如何正确预测 python 中的近期值？
[英]How do I predict the near future value correctly in python?
问题描述
I need help, I m currently deploying my LSTM model in flask python, I m trying to load my result to new csv file, but eventually, it loaded with the repeated result, so I have no idea which line of code was doing wrong, Please adjust me and give me some tips Thanks a lots! 
I need help, I m currently deploying my LSTM model in flask python, I m trying to load my result to new csv file, but eventually, it loaded with the repeated result, so I have no idea which line of code was doing wrong,请调整我并给我一些提示非常感谢！
model.py model.py
import numpy as np
from math import sqrt
from numpy import concatenate
from matplotlib import pyplot
from pandas import read_csv
from pandas import DataFrame
from pandas import concat
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from pickle import dump
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
# load dataset
np.random.seed(7)
# load the dataset
dataframe = read_csv('Sales.csv', usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
dataset = dataset.astype('float32')
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# split into train and test sets
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# reshape into X=t and Y=t+1
look_back = 1
train_X, train_Y = create_dataset(train, look_back)
test_X, test_Y = create_dataset(test, look_back)
# reshape input to be [samples, time steps, features]
train_X = np.reshape(train_X, (train_X.shape[0], 1, train_X.shape[1]))
test_X = np.reshape(test_X, (test_X.shape[0], 1, test_X.shape[1]))
model = Sequential()
model.add(LSTM(128, return_sequences=True ,input_shape=(train_X.shape[1], train_X.shape[2])))
model.add(Dropout(0.2))
model.add(LSTM(64))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')
history = model.fit(train_X, train_Y, epochs=100, batch_size=128, validation_data=(test_X, test_Y), verbose=2, shuffle=False)
#save the model
model.save('model.h5')
app.py应用程序.py
from flask import Flask, make_response, request, render_template
from pandas import DataFrame
import io
from pandas import datetime
from io import StringIO
import csv
import pandas as pd
import numpy as np
import pickle
import os
from keras.models import load_model
from sklearn.preprocessing import MinMaxScaler
import datetime
from datetime import timedelta, datetime
from dateutil.relativedelta import relativedelta
app = Flask(__name__)
@app.route('/')
def form():
return """
<html>
<body>
<h1>Let's TRY to Predict..</h1>
</br>
</br>
<p> Insert your CSV file and then download the Result
<form action="/transform" method="post" enctype="multipart/form-data">
<input type="file" name="data_file" class="btn btn-block"/>
</br>
</br>
<button type="submit" class="btn btn-primary btn-block btn-large">Predict</button>
</form>
<div class="ct-chart ct-perfect-fourth"></div>
</body>
</html>
"""
@app.route('/transform', methods=["POST"])
def transform_view():
if request.method == 'POST':
f = request.files['data_file']
if not f:
return "No file"
stream = io.StringIO(f.stream.read().decode("UTF8"), newline=None)
csv_input = csv.reader(stream)
stream.seek(0)
result = stream.read()
df = pd.read_csv(StringIO(result), usecols=[1])
#extract month value
df2 = pd.read_csv(StringIO(result))
matrix2 = df2[df2.columns[0]].to_numpy()
list1 = matrix2.tolist()
# load the model from disk
model = load_model('model.h5')
dataset = df.values
dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
dataset = np.reshape(dataset, (dataset.shape[0], 1, dataset.shape[1]))
predict = model.predict(dataset)
transform = scaler.inverse_transform(predict)
X_FUTURE = 100
transform = np.array([])
last = dataset[-1]
for i in range(X_FUTURE):
curr_prediction = model.predict(np.array([last]))
last = np.concatenate([last[1:], curr_prediction])
transform = np.concatenate([transform, curr_prediction[0]])
transform = scaler.inverse_transform([transform])[0]
dicts = []
curr_date = pd.to_datetime(list1[-1])
for i in range(X_FUTURE):
curr_date = curr_date + relativedelta(month=1)
dicts.append({'Predictions':transform[i], "Month": curr_date})
new_data = pd.DataFrame(dicts).set_index("Month")
##df_predict = pd.DataFrame(transform, columns=["predicted value"])
response = make_response(new_data.to_csv(index = True, encoding='utf8'))
response.headers["Content-Disposition"] = "attachment; filename=result.csv"
return response
if __name__ == "__main__":
app.run(debug=True, port = 9000, host = "localhost")
This is the result that loaded to the new csv file这是加载到新 csv 文件的结果
1 个解决方案
解决方案1
0 已采纳 2021-03-08 13:00:01
I think it is the case that you have correct results (meaning duplicates), your LSTM is trained correctly (but maybe with low accuracy), and duplicates are not a mistake but correct answer.我认为您的结果正确（即重复），您的 LSTM 训练正确（但准确率可能较低），重复不是错误，而是正确答案。
Regarding duplicate Month column values - the reason is that Pandas can't recognize relativedelta from dateutil package hence adding it to date gives wrong result.关于重复的 Month 列值 - 原因是 Pandas 无法识别dateutil package 中的relativedelta ，因此将其添加到 date 会产生错误的结果。 Instead try doing this curr_date = curr_date + pd.DateOffset(months = 1) , this will produces correct different dates in your Month column.而是尝试这样做curr_date = curr_date + pd.DateOffset(months = 1) ，这将在您的 Month 列中生成正确的不同日期。
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