autompg_data: The Auto-MPG dataset for regression

A function that loads the autompg dataset into NumPy arrays.

from mlxtend.data import autompg_data

Overview

The Auto-MPG dataset for regression analysis. The target (y) is defined as the miles per gallon (mpg) for 392 automobiles (6 rows containing "NaN"s have been removed. The 8 feature columns are:

Features

  1. cylinders: multi-valued discrete
  2. displacement: continuous
  3. horsepower: continuous
  4. weight: continuous
  5. acceleration: continuous
  6. model year: multi-valued discrete
  7. origin: multi-valued discrete

  8. car name: string (unique for each instance)

  9. Number of samples: 392

  10. Target variable (continuous): mpg

References

  • Source: https://archive.ics.uci.edu/ml/datasets/Auto+MPG
  • Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.

Example - Dataset overview

from mlxtend.data import autompg_data
X, y = autompg_data()

print('Dimensions: %s x %s' % (X.shape[0], X.shape[1]))
print('\nHeader: %s' % ['cylinders', 'displacement', 
                        'horsepower', 'weight', 'acceleration',
                        'model year', 'origin', 'car name'])
print('1st row', X[0])

Dimensions: 392 x 8

Header: ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model year', 'origin', 'car name']
1st row [  8.00000000e+00   3.07000000e+02   1.30000000e+02   3.50400000e+03
   1.20000000e+01   7.00000000e+01   1.00000000e+00              nan]

Note that the feature array contains a str column ("car name"), thus it is recommended to pick the features as needed and convert it into a float array for further analysis. The example below shows how to get rid of the car name column and cast the NumPy array as a float array.

X[:, :-1].astype(float)

array([[   8. ,  307. ,  130. , ...,   12. ,   70. ,    1. ],
       [   8. ,  350. ,  165. , ...,   11.5,   70. ,    1. ],
       [   8. ,  318. ,  150. , ...,   11. ,   70. ,    1. ],
       ..., 
       [   4. ,  135. ,   84. , ...,   11.6,   82. ,    1. ],
       [   4. ,  120. ,   79. , ...,   18.6,   82. ,    1. ],
       [   4. ,  119. ,   82. , ...,   19.4,   82. ,    1. ]])

API

autompg_data()

Auto MPG dataset.

  • Source : https://archive.ics.uci.edu/ml/datasets/Auto+MPG

  • Number of samples : 392

  • Continuous target variable : mpg

    Dataset Attributes:

    • 1) cylinders: multi-valued discrete
    • 2) displacement: continuous
    • 3) horsepower: continuous
    • 4) weight: continuous
    • 5) acceleration: continuous
    • 6) model year: multi-valued discrete
    • 7) origin: multi-valued discrete
    • 8) car name: string (unique for each instance)

Returns

  • X, y : [n_samples, n_features], [n_targets]

    X is the feature matrix with 392 auto samples as rows and 8 feature columns (6 rows with NaNs removed). y is a 1-dimensional array of the target MPG values.

Examples

For usage examples, please see https://rasbt.github.io/mlxtend/user_guide/data/autompg_data/