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Files Size Format Created Updated License Source
3 117kB arff csv zip 10 months ago 6 months ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at https://www.openml.org/d/10 Author: Source: Unknown - Please cite: Citation Request: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
lymph_arff 22kB arff (22kB)
lymph 15kB csv (15kB) , json (69kB)
lymph_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 13kB zip (13kB)

lymph_arff  

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lymph  

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Field information

Field Name Order Type (Format) Description
lymphatics 1 string (default)
block_of_affere 2 string (default)
bl_of_lymph_c 3 string (default)
bl_of_lymph_s 4 string (default)
by_pass 5 string (default)
extravasates 6 string (default)
regeneration_of 7 string (default)
early_uptake_in 8 string (default)
lym_nodes_dimin 9 number (default)
lym_nodes_enlar 10 number (default)
changes_in_lym 11 string (default)
defect_in_node 12 string (default)
changes_in_node 13 string (default)
changes_in_stru 14 string (default)
special_forms 15 string (default)
dislocation_of 16 string (default)
exclusion_of_no 17 string (default)
no_of_nodes_in 18 number (default)
class 19 string (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

data get https://datahub.io/machine-learning/lymph
data info machine-learning/lymph
tree machine-learning/lymph
# Get a list of dataset's resources
curl -L -s https://datahub.io/machine-learning/lymph/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/machine-learning/lymph/r/0.arff

curl -L https://datahub.io/machine-learning/lymph/r/1.csv

curl -L https://datahub.io/machine-learning/lymph/r/2.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/machine-learning/lymph/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/machine-learning/lymph/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/machine-learning/lymph/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/machine-learning/lymph/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()

Read me

The resources for this dataset can be found at https://www.openml.org/d/10

Author:
Source: Unknown -
Please cite:

Citation Request: This lymphography domain was obtained from the University Medical Centre, Institute of Oncology, Ljubljana, Yugoslavia. Thanks go to M. Zwitter and M. Soklic for providing the data. Please include this citation if you plan to use this database.

  1. Title: Lymphography Domain

  2. Sources: (a) See Above. (b) Donors: Igor Kononenko, University E.Kardelj Faculty for electrical engineering Trzaska 25 61000 Ljubljana (tel.: (38)(+61) 265-161

             Bojan Cestnik
             Jozef Stefan Institute
             Jamova 39
             61000 Ljubljana
             Yugoslavia (tel.: (38)(+61) 214-399 ext.287) 
    

    © Date: November 1988

  3. Past Usage: (sveral)

    1. Cestnik,G., Konenenko,I, & Bratko,I. (1987). Assistant-86: A Knowledge-Elicitation Tool for Sophisticated Users. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 31-45, Sigma Press. – Assistant-86: 76% accuracy
    2. Clark,P. & Niblett,T. (1987). Induction in Noisy Domains. In I.Bratko & N.Lavrac (Eds.) Progress in Machine Learning, 11-30, Sigma Press. – Simple Bayes: 83% accuracy – CN2 (99% threshold): 82%
    3. Michalski,R., Mozetic,I. Hong,J., & Lavrac,N. (1986). The Multi-Purpose Incremental Learning System AQ15 and its Testing Applications to Three Medical Domains. In Proceedings of the Fifth National Conference on Artificial Intelligence, 1041-1045. Philadelphia, PA: Morgan Kaufmann. – Experts: 85% accuracy (estimate) – AQ15: 80-82%
  4. Relevant Information: This is one of three domains provided by the Oncology Institute that has repeatedly appeared in the machine learning literature. (See also breast-cancer and primary-tumor.)

  5. Number of Instances: 148

  6. Number of Attributes: 19 including the class attribute

  7. Attribute information: — NOTE: All attribute values in the database have been entered as numeric values corresponding to their index in the list of attribute values for that attribute domain as given below.

    1. class: normal find, metastases, malign lymph, fibrosis
    2. lymphatics: normal, arched, deformed, displaced
    3. block of affere: no, yes
    4. bl. of lymph. c: no, yes
    5. bl. of lymph. s: no, yes
    6. by pass: no, yes
    7. extravasates: no, yes
    8. regeneration of: no, yes
    9. early uptake in: no, yes
    10. lym.nodes dimin: 0-3
    11. lym.nodes enlar: 1-4
    12. changes in lym.: bean, oval, round
    13. defect in node: no, lacunar, lac. marginal, lac. central
    14. changes in node: no, lacunar, lac. margin, lac. central
    15. changes in stru: no, grainy, drop-like, coarse, diluted, reticular, stripped, faint,
    16. special forms: no, chalices, vesicles
    17. dislocation of: no, yes
    18. exclusion of no: no, yes
    19. no. of nodes in: 0-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, >=70
  8. Missing Attribute Values: None

  9. Class Distribution: Class: Number of Instances: normal find: 2 metastases: 81 malign lymph: 61 fibrosis: 4

Relabeled values in attribute ‘lymphatics’ From: ‘1’ To: normal
From: ‘2’ To: arched
From: ‘3’ To: deformed
From: ‘4’ To: displaced

Relabeled values in attribute ‘block_of_affere’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘bl_of_lymph_c’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘bl_of_lymph_s’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘by_pass’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘extravasates’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘regeneration_of’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘early_uptake_in’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘changes_in_lym’ From: ‘1’ To: bean
From: ‘2’ To: oval
From: ‘3’ To: round

Relabeled values in attribute ‘defect_in_node’ From: ‘1’ To: no
From: ‘2’ To: lacunar
From: ‘3’ To: lac_margin
From: ‘4’ To: lac_central

Relabeled values in attribute ‘changes_in_node’ From: ‘1’ To: no
From: ‘2’ To: lacunar
From: ‘3’ To: lac_margin
From: ‘4’ To: lac_central

Relabeled values in attribute ‘changes_in_stru’ From: ‘1’ To: no
From: ‘2’ To: grainy
From: ‘3’ To: drop_like
From: ‘4’ To: coarse
From: ‘5’ To: diluted
From: ‘6’ To: reticular
From: ‘7’ To: stripped
From: ‘8’ To: faint

Relabeled values in attribute ‘special_forms’ From: ‘1’ To: no
From: ‘2’ To: chalices
From: ‘3’ To: vesicles

Relabeled values in attribute ‘dislocation_of’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘exclusion_of_no’ From: ‘1’ To: no
From: ‘2’ To: yes

Relabeled values in attribute ‘class’ From: ‘1’ To: normal
From: ‘2’ To: metastases
From: ‘3’ To: malign_lymph
From: ‘4’ To: fibrosis

Datapackage.json

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