def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U,
self._S,
post_normalize=post_normalize)
return self._matrix_similarity
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
#Update feature
self._foldinZeroes = {}
self.inv_S = None #since it doesn't get updated so redundent to calculate each time
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
class SVD(Algorithm):
"""
Inherits from base class Algorithm.
It computes SVD (Singular Value Decomposition) on a matrix *M*
It also provides recommendations and predictions using the reconstructed matrix *M'*
:param filename: Path to a Zip file, containing an already computed SVD (U, Sigma, and V) for a matrix *M*
:type filename: string
"""
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
def __repr__(self):
try:
s = '\n'.join(('M\':' + str(self._reconstruct_matrix()), \
'A row (U):' + str(self._reconstruct_matrix().right[1]), \
'A col (V):' + str(self._reconstruct_matrix().left[1])))
except TypeError:
s = self._data.__repr__()
return s
def load_model(self, filename):
"""
Loads SVD transformation (U, Sigma and V matrices) from a ZIP file
:param filename: path to the SVD matrix transformation (a ZIP file)
:type filename: string
"""
try:
zip = zipfile.ZipFile(filename, allowZip64=True)
except:
zip = zipfile.ZipFile(filename + '.zip', allowZip64=True)
# Options file
options = dict()
for line in zip.open('README'):
data = line.strip().split('\t')
options[data[0]] = data[1]
try:
k = int(options['k'])
except:
k = 100 #TODO: nasty!!!
# Load U, S, and V
"""
#Python 2.6 only:
#self._U = loads(zip.open('.U').read())
#self._S = loads(zip.open('.S').read())
#self._V = loads(zip.open('.V').read())
"""
try:
self._U = loads(zip.read('.U'))
except:
matrix = fromfile(zip.extract('.U', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.row_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.row_ids').split('\n') if idx]
#self._U = DenseMatrix(vectors)
self._U = DenseMatrix(vectors, OrderedSet(idx), None)
try:
self._V = loads(zip.read('.V'))
except:
matrix = fromfile(zip.extract('.V', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.col_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.col_ids').split('\n') if idx]
#self._V = DenseMatrix(vectors)
self._V = DenseMatrix(vectors, OrderedSet(idx), None)
self._S = loads(zip.read('.S'))
# Shifts for Mean Centerer Matrix
self._shifts = None
if '.shifts.row' in zip.namelist():
self._shifts = [loads(zip.read('.shifts.row')),
loads(zip.read('.shifts.col')),
loads(zip.read('.shifts.total'))
]
self._reconstruct_matrix(shifts=self._shifts, force=True)
self._reconstruct_similarity(force=True)
def save_model(self, filename, options={}):
"""
Saves SVD transformation (U, Sigma and V matrices) to a ZIP file
:param filename: path to save the SVD matrix transformation (U, Sigma and V matrices)
:type filename: string
:param options: a dict() containing the info about the SVD transformation. E.g. {'k': 100, 'min_values': 5, 'pre_normalize': None, 'mean_center': True, 'post_normalize': True}
:type options: dict
"""
if VERBOSE:
sys.stdout.write('Saving svd model to %s\n' % filename)
f_opt = open(filename + '.config', 'w')
for option, value in options.items():
f_opt.write('\t'.join((option, str(value))) + '\n')
f_opt.close()
# U, S, and V
MAX_VECTORS = 2**21
if len(self._U) < MAX_VECTORS:
self._U.dump(filename + '.U')
else:
self._U.tofile(filename + '.U')
if len(self._V) < MAX_VECTORS:
self._V.dump(filename + '.V')
else:
self._V.tofile(filename + '.V')
self._S.dump(filename + '.S')
# Shifts for Mean Centered Matrix
if self._shifts:
#(row_shift, col_shift, total_shift)
self._shifts[0].dump(filename + '.shifts.row')
self._shifts[1].dump(filename + '.shifts.col')
self._shifts[2].dump(filename + '.shifts.total')
zip = filename
if not filename.endswith('.zip') and not filename.endswith('.ZIP'):
zip += '.zip'
fp = zipfile.ZipFile(zip, 'w', allowZip64=True)
# Store Options in the ZIP file
fp.write(filename=filename + '.config', arcname='README')
os.remove(filename + '.config')
# Store matrices in the ZIP file
for extension in ['.U', '.S', '.V']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store mean center shifts in the ZIP file
if self._shifts:
for extension in ['.shifts.row', '.shifts.col', '.shifts.total']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store row and col ids file, if importing from SVDLIBC
if self._file_row_ids:
fp.write(filename=self._file_row_ids, arcname='.row_ids')
if self._file_col_ids:
fp.write(filename=self._file_col_ids, arcname='.col_ids')
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U, self._S, post_normalize=post_normalize)
return self._matrix_similarity
def _reconstruct_matrix(self, shifts=None, force=True):
if not self._matrix_reconstructed or force:
if shifts:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S, self._V, shifts=shifts)
else:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S, self._V)
return self._matrix_reconstructed
def compute(self, k=100, min_values=None, pre_normalize=None, mean_center=False, post_normalize=True, savefile=None):
"""
Computes SVD on matrix *M*, :math:`M = U \Sigma V^T`
:param k: number of dimensions
:type k: int
:param min_values: min. number of non-zeros (or non-empty values) any row or col must have
:type min_values: int
:param pre_normalize: normalize input matrix. Possible values are tfidf, rows, cols, all.
:type pre_normalize: string
:param mean_center: centering the input matrix (aka mean substraction)
:type mean_center: Boolean
:param post_normalize: Normalize every row of :math:`U \Sigma` to be a unit vector. Thus, row similarity (using cosine distance) returns :math:`[-1.0 .. 1.0]`
:type post_normalize: Boolean
:param savefile: path to save the SVD factorization (U, Sigma and V matrices)
:type savefile: string
"""
super(SVD, self).compute(min_values)
if VERBOSE:
sys.stdout.write('Computing svd k=%s, min_values=%s, pre_normalize=%s, mean_center=%s, post_normalize=%s\n'
% (k, min_values, pre_normalize, mean_center, post_normalize))
if not min_values:
sys.stdout.write('[WARNING] min_values is set to None, meaning that some funky recommendations might appear!\n')
# Get SparseMatrix
matrix = self._matrix.get()
# Mean center?
shifts, row_shift, col_shift, total_shift = (None, None, None, None)
if mean_center:
if VERBOSE:
sys.stdout.write("[WARNING] mean_center is True. svd.similar(...) might return nan's. If so, then do svd.compute(..., mean_center=False)\n")
matrix, row_shift, col_shift, total_shift = matrix.mean_center()
self._shifts = (row_shift, col_shift, total_shift)
# Pre-normalize input matrix?
if pre_normalize:
"""
Divisi2 divides each entry by the geometric mean of its row norm and its column norm.
The rows and columns don't actually become unit vectors, but they all become closer to unit vectors.
"""
if pre_normalize == 'tfidf':
matrix = matrix.normalize_tfidf() #TODO By default, treats the matrix as terms-by-documents;
# pass cols_are_terms=True if the matrix is instead documents-by-terms.
elif pre_normalize == 'rows':
matrix = matrix.normalize_rows()
elif pre_normalize == 'cols':
matrix = matrix.normalize_cols()
elif pre_normalize == 'all':
matrix = matrix.normalize_all()
else:
raise ValueError("Pre-normalize option (%s) is not correct.\n \
Possible values are: 'tfidf', 'rows', 'cols' or 'all'" % pre_normalize)
#Compute SVD(M, k)
self._U, self._S, self._V = matrix.svd(k)
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize, force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if savefile:
options = {'k': k, 'min_values': min_values, 'pre_normalize': pre_normalize, 'mean_center': mean_center, 'post_normalize': post_normalize}
self.save_model(savefile, options)
def _get_row_reconstructed(self, i, zeros=None):
if zeros:
return self._matrix_reconstructed.row_named(i)[zeros]
return self._matrix_reconstructed.row_named(i)
def _get_col_reconstructed(self, j, zeros=None):
if zeros:
return self._matrix_reconstructed.col_named(j)[zeros]
return self._matrix_reconstructed.col_named(j)
def predict(self, i, j, MIN_VALUE=None, MAX_VALUE=None):
"""
Predicts the value of :math:`M_{i,j}`, using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
:param i: row in M, :math:`M_{i \cdot}`
:type i: user or item id
:param j: col in M, :math:`M_{\cdot j}`
:type j: item or user id
:param MIN_VALUE: min. value in M (e.g. in ratings[1..5] => 1)
:type MIN_VALUE: float
:param MAX_VALUE: max. value in M (e.g. in ratings[1..5] => 5)
:type MAX_VALUE: float
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
predicted_value = self._matrix_reconstructed.entry_named(i, j) #M' = U S V^t
if MIN_VALUE:
predicted_value = max(predicted_value, MIN_VALUE)
if MAX_VALUE:
predicted_value = min(predicted_value, MAX_VALUE)
return float(predicted_value)
def recommend(self, i, n=10, only_unknowns=False, is_row=True):
"""
Recommends items to a user (or users to an item) using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
E.g. if *i* is a row and *only_unknowns* is True, it returns the higher values of :math:`M^\prime_{i,\cdot}` :math:`\\forall_j{M_{i,j}=\emptyset}`
:param i: row or col in M
:type i: user or item id
:param n: number of recommendations to return
:type n: int
:param only_unknowns: only return unknown values in *M*? (e.g. items not rated by the user)
:type only_unknowns: Boolean
:param is_row: is param *i* a row (or a col)?
:type is_row: Boolean
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
item = None
zeros = []
if only_unknowns and not self._matrix.get():
raise ValueError("Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called")
if is_row:
if only_unknowns:
zeros = self._matrix.get().row_named(i).zero_entries()
item = self._get_row_reconstructed(i, zeros)
else:
if only_unknowns:
zeros = self._matrix.get().col_named(i).zero_entries()
item = self._get_col_reconstructed(i, zeros)
return item.top_items(n)
def centroid(self, ids, is_row=True):
points = []
for id in ids:
if is_row:
point = self._U.row_named(id)
else:
point = self._V.row_named(id)
points.append(point)
M = divisi2.SparseMatrix(points)
return M.col_op(sum)/len(points) #TODO Numpy.sum?
def kmeans(self, ids, k=5, components=3, are_rows=True):
"""
K-means clustering. It uses k-means++ (http://en.wikipedia.org/wiki/K-means%2B%2B) to choose the initial centroids of the clusters
Clusterizes a list of IDs (either row or cols)
:param ids: list of row (or col) ids to cluster
:param k: number of clusters
:param components: how many eigen values use (from SVD)
:param are_rows: is param *ids* a list of rows (or cols)?
:type are_rows: Boolean
"""
if not isinstance(ids, list):
# Cluster the whole row(or col) values. It's slow!
return super(SVD, self).kmeans(ids, k=k, is_row=are_rows)
if VERBOSE:
sys.stdout.write('Computing k-means, k=%s for ids %s\n' % (k, ids))
MAX_POINTS = 150
points = []
for id in ids:
if are_rows:
points.append(self._U.row_named(id)[:components])
else:
points.append(self._V.row_named(id)[:components])
M = array(points)
# Only apply Matrix initialization if num. points is not that big!
if len(points) <= MAX_POINTS:
centers = self._kinit(array(points), k)
centroids, labels = kmeans2(M, centers, minit='matrix')
else:
centroids, labels = kmeans2(M, k, minit='random')
i = 0
clusters = dict()
for cluster in labels:
if not clusters.has_key(cluster):
clusters[cluster] = dict()
clusters[cluster]['centroid'] = centroids[cluster]
clusters[cluster]['points'] = []
point = self._U.row_named(ids[i])[:components]
centroid = clusters[cluster]['centroid']
to_centroid = self._cosine(centroid, point)
clusters[cluster]['points'].append((ids[i], to_centroid))
clusters[cluster]['points'].sort(key=itemgetter(1), reverse=True)
i += 1
return clusters
'''
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U, self._S, post_normalize=post_normalize)
return self._matrix_similarity
class Baseline(Algorithm):
def __init__(self, filename=None):
#Call parent constructor
super(Baseline, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
def __repr__(self):
try:
s = '\n'.join(('M\':' + str(self._reconstruct_matrix()), \
'A row (U):' + str(self._reconstruct_matrix().right[1]), \
'A col (V):' + str(self._reconstruct_matrix().left[1])))
except TypeError:
s = self._data.__repr__()
return s
def load_model(self, filename):
"""
Loads SVD transformation (U, Sigma and V matrices) from a ZIP file
:param filename: path to the SVD matrix transformation (a ZIP file)
:type filename: string
"""
try:
zip = zipfile.ZipFile(filename, allowZip64=True)
except:
zip = zipfile.ZipFile(filename + '.zip', allowZip64=True)
# Options file
options = dict()
for line in zip.open('README'):
data = line.strip().split('\t')
options[data[0]] = data[1]
try:
k = int(options['k'])
except:
k = 100 #TODO: nasty!!!
# Load U, S, and V
"""
#Python 2.6 only:
#self._U = loads(zip.open('.U').read())
#self._S = loads(zip.open('.S').read())
#self._V = loads(zip.open('.V').read())
"""
try:
self._U = loads(zip.read('.U'))
except:
matrix = fromfile(zip.extract('.U', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.row_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.row_ids').split('\n') if idx]
#self._U = DenseMatrix(vectors)
self._U = DenseMatrix(vectors, OrderedSet(idx), None)
try:
self._V = loads(zip.read('.V'))
except:
matrix = fromfile(zip.extract('.V', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.col_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.col_ids').split('\n') if idx]
#self._V = DenseMatrix(vectors)
self._V = DenseMatrix(vectors, OrderedSet(idx), None)
self._S = loads(zip.read('.S'))
# Shifts for Mean Centerer Matrix
self._shifts = None
if '.shifts.row' in zip.namelist():
self._shifts = [loads(zip.read('.shifts.row')),
loads(zip.read('.shifts.col')),
loads(zip.read('.shifts.total'))
]
self._reconstruct_matrix(shifts=self._shifts, force=True)
self._reconstruct_similarity(force=True)
def save_model(self, filename, options={}):
"""
Saves SVD transformation (U, Sigma and V matrices) to a ZIP file
:param filename: path to save the SVD matrix transformation (U, Sigma and V matrices)
:type filename: string
:param options: a dict() containing the info about the SVD transformation. E.g. {'k': 100, 'min_values': 5, 'pre_normalize': None, 'mean_center': True, 'post_normalize': True}
:type options: dict
"""
if VERBOSE:
sys.stdout.write('Saving svd model to %s\n' % filename)
f_opt = open(filename + '.config', 'w')
for option, value in options.items():
f_opt.write('\t'.join((option, str(value))) + '\n')
f_opt.close()
# U, S, and V
MAX_VECTORS = 2**21
if len(self._U) < MAX_VECTORS:
self._U.dump(filename + '.U')
else:
self._U.tofile(filename + '.U')
if len(self._V) < MAX_VECTORS:
self._V.dump(filename + '.V')
else:
self._V.tofile(filename + '.V')
self._S.dump(filename + '.S')
# Shifts for Mean Centered Matrix
if self._shifts:
#(row_shift, col_shift, total_shift)
self._shifts[0].dump(filename + '.shifts.row')
self._shifts[1].dump(filename + '.shifts.col')
self._shifts[2].dump(filename + '.shifts.total')
zip = filename
if not filename.endswith('.zip') and not filename.endswith('.ZIP'):
zip += '.zip'
fp = zipfile.ZipFile(zip, 'w', allowZip64=True)
# Store Options in the ZIP file
fp.write(filename=filename + '.config', arcname='README')
os.remove(filename + '.config')
# Store matrices in the ZIP file
for extension in ['.U', '.S', '.V']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store mean center shifts in the ZIP file
if self._shifts:
for extension in ['.shifts.row', '.shifts.col', '.shifts.total']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store row and col ids file, if importing from SVDLIBC
if self._file_row_ids:
fp.write(filename=self._file_row_ids, arcname='.row_ids')
if self._file_col_ids:
fp.write(filename=self._file_col_ids, arcname='.col_ids')
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U, self._S,
post_normalize=post_normalize)
return self._matrix_similarity
def _reconstruct_matrix(self, shifts=None, force=True):
if not self._matrix_reconstructed or force:
if shifts:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S,
self._V, shifts=shifts)
else:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S,
self._V, shifts)
return self._matrix_reconstructed
def _get_row_reconstructed(self, i, zeros=None):
if zeros:
return self._matrix_reconstructed.row_named(i)[zeros]
return self._matrix_reconstructed.row_named(i)
def _get_col_reconstructed(self, j, zeros=None):
if zeros:
return self._matrix_reconstructed.col_named(j)[zeros]
return self._matrix_reconstructed.col_named(j)
def compute(self, i, k=100, min_values=None, pre_normalize=None, mean_center=False,
post_normalize=True, savefile=None, v_vectors=None, col_labels=None):
self._V = DenseMatrix(v_vectors, col_labels)
# Sim. matrix = U \Sigma^2 U^T
#self._reconstruct_similarity(post_normalize=post_normalize, force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if savefile:
options = {'k': k, 'min_values': min_values, 'pre_normalize': pre_normalize,
'mean_center': mean_center,'post_normalize': post_normalize}
self.save_model(savefile, options)
def recommend(self, i, n=10, only_unknowns=False, is_row=True, save=False,
v_vectors=None,sparse_matrix_vector=None,
col_labels=None):
db = DBConn()
self.compute(i, k=100, min_values=None, pre_normalize=None, mean_center=False,
post_normalize=True,savefile=save, v_vectors=v_vectors,
col_labels=col_labels) #will use default values!
item = None
zeros = []
if is_row:
if only_unknowns:
zeros = self._matrix.get().row_named(i).zero_entries()
item = self._get_row_reconstructed(i, zeros)
else:
if only_unknowns:
zeros = []
soundcloud_artists = db.get_soundcloud_labels()
for artist in soundcloud_artists:
if not artist["index"] in sparse_matrix_vector:
zeros.append(artist["index"])
item = self._get_col_reconstructed(0, zeros)
return item.top_items(n)
class SVD(Algorithm):
"""
Inherits from base class Algorithm.
It computes SVD (Singular Value Decomposition) on a matrix *M*
It also provides recommendations and predictions using the reconstructed matrix *M'*
:param filename: Path to a Zip file, containing an already computed SVD (U, Sigma, and V) for a matrix *M*
:type filename: string
"""
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
#Update feature
self._foldinZeroes = {}
self.inv_S = None #since it doesn't get updated so redundent to calculate each time
def __repr__(self):
try:
s = '\n'.join(('M\':' + str(self._reconstruct_matrix()), \
'A row (U):' + str(self._reconstruct_matrix().right[1]), \
'A col (V):' + str(self._reconstruct_matrix().left[1])))
except TypeError:
s = self._data.__repr__()
return s
def load_model(self, filename):
"""
Loads SVD transformation (U, Sigma and V matrices) from a ZIP file
:param filename: path to the SVD matrix transformation (a ZIP file)
:type filename: string
"""
try:
zip = zipfile.ZipFile(filename, allowZip64=True)
except:
zip = zipfile.ZipFile(filename + '.zip', allowZip64=True)
# Options file
options = dict()
for line in zip.open('README'):
data = line.strip().split('\t')
options[data[0]] = data[1]
try:
k = int(options['k'])
except:
k = 100 #TODO: nasty!!!
# Load U, S, and V
"""
#Python 2.6 only:
#self._U = loads(zip.open('.U').read())
#self._S = loads(zip.open('.S').read())
#self._V = loads(zip.open('.V').read())
"""
try:
self._U = loads(zip.read('.U'))
except:
matrix = fromfile(zip.extract('.U', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k * i:k * (i + 1)])
vectors.append(v)
i += 1
try:
idx = [
int(idx.strip())
for idx in zip.read('.row_ids').split('\n') if idx
]
except:
idx = [
idx.strip() for idx in zip.read('.row_ids').split('\n')
if idx
]
#self._U = DenseMatrix(vectors)
self._U = DenseMatrix(vectors, OrderedSet(idx), None)
try:
self._V = loads(zip.read('.V'))
except:
matrix = fromfile(zip.extract('.V', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k * i:k * (i + 1)])
vectors.append(v)
i += 1
try:
idx = [
int(idx.strip())
for idx in zip.read('.col_ids').split('\n') if idx
]
except:
idx = [
idx.strip() for idx in zip.read('.col_ids').split('\n')
if idx
]
#self._V = DenseMatrix(vectors)
self._V = DenseMatrix(vectors, OrderedSet(idx), None)
self._S = loads(zip.read('.S'))
# Shifts for Mean Centerer Matrix
self._shifts = None
if '.shifts.row' in zip.namelist():
self._shifts = [
loads(zip.read('.shifts.row')),
loads(zip.read('.shifts.col')),
loads(zip.read('.shifts.total'))
]
self._reconstruct_matrix(shifts=self._shifts, force=True)
self._reconstruct_similarity(force=True)
def save_model(self, filename, options={}):
"""
Saves SVD transformation (U, Sigma and V matrices) to a ZIP file
:param filename: path to save the SVD matrix transformation (U, Sigma and V matrices)
:type filename: string
:param options: a dict() containing the info about the SVD transformation. E.g. {'k': 100, 'min_values': 5, 'pre_normalize': None, 'mean_center': True, 'post_normalize': True}
:type options: dict
"""
if VERBOSE:
sys.stdout.write('Saving svd model to %s\n' % filename)
f_opt = open(filename + '.config', 'w')
for option, value in options.items():
f_opt.write('\t'.join((option, str(value))) + '\n')
f_opt.close()
# U, S, and V
MAX_VECTORS = 2**21
if len(self._U) < MAX_VECTORS:
self._U.dump(filename + '.U')
else:
self._U.tofile(filename + '.U')
if len(self._V) < MAX_VECTORS:
self._V.dump(filename + '.V')
else:
self._V.tofile(filename + '.V')
self._S.dump(filename + '.S')
# Shifts for Mean Centered Matrix
if self._shifts:
#(row_shift, col_shift, total_shift)
self._shifts[0].dump(filename + '.shifts.row')
self._shifts[1].dump(filename + '.shifts.col')
self._shifts[2].dump(filename + '.shifts.total')
zip = filename
if not filename.endswith('.zip') and not filename.endswith('.ZIP'):
zip += '.zip'
fp = zipfile.ZipFile(zip, 'w', allowZip64=True)
# Store Options in the ZIP file
fp.write(filename=filename + '.config', arcname='README')
os.remove(filename + '.config')
# Store matrices in the ZIP file
for extension in ['.U', '.S', '.V']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store mean center shifts in the ZIP file
if self._shifts:
for extension in ['.shifts.row', '.shifts.col', '.shifts.total']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store row and col ids file, if importing from SVDLIBC
if self._file_row_ids:
fp.write(filename=self._file_row_ids, arcname='.row_ids')
if self._file_col_ids:
fp.write(filename=self._file_col_ids, arcname='.col_ids')
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U,
self._S,
post_normalize=post_normalize)
return self._matrix_similarity
def _reconstruct_matrix(self, shifts=None, force=True):
if not self._matrix_reconstructed or force:
if shifts:
self._matrix_reconstructed = divisi2.reconstruct(self._U,
self._S,
self._V,
shifts=shifts)
else:
self._matrix_reconstructed = divisi2.reconstruct(
self._U, self._S, self._V)
return self._matrix_reconstructed
def compute(self,
k=100,
min_values=None,
pre_normalize=None,
mean_center=False,
post_normalize=True,
savefile=None):
"""
Computes SVD on matrix *M*, :math:`M = U \Sigma V^T`
:param k: number of dimensions
:type k: int
:param min_values: min. number of non-zeros (or non-empty values) any row or col must have
:type min_values: int
:param pre_normalize: normalize input matrix. Possible values are tfidf, rows, cols, all.
:type pre_normalize: string
:param mean_center: centering the input matrix (aka mean substraction)
:type mean_center: Boolean
:param post_normalize: Normalize every row of :math:`U \Sigma` to be a unit vector. Thus, row similarity (using cosine distance) returns :math:`[-1.0 .. 1.0]`
:type post_normalize: Boolean
:param savefile: path to save the SVD factorization (U, Sigma and V matrices)
:type savefile: string
"""
super(SVD, self).compute(
min_values
) #creates matrix and does squish to not have empty values
if VERBOSE:
sys.stdout.write(
'Computing svd k=%s, min_values=%s, pre_normalize=%s, mean_center=%s, post_normalize=%s\n'
% (k, min_values, pre_normalize, mean_center, post_normalize))
if not min_values:
sys.stdout.write(
'[WARNING] min_values is set to None, meaning that some funky recommendations might appear!\n'
)
# Get SparseMatrix
matrix = self._matrix.get()
# Mean center?
shifts, row_shift, col_shift, total_shift = (None, None, None, None)
if mean_center:
if VERBOSE:
sys.stdout.write(
"[WARNING] mean_center is True. svd.similar(...) might return nan's. If so, then do svd.compute(..., mean_center=False)\n"
)
matrix, row_shift, col_shift, total_shift = matrix.mean_center()
self._shifts = (row_shift, col_shift, total_shift)
# Pre-normalize input matrix?
if pre_normalize:
"""
Divisi2 divides each entry by the geometric mean of its row norm and its column norm.
The rows and columns don't actually become unit vectors, but they all become closer to unit vectors.
"""
if pre_normalize == 'tfidf':
matrix = matrix.normalize_tfidf(
) #TODO By default, treats the matrix as terms-by-documents;
# pass cols_are_terms=True if the matrix is instead documents-by-terms.
elif pre_normalize == 'rows':
matrix = matrix.normalize_rows()
elif pre_normalize == 'cols':
matrix = matrix.normalize_cols()
elif pre_normalize == 'all':
matrix = matrix.normalize_all()
else:
raise ValueError("Pre-normalize option (%s) is not correct.\n \
Possible values are: 'tfidf', 'rows', 'cols' or 'all'"
% pre_normalize)
#Compute SVD(M, k)
self._U, self._S, self._V = matrix.svd(k)
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize, force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if savefile:
options = {
'k': k,
'min_values': min_values,
'pre_normalize': pre_normalize,
'mean_center': mean_center,
'post_normalize': post_normalize
}
self.save_model(savefile, options)
def _get_row_reconstructed(
self,
i,
zeros=None
): #if foldin that means it is known what the user rated and zeros contains the rated items
if zeros:
return self._matrix_reconstructed.row_named(i)[zeros]
return self._matrix_reconstructed.row_named(i)
def _get_col_reconstructed(self, j, zeros=None):
if zeros:
return self._matrix_reconstructed.col_named(j)[zeros]
return self._matrix_reconstructed.col_named(j)
def _get_row_unrated(
self, i, rated
): # use for foldin since that means users new rated items are known so no need to squish or need normal matrix
sparse_matrix = self._matrix_reconstructed.row_named(i).to_sparse()
# values: np array with the predicted ratings or ratings
# named_rows: normal array with movie names
values, named_cols = sparse_matrix.named_lists(
) #values contains a np array with predicted ratings , while named_cols contains list of labels of columns
removal_indicies = [] #array of indicies for removal
for item in rated:
index_remove = named_cols.index(item)
del named_cols[
index_remove] #since its a normal list can remove like this
removal_indicies.append(index_remove)
values = np.delete(
values, removal_indicies
) #since it's a numpy array so must remove like this
return divisiSparseVector.from_named_lists(values,
named_cols).to_dense()
def _get_col_unrated(
self, j, rated
): # use for foldin since that means users new rated items are known so no need to squish or need normal matrix
sparse_matrix = self._matrix_reconstructed.col_named(j).to_sparse()
# values: np array with the predicted ratings or ratings
# named_rows: normal array with movie names
values, named_rows = sparse_matrix.named_lists()
removal_indicies = []
for item in rated:
index_remove = named_rows.index(item)
del named_rows[index_remove]
removal_indicies.append(index_remove)
values = np.delete(values, removal_indicies)
return divisiSparseVector.from_named_lists(values,
named_rows).to_dense()
def predict(self, i, j, MIN_VALUE=None, MAX_VALUE=None):
"""
Predicts the value of :math:`M_{i,j}`, using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
:param i: row in M, :math:`M_{i \cdot}`
:type i: user or item id
:param j: col in M, :math:`M_{\cdot j}`
:type j: item or user id
:param MIN_VALUE: min. value in M (e.g. in ratings[1..5] => 1)
:type MIN_VALUE: float
:param MAX_VALUE: max. value in M (e.g. in ratings[1..5] => 5)
:type MAX_VALUE: float
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
predicted_value = self._matrix_reconstructed.entry_named(
i, j) #M' = U S V^t
if MIN_VALUE:
predicted_value = max(predicted_value, MIN_VALUE)
if MAX_VALUE:
predicted_value = min(predicted_value, MAX_VALUE)
return float(predicted_value)
def recommend(self, i, n=10, only_unknowns=False, is_row=True):
"""
Recommends items to a user (or users to an item) using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
E.g. if *i* is a row and *only_unknowns* is True, it returns the higher values of :math:`M^\prime_{i,\cdot}` :math:`\\forall_j{M_{i,j}=\emptyset}`
:param i: row or col in M
:type i: user or item id
:param n: number of recommendations to return
:type n: int
:param only_unknowns: only return unknown values in *M*? (e.g. items not rated by the user)
:type only_unknowns: Boolean
:param is_row: is param *i* a row (or a col)?
:type is_row: Boolean
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
item = None
zeros = []
seeDict = False
if only_unknowns and not self._matrix.get() and len(
self._foldinZeroes) == 0:
raise ValueError(
"Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called"
)
if not self._matrix.get():
seeDict = True
if is_row:
if only_unknowns:
if seeDict:
zeros = self._foldinZeroes[
i] #zeros in this instance contains the rated items
if len(zeros) == 0:
raise ValueError(
"Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called or youve just folded them in"
)
else:
item = self._get_row_unrated(
i, zeros
) #removing the rated items from utility row for recommendations
else:
zeros = self._matrix.get().row_named(i).zero_entries()
item = self._get_row_reconstructed(i, zeros)
else:
item = self._get_row_reconstructed(i, zeros)
else:
if only_unknowns:
if seeDict:
zeros = self._foldinZeroes[
i] #zeros in this instance contains the rated items
if len(zeros) == 0:
raise ValueError(
"Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called or you just folded them in"
)
else:
item = self._get_col_unrated(
i, zeros
) #removing the rated items from utility columns for recommendations
else:
zeros = self._matrix.get().col_named(i).zero_entries()
item = self._get_col_reconstructed(i, zeros)
else:
item = self._get_row_reconstructed(i, zeros)
return item.top_items(n)
def _calc_mean_center(
self,
matrix,
is_row=True
): #created this to use the loaded shifts and calculate the row or column shift
row_shift, col_shift, total_shift = self._shifts
total_mean = total_shift # use the global shift one
if is_row:
row_means = matrix.row_op(
np.mean) - total_mean # calculate row shift
col_means = col_shift # use already given col shifts
else:
row_means = row_shift # use already given row shifts
col_means = matrix.col_op(
np.mean) - total_mean # calculate col shifts
row_lengths = matrix.row_op(len)
col_lengths = matrix.col_op(len)
shifted = matrix.copy()
for row, col in shifted.keys():
shifted[row, col] -= (
(row_means[row] * row_lengths[row] +
col_means[col] * col_lengths[col]) /
(row_lengths[row] + col_lengths[col])) + total_mean
return (shifted, row_means, col_means, total_mean)
# return shifted
def load_updateDataTuple_foldin(self,
filename,
force=True,
sep='\t',
format={
'value': 0,
'row': 1,
'col': 2
},
pickle=False,
is_row=True,
truncate=True,
post_normalize=False):
"""
Folds-in a SINGLE user OR item. First loads a dataset file that contains a SINGLE tuple (a dataset for a single user OR item , has to be either same row or same column depending on is_row aka tuple)
For params: filename,force,sep,format,pickle then see params definition in *datamodel.Data.load()*
:param is_row: are you trying to foldin a row or a column ? yes->foldin row , no->foldin column
:type is_row: boolean
:param truncate: sometimes new users rate new items not in the original SVD matrix so would you like new items to be truncated or folded in ? default is foldin
:type truncate: boolean
:param post_normalize: Normalize every row of :math:`U \Sigma` to be a unit vector. Thus, row similarity (using cosine distance) returns :math:`[-1.0 .. 1.0]`
:type post_normalize: Boolean
"""
if force:
self._updateData = Data()
self._updateData.load(filename, force, sep, format, pickle)
if VERBOSE:
print "reading the new tuple"
if (is_row):
nDimensionLabels = self._V.all_labels()[
0] #get labels from V matrix to complete the sparse matrix
print type(nDimensionLabels)
print type(nDimensionLabels[0])
print len(nDimensionLabels)
self._singleUpdateMatrix.create(self._updateData.get(),
col_labels=nDimensionLabels,
foldin=True,
truncate=truncate)
self._foldinZeroes[self._singleUpdateMatrix.get_rows()
[0]] = self._singleUpdateMatrix.get_cols()
else:
nDimensionLabels = self._U.all_labels(
) #get labels from U matrix to complete the sparse matrix
print nDimensionLabels
self._singleUpdateMatrix.create(self._updateData.get(),
row_labels=nDimensionLabels,
foldin=True,
truncate=truncate)
self._foldinZeroes[self._singleUpdateMatrix.get_cols()
[0]] = self._singleUpdateMatrix.get_rows()
if not truncate:
additionalElements = self._singleUpdateMatrix.get_additional_elements(
)
#If it's trying to foldin a new user who has rated a new item which was not used before, then foldin the item first then foldin that user
print "dimension", len(nDimensionLabels)
print "additional elements:", additionalElements
print "length", len(additionalElements)
if len(additionalElements) != 0:
for item in additionalElements:
if (
is_row
): #if I am folding in a row then , the additionals added that shouldn't be are the columns to be folded in to the rows
self._singleAdditionalFoldin.create(
[(0, nDimensionLabels[0], item)],
row_labels=self._U.all_labels()[0])
else:
self._singleAdditionalFoldin.create(
[(0, item, nDimensionLabels[0])],
col_labels=self._V.all_labels()[0])
self._update(update_matrix=self._singleAdditionalFoldin,
is_row=not is_row)
# #update the data matrix
if VERBOSE:
print "updating the sparse matrix"
if self._matrix.get(): #if matrix not there due to load ignore it
self._matrix.update(
self._singleUpdateMatrix
) # updating the data matrix for the zeroes , also for saving the data matrix if needed
# Mean centering
if self._shifts: #if not None then it means mean_center was equal true
row_shift, col_shift, total_shift = self._shifts
meanedMatrix, rowShift, colShift, totalShift = self._calc_mean_center(
self._singleUpdateMatrix.get(), is_row=is_row)
self._singleUpdateMatrix.set(meanedMatrix)
if is_row:
values, named_rows = row_shift.to_sparse().named_lists(
) #values numpy array, named_rows normal array
valuesFold, named_rowsFold = rowShift.to_sparse().named_lists()
else:
values, named_rows = col_shift.to_sparse().named_lists(
) # values numpy array, named_rows normal array
valuesFold, named_rowsFold = colShift.to_sparse().named_lists()
values = np.concatenate((values, valuesFold))
named_rows.extend(named_rowsFold)
if is_row:
row_shift = divisiSparseVector.from_named_lists(
values, named_rows).to_dense()
else:
col_shift = divisiSparseVector.from_named_lists(
values, named_rows).to_dense()
self._shifts = (row_shift, col_shift, total_shift)
self._update(is_row=is_row, post_normalize=post_normalize)
def _construct_batch_dictionary(self, data, is_row=True):
"""
:param data: Data()
:param is_row: Boolean
:return: constructs a dictionary with the row or col as the keys (depending on which is being added) with values as the tuples
in self._batchDict
"""
key_idx = 1 #key index default is the row
if not is_row:
key_idx = 2
#collecting the significant col or row tuples at one place to fold them in at once
for item in data: #data is a list of tuples so item is 1 tuple
try:
self._batchDict[item[key_idx]].append(item)
except KeyError:
self._batchDict[item[key_idx]] = []
self._batchDict[item[key_idx]].append(item)
#batch loaded , now need to fold them in one by one
print "Batch loaded successfully"
def load_updateDataBatch_foldin(self,
filename=None,
data=None,
force=True,
sep='\t',
format={
'value': 0,
'row': 1,
'col': 2
},
pickle=False,
is_row=True,
truncate=True,
post_normalize=False):
"""
Folds in the batch users or items, first Loads a dataset file that contains Multiple tuples (users or items) or uses the preloaded data from the datamodel/data.py object then folds them in with their ratings
:param data: Contains the dataset that was loaded using the Data() class
:type data: Data()
For params: filename,force,sep,format,pickle then see params definition in *datamodel.Data.load()*
:param is_row: are you trying to foldin a row or a column ? yes->foldin row , no->foldin column
:type is_row: boolean
:param truncate: sometimes new users rate new items not in the original SVD matrix so would you like new items to be truncated or folded in ? default is foldin
:type truncate: boolean
:param post_normalize: Normalize every row of :math:`U \Sigma` to be a unit vector. Thus, row similarity (using cosine distance) returns :math:`[-1.0 .. 1.0]`
:type post_normalize: Boolean
"""
if force:
self._updateData = Data()
if filename: #not null
self._updateData.load(filename, force, sep, format,
pickle) #load array of tuples
else:
if data:
self._updateData = data
else:
raise ValueError('No data or filename set!')
print "Reading the new batch"
self._construct_batch_dictionary(self._updateData.get(), is_row)
print "Folding in batch entries"
nDimensionLabels = None
if (is_row):
nDimensionLabels = self._V.all_labels()[
0] # get labels from V matrix to complete the sparse matrix
else:
nDimensionLabels = self._U.all_labels()[
0] # get labels from U matrix to complete the sparse matrix
length_of_dict = len(self._batchDict)
i = 0
meanDenseVector = []
isbatch = True
for key_idx in self._batchDict: #data in batchDict in form {key:[(tuple)]}
i += 1
if VERBOSE:
if i % 100 == 0:
sys.stdout.write('.')
if i % 1000 == 0:
sys.stdout.write('|')
if i % 10000 == 0:
sys.stdout.write(' (%d K user)\n' % int(i / 1000))
if (is_row):
self._singleUpdateMatrix.create(self._batchDict[key_idx],
col_labels=nDimensionLabels,
foldin=True,
truncate=truncate)
else:
self._singleUpdateMatrix.create(self._batchDict[key_idx],
row_labels=nDimensionLabels,
foldin=True,
truncate=truncate)
# If it's trying to foldin a new user who has rated a new item which was not used before, then foldin the item first then foldin that user
if not truncate:
additionalElements = self._singleUpdateMatrix.get_additional_elements(
)
if len(additionalElements) != 0:
for item in additionalElements:
if (
is_row
): # if I am folding in a row then , the additionals added that shouldn't be are the columns to be folded in to the rows
self._singleAdditionalFoldin.create(
[(0, nDimensionLabels[0], item)],
row_labels=self._U.all_labels()[0])
else:
self._singleAdditionalFoldin.create(
[(0, item, nDimensionLabels[0])],
col_labels=self._V.all_labels()[0])
self._update(
update_matrix=self._singleAdditionalFoldin,
is_row=not is_row)
if self._shifts: # if not None then it means mean_center was equal true
row_shift, col_shift, total_shift = self._shifts
meanedMatrix, rowShift, colShift, totalShift = self._calc_mean_center(
self._singleUpdateMatrix.get(), is_row=is_row)
self._singleUpdateMatrix.set(meanedMatrix)
# row shift cause it's row for the time being
if is_row:
meanDenseVector.append(rowShift)
else:
meanDenseVector.append(colShift)
if self._matrix.get(): #if matrix not there due to load ignore it
self._matrix.update(
self._singleUpdateMatrix, is_batch=isbatch
) # updating the data matrix for the zeroes , also for saving the data matrix if needed
self._update(
is_row=is_row,
is_batch=isbatch) #Do foldin on the singleUpdateMatrix tuple
if VERBOSE:
sys.stdout.write('\n')
# UPDATING MEAN CENTER PART
if self._shifts:
sys.stdout.write("updating shifts")
if is_row:
values, named_rows = row_shift.to_sparse().named_lists(
) # values numpy array, named_rows normal array
else:
values, named_rows = col_shift.to_sparse().named_lists(
) # values numpy array, named_rows normal array
for vector in meanDenseVector:
valuesFold, named_rowsFold = vector.to_sparse().named_lists(
) # rowShift contains new calculated row shift
values = np.concatenate((values, valuesFold))
named_rows.extend(named_rowsFold)
if is_row:
row_shift = divisiSparseVector.from_named_lists(
values, named_rows).to_dense()
else:
col_shift = divisiSparseVector.from_named_lists(
values, named_rows).to_dense()
self._shifts = (row_shift, col_shift, total_shift)
self.update_sparse_matrix_data(is_batch=True,
squish=False,
post_normalize=post_normalize)
def update_sparse_matrix_data(self,
squishFactor=10,
is_batch=False,
squish=True,
post_normalize=False):
#update the data matrix
if is_batch:
if self._matrix.get():
if VERBOSE:
print "updating sparse index"
self._matrix.index_sparseMatrix()
if VERBOSE:
print "before updating, M=", self._matrix_reconstructed.shape
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize,
force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if VERBOSE:
print "done updating, M=", self._matrix_reconstructed.shape
if squish:
if self._matrix.get(): #if loaded model there is no matrix
if VERBOSE:
print "commiting the sparse data matrix by removing empty rows and columns divisi created"
self._matrix.squish(
squishFactor
) # updating the data matrix for the zeroes ,#NOTE: Intensive so do at end
def _update(self,
update_matrix=None,
is_row=True,
is_batch=False,
post_normalize=False):
#The function which does the actual folding-in process
if self.inv_S is None:
self.inv_S = np.zeros((self._S.shape[0], self._S.shape[0]))
for i in range(self._S.shape[0]):
self.inv_S[i, i] = self._S[
i]**-1 # creating diagonal matrix and inverting using special property of diagonal matrix
#if new is row -> V*S^-1
if is_row:
prodM = self._V.dot(self.inv_S)
# if VERBOSE:
# print "dimension of VxS^-1=", prodM.shape
else: #if new is col -> U*S^-1
prodM = self._U.dot(self.inv_S)
# if VERBOSE:
# print "dimension of UxS^-1=", prodM.shape
if update_matrix:
updateTupleMatrix = update_matrix.get()
else:
updateTupleMatrix = self._singleUpdateMatrix.get()
if not is_row:
updateTupleMatrix = updateTupleMatrix.transpose() #transpose
res = updateTupleMatrix.dot(prodM)
if is_row:
#new value can now be concatinated with U
self._U = self._U.concatenate(res)
else:
#new value can now be concatinated with V
self._V = self._V.concatenate(res)
if not is_batch: #will reconstruct all at end with batch using another function
if VERBOSE:
print "before updating, M=", self._matrix_reconstructed.shape
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize,
force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if VERBOSE:
print "done updating, M=", self._matrix_reconstructed.shape
def centroid(self, ids, is_row=True):
points = []
for id in ids:
if is_row:
point = self._U.row_named(id)
else:
point = self._V.row_named(id)
points.append(point)
M = divisi2.SparseMatrix(points)
return M.col_op(sum) / len(points) #TODO Numpy.sum?
def kmeans(self, ids, k=5, components=3, are_rows=True):
"""
K-means clustering. It uses k-means++ (http://en.wikipedia.org/wiki/K-means%2B%2B) to choose the initial centroids of the clusters
Clusterizes a list of IDs (either row or cols)
:param ids: list of row (or col) ids to cluster
:param k: number of clusters
:param components: how many eigen values use (from SVD)
:param are_rows: is param *ids* a list of rows (or cols)?
:type are_rows: Boolean
"""
if not isinstance(ids, list):
# Cluster the whole row(or col) values. It's slow!
return super(SVD, self).kmeans(ids, k=k, is_row=are_rows)
if VERBOSE:
sys.stdout.write('Computing k-means, k=%s for ids %s\n' % (k, ids))
MAX_POINTS = 150
points = []
for id in ids:
if are_rows:
points.append(self._U.row_named(id)[:components])
else:
points.append(self._V.row_named(id)[:components])
M = array(points)
# Only apply Matrix initialization if num. points is not that big!
if len(points) <= MAX_POINTS:
centers = self._kinit(array(points), k)
centroids, labels = kmeans2(M, centers, minit='matrix')
else:
centroids, labels = kmeans2(M, k, minit='random')
i = 0
clusters = dict()
for cluster in labels:
if not clusters.has_key(cluster):
clusters[cluster] = dict()
clusters[cluster]['centroid'] = centroids[cluster]
clusters[cluster]['points'] = []
point = self._U.row_named(ids[i])[:components]
centroid = clusters[cluster]['centroid']
to_centroid = self._cosine(centroid, point)
clusters[cluster]['points'].append((ids[i], to_centroid))
clusters[cluster]['points'].sort(key=itemgetter(1), reverse=True)
i += 1
return clusters
'''
class SVD(Algorithm):
"""
Inherits from base class Algorithm.
It computes SVD (Singular Value Decomposition) on a matrix *M*
It also provides recommendations and predictions using the reconstructed matrix *M'*
:param filename: Path to a Zip file, containing an already computed SVD (U, Sigma, and V) for a matrix *M*
:type filename: string
"""
def __init__(self, filename=None):
#Call parent constructor
super(SVD, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
def __repr__(self):
try:
s = '\n'.join(('M\':' + str(self._reconstruct_matrix()), \
'A row (U):' + str(self._reconstruct_matrix().right[1]), \
'A col (V):' + str(self._reconstruct_matrix().left[1])))
except TypeError:
s = self._data.__repr__()
return s
def load_model(self, filename):
"""
Loads SVD transformation (U, Sigma and V matrices) from a ZIP file
:param filename: path to the SVD matrix transformation (a ZIP file)
:type filename: string
"""
try:
zip = zipfile.ZipFile(filename, allowZip64=True)
except:
zip = zipfile.ZipFile(filename + '.zip', allowZip64=True)
#Python 2.6 only:
#self._U = loads(zip.open('.U').read())
#self._S = loads(zip.open('.S').read())
#self._V = loads(zip.open('.V').read())
self._U = loads(zip.read('.U'))
self._S = loads(zip.read('.S'))
self._V = loads(zip.read('.V'))
self._shifts = None
if '.shifts.row' in zip.namelist():
self._shifts = [
loads(zip.read('.shifts.row')),
loads(zip.read('.shifts.col')),
loads(zip.read('.shifts.total'))
]
self._reconstruct_matrix(shifts=self._shifts, force=True)
self._reconstruct_similarity(force=True)
def save_model(self, filename, options={}):
"""
Saves SVD transformation (U, Sigma and V matrices) to a ZIP file
:param filename: path to save the SVD matrix transformation (U, Sigma and V matrices)
:type filename: string
:param options: a dict() containing the info about the SVD transformation. E.g. {'k': 100, 'min_values': 5, 'pre_normalize': None, 'mean_center': True, 'post_normalize': True}
:type options: dict
"""
if VERBOSE:
sys.stdout.write('Saving svd model to %s\n' % filename)
f_opt = open(filename + '.config', 'w')
for option, value in options.items():
f_opt.write('\t'.join((option, str(value))) + '\n')
f_opt.close()
self._U.dump(filename + '.U')
self._S.dump(filename + '.S')
self._V.dump(filename + '.V')
if self._shifts:
#(row_shift, col_shift, total_shift)
self._shifts[0].dump(filename + '.shifts.row')
self._shifts[1].dump(filename + '.shifts.col')
self._shifts[2].dump(filename + '.shifts.total')
zip = filename
if not filename.endswith('.zip') and not filename.endswith('.ZIP'):
zip += '.zip'
fp = zipfile.ZipFile(zip, 'w', allowZip64=True)
#options
fp.write(filename=filename + '.config', arcname='README')
os.remove(filename + '.config')
#Store matrices
for extension in ['.U', '.S', '.V']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
#Store mean center shifts
if self._shifts:
for extension in ['.shifts.row', '.shifts.col', '.shifts.total']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U,
self._S,
post_normalize=post_normalize)
return self._matrix_similarity
def _reconstruct_matrix(self, shifts=None, force=True):
if not self._matrix_reconstructed or force:
if shifts:
self._matrix_reconstructed = divisi2.reconstruct(self._U,
self._S,
self._V,
shifts=shifts)
else:
self._matrix_reconstructed = divisi2.reconstruct(
self._U, self._S, self._V)
return self._matrix_reconstructed
def compute(self,
k=100,
min_values=None,
pre_normalize=None,
mean_center=False,
post_normalize=True,
savefile=None):
"""
Computes SVD on matrix *M*, :math:`M = U \Sigma V^T`
:param k: number of dimensions
:type k: int
:param min_values: min. number of non-zeros (or non-empty values) any row or col must have
:type min_values: int
:param pre_normalize: normalize input matrix. Possible values are tfidf, rows, cols, all.
:type pre_normalize: string
:param mean_center: centering the input matrix (aka mean substraction)
:type mean_center: Boolean
:param post_normalize: Normalize every row of :math:`U \Sigma` to be a unit vector. Thus, row similarity (using cosine distance) returns :math:`[-1.0 .. 1.0]`
:type post_normalize: Boolean
:param savefile: path to save the SVD factorization (U, Sigma and V matrices)
:type savefile: string
"""
super(SVD, self).compute(min_values)
if VERBOSE:
sys.stdout.write(
'Computing svd k=%s, min_values=%s, pre_normalize=%s, mean_center=%s, post_normalize=%s\n'
% (k, min_values, pre_normalize, mean_center, post_normalize))
if not min_values:
sys.stdout.write(
'[WARNING] min_values is set to None, meaning that some funky recommendations might appear!\n'
)
# Get SparseMatrix
matrix = self._matrix.get()
# Mean center?
shifts, row_shift, col_shift, total_shift = (None, None, None, None)
if mean_center:
if VERBOSE:
sys.stdout.write(
"[WARNING] mean_center is True. svd.similar(...) might return nan's. If so, then do svd.compute(..., mean_center=False)\n"
)
matrix, row_shift, col_shift, total_shift = matrix.mean_center()
self._shifts = (row_shift, col_shift, total_shift)
# Pre-normalize input matrix?
if pre_normalize:
"""
Divisi2 divides each entry by the geometric mean of its row norm and its column norm.
The rows and columns don't actually become unit vectors, but they all become closer to unit vectors.
"""
if pre_normalize == 'tfidf':
matrix = matrix.normalize_tfidf(
) #TODO By default, treats the matrix as terms-by-documents;
# pass cols_are_terms=True if the matrix is instead documents-by-terms.
elif pre_normalize == 'rows':
matrix = matrix.normalize_rows()
elif pre_normalize == 'cols':
matrix = matrix.normalize_cols()
elif pre_normalize == 'all':
matrix = matrix.normalize_all()
else:
raise ValueError("Pre-normalize option (%s) is not correct.\n \
Possible values are: 'tfidf', 'rows', 'cols' or 'all'"
% pre_normalize)
#Compute SVD(M, k)
self._U, self._S, self._V = matrix.svd(k)
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize, force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if savefile:
options = {
'k': k,
'min_values': min_values,
'pre_normalize': pre_normalize,
'mean_center': mean_center,
'post_normalize': post_normalize
}
self.save_model(savefile, options)
def _get_row_reconstructed(self, i, zeros=None):
if zeros:
return self._matrix_reconstructed.row_named(i)[zeros]
return self._matrix_reconstructed.row_named(i)
def _get_col_reconstructed(self, j, zeros=None):
if zeros:
return self._matrix_reconstructed.col_named(j)[zeros]
return self._matrix_reconstructed.col_named(j)
def predict(self, i, j, MIN_VALUE=None, MAX_VALUE=None):
"""
Predicts the value of :math:`M_{i,j}`, using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
:param i: row in M, :math:`M_{i \cdot}`
:type i: user or item id
:param j: col in M, :math:`M_{\cdot j}`
:type j: item or user id
:param MIN_VALUE: min. value in M (e.g. in ratings[1..5] => 1)
:type MIN_VALUE: float
:param MAX_VALUE: max. value in M (e.g. in ratings[1..5] => 5)
:type MAX_VALUE: float
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
predicted_value = self._matrix_reconstructed.entry_named(
i, j) #M' = U S V^t
if MIN_VALUE:
predicted_value = max(predicted_value, MIN_VALUE)
if MAX_VALUE:
predicted_value = min(predicted_value, MAX_VALUE)
return float(predicted_value)
def recommend(self, i, n=10, only_unknowns=False, is_row=True):
"""
Recommends items to a user (or users to an item) using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
E.g. if *i* is a row and *only_unknowns* is True, it returns the higher values of :math:`M^\prime_{i,\cdot}` :math:`\\forall_j{M_{i,j}=\emptyset}`
:param i: row or col in M
:type i: user or item id
:param n: number of recommendations to return
:type n: int
:param only_unknowns: only return unknown values in *M*? (e.g. items not rated by the user)
:type only_unknowns: Boolean
:param is_row: is param *i* a row (or a col)?
:type is_row: Boolean
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
item = None
zeros = []
if only_unknowns and not self._matrix.get():
raise ValueError(
"Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called"
)
if is_row:
if only_unknowns:
zeros = self._matrix.get().row_named(i).zero_entries()
item = self._get_row_reconstructed(i, zeros)
else:
if only_unknowns:
zeros = self._matrix.get().col_named(i).zero_entries()
item = self._get_col_reconstructed(i, zeros)
return item.top_items(n)
def centroid(self, ids, is_row=True):
points = []
for id in ids:
if is_row:
point = self._U.row_named(id)
else:
point = self._V.row_named(id)
points.append(point)
M = divisi2.SparseMatrix(points)
return M.col_op(sum) / len(points) #TODO Numpy.sum?
def kmeans(self, ids, k=5, components=3, are_rows=True):
"""
K-means clustering. It uses k-means++ (http://en.wikipedia.org/wiki/K-means%2B%2B) to choose the initial centroids of the clusters
Clusterizes a list of IDs (either row or cols)
:param ids: list of row (or col) ids to cluster
:param k: number of clusters
:param components: how many eigen values use (from SVD)
:param are_rows: is param *ids* a list of rows (or cols)?
:type are_rows: Boolean
"""
if not isinstance(ids, list):
# Cluster the whole row(or col) values. It's slow!
return super(SVD, self).kmeans(ids, k=k, is_row=are_rows)
if VERBOSE:
sys.stdout.write('Computing k-means, k=%s for ids %s\n' % (k, ids))
MAX_POINTS = 150
points = []
for id in ids:
if are_rows:
points.append(self._U.row_named(id)[:components])
else:
points.append(self._V.row_named(id)[:components])
M = array(points)
# Only apply Matrix initialization if num. points is not that big!
if len(points) <= MAX_POINTS:
centers = self._kinit(array(points), k)
centroids, labels = kmeans2(M, centers, minit='matrix')
else:
centroids, labels = kmeans2(M, k, minit='random')
i = 0
clusters = dict()
for cluster in labels:
if not clusters.has_key(cluster):
clusters[cluster] = dict()
clusters[cluster]['centroid'] = centroids[cluster]
clusters[cluster]['points'] = []
point = self._U.row_named(ids[i])[:components]
centroid = clusters[cluster]['centroid']
to_centroid = self._cosine(centroid, point)
clusters[cluster]['points'].append((ids[i], to_centroid))
clusters[cluster]['points'].sort(key=itemgetter(1), reverse=True)
i += 1
return clusters
'''
class Baseline(Algorithm):
def __init__(self, filename=None):
#Call parent constructor
super(Baseline, self).__init__()
# self._U: Eigen vector. Relates the concepts of the input matrix to the principal axes
# self._S (or \Sigma): Singular -or eigen- values. It represents the strength of each eigenvector.
# self._V: Eigen vector. Relates features to the principal axes
self._U, self._S, self._V = (None, None, None)
# Mean centered Matrix: row and col shifts
self._shifts = None
# self._matrix_reconstructed: M' = U S V^t
self._matrix_reconstructed = None
# Similarity matrix: (U \Sigma)(U \Sigma)^T = U \Sigma^2 U^T
# U \Sigma is concept_axes weighted by axis_weights.
self._matrix_similarity = SimilarityMatrix()
if filename:
self.load_model(filename)
# Row and Col ids. Only when importing from SVDLIBC
self._file_row_ids = None
self._file_col_ids = None
def __repr__(self):
try:
s = '\n'.join(('M\':' + str(self._reconstruct_matrix()), \
'A row (U):' + str(self._reconstruct_matrix().right[1]), \
'A col (V):' + str(self._reconstruct_matrix().left[1])))
except TypeError:
s = self._data.__repr__()
return s
def load_model(self, filename):
"""
Loads SVD transformation (U, Sigma and V matrices) from a ZIP file
:param filename: path to the SVD matrix transformation (a ZIP file)
:type filename: string
"""
try:
zip = zipfile.ZipFile(filename, allowZip64=True)
except:
zip = zipfile.ZipFile(filename + '.zip', allowZip64=True)
# Options file
options = dict()
for line in zip.open('README'):
data = line.strip().split('\t')
options[data[0]] = data[1]
try:
k = int(options['k'])
except:
k = 100 #TODO: nasty!!!
# Load U, S, and V
"""
#Python 2.6 only:
#self._U = loads(zip.open('.U').read())
#self._S = loads(zip.open('.S').read())
#self._V = loads(zip.open('.V').read())
"""
try:
self._U = loads(zip.read('.U'))
except:
matrix = fromfile(zip.extract('.U', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.row_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.row_ids').split('\n') if idx]
#self._U = DenseMatrix(vectors)
self._U = DenseMatrix(vectors, OrderedSet(idx), None)
try:
self._V = loads(zip.read('.V'))
except:
matrix = fromfile(zip.extract('.V', TMPDIR))
vectors = []
i = 0
while i < len(matrix) / k:
v = DenseVector(matrix[k*i:k*(i+1)])
vectors.append(v)
i += 1
try:
idx = [ int(idx.strip()) for idx in zip.read('.col_ids').split('\n') if idx]
except:
idx = [ idx.strip() for idx in zip.read('.col_ids').split('\n') if idx]
#self._V = DenseMatrix(vectors)
self._V = DenseMatrix(vectors, OrderedSet(idx), None)
self._S = loads(zip.read('.S'))
# Shifts for Mean Centerer Matrix
self._shifts = None
if '.shifts.row' in zip.namelist():
self._shifts = [loads(zip.read('.shifts.row')),
loads(zip.read('.shifts.col')),
loads(zip.read('.shifts.total'))
]
self._reconstruct_matrix(shifts=self._shifts, force=True)
self._reconstruct_similarity(force=True)
def save_model(self, filename, options={}):
"""
Saves SVD transformation (U, Sigma and V matrices) to a ZIP file
:param filename: path to save the SVD matrix transformation (U, Sigma and V matrices)
:type filename: string
:param options: a dict() containing the info about the SVD transformation. E.g. {'k': 100, 'min_values': 5, 'pre_normalize': None, 'mean_center': True, 'post_normalize': True}
:type options: dict
"""
if VERBOSE:
sys.stdout.write('Saving svd model to %s\n' % filename)
f_opt = open(filename + '.config', 'w')
for option, value in options.items():
f_opt.write('\t'.join((option, str(value))) + '\n')
f_opt.close()
# U, S, and V
MAX_VECTORS = 2**21
if len(self._U) < MAX_VECTORS:
self._U.dump(filename + '.U')
else:
self._U.tofile(filename + '.U')
if len(self._V) < MAX_VECTORS:
self._V.dump(filename + '.V')
else:
self._V.tofile(filename + '.V')
self._S.dump(filename + '.S')
# Shifts for Mean Centered Matrix
if self._shifts:
#(row_shift, col_shift, total_shift)
self._shifts[0].dump(filename + '.shifts.row')
self._shifts[1].dump(filename + '.shifts.col')
self._shifts[2].dump(filename + '.shifts.total')
zip = filename
if not filename.endswith('.zip') and not filename.endswith('.ZIP'):
zip += '.zip'
fp = zipfile.ZipFile(zip, 'w', allowZip64=True)
# Store Options in the ZIP file
fp.write(filename=filename + '.config', arcname='README')
os.remove(filename + '.config')
# Store matrices in the ZIP file
for extension in ['.U', '.S', '.V']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store mean center shifts in the ZIP file
if self._shifts:
for extension in ['.shifts.row', '.shifts.col', '.shifts.total']:
fp.write(filename=filename + extension, arcname=extension)
os.remove(filename + extension)
# Store row and col ids file, if importing from SVDLIBC
if self._file_row_ids:
fp.write(filename=self._file_row_ids, arcname='.row_ids')
if self._file_col_ids:
fp.write(filename=self._file_col_ids, arcname='.col_ids')
def _reconstruct_similarity(self, post_normalize=True, force=True):
if not self.get_matrix_similarity() or force:
self._matrix_similarity = SimilarityMatrix()
self._matrix_similarity.create(self._U, self._S, post_normalize=post_normalize)
return self._matrix_similarity
def _reconstruct_matrix(self, shifts=None, force=True):
if not self._matrix_reconstructed or force:
if shifts:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S, self._V, shifts=shifts)
else:
self._matrix_reconstructed = divisi2.reconstruct(self._U, self._S, self._V)
return self._matrix_reconstructed
def _get_row_reconstructed(self, i, zeros=None):
if zeros:
return self._matrix_reconstructed.row_named(i)[zeros]
return self._matrix_reconstructed.row_named(i)
def _get_col_reconstructed(self, j, zeros=None):
if zeros:
return self._matrix_reconstructed.col_named(j)[zeros]
return self._matrix_reconstructed.col_named(j)
def compute(self, k=100, min_values=None, pre_normalize=None, mean_center=False, post_normalize=True, savefile=None):
# Get SparseMatrix
matrix = self._matrix.get()
# Mean center?
shifts, row_shift, col_shift, total_shift = (None, None, None, None)
if mean_center:
matrix, row_shift, col_shift, total_shift = matrix.mean_center()
self._shifts = (row_shift, col_shift, total_shift)
# Pre-normalize input matrix?
if pre_normalize:
"""
Divisi2 divides each entry by the geometric mean of its row norm and its column norm.
The rows and columns don't actually become unit vectors, but they all become closer to unit vectors.
"""
if pre_normalize == 'tfidf':
matrix = matrix.normalize_tfidf() #TODO By default, treats the matrix as terms-by-documents;
# pass cols_are_terms=True if the matrix is instead documents-by-terms.
elif pre_normalize == 'rows':
matrix = matrix.normalize_rows()
elif pre_normalize == 'cols':
matrix = matrix.normalize_cols()
elif pre_normalize == 'all':
matrix = matrix.normalize_all()
else:
raise ValueError("Pre-normalize option (%s) is not correct.\n \
Possible values are: 'tfidf', 'rows', 'cols' or 'all'" % pre_normalize)
#Compute SVD(M, k)
self._U, self._S, self._V = matrix.svd(k)
# Sim. matrix = U \Sigma^2 U^T
self._reconstruct_similarity(post_normalize=post_normalize, force=True)
# M' = U S V^t
self._reconstruct_matrix(shifts=self._shifts, force=True)
if savefile:
options = {'k': k, 'min_values': min_values, 'pre_normalize': pre_normalize, 'mean_center': mean_center, 'post_normalize': post_normalize}
self.save_model(savefile, options)
def recommend(self, i, n=10, only_unknowns=False, is_row=True):
"""
Recommends items to a user (or users to an item) using reconstructed matrix :math:`M^\prime = U \Sigma_k V^T`
E.g. if *i* is a row and *only_unknowns* is True, it returns the higher values of :math:`M^\prime_{i,\cdot}` :math:`\\forall_j{M_{i,j}=\emptyset}`
:param i: row or col in M
:type i: user or item id
:param n: number of recommendations to return
:type n: int
:param only_unknowns: only return unknown values in *M*? (e.g. items not rated by the user)
:type only_unknowns: Boolean
:param is_row: is param *i* a row (or a col)?
:type is_row: Boolean
"""
if not self._matrix_reconstructed:
self.compute() #will use default values!
item = None
zeros = []
if only_unknowns and not self._matrix.get():
raise ValueError("Matrix is empty! If you loaded an SVD model you can't use only_unknowns=True, unless svd.create_matrix() is called")
if is_row:
if only_unknowns:
zeros = self._matrix.get().row_named(i).zero_entries()
item = self._get_row_reconstructed(i, zeros)
else:
if only_unknowns:
zeros = self._matrix.get().col_named(i).zero_entries()
item = self._get_col_reconstructed(i, zeros)
return item.top_items(n)