Merge branch '55-random-recommender' into 'master'

Resolve "Random recommender"

Closes #55

See merge request recommend.games/board-game-recommender!26

board_game_recommender/baseline.py

@@ -0,0 +1,85 @@
+"""Baseline recommender models."""
+
+import logging
+from typing import FrozenSet, Iterable
+
+import numpy as np
+import pandas as pd
+
+from board_game_recommender.base import BaseGamesRecommender
+
+LOGGER = logging.getLogger(__name__)
+
+
+class RandomGamesRecommender(BaseGamesRecommender):
+    """Random recommender."""
+
+    def __init__(self) -> None:
+        self.rng = np.random.default_rng()
+
+    @property
+    def known_games(self) -> FrozenSet[int]:
+        return frozenset()
+
+    @property
+    def rated_games(self) -> FrozenSet[int]:
+        return frozenset()
+
+    @property
+    def num_games(self) -> int:
+        return 0
+
+    @property
+    def known_users(self) -> FrozenSet[str]:
+        return frozenset()
+
+    @property
+    def num_users(self) -> int:
+        return 0
+
+    def _recommendation_scores(self, users: int, games: int) -> np.ndarray:
+        """Random scores."""
+        return self.rng.random((users, games))
+
+    def recommend(
+        self,
+        users: Iterable[str],
+        games: Iterable[int],
+        **kwargs,
+    ) -> pd.DataFrame:
+        """Random recommendations for certain users."""
+
+        users = list(users)
+        games = list(games)
+        scores = self._recommendation_scores(users=len(users), games=len(games))
+
+        result = pd.DataFrame(
+            index=games,
+            columns=pd.MultiIndex.from_product([users, ["score"]]),
+            data=scores.T,
+        )
+        result[pd.MultiIndex.from_product([users, ["rank"]])] = result.rank(
+            method="min",
+            ascending=False,
+        ).astype(int)
+
+        if len(users) == 1:
+            result.sort_values((users[0], "rank"), inplace=True)
+
+        return result[pd.MultiIndex.from_product([users, ["score", "rank"]])]
+
+    def recommend_as_numpy(
+        self,
+        users: Iterable[str],
+        games: Iterable[int],
+    ) -> np.ndarray:
+        """Random recommendations for certain users and games as a numpy array."""
+        users = list(users)
+        games = list(games)
+        return self._recommendation_scores(users=len(users), games=len(games))
+
+    def recommend_similar(self, games: Iterable[int], **kwargs) -> pd.DataFrame:
+        raise NotImplementedError
+
+    def similar_games(self, games: Iterable[int], **kwargs) -> pd.DataFrame:
+        raise NotImplementedError