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Steel plates fault

machine-learning

Files Size Format Created Updated License Source
3 796kB arff csv zip 10 months ago 10 months ago Open Data Commons Public Domain Dedication and License
The resources for this dataset can be found at https://www.openml.org/d/1504 Author: Semeion, Research Center of Sciences of Communication, Rome, Italy. Source: UCI Please cite: Dataset provided by Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy. Steel read more
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Data Files

Download files in this dataset

File Description Size Last changed Download
steel-plates-fault_arff 291kB arff (291kB)
steel-plates-fault 293kB csv (293kB) , json (878kB)
steel-plates-fault_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 442kB zip (442kB)

steel-plates-fault_arff  

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steel-plates-fault  

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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) Description
V1 1 number (default)
V2 2 number (default)
V3 3 number (default)
V4 4 number (default)
V5 5 number (default)
V6 6 number (default)
V7 7 number (default)
V8 8 number (default)
V9 9 number (default)
V10 10 number (default)
V11 11 year (default)
V12 12 number (default)
V13 13 number (default)
V14 14 number (default)
V15 15 number (default)
V16 16 number (default)
V17 17 number (default)
V18 18 number (default)
V19 19 number (default)
V20 20 number (default)
V21 21 number (default)
V22 22 number (default)
V23 23 number (default)
V24 24 number (default)
V25 25 number (default)
V26 26 number (default)
V27 27 number (default)
V28 28 number (default)
V29 29 number (default)
V30 30 number (default)
V31 31 number (default)
V32 32 number (default)
V33 33 number (default)
Class 34 number (default)

Integrate this dataset into your favourite tool

Use our data-cli tool designed for data wranglers:

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

# Get resources

curl -L https://datahub.io/machine-learning/steel-plates-fault/r/0.arff

curl -L https://datahub.io/machine-learning/steel-plates-fault/r/1.csv

curl -L https://datahub.io/machine-learning/steel-plates-fault/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/steel-plates-fault/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/steel-plates-fault/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/steel-plates-fault/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/steel-plates-fault/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/1504

Author: Semeion, Research Center of Sciences of Communication, Rome, Italy.
Source: UCI
Please cite: Dataset provided by Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy.

Steel Plates Faults Data Set
A dataset of steel plates’ faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.

The dataset consists of 27 features describing each fault (location, size, …) and 7 binary features indicating the type of fault (on of 7: Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults). The latter is commonly used as a binary classification target (‘common’ or ‘other’ fault.)

Attribute Information

  • V1: X_Minimum
  • V2: X_Maximum
  • V3: Y_Minimum
  • V4: Y_Maximum
  • V5: Pixels_Areas
  • V6: X_Perimeter
  • V7: Y_Perimeter
  • V8: Sum_of_Luminosity
  • V9: Minimum_of_Luminosity
  • V10: Maximum_of_Luminosity
  • V11: Length_of_Conveyer
  • V12: TypeOfSteel_A300
  • V13: TypeOfSteel_A400
  • V14: Steel_Plate_Thickness
  • V15: Edges_Index
  • V16: Empty_Index
  • V17: Square_Index
  • V18: Outside_X_Index
  • V19: Edges_X_Index
  • V20: Edges_Y_Index
  • V21: Outside_Global_Index
  • V22: LogOfAreas
  • V23: Log_X_Index
  • V24: Log_Y_Index
  • V25: Orientation_Index
  • V26: Luminosity_Index
  • V27: SigmoidOfAreas
  • V28: Pastry
  • V29: Z_Scratch
  • V30: K_Scatch
  • V31: Stains
  • V32: Dirtiness
  • V33: Bumps
  • Class: Other_Faults

Relevant Papers

1.M Buscema, S Terzi, W Tastle, A New Meta-Classifier,in NAFIPS 2010, Toronto (CANADA),26-28 July 2010, 978-1-4244-7858-6/10 ©2010 IEEE
2.M Buscema, MetaNet: The Theory of Independent Judges, in Substance Use & Misuse, 33(2), 439-461,1998

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