From c1f3383570b509930b6e59bcc5021b991283d7ad Mon Sep 17 00:00:00 2001
From: Quoc-Tuan Truong <tqtg@users.noreply.github.com>
Date: Thu, 28 Mar 2024 08:54:31 -0700
Subject: [PATCH] Add metadata file when saving model (#607)
---
cornac/models/recommender.py | 36 ++++++++++++++++++++++++++++--------
1 file changed, 28 insertions(+), 8 deletions(-)
diff --git a/cornac/models/recommender.py b/cornac/models/recommender.py
index 62a17f7f..c7080a4b 100644
--- a/cornac/models/recommender.py
+++ b/cornac/models/recommender.py
@@ -20,6 +20,7 @@ import pickle
import warnings
from datetime import datetime
from glob import glob
+import json
import numpy as np
@@ -219,7 +220,7 @@ class Recommender:
return self.__class__(**init_params)
- def save(self, save_dir=None, save_trainset=False):
+ def save(self, save_dir=None, save_trainset=False, metadata=None):
"""Save a recommender model to the filesystem.
Parameters
@@ -232,6 +233,10 @@ class Recommender:
if we want to deploy model later because train_set is
required for certain evaluation steps.
+ metadata: dict, default: None
+ Metadata to be saved with the model. This is useful
+ to store model details.
+
Returns
-------
model_file : str
@@ -246,16 +251,27 @@ class Recommender:
model_file = os.path.join(model_dir, "{}.pkl".format(timestamp))
saved_model = copy.deepcopy(self)
- pickle.dump(saved_model, open(model_file, "wb"), protocol=pickle.HIGHEST_PROTOCOL)
+ pickle.dump(
+ saved_model, open(model_file, "wb"), protocol=pickle.HIGHEST_PROTOCOL
+ )
if self.verbose:
print("{} model is saved to {}".format(self.name, model_file))
+ metadata = {} if metadata is None else metadata
+ metadata["model_classname"] = type(saved_model).__name__
+ metadata["model_file"] = os.path.basename(model_file)
+
if save_trainset:
+ trainset_file = model_file + ".trainset"
pickle.dump(
self.train_set,
- open(model_file + ".trainset", "wb"),
+ open(trainset_file, "wb"),
protocol=pickle.HIGHEST_PROTOCOL,
)
+ metadata["trainset_file"] = os.path.basename(trainset_file)
+
+ with open(model_file + ".meta", "w", encoding="utf-8") as f:
+ json.dump(metadata, f, ensure_ascii=False, indent=4)
return model_file
@@ -502,9 +518,7 @@ class Recommender:
)
item_scores = all_item_scores[item_indices]
- if (
- k != -1
- ): # O(n + k log k), faster for small k which is usually the case
+ if k != -1: # O(n + k log k), faster for small k which is usually the case
partitioned_idx = np.argpartition(item_scores, -k)
top_k_idx = partitioned_idx[-k:]
sorted_top_k_idx = top_k_idx[np.argsort(item_scores[top_k_idx])]
@@ -545,7 +559,9 @@ class Recommender:
raise ValueError(f"{user_id} is unknown to the model.")
if k < -1 or k > self.total_items:
- raise ValueError(f"k={k} is invalid, there are {self.total_users} users in total.")
+ raise ValueError(
+ f"k={k} is invalid, there are {self.total_users} users in total."
+ )
item_indices = np.arange(self.total_items)
if remove_seen:
@@ -622,7 +638,11 @@ class Recommender:
if self.stopped_epoch > 0:
print("Early stopping:")
- print("- best epoch = {}, stopped epoch = {}".format(self.best_epoch, self.stopped_epoch))
+ print(
+ "- best epoch = {}, stopped epoch = {}".format(
+ self.best_epoch, self.stopped_epoch
+ )
+ )
print(
"- best monitored value = {:.6f} (delta = {:.6f})".format(
self.best_value, current_value - self.best_value
--
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