Time Series data 与 sequential data 的区别
时间: 2024-05-02 18:21:26 浏览: 196
Time Series data 是指按时间顺序采集的数据,通常包含了时间戳和对应的数值,例如股票价格、气温等。而 sequential data 则是指按特定序列排列的数据,例如自然语言文本、音频信号等。时间戳是 Time Series data 特有的属性,而 sequential data 则不一定包含时间戳。同时,Time Series data 的数值通常具有一定的连续性和相关性,而 sequential data 中的不同元素则可能具有不同的含义和关联性。
相关问题
import pandas as pd data = pd.read_csv(C:\Users\Administrator\Desktop\pythonsjwj\weibo_senti_100k.csv') data = data.dropna(); data.shape data.head() import jieba data['data_cut'] = data['review'].apply(lambda x: list(jieba.cut(x))) data.head() with open('stopword.txt','r',encoding = 'utf-8') as f: stop = f.readlines() import re stop = [re.sub(' |\n|\ufeff','',r) for r in stop] data['data_after'] = [[i for i in s if i not in stop] for s in data['data_cut']] data.head() w = [] for i in data['data_after']: w.extend(i) num_data = pd.DataFrame(pd.Series(w).value_counts()) num_data['id'] = list(range(1,len(num_data)+1)) a = lambda x:list(num_data['id'][x]) data['vec'] = data['data_after'].apply(a) data.head() from wordcloud import WordCloud import matplotlib.pyplot as plt num_words = [''.join(i) for i in data['data_after']] num_words = ''.join(num_words) num_words= re.sub(' ','',num_words) num = pd.Series(jieba.lcut(num_words)).value_counts() wc_pic = WordCloud(background_color='white',font_path=r'C:\Windows\Fonts\simhei.ttf').fit_words(num) plt.figure(figsize=(10,10)) plt.imshow(wc_pic) plt.axis('off') plt.show() from sklearn.model_selection import train_test_split from keras.preprocessing import sequence maxlen = 128 vec_data = list(sequence.pad_sequences(data['vec'],maxlen=maxlen)) x,xt,y,yt = train_test_split(vec_data,data['label'],test_size = 0.2,random_state = 123) import numpy as np x = np.array(list(x)) y = np.array(list(y)) xt = np.array(list(xt)) yt = np.array(list(yt)) x=x[:2000,:] y=y[:2000] xt=xt[:500,:] yt=yt[:500] from sklearn.svm import SVC clf = SVC(C=1, kernel = 'linear') clf.fit(x,y) from sklearn.metrics import classification_report test_pre = clf.predict(xt) report = classification_report(yt,test_pre) print(report) from keras.optimizers import SGD, RMSprop, Adagrad from keras.utils import np_utils from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.layers.embeddings import Embedding from keras.layers.recurrent import LSTM, GRU model = Sequential() model.add(Embedding(len(num_data['id'])+1,256)) model.add(Dense(32, activation='sigmoid', input_dim=100)) model.add(LSTM(128)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary() import matplotlib.pyplot as plt import matplotlib.image as mpimg from keras.utils import plot_model plot_model(model,to_file='Lstm2.png',show_shapes=True) ls = mpimg.imread('Lstm2.png') plt.imshow(ls) plt.axis('off') plt.show() model.compile(loss='binary_crossentropy',optimizer='Adam',metrics=["accuracy"]) model.fit(x,y,validation_data=(x,y),epochs=15)
这段代码的作用是:
1. 读取一个csv文件(weibo_senti_100k.csv),并将其中的空值删除。
2. 对csv文件中的review列进行分词处理,并过滤掉停用词。
3. 用词频统计生成词云图。
4. 将数据集划分为训练集和测试集,分别使用SVM和LSTM模型进行分类预测,并输出分类报告。
具体流程如下:
1. 首先,使用pandas读取csv文件,并将其中的空值删除。
2. 然后,使用jieba对csv文件中的review列进行分词处理,并过滤掉停用词,得到分词后的结果。
3. 用词频统计生成词云图,可以通过WordCloud库实现。
4. 将分词后的结果转换成向量,并将数据集划分为训练集和测试集。使用SVM和LSTM模型进行分类预测,并输出分类报告。
其中,使用SVM模型的代码如下:
```
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import classification_report
# 将数据集划分为训练集和测试集
x, xt, y, yt = train_test_split(vec_data, data['label'], test_size=0.2, random_state=123)
# 训练SVM模型
clf = SVC(C=1, kernel='linear')
clf.fit(x, y)
# 使用测试集进行预测
test_pre = clf.predict(xt)
# 输出分类报告
report = classification_report(yt, test_pre)
print(report)
```
使用LSTM模型的代码如下:
```
from keras.optimizers import Adam
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.embeddings import Embedding
from keras.layers.recurrent import LSTM
# 将数据集划分为训练集和测试集
x, xt, y, yt = train_test_split(vec_data, data['label'], test_size=0.2, random_state=123)
# 将标签进行one-hot编码
y = np_utils.to_categorical(y, 2)
yt = np_utils.to_categorical(yt, 2)
# 构建LSTM模型
model = Sequential()
model.add(Embedding(len(num_data['id'])+1, 256))
model.add(LSTM(128))
model.add(Dense(1))
model.add(Activation('sigmoid'))
# 编译LSTM模型
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=["accuracy"])
# 训练LSTM模型
model.fit(x, y, validation_data=(xt, yt), epochs=15)
```
最后,需要注意的是,代码中有一些缺失的部分,比如数据集的读取和停用词的来源等,需要根据实际情况进行补充。
python time series predict model multi example code
Here is an example code for a time series prediction model using Python:
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import LSTM, Dense
# Load data
data = pd.read_csv('time_series_data.csv')
# Preprocess data
data['Date'] = pd.to_datetime(data['Date'])
data.set_index('Date', inplace=True)
# Split data into training and testing sets
train_size = int(len(data) * 0.8)
train_data, test_data = data.iloc[:train_size], data.iloc[train_size:]
# Define function for creating time series dataset
def create_time_series_dataset(X, y, time_steps=1):
Xs, ys = [], []
for i in range(len(X) - time_steps):
Xs.append(X.iloc[i:(i+time_steps)].values)
ys.append(y.iloc[i+time_steps])
return np.array(Xs), np.array(ys)
# Create time series dataset
time_steps = 30
X_train, y_train = create_time_series_dataset(train_data, train_data['Value'], time_steps)
X_test, y_test = create_time_series_dataset(test_data, test_data['Value'], time_steps)
# Build LSTM model
model = Sequential()
model.add(LSTM(units=64, input_shape=(time_steps, 1)))
model.add(Dense(units=1))
model.compile(optimizer='adam', loss='mse')
# Train model
model.fit(X_train, y_train, epochs=50, batch_size=32)
# Predict on test data
y_pred = model.predict(X_test)
# Evaluate model
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)
print('RMSE:', rmse)
# Plot predictions vs actual
plt.plot(y_test)
plt.plot(y_pred)
plt.legend(['Actual', 'Predicted'])
plt.show()
```
This code loads in a time series dataset, preprocesses the data, splits it into training and testing sets, creates a time series dataset with a given time step, builds an LSTM model, trains the model, makes predictions on the testing set, evaluates the model using the root mean squared error, and plots the predicted values against the actual values.
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