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[1044] Added end-to-end tests at the end of Quickstart editorial #1118

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merged 8 commits into from
May 14, 2024
Merged

[1044] Added end-to-end tests at the end of Quickstart editorial #1118

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428f047
adding preliminary changes
allincowell May 4, 2024
a32259c
[1044] adding assert statements to quickstart tutorial
allincowell May 4, 2024
b732d44
[1044] adding assert statements to quickstart tutorial
allincowell May 4, 2024
1cbdf2b
[1044] Fixed Python version
allincowell May 4, 2024
afaeccd
[1044] Fixed Python version and edited away outputs
allincowell May 4, 2024
f58b7bb
fixed nested metadata issue and using y_train_idx for accessing groun…
allincowell May 8, 2024
e8717b3
checking tests again
allincowell May 8, 2024
05cd97e
display default number of rows with examples_w_issue.head()
elisno May 14, 2024
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[1044] adding assert statements to quickstart tutorial
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@allincowell
allincowell committed May 4, 2024
commit b732d44258a94fa5fe848846b29ab8eb65a19c0f
32 changes: 16 additions & 16 deletions docs/source/tutorials/datalab/datalab_quickstart.ipynb
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Expand Up @@ -7,6 +7,22 @@
"# Datalab: A unified audit to detect all kinds of issues in data and labels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleanlab offers a `Datalab` object that can identify various issues in your machine learning datasets, such as noisy labels, outliers, (near) duplicates, drift, and other types of problems common in real-world data. These data issues may negatively impact models if not addressed. `Datalab` utilizes *any* ML model you have already trained for your data to diagnose these issues, it only requires access to either: (probabilistic) predictions from your model or its learned representations of the data.\n",
"\n",
"\n",
"**Overview of what we'll do in this tutorial:**\n",
"\n",
"- Compute out-of-sample predicted probabilities for a sample dataset using cross-validation.\n",
"- Use `Datalab` to identify issues such as noisy labels, outliers, (near) duplicates, and other types of problems \n",
"- View the issue summaries and other information about our sample dataset\n",
"\n",
"You can easily replace our demo dataset with your own image/text/tabular/audio/etc dataset, and then run the same code to discover what sort of issues lurk within it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand All @@ -32,22 +48,6 @@
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleanlab offers a `Datalab` object that can identify various issues in your machine learning datasets, such as noisy labels, outliers, (near) duplicates, drift, and other types of problems common in real-world data. These data issues may negatively impact models if not addressed. `Datalab` utilizes *any* ML model you have already trained for your data to diagnose these issues, it only requires access to either: (probabilistic) predictions from your model or its learned representations of the data.\n",
"\n",
"\n",
"**Overview of what we'll do in this tutorial:**\n",
"\n",
"- Compute out-of-sample predicted probabilities for a sample dataset using cross-validation.\n",
"- Use `Datalab` to identify issues such as noisy labels, outliers, (near) duplicates, and other types of problems \n",
"- View the issue summaries and other information about our sample dataset\n",
"\n",
"You can easily replace our demo dataset with your own image/text/tabular/audio/etc dataset, and then run the same code to discover what sort of issues lurk within it!"
]
},
{
"cell_type": "markdown",
"metadata": {},
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