Data transformation forHuggineFace model https://huggingface.co/peterkros/cvrp-model/

data_transformation.py

import tensorflow as tf 
import pickle 
import pandas as pd 

#Read DataFrame from CSV
csv_file = pd.read_csv('./results/ADM_VRP_20_1024/data_pak_calc.csv')
df = csv_file[['latitude','longitude', 'demand']].head(10)

def create_data(graph_size, num_samples, is_save=True, filename=None, is_return=False, seed=1234):
    """Generate validation dataset (with SEED) and save
    """

    CAPACITIES = {
        10: 20.,
        20: 30.,
        50: 40.,
        100: 50.
    }

    depo = [33.7111702,73.4285812]
    depo_respahed = tf.reshape(depo, (1,2))
    depo_tensor = tf.convert_to_tensor( depo_respahed, dtype=tf.float32)
    
    tensor_A = tf.convert_to_tensor(df['latitude'].values, dtype=tf.float32)
    tensor_B = tf.convert_to_tensor(df['longitude'].values, dtype=tf.float32)
    # concatenate tensors along a new axis
    concatenated_tensor = tf.concat([tensor_A, tensor_B], axis=-1)
    # reshape the concatenated tensor
    reshaped_tensor = tf.reshape(concatenated_tensor, (1,10,2))

    demand = tf.convert_to_tensor(df.demand.values, dtype=tf.float32)
    demand_reshaped = tf.reshape(demand, (1,10))

    depo, graphs, demand = (depo_tensor, reshaped_tensor, demand_reshaped)
    if is_save:
        save_to_pickle('Validation_dataset_{}.pkl'.format(filename), (depo, graphs, demand))

    if is_return:
        return tf.data.Dataset.from_tensor_slices((list(depo), list(graphs), list(demand)))

def save_to_pickle(filename, item):
    """Save to pickle
    """
    with open(filename, 'wb') as handle:
        pickle.dump(item, handle, protocol=pickle.HIGHEST_PROTOCOL)