Eligijus112

# Loading pandas
import pandas as pd

# Loading date wrangling package
from datetime import datetime

# Reading the input data
d = pd.read_csv('input/DAYTON_hourly.csv')

# Formating to datetime

Eligijus112

import numpy as np

def create_X_Y(ts: list, lag: int) -> tuple:
    """
    A method to create X and Y matrix from a time series list for the training of 
    deep learning models 
    """
    X, Y = [], []

    if len(ts) - lag <= 0:

Eligijus112

# Deep learning packages
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Defining the number of neurons in the LSTM layer
n_layer = 50

# Defining how many lags will be used in the time series
n_lag = 3

Eligijus112

# Data wrangling
import pandas as pd
import numpy as np

# Deep learning: 
from keras.models import Sequential
from keras.layers import LSTM, Dense


class DeepModelTS():

Eligijus112

import re

def clean_text(
    string: str, 
    punctuations=r'''!()-[]{};:'"\,<>./?@#$%^&*_~''',
    stop_words=['the', 'a', 'and', 'is', 'be', 'will']) -> str:
    """
    A method to clean text 
    """
    # Cleaning the urls

Eligijus112

# Defining the window for context
window = 2

# Creating a placeholder for the scanning of the word list
word_lists = []
all_text = []

for text in texts:

    # Cleaning the text

Eligijus112

def create_unique_word_dict(text:list) -> dict:
    """
    A method that creates a dictionary where the keys are unique words
    and key values are indices
    """
    # Getting all the unique words from our text and sorting them alphabetically
    words = list(set(text))
    words.sort()

    # Creating the dictionary for the unique words

Eligijus112

from scipy import sparse
import numpy as np

# Defining the number of features (unique words)
n_words = len(unique_word_dict)

# Getting all the unique words 
words = list(unique_word_dict.keys())

# Creating the X and Y matrices using one hot encoding

Eligijus112

# Deep learning: 
from keras.models import Input, Model
from keras.layers import Dense

# Defining the size of the embedding
embed_size = 2

# Defining the neural network
inp = Input(shape=(X.shape[1],))
x = Dense(units=embed_size, activation='linear')(inp)

Eligijus112

# Deep learning: 
from keras.models import Input, Model
from keras.layers import Dense

# Defining the size of the embedding
embed_size = 2

# Defining the neural network
inp = Input(shape=(X.shape[1],))
x = Dense(units=embed_size, activation='linear')(inp)