Save wide and deep model

Signed-off-by: David Davó <[email protected]>

recommenders/models/wide_deep/wide_deep_utils.py

@@ -1,7 +1,8 @@
 # Copyright (c) Recommenders contributors.
 # Licensed under the MIT License.
-from typing import Tuple, Dict, Optional, Any
+from typing import Tuple, Dict, Optional, Any, Union
 from dataclasses import dataclass, field
+from pathlib import Path
 
 import numpy as np
 import pandas as pd
@@ -15,7 +16,7 @@
 import recommenders.utils.python_utils as pu
 import recommenders.utils.torch_utils as tu
 
-@dataclass(kw_only=True, frozen=True)
+@dataclass(frozen=True)
 class WideAndDeepHyperParams:
     user_dim: int = 32
     item_dim: int = 32
@@ -182,12 +183,14 @@ def __init__(
         n_items: Optional[int] = None,
         epochs: int = 100,
         batch_size: int = 128,
-        loss_fn: str | nn.Module = 'mse',
+        loss_fn: Union[str, nn.Module] = 'mse',
         optimizer: str = 'sgd',
         l1: float = 0.0001,
         optimizer_params: dict[str, Any] = dict(),
         disable_batch_progress: bool = False,
         disable_iter_progress: bool = False,
+        model_dir: Optional[Union[str, Path]] = None,
+        save_model_iter: int = -1,
         prediction_col: str = DEFAULT_PREDICTION_COL,
     ):
         self.n_users = n_users or max(train.n_users, test.n_users)
@@ -230,16 +233,35 @@ def __init__(
         self.current_epoch = 0
         self.epochs = epochs
 
+        self.model_dir = Path(model_dir) if model_dir else None
+        self.save_model_iter = save_model_iter
+        self._check_save_model()
+
         self.train_loss_history = list()
         self.test_loss_history = list()
 
     @property
     def user_col(self) -> str:
         return self.train.user_col
 
+    @property
+    def model_path(self) -> Path:
+        return self.model_dir / f'wide_deep_state_{self.current_epoch:05d}.pth'
+
     @property
     def item_col(self) -> str:
         return self.train.item_col
+
+    def _check_save_model(self) -> bool:
+        # The two conditions should be True/False at the same time
+        if (self.save_model_iter == -1) != (self.model_dir is None):
+            raise ValueError('You should set both save_model_iter and model_dir at the same time')
+
+        if self.model_dir is not None:
+            # Check that save works
+            self.save()
+
+        return True
 
     def fit(self):
         if self.current_epoch >= self.epochs:
@@ -255,6 +277,26 @@ def fit(self):
                     test_loss=self.test_loss_history[-1],
                 )
 
+                if self.save_model_iter != -1 and self.current_epoch % self.save_model_iter == 0:
+                    self.save()
+
+    def save(self, model_path=None):
+        model_path = Path(model_path) if model_path else self.model_path
+        model_path.parent.mkdir(exist_ok=True)
+
+        torch.save(self.model.state_dict(), model_path)
+
+    def load(self, model_path=None):
+        if model_path is None:
+            print('Model path not specified, automatically loading from model dir')
+            model_path = max(self.model_dir.glob('*.pth'), key=lambda f: int(f.stem.split('_')[-1]))
+            print('  Loading', model_path)
+        else:
+            model_path = Path(model_path)
+
+        self.model.load_state_dict(torch.load(model_path))
+        self.current_epoch = int(model_path.stem.split('_')[-1])
+
     def fit_step(self):
         self.model.train()
 
@@ -292,9 +334,7 @@ def fit_step(self):
 
         self.current_epoch += 1
 
-    def recommend_k_items(
-        self, user_ids=None, item_ids=None, top_k=10, remove_seen=True,
-    ):
+    def _get_uip_cont(self, user_ids, item_ids, remove_seen: bool):
         if user_ids is None:
             user_ids = np.arange(1, self.n_users)
         if item_ids is None:
@@ -316,8 +356,13 @@ def recommend_k_items(
             cont_features = torch.from_numpy(
                 np.stack(uip.map(lambda x: self.train._get_continuous_features(*x)).values)
             )
-
-        uip = uip.to_frame(index=False)
+
+        return uip.to_frame(index=False), cont_features
+
+    def recommend_k_items(
+        self, user_ids=None, item_ids=None, top_k=10, remove_seen=True,
+    ):
+        uip, cont_features = self._get_uip_cont(user_ids, item_ids, remove_seen)
 
         with torch.no_grad():
             uip[self.prediction_col] = self.model(