pushing files to the repo from the example!

README.md

@@ -0,0 +1,285 @@
+---
+license: mit
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+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
+
+This is a HistGradientBoostingClassifier model trained on breast cancer dataset. It's trained with Halving Grid Search Cross Validation, with parameter grids on max_leaf_nodes and max_depth.
+
+## Intended uses & limitations
+
+This model is not ready to be used in production.
+
+## 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 {color: black;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 pre{padding: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable {background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-estimator:hover {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-item {z-index: 1;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-parallel-item:only-child::after {width: 0;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 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-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04 div.sk-text-repr-fallback {display: none;}</style><div id="sk-72410a5a-f2ab-48e8-8d36-6c2ba8f6eb04" class="sk-top-container"><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="ab167486-be7e-4eb5-be01-ba21adbd7469" type="checkbox" ><label for="ab167486-be7e-4eb5-be01-ba21adbd7469" 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="e9df9f06-8d9e-4379-ad72-52f461408663" type="checkbox" ><label for="e9df9f06-8d9e-4379-ad72-52f461408663" 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 |
+|----------|----------|
+| accuracy | 0.959064 |
+| f1 score | 0.959064 |
+
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+import pickle
+with open(pkl_filename, 'rb') as file:
+    clf = pickle.load(file)
+```
+
+</details>
+
+
+
+
+# Model Card Authors
+
+This model card is written by following authors:
+
+skops_user
+
+# 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:**
+```
+bibtex
+@inproceedings{...,year={2020}}
+```
+
+
+# Additional Content
+
+## Confusion matrix
+
+![Confusion matrix](confusion_matrix.png)
+
+## Hyperparameter search results
+
+<details>
+<summary> Click to expand </summary>
+
+|   iter |   n_resources |   mean_fit_time |   std_fit_time |   mean_score_time |   std_score_time |   param_max_depth |   param_max_leaf_nodes | params                                  |   split0_test_score |   split1_test_score |   split2_test_score |   split3_test_score |   split4_test_score |   mean_test_score |   std_test_score |   rank_test_score |   split0_train_score |   split1_train_score |   split2_train_score |   split3_train_score |   split4_train_score |   mean_train_score |   std_train_score |
+|--------|---------------|-----------------|----------------|-------------------|------------------|-------------------|------------------------|-----------------------------------------|---------------------|---------------------|---------------------|---------------------|---------------------|-------------------|------------------|-------------------|----------------------|----------------------|----------------------|----------------------|----------------------|--------------------|-------------------|
+|      0 |            44 |       0.0498069 |     0.0107112  |        0.0121156  |      0.0061838   |                 2 |                      5 | {'max_depth': 2, 'max_leaf_nodes': 5}   |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0492636 |     0.0187271  |        0.00738611 |      0.00245441  |                 2 |                     10 | {'max_depth': 2, 'max_leaf_nodes': 10}  |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0572055 |     0.0153176  |        0.0111395  |      0.0010297   |                 2 |                     15 | {'max_depth': 2, 'max_leaf_nodes': 15}  |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0498482 |     0.0177091  |        0.00857358 |      0.00415935  |                 5 |                      5 | {'max_depth': 5, 'max_leaf_nodes': 5}   |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0500658 |     0.00992094 |        0.00998321 |      0.00527031  |                 5 |                     10 | {'max_depth': 5, 'max_leaf_nodes': 10}  |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0525903 |     0.0151616  |        0.00874681 |      0.00462998  |                 5 |                     15 | {'max_depth': 5, 'max_leaf_nodes': 15}  |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0512018 |     0.0130152  |        0.00881834 |      0.00500514  |                10 |                      5 | {'max_depth': 10, 'max_leaf_nodes': 5}  |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.0566921 |     0.0186051  |        0.00513492 |      0.000498488 |                10 |                     10 | {'max_depth': 10, 'max_leaf_nodes': 10} |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      0 |            44 |       0.060587  |     0.04041    |        0.00987453 |      0.00529624  |                10 |                     15 | {'max_depth': 10, 'max_leaf_nodes': 15} |            0.875    |            0.5      |            0.625    |            0.75     |            0.375    |          0.625    |        0.176777  |                 5 |             0.628571 |             0.628571 |             0.628571 |             0.514286 |             0.514286 |           0.582857 |         0.0559883 |
+|      1 |           132 |       0.232459  |     0.0479878  |        0.0145514  |      0.00856422  |                10 |                      5 | {'max_depth': 10, 'max_leaf_nodes': 5}  |            0.961538 |            0.923077 |            0.923077 |            0.961538 |            0.961538 |          0.946154 |        0.0188422 |                 2 |             1        |             1        |             1        |             1        |             1        |           1        |         0         |
+|      1 |           132 |       0.272297  |     0.0228833  |        0.011561   |      0.0068272   |                10 |                     10 | {'max_depth': 10, 'max_leaf_nodes': 10} |            0.961538 |            0.923077 |            0.923077 |            0.961538 |            0.961538 |          0.946154 |        0.0188422 |                 2 |             1        |             1        |             1        |             1        |             1        |           1        |         0         |
+|      1 |           132 |       0.239161  |     0.0330412  |        0.0116591  |      0.003554    |                10 |                     15 | {'max_depth': 10, 'max_leaf_nodes': 15} |            0.961538 |            0.923077 |            0.923077 |            0.961538 |            0.961538 |          0.946154 |        0.0188422 |                 2 |             1        |             1        |             1        |             1        |             1        |           1        |         0         |
+|      2 |           396 |       0.920334  |     0.18198    |        0.0166654  |      0.00776263  |                10 |                     15 | {'max_depth': 10, 'max_leaf_nodes': 15} |            0.962025 |            0.911392 |            0.987342 |            0.974359 |            0.935897 |          0.954203 |        0.0273257 |                 1 |             1        |             1        |             1        |             1        |             1        |           1        |         0         |
+
+</details>
+
+## Classification report
+
+<details>
+<summary> Click to expand </summary>
+
+| index        |   precision |   recall |   f1-score |   support |
+|--------------|-------------|----------|------------|-----------|
+| malignant    |    0.951613 | 0.936508 |   0.944    |        63 |
+| benign       |    0.963303 | 0.972222 |   0.967742 |       108 |
+| macro avg    |    0.957458 | 0.954365 |   0.955871 |       171 |
+| weighted avg |    0.958996 | 0.959064 |   0.958995 |       171 |
+
+</details>

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,
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skops-v5dfi0sh.pkl

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