Enh/tqdm bar (#128)

* ENH: description of tqdm bars with losses
artefactory · Jul 17, 2024 · 068239f · 068239f
1 parent ed59165
commit 068239f
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diff --git a/choice_learn/models/base_model.py b/choice_learn/models/base_model.py
@@ -1,4 +1,5 @@
 """Base class for choice models."""
+
 import json
 import logging
 import os
@@ -315,6 +316,11 @@ def fit(
                     # Optimization Steps
                     epoch_losses.append(neg_loglikelihood)
 
+                    if verbose > 0:
+                        inner_range.set_description(
+                            f"Epoch Negative-LogLikeliHood: {np.sum(epoch_losses):.4f}"
+                        )
+
             # In this case we do not need to batch the sample_weights
             else:
                 if verbose > 0:
@@ -346,6 +352,11 @@ def fit(
                     # Optimization Steps
                     epoch_losses.append(neg_loglikelihood)
 
+                    if verbose > 0:
+                        inner_range.set_description(
+                            f"Epoch Negative-LogLikeliHood: {np.sum(epoch_losses):.4f}"
+                        )
+
             # Take into account last batch that may have a differnt length into account for
             # the computation of the epoch loss.
             if batch_size != -1:
@@ -358,13 +369,13 @@ def fit(
             else:
                 epoch_loss = tf.reduce_mean(epoch_losses)
             losses_history["train_loss"].append(epoch_loss)
-            desc = f"Epoch {epoch_nb} Train Loss {losses_history['train_loss'][-1].numpy()}"
+            print_loss = losses_history["train_loss"][-1].numpy()
+            desc = f"Epoch {epoch_nb} Train Loss {print_loss:.4f}"
             if verbose > 1:
                 print(
-                    f"Loop {epoch_nb} Time",
-                    time.time() - t_start,
-                    "Loss:",
-                    tf.reduce_sum(epoch_losses).numpy(),
+                    f"Loop {epoch_nb} Time:",
+                    f"{time.time() - t_start:.4f}",
+                    f"Loss: {print_loss:.4f}",
                 )
 
             # Test on val_dataset if provided
@@ -393,7 +404,7 @@ def fit(
                 test_loss = tf.reduce_mean(test_losses)
                 if verbose > 1:
                     print("Test Negative-LogLikelihood:", test_loss.numpy())
-                    desc += f", Test Loss {test_loss.numpy()}"
+                    desc += f", Test Loss {np.round(test_loss.numpy(), 4)}"
                 losses_history["test_loss"] = losses_history.get("test_loss", []) + [
                     test_loss.numpy()
                 ]
@@ -404,9 +415,8 @@ def fit(
             if self.stop_training:
                 print("Early Stopping taking effect")
                 break
-            if verbose > 0:
-                t_range.set_description(desc)
-                t_range.refresh()
+            t_range.set_description(desc)
+            t_range.refresh()
 
         temps_logs = {k: tf.reduce_mean(v) for k, v in train_logs.items()}
         self.callbacks.on_train_end(logs=temps_logs)

diff --git a/docs/index.md b/docs/index.md
@@ -123,14 +123,39 @@ If you consider this package and any of its feature useful for your research, pl
 
 The use of this software is under the MIT license, with no limitation of usage, including for commercial applications.
 
-### Contributors
-
-### Special Thanks
-
 ### Affiliations
 
 This package has been developped within the [Artefact Research Center](https://www.artefact.com/data-consulting-transformation/artefact-research-center/) in collaboration with CentraleSupélec, université Paris-Saclay.
 
 
-[![](./illustrations/logos/logo_arc.png)](https://www.artefact.com/data-consulting-transformation/artefact-research-center/) | [![](./illustrations/logos/artefact_logo.png)](https://www.artefact.com/) |  [![](./illustrations/logos/logo_CS.png)](https://mics.centralesupelec.fr/) |  [![](./illustrations/logos/logo_paris_saclay.png)](https://www.universite-paris-saclay.fr/)
-:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:
+<p align="center">
+  <a href="https://www.artefact.com/data-consulting-transformation/artefact-research-center/">
+    <img src="./docs/illustrations/logos/logo_arc.png" height="60" />
+  </a>
+  &emsp;
+  &emsp;
+  <a href="https://www.artefact.com/">
+    <img src="docs/illustrations/logos/logo_atf.png" height="65" />
+  </a>
+</p>
+
+<p align="center">
+  <a href="https://www.universite-paris-saclay.fr/">
+    <img src="./docs/illustrations/logos/logo_paris_saclay.png" height="60" />
+  </a>
+  &emsp;
+  &emsp;
+  <a href="https://mics.centralesupelec.fr/">
+    <img src="docs/illustrations/logos/logo_CS.png" height="60" />
+  </a>
+  &emsp;
+  &emsp;
+  <a href="https://www.london.edu/">
+    <img src="docs/illustrations/logos/logo_lbs.jpeg" height="60" />
+  </a>
+  &emsp;
+  &emsp;
+  <a href="https://www.insead.edu/">
+    <img src="docs/illustrations/logos/logo_insead.png" height="60" />
+  </a>
+</p>
diff --git a/notebooks/introduction/3_model_clogit.ipynb b/notebooks/introduction/3_model_clogit.ipynb
@@ -1211,9 +1211,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 2000/2000 [00:09<00:00, 210.33it/s]\n",
-      "100%|██████████| 4000/4000 [00:16<00:00, 237.61it/s]\n",
-      "100%|██████████| 20000/20000 [01:24<00:00, 236.20it/s]\n"
+      "Epoch 1999 Train Loss 0.6801: 100%|██████████| 2000/2000 [00:13<00:00, 148.87it/s]\n",
+      "Epoch 3999 Train Loss 0.6776: 100%|██████████| 4000/4000 [00:24<00:00, 160.24it/s]\n",
+      "Epoch 19999 Train Loss 0.6767: 100%|██████████| 20000/20000 [01:59<00:00, 166.89it/s]\n"
      ]
     }
    ],