find optimal clustering number using silhouette evaluation

 

To Find optimal clustering number using silhouette metrics

It evaluate clustering resulting in every k number of KMean algorithm.

And show it as figure.

Lager value is better result.

..

from sklearn.cluster import KMeans

from sklearn.metrics import silhouette_samples

import numpy as np

import matplotlib.pyplot as plt

silhouette_vals = []

sk,ek = 2,20

for i in range(sk, ek):

kmeans_plus = KMeans(n_clusters=i, init='k-means++')

pred = kmeans_plus.fit_predict(cluster_df)

silhouette_vals.append(np.mean(silhouette_samples(cluster_df, pred, metric='euclidean')))

plt.plot(range(sk, ek), silhouette_vals, marker='o')

plt.xlabel('Number of clusters')

plt.ylabel('Silhouette')

plt.show()

..

For example here, 20 k is best clustering result.

Thank you.

t-SNE visualisation example code in Python

 

Refer to code

..

from sklearn.manifold import TSNE

from keras.datasets import mnist

from sklearn.datasets import load_iris

from numpy import reshape

import seaborn as sns

import pandas as pd

iris = load_iris()

x = iris.data

y = iris.target

# from sklearn.utils import shuffle

# x, y = shuffle(x, y)

tsne = TSNE(n_components=2, verbose=1, random_state=123)

z = tsne.fit_transform(x)

df = pd.DataFrame()

df["y"] = y

df["comp-1"] = z[:,0]

df["comp-2"] = z[:,1]

sns.scatterplot(x="comp-1", y="comp-2", hue=df.y.tolist(),

palette=sns.color_palette("hls", 3),

data=df).set(title="Iris data T-SNE projection")

..

output

check ubuntu version on terminal

 

lsb_release -a

pathlib, path, pathlib.PosixPath,

 

Make path using pathlib.

refer to code

..

from pathlib import Path

image_dir = 'dataset/images'

images = '1.png'

print(str(Path(image_dir) / images))

print( type(Path(image_dir) / images ))

# dataset/images/1.png

# <class 'pathlib.PosixPath'>

..

www.marearts.com

yaml to dict, adding argparse to dict (easydict, yaml)

 

Simple code to adapt and know how to read yaml and convert it to dict.

And one more thing is add argparse param to dict which is made from yaml.

We use easydict for this.

Refer to below code, then you would understand at a glance.

..

from easydict import EasyDict

import yaml

def load_setting(setting):

with open(setting, 'r') as f:

cfg = yaml.load(f, Loader=yaml.FullLoader)

return EasyDict(cfg)

#----------------------------

cfg = load_setting('test.yaml')

print(cfg)

#{'V1': 'abc', 'V2': {'sub': [1, 2, 3]}}

#----------------------------

#----------------------------

import argparse

paser = argparse.ArgumentParser()

args = paser.parse_args("")

args.batch_size=10

args.epoch=10

#----------------------------

cfg.update(vars(args))

print(cfg, type(cfg))

#{'V1': 'abc', 'V2': {'sub': [1, 2, 3]}, 'batch_size': 10, 'epoch': 10} <class 'easydict.EasyDict'>

#----------------------------

..

Thank you.

www.marearts.com

convert simple transformer ner model to onnx

 

..

!python -m transformers.onnx --model=./checkpoint-21-epoch-11 --feature=token-classification onnx/

..

tokens to word, transformer

 

Refer to code to figure it out

how tokens consisted for a word.

Code show you tokens list for a word.

..

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("roberta-base")

example = "This is a tokenization example"

print('input sentence: ', example)

print('---')

print('tokens :')

print( tokenizer.encode(example, add_special_tokens=False, return_attention_mask=False, return_token_type_ids=False) )

print('---')

print('word and tokens :')

print({x : tokenizer.encode(x, add_special_tokens=False, return_attention_mask=False, return_token_type_ids=False) for x in example.split()})

print('---')

idx = 1

enc =[tokenizer.encode(x, add_special_tokens=False, return_attention_mask=False, return_token_type_ids=False) for x in example.split()]

desired_output = []

for token in enc:

tokenoutput = []

for ids in token:

tokenoutput.append(idx)

idx +=1

desired_output.append(tokenoutput)

print('tokens in grouped list')

print(desired_output)

print('---')

..

input sentence:  This is a tokenization example
---
tokens :
[713, 16, 10, 19233, 1938, 1246]
---
word and tokens :
{'This': [713], 'is': [354], 'a': [102], 'tokenization': [46657, 1938], 'example': [46781]}
---
tokens in grouped list
[[1], [2], [3], [4, 5], [6]]
---

Thank you.

www.marearts.com