Rahul Das RahulDas-dev

Loading dataset

import pandas as pd
from sklearn.datasets import  fetch_openml

x = fetch_openml(data_id=1461, as_frame=True, parser='pandas') 
dataset = x['frame']
print(f'dataset Shape {dataset.shape}')
dataset.head()

The Jupyter Notebook Installation for the Virtual Environment

Installation can be done pip install Jupyter. However big list dependency of Jupyter can be avoided by installting ipython kernel module only, if you have Jupyter installed Globally.

Install the ipython kernrl pip install ipykernel
Create a new kernel for your current virtual environment python -m ipykernel install --user --name=<promt>
Launch the notebook with jupyter notebbok from virtual Environment.
You should now be able to see your kernel in the IPython notebook menu: Kernel -> Change kernel

	import numpy as np

	# Frobenius norm (X) = Tr(X.X_T) X_T = Transpose of X
	# d(Frobenius norm)/dX = 2X


	# M ~ L*R
	# R = l*r - M
	# Loss_fuction = NORM(R) to be minimized
	# Loss_fuction = NORM(R)

	import numpy as np

	### Loss function : L(w₁,w₂) = 0.75(w₁ −2)² + 0.35(w₂−4)²
	### Gradient of Loss function : ∇ L = [0.75×2×(w₁ −2), 0.35×2×(w₂−4)]ᵀ
	### Hyper Parameters : Learning Rate η = 0.1 , iteration or epoch N = 100

	loss_f = lambda w1,w2: 0.75(w1-2)2 + 0.35(w2-4)**2
	grad_f = lambda w1,w2: np.array([1.5(w1-2),0.7(w2-4)])

	eta, epoch = 0.1 ,100

	import argparse
	import os

	import pikepdf


	perser = argparse.ArgumentParser()
	perser.add_argument('Path', metavar='path', help='PDF directory')
	args = perser.parse_args()

	def make_pipeline2(list_of_callables):
	def map_2_partials(item):
	func = item[0]
	#return func if len(item) < 2 else partial(func, **item[1])
	if len(item) < 2:
	return func
	else :
	return partial(func, **item[1])

	list_of_partials = map(map_2_partials, list_of_callables)

	from functools import reduce

	# creates pipeline object from list of callables
	def make_pipeline(list_of_callables):
	return reduce(lambda f,g: lambda x: g(f(x)), list_of_callables)

	add1 = lambda x: x+'1' # Oprtaion 1
	add2 = lambda x: x+'2' # Oprtaion 2
	add3 = lambda x: x+'3' # Oprtaion 3
	add4 = lambda x: x+'4' # Oprtaion 4