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README.md

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+---
+library_name: sklearn
+license: mit
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_format: pickle
+model_file: febskxmodel_hug_0.pkl
+widget:
+- structuredData:
+    backlog_minutes:
+    - 793051
+    - 474385
+    - 785116
+    backlog_num_jobs:
+    - 302
+    - 193
+    - 302
+    max_minutes:
+    - 18
+    - 360
+    - 18
+    nnodes:
+    - 1
+    - 1
+    - 1
+    running_minutes:
+    - 1934034
+    - 1934094
+    - 1934034
+    running_num_jobs:
+    - 6827
+    - 6828
+    - 6827
+---
+
+# Model description
+
+[More Information Needed]
+
+## Intended uses & limitations
+
+[More Information Needed]
+
+## Training Procedure
+
+[More Information Needed]
+
+### Hyperparameters
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter             | Value                                                                                              |
+|----------------------------|----------------------------------------------------------------------------------------------------|
+| memory                     |                                                                                                    |
+| steps                      | [('scale', StandardScaler()), ('hgbc', HistGradientBoostingClassifier(max_depth=9, max_iter=600))] |
+| verbose                    | False                                                                                              |
+| scale                      | StandardScaler()                                                                                   |
+| hgbc                       | HistGradientBoostingClassifier(max_depth=9, max_iter=600)                                          |
+| scale__copy                | True                                                                                               |
+| scale__with_mean           | True                                                                                               |
+| scale__with_std            | True                                                                                               |
+| hgbc__categorical_features |                                                                                                    |
+| hgbc__class_weight         |                                                                                                    |
+| hgbc__early_stopping       | auto                                                                                               |
+| hgbc__interaction_cst      |                                                                                                    |
+| hgbc__l2_regularization    | 0.0                                                                                                |
+| hgbc__learning_rate        | 0.1                                                                                                |
+| hgbc__loss                 | log_loss                                                                                           |
+| hgbc__max_bins             | 255                                                                                                |
+| hgbc__max_depth            | 9                                                                                                  |
+| hgbc__max_iter             | 600                                                                                                |
+| hgbc__max_leaf_nodes       | 31                                                                                                 |
+| hgbc__min_samples_leaf     | 20                                                                                                 |
+| hgbc__monotonic_cst        |                                                                                                    |
+| hgbc__n_iter_no_change     | 10                                                                                                 |
+| hgbc__random_state         |                                                                                                    |
+| hgbc__scoring              | loss                                                                                               |
+| hgbc__tol                  | 1e-07                                                                                              |
+| hgbc__validation_fraction  | 0.1                                                                                                |
+| hgbc__verbose              | 0                                                                                                  |
+| hgbc__warm_start           | False                                                                                              |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</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-1" type="checkbox" ><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[(&#x27;scale&#x27;, StandardScaler()),(&#x27;hgbc&#x27;,HistGradientBoostingClassifier(max_depth=9, max_iter=600))])</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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">StandardScaler</label><div class="sk-toggleable__content"><pre>StandardScaler()</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-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label sk-toggleable__label-arrow">HistGradientBoostingClassifier</label><div class="sk-toggleable__content"><pre>HistGradientBoostingClassifier(max_depth=9, max_iter=600)</pre></div></div></div></div></div></div></div>
+
+## Evaluation Results
+
+| Metric                | Value             |
+|-----------------------|-------------------|
+| accuracy              | 0.946168166304685 |
+| classification report | precision    recall  f1-score   support<br /><br />           0       0.97      0.98      0.98      5075<br />           1       0.74      0.57      0.64       218<br />           2       0.70      0.59      0.64       108<br />           3       0.67      0.55      0.60        86<br />           4       0.89      0.92      0.90       959<br /><br />    accuracy                           0.95      6446<br />   macro avg       0.79      0.72      0.75      6446<br />weighted avg       0.94      0.95      0.94      6446                   |
+
+# How to Get Started with the Model
+
+[More Information Needed]
+
+# 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]
+```
+
+# citation_bibtex
+
+bibtex
+@inproceedings{...,year={2024}}
+
+# get_started_code
+
+import pickle 
+with open(dtc_pkl_filename, 'rb') as file: 
+    clf = pickle.load(file)
+
+# model_card_authors
+
+Smruti Padhy
+
+# limitations
+
+This model is ready to be used in production.
+
+# model_description
+
+This is a  Histogram-based Gradient Boosting Classification Tree model trained on HPC history jobs between 1Feb-1Aug 2022, window number0
+
+# eval_method
+
+The model is evaluated using test split, on accuracy and F1 score with macro average.
+
+# confusion_matrix
+
+![confusion_matrix](confusion_matrix.png)

config.json

@@ -0,0 +1,52 @@
+{
+    "sklearn": {
+        "columns": [
+            "nnodes",
+            "max_minutes",
+            "backlog_minutes",
+            "backlog_num_jobs",
+            "running_num_jobs",
+            "running_minutes"
+        ],
+        "environment": [
+            "scikit-learn=1.2.2"
+        ],
+        "example_input": {
+            "backlog_minutes": [
+                793051,
+                474385,
+                785116
+            ],
+            "backlog_num_jobs": [
+                302,
+                193,
+                302
+            ],
+            "max_minutes": [
+                18,
+                360,
+                18
+            ],
+            "nnodes": [
+                1,
+                1,
+                1
+            ],
+            "running_minutes": [
+                1934034,
+                1934094,
+                1934034
+            ],
+            "running_num_jobs": [
+                6827,
+                6828,
+                6827
+            ]
+        },
+        "model": {
+            "file": "febskxmodel_hug_0.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification"
+    }
+}

febskxmodel_hug_0.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:96bdbfc1ea264c191432cb89d3e3065dfaf8968a8122991db333297346ed9185
+size 2668195