Deep Learning PyTorch Course, Preprocessing, Checking Missing Values

The performance of deep learning models heavily depends on the quality of the data. Therefore, data preprocessing is one of the most important processes in building deep learning models. In this course, we will explain how to perform data preprocessing using Pytorch and how to check for missing values in a dataset.

1. What is Data Preprocessing?

Data Preprocessing is the process of transforming raw data into a suitable format for analysis. This process can include several stages and typically involves the following tasks.

Handling missing values
Normalization and standardization
Categorical data encoding
Data splitting (train/validation/test)

2. Handling Missing Values

Missing Values refer to the state in which certain values in a dataset are empty. Missing values can negatively impact analysis results, so they need to be handled properly. There are various methods for handling missing values, and some of the representative methods are as follows.

Row removal: A method that deletes rows with missing values
Column removal: A method that deletes columns with missing values
Imputation: A method that replaces missing values with the mean, median, mode, etc.

3. Preprocessing and Checking for Missing Values with Pytorch

Now, let’s perform actual data preprocessing and check for missing values using Pytorch. First, we import the necessary libraries.

import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader

3.1 Creating a Dataset

Let’s create a dataset to use as an example. This dataset contains some missing values.

data = {
    'feature_1': [1.0, 2.5, np.nan, 4.5, 5.0],
    'feature_2': [np.nan, 1.5, 2.0, 2.5, 3.0],
    'label': [0, 1, 0, 1, 0]
}

df = pd.DataFrame(data)
print(df)

3.2 Checking for Missing Values

You can check for missing values using Pandas. The isnull() method can be used to identify missing values.

# Checking for missing values
missing_values = df.isnull().sum()
print("Number of missing values in each column:\n", missing_values)

3.3 Handling Missing Values

Let’s look at how to handle missing values. Here, we will use the method of replacing missing values with the mean.

# Replacing missing values with mean
df['feature_1'].fillna(df['feature_1'].mean(), inplace=True)
df['feature_2'].fillna(df['feature_2'].mean(), inplace=True)
print("After replacing missing values:\n", df)

4. Converting the Dataset to a Pytorch Dataset

Once the data preprocessing is complete, we convert the dataset by inheriting from Pytorch’s Dataset class.

class MyDataset(Dataset):
    def __init__(self, dataframe):
        self.dataframe = dataframe
        
    def __len__(self):
        return len(self.dataframe)

    def __getitem__(self, idx):
        return torch.tensor(self.dataframe.iloc[idx, :-1].values, dtype=torch.float32), \
               torch.tensor(self.dataframe.iloc[idx, -1], dtype=torch.long)

dataset = MyDataset(df)
dataloader = DataLoader(dataset, batch_size=2, shuffle=True)

5. Conclusion

In this course, we learned about the importance of data preprocessing in deep learning and methods to handle missing values. We practiced checking and handling missing values using Pytorch, which helped us learn effective ways to prepare a dataset. Since data preprocessing is a crucial step to enhance the performance of deep learning models, it must be well understood and utilized.

References

You can find more information through the following resources: