Pd auto_ml provides a pandas dataframe accessor to allow using dataframes with MindsDB (either the local native library or a local or remote mindsdb server)
Project description
Python MindsDB SDK
It enables you to connect to a midnsDB server and use it in a similar way to mindsb_native.
Install
pip install mindsdb_sdk
Example of usage
from mindsdb_sdk import SDK
# connect
mdb = SDK('http://localhost:47334')
# upload datasource
mdb.datasources['home_rentals_data'] = {'file' : 'home_rentals.csv'}
# create a new predictor and learn to predict
predictor = mdb.predictors.learn(
name='home_rentals',
datasource='home_rentals_data',
to_predict='rental_price'
)
# predict
result = predictor.predict({'initial_price': '2000','number_of_bathrooms': '1', 'sqft': '700'})
Tests
Before run tests, change SERVER and CREDENTIAL constants in tests/test.py
. After that, run python3 tests/test.py
Test file - is a good place where you can find some examples of api usage.
API Reference(WIP)
class MindsDB(server: str, params: dict)
class DataSources()
class DataSource()
class Predictors()
class Predictor()
class Proxy()
Project details
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