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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+correlation_heatmap.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,180 @@
+
+# Model description
+
+K-Means clustering model trained on wheat seeds dataset to identify 3 types of wheat seeds based on 7 morphological features. The model groups seeds into clusters that correspond to different wheat varieties.
+
+## Intended uses & limitations
+
+[More Information Needed]
+
+## Training Procedure
+
+[More Information Needed]
+
+### Hyperparameters
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter |   Value   |
+| :------------: | :-------: |
+|   algorithm    |   lloyd   |
+|     copy_x     |    True   |
+|      init      | k-means++ |
+|    max_iter    |    300    |
+|   n_clusters   |     3     |
+|     n_init     |    auto   |
+|  random_state  |     80    |
+|      tol       |   0.0001  |
+|    verbose     |     0     |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-1 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: #000;--sklearn-color-text-muted: #666;--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-1 {color: var(--sklearn-color-text);
+}#sk-container-id-1 pre {padding: 0;
+}#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-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);
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+}#sk-container-id-1 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-1 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-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;
+}#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;
+}/* Serial-specific style estimator block */#sk-container-id-1 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-1 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-1 label.sk-toggleable__label {cursor: pointer;display: flex;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;align-items: start;justify-content: space-between;gap: 0.5em;
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+}/* Estimator-specific style *//* Colorize estimator box */
+#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
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+}#sk-container-id-1 div.sk-label label.sk-toggleable__label,
+#sk-container-id-1 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-1 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-1 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-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;
+}/* Estimator-specific */
+#sk-container-id-1 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-1 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}/* on hover */
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+}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
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+}/* On hover */
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+div.sk-label-container:hover .sk-estimator-doc-link:hover,
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+}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
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+}/* Span, style for the box shown on hovering the info icon */
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+}/* On hover */
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+}#sk-container-id-1 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+}.estimator-table summary {padding: .5rem;font-family: monospace;cursor: pointer;
+}.estimator-table details[open] {padding-left: 0.1rem;padding-right: 0.1rem;padding-bottom: 0.3rem;
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+}.estimator-table .parameters-table tr:hover {background-color: #e0e0e0;
+}.estimator-table table td {border: 1px solid rgba(106, 105, 104, 0.232);
+}.user-set td {color:rgb(255, 94, 0);text-align: left;
+}.user-set td.value pre {color:rgb(255, 94, 0) !important;background-color: transparent !important;
+}.default td {color: black;text-align: left;
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+.default td i {color: black;
+}.copy-paste-icon {background-image: url(data:image/svg+xml;base64,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);background-repeat: no-repeat;background-size: 14px 14px;background-position: 0;display: inline-block;width: 14px;height: 14px;cursor: pointer;
+}
+</style><body><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>KMeans(n_clusters=3, random_state=80)</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"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>KMeans</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.7/modules/generated/sklearn.cluster.KMeans.html">?<span>Documentation for KMeans</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix=""><div class="estimator-table"><details><summary>Parameters</summary><table class="parameters-table"><tbody><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('n_clusters',this.parentElement.nextElementSibling)"></i></td><td class="param">n_clusters&nbsp;</td><td class="value">3</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('init',this.parentElement.nextElementSibling)"></i></td><td class="param">init&nbsp;</td><td class="value">&#x27;k-means++&#x27;</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('n_init',this.parentElement.nextElementSibling)"></i></td><td class="param">n_init&nbsp;</td><td class="value">&#x27;auto&#x27;</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('max_iter',this.parentElement.nextElementSibling)"></i></td><td class="param">max_iter&nbsp;</td><td class="value">300</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('tol',this.parentElement.nextElementSibling)"></i></td><td class="param">tol&nbsp;</td><td class="value">0.0001</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('verbose',this.parentElement.nextElementSibling)"></i></td><td class="param">verbose&nbsp;</td><td class="value">0</td></tr><tr class="user-set"><td><i class="copy-paste-icon"onclick="copyToClipboard('random_state',this.parentElement.nextElementSibling)"></i></td><td class="param">random_state&nbsp;</td><td class="value">80</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('copy_x',this.parentElement.nextElementSibling)"></i></td><td class="param">copy_x&nbsp;</td><td class="value">True</td></tr><tr class="default"><td><i class="copy-paste-icon"onclick="copyToClipboard('algorithm',this.parentElement.nextElementSibling)"></i></td><td class="param">algorithm&nbsp;</td><td class="value">&#x27;lloyd&#x27;</td></tr></tbody></table></details></div></div></div></div></div></div><script>function copyToClipboard(text, element) {// Get the parameter prefix from the closest toggleable contentconst toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;const originalStyle = element.style;const computedStyle = window.getComputedStyle(element);const originalWidth = computedStyle.width;const originalHTML = element.innerHTML.replace('Copied!', '');navigator.clipboard.writeText(fullParamName).then(() => {element.style.width = originalWidth;element.style.color = 'green';element.innerHTML = "Copied!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);}).catch(err => {console.error('Failed to copy:', err);element.style.color = 'red';element.innerHTML = "Failed!";setTimeout(() => {element.innerHTML = originalHTML;element.style = originalStyle;}, 2000);});return false;
+}document.querySelectorAll('.fa-regular.fa-copy').forEach(function(element) {const toggleableContent = element.closest('.sk-toggleable__content');const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';const paramName = element.parentElement.nextElementSibling.textContent.trim();const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;element.setAttribute('title', fullParamName);
+});
+</script></body>
+
+## Evaluation Results
+
+[More Information Needed]
+
+# How to Get Started with the Model
+
+```python
+from huggingface_hub import hf_hub_download
+import skops.io as sio
+import pandas as pd
+
+# Download model and test data
+hf_hub_download(repo_id='CSC310-fall25/seeds-clustering', filename='model.pkl', local_dir='.')
+hf_hub_download(repo_id='CSC310-fall25/seeds-clustering', filename='test_data.csv', local_dir='.')
+
+# Load model and data
+model = sio.load('model.pkl')
+test_data = pd.read_csv('test_data.csv')
+
+# Prepare features (exclude wheat_type for clustering)
+X_test = test_data.drop('wheat_type', axis=1)
+
+# Make cluster predictions
+cluster_labels = model.predict(X_test)
+```
+
+# Model Card Authors
+
+Christian Romualdo
+
+# Model Card Contact
+
+cromualdo@uri.edu
+
+# Citation
+
+Wheat Seeds Dataset from UCI Machine Learning Repository
+
+# Intended uses & limitations
+
+This clustering model is made for educational purposes and identifies 3 wheat seed types. Performance depends on feature scaling and random initialization.
+
+# Training Procedure
+
+Trained K-Means with 3 clusters on wheat seeds morphological data. Used 80% of data for training, 20% for testing. Evaluation metrics: Silhouette Score and Adjusted Mutual Information (AMI).
+
+# Evaluation Results
+
+Training Silhouette Score: 0.480
+Adjusted Mutual Information: 0.722
+The model shows good separation between wheat types, with clusters aligning well with true labels as shown in the confusion matrix and scatter plots.

config.json

@@ -0,0 +1,63 @@
+{
+  "sklearn": {
+    "columns": [
+      "area",
+      "perimeter",
+      "compactness",
+      "length_kernel",
+      "width_kernel",
+      "asymmetry_coeff",
+      "groove_length",
+      "wheat_type"
+    ],
+    "environment": [
+      "scikit-learn=1.3.2"
+    ],
+    "example_input": {
+      "area": [
+        15.26,
+        14.88,
+        18.6
+      ],
+      "perimeter": [
+        14.84,
+        14.57,
+        16.33
+      ],
+      "compactness": [
+        0.871,
+        0.8811,
+        0.8777
+      ],
+      "length_kernel": [
+        5.763,
+        5.554,
+        6.144
+      ],
+      "width_kernel": [
+        3.312,
+        3.333,
+        3.415
+      ],
+      "asymmetry_coeff": [
+        2.221,
+        1.018,
+        0.966
+      ],
+      "groove_length": [
+        5.22,
+        4.956,
+        5.582
+      ],
+      "wheat_type": [
+        1,
+        2,
+        3
+      ]
+    },
+    "model": {
+      "file": "model.pkl"
+    },
+    "task": "tabular-clustering"
+  }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:449ef710b6314960c624cf3563de37fe9d6ebc1f404597946fc1cf1830595e00
+size 12157

test_data.csv

@@ -0,0 +1,43 @@
+area,perimeter,compactness,length_kernel,width_kernel,asymmetry_coeff,groove_length,wheat_type
+18.98,16.57,0.8687,6.449,3.552,2.144,6.453,2
+15.6,15.11,0.858,5.832,3.286,2.725,5.752,2
+17.08,15.38,0.9079,5.832,3.683,2.956,5.484,1
+11.21,13.13,0.8167,5.279,2.687,6.169,5.275,3
+12.49,13.46,0.8658,5.267,2.967,4.421,5.002,3
+19.18,16.63,0.8717,6.369,3.681,3.357,6.229,2
+16.19,15.16,0.8849,5.833,3.421,0.903,5.307,1
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