A lightweight api for machine and deep learning experiment logging in the form of a python library.
Project description
MLPath
A lightweight api for machine and deep learning experiment logging in the form of a python library.
Installation
pip install mlpath
Get started
import the mlquest module which encompasses all the logging functionality
from mlpath import mlquest as mlq
l = mlq.l
# let's try this out
def DatasetFilter(x_param, y_param, z_param, **kwargs):
return x_param * y_param * z_param
def FeatureExtractor(p_num, k_num, l_num, **kwargs):
return p_num**k_num + l_num
def NaiveBayes(alpha, beta_param, c=0, depth_ratio=4, **kwargs):
return alpha + beta_param + c
mlq.start('NaiveBayes')
dataset = l(DatasetFilter)(14, 510, 4, m_num=63, g_num=3, h_num=4)
features = l(FeatureExtractor)(12, 2, 12)
accuracy = l(NaiveBayes)(alpha=1024, beta_param=7, c=12, depth_ratio=538, mega_p=63, g_estim=3, h=43)
mlq.log_metrics(accuracy=accuracy)
mlq.end()
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