tr_model = NMF(n_components=K, init='random', random_state=0, max_iter=1000, beta_loss=loss, solver='mu')
tr_U_f = tr_model.fit_transform(zi_tr_graph_sparse) # user features
tr_I_f = tr_model.components_ # item features
# Train lookup model with item features fixed
# 2. Train on graph_lookup: fix item_feat
zi_lu_graph = zero_index_sparse_graph(lu_graph, axis=1, convert=lu_I_zero_indexer)
zi_lu_graph = zero_index_sparse_graph(zi_lu_graph, axis=0, convert=lu_U_zero_indexer)
zi_lu_graph_sparse = csr_matrix((zi_lu_graph[1], (zi_lu_graph[0][:, 0], zi_lu_graph[0][:, 1])), shape=(n_lu_U, n_lu_I))
lu_model = trainedNMF(components_=tr_I_f[:, lu_I], n_components=K, init='random', random_state=0, max_iter=1000,
beta_loss=loss, solver='mu')
ts_U_f = lu_U_f = lu_model.transform(zi_lu_graph_sparse)
ts_I_f = tr_I_f[:, ts_I]
predictions = np.matmul(lu_U_f, ts_I_f)
zi_ts_graph = zero_index_sparse_graph(ts_graph, axis=0, convert=ts_U_zero_indexer)
zi_ts_graph = zero_index_sparse_graph(zi_ts_graph, axis=1, convert=ts_I_zero_indexer)
#
topk = torch.topk(torch.tensor(predictions), n_ts_I)
# 3. predict graph_test: evaluate
nDCG_score = nDCG(np.r_[0:ts_U.shape[0]], topk[1], zi_ts_graph[0])
m = 20
precision_score = precision_at_m(np.r_[0:ts_U.shape[0]], topk[1], zi_ts_graph[0], m=m)
print("nDCG Score for is {}".format(np.mean(nDCG_score)))
print("Precision at {} is {}".format(m, np.mean(precision_score)))
init_std=0.01, verbose=True, dtype='float32',
recompute_factors=wmf.recompute_factors_bias)
ts_U = np.unique(test[0][:, 0])
zi_test, test_convert = zero_index_sparse_graph(test)
mask_edges = train[0][np.in1d(train[0][:, 0], ts_U)]
mask_edges[:, 0] = test_convert[mask_edges[:, 0]]
test_user_features = user_features[ts_U]
predictions = np.matmul(test_user_features, item_features.T)
# Scatter update might be faster but for correctness
for edge in mask_edges:
predictions[tuple(edge)] = 0.
# Recommend top_k
topk = torch.topk(torch.tensor(predictions), I.shape[0])[1].numpy()
# Evaluate: More metrics?
nDCG_score = nDCG(np.r_[0:ts_U.shape[0]], topk, zi_test[0])
m=20
precision_score = precision_at_m(np.r_[0:ts_U.shape[0]], topk, zi_test[0], m)
print("nDCG Score for is {}".format(np.mean(nDCG_score)))
print("Precision at {} is {}".format(m, np.mean(precision_score)))
def main(data, p, q, K, M, num_iterations, alpha, lambda_reg, init_std):
# loc = "/Users/ekansh/repos/data/{}"
# ds = "nyt/"
# loss = 'kullback-leibler'
# # loss = "frobenius"
graph = (data[:, :2], data[:, 2])
graph, _ = zero_index_sparse_graph(graph, axis = 0)
graph, _ = zero_index_sparse_graph(graph, axis = 1)
U = np.unique(graph[0][:, 0])
nU = U.shape[0]
I = np.unique(graph[0][:, 1])
nI = I.shape[0]
# Split data
# Data Split
tr_graph, lu_graph, ts_graph, tr_U, lu_I = pq_samp_split(graph, p, q)
U = np.unique(graph[0][:, 0])
nU = U.shape[0]
# THIS IS CONFUSING. FIX IT!
tr_U_zero_indexer = zero_index(tr_U, True)
I = np.unique(graph[0][:, 1])
nI = I.shape[0]
n_tr_U = tr_U.shape[0]
tr_I = I
n_tr_I = nI
lu_U = ts_U = np.setdiff1d(U, tr_U, assume_unique=True)
n_lu_U = n_ts_U = lu_U.shape[0]
lu_U_zero_indexer = ts_U_zero_indexer = zero_index(lu_U, True)
n_lu_I = lu_I.shape[0]
lu_I_zero_indexer = zero_index(lu_I, True)
ts_I = np.setdiff1d(I, lu_I, assume_unique=True)
n_ts_I = ts_I.shape[0]
ts_I_zero_indexer = zero_index(ts_I, True)
## Train NMF
# K = 10
# print("Using {} loss".format(loss))
# model = NMF(n_components=K, init='random', random_state=0, beta_loss=loss, solver='mu', max_iter=1000)
zi_tr_graph = zero_index_sparse_graph(tr_graph, axis=0, convert=tr_U_zero_indexer)
zi_tr_graph_sparse = csr_matrix((zi_tr_graph[1], (zi_tr_graph[0][:, 0], zi_tr_graph[0][:, 1])), shape=(n_tr_U, n_tr_I))
S_tr_sparse = wmf.log_surplus_confidence_matrix(zi_tr_graph_sparse, alpha=alpha, epsilon=TINY)
tr_U_f, tr_I_f = wmf.factorize(S_tr_sparse, num_factors=K, lambda_reg=lambda_reg, num_iterations=num_iterations,
init_std=init_std, verbose=True, dtype='float32',
recompute_factors=wmf.recompute_factors_bias)
tr_I_f = tr_I_f.T
# Train lookup model with item features fixed
# 2. Train on graph_lookup: fix item_feat
zi_lu_graph = zero_index_sparse_graph(lu_graph, axis=1, convert=lu_I_zero_indexer)
zi_lu_graph = zero_index_sparse_graph(zi_lu_graph, axis=0, convert=lu_U_zero_indexer)
zi_lu_graph_sparse = csr_matrix((zi_lu_graph[1], (zi_lu_graph[0][:, 0], zi_lu_graph[0][:, 1])), shape=(n_lu_U, n_lu_I))
S_lu_sparse = wmf.log_surplus_confidence_matrix(zi_lu_graph_sparse, alpha=alpha, epsilon=TINY)
lu_U_f, _ = wmf.factorize(zi_lu_graph_sparse, num_factors=K, lambda_reg=lambda_reg, num_iterations=num_iterations,
init_std=init_std, verbose=True, dtype='float32',
recompute_factors=wmf.recompute_factors_bias, V=tr_I_f[:, lu_I].T)
ts_U_f = lu_U_f
ts_I_f = tr_I_f[:, ts_I]
predictions = np.matmul(ts_U_f, ts_I_f)
zi_ts_graph = zero_index_sparse_graph(ts_graph, axis=0, convert=ts_U_zero_indexer)
zi_ts_graph = zero_index_sparse_graph(zi_ts_graph, axis=1, convert=ts_I_zero_indexer)
#
topk = torch.topk(torch.tensor(predictions), n_ts_I)
nDCG_score = nDCG(np.r_[0:ts_U.shape[0]], topk[1], zi_ts_graph[0])
precision_score = precision_at_m(np.r_[0:ts_U.shape[0]], topk[1], zi_ts_graph[0], m=M)
print("nDCG Score for is {}".format(np.mean(nDCG_score)))
print("Precision at {} is {}".format(M, np.mean(precision_score)))
pass
def main(data, p, K, M, num_iterations, alpha, lambda_reg, init_std):
# loc = "/Users/ekansh/repos/data/{}"
# ds = "nyt/"
# loss = 'kullback-leibler'
# # loss = "frobenius"
graph = (data[:, :2], data[:, 2])
graph, _ = zero_index_sparse_graph(graph, axis=0)
graph, _ = zero_index_sparse_graph(graph, axis=1)
U = np.unique(graph[0][:, 0])
nU = U.shape[0]
I = np.unique(graph[0][:, 1])
nI = I.shape[0]
# Split data
train, test = edge_samp_split(graph, p)
train_sparse = csr_matrix((train[1], (train[0][:, 0], train[0][:, 1])),
shape=(nU, nI))
## Train NMF
# K = 10
# print("Using {} loss".format(loss))
# model = NMF(n_components=K, init='random', random_state=0, beta_loss=loss, solver='mu', max_iter=1000)
S = wmf.log_surplus_confidence_matrix(train_sparse,
alpha=alpha,
epsilon=TINY)
user_features, item_features = wmf.factorize(
S,
num_factors=K,
lambda_reg=lambda_reg,
num_iterations=num_iterations,
init_std=init_std,
verbose=True,
dtype='float32',
recompute_factors=wmf.recompute_factors_bias)
ts_U = np.unique(test[0][:, 0])
zi_test, test_convert = zero_index_sparse_graph(test)
mask_edges = train[0][np.in1d(train[0][:, 0], ts_U)]
mask_edges[:, 0] = test_convert[mask_edges[:, 0]]
test_user_features = user_features[ts_U]
predictions = np.matmul(test_user_features, item_features.T)
# Scatter update might be faster but for correctness
for edge in mask_edges:
predictions[tuple(edge)] = 0.
# Recommend top_k
topk = torch.topk(torch.tensor(predictions), I.shape[0])[1].numpy()
# Evaluate: More metrics?
nDCG_score = nDCG(np.r_[0:ts_U.shape[0]], topk, zi_test[0])
# m=20
precision_score = precision_at_m(np.r_[0:ts_U.shape[0]], topk, zi_test[0],
M)
print("nDCG Score for is {}".format(np.mean(nDCG_score)))
print("Precision at {} is {}".format(m, np.mean(precision_score)))
pass