Iris Dataset

import pandas as pd
import numpy as np
import time
import os.path

import warnings
warnings.filterwarnings('ignore')

# install DenMune clustering algorithm using pip command from the offecial Python repository, PyPi
# from https://pypi.org/project/denmune/
!pip install denmune

# then import it
from denmune import DenMune

# clone datasets from our repository datasets
if not os.path.exists('datasets'):
  !git clone https://github.com/egy1st/datasets

::: {.parsed-literal} Cloning into \'datasets\'... remote: Enumerating objects: 57, done. remote: Counting objects: 100% (57/57), done. remote: Compressing objects: 100% (46/46), done. remote: Total 57 (delta 9), reused 54 (delta 9), pack-reused 0 Unpacking objects: 100% (57/57), done. :::

data_path = 'datasets/denmune/uci/' 
dataset='iris' 
data_file = data_path + dataset + '.csv'

X_train = pd.read_csv(data_file, sep=',', header=None)
y_train = X_train.iloc[:, -1]
X_train = X_train.drop(X_train.columns[-1], axis=1)  

knn = 11 # k-nearest neighbor, the only parameter required by the algorithm
dm = DenMune(train_data=X_train,
             train_truth=y_train,
             k_nearest=knn,
             rgn_tsne=False)

labels, validity = dm.fit_predict(show_noise=True, show_analyzer=True)

::: {.parsed-literal} Plotting dataset Groundtruth :::

image

::: {.parsed-literal} Plotting train data :::

image

::: {.parsed-literal} Validating train data ├── exec_time │ ├── DenMune: 0.019 │ ├── NGT: 0.002 │ └── t_SNE: 0.85 ├── n_clusters │ ├── actual: 3 │ └── detected: 3 ├── n_points │ ├── dim: 4 │ ├── noise │ │ ├── type-1: 0 │ │ └── type-2: 0 │ ├── plot_size: 150 │ ├── size: 150 │ ├── strong: 84 │ └── weak │ ├── all: 66 │ ├── failed to merge: 0 │ └── succeeded to merge: 66 └── validity └── train ├── ACC: 135 ├── AMI: 0.795 ├── ARI: 0.746 ├── F1: 0.898 ├── NMI: 0.798 ├── completeness: 0.809 └── homogeneity: 0.787 :::