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 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 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 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 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
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)
print("=================================Optimal Number of Latent Factors=============================================================")
all_genres = set('|'.join(movies.genres).split('|'))
print('#of Genres - ', len(all_genres))
print("===================================NNMF collaborative filtering on popular movie trimmed set=================================================")
avg_rmse = []
k_range = range(2, 51, 2)
kf = KFold(n_splits=10)
for k in k_range:
algo = NMF(n_factors=k)
k_rmse = []
for trainset, testset in kf.split(data):
algo.fit(trainset)
predictions = algo.test(popular_trim(testset))
k_rmse.append(accuracy.rmse(predictions, verbose=False))
avg_rmse.append(np.mean(k_rmse))
print('Minimum average RMSE is ', min(avg_rmse), ' for k = ', k_range[np.argmin(avg_rmse)])
plt.plot(k_range, avg_rmse)
plt.xlabel('Number of latent factors', fontsize=15)
plt.ylabel('Average RMSE', fontsize=15)
plt.title('#latent factors vs Average RMSE for NMF Popular trimming')
plt.show()
print("========================NNMF collaborative filtering on unpopular movie trimmed set==================================")
avg_rmse = []
k_range = range(2, 51, 2)
kf = KFold(n_splits=10)
fig3.savefig(path + 'fig/Part_8_knn_preVSrec.png')
#define model for training
k_min_rmse = 18
nnmf = NMF(n_factors=k_min_rmse, random_state=1)
#train, test and rank
top_t_list = range(1, 26)
pre_list_nnmf = []
rec_list_nnmf = []
for top_t in top_t_list:
pre = 0
rec = 0
for trainset, testset in kf.split(data_raw):
nnmf.fit(trainset)
prediction = nnmf.test(testset)
G = create_dict(testset)
G_s = create_dict(prediction, if_pred=1)
R, R_s = threshold_rank_filter(G, G_s, thre=3, top_t=top_t)
#precision and recall for each fold
pre_fold = 0
rec_fold = 0
for key in R.keys():
pre_temp, rec_temp = precision_recall(R[key], R_s[key])
pre_fold += pre_temp
rec_fold += rec_temp
pre += pre_fold / len(R)
rec += rec_fold / len(R)
pre_list_nnmf.append(pre / num_fold)
rec_list_nnmf.append(rec / num_fold)
plt.figure()
plt.title("kNN: Avg Precision vs Avg Recall with 10 fold CV")
plt.xlabel("Avg Recall")
plt.ylabel("Avg Precision")
plt.plot(knn_recall, knn_prec)
plt.show(0)
"""
Question 37
"""
nmf_prec, nmf_recall = [], []
for t in ts:
precision_sum, recall_sum = 0.0, 0.0
nmf = NMF(n_factors=20, biased=False)
for trainset, testset in kf.split(data):
nmf.fit(trainset)
pred = nmf.test(testset)
precision, recall = calc_precision_recall(pred, int(t), threshold)
precision_sum += np.mean(list(precision.values()))
recall_sum += np.mean(list(recall.values()))
precision_avg = precision_sum / kf.n_splits
recall_avg = recall_sum / kf.n_splits
print(
f"NMF t: {t}, precision_avg: {precision_avg}, recall_avg: {recall_avg}"
)
nmf_prec.append(precision_avg)
nmf_recall.append(recall_avg)
plt.figure()
plt.subplot(2, 1, 1)
plt.title("NMF: Avg Precision vs t with 10 fold CV")
plt.ylabel("Avg Precision")
# In[42]:
print("Minimum average RMSE after high variance movie trimming: %.4f" %
np.min(nmf_rmse_high_var_trim))
# <font size=4>**Question 22:** Plot the ROC curves for the NNMF-based collaborative filter designed in Question 17 for threshold values [2.5,3,3.5,4]. For the ROC plotting use the optimal number of latent factors found in Question 18. For each of the plots, also report the area under the curve (AUC) value.</font>
# In[43]:
nmf_best_k = ks[np.argmin(nmf_rmse)]
trainset, testset = train_test_split(R, test_size=0.1, random_state=42)
nmf_best = NMF(n_factors=nmf_best_k, random_state=42)
nmf_best.fit(trainset)
nmf_best_pred = nmf_best.test(testset)
plot_roc_curves(testset, nmf_best_pred, 'NNMF')
# <font size=4>**Question 23:** Perform Non-negative matrix factorization on the ratings matrix R to obtain the factor matrices U and V , where U represents the user-latent factors interaction and V represents the movie-latent factors interaction (use k = 20). For each column of V , sort the movies in descending order and report the genres of the top 10 movies. Do the top 10 movies belong to a particular or a small collection of genre? Is there a connection between the latent factors and the movie genres?</font>
# In[44]:
nmf_k20 = NMF(n_factors=20, random_state=42)
nmf_k20.fit(R.build_full_trainset())
# In[45]:
item_factors = nmf_k20.qi
# In[46]:
def problems_19_20_21_rmse_pop_unpop_hv():
x_axis = range(2, 52, 2)
ratings = {}
for r in data.raw_ratings:
if r[1] not in ratings:
ratings[r[1]] = []
ratings[r[1]].append(r[2])
###############################################################################################
popular_movies = [x for x in ratings if len(ratings[x]) > 2]
unpopular_movies = [x for x in ratings if len(ratings[x]) <= 2]
###################################################################################
kf = KFold(n_splits=10)
rmse_popular_store = []
for i in x_axis:
algo = NMF(i, verbose=False)
accu = []
for trainset, testset in kf.split(data):
algo.fit(trainset)
test_trim = [x for x in testset if x[1] in popular_movies]
predictions = algo.test(test_trim)
accu.append(accuracy.rmse(predictions, verbose=True))
s = np.mean(accu)
rmse_popular_store.append(s)
plotgraphs(x_axis, rmse_popular_store, 'K', 'Mean RMSE scores',
'Plot of popular movies', 'q19_rmse_popular_movies.png')
plotgraphs(x_axis, rmse_popular_store, 'K', 'Mean RMSE scores',
'Plot of popular movies')
##########################################################################################
kf = KFold(n_splits=10)
rmse_unpopular_store = []
for i in x_axis:
algo = NMF(i, verbose=False)
accu = []
for trainset, testset in kf.split(data):
algo.fit(trainset)
test_trim = [x for x in testset if x[1] in unpopular_movies]
predictions = algo.test(test_trim)
accu.append(accuracy.rmse(predictions, verbose=True))
s = np.mean(accu)
rmse_unpopular_store.append(s)
plotgraphs(x_axis, rmse_unpopular_store, 'K', 'Mean RMSE scores',
'Plot of unpopular movies', 'q20_rmse_unpopular_movies.png')
plotgraphs(x_axis, rmse_unpopular_store, 'K', 'Mean RMSE scores',
'Plot of unpopular movies')
############ rates "key" id, values are ratings #######################################
movie_var = {}
for k in ratings:
# print(k)
movie_var[k] = np.var(ratings[k])
####################################################################################
highvar_movies = [
x for x in ratings if len(ratings[x]) >= 5 and movie_var[x] >= 2
]
##################################################################################
kf = KFold(n_splits=10)
rmse_highvar_store = []
for i in x_axis:
algo = NMF(i, verbose=False)
accu = []
for trainset, testset in kf.split(data):
algo.fit(trainset)
test_trim = [x for x in testset if x[1] in highvar_movies]
predictions = algo.test(test_trim)
accu.append(accuracy.rmse(predictions, verbose=True))
s = np.mean(accu)
rmse_highvar_store.append(s)
pd.DataFrame(rmse_highvar_store).to_csv("rmse_highvar_store_21.csv")
plotgraphs(x_axis, rmse_highvar_store, 'K', 'Mean RMSE scores',
'Plot of high variance movies', 'q21_rmse_high_var_movies.png')