用Python解决一个简单的水果分类问题

在本文中，我们将使用Python中最流行的机器学习工具Scikit-learn在Python中实现几种机器学习算法。使用简单的数据集来训练分类器以区分不同类型的水果。

本文的目的是确定最适合手头问题的机器学习算法; 因此，我们想要比较不同的算法，选择效果最好的算法。

数据

水果数据集由爱丁堡大学的Iain Murray博士创建。他买了几十个不同品种的桔子、柠檬和苹果，并记录了他们的尺寸。

让我们看一下数据的前几行。

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

数据集的每一行表示水果的一个部分，由列表中的几个特征表示。

我们的数据集中有59个水果和7个特征：

print(fruits.shape)

（59,7）

我们的数据集中有四种类型的水果：

print(fruits['fruit_name'].unique())

['苹果''橘子（mandarin）''橙子''柠檬']

除橘子外，数据非常平衡。我们必须坚持下去。

print(fruits.groupby('fruit_name').size())

import seaborn as sns
sns.countplot(fruits['fruit_name'],label="Count")
plt.show()

可视化

• 每个数字变量的方形图将使我们更清楚地了解输入变量的分布：

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()

• 颜色分数近似于高斯分布。

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()

• 一些属性对是相关的（质量和宽度）。这表明高度相关性和可预测的关系。

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')

统计摘要

我们可以看到数值没有相同的比例。我们需要对我们为训练集计算的测试集扩展应用。

创建训练和测试集扩展到应用。

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)

构建模型

Logistic回归

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)))

Logistic回归分类器在训练集上的准确率:0.70

Logistic回归分类器在测试集上的准确率:0.40

决策树

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)))

决策树分类器在训练集上的准确率:1.00

决策树分类器在测试集上的准确率:0.73

K-Nearest Neighbors

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)))

K-NN分类器在训练集上的准确率:0.95

K-NN分类器在测试集上的准确率:1.00

线性判别分析

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)))

LDA分类器在训练集上的准确率:0.86

LDA分类器在测试集上的准确率:0.67

高斯朴素贝叶斯

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)))

GNB分类器在训练集上的准确率:0.86

GNB分类器在测试集上的准确率:0.67

支持向量机

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)))

SVM分类器在训练集上的准确率:0.61

SVM分类器在测试集上的准确率:0.33

KNN算法是我们尝试过的最准确的模型。混淆矩阵表示测试集没有发生错误。但是，测试集非常小。

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))

绘制k-NN分类器的决策边界

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')

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])

对于这个特定的数据集，当k = 5时，我们获得最高的准确度

总结

在本文中，我们关注预测的准确性。我们的目标是学习具有良好泛化性能的模型。这种模型使预测精度最大化。我们确定了最适合手头问题的机器学习算法（即水果类型分类）; 因此，我们比较了不同的算法并选择了性能最佳的算法。