From 85c1604292d37e5670efc4d15e06c0cbdbcd8c2c Mon Sep 17 00:00:00 2001
From: alekseykalyagin <aakalyagin@gmail.com>
Date: Fri, 13 Dec 2024 16:56:31 +0300
Subject: [PATCH] Remove fit_recommend function

---
 rectools/model_selection/cross_validate.py | 77 +++++++++-------------
 1 file changed, 32 insertions(+), 45 deletions(-)

diff --git a/rectools/model_selection/cross_validate.py b/rectools/model_selection/cross_validate.py
index 3107ce79..f23d778c 100644
--- a/rectools/model_selection/cross_validate.py
+++ b/rectools/model_selection/cross_validate.py
@@ -15,8 +15,6 @@
 import typing as tp
 from contextlib import contextmanager
 
-import pandas as pd
-
 from rectools.columns import Columns
 from rectools.dataset import Dataset
 from rectools.metrics import calc_metrics
@@ -124,47 +122,6 @@ def cross_validate(  # pylint: disable=too-many-locals
             ]
         }
     """
-
-    def fit_recommend(model: ModelBase, ref_model: bool = False) -> tp.Tuple[pd.DataFrame, tp.Dict[str, tp.Any]]:
-        """
-        Trains the given recommendation model on a dataset split and generates recommendations.
-
-        Parameters
-        ----------
-        model : ModelBase
-            The recommendation model to be trained and used for generating recommendations.
-            Must be an instance of a subclass of `rectools.models.base.ModelBase`.
-        ref_model : bool, optional, default False
-            Indicates whether the model is a reference model used for comparison. If True,
-            and `validate_ref_models` is False, this model's recommendations may be reused
-            across splits without being refitted.
-
-        Returns
-        -------
-        tuple(pd.DataFrame, dict)
-            - A DataFrame with recommendations.
-            - A dictionary containing timing metrics (`fit_time` and `recommend_time`), if
-              `compute_timings` is enabled; otherwise, an empty dictionary.
-        """
-        timings: tp.Optional[tp.Dict[str, float]] = (
-            {} if compute_timings and (validate_ref_models or not ref_model) else None
-        )
-
-        with compute_timing("fit_time", timings):
-            model.fit(fold_dataset)
-
-        with compute_timing("recommend_time", timings):
-            reco = model.recommend(
-                users=test_users,
-                dataset=fold_dataset,
-                k=k,
-                filter_viewed=filter_viewed,
-                items_to_recommend=items_to_recommend,
-                on_unsupported_targets=on_unsupported_targets,
-            )
-
-        return reco, (timings or {})
-
     split_iterator = splitter.split(dataset.interactions, collect_fold_stats=True)
 
     split_infos = []
@@ -191,7 +148,22 @@ def fit_recommend(model: ModelBase, ref_model: bool = False) -> tp.Tuple[pd.Data
         ref_res = {}
         for model_name in ref_models or []:
             model = models[model_name]
-            ref_reco[model_name], ref_res[model_name] = fit_recommend(model, ref_model=True)
+            ref_timings: tp.Optional[tp.Dict[str, float]] = {} if compute_timings and validate_ref_models else None
+
+            with compute_timing("fit_time", ref_timings):
+                model.fit(fold_dataset)
+
+            with compute_timing("recommend_time", ref_timings):
+                ref_reco[model_name] = model.recommend(
+                    users=test_users,
+                    dataset=fold_dataset,
+                    k=k,
+                    filter_viewed=filter_viewed,
+                    items_to_recommend=items_to_recommend,
+                    on_unsupported_targets=on_unsupported_targets,
+                )
+
+            ref_res[model_name] = ref_timings or {}
 
         # ### Generate recommendations and calc metrics
         for model_name, model in models.items():
@@ -201,7 +173,22 @@ def fit_recommend(model: ModelBase, ref_model: bool = False) -> tp.Tuple[pd.Data
                 reco = ref_reco[model_name]
                 model_res = ref_res[model_name]
             else:
-                reco, model_res = fit_recommend(model)
+                timings: tp.Optional[tp.Dict[str, float]] = {} if compute_timings else None
+
+                with compute_timing("fit_time", timings):
+                    model.fit(fold_dataset)
+
+                with compute_timing("recommend_time", timings):
+                    reco = model.recommend(
+                        users=test_users,
+                        dataset=fold_dataset,
+                        k=k,
+                        filter_viewed=filter_viewed,
+                        items_to_recommend=items_to_recommend,
+                        on_unsupported_targets=on_unsupported_targets,
+                    )
+
+                model_res = timings or {}
 
             metric_values = calc_metrics(
                 metrics,