用Python實作水果分類器

程式原始碼：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Solving%20A%20Simple%20Classification%20Problem%20with%20Python.ipynb

水果資料來源：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/fruit_data_with_colors.txt

我們先滙入pandas和matplotlib.pyplot函式，分別用來處理資料以及繪製統計圖。首先我們先使用read_table函式來讀取fruit_data_with_colors.txt的資料，並把資料集存在fruits變數中，然後呼叫head()讀取資料前五筆資料，其程式如下：

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') print(fruits.head())

執行結果：

若要資料集的輪廓，可以讀取shape，其程式如下：

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') print(fruits.shape)

執行結果：共有59筆資料，有7個欄位。

若想要知道資料集中有幾種水果，可以先讀取fruit_name欄位中的資料，再使用unique()來過濾掉重覆值。

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') print(fruits['fruit_name'].unique())

執行結果：可以看出四種水果

接下來我們來繪製數量統計圖，這時我們採用seaborn函式庫。

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') import seaborn as sns sns.countplot(fruits['fruit_name'],label="Count") plt.show()

執行結果：

為了觀察資料的變異值，我們選用盒鬚圖來繪製。

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') fruits.drop('fruit_label', axis=1).plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False, figsize=(9,9), title='Box Plot for each input variable') plt.savefig('fruits_box') plt.show()

執行結果：

接下來我們使用直方圖來表示各輸入值的分佈情形：

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') import pylab as pl fruits.drop('fruit_label' ,axis=1).hist(bins=30, figsize=(9,9)) pl.suptitle("Histogram for each numeric input variable") plt.savefig('fruits_hist') plt.show()

執行結果：

我們來看看各變數間的相依關係：

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') from pandas.tools.plotting import scatter_matrix from matplotlib import cm feature_names = ['mass', 'width', 'height', 'color_score'] X = fruits[feature_names] y = fruits['fruit_label'] cmap = cm.get_cmap('gnuplot') scatter = pd.scatter_matrix(X, c = y, marker = 'o', s=40, hist_kwds={'bins':15}, figsize=(9,9), cmap = cmap) plt.suptitle('Scatter-matrix for each input variable') plt.savefig('fruits_scatter_matrix') plt.show()

執行結果：

求邏輯迴歸分類值：

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import pandas as pd import matplotlib.pyplot as plt fruits = pd.read_table('fruit_data_with_colors.txt') from pandas.tools.plotting import scatter_matrix from matplotlib import cm feature_names = ['mass', 'width', 'height', 'color_score'] X = fruits[feature_names] y = fruits['fruit_label'] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from sklearn.linear_model import LogisticRegression logreg = LogisticRegression() logreg.fit(X_train, y_train) print('Accuracy of Logistic regression classifier on training set: {:.2f}' .format(logreg.score(X_train, y_train))) print('Accuracy of Logistic regression classifier on test set: {:.2f}' .format(logreg.score(X_test, y_test)))

執行結果：

對於有關其他統計分析可以參考：http://www.atyun.com/14092.html