用随机森林做分类

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

import torch

import torch.fft as fft

df = pd.read_csv('train.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:-20,:-1]

label=nmp[:-20,-1]#（210，240）

feature=torch.fft.fft(torch.Tensor(feature))

feature=torch.abs(feature)/240*2

feature=feature.detach().numpy()

sum=1

li=[]

for i in range(feature.shape[0]):

    index=feature[i,:]>=0.2

    index=index.astype(np.int)

    index=np.nonzero(index)

    for j in index:

        for j1 in j:

            if j1 not in li:

                li.append(j1)

print(li)

print(len(li))

df = pd.read_csv('train.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:-20,:-1]

label=nmp[:-20,-1]#（210，240）

feature=torch.fft.fft(torch.Tensor(feature))

feature=torch.abs(feature)/240*2

feature=feature[:,li]

feature=feature.detach().numpy()

test_feature=nmp[-20:,:-1]

test_label=nmp[-20:,-1]#（210，240）

test_feature=torch.fft.fft(torch.Tensor(test_feature))

test_feature=torch.abs(test_feature)/240*2

test_feature=test_feature[:,li]

from torch import nn

import torch

label=label.reshape(-1,1)

test_label=test_label.reshape(-1,1)

from sklearn import svm

import matplotlib.pyplot as plt

from sklearn import tree

from sklearn.ensemble import RandomForestClassifier

clf=RandomForestClassifier(n_estimators=2000,max_depth=8) # .SVC（）就是 SVM 的方程，参数 kernel 为线性核函数

# 训练分类器

准确率0.83效果不太好。

import sklearn

from sklearn.metrics import accuracy_score

clf.fit(feature, label)

w=clf.predict(feature)

pr=accuracy_score(label, w)

print(pr)

w=clf.predict(test_feature)

pr=accuracy_score(test_label, w)

print(pr)

df = pd.read_csv('test.csv')

df=df.drop(['ID'],axis=1)

nmp=df.to_numpy()

feature=nmp[:,:]

feature=torch.fft.fft(torch.Tensor(feature))

feature=torch.abs(feature)/240*2

feature=torch.Tensor(feature[:,li])

feature=feature.detach().numpy()

out=clf.predict(feature)

out=pd.DataFrame(out)

out.columns = ['CLASS']

w=[]

for k in range(out.shape[0]):

    w.append(k+210)

out['ID']=np.reshape(w,(-1,1))

out[['ID','CLASS']].to_csv('out.csv',index=False)