pushing files to the repo from the example!
Browse files- README.md +3 -3
- config.json +2 -1
- init_repo_MLstructureMining.py +4 -1
- skops-8nefprdu.pkl +3 -0
README.md
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@@ -4,7 +4,7 @@ tags:
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- sklearn
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- skops
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- tabular-classification
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model_file: skops-
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widget:
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structuredData:
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area error:
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@@ -188,7 +188,7 @@ The model is trained with below hyperparameters.
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The model plot is below.
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<style>#sk-
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## Evaluation Results
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@@ -207,7 +207,7 @@ Use the code below to get started with the model.
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import joblib
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import json
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import pandas as pd
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clf = joblib.load(skops-
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with open("config.json") as f:
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config = json.load(f)
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
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- sklearn
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- skops
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- tabular-classification
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model_file: skops-8nefprdu.pkl
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widget:
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structuredData:
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area error:
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The model plot is below.
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<style>#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f {color: black;background-color: white;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f pre{padding: 0;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-toggleable {background-color: white;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f 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-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-estimator:hover {background-color: #d4ebff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-item {z-index: 1;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-parallel-item:only-child::after {width: 0;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f 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 the default 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-0c1d7c13-a4c8-4590-87aa-1f83604d531f div.sk-text-repr-fallback {display: none;}</style><div id="sk-0c1d7c13-a4c8-4590-87aa-1f83604d531f" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</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="fda069f1-3c89-4cdd-93a3-300250ad04ac" type="checkbox" ><label for="fda069f1-3c89-4cdd-93a3-300250ad04ac" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={'max_depth': [2, 5, 10],'max_leaf_nodes': [5, 10, 15]},random_state=42)</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f08bbe7b-c0ba-40eb-983c-ca33a0c7f783" type="checkbox" ><label for="f08bbe7b-c0ba-40eb-983c-ca33a0c7f783" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div></div></div></div></div></div>
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## Evaluation Results
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import joblib
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import json
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import pandas as pd
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clf = joblib.load(skops-8nefprdu.pkl)
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with open("config.json") as f:
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config = json.load(f)
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clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
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config.json
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"worst fractal dimension"
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],
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"environment": [
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"xgboost=1.5.2"
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],
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"example_input": {
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]
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},
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"model": {
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"file": "skops-
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},
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"task": "tabular-classification"
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}
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"worst fractal dimension"
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],
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"environment": [
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"scikit-learn=1.0.2",
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"xgboost=1.5.2"
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],
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"example_input": {
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]
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},
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"model": {
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"file": "skops-8nefprdu.pkl"
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},
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"task": "tabular-classification"
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}
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init_repo_MLstructureMining.py
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).fit(X_train, y_train)
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model.score(X_test, y_test)# The file name is not significant, here we choose to save it with a `pkl`
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# extension.
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_, pkl_name = mkstemp(prefix="skops-", suffix=".pkl")
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with open(pkl_name, mode="bw") as f:
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pickle.dump(model, file=f)
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local_repo = mkdtemp(prefix="skops-")
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hub_utils.init(
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model=pkl_name,
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requirements=[f"xgboost={xgboost.__version__}"],
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dst=local_repo,
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task="tabular-classification",
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data=X_test,
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# Add this script itself to the files to be uploaded for reproducibility
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hub_utils.add_files(__file__, dst=local_repo)
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model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
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model_card.save(Path(local_repo) / "README.md")
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).fit(X_train, y_train)
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model.score(X_test, y_test)# The file name is not significant, here we choose to save it with a `pkl`
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# extension.
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_, pkl_name = mkstemp(prefix="skops-", suffix=".pkl")
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with open(pkl_name, mode="bw") as f:
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pickle.dump(model, file=f)
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local_repo = mkdtemp(prefix="skops-")
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hub_utils.init(
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model=pkl_name,
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requirements=[f"scikit-learn={sklearn.__version__}", f"xgboost={xgboost.__version__}"],
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dst=local_repo,
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task="tabular-classification",
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data=X_test,
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# Add this script itself to the files to be uploaded for reproducibility
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hub_utils.add_files(__file__, dst=local_repo)
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print(os.listdir(local_repo))
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model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
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model_card.save(Path(local_repo) / "README.md")
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skops-8nefprdu.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:0db0cd8b1acd1f6deab5e7706d350fa7d917ce83a668ea74bf277b99a99dc398
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size 242801
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