pushing files to the repo from the example!

README.md

@@ -1,7 +1,235 @@
 ---
-license: apache-2.0
-language:
-- en
+library_name: sklearn
 tags:
-- chemistry
----
+- sklearn
+- skops
+- tabular-classification
+model_file: skops-c52_f3uq.pkl
+widget:
+  structuredData:
+    area error:
+    - 30.29
+    - 96.05
+    - 48.31
+    compactness error:
+    - 0.01911
+    - 0.01652
+    - 0.01484
+    concave points error:
+    - 0.01037
+    - 0.0137
+    - 0.01093
+    concavity error:
+    - 0.02701
+    - 0.02269
+    - 0.02813
+    fractal dimension error:
+    - 0.003586
+    - 0.001698
+    - 0.002461
+    mean area:
+    - 481.9
+    - 1130.0
+    - 748.9
+    mean compactness:
+    - 0.1058
+    - 0.1029
+    - 0.1223
+    mean concave points:
+    - 0.03821
+    - 0.07951
+    - 0.08087
+    mean concavity:
+    - 0.08005
+    - 0.108
+    - 0.1466
+    mean fractal dimension:
+    - 0.06373
+    - 0.05461
+    - 0.05796
+    mean perimeter:
+    - 81.09
+    - 123.6
+    - 101.7
+    mean radius:
+    - 12.47
+    - 18.94
+    - 15.46
+    mean smoothness:
+    - 0.09965
+    - 0.09009
+    - 0.1092
+    mean symmetry:
+    - 0.1925
+    - 0.1582
+    - 0.1931
+    mean texture:
+    - 18.6
+    - 21.31
+    - 19.48
+    perimeter error:
+    - 2.497
+    - 5.486
+    - 3.094
+    radius error:
+    - 0.3961
+    - 0.7888
+    - 0.4743
+    smoothness error:
+    - 0.006953
+    - 0.004444
+    - 0.00624
+    symmetry error:
+    - 0.01782
+    - 0.01386
+    - 0.01397
+    texture error:
+    - 1.044
+    - 0.7975
+    - 0.7859
+    worst area:
+    - 677.9
+    - 1866.0
+    - 1156.0
+    worst compactness:
+    - 0.2378
+    - 0.2336
+    - 0.2394
+    worst concave points:
+    - 0.1015
+    - 0.1789
+    - 0.1514
+    worst concavity:
+    - 0.2671
+    - 0.2687
+    - 0.3791
+    worst fractal dimension:
+    - 0.0875
+    - 0.06589
+    - 0.08019
+    worst perimeter:
+    - 96.05
+    - 165.9
+    - 124.9
+    worst radius:
+    - 14.97
+    - 24.86
+    - 19.26
+    worst smoothness:
+    - 0.1426
+    - 0.1193
+    - 0.1546
+    worst symmetry:
+    - 0.3014
+    - 0.2551
+    - 0.2837
+    worst texture:
+    - 24.64
+    - 26.58
+    - 26.0
+---
+
+# Model description
+
+[More Information Needed]
+
+## Intended uses & limitations
+
+[More Information Needed]
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter                  | Value                                                    |
+|---------------------------------|----------------------------------------------------------|
+| aggressive_elimination          | False                                                    |
+| cv                              | 5                                                        |
+| error_score                     | nan                                                      |
+| estimator__categorical_features |                                                          |
+| estimator__early_stopping       | auto                                                     |
+| estimator__l2_regularization    | 0.0                                                      |
+| estimator__learning_rate        | 0.1                                                      |
+| estimator__loss                 | auto                                                     |
+| estimator__max_bins             | 255                                                      |
+| estimator__max_depth            |                                                          |
+| estimator__max_iter             | 100                                                      |
+| estimator__max_leaf_nodes       | 31                                                       |
+| estimator__min_samples_leaf     | 20                                                       |
+| estimator__monotonic_cst        |                                                          |
+| estimator__n_iter_no_change     | 10                                                       |
+| estimator__random_state         |                                                          |
+| estimator__scoring              | loss                                                     |
+| estimator__tol                  | 1e-07                                                    |
+| estimator__validation_fraction  | 0.1                                                      |
+| estimator__verbose              | 0                                                        |
+| estimator__warm_start           | False                                                    |
+| estimator                       | HistGradientBoostingClassifier()                         |
+| factor                          | 3                                                        |
+| max_resources                   | auto                                                     |
+| min_resources                   | exhaust                                                  |
+| n_jobs                          | -1                                                       |
+| param_grid                      | {'max_leaf_nodes': [5, 10, 15], 'max_depth': [2, 5, 10]} |
+| random_state                    | 42                                                       |
+| refit                           | True                                                     |
+| resource                        | n_samples                                                |
+| return_train_score              | True                                                     |
+| scoring                         |                                                          |
+| verbose                         | 0                                                        |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<style>#sk-44efa000-ff3f-4566-a6bf-9321209cde4e {color: black;background-color: white;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e pre{padding: 0;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-toggleable {background-color: white;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e 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-44efa000-ff3f-4566-a6bf-9321209cde4e 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-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-estimator:hover {background-color: #d4ebff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-item {z-index: 1;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-parallel-item:only-child::after {width: 0;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e 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-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-44efa000-ff3f-4566-a6bf-9321209cde4e 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-44efa000-ff3f-4566-a6bf-9321209cde4e div.sk-text-repr-fallback {display: none;}</style><div id="sk-44efa000-ff3f-4566-a6bf-9321209cde4e" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={&#x27;max_depth&#x27;: [2, 5, 10],&#x27;max_leaf_nodes&#x27;: [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="ac50b731-f92e-4ee9-8158-e4b19abe57a8" type="checkbox" ><label for="ac50b731-f92e-4ee9-8158-e4b19abe57a8" class="sk-toggleable__label sk-toggleable__label-arrow">HalvingGridSearchCV</label><div class="sk-toggleable__content"><pre>HalvingGridSearchCV(estimator=HistGradientBoostingClassifier(), n_jobs=-1,param_grid={&#x27;max_depth&#x27;: [2, 5, 10],&#x27;max_leaf_nodes&#x27;: [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="7e8aa589-efb5-47fd-acb4-21b96bdbda86" type="checkbox" ><label for="7e8aa589-efb5-47fd-acb4-21b96bdbda86" 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>
+
+## Evaluation Results
+
+You can find the details about evaluation process and the evaluation results.
+
+
+
+| Metric   | Value   |
+|----------|---------|
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import joblib
+import json
+import pandas as pd
+clf = joblib.load(skops-c52_f3uq.pkl)
+with open("config.json") as f:
+    config = json.load(f)
+clf.predict(pd.DataFrame.from_dict(config["sklearn"]["example_input"]))
+```
+
+
+# Model Card Authors
+
+This model card is written by following authors:
+
+[More Information Needed]
+
+# Model Card Contact
+
+You can contact the model card authors through following channels:
+[More Information Needed]
+
+# Citation
+
+Below you can find information related to citation.
+
+**BibTeX:**
+```
+[More Information Needed]
+```

config.json

@@ -0,0 +1,195 @@
+{
+    "sklearn": {
+        "columns": [
+            "mean radius",
+            "mean texture",
+            "mean perimeter",
+            "mean area",
+            "mean smoothness",
+            "mean compactness",
+            "mean concavity",
+            "mean concave points",
+            "mean symmetry",
+            "mean fractal dimension",
+            "radius error",
+            "texture error",
+            "perimeter error",
+            "area error",
+            "smoothness error",
+            "compactness error",
+            "concavity error",
+            "concave points error",
+            "symmetry error",
+            "fractal dimension error",
+            "worst radius",
+            "worst texture",
+            "worst perimeter",
+            "worst area",
+            "worst smoothness",
+            "worst compactness",
+            "worst concavity",
+            "worst concave points",
+            "worst symmetry",
+            "worst fractal dimension"
+        ],
+        "environment": [
+            "scikit-learn=1.0.2"
+        ],
+        "example_input": {
+            "area error": [
+                30.29,
+                96.05,
+                48.31
+            ],
+            "compactness error": [
+                0.01911,
+                0.01652,
+                0.01484
+            ],
+            "concave points error": [
+                0.01037,
+                0.0137,
+                0.01093
+            ],
+            "concavity error": [
+                0.02701,
+                0.02269,
+                0.02813
+            ],
+            "fractal dimension error": [
+                0.003586,
+                0.001698,
+                0.002461
+            ],
+            "mean area": [
+                481.9,
+                1130.0,
+                748.9
+            ],
+            "mean compactness": [
+                0.1058,
+                0.1029,
+                0.1223
+            ],
+            "mean concave points": [
+                0.03821,
+                0.07951,
+                0.08087
+            ],
+            "mean concavity": [
+                0.08005,
+                0.108,
+                0.1466
+            ],
+            "mean fractal dimension": [
+                0.06373,
+                0.05461,
+                0.05796
+            ],
+            "mean perimeter": [
+                81.09,
+                123.6,
+                101.7
+            ],
+            "mean radius": [
+                12.47,
+                18.94,
+                15.46
+            ],
+            "mean smoothness": [
+                0.09965,
+                0.09009,
+                0.1092
+            ],
+            "mean symmetry": [
+                0.1925,
+                0.1582,
+                0.1931
+            ],
+            "mean texture": [
+                18.6,
+                21.31,
+                19.48
+            ],
+            "perimeter error": [
+                2.497,
+                5.486,
+                3.094
+            ],
+            "radius error": [
+                0.3961,
+                0.7888,
+                0.4743
+            ],
+            "smoothness error": [
+                0.006953,
+                0.004444,
+                0.00624
+            ],
+            "symmetry error": [
+                0.01782,
+                0.01386,
+                0.01397
+            ],
+            "texture error": [
+                1.044,
+                0.7975,
+                0.7859
+            ],
+            "worst area": [
+                677.9,
+                1866.0,
+                1156.0
+            ],
+            "worst compactness": [
+                0.2378,
+                0.2336,
+                0.2394
+            ],
+            "worst concave points": [
+                0.1015,
+                0.1789,
+                0.1514
+            ],
+            "worst concavity": [
+                0.2671,
+                0.2687,
+                0.3791
+            ],
+            "worst fractal dimension": [
+                0.0875,
+                0.06589,
+                0.08019
+            ],
+            "worst perimeter": [
+                96.05,
+                165.9,
+                124.9
+            ],
+            "worst radius": [
+                14.97,
+                24.86,
+                19.26
+            ],
+            "worst smoothness": [
+                0.1426,
+                0.1193,
+                0.1546
+            ],
+            "worst symmetry": [
+                0.3014,
+                0.2551,
+                0.2837
+            ],
+            "worst texture": [
+                24.64,
+                26.58,
+                26.0
+            ]
+        },
+        "model": {
+            "file": "skops-c52_f3uq.pkl"
+        },
+        "task": "tabular-classification"
+    }
+}

init_repo_MLstructureMining.py

@@ -0,0 +1,78 @@
+import json
+import os
+import pickle
+from pathlib import Path
+from tempfile import mkdtemp, mkstemp
+from uuid import uuid4
+
+import numpy as np
+import xgboost
+import sklearn
+from huggingface_hub import HfApi
+from sklearn.datasets import load_breast_cancer
+from sklearn.ensemble import HistGradientBoostingClassifier
+from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.model_selection import HalvingGridSearchCV, train_test_split
+
+from skops import card, hub_utils
+
+# Data
+X, y = load_breast_cancer(as_frame=True, return_X_y=True)
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.3, random_state=42
+)
+print("X's summary: ", X.describe())
+print("y's summary: ", y.describe())
+
+# # Train model
+param_grid = {
+    "max_leaf_nodes": [5, 10, 15],
+    "max_depth": [2, 5, 10],
+}
+
+model = HalvingGridSearchCV(
+    estimator=HistGradientBoostingClassifier(),
+    param_grid=param_grid,
+    random_state=42,
+    n_jobs=-1,
+).fit(X_train, y_train)
+model.score(X_test, y_test)# The file name is not significant, here we choose to save it with a `pkl`
+# extension.
+_, pkl_name = mkstemp(prefix="skops-", suffix=".pkl")
+with open(pkl_name, mode="bw") as f:
+    pickle.dump(model, file=f)
+
+local_repo = mkdtemp(prefix="skops-")
+hub_utils.init(
+    model=pkl_name,
+    requirements=[f"scikit-learn={sklearn.__version__}"],
+    dst=local_repo,
+    task="tabular-classification",
+    data=X_test,
+)
+if "__file__" in locals():  # __file__ not defined during docs built
+    # Add this script itself to the files to be uploaded for reproducibility
+    hub_utils.add_files(__file__, dst=local_repo)
+
+model_card = card.Card(model, metadata=card.metadata_from_config(Path(local_repo)))
+model_card.save(Path(local_repo) / "README.md")
+
+
+# you can put your own token here, or set it as an environment variable before
+# running this script.
+token = "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"
+
+repo_name = f"MLstructureMining"
+user_name = HfApi().whoami(token=token)["name"]
+repo_id = f"{user_name}/{repo_name}"
+print(f"Creating and pushing to repo: {repo_id}")
+
+
+hub_utils.push(
+    repo_id=repo_id,
+    source=local_repo,
+    token=token,
+    commit_message="pushing files to the repo from the example!",
+    create_remote=True,
+    private=True,
+)

skops-c52_f3uq.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c66fc4f332860d0670b4bd4d5e63bc66d5c635de6603524808c2976d79b0b5c7
+size 242801