scikit-learn实现近邻算法分类的示例
scikit-learn库
scikit-learn已经封装好很多数据挖掘的算法
现介绍数据挖掘框架的搭建方法
- 转换器（Transformer）用于数据预处理，数据转换
- 流水线（Pipeline）组合数据挖掘流程，方便再次使用（封装）
- 估计器（Estimator）用于分类，聚类，回归分析（各种算法对象）
- 所有的估计器都有下面2个函数
- fit() 训练
- 用法：estimator.fit(X_train, y_train)，
- estimator = KNeighborsClassifier() 是scikit-learn算法对象
- X_train = dataset.data 是numpy数组
- y_train = dataset.target 是numpy数组
- predict() 预测
- 用法：estimator.predict(X_test)
- estimator = KNeighborsClassifier() 是scikit-learn算法对象
- X_test = dataset.data 是numpy数组
%matplotlib inline # Ionosphere数据集 # https://archive.ics.uci.edu/ml/machine-learning-databases/ionosphere/ # 下载ionosphere.data和ionosphere.names文件，放在 ./data/Ionosphere/ 目录下 import os home_folder = os.path.expanduser("~") print(home_folder) # home目录 # Change this to the location of your dataset home_folder = "." # 改为当前目录 data_folder = os.path.join(home_folder, "data") print(data_folder) data_filename = os.path.join(data_folder, "ionosphere.data") print(data_filename) import csv import numpy as np # Size taken from the dataset and is known已知数据集形状 X = np.zeros((351, 34), dtype='float') y = np.zeros((351,), dtype='bool') with open(data_filename, 'r') as input_file: reader = csv.reader(input_file) for i, row in enumerate(reader): # Get the data, converting each item to a float data = [float(datum) for datum in row[:-1]] # Set the appropriate row in our dataset用真实数据覆盖掉初始化的0 X[i] = data # 1 if the class is 'g', 0 otherwise y[i] = row[-1] == 'g' # 相当于if row[-1]=='g': y[i]=1 else: y[i]=0 # 数据预处理 from sklearn.cross_validation import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=14) print("训练集数据有 {} 条".format(X_train.shape[0])) print("测试集数据有 {} 条".format(X_test.shape[0])) print("每条数据有 {} 个features".format(X_train.shape[1])) 训练集数据有 263 条
测试集数据有 88 条
每条数据有 34 个features
# 实例化算法对象->训练->预测->评价 from sklearn.neighbors import KNeighborsClassifier estimator = KNeighborsClassifier() estimator.fit(X_train, y_train) y_predicted = estimator.predict(X_test) accuracy = np.mean(y_test == y_predicted) * 100 print("准确率 {0:.1f}%".format(accuracy)) # 其他评价方式 from sklearn.cross_validation import cross_val_score scores = cross_val_score(estimator, X, y, scoring='accuracy') average_accuracy = np.mean(scores) * 100 print("平均准确率 {0:.1f}%".format(average_accuracy)) avg_scores = [] all_scores = [] parameter_values = list(range(1, 21)) # Including 20 for n_neighbors in parameter_values: estimator = KNeighborsClassifier(n_neighbors=n_neighbors) scores = cross_val_score(estimator, X, y, scoring='accuracy') avg_scores.append(np.mean(scores)) all_scores.append(scores) 准确率 86.4%
平均准确率 82.3%
from matplotlib import pyplot as plt plt.figure(figsize=(32,20)) plt.plot(parameter_values, avg_scores, '-o', linewidth=5, markersize=24) #plt.axis([0, max(parameter_values), 0, 1.0]) for parameter, scores in zip(parameter_values, all_scores): n_scores = len(scores) plt.plot([parameter] * n_scores, scores, '-o') 
plt.plot(parameter_values, all_scores, 'bx') 
from collections import defaultdict all_scores = defaultdict(list) parameter_values = list(range(1, 21)) # Including 20 for n_neighbors in parameter_values: for i in range(100): estimator = KNeighborsClassifier(n_neighbors=n_neighbors) scores = cross_val_score(estimator, X, y, scoring='accuracy', cv=10) all_scores[n_neighbors].append(scores) for parameter in parameter_values: scores = all_scores[parameter] n_scores = len(scores) plt.plot([parameter] * n_scores, scores, '-o') 
plt.plot(parameter_values, avg_scores, '-o') 
