XAISuite General Demo
======================
  
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   .. rubric:: XAISuite General Demo
      :name: xaisuite-general-demo

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   Welcome to the XAISuite General Demo. Here you'll find countless
   examples about how to use XAISuite for easier machine learning model
   training, explanation, and explanation comparison.

   First, start by installing the XAISuite library.

   ``!pip install XAISuite==2.8.3``

   Then, import the library. You may need to restart runtime for this to
   work correctly.

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   .. code:: python

      from xaisuite import*

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   Run the following code to see a mini doc of XAISuite classes and
   functions.

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   .. code:: python

      help(DataLoader)
      help(DataProcessor)
      help(ModelTrainer)
      help(InsightGenerator)

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   Let's first start with Data Loading. The
   ``xaisuite.dataHandler.DataLoader``\ class allows loading of data
   from different sources.

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   .. code:: python

      DataLoader(make_classification)

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   .. code:: python

      DataLoader(load_diabetes, return_X_y = True)

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   .. code:: python

      import numpy as np
      data = np.zeros(20)
      DataLoader(data)

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   .. code:: python

      import pandas as pd
      data = pd.DataFrame([[1, 2, 3], [3, 4, 5]])
      DataLoader(data)

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   Another option: ``DataLoader('path/to/local/file')``

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   DataLoader also has options to specify the variable names, target
   name, categorical variables, etc. If not specified, these values are
   inferred.

   Visualize variables loaded by ``DataLoader``

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   .. code:: python

      load_data = DataLoader(make_classification, n_features = 10)
      load_data.plot()

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   XAISuiteGeneralDemoOutput1

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   For optimal model training, additional processing must be done to the
   data. This is where ``xaisuite.dataHandler.DataProcessor`` comes into
   play. You can process data with default parameters or pass in your
   own transforms (like from the ``sklearn.preprocessing`` library).

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   .. code:: python

      load_data = DataLoader(make_classification, n_features = 10)
      DataProcessor(load_data)

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   .. code:: python

      load_data = DataLoader(load_diabetes, return_X_y = True)
      DataProcessor(load_data, test_size = 0.1)

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   You can also use transform components, a short example of which is
   given below with placeholders:

   ``from sklearn.preprocessing import StandardScaler``

   ``load_data = DataLoader(load_diabetes, return_X_y = True)``

   ``DataProcessor(load_data, test_size = 0.1, target_transform = "component: TargetTransform()")``

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   To train a model, simply do:

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   .. code:: python

      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)

      ModelTrainer("SVR", process_data)

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   You can also pass in a model directly without using a String
   representation.

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   .. code:: python

      from sklearn.svm import SVR
      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)

      ModelTrainer(SVR, process_data, epsilon = 0.2)

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   For explaining, simply list the desired explanations.

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   .. code:: python

      from sklearn.svm import SVR
      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)

      ModelTrainer(SVR, process_data, explainers = ["lime", "shap"], epsilon = 0.2)

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   You can pass in arguments to the explainers:

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   .. code:: python

      from sklearn.svm import SVR
      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)

      ModelTrainer(SVR, process_data, explainers = {"lime": {"feature_selection": "none"}, "shap": {}}, epsilon = 0.2)

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   To access the explanations, use the ``getExplanationsFor``,
   ``getAllExplanations``, or ``getSummaryExplanations`` functions. Use
   ``plotExplanations`` for explanation visualization.

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   .. code:: python

      from sklearn.svm import SVR
      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)
      train_model = ModelTrainer(SVR, process_data, explainers = {"lime": {"feature_selection": "none"}, "shap": {}}, epsilon = 0.2)

      explanations = train_model.getExplanationsFor([]) # Gets all explanations. You can also request explanations for a specific instance
      train_model.plotExplanations("lime", 1) #Display the lime explainer results for the 2nd instance returned by getExplanationsFor()

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   ``Model score is 0.22886080630718109``

   ``Generating explanations.``

   ``0%|          | 0/45 [00:00<?, ?it/s]``

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   XAISuiteGeneralDemoOutput2

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   Calculate similarity between explainers using the Shreyan Distance

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   .. code:: python

      from sklearn.svm import SVR
      load_data = DataLoader(load_diabetes, return_X_y = True)
      process_data = DataProcessor(load_data, test_size = 0.1)
      train_model = ModelTrainer(SVR, process_data, explainers = {"lime": {"feature_selection": "none"}, "shap": {}}, epsilon = 0.2)
      explanations = train_model.getExplanationsFor([])

      insights = InsightGenerator(explanations)
      print(insights.calculateExplainerSimilarity("lime", "shap"))

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   ``Model score is 0.14626289816154203``

   ``Generating explanations.``

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   ``0.8081355932203389``

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   *NOTE*: For examples using tensorflow or pytorch models, check out
   our other demos.

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   .. code:: python