Python client for accessing Fero API
Project description
fero_client
fero
is a client side python library intended to help users interact with the Fero Machine Learning website. The primary entrypoint for interacting with the Fero Application is via the Fero
client class
Quickstart
from fero import Fero
# create a client
fero_client = Fero()
# get an analysis
analyses = fero_client.search_analyses(name="quality-example-data")
analysis = analyses[0]
# Create a data frame with the expected input columns
df = pd.DataFrame([{"value": 5, "value2": 2}])
# Make a prediction
prediction = analysis.make_prediction(df)
print(prediction)
'''
value value2 target_high target_low target_mid
0 5 2 100 75 88
'''
Providing Credentials
The simplest way to provide your Fero login credentials to the API is as arguments to the Fero
object on initialization.
fero_client = Fero(username="<your username>", password="<your password>")
However, while this is fine for interactive shells it's not ideal for a publicly viewable script. To account for this Fero
also supports setting the FERO_USERNAME
and FERO_PASSWORD
environment variables or storing your username and password in a .fero
in the home directory. This file needs to be in the the following format.
FERO_USERNAME=fero_user
FERO_PASSWORD=shouldBeAGoodPassword
Finding An Analysis
An Analysis
object is the how Fero exposes ML models to the API. The Fero client provides two different methods to find an Analysis
. The first is Fero.get_analysis
which takes a single UUID string and attempts to lookup the analysis by its unique id. The second method is Fero.search_analyses
which will return a list of available Analysis
objects. If no keyword arguments are provided, it will return all analyses you have available on the Fero website. Optionally, name
can be provided to filter to only analyses matching that name.
Examples
from fero import Fero
fero_client = Fero()
# get a specific analysis
analysis = fero_client.get_analysis('5dfbbb63-8ad4-4638-9fdb-61e39952d3cf')
# get all available analyses
all_analyses = fero.search_analyses()
# only get "quality" analyses
quality_only = fero.search_analyses(name="quality")
Using an Analysis
Along with associated properties such as name
and uuid
, an Analysis
provides two methods for interacting with Fero.
The first is Analysis.has_trained_model
which is simply a boolean check that a model has finished training. This will be false if the Analysis is still training or there was an error training and it has not been revised.
The second method is Analysis.make_prediction
which, as its name implies, makes a prediction using the latest model associated with the analysis. This function can take either a pandas data frame with columns matching the expected inputs(factors) for the model or a list of dictionaries with each dictionary containing a key/value pairs for each factor. A prediction will be made for each row in the data frame or each dictionary in the list.
The return value will either be a data frame with each target value predicted by Fero added to each row or keys for each target added to each dictionary depending on the initial input type. These values will have the suffixes _low
, _mid
, _high
added to each target name to indicate the range of the prediction.
Example
raw_data = [{"value": 5, "value2": 2}]
# Using a data frame
df = pd.DataFrame([raw_data])
prediction = analysis.make_prediction(df)
print(prediction)
'''
value value2 target_high target_low target_mid
0 5 2 100 75 88
'''
# Using a list of dicts
print(analysis.make_prediction(raw_data))
'''
[{"value": 5, "value2": 2, "target_high": 100, "target_low": 75, "target_mid": 88}]
'''
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.