def fit(self, topK=50, shrink=100, normalize=True, similarity="cosine"):
similarity_object = Compute_Similarity_Python(self.URM.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity)
self.W_sparse = similarity_object.compute_similarity()
def compute_similarity_matrix(self, URM_train, shrink, topK, normalize,
similarity):
similarity_object = Compute_Similarity_Python(URM_train.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity)
return similarity_object.compute_similarity()
def compute_similarity(self, UCM):
similarity_object = Compute_Similarity_Python(
UCM.T,
shrink=self.shrink,
topK=self.topK,
normalize=self.normalize,
similarity=self.similarity,
asymmetric_alpha=self.asymmetric_alpha)
return similarity_object.compute_similarity()
def fit(self, topK=50, shrink=100, normalize=True, similarity="cosine"):
#TODO Verify if there is more than one feature!
for ICM in self.ICM:
similarity_object = Compute_Similarity_Python(
ICM.T,
shrink=shrink,
topK=topK,
normalize=normalize,
similarity=similarity)
self.W_sparse.append(similarity_object.compute_similarity())
def compute_similarity_cbf(self,
ICM,
top_k,
shrink,
normalize=True,
similarity="cosine"):
# Compute similarities for weighted features recommender
similarity_object = Compute_Similarity_Python(ICM.T,
shrink=shrink,
topK=top_k,
normalize=normalize,
similarity=similarity)
w_sparse = similarity_object.compute_similarity()
return w_sparse
def __init__(self,
dataMatrix,
use_implementation="density",
similarity=None,
**args):
"""
Interface object that will call the appropriate similarity implementation
:param dataMatrix:
:param use_implementation: "density" will choose the most efficient implementation automatically
"cython" will use the cython implementation, if available. Most efficient for sparse matrix
"python" will use the python implementation. Most efficent for dense matrix
:param similarity: the type of similarity to use, see SimilarityFunction enum
:param args: other args required by the specific similarity implementation
"""
assert np.all(np.isfinite(dataMatrix.data)), \
"Compute_Similarity: Data matrix contains {} non finite values".format(np.sum(np.logical_not(np.isfinite(dataMatrix.data))))
self.dense = False
if similarity == "euclidean":
# This is only available here
self.compute_similarity_object = Compute_Similarity_Euclidean(
dataMatrix, **args)
else:
assert not (dataMatrix.shape[0] == 1 and dataMatrix.nnz == dataMatrix.shape[1]),\
"Compute_Similarity: data has only 1 feature (shape: {}) with dense values," \
" vector and set based similarities are not defined on 1-dimensional dense data," \
" use Euclidean similarity instead.".format(dataMatrix.shape)
if similarity is not None:
args["similarity"] = similarity
if use_implementation == "density":
if isinstance(dataMatrix, np.ndarray):
self.dense = True
elif isinstance(dataMatrix, sps.spmatrix):
shape = dataMatrix.shape
num_cells = shape[0] * shape[1]
sparsity = dataMatrix.nnz / num_cells
self.dense = sparsity > 0.5
else:
print(
"Compute_Similarity: matrix type not recognized, calling default..."
)
use_implementation = "python"
if self.dense:
print("Compute_Similarity: detected dense matrix")
use_implementation = "python"
else:
use_implementation = "cython"
if use_implementation == "cython":
try:
from Base.Similarity.Cython.Compute_Similarity_Cython import Compute_Similarity_Cython
self.compute_similarity_object = Compute_Similarity_Cython(
dataMatrix, **args)
except ImportError:
print(
"Unable to load Cython Compute_Similarity, reverting to Python"
)
self.compute_similarity_object = Compute_Similarity_Python(
dataMatrix, **args)
elif use_implementation == "python":
self.compute_similarity_object = Compute_Similarity_Python(
dataMatrix, **args)
else:
raise ValueError(
"Compute_Similarity: value for argument 'use_implementation' not recognized"
)
def __init__(self, dataMatrix, use_implementation = "density", similarity = None, **args):
"""
Interface object that will call the appropriate similarity implementation
:param dataMatrix:
:param use_implementation: "density" will choose the most efficient implementation automatically
"cython" will use the cython implementation, if available. Most efficient for sparse matrix
"python" will use the python implementation. Most efficent for dense matrix
:param similarity: the type of similarity to use, see SimilarityFunction enum
:param args: other args required by the specific similarity implementation
"""
self.dense = False
if similarity == "euclidean":
# This is only available here
self.compute_similarity_object = Compute_Similarity_Euclidean(dataMatrix, **args)
else:
if similarity is not None:
args["similarity"] = similarity
if use_implementation == "density":
if isinstance(dataMatrix, np.ndarray):
self.dense = True
elif isinstance(dataMatrix, sps.spmatrix):
shape = dataMatrix.shape
num_cells = shape[0]*shape[1]
sparsity = dataMatrix.nnz/num_cells
self.dense = sparsity > 0.5
else:
print("Compute_Similarity: matrix type not recognized, calling default...")
use_implementation = "python"
if self.dense:
print("Compute_Similarity: detected dense matrix")
use_implementation = "python"
else:
use_implementation = "cython"
if use_implementation == "cython":
try:
from Base.Similarity.Cython.Compute_Similarity_Cython import Compute_Similarity_Cython
self.compute_similarity_object = Compute_Similarity_Cython(dataMatrix, **args)
except ImportError:
print("Unable to load Cython Compute_Similarity, reverting to Python")
self.compute_similarity_object = Compute_Similarity_Python(dataMatrix, **args)
elif use_implementation == "python":
self.compute_similarity_object = Compute_Similarity_Python(dataMatrix, **args)
else:
raise ValueError("Compute_Similarity: value for argument 'use_implementation' not recognized")