Implementing Active Learning with Scikit-learn

Table of Contents

1. Core Concepts of Active Learning
2. Typical Usage Scenarios
3. Implementing Active Learning with Scikit-learn
   • Step 1: Load and Prepare the Data
   • Step 2: Select an Initial Training Set
   • Step 3: Train a Model
   • Step 4: Select Instances to Label
   • Step 5: Update the Training Set
   • Step 6: Repeat Steps 3 - 5
4. Common Pitfalls
5. Best Practices
6. Conclusion
7. References

Core Concepts of Active Learning

Active learning is based on the idea that not all data points are equally informative for training a machine learning model. By selectively choosing which instances to label, active learning can focus on the most informative data points and achieve better performance with fewer labeled instances.

There are three main types of active learning strategies:

• Uncertainty Sampling: Selects instances for which the model is most uncertain about the label. For example, in a binary classification problem, the model may select instances with a predicted probability close to 0.5.
• Query-by-Committee: Trains multiple models (a committee) on the current training set and selects instances for which the committee members disagree the most.
• Density-Based Sampling: Selects instances that are in regions of high data density. These instances are likely to be representative of the overall data distribution.

Typical Usage Scenarios

Active learning is particularly useful in the following scenarios:

• Limited Labeled Data: When the amount of labeled data is limited and the cost of labeling new data is high, active learning can help to reduce the labeling effort.
• Dynamic Data Streams: In applications where new data is continuously arriving, active learning can be used to select the most informative new instances to label.
• High-Dimensional Data: In high-dimensional spaces, it can be difficult to collect enough labeled data to train a model. Active learning can help to focus on the most relevant dimensions and instances.

Implementing Active Learning with Scikit-learn

Let’s walk through the steps of implementing active learning using Scikit-learn. We will use the popular Iris dataset as an example.

Step 1: Load and Prepare the Data

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 2: Select an Initial Training Set

import numpy as np

# Select a small initial training set
n_initial = 10
initial_indices = np.random.choice(len(X_train), size=n_initial, replace=False)
X_initial = X_train[initial_indices]
y_initial = y_train[initial_indices]

# Create a pool of unlabeled instances
remaining_indices = np.setdiff1d(np.arange(len(X_train)), initial_indices)
X_pool = X_train[remaining_indices]

Step 3: Train a Model

from sklearn.linear_model import LogisticRegression

# Train a logistic regression model on the initial training set
model = LogisticRegression()
model.fit(X_initial, y_initial)

Step 4: Select Instances to Label

# Use uncertainty sampling to select the most uncertain instances
proba = model.predict_proba(X_pool)
max_proba = np.max(proba, axis=1)
uncertain_indices = np.argsort(max_proba)[:5]  # Select the 5 most uncertain instances

Step 5: Update the Training Set

# Add the selected instances to the training set
X_new = X_pool[uncertain_indices]
y_new = [input(f"Enter the label for instance {x}: ") for x in X_new]
y_new = np.array(y_new, dtype=int)

X_initial = np.vstack((X_initial, X_new))
y_initial = np.hstack((y_initial, y_new))

# Remove the selected instances from the pool
remaining_indices = np.setdiff1d(np.arange(len(X_pool)), uncertain_indices)
X_pool = X_pool[remaining_indices]

Step 6: Repeat Steps 3 - 5

# Repeat the active learning process for a few iterations
for _ in range(5):
    # Train the model on the updated training set
    model.fit(X_initial, y_initial)
    
    # Select the most uncertain instances
    proba = model.predict_proba(X_pool)
    max_proba = np.max(proba, axis=1)
    uncertain_indices = np.argsort(max_proba)[:5]
    
    # Add the selected instances to the training set
    X_new = X_pool[uncertain_indices]
    y_new = [input(f"Enter the label for instance {x}: ") for x in X_new]
    y_new = np.array(y_new, dtype=int)
    
    X_initial = np.vstack((X_initial, X_new))
    y_initial = np.hstack((y_initial, y_new))
    
    # Remove the selected instances from the pool
    remaining_indices = np.setdiff1d(np.arange(len(X_pool)), uncertain_indices)
    X_pool = X_pool[remaining_indices]

# Evaluate the model on the test set
accuracy = model.score(X_test, y_test)
print(f"Final accuracy: {accuracy}")

Common Pitfalls

• Overfitting: Active learning can lead to overfitting if the model is trained on a small and unrepresentative set of labeled instances. To avoid overfitting, it is important to use proper validation techniques and regularization.
• Poor Sampling Strategy: Choosing an inappropriate sampling strategy can result in selecting uninformative instances. It is important to understand the characteristics of the data and choose a sampling strategy that is suitable for the problem.
• Labeling Errors: If the labels provided by the user are incorrect, it can degrade the performance of the model. It is important to have a mechanism for verifying and correcting the labels.

Best Practices

• Use Multiple Sampling Strategies: Combining different sampling strategies can help to select a more diverse and informative set of instances.
• Monitor the Performance: Regularly evaluate the performance of the model on a validation set to ensure that the active learning process is improving the model’s performance.
• Use Active Learning Libraries: There are several Python libraries available for active learning, such as modAL, which provide more advanced functionality and algorithms.

Conclusion

Active learning is a powerful technique for reducing the amount of labeled data required to train a machine learning model. By selectively choosing which instances to label, active learning can focus on the most informative data points and achieve better performance with fewer labeled instances. In this blog post, we have explored how to implement active learning using Scikit-learn, including core concepts, typical usage scenarios, common pitfalls, and best practices. We hope that this post has helped you to understand active learning and how to apply it effectively in real-world situations.

References

• Scikit-learn documentation: https://scikit-learn.org/stable/
• Active learning literature review: Settles, B. (2012). Active learning literature survey. University of Wisconsin–Madison Department of Computer Sciences.
• modAL documentation: https://modal-python.readthedocs.io/en/latest/