def __init__(self, URM_train, ICM, S_matrix_target):
super(CFW_D_Similarity_Linalg, self).__init__(URM_train)
if (URM_train.shape[1] != ICM.shape[0]):
raise ValueError(
"Number of items not consistent. URM contains {} but ICM contains {}"
.format(URM_train.shape[1], ICM.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.shape[0]):
raise ValueError(
"Number of items not consistent. S_matrix contains {} but ICM contains {}"
.format(S_matrix_target.shape[0], ICM.shape[0]))
self.S_matrix_target = check_matrix(S_matrix_target, 'csr')
self.ICM = check_matrix(ICM, 'csr')
self.n_items = self.URM_train.shape[1]
self.n_users = self.URM_train.shape[0]
self.n_features = self.ICM.shape[1]
self.sparse_weights = True
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.URM_train = self.URM_train.astype(np.float32)
self.URM_train = okapi_BM_25(self.URM_train.T).T
self.URM_train = check_matrix(self.URM_train, 'csr')
elif feature_weighting == "TF-IDF":
self.URM_train = self.URM_train.astype(np.float32)
self.URM_train = TF_IDF(self.URM_train.T).T
self.URM_train = check_matrix(self.URM_train, 'csr')
similarity = Compute_Similarity(self.URM_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 __init__(self, URM_train, Similarity_1, Similarity_2):
super(ItemKNNSimilarityHybridRecommender, self).__init__(URM_train)
if Similarity_1.shape != Similarity_2.shape:
raise ValueError(
"ItemKNNSimilarityHybridRecommender: similarities have different size, S1 is {}, S2 is {}"
.format(Similarity_1.shape, Similarity_2.shape))
# CSR is faster during evaluation
self.Similarity_1 = check_matrix(Similarity_1.copy(), 'csr')
self.Similarity_2 = check_matrix(Similarity_2.copy(), 'csr')
def get_S_incremental_and_set_W(self):
self.S_incremental = self.cythonEpoch.get_S()
if self.train_with_sparse_weights:
self.W_sparse = self.S_incremental
self.W_sparse = check_matrix(self.W_sparse, format='csr')
else:
self.W_sparse = similarityMatrixTopK(self.S_incremental,
k=self.topK)
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def fit(self, lambda_user=10, lambda_item=25):
self.lambda_user = lambda_user
self.lambda_item = lambda_item
self.n_items = self.URM_train.shape[1]
# convert to csc matrix for faster column-wise sum
self.URM_train = check_matrix(self.URM_train, 'csc', dtype=np.float32)
# 1) global average
self.mu = self.URM_train.data.sum(
dtype=np.float32) / self.URM_train.data.shape[0]
# 2) item average bias
# compute the number of non-zero elements for each column
col_nnz = np.diff(self.URM_train.indptr)
# it is equivalent to:
# col_nnz = X.indptr[1:] - X.indptr[:-1]
# and it is **much faster** than
# col_nnz = (X != 0).sum(axis=0)
URM_train_unbiased = self.URM_train.copy()
URM_train_unbiased.data -= self.mu
self.item_bias = URM_train_unbiased.sum(axis=0) / (col_nnz +
self.lambda_item)
self.item_bias = np.asarray(self.item_bias).ravel(
) # converts 2-d matrix to 1-d array without anycopy
# 3) user average bias
# NOTE: the user bias is *useless* for the sake of ranking items. We just show it here for educational purposes.
# first subtract the item biases from each column
# then repeat each element of the item bias vector a number of times equal to col_nnz
# and subtract it from the data vector
URM_train_unbiased.data -= np.repeat(self.item_bias, col_nnz)
# now convert the csc matrix to csr for efficient row-wise computation
URM_train_unbiased_csr = URM_train_unbiased.tocsr()
row_nnz = np.diff(URM_train_unbiased_csr.indptr)
# finally, let's compute the bias
self.user_bias = URM_train_unbiased_csr.sum(
axis=1).ravel() / (row_nnz + self.lambda_user)
# 4) precompute the item ranking by using the item bias only
# the global average and user bias won't change the ranking, so there is no need to use them
#self.item_ranking = np.argsort(self.bi)[::-1]
self.URM_train = check_matrix(self.URM_train, 'csr', dtype=np.float32)
def applyAdjustedCosine(self):
"""
Remove from every data point the average for the corresponding row
:return:
"""
self.dataMatrix = check_matrix(self.dataMatrix, 'csr')
interactionsPerRow = np.diff(self.dataMatrix.indptr)
nonzeroRows = interactionsPerRow > 0
sumPerRow = np.asarray(self.dataMatrix.sum(axis=1)).ravel()
rowAverage = np.zeros_like(sumPerRow)
rowAverage[nonzeroRows] = sumPerRow[nonzeroRows] / interactionsPerRow[
nonzeroRows]
# Split in blocks to avoid duplicating the whole data structure
start_row = 0
end_row = 0
blockSize = 1000
while end_row < self.n_rows:
end_row = min(self.n_rows, end_row + blockSize)
self.dataMatrix.data[self.dataMatrix.indptr[start_row]:self.dataMatrix.indptr[end_row]] -= \
np.repeat(rowAverage[start_row:end_row], interactionsPerRow[start_row:end_row])
start_row += blockSize
def remove_empty_rows_and_cols(URM, ICM=None):
URM = check_matrix(URM, "csr")
rows = URM.indptr
numRatings = np.ediff1d(rows)
user_mask = numRatings >= 1
URM = URM[user_mask, :]
cols = URM.tocsc().indptr
numRatings = np.ediff1d(cols)
item_mask = numRatings >= 1
URM = URM[:, item_mask]
removedUsers = np.arange(0, len(user_mask))[np.logical_not(user_mask)]
removedItems = np.arange(0, len(item_mask))[np.logical_not(item_mask)]
if ICM is not None:
ICM = ICM[item_mask, :]
return URM.tocsr(), ICM.tocsr(), removedUsers, removedItems
return URM.tocsr(), removedUsers, removedItems
def __init__(self, URM_train, verbose=True):
super(BaseClassicRecommender, self).__init__()
self.URM_train = check_matrix(URM_train.copy(),
'csr',
dtype=np.float32)
self.URM_train.eliminate_zeros()
self.n_users, self.n_items = self.URM_train.shape
self.verbose = verbose
self.filterTopPop = False
self.filterTopPop_ItemsID = np.array([], dtype=np.int)
self.items_to_ignore_flag = False
self.items_to_ignore_ID = np.array([], dtype=np.int)
self._cold_user_mask = np.ediff1d(self.URM_train.indptr) == 0
if self._cold_user_mask.any():
self._print("URM Detected {} ({:.2f} %) cold users.".format(
self._cold_user_mask.sum(),
self._cold_user_mask.sum() / self.n_users * 100))
self._cold_item_mask = np.ediff1d(self.URM_train.tocsc().indptr) == 0
if self._cold_item_mask.any():
self._print("URM Detected {} ({:.2f} %) cold items.".format(
self._cold_item_mask.sum(),
self._cold_item_mask.sum() / self.n_items * 100))
def applyPearsonCorrelation(self):
"""
Remove from every data point the average for the corresponding column
:return:
"""
self.dataMatrix = check_matrix(self.dataMatrix, 'csc')
interactionsPerCol = np.diff(self.dataMatrix.indptr)
nonzeroCols = interactionsPerCol > 0
sumPerCol = np.asarray(self.dataMatrix.sum(axis=0)).ravel()
colAverage = np.zeros_like(sumPerCol)
colAverage[nonzeroCols] = sumPerCol[nonzeroCols] / interactionsPerCol[
nonzeroCols]
# Split in blocks to avoid duplicating the whole data structure
start_col = 0
end_col = 0
blockSize = 1000
while end_col < self.n_columns:
end_col = min(self.n_columns, end_col + blockSize)
self.dataMatrix.data[self.dataMatrix.indptr[start_col]:self.dataMatrix.indptr[end_col]] -= \
np.repeat(colAverage[start_col:end_col], interactionsPerCol[start_col:end_col])
start_col += blockSize
def __init__(self, URM_recommendations_items):
super(PredefinedListRecommender, self).__init__()
# convert to csc matrix for faster column-wise sum
self.URM_recommendations = check_matrix(URM_recommendations_items,
'csr',
dtype=np.int)
self.URM_train = sps.csr_matrix((self.URM_recommendations.shape))
def _build_confidence_matrix(self, confidence_scaling):
if confidence_scaling == 'linear':
self.C = self._linear_scaling_confidence()
else:
self.C = self._log_scaling_confidence()
self.C_csc = check_matrix(self.C.copy(),
format="csc",
dtype=np.float32)
def fit(self, topK=100, alpha=0.5):
self.topK = topK
self.alpha = alpha
W_sparse = self.Similarity_1 * self.alpha + self.Similarity_2 * (
1 - self.alpha)
self.W_sparse = similarityMatrixTopK(W_sparse, k=self.topK)
self.W_sparse = check_matrix(self.W_sparse, format='csr')
def fit(self, W_sparse, selectTopK=False, topK=100):
assert W_sparse.shape[0] == W_sparse.shape[1],\
"ItemKNNCustomSimilarityRecommender: W_sparse matrice is not square. Current shape is {}".format(W_sparse.shape)
assert self.URM_train.shape[1] == W_sparse.shape[0],\
"ItemKNNCustomSimilarityRecommender: URM_train and W_sparse matrices are not consistent. " \
"The number of columns in URM_train must be equal to the rows in W_sparse. " \
"Current shapes are: URM_train {}, W_sparse {}".format(self.URM_train.shape, W_sparse.shape)
if selectTopK:
W_sparse = similarityMatrixTopK(W_sparse, k=topK)
self.W_sparse = check_matrix(W_sparse, format='csr')
def removeFeatures(ICM,
minOccurrence=5,
maxPercOccurrence=0.30,
reconcile_mapper=None):
"""
The function eliminates the values associated to feature occurring in less than the minimal percentage of items
or more then the max. Shape of ICM is reduced deleting features.
:param ICM:
:param minPercOccurrence:
:param maxPercOccurrence:
:param reconcile_mapper: DICT mapper [token] -> index
:return: ICM
:return: deletedFeatures
:return: DICT mapper [token] -> index
"""
ICM = check_matrix(ICM, 'csc')
n_items = ICM.shape[0]
cols = ICM.indptr
numOccurrences = np.ediff1d(cols)
feature_mask = np.logical_and(
numOccurrences >= minOccurrence,
numOccurrences <= n_items * maxPercOccurrence)
ICM = ICM[:, feature_mask]
deletedFeatures = np.arange(
0, len(feature_mask))[np.logical_not(feature_mask)]
print(
"RemoveFeatures: removed {} features with less then {} occurrencies, removed {} features with more than {} occurrencies"
.format(sum(numOccurrences < minOccurrence), minOccurrence,
sum(numOccurrences > n_items * maxPercOccurrence),
int(n_items * maxPercOccurrence)))
if reconcile_mapper is not None:
reconcile_mapper = reconcile_mapper_with_removed_tokens(
reconcile_mapper, deletedFeatures)
return ICM, deletedFeatures, reconcile_mapper
return ICM, deletedFeatures
def fit(self,
l1_ratio=0.1,
positive_only=True,
topK=100,
workers=multiprocessing.cpu_count()):
assert l1_ratio >= 0 and l1_ratio <= 1, "SLIM_ElasticNet: l1_ratio must be between 0 and 1, provided value was {}".format(
l1_ratio)
self.l1_ratio = l1_ratio
self.positive_only = positive_only
self.topK = topK
self.workers = workers
self.URM_train = check_matrix(self.URM_train, 'csc', dtype=np.float32)
n_items = self.URM_train.shape[1]
# fit item's factors in parallel
#oggetto riferito alla funzione nel quale predefinisco parte dell'input
_pfit = partial(self._partial_fit, X=self.URM_train, topK=self.topK)
#creo un pool con un certo numero di processi
pool = Pool(processes=self.workers)
#avvio il pool passando la funzione (con la parte fissa dell'input)
#e il rimanente parametro, variabile
res = pool.map(_pfit, np.arange(n_items))
# res contains a vector of (values, rows, cols) tuples
values, rows, cols = [], [], []
for values_, rows_, cols_ in res:
values.extend(values_)
rows.extend(rows_)
cols.extend(cols_)
# generate the sparse weight matrix
self.W_sparse = sps.csr_matrix((values, (rows, cols)),
shape=(n_items, n_items),
dtype=np.float32)
def _linear_scaling_confidence(self):
C = check_matrix(self.URM_train, format="csr", dtype=np.float32)
C.data = 1.0 + self.alpha * C.data
return C
def compute_similarity(self, start_col=None, end_col=None, block_size=100):
"""
Compute the similarity for the given dataset
:param self:
:param start_col: column to begin with
:param end_col: column to stop before, end_col is excluded
:return:
"""
values = []
rows = []
cols = []
start_time = time.time()
start_time_print_batch = start_time
processedItems = 0
if self.adjusted_cosine:
self.applyAdjustedCosine()
elif self.pearson_correlation:
self.applyPearsonCorrelation()
elif self.tanimoto_coefficient or self.dice_coefficient or self.tversky_coefficient:
self.useOnlyBooleanInteractions()
# We explore the matrix column-wise
self.dataMatrix = check_matrix(self.dataMatrix, 'csc')
# Compute sum of squared values to be used in normalization
sumOfSquared = np.array(self.dataMatrix.power(2).sum(axis=0)).ravel()
# Tanimoto does not require the square root to be applied
if not (self.tanimoto_coefficient or self.dice_coefficient
or self.tversky_coefficient):
sumOfSquared = np.sqrt(sumOfSquared)
if self.asymmetric_cosine:
sumOfSquared_to_1_minus_alpha = np.power(
sumOfSquared, 2 * (1 - self.asymmetric_alpha))
sumOfSquared_to_alpha = np.power(sumOfSquared,
2 * self.asymmetric_alpha)
self.dataMatrix = check_matrix(self.dataMatrix, 'csc')
start_col_local = 0
end_col_local = self.n_columns
if start_col is not None and start_col > 0 and start_col < self.n_columns:
start_col_local = start_col
if end_col is not None and end_col > start_col_local and end_col < self.n_columns:
end_col_local = end_col
start_col_block = start_col_local
this_block_size = 0
# Compute all similarities for each item using vectorization
while start_col_block < end_col_local:
end_col_block = min(start_col_block + block_size, end_col_local)
this_block_size = end_col_block - start_col_block
# All data points for a given item
item_data = self.dataMatrix[:, start_col_block:end_col_block]
item_data = item_data.toarray().squeeze()
# If only 1 feature avoid last dimension to disappear
if item_data.ndim == 1:
item_data = np.atleast_2d(item_data)
if self.use_row_weights:
this_block_weights = self.dataMatrix_weighted.T.dot(item_data)
else:
# Compute item similarities
this_block_weights = self.dataMatrix.T.dot(item_data)
for col_index_in_block in range(this_block_size):
if this_block_size == 1:
this_column_weights = this_block_weights
else:
this_column_weights = this_block_weights[:,
col_index_in_block]
columnIndex = col_index_in_block + start_col_block
this_column_weights[columnIndex] = 0.0
# Apply normalization and shrinkage, ensure denominator != 0
if self.normalize:
if self.asymmetric_cosine:
denominator = sumOfSquared_to_alpha[
columnIndex] * sumOfSquared_to_1_minus_alpha + self.shrink + 1e-6
else:
denominator = sumOfSquared[
columnIndex] * sumOfSquared + self.shrink + 1e-6
this_column_weights = np.multiply(this_column_weights,
1 / denominator)
# Apply the specific denominator for Tanimoto
elif self.tanimoto_coefficient:
denominator = sumOfSquared[
columnIndex] + sumOfSquared - this_column_weights + self.shrink + 1e-6
this_column_weights = np.multiply(this_column_weights,
1 / denominator)
elif self.dice_coefficient:
denominator = sumOfSquared[
columnIndex] + sumOfSquared + self.shrink + 1e-6
this_column_weights = np.multiply(this_column_weights,
1 / denominator)
elif self.tversky_coefficient:
denominator = this_column_weights + \
(sumOfSquared[columnIndex] - this_column_weights)*self.tversky_alpha + \
(sumOfSquared - this_column_weights)*self.tversky_beta + self.shrink + 1e-6
this_column_weights = np.multiply(this_column_weights,
1 / denominator)
# If no normalization or tanimoto is selected, apply only shrink
elif self.shrink != 0:
this_column_weights = this_column_weights / self.shrink
#this_column_weights = this_column_weights.toarray().ravel()
# Sort indices and select TopK
# Sorting is done in three steps. Faster then plain np.argsort for higher number of items
# - Partition the data to extract the set of relevant items
# - Sort only the relevant items
# - Get the original item index
relevant_items_partition = (
-this_column_weights).argpartition(self.TopK -
1)[0:self.TopK]
relevant_items_partition_sorting = np.argsort(
-this_column_weights[relevant_items_partition])
top_k_idx = relevant_items_partition[
relevant_items_partition_sorting]
# Incrementally build sparse matrix, do not add zeros
notZerosMask = this_column_weights[top_k_idx] != 0.0
numNotZeros = np.sum(notZerosMask)
values.extend(this_column_weights[top_k_idx][notZerosMask])
rows.extend(top_k_idx[notZerosMask])
cols.extend(np.ones(numNotZeros) * columnIndex)
# Add previous block size
processedItems += this_block_size
if time.time(
) - start_time_print_batch >= 30 or end_col_block == end_col_local:
columnPerSec = processedItems / (time.time() - start_time +
1e-9)
print(
"Similarity column {} ( {:2.0f} % ), {:.2f} column/sec, elapsed time {:.2f} min"
.format(
processedItems, processedItems /
(end_col_local - start_col_local) * 100, columnPerSec,
(time.time() - start_time) / 60))
sys.stdout.flush()
sys.stderr.flush()
start_time_print_batch = time.time()
start_col_block += block_size
# End while on columns
W_sparse = sps.csr_matrix((values, (rows, cols)),
shape=(self.n_columns, self.n_columns),
dtype=np.float32)
return W_sparse
def fit(self,
topK=500,
alpha=.5,
min_rating=0,
implicit=True,
normalize_similarity=False):
self.topK = topK
self.alpha = alpha
self.min_rating = min_rating
self.implicit = implicit
self.normalize_similarity = normalize_similarity
#
# if X.dtype != np.float32:
# print("P3ALPHA fit: For memory usage reasons, we suggest to use np.float32 as dtype for the dataset")
if self.min_rating > 0:
self.URM_train.data[self.URM_train.data < self.min_rating] = 0
self.URM_train.eliminate_zeros()
if self.implicit:
self.URM_train.data = np.ones(self.URM_train.data.size,
dtype=np.float32)
# Pui is the row-normalized urm
Pui = normalize(self.URM_train, norm='l1', axis=1)
# Piu is the column-normalized, "boolean" urm transposed
X_bool = self.URM_train.transpose(copy=True)
X_bool.data = np.ones(X_bool.data.size, np.float32)
# ATTENTION: axis is still 1 because i transposed before the normalization
Piu = normalize(X_bool, norm='l1', axis=1)
del (X_bool)
# Alfa power
if self.alpha != 1.:
Pui = Pui.power(self.alpha)
Piu = Piu.power(self.alpha)
# Final matrix is computed as Pui * Piu * Pui
# Multiplication unpacked for memory usage reasons
block_dim = 200
d_t = Piu
# Use array as it reduces memory requirements compared to lists
dataBlock = 10000000
rows = np.zeros(dataBlock, dtype=np.int32)
cols = np.zeros(dataBlock, dtype=np.int32)
values = np.zeros(dataBlock, dtype=np.float32)
numCells = 0
start_time = time.time()
start_time_printBatch = start_time
for current_block_start_row in range(0, Pui.shape[1], block_dim):
if current_block_start_row + block_dim > Pui.shape[1]:
block_dim = Pui.shape[1] - current_block_start_row
similarity_block = d_t[
current_block_start_row:current_block_start_row +
block_dim, :] * Pui
similarity_block = similarity_block.toarray()
for row_in_block in range(block_dim):
row_data = similarity_block[row_in_block, :]
row_data[current_block_start_row + row_in_block] = 0
best = row_data.argsort()[::-1][:self.topK]
notZerosMask = row_data[best] != 0.0
values_to_add = row_data[best][notZerosMask]
cols_to_add = best[notZerosMask]
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] = current_block_start_row + row_in_block
cols[numCells] = cols_to_add[index]
values[numCells] = values_to_add[index]
numCells += 1
if time.time() - start_time_printBatch > 60:
print(
"Processed {} ( {:.2f}% ) in {:.2f} minutes. Rows per second: {:.0f}"
.format(
current_block_start_row,
100.0 * float(current_block_start_row) / Pui.shape[1],
(time.time() - start_time) / 60,
float(current_block_start_row) /
(time.time() - start_time)))
sys.stdout.flush()
sys.stderr.flush()
start_time_printBatch = time.time()
self.W_sparse = sps.csr_matrix(
(values[:numCells], (rows[:numCells], cols[:numCells])),
shape=(Pui.shape[1], Pui.shape[1]))
if self.normalize_similarity:
self.W_sparse = normalize(self.W_sparse, norm='l1', axis=1)
if self.topK != False:
self.W_sparse = similarityMatrixTopK(self.W_sparse, k=self.topK)
self.W_sparse = check_matrix(self.W_sparse, format='csr')
self.recs = self.URM_train.dot(self.W_sparse)
def set_URM_train(self,
URM_train_new,
estimate_model_for_cold_users=False,
topK=100,
**kwargs):
"""
:param URM_train_new:
:param estimate_item_similarity_for_cold_users: Set to TRUE if you want to estimate the item-item similarity for cold users to be used as in a KNN algorithm
:param topK: 100
:param kwargs:
:return:
"""
assert self.URM_train.shape == URM_train_new.shape, "{}: set_URM_train old and new URM train have different shapes".format(
self.RECOMMENDER_NAME)
if len(kwargs) > 0:
print(
"{}: set_URM_train keyword arguments not supported for this recommender class. Received: {}"
.format(self.RECOMMENDER_NAME, kwargs))
self.URM_train = check_matrix(URM_train_new.copy(),
'csr',
dtype=np.float32)
self.URM_train.eliminate_zeros()
if estimate_model_for_cold_users == "itemKNN":
print("{}: Estimating ItemKNN model from ITEM latent factors...".
format(self.RECOMMENDER_NAME))
W_sparse = compute_W_sparse_from_item_latent_factors(
self.ITEM_factors, topK=topK)
self._ItemKNNRecommender = ItemKNNCustomSimilarityRecommender(
self.URM_train)
self._ItemKNNRecommender.fit(W_sparse, topK=topK)
self._cold_user_KNN_model_available = True
self._warm_user_KNN_mask = np.ediff1d(self.URM_train.indptr) > 0
print(
"{}: Estimating ItemKNN model from ITEM latent factors... done!"
.format(self.RECOMMENDER_NAME))
elif estimate_model_for_cold_users == "mean_item_factors":
print(
"{}: Estimating USER latent factors from ITEM latent factors..."
.format(self.RECOMMENDER_NAME))
self._cold_user_mask = np.ediff1d(self.URM_train.indptr) == 0
profile_length = np.ediff1d(self.URM_train.indptr)
profile_length_sqrt = np.sqrt(profile_length)
self.USER_factors = self.URM_train.dot(self.ITEM_factors)
#Divide every row for the sqrt of the profile length
for user_index in range(self.n_users):
if profile_length_sqrt[user_index] > 0:
self.USER_factors[
user_index, :] /= profile_length_sqrt[user_index]
print(
"{}: Estimating USER latent factors from ITEM latent factors... done!"
.format(self.RECOMMENDER_NAME))
def _log_scaling_confidence(self):
C = check_matrix(self.URM_train, format="csr", dtype=np.float32)
C.data = 1.0 + self.alpha * np.log(1.0 + C.data / self.epsilon)
return C
def fit(self,
l1_ratio=0.1,
alpha=1.0,
positive_only=True,
topK=100,
verbose=True):
assert l1_ratio >= 0 and l1_ratio <= 1, "{}: l1_ratio must be between 0 and 1, provided value was {}".format(
self.RECOMMENDER_NAME, l1_ratio)
self.l1_ratio = l1_ratio
self.positive_only = positive_only
self.topK = topK
# initialize the ElasticNet model
self.model = ElasticNet(alpha=alpha,
l1_ratio=self.l1_ratio,
positive=self.positive_only,
fit_intercept=False,
copy_X=False,
precompute=True,
selection='random',
max_iter=100,
tol=1e-4)
URM_train = check_matrix(self.URM_train, 'csc', dtype=np.float32)
n_items = URM_train.shape[1]
# Use array as it reduces memory requirements compared to lists
dataBlock = 10000000
rows = np.zeros(dataBlock, dtype=np.int32)
cols = np.zeros(dataBlock, dtype=np.int32)
values = np.zeros(dataBlock, dtype=np.float32)
numCells = 0
start_time = time.time()
start_time_printBatch = start_time
# fit each item's factors sequentially (not in parallel)
for currentItem in range(n_items):
# get the target column
y = URM_train[:, currentItem].toarray()
# set the j-th column of X to zero
start_pos = URM_train.indptr[currentItem]
end_pos = URM_train.indptr[currentItem + 1]
current_item_data_backup = URM_train.data[start_pos:end_pos].copy()
URM_train.data[start_pos:end_pos] = 0.0
# fit one ElasticNet model per column
self.model.fit(URM_train, y)
# self.model.coef_ contains the coefficient of the ElasticNet model
# let's keep only the non-zero values
# Select topK values
# Sorting is done in three steps. Faster then plain np.argsort for higher number of items
# - Partition the data to extract the set of relevant items
# - Sort only the relevant items
# - Get the original item index
# nonzero_model_coef_index = self.model.coef_.nonzero()[0]
# nonzero_model_coef_value = self.model.coef_[nonzero_model_coef_index]
nonzero_model_coef_index = self.model.sparse_coef_.indices
nonzero_model_coef_value = self.model.sparse_coef_.data
local_topK = min(len(nonzero_model_coef_value) - 1, self.topK)
relevant_items_partition = (
-nonzero_model_coef_value
).argpartition(local_topK)[0:local_topK]
relevant_items_partition_sorting = np.argsort(
-nonzero_model_coef_value[relevant_items_partition])
ranking = relevant_items_partition[
relevant_items_partition_sorting]
for index in range(len(ranking)):
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] = nonzero_model_coef_index[ranking[index]]
cols[numCells] = currentItem
values[numCells] = nonzero_model_coef_value[ranking[index]]
numCells += 1
# finally, replace the original values of the j-th column
URM_train.data[start_pos:end_pos] = current_item_data_backup
if verbose and (time.time() - start_time_printBatch > 300
or currentItem == n_items - 1):
print(
"{}: Processed {} ( {:.2f}% ) in {:.2f} minutes. Items per second: {:.0f}"
.format(self.RECOMMENDER_NAME, currentItem + 1,
100.0 * float(currentItem + 1) / n_items,
(time.time() - start_time) / 60,
float(currentItem) / (time.time() - start_time)))
sys.stdout.flush()
sys.stderr.flush()
start_time_printBatch = time.time()
# generate the sparse weight matrix
self.W_sparse = sps.csr_matrix(
(values[:numCells], (rows[:numCells], cols[:numCells])),
shape=(n_items, n_items),
dtype=np.float32)
def _generateTrainData_low_ram(self):
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._writeLog(
self.RECOMMENDER_NAME +
": 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._writeLog(
self.RECOMMENDER_NAME +
": 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 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 {} ( {:.2f} %) ".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._writeLog(
self.RECOMMENDER_NAME +
": Content S structure has {} out of {} ( {:.2f}%) 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._writeLog(
self.RECOMMENDER_NAME +
": 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))
