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
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#這行是在ipython notebook的介面裏專用，如果在其他介面則可以拿掉
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%matplotlib inline
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​
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn import datasets
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from sklearn.decomposition import PCA
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​
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# import some data to play with
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iris = datasets.load_iris()
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X = iris.data[:, :2]  # we only take the first two features.
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Y = iris.target
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​
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x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
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y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
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​
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plt.figure(2, figsize=(8, 6))
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plt.clf()
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# Plot the training points
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plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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​
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plt.xlim(x_min, x_max)
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plt.ylim(y_min, y_max)
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plt.xticks(())
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plt.yticks(())
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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
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for key,value in iris.items() :
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    try:
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        print (key,value.shape)
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    except:
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        print (key)
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print(iris['feature_names'])
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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
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X_reduced = PCA(n_components=3).fit_transform(iris.data)
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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。
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# To getter a better understanding of interaction of the dimensions
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# plot the first three PCA dimensions
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fig = plt.figure(1, figsize=(8, 6))
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ax = Axes3D(fig, elev=-150, azim=110)
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ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2], c=Y,
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           cmap=plt.cm.Paired)
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ax.set_title("First three PCA directions")
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ax.set_xlabel("1st eigenvector")
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ax.w_xaxis.set_ticklabels([])
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ax.set_ylabel("2nd eigenvector")
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ax.w_yaxis.set_ticklabels([])
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ax.set_zlabel("3rd eigenvector")
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ax.w_zaxis.set_ticklabels([])
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​
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plt.show()
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png
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#接著我們嘗試將這個機器學習資料之描述檔顯示出來
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print(iris['DESCR'])
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Iris Plants Database
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​
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Notes
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-----
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Data Set Characteristics:
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    :Number of Instances: 150 (50 in each of three classes)
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    :Number of Attributes: 4 numeric, predictive attributes and the class
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    :Attribute Information:
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        - sepal length in cm
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        - sepal width in cm
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        - petal length in cm
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        - petal width in cm
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        - class:
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                - Iris-Setosa
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                - Iris-Versicolour
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                - Iris-Virginica
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    :Summary Statistics:
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​
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    ============== ==== ==== ======= ===== ====================
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                    Min  Max   Mean    SD   Class Correlation
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    ============== ==== ==== ======= ===== ====================
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    sepal length:   4.3  7.9   5.84   0.83    0.7826
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    sepal width:    2.0  4.4   3.05   0.43   -0.4194
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    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
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    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
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    ============== ==== ==== ======= ===== ====================
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​
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    :Missing Attribute Values: None
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    :Class Distribution: 33.3% for each of 3 classes.
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    :Creator: R.A. Fisher
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    :Donor: Michael Marshall (MARSHALL%[email protected])
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    :Date: July, 1988
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​
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This is a copy of UCI ML iris datasets.
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http://archive.ics.uci.edu/ml/datasets/Iris
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​
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The famous Iris database, first used by Sir R.A Fisher
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​
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This is perhaps the best known database to be found in the
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pattern recognition literature.  Fisher's paper is a classic in the field and
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is referenced frequently to this day.  (See Duda & Hart, for example.)  The
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data set contains 3 classes of 50 instances each, where each class refers to a
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type of iris plant.  One class is linearly separable from the other 2; the
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latter are NOT linearly separable from each other.
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​
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References
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----------
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   - Fisher,R.A. "The use of multiple measurements in taxonomic problems"
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     Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
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     Mathematical Statistics" (John Wiley, NY, 1950).
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   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
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     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
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   - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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     Structure and Classification Rule for Recognition in Partially Exposed
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     Environments".  IEEE Transactions on Pattern Analysis and Machine
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     Intelligence, Vol. PAMI-2, No. 1, 67-71.
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   - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE Transactions
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     on Information Theory, May 1972, 431-433.
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   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al"s AUTOCLASS II
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     conceptual clustering system finds 3 classes in the data.
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   - Many, many more ...
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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。
分類法 Classification
​EX 3: Plot classification probability​
特徵選擇 Feature Selection
​Ex 5: Test with permutations the significance of a classification score​
​Ex 6: Univariate Feature Selection​
通用範例 General Examples
​Ex 2: Concatenating multiple feature extraction methods​
Ex 2: Plot randomly generated classification dataset 分類數據集
Ex 4: Plot randomly generated multilabel dataset 多標籤數據集
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Contents
機器學習資料集/ 範例三: The iris dataset
(一)引入函式庫及內建手寫數字資料庫
(二)資料集介紹
(三)應用範例介紹