Dimensionality reduction and data sampling: Worked Example — Data Manipulation and Preparation (NVIDIA-Certified Associate: Accelerated Data Science)

Dimensionality Reduction and Data Sampling: Worked Example

In the NVIDIA-Certified Associate: Accelerated Data Science exam, dimensionality reduction and data sampling are critical techniques for managing large, high-dimensional datasets efficiently. This worked example demonstrates how to apply these concepts using GPU-accelerated libraries such as cuDF and RAPIDS to prepare data for machine learning.

Scenario

You are given a large dataset containing 100,000 customer records with 200 features each, including numerical and categorical variables. The goal is to reduce the feature space to improve model training speed and performance while maintaining essential information. Additionally, you need to create a representative sample of the dataset for exploratory analysis.

Step 1: Load and Inspect the Data

Using cuDF, load the dataset into a GPU DataFrame for efficient processing.

• Import cuDF and read the data from a CSV or Parquet file.
• Inspect the shape and feature types.

Code snippet

Note: This is a conceptual example; actual code execution requires a GPU environment.

import cudf

df = cudf.read_parquet('customer_data.parquet') print(f"Dataset shape: {df.shape}") print(df.dtypes)

Step 2: Handle Missing Values and Data Cleaning

Before dimensionality reduction, ensure data quality by imputing missing values or removing incomplete records. For numerical features, use mean or median imputation; for categorical, use the mode or a placeholder.

Step 3: Encode Categorical Variables

Convert categorical variables into numerical representations using one-hot encoding or label encoding with cuDF or RAPIDS utilities.

Step 4: Apply Dimensionality Reduction

Use Principal Component Analysis (PCA) from cuML (part of RAPIDS) to reduce the 200 features to a smaller set of principal components that capture most of the variance.

• Standardize the numerical features.
• Fit PCA and choose the number of components to retain (e.g., enough to explain 95% of variance).

Code snippet

from cuml.preprocessing import StandardScaler from cuml.decomposition import PCA

Separate numerical features

num_features = df.select_dtypes(include=['float32', 'int32']).columns

Standardize

scaler = StandardScaler() X_scaled = scaler.fit_transform(df[num_features])

PCA

pca = PCA(n_components=0.95) # retain 95% variance X_reduced = pca.fit_transform(X_scaled) print(f"Reduced shape: {X_reduced.shape}")

Step 5: Data Sampling

To create a manageable subset for exploratory analysis, perform stratified sampling to maintain class distribution (if applicable) or random sampling otherwise.

• Use cuDF's sampling methods with a specified fraction or number of rows.
• Ensure the sample is representative to avoid bias.

Code snippet

Random sample 10% of data

sample_df = df.sample(frac=0.1, random_state=42) print(f"Sample shape: {sample_df.shape}")

Step 6: Store Processed Data Efficiently

Save the reduced and sampled datasets in Parquet format for efficient storage and fast loading in subsequent workflows.

Code snippet

sample_df.to_parquet('customer_sample.parquet')

For PCA results, convert to cuDF DataFrame and save

import cudf pca_df = cudf.DataFrame(X_reduced) pca_df.to_parquet('customer_pca.parquet')

Summary

This example illustrates the step-by-step process of reducing dimensionality using PCA and generating a representative data sample with GPU-accelerated tools from RAPIDS. Mastery of these techniques is essential for efficient data manipulation and preparation in accelerated data science workflows, as emphasized in the NVIDIA-Certified Associate: Accelerated Data Science certification.

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