This repository provides an implementation of experiments in our ICLR2020 paper

@inproceedings{
Awasthi2020Learning,
title={Learning from Rules Generalizing Labeled Exemplars},
author={Abhijeet Awasthi and Sabyasachi Ghosh and Rasna Goyal and Sunita Sarawagi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SkeuexBtDr}
}

This code has been developed with

• python 3.6
• tensorflow 1.12.0
• numpy 1.17.2
• snorkel 0.9.1
• tensorflow_hub 0.7.0

We have currently released processed version of 4 datastes used in our paper. Following datasets can be found in data/ directory

• TREC - Question Classification task (Source: http://cogcomp.org/Data/QA/QC/)
• MITR - Slot filling task (Source: https://groups.csail.mit.edu/sls/downloads/restaurant/)
• YOUTUBE - Spam Classification task of youtube comments (Source: http://www.dt.fee.unicamp.br/~tiago//youtubespamcollection)
• SMS - Spam classification task of text messages (Source: https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection)
• Please see the README files in individual dataset directiories for more information

• d_processed.p (d set: labeled data -- In paper we refer to this is as the "L" dataset)
• U_processed.p (U set: unlabeled data -- In paper as well this is referred as "U" dataset)
• validation_processed.p (validation data)
• test_processed.p (test data)
• NOTE U_processed.p for YOUTUBE and MITR is unavailable on GitHub due to larger size. You can download entire data dir from this link

• x : feature representation of instances
  • shape : [num_instances, num_features]
• l : Class Labels assigned by rules
  • shape : [num_instances, num_rules]
  • class labels belong to {0, 1, 2, .. num_classes-1}
  • l[i][j] provides the class label provided by jth rule on ith instance
  • if jth rule doesn't cover ith instance, then l[i][j] = num_classes (convention)
  • in snorkel, convention is to keep l[i][j] = -1, if jth rule doesn't cover ith instance
• m : Rule coverage mask
  • A binary matrix of shape [num_instances, num_rules]
  • m[i][j] = 1 if jth rule cover ith instance
  • m[i][j] = 0 otherwise
• L : Instance labels
  • shape : [num_instances, 1]
  • L[i] = label of ith instance, if label is available i.e. if instance is from labeled set d
  • Else, L[i] = num_clases if instances comes from the unlabeled set U
  • class labels belong to {0, 1, 2, .. num_classes-1}
• d : binary matrix of shape [num_instances, 1]
  • d[i]=1 if instance belongs to labeled data (d), d[i]=0 otherwise
  • d[i]=1 for all instances is from d_processed.p
  • d[i]=0 for all instances in other 3 pickles {U,validation,test}_processed.p
• r : A binary matrix of shape [num_instances, num_rules]
  • r[i][j]=1 if jth rule was associated with ith instance
  • Highly sparse matrix
  • r is a 0 matrix in all the pickles except d_processed.p
  • Note that this is different from rule coverage mask "m"
  • This matrix defines the coupled rule,example pairs.

From src/hls

• For reproducing numbers in Table 1, Row 1
  • python3 get_rule_related_statistics.py ../../data/TREC 6 None
  • This also provides Majority Vote accuracy in Table2 Column2 (Question dataset)
• For training, saving and testing a snorkel model
  • python3 run_snorkel.py ../../data/TREC 6 None
  • (RUN THIS BEFORE EXPERIMENTS WHICH DEPEND ON SNORKEL LABELS) if snorkel model is not already saved in the dataset directory.
  • We have released pre-trained snorkel models in each dataset directory with name "saved_label_model" )
• For reproducing (approximately) numbers in Table2 Column2 (Question dataset)
  • use train_TREC.sh for training models for different loss functions
  • use test_TREC.sh for testing models for different loss functions
  • best hyperparameters are already set in these scripts
  • both of the above scripts use TREC.sh
• For reproducing numbers (approximately) for other datasets follow steps same as above, with TREC replaced by the dataset name.

• f network refes to the classification network
• w network refers to the rule network

• analyze_w_predictions.py - Used for diagnostics (Old Precision Vs Denoised Precision in Figure 3)
• checkpoint.py - Load/Save checkpoints (Uses code from checkmate)
• config.py - All configuration options go here
• data_feeders.py - all kind of data handling for training and testing.
• data_feeder_utils.py - Load train/test data from processed pickles
• data_utils.py - Other utilities related to data processing
• generalized_cross_entropy_utils.py - Implementation of a noise tolerant loss functions
• get_rule_related_statistics.py - For reproducing numbers in Table 1
• hls_data_types.py - some basic data types used in data_feeders.py
• hls_model.py - Creates train ops All the loss functions are defined here
• hls_test.py - Runs inference using f or w.
  • Inference on f tests the classification network (valid for all the loss functions)
  • Inference on w is used to analyze the denoised rule-precision obtained by w network
  • Inference on w is only meaningful for ImplyLoss and Posterior Reg. method since only these involve a rule (w) network.
• hls_train.py - Two modes:
  • f_d (simply trains f network on labeled data)
  • f_d_U : used for all other modes which utilize unlabeled data
• learn2reweight_utils.py - utilities for implementing L2R method
• main.py - entry point
• metrics_utils.py - utilities for computing metrics
• networks.py - implementation of f network (classification network) and w network (rule network)
• pr_utils.py - utilities for implementing Posterior Reg. method
• run_snorkel.py - training, saving and testing a snorkel model
• snorkel_utils.py - utilitiy to convert l in our format to l in snorkel's format
• test_"DATASET_NAME".sh - model testing (inference) script
  • e.g. test_TREC.sh runs inference for models trained on TREC dataset
• "train_"DATASET_NAME".sh - model training script
  • e.g. train_TREC.sh trains models on TREC dataset
• "DATASET_NAME".sh - test_"DATASET_NAME".sh and train_"DATASET_NAME".sh use "DATASET_NAME".sh
• utils.py - misc. utilities