def recommend(learner):
"""
Take a list of coauthors and read in the complete graph into a sparse
matrix X such that X_ij = k means author i has worked with j, k times. Then
do matrix factorisation on the resulting methods.
"""
outputDir = PathDefaults.getOutputDir() + "erasm/"
matrixFileName = outputDir + "Toy"
numExamples = 50
numFolds = 5
X = scipy.io.mmread(matrixFileName)
X = scipy.sparse.csr_matrix(X)
logging.debug("Loaded matrix " + str(X.shape) + " with " + str(X.getnnz()) + " non zeros")
X = X.tocsr()
X = X[0:numExamples ,:]
X, maxS = preprocess(X)
#Take out some ratings to form a training set
rowInds, colInds = X.nonzero()
randInds = numpy.random.permutation(rowInds.shape[0])
indexList = Sampling.crossValidation(numFolds, rowInds.shape[0])
paramList = []
for j, (trnIdx, tstIdx) in enumerate(indexList):
trainInds = randInds[trnIdx]
testInds = randInds[tstIdx]
trainX = SparseUtils.selectMatrix(X, rowInds[trainInds], colInds[trainInds]).tocsr()
testX = SparseUtils.selectMatrix(X, rowInds[testInds], colInds[testInds]).tocsr()
paramList.append((trainX, testX, learner))
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
results = pool.map(computeTestError, paramList)
#results = map(computeTestError, paramList)
testErrors = numpy.array(results)
meanTestErrors = testErrors.mean()
logging.debug("Test errors = " + str(meanTestErrors))
errorFileName = outputDir + "results_" + learner.name()
numpy.savez(errorFileName, meanTestErrors)
logging.debug("Saved results as " + errorFileName)
def testSelectMatrix(self):
numRows = 10
numCols = 10
A = scipy.sparse.rand(numRows, numCols, 0.5, "csr")
#Select first row
rowInds = numpy.zeros(numCols)
colInds = numpy.arange(10)
newA = SparseUtils.selectMatrix(A, rowInds, colInds)
for i in range(numCols):
self.assertEquals(A[0, i], newA[0, i])
for i in range(1, numRows):
for j in range(numCols):
self.assertEquals(newA[i, j], 0)
