Installation
pip install glowfi.sh
Setup
from glowfish import glowfish
glower = glowfish.Glower('<GLOWFISH_SID>', '<GLOWFISH_AUTH_TOKEN>')
Useage
Get ready for some simple machine learning...
Training
response = glower.train({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, { # the response set
'class': [4, 3, 5, 6, ...etc]
})
Training using CSVs
response = glower.train_csv('./data_set.csv', './response.csv')
Predict It's important to note that predicting will throw an error if you have not trained against a data set first.
response = glower.predict({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
})
Predict using CSVs
response = glower.predict_csv('./data_set.csv')
Clustering
response = glower.cluster({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
})
Clustering using CSVs
response = glower.cluster_csv('./data_set.csv')
Feature Selection
response = glower.feature_select({ # the data set
'feature_name1': [1, 2, 3, 4, ...etc],
'feature_name2': [9, 4, 5, 6, ...etc]
}, { # the response set
'class': [4, 3, 5, 6, ...etc]
})
Feature Selection using CSVs
response = glower.feature_select_csv('./data_set.csv', './response.csv')
Filter Train # userids, productids, then ratings response = glower.filter_train(userids=[1, 2, 3, 4, ...etc],productids=[1, 2, 3, 4, ...etc],ratings=[1, 2, 3, 4, ...etc])
Filter Predict # userids, productids, then ratings response = glower.filter_predict(userids=[1, 2, 3, 4, ...etc],productids=[1, 2, 3, 4, ...etc],ratings=[1, 2, 3, 4, ...etc])
CSV File Format
Data Set
Feature 1, Feature 2, Feature 3,
1, 2, 3,
4, 5, 6,
7, 8, 9
Response Set
Response Key
1
2
3
Further Documentation
Docs - http://glowfish.readme.io/
Registration - http://glowfi.sh/