skops What is skops?
[skops](https://skops.readthedocs.io/en/stable/index.html) is a neat Python library for productionizing [scikit-learn](https://scikit-learn.org) models. It not only has tools for saving and sharing the models safely, but also allows you to add a Model Card with additional context about your models. skops is a collaboration between core scikit-learn developers at [Probabl](https://probabl.ai) and engineers at [Hugging Face](https://huggingface.co). I'm studying for the [Scikit-Learn Expert Certification](https://certification.probabl.ai/scikit-learn-expert) and it requires familiarity with [skops](https://skops.readthedocs.io/en/stable/index.html), so I thought this would be a good excuse to dive deep and explain skops features.
Why skops?
scikit-learn models are often still saved with libraries like `pickle`, `cloudpickle` or `joblib`, which are all based on Python's [pickle](https://docs.python.org/3/library/pickle.html) protocol. There is a security risk because a pickled file can execute arbitrary code when it is loaded. This is especially problematic if you want to use models that are shared on public Internet platforms like the [Hugging Face Model Hub](https://huggingface.co/models). skops strives to fix this with [model persistence](https://skops.readthedocs.io/en/stable/persistence.html) and includes tools for safe sharing of models.
Safe, load and check with skops
The syntax for using skops is very similar to the pickle-based libraries you might already be familiar with. Let's start out with a very simple scikit-learn pipeline.
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import HistGradientBoostingClassifier
pipe = make_pipeline(StandardScaler(with_mean=False),
HistGradientBoostingClassifier(max_depth=6, learning_rate=0.01))
Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),
('lr',
HistGradientBoostingClassifier(learning_rate=0.01,
max_depth=6))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),
('lr',
HistGradientBoostingClassifier(learning_rate=0.01,
max_depth=6))])StandardScaler(with_mean=False)
HistGradientBoostingClassifier(learning_rate=0.01, max_depth=6)
Saving and loading with skops is similar to joblib:
import skops.io as sio
import joblib
# Joblib way
joblib.dump(pipe, "pipe.joblib")
pipe = joblib.load("pipe.joblib")
# Skops way
sio.dump(pipe, "pipe.skops")
unknown_types = sio.get_untrusted_types(file="pipe.skops") # Check safety of untrusted types
pipe = sio.load("pipe.skops", trusted_types=unknown_types)
Skops has additional tools for compressing models, which can be helpful for deployment.
from zipfile import ZIP_DEFLATED
sio.dump(pipe, "pipe.skops", compression=ZIP_DEFLATED, compresslevel=9)
Once a model is saved we can analyze the entire pipeline as a tree. The overview is very detailed, even for a simple pipeline like this one.
sio.visualize("pipe.skops")
root: sklearn.pipeline.Pipeline
└── attrs: builtins.dict
├── steps: builtins.list
│ ├── content: builtins.tuple
│ │ ├── content: json-type("scaler")
│ │ └── content: sklearn.preprocessing._data.StandardScaler
│ │ └── attrs: builtins.dict
│ │ ├── with_mean: json-type(false)
│ │ ├── with_std: json-type(true)
│ │ ├── n_features_in_: json-type(10)
│ │ ├── ...
│ │ ├── mean_: numpy.ndarray
│ │ ├── var_: numpy.ndarray
│ │ └── _sklearn_version: json-type("1.5.2")
│ └── content: builtins.tuple
│ ├── content: json-type("lr")
│ └── content: sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier
│ └── attrs: builtins.dict
... Additionally, skops provides tools for pushing to the Hugging Face Hub. It's a bit of an intricate process, so I won't go into detail here. If you want to learn more check out [this guide](https://skops.readthedocs.io/en/stable/hf_hub.html).
sio.push("pipe.skops", repo_id="fasthtml/pipe")
Model Card
The skops feature I'm most excited is the [Model Card](https://skops.readthedocs.io/en/stable/model_card.html). This structure can hold a model with descriptions, metrics, hyperparameters, feature importance, plots, tables and more. Packaging a model like this is a great way to share its full context. The community can understand and discuss the model without have to backtrack or speculate about things that got lost in the model training process. Let's create a model card!
from skops import card
model_card = card.Card(model=pipe, model_diagram=True)
# Convenience methods are available for certain sections
model_card.add_metrics(**{"accuracy": 0.9, "f1_score": 0.8})
# General method for adding context
model_card.add(**{
"Citation": '@article{lepelaars2024, title={Writing Cool Model Cards}, author={Lepelaars, Carlo}, journal={Journal of Very Serious Machine Learning}, year={2024}, abstract={"Hi, this is my paper!"}}',
"Model Card Authors": "Carlo Lepelaars",
"Model Card Contact": "https://carlo.ai",
"Model description": "A very serious classification model.",
"Model description/Intended uses & limitations": "Don't try this model at home!",
"Model description/Evaluation Results": "Metrics are looking very good!",
"Model description/Training Procedure": "Trained on the full test set",
})
# Save as markdown
model_card.save("my_model_card.md")
There are three ways to effectively render the model card in this blog post using FastHTML:
# 1. Use the `MarkdownJS()` header from fasthtml.common
from fasthtml.common import *
app, rt = fast_app(hdrs=[MarkdownJS()])
@rt("/")
def get():
"Show off the model card"
return Div(model_card.render(), cls="marked")
serve()
##########################################
# 2. Use `markdown` from mistletoe
# !pip install mistletoe
from mistletoe import markdown
Div(NotStr(markdown(model_card.render())))
##########################################
# 3. Use `render_md` from fh-frankenui
# !pip install fh-frankenui
# !pip install mistletoe
# !pip install lxml
from fh_frankenui.core import *
Div(render_md(model_card.render()))
Model Card Rendered
The full model card render looks like this:
# Model description
A very serious classification model.
## Intended uses & limitations
Don't try this model at home!
## Training Procedure
Trained on the full test set
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|--------------------------|------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('scaler', StandardScaler(with_mean=False)), ('lr', HistGradientBoostingClassifier(learning_rate=0.01, max_depth=6))] |
| verbose | False |
| scaler | StandardScaler(with_mean=False) |
| lr | HistGradientBoostingClassifier(learning_rate=0.01, max_depth=6) |
| scaler__copy | True |
| scaler__with_mean | False |
| scaler__with_std | True |
| lr__categorical_features | warn |
| lr__class_weight | |
| lr__early_stopping | auto |
| lr__interaction_cst | |
| lr__l2_regularization | 0.0 |
| lr__learning_rate | 0.01 |
| lr__loss | log_loss |
| lr__max_bins | 255 |
| lr__max_depth | 6 |
| lr__max_features | 1.0 |
| lr__max_iter | 100 |
| lr__max_leaf_nodes | 31 |
| lr__min_samples_leaf | 20 |
| lr__monotonic_cst | |
| lr__n_iter_no_change | 10 |
| lr__random_state | |
| lr__scoring | loss |
| lr__tol | 1e-07 |
| lr__validation_fraction | 0.1 |
| lr__verbose | 0 |
| lr__warm_start | False |
</details>
### Model Plot
<style>#sk-container-id-2 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
}#sk-container-id-2 {color: var(--sklearn-color-text);
}#sk-container-id-2 pre {padding: 0;
}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;
}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;
}#sk-container-id-2 div.sk-text-repr-fallback {display: none;
}div.sk-parallel-item,
div.sk-serial,
div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
}/* Parallel-specific style estimator block */#sk-container-id-2 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;
}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;
}/* Serial-specific style estimator block */#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
}/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
clickable and can be expanded/collapsed.
- Pipeline and ColumnTransformer use this feature and define the default style
- Estimators will overwrite some part of the style using the `sk-estimator` class
*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-2 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
}/* Toggleable label */
#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
}#sk-container-id-2 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
}/* Toggleable content - dropdown */#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-2 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-2 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
}/* Estimator-specific style *//* Colorize estimator box */
#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
}#sk-container-id-2 div.sk-label label.sk-toggleable__label,
#sk-container-id-2 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
}/* On hover, darken the color of the background */
#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
}/* Label box, darken color on hover, fitted */
#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
}/* Estimator label */#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
}#sk-container-id-2 div.sk-label-container {text-align: center;
}/* Estimator-specific */
#sk-container-id-2 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
}#sk-container-id-2 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
}/* on hover */
#sk-container-id-2 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
}#sk-container-id-2 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
a:link.sk-estimator-doc-link,
a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
}.sk-estimator-doc-link.fitted,
a:link.sk-estimator-doc-link.fitted,
a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
}/* On hover */
div.sk-estimator:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover,
div.sk-label-container:hover .sk-estimator-doc-link:hover,
.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover,
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}/* Span, style for the box shown on hovering the info icon */
.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
}.sk-estimator-doc-link:hover span {display: block;
}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-2 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
}#sk-container-id-2 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
}/* On hover */
#sk-container-id-2 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
}#sk-container-id-2 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
}
</style><div id="sk-container-id-2" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),('lr',HistGradientBoostingClassifier(learning_rate=0.01,max_depth=6))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow "> Pipeline<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></label><div class="sk-toggleable__content "><pre>Pipeline(steps=[('scaler', StandardScaler(with_mean=False)),('lr',HistGradientBoostingClassifier(learning_rate=0.01,max_depth=6))])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow "> StandardScaler<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content "><pre>StandardScaler(with_mean=False)</pre></div> </div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-6" type="checkbox" ><label for="sk-estimator-id-6" class="sk-toggleable__label sk-toggleable__label-arrow "> HistGradientBoostingClassifier<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html">?<span>Documentation for HistGradientBoostingClassifier</span></a></label><div class="sk-toggleable__content "><pre>HistGradientBoostingClassifier(learning_rate=0.01, max_depth=6)</pre></div> </div></div></div></div></div></div>
## Evaluation Results
Metrics are looking very good!
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
Carlo Lepelaars
# Model Card Contact
https://carlo.ai
# Citation
@article{lepelaars2024, title={Writing Cool Model Cards}, author={Lepelaars, Carlo}, journal={Journal of Very Serious Machine Learning}, year={2024}, abstract={"Hi, this is my paper!"}}
Pretty cool, right?! And we haven't even explored the ability to add plots, custom tables and feature importance. Check out [this guide](https://skops.readthedocs.io/en/stable/auto_examples/plot_custom_model_card.html) and [this guide](https://skops.readthedocs.io/en/stable/auto_examples/plot_california_housing.html#sphx-glr-auto-examples-plot-california-housing-py) for full data science workflow examples includes skops. Hope you learned something new today!
