第4大题汇总，以此为准

Q1

4_1和4_2作为训练集

import pandas as pd

train1 = pd.read_table('question_4_1.txt', sep='\t', header=None)

train2 = pd.read_table('question_4_2.txt', sep='\t', header=None)

train = pd.concat([train1, train2], axis=0)

names = ['text','sentiment']

train.columns = names

4_3作为测试集

test = pd.read_table('question_4_3.txt', sep='\t', header=None)

names = ['text','sentiment']

test.columns = names

from sklearn.ensemble import RandomForestClassifier

import matplotlib.pyplot as plt

import seaborn as sns

sns.set(color_codes=True)

train_data = train['sentiment'].values

test_data = test['sentiment'].values

plt.figure(figsize=(12,8))

sns.countplot(x='label', data=train)

plt.title('Distribution of Numbers')

plt.xlabel('Numbers');

print("Number of features used for training: \t", len(train),

"\nNumber of features used for testing: \t", len(test))

# 开始使用随机森林分类器

clf = RandomForestClassifier(n_estimators=100) # 定义决策树的个数为100

# 开始训练，训练的X数据格式为[[]]，训练的y值为[]也就是经过ravel后的数据

model = clf.fit(train[:,1:], train[:,0].ravel())

# 然后预测

output = model.predict(test[:,1:]

# 利用

clf = RandomForestClassifier(n_estimators=100) # 100 trees

# 用全部训练数据来做训练

target = train_data[:,0].ravel()

train = train_data[:,1:]

model = clf.fit(train, target)

# 用测试集数据来预测最终结果

output = model.predict(test_data)

print output

# 输出预测结果

pd.DataFrame({"ImageId": range(1, len(output)+1), "Label": output}).to_csv('out.csv', index=False, header=True)

Q2

# 计算准确度

acc = np.mean(output == test[:,0].ravel()) *100

print("The accuracy of the pure RandomForest classifier is: \t", acc, "%")