04_data_science

Data Science and ML

Data Science and Machine Learning

Purpose

The Data Science category connects your logic to standard machine learning libraries. It lets you build complex systems like neural networks or data clustering with very little code. Because Python has the best tools for this, the system almost always uses Python to run these tasks.

Prerequisites

  • Database level. These are considered level 4 algorithms.
  • Data setup. Your data should be organized into lists that the system can turn into math blocks.

Dependencies

  • src/registry/categories/data_science.ts. This is where the machine learning logic is defined.
  • External tools. You will need libraries like NumPy, Scikit-Learn, or PyTorch installed.

How it works

The system acts as a bridge to standard libraries.

  1. Importing tools. It adds the necessary library imports to the top of your file.
  2. Handling math. It turns your data into tensors or math blocks that libraries can understand.
  3. Using the GPU. If your computer has a graphics card, the system automatically tries to use it to make the math run faster.

Implementation details

  • Fast math. For tasks like clustering, the system wraps your data in a special NumPy object.
  • Learning structures. When you define a neural network, the system writes the whole structure, including how data flows through it.
  • Smart search. Our RAG tools help you ingest text data into a format that AI models can use.

Examples

Clustering points:

ares
read points as vector<vector<int>>
use kmeans on points with k=3

Python result:

python
from sklearn.cluster import KMeans
import numpy as np

n_points = int(input())
points = []
for _ in range(n_points):
    n__temp = int(input())
    _temp = [int(x) for x in input().split()[:n__temp]]
    points.append(_temp)

kmeans = KMeans(n_clusters=3, random_state=42).fit(np.array(points))
print(f"Centroids: {kmeans.cluster_centers_}")

Technical model

Clustering goal: The system tries to find the best central points so that every data point is as close as possible to one of those centers.

Linear regression: ARES uses specific math libraries in C++ to find the best line through your data points.

Complexity

  • Testing. Check that the error in our models decreases as the system learns from the data.
  • Speed. Training a model takes time based on the number of items and the complexity of the layers.

Traces

This is what happens when you transform data for a neural network:

  1. The system adds the Torch library.
  2. It turns your data into a math tensor.
  3. It changes the shape of the tensor to match what the model expects.
  4. It moves the tensor to the GPU if one is available.

Edge cases

  • No GPU. If you do not have a graphics card, the system automatically uses your main processor instead.
  • Unscaled data. The system does not automatically scale your data, so you should ensure your numbers are in the right range before starting.

Failure modes

Sometimes the system might stop. This often happens if the shape of your data is wrong. If you try to change a list of 10 items into a grid of 5 by 5, the math will not work, and the program will stop. Another problem is missing tools. If the machine learning libraries are not installed on your computer, the program will not find them and will exit with an error.

ARES