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| # -*- coding: utf-8 -*- | |
| """Load Model.ipynb | |
| Automatically generated by Colaboratory. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1CnM4IpjNR-VDFbqUuaVm96Q0cMkzkeAu | |
| """ | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import keras | |
| import tensorflow as tf | |
| import requests | |
| import numpy as np | |
| import csv | |
| from datetime import date | |
| from datetime import timedelta | |
| from bs4 import BeautifulSoup | |
| """To get the training data mean and std for the features""" | |
| path = "" | |
| def preprocessdata(): | |
| with open(path + 'preprocessing_data.csv', newline='') as f: | |
| reader = csv.reader(f) | |
| data = list(reader) | |
| version = int(*data[0]) | |
| mean = data[2] | |
| std = data[3] | |
| for i in range(len(mean)): | |
| mean[i] = float(mean[i]) | |
| std[i] = float(std[i]) | |
| return version, mean, std | |
| def normalize(data, mean, std): | |
| data['Temp (F)'] = (data['Temp (F)'] - mean[0])/std[0] | |
| data['Humidity'] = (data['Humidity'] - mean[1])/std[1] | |
| data['Wind Speed (in HG)'] = (data['Wind Speed (in HG)'] - mean[2])/std[2] | |
| data['Wind Gust (MPH)'] = (data['Wind Gust (MPH)'] - mean[3])/std[3] | |
| return data | |
| def getdata(mean, std): | |
| pd_data = pd.read_csv(path+'weather_data.csv') | |
| pd_data = pd_data.iloc[-60:] | |
| pd_data.reset_index(drop=True, inplace=True) | |
| time_data = pd.DataFrame() | |
| time_data['Time'] = pd.to_datetime(pd_data['Time PST']) | |
| pd_data = pd_data.drop(['Time PST'], axis = 1) | |
| pd_data['Temp (F)'] = pd_data['Temp (F)'].astype(int) | |
| pd_data['Humidity'] = pd_data['Humidity'].astype(int) | |
| pd_data['Wind Speed (in HG)'] = pd_data['Wind Speed (in HG)'].astype(float) | |
| pd_data['Wind Gust (MPH)'] = pd_data['Wind Gust (MPH)'].astype(float) | |
| df = normalize(pd_data, mean, std) | |
| return df | |
| def model_predict(df, model): | |
| tf_data = tf.convert_to_tensor(df) | |
| tf_data = tf.reshape(tf_data, [1, *tf_data.shape]) | |
| prediction = model.predict(tf_data, verbose = 0) | |
| df.loc[len(df.index)] = [*prediction[0]] | |
| df = df.iloc[-60:] | |
| return prediction[0][0] | |
| def main(): | |
| version, mean, std = preprocessdata() | |
| df = getdata(mean, std) | |
| today = date.today() - timedelta(days = 1) | |
| result = pd.DataFrame(columns=['Time PST', 'Temp (F)']) | |
| model = tf.keras.models.load_model(path + 'LTSM{}.h5'.format(version)) | |
| for i in range(24): | |
| temperature = int(model_predict(df, model)*std[0] + mean[0]) | |
| time = "" | |
| if i < 10: | |
| time = "{} 0{}:57:00 AM".format(today, i) | |
| #print(time + ', Temp {} (F)'.format(temperature)) | |
| elif i < 12: | |
| time = "{} {}:57:00 AM".format(today, i) | |
| #print(time + ', Temp {} (F)'.format(temperature)) | |
| else: | |
| time = "{} 0{}:57:00 PM".format(today, i) | |
| #print(time + ', Temp {} (F)'.format(temperature)) | |
| result.loc[len(result.index)] = [time, temperature] | |
| result.to_csv(path+'model_prediction.csv', mode='a', index=False, header=False) | |
| remove_dup = pd.read_csv(path+'model_prediction.csv') | |
| remove_dup = remove_dup.drop_duplicates(subset=['Time PST']) | |
| remove_dup.to_csv(path+'model_prediction.csv', mode = 'w', index=False, header=True) | |
| if __name__ == "__main__": | |
| main() | |