uploading the model

README.md

@@ -1,3 +1,129 @@
 ---
-license: apache-2.0
+library_name: sklearn
+tags:
+- sklearn
+- skops
+- tabular-classification
+model_file: model.pkl
+widget:
+  structuredData:
+    angel_n_rounds:
+    - 0.0
+    - 0.0
+    - 0.0
+    pre_seed_n_rounds:
+    - 0.0
+    - 0.0
+    - 0.0
+    seed_funding:
+    - 1250000.0
+    - 800000.0
+    - 8000000.0
+    seed_n_rounds:
+    - 1.0
+    - 3.0
+    - 1.0
+    time_first_funding:
+    - 1270.0
+    - 1856.0
+    - 689.0
+    time_till_series_a:
+    - 1455.0
+    - 1667.0
+    - 1559.0
 ---
+
+# Model description
+
+[More Information Needed]
+
+## Intended uses & limitations
+
+[More Information Needed]
+
+## Training Procedure
+
+### Hyperparameters
+
+The model is trained with below hyperparameters.
+
+<details>
+<summary> Click to expand </summary>
+
+| Hyperparameter                                | Value                                                                                              |
+|-----------------------------------------------|----------------------------------------------------------------------------------------------------|
+| memory                                        |                                                                                                    |
+| steps                                         | [('transformation', ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(),<br />                                 ['time_first_funding', 'seed_funding',<br />                                  'time_till_series_a'])])), ('model', LogisticRegression(penalty='none', random_state=0))]                                                                                                    |
+| verbose                                       | False                                                                                              |
+| transformation                                | ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(),<br />                                 ['time_first_funding', 'seed_funding',<br />                                  'time_till_series_a'])])                                                                                                    |
+| model                                         | LogisticRegression(penalty='none', random_state=0)                                                 |
+| transformation__n_jobs                        |                                                                                                    |
+| transformation__remainder                     | drop                                                                                               |
+| transformation__sparse_threshold              | 0.3                                                                                                |
+| transformation__transformer_weights           |                                                                                                    |
+| transformation__transformers                  | [('min_max_scaler', MinMaxScaler(), ['time_first_funding', 'seed_funding', 'time_till_series_a'])] |
+| transformation__verbose                       | False                                                                                              |
+| transformation__verbose_feature_names_out     | True                                                                                               |
+| transformation__min_max_scaler                | MinMaxScaler()                                                                                     |
+| transformation__min_max_scaler__clip          | False                                                                                              |
+| transformation__min_max_scaler__copy          | True                                                                                               |
+| transformation__min_max_scaler__feature_range | (0, 1)                                                                                             |
+| model__C                                      | 1.0                                                                                                |
+| model__class_weight                           |                                                                                                    |
+| model__dual                                   | False                                                                                              |
+| model__fit_intercept                          | True                                                                                               |
+| model__intercept_scaling                      | 1                                                                                                  |
+| model__l1_ratio                               |                                                                                                    |
+| model__max_iter                               | 100                                                                                                |
+| model__multi_class                            | auto                                                                                               |
+| model__n_jobs                                 |                                                                                                    |
+| model__penalty                                | none                                                                                               |
+| model__random_state                           | 0                                                                                                  |
+| model__solver                                 | lbfgs                                                                                              |
+| model__tol                                    | 0.0001                                                                                             |
+| model__verbose                                | 0                                                                                                  |
+| model__warm_start                             | False                                                                                              |
+
+</details>
+
+### Model Plot
+
+The model plot is below.
+
+<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;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;min_max_scaler&#x27;,MinMaxScaler(),[&#x27;time_first_funding&#x27;,&#x27;seed_funding&#x27;,&#x27;time_till_series_a&#x27;])])),(&#x27;model&#x27;, LogisticRegression(penalty=&#x27;none&#x27;, random_state=0))])</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;transformation&#x27;,ColumnTransformer(transformers=[(&#x27;min_max_scaler&#x27;,MinMaxScaler(),[&#x27;time_first_funding&#x27;,&#x27;seed_funding&#x27;,&#x27;time_till_series_a&#x27;])])),(&#x27;model&#x27;, LogisticRegression(penalty=&#x27;none&#x27;, random_state=0))])</pre></div></div></div><div class="sk-serial"><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-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label sk-toggleable__label-arrow">transformation: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(transformers=[(&#x27;min_max_scaler&#x27;, MinMaxScaler(),[&#x27;time_first_funding&#x27;, &#x27;seed_funding&#x27;,&#x27;time_till_series_a&#x27;])])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label 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">min_max_scaler</label><div class="sk-toggleable__content"><pre>[&#x27;time_first_funding&#x27;, &#x27;seed_funding&#x27;, &#x27;time_till_series_a&#x27;]</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-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label sk-toggleable__label-arrow">MinMaxScaler</label><div class="sk-toggleable__content"><pre>MinMaxScaler()</pre></div></div></div></div></div></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-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(penalty=&#x27;none&#x27;, random_state=0)</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]
+```
+
+# model_card_authors
+
+jirko
+
+# model_description
+
+just the temporal regression with reduced input features

config.json

@@ -0,0 +1,52 @@
+{
+    "sklearn": {
+        "columns": [
+            "time_first_funding",
+            "seed_funding",
+            "time_till_series_a",
+            "seed_n_rounds",
+            "angel_n_rounds",
+            "pre_seed_n_rounds"
+        ],
+        "environment": [
+            "scikit-learn=1.1.3"
+        ],
+        "example_input": {
+            "angel_n_rounds": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "pre_seed_n_rounds": [
+                0.0,
+                0.0,
+                0.0
+            ],
+            "seed_funding": [
+                1250000.0,
+                800000.0,
+                8000000.0
+            ],
+            "seed_n_rounds": [
+                1.0,
+                3.0,
+                1.0
+            ],
+            "time_first_funding": [
+                1270.0,
+                1856.0,
+                689.0
+            ],
+            "time_till_series_a": [
+                1455.0,
+                1667.0,
+                1559.0
+            ]
+        },
+        "model": {
+            "file": "model.pkl"
+        },
+        "model_format": "pickle",
+        "task": "tabular-classification"
+    }
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db86051ca41cbc1efcd37c13ce4c845062a29b3d610808bae8b397fbb1c0d4e5
+size 2054