Metadata-Version: 2.1
Name: debiai
Version: 0.15.1
Summary: DebiAI python module
Home-page: https://github.com/debiai/py-debiai
Author: IRT-SystemX
Author-email: debiai@irt-systemx.fr
License: Apache 2.0
Description: # DebiAI Python module
        [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
        
        This Python module is an interface with DebiAI, you can use directly it in your Python project workflow to provide DebiAI with data.
        
        ## Features :
        - Create projects
        - Insert your project data
        - Insert model metadata and model results
        - Recovery of the samples selections made with the dashboard
        - Create tf.dataset from the project or selection
        
        ## Requierements:
        * A running DebiAI instance
        * Numpy
        * Pandas
        * Eventualy Tensorflow
        
        ## Quickstart
        
        ```python
        from debiai import debiai
        import pandas as pd
        import numpy as np
        
        DEBIAI_BACKEND_URL = "http://localhost:3000/"
        DEBIAI_PROJECT_NAME = "Hello DebiAI"
        
        # Initialisation
        my_debiai = debiai.Debiai(DEBIAI_BACKEND_URL)
        
        # Creating a project
        debiai_project = my_debiai.create_project(DEBIAI_PROJECT_NAME)
        
        # Creating the project block structure
        block_structure = [
            {
                # The sample: an image with contexts, GDT and an ID
                "name": "Image ID",
                "contexts": [
                    {"name": "My context 1",     "type": "text"},
                    {"name": "My context 2",     "type": "number"}
                ],
                "groundTruth": [
                    {"name": "My groundtruth 1", "type": "number"}
                ]
            }
        ]
        
        debiai_project.set_blockstructure(block_structure)
        
        
        # ======== Adding the project samples ========
        # Adding samples with a dataframe
        samples_df = pd.DataFrame({
            "Image ID":         ["image-1", "image-2", "image-3"],
            "My context 1":     ["A", "B", "C"],
            "My context 2":     [0.28, 0.388, 0.5],
            "My groundtruth 1": [8, 7, 19],
        })
        
        debiai_project.add_samples_pd(samples_df)
        
        # The project samples are ready to be analysed with the dashboard
        
        
        # ===== Adding the project model results =====
        # Setting the project models expected results
        expected_results = [
            {"name": "Model result",     "type": "number"},
            {"name": "Model confidence", "type": "number"},
            {"name": "Model error",      "type": "text"},
        ]
        
        debiai_project.set_expected_results(expected_results)
        
        # Create the models
        debiai_model_1 = debiai_project.create_model("Model 1")
        debiai_model_2 = debiai_project.create_model("Model 2")
        
        # Adding results with a numpy Array
        results_np = np.array(
            [["Image ID", "Model result", "Model confidence", "Model error"],
             ["image-1", 3,  0.98, "yes"],
             ["image-2", 7,  0.97, "no"],
             ["image-3", 10, 0.8, "yes"]]
        )
        
        debiai_model_1.add_results_np(results_np)
        
        # Adding results with a dataframe
        results_df = pd.DataFrame({
            "Image ID": ["image-1", "image-2", "image-3"],
            "Model result": [5, 7, 19],
            "Model confidence": [0.22, 0.8, 0.9],
            "Model error": ["yes", "no", "no"],
        })
        
        debiai_model_2.add_results_df(results_df)
        
        # The model results are ready to be analysed with the Debiai dashboard
        ```
        <img src="./images/quickstart_results.png">
        
        ## Installation
        
        ### With pip :
        
        Comming soon
        
        ### By building the package :
        
        **Requirements :**
        * setuptools
        * wheel
        * pip
        
        
        Clone the module repository :
        ```bash
        git clone git@github.com:DebiAI/py-debiai.git
        
        cd pythonModule
        ```
        Execute
        ```bash
        ./build_package.sh
        ```
        Install
        ```bash
        pip install build_package/*.tar.gz
        ```
        You can now use the DebiAI module inside your script with `from debiai import debiai`
        
        _(if any script does not work due to bad permissions, use `chmod +x *.sh`_
        
        ### Update
        
        ```bash
        cd pythonModule
        
        git pull
        
        ./build_package.sh
        
        pip install build_package/*.tar.gz
        ```
        
        # Documentation
        
        Comming soon
        
        ---
        
        <p align="center" style="display:flex; align-items:center; justify-content:space-around" >
          Developed by :
          <a href="https://www.irt-systemx.fr/" title="IRT SystemX">
           <img src="https://www.irt-systemx.fr/wp-content/uploads/2013/03/system-x-logo.jpeg"  height="70">
          </a>
          Integrated in :
          <a href="https://www.confiance.ai/" title="Conf AI">
           <img src="https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg"  height="70">
          </a>
        </p>
        
        ---
        
Keywords: DebiAI,Data vis,AI,Bias
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
