# Copyright 2022-2024 MTS (Mobile Telesystems)
#
# Licensed 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.
"""Random Model."""
import random
import typing as tp
import numpy as np
from tqdm.auto import tqdm
from rectools import InternalIds
from rectools.dataset import Dataset
from rectools.types import AnyIdsArray, InternalId, InternalIdsArray
from rectools.utils import fast_isin_for_sorted_test_elements
from .base import ModelBase, Scores, SemiInternalRecoTriplet
from .utils import get_viewed_item_ids
[docs]class _RandomGen:
def __init__(self, random_state: tp.Optional[int] = None) -> None:
self.python_gen = random.Random(random_state) # nosec
self.np_gen = np.random.default_rng(random_state)
[docs]class _RandomSampler:
def __init__(self, values: np.ndarray, random_gen: _RandomGen) -> None:
self.python_gen = random_gen.python_gen
self.np_gen = random_gen.np_gen
self.values = values
self.values_list = list(values) # for random.sample
def sample(self, n: int) -> np.ndarray:
if n < 25: # Empiric value, for optimization
sampled = np.asarray(self.python_gen.sample(self.values_list, n))
else:
sampled = self.np_gen.choice(self.values, n, replace=False)
return sampled
[docs]class RandomModel(ModelBase):
"""
Model generating random recommendations.
By default all items that are present
in `dataset.item_id_map` will be used for recommendations.
Numbers ranging from <n recommendations for user> to 1 will be used as a "score" in recommendations.
Parameters
----------
random_state : int, optional, default ``None``
Pseudorandom number generator state to control the sampling.
verbose : int, default ``0``
Degree of verbose output. If ``0``, no output will be provided.
"""
recommends_for_warm = False
recommends_for_cold = True
def __init__(self, random_state: tp.Optional[int] = None, verbose: int = 0):
super().__init__(verbose=verbose)
self.random_state = random_state
self.random_gen = _RandomGen(random_state)
self.all_item_ids: np.ndarray
def _fit(self, dataset: Dataset) -> None: # type: ignore
self.all_item_ids = dataset.item_id_map.internal_ids
def _recommend_u2i(
self,
user_ids: InternalIdsArray,
dataset: Dataset,
k: int,
filter_viewed: bool,
sorted_item_ids_to_recommend: tp.Optional[InternalIdsArray],
) -> tp.Tuple[InternalIds, InternalIds, Scores]:
if filter_viewed:
user_items = dataset.get_user_item_matrix(include_weights=False)
item_ids = sorted_item_ids_to_recommend if sorted_item_ids_to_recommend is not None else self.all_item_ids
sampler = _RandomSampler(item_ids, self.random_gen)
all_user_ids = []
all_reco_ids: tp.List[InternalId] = []
all_scores: tp.List[float] = []
for user_id in tqdm(user_ids, disable=self.verbose == 0):
if filter_viewed:
viewed_ids = get_viewed_item_ids(user_items, user_id) # sorted
n_reco = k + viewed_ids.size
else:
n_reco = k
n_reco = min(n_reco, item_ids.size)
reco_ids = sampler.sample(n_reco)
if filter_viewed:
reco_ids = reco_ids[fast_isin_for_sorted_test_elements(reco_ids, viewed_ids, invert=True)][:k]
reco_scores = np.arange(reco_ids.size, 0, -1)
all_user_ids.extend([user_id] * len(reco_ids))
all_reco_ids.extend(reco_ids.tolist())
all_scores.extend(reco_scores.tolist())
return all_user_ids, all_reco_ids, all_scores
def _recommend_i2i(
self,
target_ids: InternalIdsArray,
dataset: Dataset,
k: int,
sorted_item_ids_to_recommend: tp.Optional[InternalIdsArray],
) -> tp.Tuple[InternalIds, InternalIds, Scores]:
return self._recommend_u2i(target_ids, dataset, k, False, sorted_item_ids_to_recommend)
def _recommend_cold(
self,
target_ids: AnyIdsArray,
dataset: Dataset,
k: int,
sorted_item_ids_to_recommend: tp.Optional[InternalIdsArray],
) -> SemiInternalRecoTriplet:
item_ids = sorted_item_ids_to_recommend if sorted_item_ids_to_recommend is not None else self.all_item_ids
sampler = _RandomSampler(item_ids, self.random_gen)
n_reco = min(k, item_ids.size)
reco_ids_lst = []
for _ in tqdm(target_ids, disable=self.verbose == 0):
reco_ids = sampler.sample(n_reco)
reco_ids_lst.append(reco_ids)
reco_item_ids = np.concatenate(reco_ids_lst)
reco_target_ids = np.repeat(target_ids, n_reco)
reco_scores = np.tile(np.arange(n_reco, 0, -1), len(target_ids))
return reco_target_ids, reco_item_ids, reco_scores