Active learning with Python

Active learning is a machine learning technique where a model is able to interactively query a user or some other information source to obtain the desired outputs at new data points. It helps in training a model with less labeled data by selecting the most informative samples for annotation. Let's start by importing the necessary libraries for active learning in Python: 
import numpy as np
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
from modAL.models import ActiveLearner
from modAL.uncertainty import uncertainty_sampling

Next, we will generate a synthetic dataset using the make_classification function from sklearn and split it into initial training and test sets: 
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=0)
X_initial = X[:10]
y_initial = y[:10]
X_pool = X[10:]
y_pool = y[10:]

Now, we will define the base estimator for our active learner, which in this case will be a RandomForestClassifier: 
learner = ActiveLearner(estimator=RandomForestClassifier(), X_training=X_initial, y_training=y_initial)

The next step involves creating a query strategy, which will determine which samples should be labeled next. Here, we will use uncertainty sampling: 
n_queries = 10
for i in range(n_queries):
    query_idx, query_instance = learner.query(X_pool, n_instances=1)
    learner.teach(X_pool[query_idx], y_pool[query_idx])
    X_pool = np.delete(X_pool, query_idx, axis=0)
    y_pool = np.delete(y_pool, query_idx)

By iterating through the query strategy for a specified number of queries, the active learner will select the most informative samples from the pool and update the model accordingly. This is how active learning can be implemented in Python using the modAL library. By selecting the most valuable data points for annotation, active learning can significantly reduce the amount of labeled data required for training a machine learning model.

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