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4_lstm_model.py
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4_lstm_model.py
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#%% This script preprocesses the wikipedia data and trains the LSTM Network.
# The LSTM Network utility functions can be found in LSTM.py
from LSTM import LSTMN, WikiDocData, split_data, train_lstmn, batcher, process_batch
import pickle
import numpy as np
import os
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
from torch import optim
import torch.nn as nn
from sklearn import metrics
# Import namespace
from argparse import Namespace
from keras.preprocessing.text import Tokenizer # Use keras for tokenization & preprocessing
import itertools
# Helper functions
from preprocess_utils import tokenize_text
# Base directory
bd = "data/HAN"
# Create a map for the different sentence lengths
slmap = {}
for sl in ["S8", "S10", "S12", "S15"]:
idx=int(sl.strip("S"))
slmap[idx] = {"tokenizer": os.path.join(bd, "tokenizer_{}.pickle".format(sl)),
"embedding": os.path.join(bd, "HAN_embeddings_{}.pickle".format(sl)),
"input_data": os.path.join(bd, "data_{}.pickle".format(sl))}
# Model settings
args = Namespace(
# Tokenizer
data_files_map=slmap,
# File to save results
out_file='results/LSTM_trials.csv',
# Number of times to evaluate bayesian search for hyperparams
# NB: the HAN is very expensive to run even on a GPU ##########################
max_evals=300,
# Embedding size
embedding_dim=300,
# NN settings
embedding_trainable=False,
# Batch size
batch_size=128
)
#%% Load the data for one of the sentence lengths
sent_length = 15
# Load
with open(args.data_files_map[sent_length]["tokenizer"], "rb") as inFile:
tokenizer = pickle.load(inFile)
with open(args.data_files_map[sent_length]["embedding"], "rb") as inFile:
FTEMB = torch.tensor(pickle.load(inFile)).to(device)
with open(args.data_files_map[sent_length]["input_data"], "rb") as inFile:
data = pickle.load(inFile)
# Unpack
train_x, train_y = data["train_x"], data["train_y"]
docs_vectorized_lstm = []
doc_lstm = []
for doc in train_x:
doc_lstm = []
for sent in doc:
doc_lstm = doc_lstm + sent
docs_vectorized_lstm.append(doc_lstm)
train_x = docs_vectorized_lstm
labels_vect = data["labels_vectorized"]
idx_to_label = data["idx_to_label"]
label_to_idx = data["labels_to_idx"]
#%% View the max length of all sentences in all documents
# Max length of the documents
max_seq_doc = max([len(doc) for doc in train_x])
# View
print((max_seq_doc))
#%% Class weights (these are the same for each)
# Class weights
# Preprocess outcome label
train_y_ohe = np.zeros((len(train_y), len(data["labels_to_idx"])))
for idx,lbl in enumerate(train_y):
train_y_ohe[idx, lbl] = 1
# These weights are unnormalized but that's what pytorch is expecting
cw = torch.tensor(np.max(np.sum(train_y_ohe, axis=0)) / (np.sum(train_y_ohe, axis=0))).type(torch.float).to(device)
#%% Unique classes
num_classes = len(np.unique(labels_vect))
#%% Use hyperopt (Bayesian hyperparameter optimization) to search for good hyperparams
from hyperopt import STATUS_OK
import csv
from hyperopt import hp
# Optimizer
from hyperopt import tpe
# Save basic training information
from hyperopt import Trials
# Optimizer criterion
from hyperopt import fmin
# Use skorch for cross-validation
from skorch import NeuralNet
from skorch.dataset import CVSplit
# Function that sets up model and outputs and returns validation loss
def LSTMN_search(parameters):
"""Set up, run and evaluate a LSTMN"""
# Based on the parameters, load various settings
sent_length = parameters["sent_length"]
# Load data
with open(args.data_files_map[sent_length]["tokenizer"], "rb") as inFile:
tokenizer = pickle.load(inFile)
with open(args.data_files_map[sent_length]["embedding"], "rb") as inFile:
FTEMB = torch.tensor(pickle.load(inFile)).to(device)
with open(args.data_files_map[sent_length]["input_data"], "rb") as inFile:
data = pickle.load(inFile)
# Unpack
train_x, train_y = data["train_x"], data["train_y"]
docs_vectorized_lstm = []
doc_lstm = []
for doc in train_x:
doc_lstm = []
for sent in doc:
doc_lstm = doc_lstm + sent
docs_vectorized_lstm.append(doc_lstm)
train_x = docs_vectorized_lstm
labels_vect = data["labels_vectorized"]
idx_to_label = data["idx_to_label"]
label_to_idx = data["labels_to_idx"]
train, val = split_data(train_x, train_y, 6754, p=0.05)
train_x = train[0]
train_y = train[1]
# Set up the model
WikiLSTM = LSTMN(FTEMB,
args.batch_size,
num_classes,
bidirectional = parameters["bidirectional"],
nb_lstm_layers = parameters["nb_lstm_layers"],
nb_lstm_units = parameters["nb_lstm_units"],
dropout_prop=parameters["dropout_prop"])
# To cuda
WikiLSTM.to(device)
# Set up optimizer
optimizer = optim.Adam(WikiLSTM.parameters(), lr=parameters["learning_rate"])
# Criterion
if parameters["use_class_weights"]:
criterion = nn.CrossEntropyLoss(weight=cw)
else:
criterion = nn.CrossEntropyLoss()
# Run the model
WikiLSTM_out, history = train_lstmn(train_x, train_y, WikiLSTM, optimizer, criterion,
epochs=10, val_split=0.1, batch_size=args.batch_size,
device=device)
# Max accuracy
which_min = int(np.argmin(history["validation_loss"]))
# Write to file
with open(args.out_file, 'a') as of_connection:
writer = csv.writer(of_connection)
writer.writerow([parameters,
which_min,
np.round(history["training_loss"][which_min], 4),
np.round(history["validation_accuracy"][which_min], 4),
np.round(history["validation_loss"][which_min], 4),
np.round(history["validation_f1"][which_min], 4),
np.round(history["validation_precision"][which_min], 4),
np.round(history["validation_recall"][which_min], 4)])
# Return cross-validation loss
# NB: we are minimizing here zo we need to take 1-accuracy
return({"loss": history["validation_loss"][which_min], "parameters": parameters, "iteration": which_min, 'status':STATUS_OK})
# Define the search space
space = {
'nb_lstm_units': hp.choice('nb_lstm_units', [32,64,128]),
'nb_lstm_layers': hp.choice('nb_lstm_layers', [1,2]),
'bidirectional': hp.choice('bidirectional', [False, True]),
'sent_length': hp.choice("sent_length", [8, 10, 12, 15]),
'use_class_weights': hp.choice("use_class_weights", [True, False]),
'learning_rate': hp.loguniform('learning_rate', np.log(0.001), np.log(0.03)),
'dropout_prop': hp.uniform("dropout", 0, 0.5)
}
#%% Test space
# Test if works
from hyperopt.pyll.stochastic import sample
parameters = sample(space)
print(parameters)
po = LSTMN_search(parameters)
#%% Run the optimizer
# Algorithm
tpe_algorithm = tpe.suggest
# Trials object to track progress
bayes_trials = Trials()
# File to save first results
with open(args.out_file, 'w') as of_connection:
writer = csv.writer(of_connection)
# Write the headers to the file
writer.writerow(['params',
'iteration',
'train_loss',
'val_accuracy',
'val_loss',
"val_f1",
"val_precision",
"val_recall"])
# Optimize
import datetime
begin = datetime.datetime.now()
best = fmin(fn = LSTMN_search, space = space, algo = tpe.suggest,
max_evals = args.max_evals, trials = bayes_trials)
finish = datetime.datetime.now()
t_spent = begin-finish
print("began at: " + begin.strftime("%Y-%m-%d %H:%M"))
print("ended at: " + finish.strftime("%Y-%m-%d %H:%M"))
print("total time: " + str(t_spent))
#%% Train LSTMN on best parameters and train data
max_sent_length = 8
# Load data for the sentence length
sent_length = max_sent_length
with open(args.data_files_map[sent_length]["tokenizer"], "rb") as inFile:
tokenizer = pickle.load(inFile)
with open(args.data_files_map[sent_length]["embedding"], "rb") as inFile:
FTEMB = torch.tensor(pickle.load(inFile)).to(device)
with open(args.data_files_map[sent_length]["input_data"], "rb") as inFile:
data = pickle.load(inFile)
# Unpack
train_x, train_y = data["train_x"], data["train_y"]
docs_vectorized_lstm = []
doc_lstm = []
for doc in train_x:
doc_lstm = []
for sent in doc:
doc_lstm = doc_lstm + sent
docs_vectorized_lstm.append(doc_lstm)
train_x = docs_vectorized_lstm
labels_vect = data["labels_vectorized"]
idx_to_label = data["idx_to_label"]
label_to_idx = data["labels_to_idx"]
#%%
best = { ###########################################################################################################
"bidirectional" : True,
"dropout_prop" : 0.010131,
"learning_rate" : 0.020356,
"nb_lstm_units" : 32,
"nb_lstm_layers" : 1,
"use_class_weights" : True,
"batch_size" : 128,
"num_classes" : len(np.unique(labels_vect)),
"epochs" : 9
}
# Set up the model
WikiLSTM = LSTMN(FTEMB, best["batch_size"], best["num_classes"],
best["bidirectional"], best["nb_lstm_layers"],
best["nb_lstm_units"], best["dropout_prop"])
# To cuda
WikiLSTM.to(device)
# Set up optimizer
optimizer = optim.Adam(WikiLSTM.parameters(), lr= best["learning_rate"])
# Criterion
if best["use_class_weights"]:
criterion = nn.CrossEntropyLoss(weight=cw)
else:
criterion = nn.CrossEntropyLoss()
# Training routine
WikiLSTM_out, history = train_lstmn(train_x, train_y, WikiLSTM, optimizer, criterion,
epochs = best["epochs"], val_split = 0.1, batch_size = best["batch_size"],
device = device)
#%% Save model
torch.save(WikiLSTM_out.state_dict(), "models/LSTM.pt")
#%% Get test data
test_x, test_y = data["test_x"], data["test_y"]
docs_vectorized_lstm = []
doc_lstm = []
for doc in test_x:
doc_lstm = []
for sent in doc:
doc_lstm = doc_lstm + sent
docs_vectorized_lstm.append(doc_lstm)
test_x = docs_vectorized_lstm
#%% WikiDocData
test = WikiDocData(test_x, test_y)
train = WikiDocData(train_x, train_y)
#%% Evaluate the model on test data
# For now, just make a single batch of the test data for evaluation
valbatch = batcher(test, len(test.X))
# Preprocess
seqs, lens = process_batch(valbatch, device = device)
# Predict
with torch.no_grad():
WikiLSTM_out.eval()
probs = WikiLSTM_out(seqs, lens, batch_size = 1001)
#%% Same for train data
def predict_LSTM(model, dataset, batch_size = 128, device = "cpu"):
"""
Create predictions for a HAN
:param model: LSTM model
:param dataset: WikiDocData dataset
:param batch_size: size of the input batches to the model
:param device: device on which the model is run
:return: tuple containing predictions and ground truth labels
"""
n = len(dataset.X)
total = n // batch_size
remainder = n % batch_size
# Make indices
idx = []
start_idx = 0
for batch_idx in range(1, total+1):
idx.append((start_idx, batch_idx * batch_size))
start_idx += batch_size
# If remainder
if remainder > 0:
idx.append((start_idx, start_idx + remainder))
# For each pair, predict
predictions = []
ground_truth = []
for start_idx, stop_idx in idx:
# Get batch
inbatch = [dataset.__getitem__(idx) for idx in range(start_idx, stop_idx)]
# Process batch
seqs, lens = process_batch(inbatch, device = device)
# Batch size
bs = len(seqs)
# Predict
with torch.no_grad():
model.eval()
probs = model(seqs, lens, batch_size = bs)
# To classes
out = torch.argmax(probs, dim=1).cpu().numpy()
# Get true label
ytrue = [batch[1] for batch in inbatch]
ytrue = torch.tensor(ytrue).cpu().numpy()
# Cat together
predictions.append(out)
ground_truth.append(ytrue)
# Stack predictions & ground truth
return(np.hstack(predictions), np.hstack(ground_truth))
#%% On Train
# Predict train
yhat, ytrue = predict_LSTM(WikiLSTM_out, train, device = device)
# Print classification report
print(metrics.classification_report(ytrue, yhat, target_names = list(label_to_idx.keys())))
#%% On test
# Predict test
yhat, ytrue = predict_LSTM(WikiLSTM_out, test, device = device)
# Print classification report
print(metrics.classification_report(ytrue, yhat, target_names = list(label_to_idx.keys())))
#%% Save to csv
import pandas as pd
out_preds = pd.DataFrame({"yhat": yhat, "ytrue":ytrue})
out_preds.to_csv("predictions/LSTM.csv", index=False)
#%%
# Predict
with torch.no_grad():
WikiLSTM_out.eval()
probs = WikiLSTM_out(seqs, lens, batch_size = len(batche[0].X))
# %% Classes
# To classes
out = torch.argmax(probs, dim=1).cpu().numpy()
# Get true label
ytrue = [batch[1] for batch in valbatch]
ytrue = torch.tensor(ytrue).cpu().numpy()
# Accuracy
print(sum(out == ytrue)/len(out))
#%% Print classification report
# Print classification report
print(metrics.classification_report(ytrue, yhat, target_names = list(label_to_idx.keys())))
#%% Save model
torch.save(WikiLSTM_out.state_dict(), "models/LSTM.pt")