#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License.  You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""
Import sample data for similar product engine
"""

import predictionio
import argparse
import random

SEED = 3

def import_events(client):
  random.seed(SEED)
  count = 0
  print(client.get_status())
  print("Importing data...")

  # generate 10 users, with user ids u1,u2,....,u10
  user_ids = ["u%s" % i for i in range(1, 11)]
  for user_id in user_ids:
    print("Set user", user_id)
    client.create_event(
      event="$set",
      entity_type="user",
      entity_id=user_id
    )
    count += 1

  # generate 50 items, with item ids i1,i2,....,i50
  # random assign 1 to 4 categories among c1-c6 to items
  categories = ["c%s" % i for i in range(1, 7)]
  item_ids = ["i%s" % i for i in range(1, 51)]
  for item_id in item_ids:
    print("Set item", item_id)
    client.create_event(
      event="$set",
      entity_type="item",
      entity_id=item_id,
      properties={
        "categories" : random.sample(categories, random.randint(1, 4))
      }
    )
    count += 1

  # each user randomly viewed 10 items
  for user_id in user_ids:
    for viewed_item in random.sample(item_ids, 10):
      print("User", user_id ,"views item", viewed_item)
      client.create_event(
        event="view",
        entity_type="user",
        entity_id=user_id,
        target_entity_type="item",
        target_entity_id=viewed_item
      )
      count += 1

  # each user randomly liked/disliked 10 items
  for user_id in user_ids:
    for viewed_item in random.sample(item_ids, 10):
      if random.choice((False, True)) :
        print "User", user_id ,"likes item", viewed_item
        client.create_event(
          event="like",
          entity_type="user",
          entity_id=user_id,
          target_entity_type="item",
          target_entity_id=viewed_item
        )
      else:
        print "User", user_id ,"dislikes item", viewed_item
        client.create_event(
          event="dislike",
          entity_type="user",
          entity_id=user_id,
          target_entity_type="item",
          target_entity_id=viewed_item
        )
      count += 1

  print("%s events are imported." % count)

if __name__ == '__main__':
  parser = argparse.ArgumentParser(
    description="Import sample data for similar product engine")
  parser.add_argument('--access_key', default='invald_access_key')
  parser.add_argument('--url', default="http://localhost:7070")

  args = parser.parse_args()
  print(args)

  client = predictionio.EventClient(
    access_key=args.access_key,
    url=args.url,
    threads=5,
    qsize=500)
  import_events(client)