pip install -r requirements.txt
For the finally selected feedforward model
python server.py --model model_nn.pkl
For the not selected LSTM model
python server.py --model model_lstm.pkl
Model with pretrained GloVe embedding is not shared on github as model size is very large.
- NN model
python train.py --model_type nn
- BiLSTM Attention model
python train.py --model_type lstm
- BiLSTM Attention model GloVe model
wget http://nlp.stanford.edu/data/glove.6B.zip
unzip glove.6B.zip
python train.py --model_type lstm --embed_path glove.6B.100d.txt
- NN model
python eval_model.py --model_type nn --model_data_file model_lstm.pkl
- BiLSTM Attention model
python eval_model.py --model_type lstm --model_data_file model_lstm.pkl
- BiLSTM Attention model GloVe model
python eval_model.py --model_type lstm --model_data_file model_lstmglove.pkl
I have used multi label classification methodology to solve the problem. This allows the model to label multiple classes for a given text prompt. This allows me to handle labels like 'flight+flightno' as two seperate intent labels 'flight' and 'flightno' and predict the probability of each seperately.
flight 3426
airfare 403
ground_service 235
airline 148
abbreviation 108
aircraft 78
flight_time 52
quantity 49
distance 20
city 18
airport 18
ground_fare 17
flight+airfare 17
capacity 16
flight_no 12
meal 6
restriction 5
airline+flight_no 2
ground_service+ground_fare 1
airfare+flight_time 1
cheapest 1
aircraft+flight+flight_no 1
The possibility of multiple classes for a text input motivates me to structure the problem as multilabel classification.
cheapest 1
restriction 5
meal 6
flight_no 15
capacity 16
ground_fare 18
airport 18
city 18
distance 20
quantity 49
flight_time 53
aircraft 79
abbreviation 108
airline 150
ground_service 236
airfare 421
flight 3444
- The imbalanced distribution in positive and negative class motivated me to use weighted binary cross entropy as loss function.
- When splitting the dataset in train and validation, iterative stratification is used to split the dataset proportionally based on class distribution.
- During evaluation I compare the confusion matrix and f1 score for each label. I also use macro/micro averaged scores to compare results.
The text size is fairly small with most of input in range of 10-15 words. RNN based model like LSTM should be able to model sequence of this size.
I have used following python libraries to help build my solution
pytorch- This is used to implement full flow of building machine learning model. Loading the data in batches, passing to the model and backpropagation.Lightning- This helps in checkpointing the model and keep track of performance of different model versions and hyperparameter tunin. It also remove a lot of boilerplate code in pytorch like backprop, setting gradients.sklearn- This is used for baseline model and calculating performance metrics for classification tasks
Model load after app run is the bug
I have experimented with three different models.
- Tfidf based feature could work for this problem as some keywords would be more important for some classes.
- Simple Neural Network can handle tfidf based feature.
- LSTM model better encodes the sequence information from text.
- BiLSTM allows model to encode tokens using previous and next tokens.
- Attention layer allows the model to decide how much to attend to each token when predicting labels.
Bi-LSTM with Attention with pretrained GloVe embeddings -> Same as previous but with pretrained glove embeddings.
Using pretrained embedding would allow model to handle words it has not seen before better.
I experimented with different losses like Binary Cross Entropy, Margin Loss and Weighted Binary Cross Entropy. Weighted Binary Cross Entropy works best for this problem as it handles the imbalanced label distribution well.
AdamW optimizer is used to update the weights.
Hyperparameters like hidden layer size and embedding dimension are tuned based on performance on validation dataset.
Tensorboard logging is used to log metrics during training. Train and validation loss is logged to track the improvement of model across epochs. Area under ROC is logged for validation data to track how model performs during training for different labels.
F1 score for each label is analyzed. Final model is selected based on macro/micro averaged f1 on test dataset. NN model outperform LSTM model by small margin
- Precision Recall F1
precision recall f1-score support
aircraft 0.74 0.85 0.79 20
airfare 0.80 0.89 0.84 81
ground_fare 0.21 0.75 0.33 4
quantity 0.38 0.38 0.38 13
capacity 0.00 0.00 0.00 2
flight 0.96 0.96 0.96 698
ground_service 0.80 1.00 0.89 45
airline 0.66 0.92 0.77 25
abbreviation 0.62 0.91 0.74 23
cheapest 0.00 0.00 0.00 0
distance 0.08 1.00 0.14 1
meal 0.00 0.00 0.00 0
city 0.00 0.00 0.00 2
flight_no 0.00 0.00 0.00 4
restriction 0.00 0.00 0.00 1
airport 0.00 0.00 0.00 3
flight_time 0.57 0.57 0.57 7
micro avg 0.87 0.93 0.90 929
macro avg 0.34 0.48 0.38 929
weighted avg 0.89 0.93 0.90 929
samples avg 0.90 0.93 0.91 929
- Confusion Matrix
precision recall f1-score support
airline 0.56 0.73 0.64 30
city 0.06 0.20 0.10 5
flight_no 0.00 0.00 0.00 9
airport 0.75 0.46 0.57 13
flight_time 0.00 0.00 0.00 1
aircraft 0.28 1.00 0.43 8
abbreviation 0.39 1.00 0.57 26
quantity 0.06 0.33 0.11 3
distance 0.24 0.50 0.32 10
capacity 0.89 0.81 0.85 21
meal 0.00 0.00 0.00 6
restriction 0.00 0.00 0.00 0
cheapest 0.00 0.00 0.00 0
flight 0.94 0.96 0.95 627
ground_service 0.69 1.00 0.82 36
airfare 0.85 0.74 0.79 61
ground_fare 0.22 0.57 0.32 7
micro avg 0.79 0.90 0.84 863
macro avg 0.35 0.49 0.38 863
weighted avg 0.84 0.90 0.86 863
samples avg 0.87 0.90 0.88 863
precision recall f1-score support
airport 0.00 0.00 0.00 3
restriction 0.00 0.00 0.00 1
airline 0.39 0.92 0.55 25
airfare 0.65 0.84 0.73 81
distance 0.00 0.00 0.00 1
capacity 0.33 1.00 0.50 2
cheapest 0.00 0.00 0.00 0
aircraft 0.83 0.75 0.79 20
meal 0.00 0.00 0.00 0
ground_service 0.49 1.00 0.66 45
flight 0.96 0.97 0.96 698
city 0.00 0.00 0.00 2
abbreviation 0.48 0.96 0.64 23
flight_no 0.00 0.00 0.00 4
flight_time 0.33 0.43 0.38 7
ground_fare 0.00 0.00 0.00 4
quantity 0.83 0.77 0.80 13
micro avg 0.82 0.93 0.87 929
macro avg 0.31 0.45 0.35 929
weighted avg 0.85 0.93 0.88 929
samples avg 0.88 0.93 0.90 929
- Confusion Matrix
precision recall f1-score support
airport 0.00 0.00 0.00 13
restriction 0.00 0.00 0.00 0
airline 0.43 0.87 0.58 30
airfare 0.62 0.74 0.67 61
distance 0.67 0.20 0.31 10
capacity 0.84 0.76 0.80 21
cheapest 0.00 0.00 0.00 0
aircraft 0.31 1.00 0.47 8
meal 0.00 0.00 0.00 6
ground_service 0.29 0.92 0.45 36
flight 0.94 0.98 0.96 627
city 0.09 0.20 0.13 5
abbreviation 0.35 1.00 0.52 26
flight_no 0.00 0.00 0.00 9
flight_time 0.06 1.00 0.11 1
ground_fare 0.50 0.29 0.36 7
quantity 0.23 1.00 0.38 3
micro avg 0.73 0.90 0.80 863
macro avg 0.31 0.53 0.34 863
weighted avg 0.80 0.90 0.83 863
samples avg 0.84 0.91 0.86 863
precision recall f1-score support
flight 0.94 0.93 0.93 698
airline 0.22 0.44 0.29 25
flight_time 0.00 0.00 0.00 7
restriction 0.00 0.00 0.00 1
meal 0.00 0.00 0.00 0
distance 0.00 0.00 0.00 1
ground_fare 1.00 0.25 0.40 4
flight_no 0.00 0.00 0.00 4
airfare 0.40 0.73 0.52 81
airport 0.00 0.00 0.00 3
capacity 0.00 0.00 0.00 2
quantity 0.19 0.54 0.29 13
cheapest 0.00 0.00 0.00 0
aircraft 0.27 0.60 0.37 20
abbreviation 0.46 1.00 0.63 23
ground_service 0.40 0.98 0.57 45
city 0.03 0.50 0.06 2
micro avg 0.69 0.87 0.77 929
macro avg 0.23 0.35 0.24 929
weighted avg 0.79 0.87 0.81 929
samples avg 0.81 0.87 0.82 929
- Confusion Matrix
precision recall f1-score support
flight 0.93 0.96 0.94 627
airline 0.32 0.60 0.42 30
flight_time 0.00 0.00 0.00 1
restriction 0.00 0.00 0.00 0
meal 0.00 0.00 0.00 6
distance 0.00 0.00 0.00 10
ground_fare 0.00 0.00 0.00 7
flight_no 0.00 0.00 0.00 9
airfare 0.26 0.44 0.33 61
airport 0.00 0.00 0.00 13
capacity 0.89 0.81 0.85 21
quantity 0.05 1.00 0.10 3
cheapest 0.00 0.00 0.00 0
aircraft 0.12 0.75 0.21 8
abbreviation 0.41 1.00 0.58 26
ground_service 0.25 0.92 0.39 36
city 0.00 0.00 0.00 5
micro avg 0.61 0.84 0.71 863
macro avg 0.19 0.38 0.22 863
weighted avg 0.75 0.84 0.78 863
samples avg 0.79 0.85 0.79 863