Chameleon Dataset

import pandas as pd
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: 52, done. remote: Counting objects: 100% (52/52), done. remote: Compressing objects: 100% (43/43), done. remote: Total 52 (delta 8), reused 49 (delta 8), pack-reused 0 Unpacking objects: 100% (52/52), done. :::

data_path = 'datasets/denmune/chameleon/'  

#@title  { run: "auto", vertical-output: true, form-width: "50%" }
chameleon_dataset = "t7.10k" #@param ["t4.8k", "t5.8k", "t7.10k", "t8.8k"]
show_noize_checkbox = True #@param {type:"boolean"}
show_analyzer_checkbox = True #@param {type:"boolean"}
k_nearest_slider = 39 #@param {type:"slider", min:1, max:100, step:1}

# train file
data_file = data_path + chameleon_dataset + '.csv'
X_train = pd.read_csv(data_file, sep=',', header=None)

dm = DenMune(train_data=X_train, k_nearest=k_nearest_slider, rgn_tsne=False)
labels, validity = dm.fit_predict(show_noise=show_noize_checkbox,
                                  show_analyzer=show_analyzer_checkbox)

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

image

::: {.parsed-literal} Validating train data ├── exec_time │ ├── DenMune: 9.7 │ ├── NGT: 0.612 │ └── t_SNE: 0 ├── n_clusters │ ├── actual: 0 │ └── detected: 9 └── n_points ├── dim: 2 ├── noise │ ├── type-1: 0 │ └── type-2: 516 ├── plot_size: 10000 ├── size: 10000 ├── strong: 5860 └── weak ├── all: 4140 ├── failed to merge: 516 └── succeeded to merge: 3624 :::

data_path = 'datasets/denmune/chameleon/'  

#@title  { run: "auto", vertical-output: true, form-width: "50%" }
chameleon_dataset = "clusterable" #@param ["t4.8k", "t5.8k", "t7.10k", "t8.8k", "clusterable"]
show_noize_checkbox = True #@param {type:"boolean"}
show_analyzer_checkbox = True #@param {type:"boolean"}
k_nearest_slider = 24 #@param {type:"slider", min:1, max:100, step:1}

# train file
data_file = data_path + chameleon_dataset + '.csv'
X_train = pd.read_csv(data_file, sep=',', header=None)

dm = DenMune(train_data=X_train, k_nearest=k_nearest_slider, rgn_tsne=False)
labels, validity = dm.fit_predict(show_noise=show_noize_checkbox,
                                  show_analyzer=show_analyzer_checkbox)

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

image

::: {.parsed-literal} Validating train data ├── exec_time │ ├── DenMune: 1.393 │ ├── NGT: 0.121 │ └── t_SNE: 0 ├── n_clusters │ ├── actual: 0 │ └── detected: 8 └── n_points ├── dim: 2 ├── noise │ ├── type-1: 0 │ └── type-2: 141 ├── plot_size: 2309 ├── size: 2309 ├── strong: 1352 └── weak ├── all: 957 ├── failed to merge: 141 └── succeeded to merge: 816 :::