add regression example - not available

notebooks/mls-plugin.ipynb

+%% Cell type:code id:512ef6ad-33ab-4170-95c3-766c148eceb6 tags:
+
+``` python
+import numpy as np
+```
+
+%% Cell type:markdown id:162a4d10-249d-492b-96b6-2b6a912a5c7f tags:
+
+# FROM
+https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py
+
+%% Cell type:code id:56b4295d-72e8-48ff-8337-800e958d2c96 tags:
+
+``` python
+from sklearn import datasets, linear_model
+from sklearn.metrics import mean_squared_error, r2_score
+
+# Load the diabetes dataset
+diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)
+```
+
+%% Cell type:code id:51305061-20a6-4939-96c2-80b87e14113e tags:
+
+``` python
+# Use only one feature
+diabetes_X = diabetes_X[:, np.newaxis, 2]
+
+# Split the data into training/testing sets
+diabetes_X_train = diabetes_X[:-20]
+diabetes_X_test = diabetes_X[-20:]
+
+# Split the targets into training/testing sets
+diabetes_y_train = diabetes_y[:-20]
+diabetes_y_test = diabetes_y[-20:]
+
+# Create linear regression object
+regr = linear_model.LinearRegression()
+
+# Train the model using the training sets
+regr.fit(diabetes_X_train, diabetes_y_train)
+
+# Make predictions using the testing set
+diabetes_y_pred = regr.predict(diabetes_X_test)
+```
+
+%% Cell type:code id:30ed4160-070f-4119-9394-dd4e84450764 tags:
+
+``` python
+from sklearn.metrics import mean_squared_error
+
+mse = mean_squared_error(diabetes_y_test, diabetes_y_pred)
+```
+
+%% Cell type:code id:05afb8c6-71d7-4a84-be85-b3d4723e2329 tags:
+
+``` python
+## mlschema
+from mlsconverters import export
+
+export(regr, evaluation_measure=(mean_squared_error, mse))
+```
+
+%%%% Output: error
+
+    ---------------------------------------------------------------------------
+    ValueError                                Traceback (most recent call last)
+    /tmp/ipykernel_513/4199068773.py in <module>
+          2 from mlsconverters import export
+          3
+    ----> 4 export(regr, evaluation_measure=(mean_squared_error, mse))
+
+    /opt/conda/lib/python3.9/site-packages/mlsconverters/__init__.py in export(model, force, **kwargs)
+         27
+         28 def export(model, force=False, **kwargs):
+    ---> 29     mls = _extract_mls(model, **kwargs)
+         30     io.log_renku_mls(mls, str(model.__hash__()), force)
+    /opt/conda/lib/python3.9/site-packages/mlsconverters/__init__.py in _extract_mls(model, **kwargs)
+         11         from . import sklearn
+         12
+    ---> 13         return sklearn.to_mls(model, **kwargs)
+         14     elif model.__module__.startswith("xgboost"):
+         15         from . import xgboost
+    /opt/conda/lib/python3.9/site-packages/mlsconverters/sklearn.py in to_mls(sklearn_model, **kwargs)
+        110     if EVALUATION_MEASURE_KEY in kwargs:
+        111         eval_measure = kwargs[EVALUATION_MEASURE_KEY]
+    --> 112         output_values.append(evaluation_measure(eval_measure[0], eval_measure[1]))
+        113     model = Run(model_hash, implementation, input_values, output_values, algo)
+        114     return RunSchema().dumps(model)
+    /opt/conda/lib/python3.9/site-packages/mlsconverters/sklearn.py in evaluation_measure(func, value)
+         42             )
+         43         else:
+    ---> 44             raise ValueError("unsupported evaluation measure")
+         45
+         46
+    ValueError: unsupported evaluation measure
+
+%% Cell type:markdown id:8ecd75fc-a0da-402f-a4a0-81531511a4db tags:
+
+# MLS converters only supports the following metrics:
+- accuracy_score (classification)
+- roc_auc_score (classification)
+- f1_score (classification)

requirements.txt

 renku-mls
-mlschema-converters
\ No newline at end of file
+mlschema-converters
+sklearn
+numpy
\ No newline at end of file