update classification

notebooks/mls-plugin-classification.ipynb

 %% Cell type:code id:b8fa1017-812e-4a6b-9cf5-d69be1a718cb tags:
 
 ``` python
 import numpy as np
 ```
 
 %% Cell type:markdown id:e1a3af63-e023-4d31-b550-aede5d359569 tags:
 
 # FROM
 https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py
 
 %% Cell type:code id:1bc291cd-0192-4611-b77e-7e941238e7fc tags:
 
 ``` python
 # Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
 # License: BSD 3 clause
 
 # Standard scientific Python imports
 import matplotlib.pyplot as plt
 
 # Import datasets, classifiers and performance metrics
 from sklearn import datasets, svm, metrics
 from sklearn.model_selection import train_test_split
 ```
 
 %% Cell type:code id:c521b6ba-f363-4444-a929-915b3962ec9c tags:
 
 ``` python
 digits = datasets.load_digits()
 
 _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
 for ax, image, label in zip(axes, digits.images, digits.target):
     ax.set_axis_off()
     ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
     ax.set_title("Training: %i" % label)
 ```
 
 %%%% Output: display_data
 
     ![](data:image/png;base64,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)
 
 %% Cell type:code id:95670bd9-85b5-4f46-a8bb-68b9d8a7f210 tags:
 
 ``` python
 # flatten the images
 n_samples = len(digits.images)
 data = digits.images.reshape((n_samples, -1))
 
 # Create a classifier: a support vector classifier
 clf = svm.SVC(gamma=0.001)
 
 # Split data into 50% train and 50% test subsets
 X_train, X_test, y_train, y_test = train_test_split(
     data, digits.target, test_size=0.5, shuffle=False
 )
 
 # Learn the digits on the train subset
 clf.fit(X_train, y_train)
 
 # Predict the value of the digit on the test subset
 predicted = clf.predict(X_test)
 ```
 
 %% Cell type:code id:b7e1ce4d-5b4b-43ba-97e4-f2c9e61339b4 tags:
 
 ``` python
 _, axes = plt.subplots(nrows=1, ncols=4, figsize=(10, 3))
 for ax, image, prediction in zip(axes, X_test, predicted):
     ax.set_axis_off()
     image = image.reshape(8, 8)
     ax.imshow(image, cmap=plt.cm.gray_r, interpolation="nearest")
     ax.set_title(f"Prediction: {prediction}")
 ```
 
 %%%% Output: display_data
 
     ![](data:image/png;base64,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)
 
 %% Cell type:code id:13fe2601-b30e-4115-b783-7aef14622d6b tags:
 
 ``` python
 from sklearn.metrics import accuracy_score
 ```
 
 %% Cell type:code id:5170f2c1-d181-4b17-9d9f-cbbf25edfa3d tags:
 
 ``` python
 from mlsconverters import export
 ```
 
 %% Cell type:code id:21b21aec-5b1c-4b5c-8a2f-13ad9ff62ed0 tags:
 
 ``` python
 ## MLS to schema
 acc = accuracy_score(y_test, predicted)
 
 export(clf, evaluation_measure=(accuracy_score, acc))
 ```
 
 %% Cell type:code id:196c78ba-3f86-429d-914e-19be240023c6 tags:
 
 ``` python
 !cd ../; renku mls leaderboard
 ```
 
 %%%% Output: stream
 
-    Error: Project version is outdated and a migration is required.
-    Run `renku migrate` command to fix the issue.
+    +--------+-------+--------+----------+
+    | Run ID | Model | Inputs | accuracy |
+    +--------+-------+--------+----------+
+    +--------+-------+--------+----------+
+
+%% Cell type:code id:a61d638b-6795-4a26-b376-69a7c868b0fb tags:
+
+``` python
+!renku --version
+```
+
+%%%% Output: stream
+
+    1.0.2
+
+%% Cell type:code id:4489f54d-4297-4c21-99cf-9f794272afb5 tags:
+
+``` python
+!cd ../; renku mls params
+```
+
+%%%% Output: stream
+
+    +--------+-------+------------------+
+    | Run ID | Model | Hyper-Parameters |
+    +--------+-------+------------------+
+    +--------+-------+------------------+