【教程】簡單教程：用Python解決簡單的水果分類問題
阿新 • • 發佈：2022-05-03
在這篇文章中，我們將使用Python中最流行的機器學習工具scikit- learn,在Python中實現幾種機器學習演算法。使用簡單的資料集來訓練分類器區分不同型別的水果。這篇文章的目的是識別出最適合當前問題的機器學習演算法。因此，我們要比較不同的演算法，選擇效能最好的演算法。讓我們開始吧!
資料
水果資料集由愛丁堡大學的Iain Murray博士建立。他買了幾十個不同種類的橘子、檸檬和蘋果，並把它們的尺寸記錄在一張桌子上。密歇根大學的教授們對水果資料進行了些微的格式化，可以從這裡下載。
下載地址：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/fruit_data_with_colors.txt
讓我們先看一看資料的前幾行。
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
fruits = pd.read_table('fruit_data_with_colors.txt')
fruits.head()
圖1
資料集的每一行表示一個水果塊，它由表中的幾個特性表示。
在資料集中有59個水果和7個特徵:
print(fruits.shape)(59, 7)
在資料集中有四種水果:
print(fruits['fruit_name'].unique())[“蘋果”柑橘”“橙子”“檸檬”]
除了柑橘，資料是相當平衡的。我們只好接著進行下一步。
print(fruits.groupby('fruit_name').size())
圖2
import seaborn as sns
sns.countplot(fruits['fruit_name'],label="Count")
plt.show()
圖3
視覺化
- 每個數字變數的箱線圖將使我們更清楚地瞭解輸入變數的分佈:
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()
圖4
- 看起來顏色分值近似於高斯分佈。
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()
圖5
- 一些成對的屬性是相關的(質量和寬度)。這表明了高度的相關性和可預測的關係。
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')
圖6
統計摘要
圖7
我們可以看到數值沒有相同的縮放比例。我們需要將縮放比例擴充套件應用到我們為訓練集計算的測試集上。
建立訓練和測試集，並應用縮放比例
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)構建模型
邏輯迴歸
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)))訓練集中邏輯迴歸分類器的精確度:0.70
測試集中邏輯迴歸分類器的精確度:0.40
決策樹
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier().fit(X_train, y_train)
print('Accuracy of Decision Tree classifier on training set: {:.2f}'
.format(clf.score(X_train, y_train)))
print('Accuracy of Decision Tree classifier on test set: {:.2f}'
.format(clf.score(X_test, y_test)))訓練集中決策樹分類器的精確度:1.00
測試集中決策樹分類器的精確度:0.73
K-Nearest Neighbors（K-NN ）
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
print('Accuracy of K-NN classifier on training set: {:.2f}'
.format(knn.score(X_train, y_train)))
print('Accuracy of K-NN classifier on test set: {:.2f}'
.format(knn.score(X_test, y_test)))訓練集中K-NN 分類器的精確度:0.95
測試集中K-NN 分類器的精確度:1.00
線性判別分析
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
lda = LinearDiscriminantAnalysis()
lda.fit(X_train, y_train)
print('Accuracy of LDA classifier on training set: {:.2f}'
.format(lda.score(X_train, y_train)))
print('Accuracy of LDA classifier on test set: {:.2f}'
.format(lda.score(X_test, y_test)))訓練集中LDA分類器的精確度:0.86
測試集中LDA分類器的精確度:0.67
高斯樸素貝葉斯
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
print('Accuracy of GNB classifier on training set: {:.2f}'
.format(gnb.score(X_train, y_train)))
print('Accuracy of GNB classifier on test set: {:.2f}'
.format(gnb.score(X_test, y_test)))訓練集中GNB分類器的精確度:0.86
測試集中GNB分類器的精確度:0.67
支援向量機
from sklearn.svm import SVC
svm = SVC()
svm.fit(X_train, y_train)
print('Accuracy of SVM classifier on training set: {:.2f}'
.format(svm.score(X_train, y_train)))
print('Accuracy of SVM classifier on test set: {:.2f}'
.format(svm.score(X_test, y_test)))訓練集中SVM分類器的精確度:0.61
測試集中SVM分類器的精確度:0.33
KNN演算法是我們嘗試過的最精確的模型。混淆矩陣提供了在測試集上沒有錯誤的指示。但是，測試集非常小。
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
pred = knn.predict(X_test)
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred))
圖8
繪製k-NN分類器的決策邊界
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap, BoundaryNorm
import matplotlib.patches as mpatches
import matplotlib.patches as mpatches
X = fruits[['mass', 'width', 'height', 'color_score']]
y = fruits['fruit_label']
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
def plot_fruit_knn(X, y, n_neighbors, weights):
X_mat = X[['height', 'width']].as_matrix()
y_mat = y.as_matrix()
# Create color maps
cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF','#AFAFAF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF','#AFAFAF'])
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(X_mat, y_mat)
# Plot the decision boundary by assigning a color in the color map
# to each mesh point.
mesh_step_size = .01 # step size in the mesh
plot_symbol_size = 50
x_min, x_max = X_mat[:, 0].min() - 1, X_mat[:, 0].max() + 1
y_min, y_max = X_mat[:, 1].min() - 1, X_mat[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, mesh_step_size),
np.arange(y_min, y_max, mesh_step_size))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure()
plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot training points
plt.scatter(X_mat[:, 0], X_mat[:, 1], s=plot_symbol_size, c=y, cmap=cmap_bold, edgecolor = 'black')
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
patch0 = mpatches.Patch(color='#FF0000', label='apple')
patch1 = mpatches.Patch(color='#00FF00', label='mandarin')
patch2 = mpatches.Patch(color='#0000FF', label='orange')
patch3 = mpatches.Patch(color='#AFAFAF', label='lemon')
plt.legend(handles=[patch0, patch1, patch2, patch3])
plt.xlabel('height (cm)')
plt.ylabel('width (cm)')
plt.title("4-Class classification (k = %i, weights = '%s')"
% (n_neighbors, weights))
plt.show()
plot_fruit_knn(X_train, y_train, 5, 'uniform')
圖9
k_range = range(1, 20)
scores = []
for k in k_range:
knn = KNeighborsClassifier(n_neighbors = k)
knn.fit(X_train, y_train)
scores.append(knn.score(X_test, y_test))
plt.figure()
plt.xlabel('k')
plt.ylabel('accuracy')
plt.scatter(k_range, scores)
plt.xticks([0,5,10,15,20])
圖10
對於這個特定的資料集，當k = 5時，我們獲得了最高精確度。
結語
在這篇文章中，我們關注的是預測的準確度。我們的目標是學習一個具有良好泛化效能的模型。這樣的模型使預測準確度最大化。通過比較不同的演算法，我們確定了最適合當前問題的機器學習演算法(即水果型別分類)。
原始碼地址：https://github.com/susanli2016/Machine-Learning-with-Python/blob/master/Solving%20A%20Simple%20Classification%20Problem%20with%20Python.ipynb