp'],
cv=5,
verbose=True)
# Results To Serie List
tmp = pd.DataFrame.from_dict(results).mean(axis=0)
tmp = tmp.append(
pd.Series([str(algorithm).split(' ')[0].split('.')[-1]],
