AutoML Process Workflow

[Anon_User]

Can you explain the workings of the AutoML process in detail? Specifically, how does it automate the various stages of building a machine learning model?

[Ehsan.Kourkchi]

AutoML, or Automated Machine Learning, is a process that automates the end-to-end process of applying machine learning to real-world problems. The AutoML process typically involves several stages, including data preprocessing, feature engineering, model selection, hyperparameter optimization, and model evaluation. The SensiML AutoML engine simplifies and automates these stages to enable users, even those without extensive machine learning expertise, to develop effective machine learning models for edge devices. Here’s a detailed breakdown of how the AutoML process works:

1. Data Collection and Preprocessing

Data Collection:

• Sensor Data Acquisition: Users import their previously collected data or collect fresh raw time-series data from their intended sensors. These sensors can be accelerometers, gyroscopes, microphones, chemical detectors, temperature, EKG, etc. SensiML Data Studio facilitates this by allowing users to collect and annotate data efficiently.

Data Preprocessing:

• Cleaning: Remove noise and irrelevant data to ensure quality. Some pipeline modules are specifically designed to filter and transform the data and remove outliers.
• Segmentation: Divide the continuous stream of sensor data into segments based on events of interest or fixed time intervals. Data segmentation can be either performed manually or by designing effective functions to isolate the objective events to be classified.
• Normalization: Scale the data to a standard range to ensure uniformity and improve model performance.

2. Feature Engineering

Feature Extraction:

• Automated Feature Extraction: Extract meaningful features from raw sensor data using predefined algorithms. This includes calculating statistical, spectral, and temporal features.
• Custom Features: Users can define custom features if needed using the offered functionalities or by introducing a new set of customized features.

Feature Selection:

• Relevance Assessment: Evaluate the relevance and importance of different features.
• Dimensionality Reduction: Reduce the number of features to simplify the model without losing significant information, using techniques such as PCA (Principal Component Analysis) or feature importance ranking.

3. Model Selection

Algorithm Choice:

• Library of Algorithms: SensiML provides a library of machine learning algorithms suitable for different types of data and tasks, including linear regression, boosted decision trees, pattern machinate engine, neural networks.
• Automated Algorithm Selection: Based on the characteristics of the data, the AutoML process can automatically suggest the most appropriate algorithms.

SensiML leverages genetic algorithms (GAs) as part of its AutoML toolkit to automate the process of finding the most suitable machine learning (ML) algorithm and hyperparameters for a given dataset. Genetic algorithms are a type of optimization algorithm inspired by the principles of natural selection and genetics.

Applying Genetic Algorithms to ML Algorithm Selection

Here’s how SensiML applies genetic algorithms to the process of selecting the best ML algorithm and features:

1. Initial Population Generation

   Diverse Pool of Algorithms: The initial population consists of a diverse set of candidate ML algorithms and feature sets. Users can event choose a set up the best algorithms that they want to consider, including Hierarchical clustering, RBF with Neuron Allocation Optimization, Random Forest, xGBoost, and neural Networks.

Random Initialization: Each individual (candidate solution) in the population is initialized with randomly selected set of engineered features for the chosen algorithm. The hyperparamters of each model can also be randomly chosen from a set of parameters.

2. Fitness Evaluation

   Performance Metrics: Each individual in the population is evaluated based on its performance on a validation dataset. Common metrics include accuracy, precision, recall, F1 score, etc.
   Model Training and Validation: For each individual, the corresponding ML algorithm is trained and validated, and its performance metric is recorded as its fitness score.

3. Selection

   Best Performers: Individuals with the highest fitness scores are selected to be parents for the next generation.

4. Crossover (Recombination)

   Combining Solutions: Pairs of parents are combined to create offspring. This can involve exchanging parts of their feature sets or combining their algorithmic structures.

5. Mutation

   Introducing Variability: Some individuals are chosen randomly. Either their features are altered or some of their algorithmic components, or both. This is to explore new areas of the solution space.
   Mutation Rate: Control the rate of mutation to balance exploration and exploitation. Too high a rate can disrupt convergence, while too low a rate can lead to stagnation. It should be noted that these parameters are not currently exposed to general users in the UI to have a nicer user experience. If needed, one can play with these parameters too to explore more possibilities.

6. Iteration and Convergence

   New Generation: The new generation of individuals (comprising selected parents and their offspring) replaces the current population.
   Iterative Improvement: The entire process is repeated over multiple generations until achieving the desirable result.

Advantages of Using Genetic Algorithms

• Exploration of Large Search Spaces: GAs can explore a large space of algorithms and hyperparameters and feature combinations, potentially finding better solutions than manual tuning.
• Adaptability: GAs can adapt to different types of problems and datasets, making them versatile for various ML tasks.
• **Parallelism: **The evaluation of individuals can be parallelized, speeding up the optimization process.

4. Hyperparameter Optimization

The SensiML autoML pipline, manily finds the optimized hyperparametes in the GA process. However, there are parameters that can be tuned and there is a lot of room for imrovments and innovations in this domain. Here are some general methods used to optimize hyperparameters:

• Grid Search: Evaluate a predefined set of hyperparameters. This method can be imple,emted as partf of the pipeline in the future or pursuoiut by users separately together with the SensiML python SDK.
• Random Search: Randomly sample hyperparameter combinations.
• Bayesian Optimization: Use past evaluation results to choose the next set of hyperparameters to try, focusing on the most promising regions of the parameter space.
• Automated Tuning: AutoML automates the process of hyperparameter tuning to find the best configuration for the chosen model. This is the main method SensiML currently uses.