Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

docs: clarify usage of output column generated by the exporter #618

Merged
merged 6 commits into from
Jul 5, 2024
+28 −27
Merged

docs: clarify usage of output column generated by the exporter #618

Show file tree
Hide file tree
Changes from 2 commits
Commits
Show all changes
6 commits
Select commit Hold shift + click to select a range
ac2beb5
clerify column output in the docs
gcroci2 Jul 1, 2024
a3de3f3
clarify column output in the tutorial
gcroci2 Jul 1, 2024
ed561dc
Update docs/getstarted.md
gcroci2 Jul 5, 2024
8f6c940
specify classes order
gcroci2 Jul 5, 2024
7838cf9
fix class name
gcroci2 Jul 5, 2024
c822e1b
add capri capri classes condition
gcroci2 Jul 5, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 2 additions & 0 deletions docs/getstarted.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -391,6 +391,8 @@ output_test = pd.read_hdf(os.path.join("<output_folder_path>", "output_exporter.

The dataframes contain `phase`, `epoch`, `entry`, `output`, `target`, and `loss` columns, and can be easily used to visualize the results.

For classification tasks, the `output` column contains the results from a [softmax function](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html). This means each entry in the `output` column is a list with one element for each class. Each element represents the probability of that class occurring.
gcroci2 marked this conversation as resolved.
Show resolved Hide resolved

Example for plotting training loss curves using [Plotly Express](https://plotly.com/python/plotly-express/):

```python
Expand Down
24 changes: 10 additions & 14 deletions tutorials/training.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -420,12 +420,8 @@
"metadata": {},
"outputs": [],
"source": [
"output_train = pd.read_hdf(\n",
" os.path.join(output_path, f\"gnn_{task}\", \"output_exporter.hdf5\"), key=\"training\"\n",
")\n",
"output_test = pd.read_hdf(\n",
" os.path.join(output_path, f\"gnn_{task}\", \"output_exporter.hdf5\"), key=\"testing\"\n",
")\n",
"output_train = pd.read_hdf(os.path.join(output_path, f\"gnn_{task}\", \"output_exporter.hdf5\"), key=\"training\")\n",
"output_test = pd.read_hdf(os.path.join(output_path, f\"gnn_{task}\", \"output_exporter.hdf5\"), key=\"testing\")\n",
"output_train.head()"
]
},
Expand All @@ -436,7 +432,11 @@
"source": [
"The dataframes contain `phase`, `epoch`, `entry`, `output`, `target`, and `loss` columns, and can be easily used to visualize the results.\n",
"\n",
"For example, the loss across the epochs can be plotted for the training and the validation sets:\n"
"For classification tasks, as in the current tutorial, the `output` column contains the results from a [softmax function](https://pytorch.org/docs/stable/generated/torch.nn.functional.softmax.html). This means each entry in the `output` column is a list with one element for each class. Each element represents the probability of that class occurring.\n",
gcroci2 marked this conversation as resolved.
Show resolved Hide resolved
"\n",
"Here specifically, the `output` column contains a list with two elements, respectively representing the predicted probabilities that the data point is 0 (first element of the list, representing non-binder class) and 1 (second element of the list, representing binder class).\n",
gcroci2 marked this conversation as resolved.
Show resolved Hide resolved
"\n",
"The loss across the epochs can be plotted for the training and the validation sets:\n"
]
},
{
Expand Down Expand Up @@ -671,12 +671,8 @@
"metadata": {},
"outputs": [],
"source": [
"output_train = pd.read_hdf(\n",
" os.path.join(output_path, f\"cnn_{task}\", \"output_exporter.hdf5\"), key=\"training\"\n",
")\n",
"output_test = pd.read_hdf(\n",
" os.path.join(output_path, f\"cnn_{task}\", \"output_exporter.hdf5\"), key=\"testing\"\n",
")\n",
"output_train = pd.read_hdf(os.path.join(output_path, f\"cnn_{task}\", \"output_exporter.hdf5\"), key=\"training\")\n",
"output_test = pd.read_hdf(os.path.join(output_path, f\"cnn_{task}\", \"output_exporter.hdf5\"), key=\"testing\")\n",
"output_train.head()"
]
},
Expand Down Expand Up @@ -767,7 +763,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.13"
"version": "3.10.12"
},
"orig_nbformat": 4
},
Expand Down
Loading