pushing files to the repo from the example!

NYC_SQF_ARR_MLP.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbf19008614e076368a6994b4f4a748d12dde0741dc1027f117eaffc6a70f85b
+size 62326

README.md

@@ -0,0 +1,261 @@
+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_format: pickle
+model_file: NYC_SQF_ARR_MLP.pkl
+widget:
+- structuredData:
+    ASK_FOR_CONSENT_FLG:
+    - 1
+    - 1
+    - 0
+    CONSENT_GIVEN_FLG:
+    - 1
+    - 0
+    - 0
+    FIREARM_FLAG:
+    - 0
+    - 0
+    - 0
+    FRISKED_FLAG:
+    - 1
+    - 1
+    - 1
+    ISSUING_OFFICER_RANK:
+    - 10
+    - 13
+    - 9
+    KNIFE_CUTTER_FLAG:
+    - 0
+    - 0
+    - 0
+    OTHER_CONTRABAND_FLAG:
+    - 1
+    - 0
+    - 0
+    OTHER_WEAPON_FLAG:
+    - 0
+    - 0
+    - 0
+    SEARCHED_FLAG:
+    - 1
+    - 0
+    - 1
+    STOP_ID:
+    - 8283
+    - 4532
+    - 14787
+    STOP_LOCATION_PRECINCT:
+    - 90
+    - 40
+    - 106
+    SUPERVISING_OFFICER_RANK:
+    - 12
+    - 7
+    - 12
+    SUSPECT_BODY_BUILD_TYPE:
+    - 2
+    - 3
+    - 3
+    SUSPECT_HEIGHT:
+    - 5.7
+    - 5.1
+    - 5.6
+    SUSPECT_RACE_DESCRIPTION:
+    - 7
+    - 3
+    - 7
+    SUSPECT_REPORTED_AGE:
+    - 22.0
+    - 20.0
+    - 30.0
+    SUSPECT_SEX:
+    - 1
+    - 2
+    - 2
+    SUSPECT_WEIGHT:
+    - 135.0
+    - 170.0
+    - 150.0
+    WEAPON_FOUND_FLAG:
+    - 0
+    - 0
+    - 0
+    __index_level_0__:
+    - 8282
+    - 4531
+    - 14786
+---
+
+# 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                    | [('scaler', MinMaxScaler()), ('mlp', MLPClassifier())] |
+| verbose                  | False                                                  |
+| scaler                   | MinMaxScaler()                                         |
+| mlp                      | MLPClassifier()                                        |
+| scaler__clip             | False                                                  |
+| scaler__copy             | True                                                   |
+| scaler__feature_range    | (0, 1)                                                 |
+| mlp__activation          | relu                                                   |
+| mlp__alpha               | 0.0001                                                 |
+| mlp__batch_size          | auto                                                   |
+| mlp__beta_1              | 0.9                                                    |
+| mlp__beta_2              | 0.999                                                  |
+| mlp__early_stopping      | False                                                  |
+| mlp__epsilon             | 1e-08                                                  |
+| mlp__hidden_layer_sizes  | (100,)                                                 |
+| mlp__learning_rate       | constant                                               |
+| mlp__learning_rate_init  | 0.001                                                  |
+| mlp__max_fun             | 15000                                                  |
+| mlp__max_iter            | 200                                                    |
+| mlp__momentum            | 0.9                                                    |
+| mlp__n_iter_no_change    | 10                                                     |
+| mlp__nesterovs_momentum  | True                                                   |
+| mlp__power_t             | 0.5                                                    |
+| mlp__random_state        |                                                        |
+| mlp__shuffle             | True                                                   |
+| mlp__solver              | adam                                                   |
+| mlp__tol                 | 0.0001                                                 |
+| mlp__validation_fraction | 0.1                                                    |
+| mlp__verbose             | False                                                  |
+| mlp__warm_start          | False                                                  |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-3 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--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-3 {color: var(--sklearn-color-text);
+}#sk-container-id-3 pre {padding: 0;
+}#sk-container-id-3 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-3 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);
+}#sk-container-id-3 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 thedefault 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-3 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-3 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-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;
+}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
+}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
+}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;
+}/* Serial-specific style estimator block */#sk-container-id-3 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-3 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-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
+}#sk-container-id-3 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
+}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
+}/* Toggleable content - dropdown */#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
+}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
+}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
+}/* Estimator-specific style *//* Colorize estimator box */
+#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}#sk-container-id-3 div.sk-label label.sk-toggleable__label,
+#sk-container-id-3 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-3 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-3 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-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
+}#sk-container-id-3 div.sk-label-container {text-align: center;
+}/* Estimator-specific */
+#sk-container-id-3 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-3 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}/* on hover */
+#sk-container-id-3 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
+a:link.sk-estimator-doc-link,
+a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
+}.sk-estimator-doc-link.fitted,
+a:link.sk-estimator-doc-link.fitted,
+a:visited.sk-estimator-doc-link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* On hover */
+div.sk-estimator:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover,
+div.sk-label-container:hover .sk-estimator-doc-link:hover,
+.sk-estimator-doc-link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover,
+div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+.sk-estimator-doc-link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}/* Span, style for the box shown on hovering the info icon */
+.sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
+}.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
+}.sk-estimator-doc-link:hover span {display: block;
+}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-3 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}#sk-container-id-3 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* On hover */
+#sk-container-id-3 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}#sk-container-id-3 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+}
+</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()), (&#x27;mlp&#x27;, MLPClassifier())])</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 fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-7" type="checkbox" ><label for="sk-estimator-id-7" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;Pipeline<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.pipeline.Pipeline.html">?<span>Documentation for Pipeline</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()), (&#x27;mlp&#x27;, MLPClassifier())])</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-8" type="checkbox" ><label for="sk-estimator-id-8" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;MinMaxScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html">?<span>Documentation for MinMaxScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>MinMaxScaler()</pre></div> </div></div><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-9" type="checkbox" ><label for="sk-estimator-id-9" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;MLPClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.neural_network.MLPClassifier.html">?<span>Documentation for MLPClassifier</span></a></label><div class="sk-toggleable__content fitted"><pre>MLPClassifier()</pre></div> </div></div></div></div></div></div>
+
+## Evaluation Results
+
+| Metric    |    Value |
+|-----------|----------|
+| accuracy  | 0.854977 |
+| f1 score  | 0.755843 |
+| precision | 0.811757 |
+| recall    | 0.707135 |
+
+# 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]
+```
+
+# eval_method
+
+The model is evaluated using test split, on accuracy, precision, recall and f1.

config.json

@@ -0,0 +1,136 @@
+{
+    "sklearn": {
+        "columns": [
+            "STOP_ID",
+            "FRISKED_FLAG",
+            "SEARCHED_FLAG",
+            "STOP_LOCATION_PRECINCT",
+            "SUSPECT_REPORTED_AGE",
+            "SUSPECT_SEX",
+            "SUSPECT_RACE_DESCRIPTION",
+            "SUSPECT_HEIGHT",
+            "SUSPECT_WEIGHT",
+            "SUSPECT_BODY_BUILD_TYPE",
+            "OTHER_CONTRABAND_FLAG",
+            "FIREARM_FLAG",
+            "KNIFE_CUTTER_FLAG",
+            "OTHER_WEAPON_FLAG",
+            "WEAPON_FOUND_FLAG",
+            "ASK_FOR_CONSENT_FLG",
+            "CONSENT_GIVEN_FLG",
+            "ISSUING_OFFICER_RANK",
+            "SUPERVISING_OFFICER_RANK",
+            "__index_level_0__"
+        ],
+        "environment": [
+            "scikit-learn=1.5.2"
+        ],
+        "example_input": {
+            "ASK_FOR_CONSENT_FLG": [
+                1,
+                1,
+                0
+            ],
+            "CONSENT_GIVEN_FLG": [
+                1,
+                0,
+                0
+            ],
+            "FIREARM_FLAG": [
+                0,
+                0,
+                0
+            ],
+            "FRISKED_FLAG": [
+                1,
+                1,
+                1
+            ],
+            "ISSUING_OFFICER_RANK": [
+                10,
+                13,
+                9
+            ],
+            "KNIFE_CUTTER_FLAG": [
+                0,
+                0,
+                0
+            ],
+            "OTHER_CONTRABAND_FLAG": [
+                1,
+                0,
+                0
+            ],
+            "OTHER_WEAPON_FLAG": [
+                0,
+                0,
+                0
+            ],
+            "SEARCHED_FLAG": [
+                1,
+                0,
+                1
+            ],
+            "STOP_ID": [
+                8283,
+                4532,
+                14787
+            ],
+            "STOP_LOCATION_PRECINCT": [
+                90,
+                40,
+                106
+            ],
+            "SUPERVISING_OFFICER_RANK": [
+                12,
+                7,
+                12
+            ],
+            "SUSPECT_BODY_BUILD_TYPE": [
+                2,
+                3,
+                3
+            ],
+            "SUSPECT_HEIGHT": [
+                5.7,
+                5.1,
+                5.6
+            ],
+            "SUSPECT_RACE_DESCRIPTION": [
+                7,
+                3,
+                7
+            ],
+            "SUSPECT_REPORTED_AGE": [
+                22.0,
+                20.0,
+                30.0
+            ],
+            "SUSPECT_SEX": [
+                1,
+                2,
+                2
+            ],
+            "SUSPECT_WEIGHT": [
+                135.0,
+                170.0,
+                150.0
+            ],
+            "WEAPON_FOUND_FLAG": [
+                0,
+                0,
+                0
+            ],
+            "__index_level_0__": [
+                8282,
+                4531,
+                14786
+            ]
+        },
+        "model": {
+            "file": "NYC_SQF_ARR_MLP.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification"
+    }
+}