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
class AcmeSupermarket:
def __init__(self, transactions_filepath):
self.schema = 'Acme-Supermarket-Recommendations'
self.transactions_filepath = transactions_filepath
self.database = Mongo(self.schema)
def load(self):
self.database.connect()
purchases = self.database.database.purchases.find()
transactions = numpy.array([])
i = 0
row_starts = numpy.array([0])
for purchase in purchases:
i += 1
purchase_id = purchase['_id']
purchase_lines = self.database.database.purchase_lines.find({'purchase_id': purchase_id})
transaction = numpy.array([line['product_id'] for line in purchase_lines], dtype='i4')
row_starts = numpy.append(row_starts, row_starts[-1] + transaction.size)
transactions = numpy.concatenate((transactions, transaction))
row_ends = numpy.concatenate((row_starts, [transactions.size]))
lengths = numpy.diff(row_ends)
pad_lengths = numpy.max(lengths) - lengths
pad_indices = numpy.repeat(row_ends[1:], pad_lengths)
transactions_padded = numpy.insert(
transactions, pad_indices, -1).reshape(-1, numpy.max(lengths))
numpy.save(self.transactions_filepath, transactions_padded)
self.database.close()
def save_rules(self, rules):
self.database.save_rules(rules)
