v.0.10

On 2022.08.17

This version has been deprecated. If used with the current API version it can produce unexpected behaviour or errors.

The package shimoku-api-python is no longer maintained

To get the new version 🎁🎁🎁

pip install --upgrade shimoku-api-python
pip install --upgrade shimoku-components-catalog

This version brings Machine Learning features through our API by the first time!!

Fixes

• For readability, renamed s.file.* by s.io.*:

Before: s.file.get_dataframe(...)

Now: s.io.get_dataframe(...)

• Fixed IO for large dataframes now you can POST and GET dataframes of large sizes without size limit.

• Fix path and subpaths. Now Apps with multiple words can be sorted too: s.plt.set_apps_orders() and s.plt.set_sub_path_orders()

• Fixes in the default look&feel of different charts and with new features to see data, download image, zoom in and out and change chart type:

  • Heatmap s.plt.heatmap()

  

• Barchart s.plt.bar() and Linechart s.plt.line()

Improvements

• Improvements in the Input Forms look&feel

• Easier to initialize the Shimoku Client

import shimoku_api_python as Shimoku

# Still allowed
s = Shimoku.Client(
    config={'access_token': api_key},    
    universe_id=universe_id,    
    environment=environment
)

# Easier
s = Shimoku.Client(
    access_token=api_key,
    universe_id=universe_id,    
    environment=environment
)

• Added IO methods for Machine Learning models:

from sklearn import svm
from sklearn import datasets

clf = svm.SVC()
X, y = datasets.load_iris(return_X_y=True)
clf.fit(X, y)

s.io.post_ai_model(
    business_id=business_id,
    app_name='test',
    model_name='model-object-test',
    model=clf,
)

model = s.io.get_ai_model(
    business_id=business_id,
    app_name='test',
    model_name='model-object-test',
)

New

• Added method to clear a whole business (tabula rasa) shimoku.plt.clear_business()

Before

s.plt.clear_business()

• Added method to retrieve input forms data

input_data: List[Dict] = s.plt.get_input_forms(menu_path='test/input-form')

Machine Learning!

• AI for Machine Learning features. Now users can use Shimoku's Machine Learning from the SDK just passing a test dataframe and specifying the Model endpoint (ask to Shimoku about what endpoints are available). You can decide whether to add Explainability or not to the predictions

df_pred, df_error = s.ai.predict_categorical(
    df_test=df_test_fail,
    model_endpoint=model_endpoint,
    explain=False,
)

Also predictive tables have been added that create a table with the prediction

target_column: str = 'NRO_POL'
column_to_predict: str = 'churn_probability'
menu_path: str = 'AI/Churn'
order: int = 0

s.ai.predictive_table(
   df_test=df_test,
   model_endpoint=model_endpoint,    
   target_column=target_column,
   column_to_predict=column_to_predict,  
   prediction_type='categorical',
   explain=True,  
   menu_path=menu_path, order=order,    
   add_filter_by_column_to_predict=False,
   add_search_by_target_column=True,
   extra_filter_columns=None,
   extra_search_columns=None
)