# Annotate text data using Active Learning with Cleanlab

Authored by: [Aravind Putrevu](https://huggingface.co/aravindputrevu)

In this notebook, I highlight the use of [active learning](https://arxiv.org/abs/2301.11856) to improve a fine-tuned Hugging Face Transformer for text classification, while keeping the total number of collected labels from human annotators low. When resource constraints prevent you from acquiring labels for the entirety of your data, active learning aims to save both time and money by selecting which examples data annotators should spend their effort labeling.

## What is Active Learning?

Active Learning helps prioritize what data to label in order to maximize the performance of a supervised machine learning model trained on the labeled data. This process usually happens iteratively — at each round, active learning tells us which examples we should collect additional annotations for to improve our current model the most under a limited labeling budget. [ActiveLab](https://arxiv.org/abs/2301.11856) is an active learning algorithm that is particularly useful when the labels coming from human annotators are noisy and when we should collect one more annotation for a previously annotated example (whose label seems suspect) vs. for a not-yet-annotated example.  After collecting these new annotations for a batch of data to increase our training dataset, we re-train our model and evaluate its test accuracy.

![ActiveLab thumb.webp](data:image/webp;base64,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)

In this notebook, I consider a binary text classification task: predicting whether a specific phrase is polite or impolite.

Active learning with ActiveLab is much better than random selection when it comes to collecting additional annotations for Transformer models. It consistently produces much better models with approximately 50% less error rate, regardless of the total labeling budget.

The rest of this notebook walks through the open-source code you can use to achieve these results.

## Setting up the environment

```python
!pip install datasets==2.20.0 transformers==4.25.1 scikit-learn==1.1.2 matplotlib==3.5.3 cleanlab
```

```python
import pandas as pd
pd.set_option('max_colwidth', None)
import numpy as np
import random
import transformers
import datasets
import matplotlib.pyplot as plt

from cleanlab.multiannotator import get_majority_vote_label, get_active_learning_scores, get_label_quality_multiannotator
from transformers import AutoTokenizer, AutoModel
from transformers import AutoModelForSequenceClassification
from transformers import TrainingArguments, Trainer
from datasets import load_dataset, Dataset, DatasetDict, ClassLabel
from sklearn.metrics import accuracy_score
from sklearn.model_selection import StratifiedKFold
from scipy.special import softmax
from datetime import datetime
```

## Collecting and Organizing Data

Here we download the data that we need for this notebook.

```python
labeled_data_file = {"labeled": "X_labeled_full.csv"}
unlabeled_data_file = {"unlabeled": "X_labeled_full.csv"}
test_data_file = {"test": "test.csv"}

X_labeled_full = load_dataset("Cleanlab/stanford-politeness", split="labeled", data_files=labeled_data_file)
X_unlabeled = load_dataset("Cleanlab/stanford-politeness", split="unlabeled", data_files=unlabeled_data_file)
test = load_dataset("Cleanlab/stanford-politeness", split="test", data_files=test_data_file)

!wget -nc -O 'extra_annotations.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/extra_annotations.npy?download=true'

extra_annotations = np.load("extra_annotations.npy",allow_pickle=True).item()
```

```python
X_labeled_full = X_labeled_full.to_pandas()
X_labeled_full.set_index('id', inplace=True)
X_unlabeled = X_unlabeled.to_pandas()
X_unlabeled.set_index('id', inplace=True)
test = test.to_pandas()
```

## Classifying the Politeness of Text

We are using [Stanford Politeness Corpus](https://convokit.cornell.edu/documentation/wiki_politeness.html) as the Dataset.

It is structured as a binary text classification task, to classify whether each phrase is polite or impolite. Human annotators are given a selected text phrase and they provide an (imperfect) annotation regarding its politeness: **0** for impolite and **1** for polite.

Training a Transformer classifier on the annotated data, we measure model accuracy over a set of held-out test examples, where I feel confident about their ground truth labels because they are derived from a consensus amongst 5 annotators who labeled each of these examples.

As for the training data, we have:

- `X_labeled_full`: our initial training set with just a small set of 100 text examples labeled with 2 annotations per example.
- `X_unlabeled`: large set of 1900 unlabeled text examples we can consider having annotators label.
- `extra_annotations`: pool of additional annotations we pull from when an annotation is requested for an example

## Visualize Data

```python
# Multi-annotated Data
X_labeled_full.head()
```

```python
# Unlabeled Data
X_unlabeled.head()
```

```python
# extra_annotations contains the annotations that we will use when an additional annotation is requested.
extra_annotations

# Random sample of extra_annotations to see format.
{k:extra_annotations[k] for k in random.sample(extra_annotations.keys(), 5)}
```

# View Some Examples From Test Set

```python
>>> num_to_label = {0:'Impolite', 1:"Polite"}
>>> for i in range(2):
...     print(f"{num_to_label[i]} examples:")
...     subset=test[test.label==i][['text']].sample(n=3, random_state=2)
...     print(subset)
```

Impolite examples:

Impolite Examples:

|     |                                                                                                    text                                                                                                    |
|----:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 120 |                                                                       And wasting our time as well. I can only repeat: why don't you do constructive work by adding contents about your beloved Makedonia? |
| 150 | Rather than tell me how wrong I was to close certain afd's maybe your time would be better spent dealing with the current afd backlog . If my decisions were so wrong why haven't you re-opened them? |
| 326 |                                                                                                                            This was supposed to have been moved to  per the CFD. Why wasn't it moved? |

Polite Examples:

|     |                                                            text                                                            |
|----:|:--------------------------------------------------------------------------------------------------------------------------:|
| 498 |                    Hi there, I've raised the possibility of unprotecting the tamazepam page . What are your thoughts? |
| 132 |                                                Due to certain Edits the page alignment has changed. Could you please help? |
| 131 | I'm glad you're pleased with the general appearance. Before I label all the streets, is the text size, font style, etc OK? |

# Helper Methods
The following section contains all of the helper methods needed for this notebook.

`get_idx_to_label` is designed for use in active learning scenarios, particularly when dealing with a mixture of labeled and unlabeled data. Its primary goal is to determine which examples (from both labeled and unlabeled datasets) should be selected for additional annotations based on their active learning scores. 

```python
# Helper method to get indices of examples with the lowest active learning score to collect more labels for.
def get_idx_to_label(
    X_labeled_full,
    X_unlabeled,
    extra_annotations,
    batch_size_to_label,
    active_learning_scores,
    active_learning_scores_unlabeled=None,
):
    if active_learning_scores_unlabeled is None:
        active_learning_scores_unlabeled = np.array([])

    to_label_idx = []
    to_label_idx_unlabeled = []

    num_labeled = len(active_learning_scores)
    active_learning_scores_combined = np.concatenate((active_learning_scores, active_learning_scores_unlabeled))
    to_label_idx_combined = np.argsort(active_learning_scores_combined)

    # We want to collect the n=batch_size best examples to collect another annotation for.
    i = 0
    while (len(to_label_idx)+len(to_label_idx_unlabeled)) 
    div.output_stderr {
    display: none;
    }

```

## Methodology Used

For each **active learning** round we:

1. Compute ActiveLab consensus labels for each training example derived from all annotations collected thus far.
2. Train our Transformer classification model on the current training set using these consensus labels.
3. Evaluate test accuracy on the test set (which has high-quality ground truth labels).
4. Run cross-validation to get out-of-sample predicted class probabilities from our model for the entire training set and unlabeled set.
5. Get ActiveLab active learning scores for each example in the training set and unlabeled set. These scores estimate how informative it would be to collect another annotation for each example.
6. Select a subset (*n = batch_size*) of examples with the lowest active learning scores.
7. Collect one additional annotation for each of the *n* selected examples.
8. Add the new annotations (and new previously non-annotated examples if selected) to our training set for the next iteration.

I subsequently compare models trained on data labeled via active learning vs. data labeled via **random selection**.  For each random selection round, I use majority vote consensus instead of ActiveLab consensus (in Step 1) and then just randomly select the **n** examples to collect an additional label for instead of using ActiveLab scores (in Step 6).

More intuition on Activelab Consensus labels and Active learning scores are shared further in the notebook.  

![activelab.png](data:image/png;base64,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)

### Model Training and Evaluation

I first tokenize my test and train sets, and then initialize a pre-trained DistilBert Transformer model. Fine-tuning DistilBert with 300 training steps produced a good balance between accuracy and training time for my data. This classifier outputs predicted class probabilities which I convert to class predictions before evaluating their accuracy.

### Use Active Learning Scores to Decide what to Label Next
During each round of Active Learning, we fit our Transformer model via 3-fold cross-validation on the current training set. This allows us to get out-of-sample predicted class probabilities for each example in the training set and we can also use the trained Transformer to get out-of-sample predicted class probabilities for each example in the unlabeled pool. All of this is internally implemented in the `get_pred_probs` helper method. The use of out-of-sample predictions helps us avoid bias due to potential overfitting.

Once I have these probabilistic predictions, I pass them into the `get_active_learning_scores` method from the open-source [cleanlab](https://github.com/cleanlab/cleanlab) package, which implements the [ActiveLab algorithm](https://arxiv.org/abs/2301.11856).  This method provides us with scores for all of our labeled and unlabeled data. Lower scores indicate data points for which collecting one additional label should be most informative for our current model (scores are directly comparable between labeled and unlabeled data).

I form a batch of examples with the lowest scores as the examples to collect an annotation for (via the `get_idx_to_label` method). Here I always collect the exact same number of annotations in each round (under both the active learning and random selection approaches). For this application, I limit the maximum number of annotations per example to 5 (don’t want to spend effort labeling the same example over and over again).

### Adding new Annotations
The `combined_example_ids` are the ids of the text examples we want to collect an annotation for. For each of these, we use the `get_annotation` helper method to collect a new annotation from an annotator. Here, we prioritize selecting annotations from annotators who have already annotated another example. If none of the annotators for the given example exist in the training set, we randomly select one. In this case, we add a new column to our training set which represents the new annotator. Finally, we add the newly collected annotation to the training set. If the corresponding example was previously non-annotated, we also add it to the training set and remove it from the unlabeled collection.

We’ve now completed one round of collecting new annotations and retrain the Transformer model on the updated training set.  We repeat this process in multiple rounds to keep growing the training dataset and improving our model.

```python
# For this Active Learning demo, we add 25 additional annotations to the training set
# each iteration, for 25 rounds.
num_rounds = 25
batch_size_to_label = 25
model_accuracy_arr = np.full(num_rounds, np.nan)

# The 'selection_method' varible determines if we use ActiveLab or random selection
# to choose the new annotations each round.
selection_method = 'random'
# selection_method = 'active_learning'

# Each round we:
# - train our model
# - evaluate on unchanging test set
# - collect and add new annotations to training set
for i in range(num_rounds):

    # X_labeled_full is updated each iteration. We drop the text column which leaves us with just the annotations.
    multiannotator_labels = X_labeled_full.drop(['text'], axis=1)

    # Use majority vote when using random selection to select the consensus label for each example.
    if i == 0 or selection_method == 'random':
        consensus_labels = get_majority_vote_label(multiannotator_labels)

    # When using ActiveLab, use cleanlab's CrowdLab to select the consensus label for each example.
    else:
        results = get_label_quality_multiannotator(
            multiannotator_labels,
            pred_probs_labeled,
            calibrate_probs=True,
        )
        consensus_labels = results["label_quality"]["consensus_label"].values

    # We only need the text and label columns.
    train_set = X_labeled_full[['text']]
    train_set['label'] = consensus_labels
    test_set = test[['text', 'label']]

    # Train our Transformer model on the full set of labeled data to evaluate model accuracy for the current round.
    # This is an optional step for demonstration purposes, in practical applications
    # you may not have ground truth labels.
    trainer = get_trainer(train_set, test_set)
    trainer.train()
    eval_metrics = trainer.evaluate()
    # set statistics
    model_accuracy_arr[i] = eval_metrics['eval_accuracy']

    # For ActiveLab, we need to run cross-validation to get out-of-sample predicted probabilites.
    if selection_method == 'active_learning':
        pred_probs, pred_probs_unlabeled = get_pred_probs(train_set, X_unlabeled)

        # Compute active learning scores.
        active_learning_scores, active_learning_scores_unlabeled = get_active_learning_scores(
            multiannotator_labels, pred_probs, pred_probs_unlabeled
        )

        # Get the indices of examples to collect more labels for.
        chosen_examples_labeled, chosen_examples_unlabeled = get_idx_to_label(
            X_labeled_full,
            X_unlabeled,
            extra_annotations,
            batch_size_to_label,
            active_learning_scores,
            active_learning_scores_unlabeled,
        )

    # We don't need to run cross-validation, just get random examples to collect annotations for.
    if selection_method == 'random':
        chosen_examples_labeled, chosen_examples_unlabeled = get_idx_to_label_random(
        X_labeled_full,
        X_unlabeled,
        extra_annotations,
        batch_size_to_label
        )

    unlabeled_example_ids = np.array([])
    # Check to see if we still have unlabeled examples left.
    if X_unlabeled is not None:
        # Get unlabeled text examples we want to collect annotations for.
        new_text = X_unlabeled.iloc[chosen_examples_unlabeled]
        unlabeled_example_ids = new_text.index.values
        num_ex, num_annot = len(new_text), multiannotator_labels.shape[1]
        empty_annot = pd.DataFrame(data = np.full((num_ex, num_annot), np.NaN), columns = multiannotator_labels.columns, index=unlabeled_example_ids)
        new_unlabeled_df = pd.concat([new_text, empty_annot], axis=1)

        # Combine unlabeled text examples with existing, labeled examples.
        X_labeled_full = pd.concat([X_labeled_full, new_unlabeled_df], axis=0)

        # Remove examples from X_unlabeled and check if empty.
        # Once it is empty we set it to None to handle appropriately elsewhere.
        X_unlabeled = X_unlabeled.drop(new_text.index)
        if X_unlabeled.empty:
            X_unlabeled = None

    if selection_method == 'active_learning':
        # Update pred_prob arrays with newly added examples if necessary.
        if pred_probs_unlabeled is not None and len(chosen_examples_unlabeled) != 0:
            pred_probs_new = pred_probs_unlabeled[chosen_examples_unlabeled, :]
            pred_probs_labeled = np.concatenate((pred_probs, pred_probs_new))
            pred_probs_unlabeled = np.delete(
                pred_probs_unlabeled, chosen_examples_unlabeled, axis=0
            )
        # Otherwise we have nothing to modify.
        else:
            pred_probs_labeled = pred_probs

    # Get combined list of text ID's to relabel.
    labeled_example_ids = X_labeled_full.iloc[chosen_examples_labeled].index.values
    combined_example_ids = np.concatenate([labeled_example_ids, unlabeled_example_ids])

    # Now we collect annotations for the selected examples.
    for example_id in combined_example_ids:
        # Choose which annotator to collect annotation from.
        chosen_annotator = get_annotator(example_id)
        # Collect new annotation.
        new_annotation = get_annotation(example_id, chosen_annotator)
        # New annotator has been selected.
        if chosen_annotator not in X_labeled_full.columns.values:
            empty_col = np.full((len(X_labeled_full),), np.nan)
            X_labeled_full[chosen_annotator] = empty_col

        # Add selected annotation to the training set.
        X_labeled_full.at[example_id, chosen_annotator] = new_annotation
```

## Results

After running 25 rounds of active learning (labeling batches of data and retraining the Transformer model), collecting 25 annotations in each round. I repeated all of this, the next time using random selection to choose which examples to annotate in each round — as a baseline comparison. Before additional data are annotated, both approaches start with the same initial training set of 100 examples (hence achieving roughly the same Transformer accuracy in the first round).  Because of inherent stochasticity in training Transformers, I ran this entire process five times (for each data labeling strategy) and report the standard deviation (shaded region) and mean (solid line) of test accuracies across the five replicate runs.

```python
# Get numpy array of results.
!wget -nc -O 'random_acc.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/activelearn_acc.npy'
!wget -nc -O 'activelearn_acc.npy' 'https://huggingface.co/datasets/Cleanlab/stanford-politeness/resolve/main/random_acc.npy'
```

```python
# Helper method to compute std dev across 2D array of accuracies.
def compute_std_dev(accuracy):
    def compute_std_dev_ind(accs):
        mean = np.mean(accs)
        std_dev = np.std(accs)
        return np.array([mean - std_dev, mean + std_dev])

    std_dev = np.apply_along_axis(compute_std_dev_ind, 0, accuracy)
    return std_dev
```

```python
>>> al_acc = np.load('activelearn_acc.npy')
>>> rand_acc = np.load('random_acc.npy')

>>> rand_acc_std = compute_std_dev(rand_acc)
>>> al_acc_std = compute_std_dev(al_acc)

>>> plt.plot(range(1, al_acc.shape[1]+1), np.mean(al_acc, axis=0), label="active learning", color='green')
>>> plt.fill_between(range(1, al_acc.shape[1]+1), al_acc_std[0], al_acc_std[1], alpha=0.3, color='green')

>>> plt.plot(range(1, rand_acc.shape[1]+1), np.mean(rand_acc, axis=0), label="random", color='red')
>>> plt.fill_between(range(1, rand_acc.shape[1]+1), rand_acc_std[0], rand_acc_std[1], alpha=0.1, color='red')

>>> plt.hlines(y=0.9, xmin=1.0, xmax=25.0, color='black', linestyle='dotted')
>>> plt.legend()
>>> plt.xlabel("Round Number")
>>> plt.ylabel("Test Accuracy")
>>> plt.title("ActiveLab vs Random Annotation Selection --- 5 Runs")
>>> plt.savefig("al-results.png")
>>> plt.show()
```

We see that choosing what data to annotate next has drastic effects on model performance. Active learning using ActiveLab consistently outperforms random selection by a significant margin at each round. For example, in round 4 with 275 total annotations in the training set, we obtain 91% accuracy via active learning vs. only 76% accuracy without a clever selection strategy of what to annotate. Overall, the resulting Transformer models fit on the dataset constructed via active learning have around **50%** of the error-rate, no matter the total labeling budget!

**When labeling data for text classification, you should consider active learning with the re-labeling option to better account for imperfect annotators.**