You can use fuzzy.ai from your Python project using the Open Source library.
You can download the software at: https://github.com/fuzzy-ai/python
You can also fork the repository on Github.
It uses pytest. However, you need to have a Fuzzy.ai API key to make it work.
The test script (not the SDK itself!) looks for the API key in the FUZZY_IO_KEY environment variable. So you can run the test something like this:
FUZZY_IO_KEY=<yourkeyhere> python -m pytest
When you use the fuzzyio module, you always have to provide your API key first. Use the setup() function to do that:
import fuzzyio fuzzyio.setup(YOUR_API_KEY)
To have a Fuzzy.ai agent make a decision for you, use the evaluate() function of the fuzzyio module:
from __future__ import print_function agent_id = "AGENTIDHERE" inputs = dict(height=188, weight=88.7) outputs = fuzzyio.evaluate(agent_id, inputs) print outputs["run_distance"]
If you need to provide feedback on the evaluation, use the evaluate_with_id() function to get an ID for the evaluation, and then provide that to the feedback() function:
agent_id = "AGENTIDHERE" inputs = dict(height=188, weight=88.7) (outputs, evaluation_id) = fuzzyio.evaluate_with_id(agent_id, inputs) # Real-world usage of the run_distance will return some performance # metric. fuzzyio.feedback(evaluation_id, dict(weight_loss=0.25))
All of the Fuzzy.ai API is available through this SDK.
The Agent class represents a single agent. It includes evaluate() and evaluate_with_id() methods as well as save() and delete() to change the agent on the server. Use that last part carefully!
The Evaluation class represents a single evaluation. It includes a get() method to fetch details about the evaluation and the feedback() method to fetch feedback on the evaluation.
The Feedback class represents a single feedback data point. It has a save() method to generate feedback for an evaluation.