Iris (Classification)

One of the more famous classification problems, we can load the classic Iris Dataset saved directly to Scikitlearn using the dataset submodule.

Loading the Data

from sklearn.datasets import load_iris

data = load_iris()

Doing so gives us a Bunch object

type(data)

sklearn.utils.Bunch

Which is basically a dictionary, but with some other stuff

data.__class__.__bases__

(dict,)

Inspecting the Data

Let’s look at the keys

data.keys()

dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])

The data and target keys are just numpy arrays

print(type(data['data']), data['data'].shape)
print(type(data['target']), data['target'].shape)

<class 'numpy.ndarray'> (150, 4)
<class 'numpy.ndarray'> (150,)

Whereas feature_names are just that

print(data['feature_names'])

['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']

And target_names are the un-tokenized labels for the target array.

print(data['target_names'])

['setosa' 'versicolor' 'virginica']

Using the Data

Sklearn

Data’s already broken up by X and y so let’s assign it as such.

X = data['data']
y = data['target']

Done deal.

Pandas

A bit trickier, basically, we want to merge our X and y together

import numpy as np

values = np.c_[X, y]

Then stuff those into a DataFrame

import pandas as pd

df = pd.DataFrame(values)
df.head()

And label the data accordingly

cols = data['feature_names'] + ['flower_names']

df.columns = cols
df.head()

And if we wanted to un-encode the flower_names column, we’d make a dictionary mapping number to flower name.

# verbose, but also generic
d = dict(zip(range(len(data['target_names'])), data['target_names']))
d

{0: 'setosa', 1: 'versicolor', 2: 'virginica'}

And throw it up against the flower_names column.

df['flower_names'] = df['flower_names'].map(d)
df.head()

Some More Description

The DESCR key gives a pretty good overview of what we’re dealing with

print(data['DESCR'])

Iris Plants Database
====================

Notes
-----
Data Set Characteristics:
    :Number of Instances: 150 (50 in each of three classes)
    :Number of Attributes: 4 numeric, predictive attributes and the class
    :Attribute Information:
        - sepal length in cm
        - sepal width in cm
        - petal length in cm
        - petal width in cm
        - class:
                - Iris-Setosa
                - Iris-Versicolour
                - Iris-Virginica
    :Summary Statistics:

    ============== ==== ==== ======= ===== ====================
                    Min  Max   Mean    SD   Class Correlation
    ============== ==== ==== ======= ===== ====================
    sepal length:   4.3  7.9   5.84   0.83    0.7826
    sepal width:    2.0  4.4   3.05   0.43   -0.4194
    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
    ============== ==== ==== ======= ===== ====================

    :Missing Attribute Values: None
    :Class Distribution: 33.3% for each of 3 classes.
    :Creator: R.A. Fisher
    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
    :Date: July, 1988

This is a copy of UCI ML iris datasets.
http://archive.ics.uci.edu/ml/datasets/Iris

The famous Iris database, first used by Sir R.A Fisher

This is perhaps the best known database to be found in the
pattern recognition literature.  Fisher's paper is a classic in the field and
is referenced frequently to this day.  (See Duda & Hart, for example.)  The
data set contains 3 classes of 50 instances each, where each class refers to a
type of iris plant.  One class is linearly separable from the other 2; the
latter are NOT linearly separable from each other.

References
----------
   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
     Mathematical Statistics" (John Wiley, NY, 1950).
   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
     Structure and Classification Rule for Recognition in Partially Exposed
     Environments".  IEEE Transactions on Pattern Analysis and Machine
     Intelligence, Vol. PAMI-2, No. 1, 67-71.
   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
     on Information Theory, May 1972, 431-433.
   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
     conceptual clustering system finds 3 classes in the data.
   - Many, many more ...