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Automatically labeling training data

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# Reef: Overcoming the Barrier to Labeling Training Data
*Code for VLDB 2019 paper [Snuba: Automating Weak Supervision to Label Training Data](http://www.vldb.org/pvldb/vol12/p223-varma.pdf)*

Reef is an automated system for labeling training data based on a small labeled dataset. Reef utilizes ideas from program synthesis to *automatically* generate a set of interpretable heuristics that are then used to label unlabeled training data efficiently.

## Installation
**Reef uses Python 2.** The Python package requirements are in the file `requirements.txt`. If you have Snorkel, can set a flag [here](https://github.com/HazyResearch/reef/blob/c20c248fc091a7caf4dc59d355f833e6d589e2fe/program_synthesis/heuristic_generator.py#L155) as True but there is a simple version of learning heuristic accuracies in this repo as well.

## Reef Workflow Overview
The inputs to Reef are the following: 
* A labeled dataset, which contains a numerical feature matrix and a vector of ground truth labels (currently only supports binary classification)
* An unlabeled dataset, which contains a numerical feature matrix

The following is the overall workflow Reef follows to label training data automatically. The overall process is encoded in `[1] generate_reef_labels.ipynb` and the main file `program_synthesis/heuristic_generator.py`
1. Using the labeled dataset, Reef generates heuristics like decision trees, or small logistic regression models.  The synthesis code is in `program_synthesis/synthesizer.py`. 
	1. A heuristic is generated for each possible combination of `c` features, where `c` is the cardinality. For example, with `c=1` and 10 features, 10 heuristics will be generated.
	2. For each generated heuristic, a [*beta* parameter](https://github.com/HazyResearch/reef/blob/4bb29e26ec99c4ab99d0cb644183ff2df35abfa9/program_synthesis/synthesizer.py#L94) is calculated. This represents the minimum confidence level at which the heuristics will assign a label. This is done by maximizing the F1 score on the labeled dataset. 
2. These heuristics are passed to a [pruner](https://github.com/HazyResearch/reef/blob/4bb29e26ec99c4ab99d0cb644183ff2df35abfa9/program_synthesis/heuristic_generator.py#L51) that selects the best heuristic by maximizing a combination of the F1 score on the labeled dataset and diversity in terms of how many points it labels that previously selected heuristics don’t. 
3. The selected heuristic and previously chosen heuristics are then passed to the [verifier](https://github.com/HazyResearch/reef/blob/4bb29e26ec99c4ab99d0cb644183ff2df35abfa9/program_synthesis/verifier.py#L21) which learns accuracies for the heuristics based on the labels the heuristics assign to the *unlabeled* dataset. 
4. Finally, Reef calculates the probabilistic labels the heuristics assign to the labeled dataset and pass datapoint with [low confidence labels](https://github.com/HazyResearch/reef/blob/4bb29e26ec99c4ab99d0cb644183ff2df35abfa9/program_synthesis/heuristic_generator.py#L141) to the synthesizer. We repeat this procedure in an iterative manner.

## Tutorial
The tutorial notebooks are based on a text-based plot classification dataset. We go through generating heuristics with Reef and then train a simple LSTM model to see how an end model trained with Reef labels compares to an end model trained with ground truth training labels.

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