Eligijus112

# Data wrangling 
import pandas as pd

# Deep learning 
import tensorflow as tf
import keras 

# Import feature engineering functions
from utils import create_date_vars, distance_calculation, custom_transform

Eligijus112

import pandas as pd 
import numpy as np 
import keras

# Defining the class for the batches creation 
class DataGenerator(keras.utils.Sequence):
    def __init__(
        self, 
        csv_generator: pd.io.parsers.readers.TextFileReader,
        n_batches: int,

Eligijus112

# Importing the feature engineering pipeline 
from utils import ft_engineering_pipeline

# Data wrangling
import pandas as pd

# Memory tracking
from memory_profiler import profile

# Command line arguments 

Eligijus112

# Deep learning 
import tensorflow as tf
import keras

# Memory tracking
from memory_profiler import profile

@profile
def create_model(
    input_size: int, 

Eligijus112

import pandas as pd 
from sklearn.preprocessing import OneHotEncoder
import numpy as np

# Defining the function for dummy creation 
def create_dummy(df: pd.DataFrame, dummy_var_list: list) -> Tuple:
    """
    Creates dummy variables for the variables in dummy_var_list

    Returns a tuple of the following

Eligijus112

import numpy as np
import pandas as pd

# Defining the function for distance calculation
def distance_calculation(df: pd.DataFrame) -> pd.DataFrame:
    """
    Calculates the distance between two points on the earth's surface.

    The distance is in meters
    """

Eligijus112

import pandas as pd 
from datetime import datetime
import numpy as np 
import re

# To datetime conversion 
def to_datetime(x: str) -> datetime:
    """
    Converts a string to a datetime object
    An example of the string is 2010-02-02 17:24:55

Eligijus112

Eligijus112

def elastic_net_to_keras(alpha: float, l1_ratio: float):
    """
    Converts ElasticNet parameters from sklearn to Keras regularizers.
    
    Arguments
    ---------
    alpha: float
        The regularization strength of the model.
    l1_ratio: float
        The l1 regularization ratio of the model.

Eligijus112

# Importing the package
import tensorflow as tf


class NMSE(tf.keras.losses.Loss):
    def __init__(self):
        super().__init__()

    def call(self, y_true, y_pred):
        # Calculating the mse;