featurizer-api-client 1.0.0

Featurizer API client

Featurizer API client

This package provides a lightweight client application for the Featurizer API RESTFull server application. The package implements FeaturizerApiClient class enabling fast and easy method-based calls to all endpoints accessible on the API. To make working with the client a piece of cake, it provides full-documented example scripts for each of the supported endpoints. For more information about the Featurizer API, please read the official readme and documentation.

The full programming sphinx-generated docs can be seen in docs/.

Endpoints:

1. featurization endpoints (/featurize)
   1. /featurize - calls .extract on the specified features-extractor (featurizer interface)
2. security endpoints (/signup, /login, and /refresh)
   1. /signup - signs-up a new user.
   2. /login - logs-in an existing user (obtains access and refresh authorization tokens).
   3. /refresh - refreshes an expired access token (obtains refreshed authorization access token).

Contents:

1. Installation
2. Configuration
3. Data
4. Examples
5. License
6. Contributors

---

Installation

pip install featurizer-api-client

Configuration

The package provides the following configuration of the FeaturizerApiClient object during the instantiation:

1. API deployment specific configuration: it supports the configuration of the host (IP address), port (port number), and other settings related to the deployment and operation of the Featurizer API (for more information, see the docs/).
2. API client specific configuration: it supports the configuration of the logging (logging_configuration). In this version, the package provides logging of the successful as well as unsuccessful /featurize endpoint calls (for more information, see the docs/).

Data

The full description of the requirements on input/output data (format, shape, etc.) can be found here.

Examples

In general, every time a client is used, the FeaturizerApiClient class must be instantiated. Next, all endpoint-specific data must be prepared. And finally, the endpoint-specific methods can be called. The full example scripts for each of the supported endpoints are placed at ./examples (simplified examples are shown bellow).

Client instantiation

from pprint import pprint
from http import HTTPStatus
from featurizer_api_client import FeaturizerApiClient

# Prepare the featurizer API client settings
#
# ---------------------------------------------- #
# Must be same as for the running Featurizer API #
# ---------------------------------------------- #
#
# 1. host (IP address)
# 2. port (port number)
# 3. request verification
# 4. request timeout in seconds
host = "http://127.0.0.1"
port = 5000
verify = True
timeout = 2

# Instantiate the featurizer API client
client = FeaturizerApiClient(host=host, port=port, verify=verify, timeout=timeout)

User sign-up

# This example assumes the presence of the client instantiation code

# TODO: prepare data for a new user (see the API's requirements on the password)
#
# 1. username
# 2. password (e.g. can be generated with https://passwordsgenerator.net/)
username = "<TODO: FILL-IN>"
password = "<TODO: FILL-IN>"

print("\n-- [01] example --")
print(f"Signing-up a new user with username: {username} and password: {password}\n")

# Sign-up a new user
response, status_code = client.sign_up(username, password)

# Check the output
if status_code == HTTPStatus.OK:
    print("Successfully signed-up a new user")
else:
    print(f"The request was unsuccessful ({status_code}): {response}")

print("Response:")
pprint(response)

User log-in

# This example assumes the presence of the client instantiation code

# TODO: prepare data for an existing user (data from: user sign-up)
#
# 1. username
# 2. password
username = "<TODO: FILL-IN>"
password = "<TODO: FILL-IN>"

print("\n-- [02] example --")
print(f"Logging-in an existing user with username: {username} and password: {password}\n")

# Log-in an existing user
response, status_code = client.log_in(username, password)

# Check the output
if status_code == HTTPStatus.OK:
    print("Successfully logged-in an existing user")
else:
    print(f"The request was unsuccessful ({status_code}): {response}")

print("Response:")
pprint(response)

Expired access token refresh

# This example assumes the presence of the client instantiation code

# TODO: prepare data for request authorization (refresh token from: user log-in)
refresh_token = "<TODO: FILL-IN>"

print("\n-- [03] example --")
print("Refreshing an expired access token\n")

# Refresh an expired access token
response, status_code = client.refresh_access_token(refresh_token)

# Check the output
if status_code == HTTPStatus.OK:
    print("Successfully refreshed an expired access token")
else:
    print(f"The request was unsuccessful ({status_code}): {response}")

print("Response:")
pprint(response)

Featurization

# This example assumes the presence of the client instantiation code

import numpy

# TODO: prepare data for request authorization (access token and refresh token)
access_token = "<TODO: FILL-IN>"
refresh_token = ">TODO: FILL-IN>"

# TODO: prepare pipeline of features
#
# Example:
# features_pipeline = [
#     {
#         "name": "feature 1",
#         "args": {"abc": 123, "def": 456}
#     },
#     {
#         "name": "feature 2",
#         "args": {"ghi": "simple", "jkl": False}
#     },
#     {
#         "name": "feature 3",
#         "args": {}
#     }
# ]
features_pipeline = "<TODO: FILL-IN>"

# TODO: prepare feature extractor configuration
extractor_configuration = "<TODO: FILL-IN>"

# TODO: prepare featurizer data (sample values/labels)
#
# ----------------------------------------------------- #
# Must meet the data requirements of the Featurizer API #
# ----------------------------------------------------- #
#
# Example (10 subjects, each having 100 1-D samples):
# sample_values = numpy.random.rand(10, 1, 100)
# sample_labels = None
sample_values = "<TODO: FILL-IN>"
sample_labels = None

print("\n-- [04] example --")
print(f"Calling .featurize(...) on a feature extractor\n")

# Call .featurize(...) on a feature extractor
response, status_code = client.featurize(
    features_pipeline=features_pipeline,
    sample_values=sample_values,
    sample_labels=sample_labels,
    extractor_configuration=extractor_configuration,
    access_token=access_token,
    refresh_token=refresh_token)

# Check the output
if status_code == HTTPStatus.OK:
    print("Successfully called .featurize(...)")
else:
    print(f"The request was unsuccessful ({status_code}): {response}")

print("Response:")
pprint(response)

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributors

This package is developed by the members of Brain Diseases Analysis Laboratory. For more information, please contact the head of the laboratory Jiri Mekyska [e-mail] or the main developer: Zoltan Galaz [e-mail].