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@@ -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
+skops-4mj4y_67.skops filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,259 @@
+---
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_format: skops
+model_file: skops-4mj4y_67.skops
+widget:
+- structuredData:
+    amplitude_cutoff:
+    - .nan
+    - .nan
+    - .nan
+    amplitude_cv_median:
+    - .nan
+    - .nan
+    - .nan
+    amplitude_cv_range:
+    - .nan
+    - .nan
+    - .nan
+    amplitude_median:
+    - -231.14950561523438
+    - -32.41670227050781
+    - -49.5401496887207
+    drift_mad:
+    - .nan
+    - .nan
+    - .nan
+    drift_ptp:
+    - .nan
+    - .nan
+    - .nan
+    drift_std:
+    - .nan
+    - .nan
+    - .nan
+    firing_range:
+    - 1.8000000000000007
+    - 3.2399999999999984
+    - 1.4399999999999995
+    firing_rate:
+    - 14.4
+    - 14.6
+    - 13.8
+    isi_violations_count:
+    - 0.0
+    - 0.0
+    - 0.0
+    isi_violations_ratio:
+    - 0.0
+    - 0.0
+    - 0.0
+    num_spikes:
+    - 144.0
+    - 146.0
+    - 138.0
+    presence_ratio:
+    - .nan
+    - .nan
+    - .nan
+    rp_contamination:
+    - 0.0
+    - 0.0
+    - 0.0
+    rp_violations:
+    - 0.0
+    - 0.0
+    - 0.0
+    sd_ratio:
+    - 0.5912728859813103
+    - 1.1242492492431155
+    - 0.7087562828230378
+    sliding_rp_violation:
+    - 0.14
+    - 0.13
+    - 0.145
+    snr:
+    - 40.52572890814601
+    - 6.3489456520122625
+    - 9.014227884573495
+    sync_spike_2:
+    - 0.0
+    - 0.0
+    - 0.007246376811594203
+    sync_spike_4:
+    - 0.0
+    - 0.0
+    - 0.0
+    sync_spike_8:
+    - 0.0
+    - 0.0
+    - 0.0
+---
+
+# 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                                | [('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler()), ('classifier', RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3,<br />                       min_samples_split=3, n_estimators=103,<br />                       random_state=404159593))]                                  |
+| verbose                              | False                            |
+| imputer                              | SimpleImputer(strategy='median') |
+| scaler                               | StandardScaler()                 |
+| classifier                           | RandomForestClassifier(class_weight='balanced_subsample', min_samples_leaf=3,<br />                       min_samples_split=3, n_estimators=103,<br />                       random_state=404159593)                                  |
+| imputer__add_indicator               | False                            |
+| imputer__copy                        | True                             |
+| imputer__fill_value                  |                                  |
+| imputer__keep_empty_features         | False                            |
+| imputer__missing_values              | nan                              |
+| imputer__strategy                    | median                           |
+| scaler__copy                         | True                             |
+| scaler__with_mean                    | True                             |
+| scaler__with_std                     | True                             |
+| classifier__bootstrap                | True                             |
+| classifier__ccp_alpha                | 0.0                              |
+| classifier__class_weight             | balanced_subsample               |
+| classifier__criterion                | gini                             |
+| classifier__max_depth                |                                  |
+| classifier__max_features             | sqrt                             |
+| classifier__max_leaf_nodes           |                                  |
+| classifier__max_samples              |                                  |
+| classifier__min_impurity_decrease    | 0.0                              |
+| classifier__min_samples_leaf         | 3                                |
+| classifier__min_samples_split        | 3                                |
+| classifier__min_weight_fraction_leaf | 0.0                              |
+| classifier__monotonic_cst            |                                  |
+| classifier__n_estimators             | 103                              |
+| classifier__n_jobs                   |                                  |
+| classifier__oob_score                | False                            |
+| classifier__random_state             | 404159593                        |
+| classifier__verbose                  | 0                                |
+| classifier__warm_start               | False                            |
+
+</details>
+
+### Model Plot
+
+<style>#sk-container-id-8 {/* 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-8 {color: var(--sklearn-color-text);
+}#sk-container-id-8 pre {padding: 0;
+}#sk-container-id-8 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-8 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-8 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-8 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-8 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-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;
+}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
+}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
+}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;
+}/* Serial-specific style estimator block */#sk-container-id-8 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-8 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-8 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-8 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-8 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
+}/* Toggleable content - dropdown */#sk-container-id-8 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-8 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-8 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-8 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
+}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
+}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-8 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-8 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-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-8 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-8 div.sk-label label.sk-toggleable__label,
+#sk-container-id-8 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-8 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-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
+}#sk-container-id-8 div.sk-label-container {text-align: center;
+}/* Estimator-specific */
+#sk-container-id-8 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-8 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}/* on hover */
+#sk-container-id-8 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-8 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-8 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-8 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-8 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-8 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+}
+</style><div id="sk-container-id-8" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, StandardScaler()),(&#x27;classifier&#x27;,RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;,min_samples_leaf=3, min_samples_split=3,n_estimators=103,random_state=404159593))])</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-26" type="checkbox" ><label for="sk-estimator-id-26" 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/1.4/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;imputer&#x27;, SimpleImputer(strategy=&#x27;median&#x27;)),(&#x27;scaler&#x27;, StandardScaler()),(&#x27;classifier&#x27;,RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;,min_samples_leaf=3, min_samples_split=3,n_estimators=103,random_state=404159593))])</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-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;SimpleImputer<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html">?<span>Documentation for SimpleImputer</span></a></label><div class="sk-toggleable__content fitted"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</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-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;StandardScaler<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html">?<span>Documentation for StandardScaler</span></a></label><div class="sk-toggleable__content fitted"><pre>StandardScaler()</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-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;RandomForestClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html">?<span>Documentation for RandomForestClassifier</span></a></label><div class="sk-toggleable__content fitted"><pre>RandomForestClassifier(class_weight=&#x27;balanced_subsample&#x27;, min_samples_leaf=3,min_samples_split=3, n_estimators=103,random_state=404159593)</pre></div> </div></div></div></div></div></div>
+
+## Evaluation Results
+
+[More Information Needed]
+
+# 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]
+```

config.json

@@ -0,0 +1,142 @@
+{
+    "sklearn": {
+        "columns": [
+            "amplitude_cutoff",
+            "amplitude_cv_median",
+            "amplitude_cv_range",
+            "amplitude_median",
+            "drift_ptp",
+            "drift_std",
+            "drift_mad",
+            "firing_range",
+            "firing_rate",
+            "isi_violations_ratio",
+            "isi_violations_count",
+            "num_spikes",
+            "presence_ratio",
+            "rp_contamination",
+            "rp_violations",
+            "sd_ratio",
+            "sliding_rp_violation",
+            "snr",
+            "sync_spike_2",
+            "sync_spike_4",
+            "sync_spike_8"
+        ],
+        "environment": [
+            "scikit-learn=1.4.0"
+        ],
+        "example_input": {
+            "amplitude_cutoff": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "amplitude_cv_median": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "amplitude_cv_range": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "amplitude_median": [
+                -231.14950561523438,
+                -32.41670227050781,
+                -49.5401496887207
+            ],
+            "drift_mad": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "drift_ptp": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "drift_std": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "firing_range": [
+                1.8000000000000007,
+                3.2399999999999984,
+                1.4399999999999995
+            ],
+            "firing_rate": [
+                14.4,
+                14.6,
+                13.8
+            ],
+            "isi_violations_count": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "isi_violations_ratio": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "num_spikes": [
+                144.0,
+                146.0,
+                138.0
+            ],
+            "presence_ratio": [
+                NaN,
+                NaN,
+                NaN
+            ],
+            "rp_contamination": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "rp_violations": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "sd_ratio": [
+                0.5912728859813103,
+                1.1242492492431155,
+                0.7087562828230378
+            ],
+            "sliding_rp_violation": [
+                0.14,
+                0.13,
+                0.145
+            ],
+            "snr": [
+                40.52572890814601,
+                6.3489456520122625,
+                9.014227884573495
+            ],
+            "sync_spike_2": [
+                0.0,
+                0.0,
+                0.007246376811594203
+            ],
+            "sync_spike_4": [
+                0.0,
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+}

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