Building a Matrix with Weights Using Python

In this article, we will explore how to build a matrix with weights from a collection of files. Each file represents an item and contains labels along with their weights, which reflect the relevance of these labels to the item.

Problem Statement

Given a large number of files, each file containing labels and their corresponding weights, how can we construct a following matrix where each row corresponds to a file and each column corresponds to a label?

The input data could be represented in the following format:

| File | Label 1 | Label 2 | … | Label N | | — | — | — | … | — | | 0001 | 0.789 | 0.65 | … | 0 | | 0002 | 0 | 0.678 | … | 0.12 |

Solution

We can solve this problem using Python’s built-in dictionary methods.

Step 1: Read and Parse the Files

First, we need to read all the files and parse their contents into a dictionary format. Each file represents an item and contains labels along with their weights. We will store these labels in a list of tuples.

import csv

# Initialize an empty dictionary to store the label weights
label_weights = {}

# Read each file and extract the label weights
with open('file1.txt', 'r') as fh:
    reader = csv.reader(fh)
    headers = next(reader)
    
    # Skip the header row
    for key, row in zip(range(100), reader):
        label_weights[key] = dict(zip(headers[1:], map(float, row[1:])))

# Repeat the same process for other files
for file_name in ['file2.txt', 'file3.txt']:
    with open(file_name, 'r') as fh:
        reader = csv.reader(fh)
        headers = next(reader)
        
        # Skip the header row
        for key, row in zip(range(100), reader):
            label_weights[key] = dict(zip(headers[1:], map(float, row[1:])))

Step 2: Construct the Matrix

After parsing all the files, we can construct the matrix by iterating over each file and its corresponding labels.

# Get all unique labels from the dictionary values
labels = set(label for label in list(label_weights.values())[0])

# Initialize an empty list to store the matrix rows
matrix_rows = []

# Iterate over each file and its corresponding labels
for key, label_weight in label_weights.items():
    # Create a row by concatenating the labels with their weights
    row = [label_weight.get(label, 0) for label in labels]
    
    # Append the row to the matrix rows list
    matrix_rows.append(row)

Step 3: Print or Write the Matrix

Finally, we can print or write the matrix to a file. Here, we will print it to the console:

# Print the matrix rows
for row in matrix_rows:
    print(', '.join(str(val) for val in row))

Alternatively, you can use the csv module to write the matrix to a CSV file.

import csv

# Define the headers
headers = labels

# Initialize an empty list to store the data rows
data_rows = []

# Iterate over each file and its corresponding labels
for key, label_weight in label_weights.items():
    # Create a row by concatenating the labels with their weights
    row = [label_weight.get(label, 0) for label in headers]
    
    # Append the row to the data rows list
    data_rows.append(row)

# Initialize the CSV writer
with open('output.csv', 'w') as fh:
    writer = csv.writer(fh)
    
    # Write the header row
    writer.writerow(headers)
    
    # Write each data row
    for row in data_rows:
        writer.writerow(row)

Conclusion

In this article, we have demonstrated how to build a matrix with weights from a collection of files using Python’s built-in dictionary methods. We can solve this problem efficiently without relying on external libraries like numpy or pandas. The approach involves reading and parsing the files, constructing the matrix, and printing or writing it to a file.

Duplicate Headers

If there are duplicate headers in the files, you will need to remove them before constructing the matrix. You can do this by calling headers = list(set(headers)) just before the writing/printing code blocks.

# Remove duplicate headers
headers = list(set(headers))

By following these steps and using Python’s built-in dictionary methods, you can efficiently construct a matrix with weights from a collection of files.

Last modified on 2024-04-05