Ex 3: The iris 鳶尾花資料集

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機器學習資料集/ 範例三: The iris dataset

http://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html

這個範例目的是介紹機器學習範例資料集中的iris 鳶尾花資料集

(一)引入函式庫及內建手寫數字資料庫

#這行是在ipython notebook的介面裏專用,如果在其他介面則可以拿掉
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
Y = iris.target
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
plt.figure(2, figsize=(8, 6))
plt.clf()
# Plot the training points
plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
png

(二)資料集介紹

iris = datasets.load_iris() 將一個dict型別資料存入iris,我們可以用下面程式碼來觀察裏面資料

for key,value in iris.items() :
try:
print (key,value.shape)
except:
print (key)
print(iris['feature_names'])

顯示

說明

('target_names', (3L,))

共有三種鳶尾花 setosa, versicolor, virginica

('data', (150L, 4L))

有150筆資料,共四種特徵

('target', (150L,))

這150筆資料各是那一種鳶尾花

DESCR

資料之描述

feature_names

四個特徵代表的意義,分別為 萼片(sepal)之長與寬以及花瓣(petal)之長與寬

為了用視覺化方式呈現這個資料集,下面程式碼首先使用PCA演算法將資料維度降低至3

X_reduced = PCA(n_components=3).fit_transform(iris.data)

接下來將三個維度的資料立用mpl_toolkits.mplot3d.Axes3D 建立三維繪圖空間,並利用 scatter以三個特徵資料數值當成座標繪入空間,並以三種iris之數值 Y,來指定資料點的顏色。我們可以看出三種iris中,有一種明顯的可以與其他兩種區別,而另外兩種則無法明顯區別。

# To getter a better understanding of interaction of the dimensions
# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
cmap=plt.cm.Paired)
ax.set_title("First three PCA directions")
ax.set_xlabel("1st eigenvector")
ax.w_xaxis.set_ticklabels([])
ax.set_ylabel("2nd eigenvector")
ax.w_yaxis.set_ticklabels([])
ax.set_zlabel("3rd eigenvector")
ax.w_zaxis.set_ticklabels([])
plt.show()
png
#接著我們嘗試將這個機器學習資料之描述檔顯示出來
print(iris['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 ...

這個描述檔說明了這個資料集是在 1936年時由Fisher建立,為圖形識別領域之重要經典範例。共例用四種特徵來分類三種鳶尾花

(三)應用範例介紹

在整個scikit-learn應用範例中,有以下幾個範例是利用了這組iris資料集。