def compute_ratings_matrix(ratings_matrix_file):
"""
Computes the rating matrix
Input:
ratings_matrix_file: Filename output rating matrix
"""
mongo = Mongo('Acme-Supermarket')
mongo.connect()
matrix_file = ratings_matrix_file
hdf5_matrix = tables.openFile(matrix_file, mode='w')
filters = tables.Filters(complevel=5, complib='blosc')
products = mongo.database.products.find({}, {'_id': 1})
products = [p['_id'] for p in products]
products = numpy.concatenate((numpy.array([-1]), products))
products_count = mongo.database.products.count()
customers = mongo.database.actors.find({'_type': 'Customer'}, {'_id': 1})
customers = [c['_id'] for c in customers]
customers_count = mongo.database.actors.count({'_type': 'Customer'})
data_storage = hdf5_matrix.createEArray(hdf5_matrix.root,
'data',
tables.UInt32Atom(),
shape=(0, products_count + 1),
filters=filters,
expectedrows=customers_count)
data_storage.append(products[:][None])
for customer_id in customers:
# Each column 0: Customer IDs
# Product ratings in columns 1+
row = numpy.zeros((products_count + 1, ))
row[0] = customer_id
ratings = mongo.database.rates.find({'customer_id': customer_id}, {
'product_id': 1,
'value': 1
})
for rating in ratings:
row[numpy.where(
products == rating['product_id'])[0][0]] = rating['value']
data_storage.append(row[:][None])
hdf5_matrix.close()
mongo.disconnect()
return matrix_file
