kabirahuja2431

'''
A simple python script to figure out which 4 types of attacks will be
super effective to maximum types of pokemons.

Inorder to run this script you will need:
    - numpy
    - pandas
    - pokemon chart available here: https://github.com/zonination/pokemon-chart

Clone the pokemon chart repo and keep it in the current working directory

kabirahuja2431

from torch.utils.data import Dataset

class CustomDataset(Dataset):
    # A pytorch dataset class for holding data for a text classification task.
    def __init__(self, filename):
        '''
        Takes as input the name of a file containing sentences with a classification label (comma separated) in each line.
        Stores the text data in a member variable X and labels in y
        '''

kabirahuja2431

from torch.utils.data import DataLoader

#Create a dataset object
dataset = CutsomDataset('path_to/somefile')

#Wrap it around a dataloader
dataloader = DataLoader(dataset, batch_size = 64, num_workers = 5)

kabirahuja2431

for X, y in dataloader:
  
  print(len(X)) # 64
  print(y.shape) # (64,)
  
  ###Do something with X and y
  
  ###

kabirahuja2431

file = open('path_to/somefile') #Creates an iterator pointing to the first element of a file

for line in file:
  print(line)
  
file.close()

kabirahuja2431

from torch.utils.data import IterableDataset

class CustomIterableDataset(IterableDataset):

    def __init__(self, filename):

        #Store the filename in object's memory
        self.filename = filename

        #And that's it, we no longer need to store the contents in the memory

kabirahuja2431

#Creating the iterable dataset object
dataset     = CustomIterableDataset('path_to/somefile')
#Creating the dataloader
dataloader  = DataLoader(dataset, batch_size = 64)

for data in dataloader:
    #Data is a list containing 64 (=batch_size) consecutive lines of the file
    print(len(data)) #[64,]
    
    #We still need to separate the text and labels from each other and preprocess the text

kabirahuja2431

def square(x):
    return x**2

itr1 = range(5)
for i in itr1:
    print(i)
'''
Prints
0
1

kabirahuja2431

class CustomIterableDatasetv1(IterableDataset):

    def __init__(self, filename):

        #Store the filename in object's memory
        self.filename = filename

        #And that's it, we no longer need to store the contents in the memory

    def preprocess(self, text):

kabirahuja2431

dataset = CustomIterableDatasetv1('path_to/somefile')
dataloader = DataLoader(dataset, batch_size = 64)

for X, y in dataloader:
    print(len(X)) # 64
    print(y.shape) # (64,)

    ### Do something with X and y

    ###