101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
import torch
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import pandas as pd
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import os
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from sklearn import preprocessing
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from sklearn.utils import shuffle
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from torch import nn, optim
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from torch.autograd.variable import Variable
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from torchvision import transforms, datasets, utils
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from torch.utils.data import Dataset, DataLoader
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class ToTensor(object):
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"""Convert ndarrays in sample to Tensors."""
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def __call__(self, sample):
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# print (sample.values[2])
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# print (torch.from_numpy(sample.values)[2].item())
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return torch.from_numpy(sample.values)
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class DataSet(Dataset):
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"""Face Landmarks dataset."""
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def __init__(self, csv_file, root_dir, transform=transforms.Compose([ToTensor()]), training_porcentage=0.7, shuffle_db=False):
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"""
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Args:
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csv_file (string): Path to the csv file with annotations.
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root_dir (string): Directory with all the images.
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transform (callable, optional): Optional transform to be applied
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on a sample.
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"""
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# self.data = pd.read_csv(csv_file).head(100000)
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self.file = pd.read_csv(csv_file)
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if (shuffle):
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self.file = shuffle(self.file)
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self.data = self.file.head(int(self.file.shape[0]*training_porcentage))
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self.test_data = self.file.tail(int(self.file.shape[0]*(1-training_porcentage)))
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self.root_dir = root_dir
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self.transform = transform
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def __len__(self):
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return len(self.data)
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def __getitem__(self, idx):
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item = self.data.iloc[idx]
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if self.transform:
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item = self.transform(item)
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return item
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def get_columns(self):
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return self.data.columns
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class DataAtts():
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def __init__(self, file_name):
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if file_name == "original_data/data.csv":
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self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set"
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self.class_name = "diagnosis"
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self.values_names = {0: "Benign", 1: "Malignant"}
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self.class_len = 32
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self.fname="data"
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elif file_name == "original_data/creditcard.csv":
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self.message = "Credit Card Fraud Detection"
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self.class_name = "Class"
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self.values_names = {0: "No Frauds", 1: "Frauds"}
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self.class_len = 31
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self.fname="creditcard"
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elif file_name == "original_data/diabetes.csv":
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self.message="Pima Indians Diabetes Database"
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self.class_name = "Outcome"
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self.values_names = {0: "Normal", 1: "Diabets"}
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self.class_len = 9
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self.fname="diabetes"
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elif file_name == "original_data/data_escalonated.csv":
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self.message = "Breast Cancer Wisconsin (Diagnostic) Data Set eSCALONATED"
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self.class_name = "diagnosis"
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self.values_names = {0: "Benign", 1: "Malignant"}
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self.class_len = 32
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self.fname="data_escalonated"
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elif file_name == "original_data/creditcard_escalonated.csv":
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self.message = "Credit Card Fraud Detection eSCALONATED"
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self.class_name = "Class"
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self.values_names = {0: "No Frauds", 1: "Frauds"}
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self.class_len = 31
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self.fname="creditcard_escalonated"
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elif file_name == "original_data/diabetes_escalonated.csv":
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self.message="Pima Indians Diabetes Database eSCALONATED"
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self.class_name = "Outcome"
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self.values_names = {0: "Normal", 1: "Diabets"}
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self.class_len = 9
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self.fname="diabetes_escalonated"
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elif file_name == "original_data/creditcard_1s_escalonated.csv":
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self.message = "Credit Card Fraud Detection eSCALONATED"
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self.class_name = "Class"
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self.values_names = {0: "No Frauds", 1: "Frauds"}
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self.class_len = 31
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self.fname="creditcard_1s_escalonated"
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else:
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print("File not found, exiting")
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exit(1)
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