Lymph

Files	Size	Format	Created	Updated	License	Source
3	117kB	arff csv zip	4 years ago	4 years 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	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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This is a preview version. There might be more data in the original version.

Field information

Field Name	Order	Type (Format)
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)

Not found what you're looking for?

You can search over a thousand datasets on datahub

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.

Title: Lymphography Domain
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
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%
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.)
Number of Instances: 148
Number of Attributes: 19 including the class attribute
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
Missing Attribute Values: None
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

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