def fit(self,
topK=50,
shrink=100,
similarity='cosine',
normalize=True,
feature_weighting="none",
**similarity_args):
self.topK = topK
self.shrink = shrink
if feature_weighting not in self.FEATURE_WEIGHTING_VALUES:
raise ValueError(
"Value for 'feature_weighting' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.FEATURE_WEIGHTING_VALUES, feature_weighting))
if feature_weighting == "BM25":
self.UCM_train = self.UCM_train.astype(np.float32)
self.UCM_train = okapi_BM_25(self.UCM_train)
elif feature_weighting == "TF-IDF":
self.UCM_train = self.UCM_train.astype(np.float32)
self.UCM_train = TF_IDF(self.UCM_train)
similarity = Compute_Similarity(self.UCM_train.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity,
**similarity_args)
self.W_sparse = similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def fit(self,
topK=None,
l2_norm=1e3,
normalize_matrix=False,
verbose=True):
self.verbose = verbose
start_time = time.time()
self._print("Fitting model... ")
if normalize_matrix:
# Normalize rows and then columns
self.URM_train = normalize(self.URM_train, norm='l2', axis=1)
self.URM_train = normalize(self.URM_train, norm='l2', axis=0)
self.URM_train = sps.csr_matrix(self.URM_train)
# Grahm matrix is X^t X, compute dot product
similarity = Compute_Similarity(self.URM_train,
shrink=0,
topK=self.URM_train.shape[1],
normalize=False,
similarity="cosine")
grahm_matrix = similarity.compute_similarity().toarray()
diag_indices = np.diag_indices(grahm_matrix.shape[0])
# The Compute_Similarity object ensures the diagonal of the similarity matrix is zero
# in this case we need the diagonal as well, which is just the item popularity
item_popularity = np.ediff1d(self.URM_train.tocsc().indptr)
grahm_matrix[diag_indices] = item_popularity + l2_norm
P = np.linalg.inv(grahm_matrix)
B = P / (-np.diag(P))
B[diag_indices] = 0.0
new_time_value, new_time_unit = seconds_to_biggest_unit(time.time() -
start_time)
self._print("Fitting model... done in {:.2f} {}".format(
new_time_value, new_time_unit))
# Check if the matrix should be saved in a sparse or dense format
# The matrix is sparse, regardless of the presence of the topK, if nonzero cells are less than sparse_threshold_quota %
if topK is not None:
B = similarityMatrixTopK(B, k=topK, verbose=False)
if self._is_content_sparse_check(B):
self._print("Detected model matrix to be sparse, changing format.")
self.W_sparse = check_matrix(B, format='csr', dtype=np.float32)
else:
self.W_sparse = check_matrix(B, format='npy', dtype=np.float32)
self._W_sparse_format_checked = True
self._compute_item_score = self._compute_score_W_dense
def compute_W_sparse(self):
self.similarity = Compute_Similarity(
self.ICM.T,
shrink=0,
topK=self.topK,
normalize=self.normalize_similarity,
row_weights=self.D_best)
self.W_sparse = self.similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def compute_W_sparse(self, model_to_use="best"):
if model_to_use == "last":
feature_weights = self.D_incremental
elif model_to_use == "best":
feature_weights = self.D_best
else:
assert False, "{}: compute_W_sparse, 'model_to_use' parameter not recognized".format(
self.RECOMMENDER_NAME)
self.similarity = Compute_Similarity(
self.ICM.T,
shrink=0,
topK=self.topK,
normalize=self.normalize_similarity,
row_weights=feature_weights)
self.W_sparse = self.similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
class CFW_D_Similarity_Cython(BaseItemCBFRecommender,
BaseItemSimilarityMatrixRecommender,
Incremental_Training_Early_Stopping):
RECOMMENDER_NAME = "CFW_D_Similarity_Cython"
INIT_TYPE_VALUES = ["random", "one", "BM25", "TF-IDF"]
def __init__(self, URM_train, ICM_train, S_matrix_target):
super(CFW_D_Similarity_Cython, self).__init__(URM_train, ICM_train)
if (URM_train.shape[1] != ICM_train.shape[0]):
raise ValueError(
"Number of items not consistent. URM contains {} but ICM contains {}"
.format(URM_train.shape[1], ICM_train.shape[0]))
if (S_matrix_target.shape[0] != S_matrix_target.shape[1]):
raise ValueError(
"Items imilarity matrix is not square: rows are {}, columns are {}"
.format(S_matrix_target.shape[0], S_matrix_target.shape[1]))
if (S_matrix_target.shape[0] != ICM_train.shape[0]):
raise ValueError(
"Number of items not consistent. S_matrix contains {} but ICM contains {}"
.format(S_matrix_target.shape[0], ICM_train.shape[0]))
self.S_matrix_target = check_matrix(S_matrix_target, 'csr')
self.ICM = check_matrix(ICM_train, 'csr')
self.n_features = self.ICM.shape[1]
def fit(self,
show_max_performance=False,
precompute_common_features=False,
learning_rate=0.1,
positive_only_D=True,
initialization_mode_D="random",
normalize_similarity=False,
use_dropout=True,
dropout_perc=0.3,
l1_reg=0.0,
l2_reg=0.0,
epochs=50,
topK=300,
add_zeros_quota=0.0,
log_file=None,
verbose=False,
sgd_mode='adagrad',
gamma=0.9,
beta_1=0.9,
beta_2=0.999,
**earlystopping_kwargs):
if initialization_mode_D not in self.INIT_TYPE_VALUES:
raise ValueError(
"Value for 'initialization_mode_D' not recognized. Acceptable values are {}, provided was '{}'"
.format(self.INIT_TYPE_VALUES, initialization_mode_D))
# Import compiled module
from Recommenders.FeatureWeighting.Cython.CFW_D_Similarity_Cython_SGD import CFW_D_Similarity_Cython_SGD
self.show_max_performance = show_max_performance
self.normalize_similarity = normalize_similarity
self.learning_rate = learning_rate
self.add_zeros_quota = add_zeros_quota
self.l1_reg = l1_reg
self.l2_reg = l2_reg
self.epochs = epochs
self.topK = topK
self.log_file = log_file
self.verbose = verbose
self._generate_train_data()
weights_initialization_D = None
if initialization_mode_D == "random":
weights_initialization_D = np.random.normal(
0.001, 0.1, self.n_features).astype(np.float64)
elif initialization_mode_D == "one":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "zero":
weights_initialization_D = np.zeros(self.n_features,
dtype=np.float64)
elif initialization_mode_D == "BM25":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = okapi_BM_25(self.ICM)
elif initialization_mode_D == "TF-IDF":
weights_initialization_D = np.ones(self.n_features,
dtype=np.float64)
self.ICM = self.ICM.astype(np.float32)
self.ICM = TF_IDF(self.ICM)
else:
raise ValueError(
"CFW_D_Similarity_Cython: 'init_type' not recognized")
# Instantiate fast Cython implementation
self.FW_D_Similarity = CFW_D_Similarity_Cython_SGD(
self.row_list,
self.col_list,
self.data_list,
self.n_features,
self.ICM,
precompute_common_features=precompute_common_features,
positive_only_D=positive_only_D,
weights_initialization_D=weights_initialization_D,
use_dropout=use_dropout,
dropout_perc=dropout_perc,
learning_rate=learning_rate,
l1_reg=l1_reg,
l2_reg=l2_reg,
sgd_mode=sgd_mode,
verbose=self.verbose,
gamma=gamma,
beta_1=beta_1,
beta_2=beta_2)
if self.verbose:
print(self.RECOMMENDER_NAME + ": Initialization completed")
self.D_incremental = self.FW_D_Similarity.get_weights()
self.D_best = self.D_incremental.copy()
self._train_with_early_stopping(epochs,
algorithm_name=self.RECOMMENDER_NAME,
**earlystopping_kwargs)
self.compute_W_sparse(model_to_use="best")
sys.stdout.flush()
def _prepare_model_for_validation(self):
self.D_incremental = self.FW_D_Similarity.get_weights()
self.compute_W_sparse(model_to_use="last")
def _update_best_model(self):
self.D_best = self.D_incremental.copy()
def _run_epoch(self, num_epoch):
self.loss = self.FW_D_Similarity.fit()
def _generate_train_data(self):
if self.verbose:
print(self.RECOMMENDER_NAME + ": Generating train data")
start_time_batch = time.time()
# Here is important only the structure
self.similarity = Compute_Similarity(self.ICM.T,
shrink=0,
topK=self.topK,
normalize=False)
S_matrix_contentKNN = self.similarity.compute_similarity()
S_matrix_contentKNN = check_matrix(S_matrix_contentKNN, "csr")
self._print("Collaborative S density: {:.2E}, nonzero cells {}".format(
self.S_matrix_target.nnz / self.S_matrix_target.shape[0]**2,
self.S_matrix_target.nnz))
self._print("Content S density: {:.2E}, nonzero cells {}".format(
S_matrix_contentKNN.nnz / S_matrix_contentKNN.shape[0]**2,
S_matrix_contentKNN.nnz))
if self.normalize_similarity:
# Compute sum of squared
sum_of_squared_features = np.array(
self.ICM.T.power(2).sum(axis=0)).ravel()
sum_of_squared_features = np.sqrt(sum_of_squared_features)
num_common_coordinates = 0
estimated_n_samples = int(S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 1.2)
self.row_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.col_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.data_list = np.zeros(estimated_n_samples, dtype=np.float64)
num_samples = 0
for row_index in range(self.n_items):
start_pos_content = S_matrix_contentKNN.indptr[row_index]
end_pos_content = S_matrix_contentKNN.indptr[row_index + 1]
content_coordinates = S_matrix_contentKNN.indices[
start_pos_content:end_pos_content]
start_pos_target = self.S_matrix_target.indptr[row_index]
end_pos_target = self.S_matrix_target.indptr[row_index + 1]
target_coordinates = self.S_matrix_target.indices[
start_pos_target:end_pos_target]
# Chech whether the content coordinate is associated to a non zero target value
# If true, the content coordinate has a collaborative non-zero value
# if false, the content coordinate has a collaborative zero value
is_common = np.in1d(content_coordinates, target_coordinates)
num_common_in_current_row = is_common.sum()
num_common_coordinates += num_common_in_current_row
for index in range(len(is_common)):
if num_samples == estimated_n_samples:
dataBlock = 1000000
self.row_list = np.concatenate(
(self.row_list, np.zeros(dataBlock, dtype=np.int32)))
self.col_list = np.concatenate(
(self.col_list, np.zeros(dataBlock, dtype=np.int32)))
self.data_list = np.concatenate(
(self.data_list, np.zeros(dataBlock,
dtype=np.float64)))
if is_common[index]:
# If cell exists in target matrix, add its value
# Otherwise it will remain zero with a certain probability
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
new_data_value = self.S_matrix_target[row_index, col_index]
if self.normalize_similarity:
new_data_value *= sum_of_squared_features[
row_index] * sum_of_squared_features[col_index]
self.data_list[num_samples] = new_data_value
num_samples += 1
elif np.random.rand() <= self.add_zeros_quota:
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
self.data_list[num_samples] = 0.0
num_samples += 1
if self.verbose and (time.time() - start_time_batch > 30
or num_samples == S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota)):
print(self.RECOMMENDER_NAME +
": Generating train data. Sample {} ({:4.1f}%) ".format(
num_samples, num_samples / S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 100))
sys.stdout.flush()
sys.stderr.flush()
start_time_batch = time.time()
self._print(
"Content S structure has {} out of {} ({:4.1f}%) nonzero collaborative cells"
.format(num_common_coordinates, S_matrix_contentKNN.nnz,
num_common_coordinates / S_matrix_contentKNN.nnz * 100))
# Discard extra cells at the left of the array
self.row_list = self.row_list[:num_samples]
self.col_list = self.col_list[:num_samples]
self.data_list = self.data_list[:num_samples]
data_nnz = sum(np.array(self.data_list) != 0)
data_sum = sum(self.data_list)
collaborative_nnz = self.S_matrix_target.nnz
collaborative_sum = sum(self.S_matrix_target.data)
self._print(
"Nonzero collaborative cell sum is: {:.2E}, average is: {:.2E}, "
"average over all collaborative data is {:.2E}".format(
data_sum, data_sum / data_nnz,
collaborative_sum / collaborative_nnz))
def compute_W_sparse(self, model_to_use="best"):
if model_to_use == "last":
feature_weights = self.D_incremental
elif model_to_use == "best":
feature_weights = self.D_best
else:
assert False, "{}: compute_W_sparse, 'model_to_use' parameter not recognized".format(
self.RECOMMENDER_NAME)
self.similarity = Compute_Similarity(
self.ICM.T,
shrink=0,
topK=self.topK,
normalize=self.normalize_similarity,
row_weights=feature_weights)
self.W_sparse = self.similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def set_ICM_and_recompute_W(self, ICM_new, recompute_w=True):
self.ICM = ICM_new.copy()
if recompute_w:
self.compute_W_sparse(model_to_use="best")
def save_model(self, folder_path, file_name=None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
print("{}: Saving model in file '{}'".format(self.RECOMMENDER_NAME,
folder_path + file_name))
data_dict_to_save = {
"D_best": self.D_best,
"topK": self.topK,
"W_sparse": self.W_sparse,
"normalize_similarity": self.normalize_similarity
}
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name,
data_dict_to_save=data_dict_to_save)
print("{}: Saving complete".format(self.RECOMMENDER_NAME))
def _generate_train_data(self):
if self.verbose:
print(self.RECOMMENDER_NAME + ": Generating train data")
start_time_batch = time.time()
# Here is important only the structure
self.similarity = Compute_Similarity(self.ICM.T,
shrink=0,
topK=self.topK,
normalize=False)
S_matrix_contentKNN = self.similarity.compute_similarity()
S_matrix_contentKNN = check_matrix(S_matrix_contentKNN, "csr")
self._print("Collaborative S density: {:.2E}, nonzero cells {}".format(
self.S_matrix_target.nnz / self.S_matrix_target.shape[0]**2,
self.S_matrix_target.nnz))
self._print("Content S density: {:.2E}, nonzero cells {}".format(
S_matrix_contentKNN.nnz / S_matrix_contentKNN.shape[0]**2,
S_matrix_contentKNN.nnz))
if self.normalize_similarity:
# Compute sum of squared
sum_of_squared_features = np.array(
self.ICM.T.power(2).sum(axis=0)).ravel()
sum_of_squared_features = np.sqrt(sum_of_squared_features)
num_common_coordinates = 0
estimated_n_samples = int(S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 1.2)
self.row_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.col_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.data_list = np.zeros(estimated_n_samples, dtype=np.float64)
num_samples = 0
for row_index in range(self.n_items):
start_pos_content = S_matrix_contentKNN.indptr[row_index]
end_pos_content = S_matrix_contentKNN.indptr[row_index + 1]
content_coordinates = S_matrix_contentKNN.indices[
start_pos_content:end_pos_content]
start_pos_target = self.S_matrix_target.indptr[row_index]
end_pos_target = self.S_matrix_target.indptr[row_index + 1]
target_coordinates = self.S_matrix_target.indices[
start_pos_target:end_pos_target]
# Chech whether the content coordinate is associated to a non zero target value
# If true, the content coordinate has a collaborative non-zero value
# if false, the content coordinate has a collaborative zero value
is_common = np.in1d(content_coordinates, target_coordinates)
num_common_in_current_row = is_common.sum()
num_common_coordinates += num_common_in_current_row
for index in range(len(is_common)):
if num_samples == estimated_n_samples:
dataBlock = 1000000
self.row_list = np.concatenate(
(self.row_list, np.zeros(dataBlock, dtype=np.int32)))
self.col_list = np.concatenate(
(self.col_list, np.zeros(dataBlock, dtype=np.int32)))
self.data_list = np.concatenate(
(self.data_list, np.zeros(dataBlock,
dtype=np.float64)))
if is_common[index]:
# If cell exists in target matrix, add its value
# Otherwise it will remain zero with a certain probability
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
new_data_value = self.S_matrix_target[row_index, col_index]
if self.normalize_similarity:
new_data_value *= sum_of_squared_features[
row_index] * sum_of_squared_features[col_index]
self.data_list[num_samples] = new_data_value
num_samples += 1
elif np.random.rand() <= self.add_zeros_quota:
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
self.data_list[num_samples] = 0.0
num_samples += 1
if self.verbose and (time.time() - start_time_batch > 30
or num_samples == S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota)):
print(self.RECOMMENDER_NAME +
": Generating train data. Sample {} ({:4.1f}%) ".format(
num_samples, num_samples / S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 100))
sys.stdout.flush()
sys.stderr.flush()
start_time_batch = time.time()
self._print(
"Content S structure has {} out of {} ({:4.1f}%) nonzero collaborative cells"
.format(num_common_coordinates, S_matrix_contentKNN.nnz,
num_common_coordinates / S_matrix_contentKNN.nnz * 100))
# Discard extra cells at the left of the array
self.row_list = self.row_list[:num_samples]
self.col_list = self.col_list[:num_samples]
self.data_list = self.data_list[:num_samples]
data_nnz = sum(np.array(self.data_list) != 0)
data_sum = sum(self.data_list)
collaborative_nnz = self.S_matrix_target.nnz
collaborative_sum = sum(self.S_matrix_target.data)
self._print(
"Nonzero collaborative cell sum is: {:.2E}, average is: {:.2E}, "
"average over all collaborative data is {:.2E}".format(
data_sum, data_sum / data_nnz,
collaborative_sum / collaborative_nnz))
def compute_W_sparse(self, use_D=True, use_V=True, model_to_use="best"):
assert model_to_use in [
"last", "best"
], "{}: compute_W_sparse, 'model_to_use' parameter not recognized".format(
self.RECOMMENDER_NAME)
if self.verbose:
print("FBSM_Rating_Cython: Building similarity matrix...")
start_time = time.time()
start_time_print_batch = start_time
# Diagonal
if use_D:
if model_to_use == "last":
D = self.D_incremental
else:
D = self.D_best
similarity = Compute_Similarity(self.ICM.T,
shrink=0,
topK=self.topK,
normalize=False,
row_weights=D)
self.W_sparse = similarity.compute_similarity()
else:
self.W_sparse = sps.csr_matrix((self.n_items, self.n_items))
if use_V:
if model_to_use == "last":
V = self.V_incremental
else:
V = self.V_best
# V * V.T
W1 = self.ICM.dot(V.T)
#self.W_sparse += W1.dot(W1.T)
# Use array as it reduces memory requirements compared to lists
dataBlock = 10000000
values = np.zeros(dataBlock, dtype=np.float32)
rows = np.zeros(dataBlock, dtype=np.int32)
cols = np.zeros(dataBlock, dtype=np.int32)
numCells = 0
for numItem in range(self.n_items):
V_weights = W1[numItem, :].dot(W1.T)
V_weights[numItem] = 0.0
relevant_items_partition = (
-V_weights).argpartition(self.topK - 1)[0:self.topK]
relevant_items_partition_sorting = np.argsort(
-V_weights[relevant_items_partition])
top_k_idx = relevant_items_partition[
relevant_items_partition_sorting]
# Incrementally build sparse matrix, do not add zeros
notZerosMask = V_weights[top_k_idx] != 0.0
numNotZeros = np.sum(notZerosMask)
values_to_add = V_weights[top_k_idx][notZerosMask]
rows_to_add = top_k_idx[notZerosMask]
cols_to_add = np.ones(numNotZeros) * numItem
for index in range(len(values_to_add)):
if numCells == len(rows):
rows = np.concatenate(
(rows, np.zeros(dataBlock, dtype=np.int32)))
cols = np.concatenate(
(cols, np.zeros(dataBlock, dtype=np.int32)))
values = np.concatenate(
(values, np.zeros(dataBlock, dtype=np.float32)))
rows[numCells] = rows_to_add[index]
cols[numCells] = cols_to_add[index]
values[numCells] = values_to_add[index]
numCells += 1
if self.verbose and (time.time() - start_time_print_batch >= 30
or numItem == self.n_items - 1):
columnPerSec = numItem / (time.time() - start_time)
print(
"Weighted similarity column {} ( {:2.0f} % ), {:.2f} column/sec, elapsed time {:.2f} min"
.format(numItem, numItem / self.n_items * 100,
columnPerSec, (time.time() - start_time) / 60))
sys.stdout.flush()
sys.stderr.flush()
start_time_print_batch = time.time()
V_weights = sps.csr_matrix(
(values[:numCells], (rows[:numCells], cols[:numCells])),
shape=(self.n_items, self.n_items),
dtype=np.float32)
self.W_sparse += V_weights
self.W_sparse = check_matrix(self.W_sparse, format='csr')
if self.verbose:
print("FBSM_Rating_Cython: Building similarity matrix... complete")
class CFW_D_Similarity_Linalg(BaseItemCBFRecommender,
BaseItemSimilarityMatrixRecommender):
RECOMMENDER_NAME = "CFW_D_Similarity_Linalg"
def __init__(self, URM_train, ICM_train, S_matrix_target):
super(CFW_D_Similarity_Linalg, self).__init__(URM_train, ICM_train)
if (URM_train.shape[1] != ICM_train.shape[0]):
raise ValueError(
"Number of items not consistent. URM contains {} but ICM contains {}"
.format(URM_train.shape[1], ICM_train.shape[0]))
if (S_matrix_target.shape[0] != S_matrix_target.shape[1]):
raise ValueError(
"Items imilarity matrix is not square: rows are {}, columns are {}"
.format(S_matrix_target.shape[0], S_matrix_target.shape[1]))
if (S_matrix_target.shape[0] != ICM_train.shape[0]):
raise ValueError(
"Number of items not consistent. S_matrix contains {} but ICM contains {}"
.format(S_matrix_target.shape[0], ICM_train.shape[0]))
self.S_matrix_target = check_matrix(S_matrix_target, 'csr')
self.ICM = check_matrix(ICM_train, 'csr')
self.n_features = self.ICM.shape[1]
def fit(self,
show_max_performance=False,
loss_tolerance=1e-6,
iteration_limit=50000,
damp_coeff=0.0,
topK=300,
add_zeros_quota=0.0,
normalize_similarity=False):
self.show_max_performance = show_max_performance
self.add_zeros_quota = add_zeros_quota
self.normalize_similarity = normalize_similarity
self.topK = topK
self._generate_train_data()
commonFeatures = self.ICM[self.row_list].multiply(
self.ICM[self.col_list])
linalg_result = linalg.lsqr(commonFeatures,
self.data_list,
show=False,
atol=loss_tolerance,
btol=loss_tolerance,
iter_lim=iteration_limit,
damp=damp_coeff)
# res = linalg.lsmr(commonFeatures, self.data_list, show = False, atol=loss_tolerance, btol=loss_tolerance,
# maxiter = iteration_limit, damp=damp_coeff)
self.D_best = linalg_result[0].copy()
self.loss = linalg_result[3]
self.compute_W_sparse()
def _generate_train_data(self):
if self.verbose:
print(self.RECOMMENDER_NAME + ": Generating train data")
start_time_batch = time.time()
# Here is important only the structure
self.similarity = Compute_Similarity(self.ICM.T,
shrink=0,
topK=self.topK,
normalize=False)
S_matrix_contentKNN = self.similarity.compute_similarity()
S_matrix_contentKNN = check_matrix(S_matrix_contentKNN, "csr")
self._print("Collaborative S density: {:.2E}, nonzero cells {}".format(
self.S_matrix_target.nnz / self.S_matrix_target.shape[0]**2,
self.S_matrix_target.nnz))
self._print("Content S density: {:.2E}, nonzero cells {}".format(
S_matrix_contentKNN.nnz / S_matrix_contentKNN.shape[0]**2,
S_matrix_contentKNN.nnz))
if self.normalize_similarity:
# Compute sum of squared
sum_of_squared_features = np.array(
self.ICM.T.power(2).sum(axis=0)).ravel()
sum_of_squared_features = np.sqrt(sum_of_squared_features)
num_common_coordinates = 0
estimated_n_samples = int(S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 1.2)
self.row_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.col_list = np.zeros(estimated_n_samples, dtype=np.int32)
self.data_list = np.zeros(estimated_n_samples, dtype=np.float64)
num_samples = 0
for row_index in range(self.n_items):
start_pos_content = S_matrix_contentKNN.indptr[row_index]
end_pos_content = S_matrix_contentKNN.indptr[row_index + 1]
content_coordinates = S_matrix_contentKNN.indices[
start_pos_content:end_pos_content]
start_pos_target = self.S_matrix_target.indptr[row_index]
end_pos_target = self.S_matrix_target.indptr[row_index + 1]
target_coordinates = self.S_matrix_target.indices[
start_pos_target:end_pos_target]
# Chech whether the content coordinate is associated to a non zero target value
# If true, the content coordinate has a collaborative non-zero value
# if false, the content coordinate has a collaborative zero value
is_common = np.in1d(content_coordinates, target_coordinates)
num_common_in_current_row = is_common.sum()
num_common_coordinates += num_common_in_current_row
for index in range(len(is_common)):
if num_samples == estimated_n_samples:
dataBlock = 1000000
self.row_list = np.concatenate(
(self.row_list, np.zeros(dataBlock, dtype=np.int32)))
self.col_list = np.concatenate(
(self.col_list, np.zeros(dataBlock, dtype=np.int32)))
self.data_list = np.concatenate(
(self.data_list, np.zeros(dataBlock,
dtype=np.float64)))
if is_common[index]:
# If cell exists in target matrix, add its value
# Otherwise it will remain zero with a certain probability
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
new_data_value = self.S_matrix_target[row_index, col_index]
if self.normalize_similarity:
new_data_value *= sum_of_squared_features[
row_index] * sum_of_squared_features[col_index]
self.data_list[num_samples] = new_data_value
num_samples += 1
elif np.random.rand() <= self.add_zeros_quota:
col_index = content_coordinates[index]
self.row_list[num_samples] = row_index
self.col_list[num_samples] = col_index
self.data_list[num_samples] = 0.0
num_samples += 1
if self.verbose and (time.time() - start_time_batch > 30
or num_samples == S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota)):
print(self.RECOMMENDER_NAME +
": Generating train data. Sample {} ({:4.1f}%) ".format(
num_samples, num_samples / S_matrix_contentKNN.nnz *
(1 + self.add_zeros_quota) * 100))
sys.stdout.flush()
sys.stderr.flush()
start_time_batch = time.time()
self._print(
"Content S structure has {} out of {} ({:4.1f}%) nonzero collaborative cells"
.format(num_common_coordinates, S_matrix_contentKNN.nnz,
num_common_coordinates / S_matrix_contentKNN.nnz * 100))
# Discard extra cells at the left of the array
self.row_list = self.row_list[:num_samples]
self.col_list = self.col_list[:num_samples]
self.data_list = self.data_list[:num_samples]
data_nnz = sum(np.array(self.data_list) != 0)
data_sum = sum(self.data_list)
collaborative_nnz = self.S_matrix_target.nnz
collaborative_sum = sum(self.S_matrix_target.data)
self._print(
"Nonzero collaborative cell sum is: {:.2E}, average is: {:.2E}, "
"average over all collaborative data is {:.2E}".format(
data_sum, data_sum / data_nnz,
collaborative_sum / collaborative_nnz))
def compute_W_sparse(self):
self.similarity = Compute_Similarity(
self.ICM.T,
shrink=0,
topK=self.topK,
normalize=self.normalize_similarity,
row_weights=self.D_best)
self.W_sparse = self.similarity.compute_similarity()
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def save_model(self, folder_path, file_name=None):
if file_name is None:
file_name = self.RECOMMENDER_NAME
print("{}: Saving model in file '{}'".format(self.RECOMMENDER_NAME,
folder_path + file_name))
data_dict_to_save = {
"D_best": self.D_best,
"topK": self.topK,
"W_sparse": self.W_sparse,
"normalize_similarity": self.normalize_similarity
}
dataIO = DataIO(folder_path=folder_path)
dataIO.save_data(file_name=file_name,
data_dict_to_save=data_dict_to_save)
print("{}: Saving complete".format(self.RECOMMENDER_NAME))