debiai 0.15.1

DebiAI python module

DebiAI Python module

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

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

Installation

With pip :

Comming soon

By building the package :

Requirements :

• setuptools
• wheel
• pip

Clone the module repository :

git clone [e-mail]:DebiAI/py-debiai.git

cd pythonModule

Execute

./build_package.sh

Install

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

cd pythonModule

git pull

./build_package.sh

pip install build_package/*.tar.gz

Documentation

Comming soon

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Developed by : Integrated in :

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