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2_baseline_model.py
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2_baseline_model.py
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#%% Baseline model
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import pickle
import numpy as np
import os
from keras.preprocessing.text import Tokenizer # Use keras for tokenization & preprocessing
from keras import preprocessing
import matplotlib.pyplot as plt
from model_utils import load_FT, Embedding_FastText, WikiData, split
# Details for this script
from argparse import Namespace
# Model settings
args = Namespace(
# Tokenizer
tokenizer="data/other/tokenizer.pickle",
# Embedding
embedding="data/other/embedding_matrix.pickle",
# Input data
input_data="data/other/vectorized_input_data.pickle",
# File to save results
out_file='results/basicNN_trials.csv',
# Number of times to evaluate bayesian search for hyperparams
max_evals=500,
# Embedding size
embedding_dim=300,
# Max length of text sequences
seq_max_len=150,
# NN settings
embedding_trainable = False
)
#%% Load pre-processed data
# Tokenizer
with open(args.tokenizer, "rb") as inFile:
tokenizer = pickle.load(inFile)
# Embedding
with open(args.embedding, "rb") as inFile:
FTEMB = pickle.load(inFile)
# Data
with open(args.input_data, "rb") as inFile:
input_data = pickle.load(inFile)
# Unroll data
train_x=input_data["train_x"]
train_y=input_data["train_y"]
category_map=input_data["catmap"]
# Shuffle data
X, y = train_x, np.array(train_y)
np.random.seed(4352)
rp = np.random.permutation(y.shape[0])
X = X[rp,:]
y = y[rp]
# Also for test
Xt, yt = input_data["test_x"], np.array(input_data["test_y"])
np.random.seed(6666)
rpt = np.random.permutation(yt.shape[0])
Xt = Xt[rpt,:]
yt = yt[rpt]
# To wikidata class
WD = WikiData(train_x, train_y)
test = WikiData(input_data["test_x"], input_data["test_y"])
#%% One-layer NN with softmax on top
class BaselineNN(nn.Module):
def __init__(self, weights, num_classes, hidden_dim, p_dropout = 0, use_batch_norm = True):
super(BaselineNN, self).__init__()
# Set dropout percentage
self._p_dropout = p_dropout
# Get embedding dimensions
self.weights_dim = weights.shape[1]
# Set up embedding
self.embedding = Embedding_FastText(weights, freeze_layer = True)
# Set up hidden layer
self.linear1 = nn.Linear(self.weights_dim, hidden_dim)
# Set up batch norm
self.bn = nn.BatchNorm1d(num_features=hidden_dim)
self.use_batch_norm = use_batch_norm
# Set up softmax layer
self.linear2 = nn.Linear(hidden_dim, num_classes)
def forward(self, input, dropout = 0):
# Call embedding
embedded = self.embedding(input).sum(dim=1)
# Predict
yhat = self.linear1(embedded)
# Apply batch norm
if self.use_batch_norm:
yhat = self.bn(yhat)
# Activation
yhat = F.relu(yhat)
# Dropout
yhat = F.dropout(yhat, p = self._p_dropout)
# Linear
yhat = self.linear2(yhat)
# Probabilities
yhat = F.softmax(yhat, dim=1)
return(yhat)
# Class weights
# Preprocess outcome label
train_y_ohe = np.zeros((len(train_y), len(category_map)))
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)
#%% Callback functions
from sklearn import metrics
def f1_score(net, X, y):
"""Compute the F1 score"""
ypred = net.predict(X)
out_class = np.argmax(ypred, axis=1)
return(metrics.f1_score(y, out_class, average="weighted"))
def precision_score(net, X, y):
"""Compute precision"""
ypred = net.predict(X)
out_class = np.argmax(ypred, axis=1)
return(metrics.precision_score(y, out_class, average="weighted"))
def recall_score(net, X, y):
"""Compute recall"""
ypred = net.predict(X)
out_class = np.argmax(ypred, axis=1)
return(metrics.recall_score(y, out_class, average="weighted"))
def accuracy_score(net, X, y):
"""Compute accuracy"""
ypred = net.predict(X)
out_class = np.argmax(ypred, axis=1)
return(metrics.accuracy_score(y, out_class))
#%% 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
# Tracking precision//recall//F1
from sklearn import metrics
import skorch
# Function that sets up model and outputs and returns validation loss
def baselineNN_search(parameters):
"""Set up, run and evaluate a baseline neural network"""
# CV with skorch
net = NeuralNet(
# Module
module=BaselineNN,
# Module settings
module__hidden_dim = parameters["hidden_units"],
module__p_dropout = parameters["dropout"],
module__use_batch_norm = parameters["use_batch_norm"],
module__weights = FTEMB, # These are word embeddings
module__num_classes = len(category_map),
# Epochs & learning rate
max_epochs=25,
lr=parameters["learning_rate"],
# Optimizer
optimizer=optim.Adam if parameters["optimizer"] == "Adam" else optim.RMSprop,
# Loss function
criterion=nn.CrossEntropyLoss,
criterion__weight = cw,
# Shuffle training data on each epoch
iterator_train__shuffle=True,
# Batch size
batch_size = 128,
train_split = CVSplit(cv=5),
# Device
device = device,
# Callbacks
callbacks=[
skorch.callbacks.EpochScoring(f1_score, use_caching=True, name="valid_f1"),
skorch.callbacks.EpochScoring(precision_score, use_caching=True, name="valid_precision"),
skorch.callbacks.EpochScoring(recall_score, use_caching=True, name="valid_recall"),
skorch.callbacks.EpochScoring(accuracy_score, use_caching=True, name="valid_accuracy")
]
)
# Verbose to false
net.verbose = 1
# Fit
net = net.fit(WD)
# Get train / validation history
train_loss = net.history[:,"train_loss"]
val_loss = net.history[:, "valid_loss"]
val_accuracy = net.history[:, "valid_accuracy"]
val_f1 = net.history[:,"valid_f1"]
val_precision = net.history[:,"valid_precision"]
val_recall = net.history[:,"valid_recall"]
# Min loss
which_min = np.argmin(val_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(train_loss[which_min], 4),
np.round(val_accuracy[which_min], 4),
np.round(val_loss[which_min], 4),
np.round(val_f1[which_min], 4),
np.round(val_precision[which_min], 4),
np.round(val_recall[which_min], 4)])
# Return cross-validation loss
return({"loss": val_loss[which_min], "parameters": parameters, "iteration": which_min, 'status':STATUS_OK})
# Define the search space
space = {
'hidden_units': hp.choice('hidden_units', [64,128,256,512]),
'optimizer': hp.choice("optimizer", ["Adam", "RMSprop"]),
'use_batch_norm': hp.choice("use_batch_norm", [True, False]),
'dropout': hp.uniform("dropout", 0, 0.5),
'learning_rate': hp.loguniform('learning_rate', np.log(0.001), np.log(0.02))
}
# Test if works
from hyperopt.pyll.stochastic import sample
params = sample(space)
po = baselineNN_search(params)
#%% Run bayesian optimization
# 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
best = fmin(fn = baselineNN_search, space = space, algo = tpe.suggest,
max_evals = args.max_evals, trials = bayes_trials)
#%% Fit on train & test
import skorch
from sklearn import metrics
from skorch import NeuralNet
from skorch.dataset import CVSplit
# Best parameters
best = Namespace(
hidden_units=512,
dropout=0.01285,
learning_rate=0.001435,
optimizer=optim.Adam,
use_batch_norm=True,
iterations=23,
batch_size=128
)
# Run the model with the best parameters
net = NeuralNet(
# Module
module=BaselineNN,
# Module settings
module__hidden_dim = best.hidden_units,
module__p_dropout = best.dropout,
module__use_batch_norm = best.use_batch_norm,
module__weights = FTEMB,
module__num_classes = len(category_map),
# Epochs & learning rate
max_epochs=best.iterations,
lr=best.learning_rate,
# Optimizer
optimizer=best.optimizer,
# Loss function
criterion=nn.CrossEntropyLoss,
criterion__weight = cw,
# Shuffle training data on each epoch
iterator_train__shuffle=True,
# Batch size
batch_size = best.batch_size,
train_split = CVSplit(cv=5),
# Device
device = device,
# Callbacks
callbacks=[
skorch.callbacks.EpochScoring(f1_score, use_caching=True, name="valid_f1"),
skorch.callbacks.EpochScoring(precision_score, use_caching=True, name="valid_precision"),
skorch.callbacks.EpochScoring(recall_score, use_caching=True, name="valid_recall"),
skorch.callbacks.EpochScoring(accuracy_score, use_caching=True, name="valid_accuracy")
]
)
# Verbose to false
net.verbose = 1
#%% Fit the model
io = net.fit(WD)
# Save model
net.save_params(f_params='models/baselineNN.pkl')
#%% Or load it from disk
net.initialize()
net.load_params(f_params="models/baselineNN.pkl")
#%% Predict on train
# Out
yhat = net.predict(WD)
# Classes
yhatc = yhat.argmax(axis=1)
# True labels
ytrue = WD.y
(ytrue == yhatc).sum() / yhatc.size
# Classification report
from sklearn import metrics
print(metrics.classification_report(ytrue, yhatc, target_names=list(category_map.values())))
metrics.confusion_matrix(ytrue, yhatc)
#%% Predict on test
# Out
yhat = net.predict(test)
# Classes
yhatc = yhat.argmax(axis=1)
# True labels
ytrue = test.y
(ytrue == yhatc).sum() / yhatc.size
# Classification report
from sklearn import metrics
print(metrics.classification_report(ytrue, yhatc, target_names=list(category_map.values())))
metrics.confusion_matrix(ytrue, yhatc)
#%% Save predictions
import pandas as pd
out_preds = pd.DataFrame({"yhat": yhatc, "ytrue":ytrue})
# Save
out_preds.to_csv("predictions/baseline.csv", index=False)
#%% Save probabilities
probs_out = pd.DataFrame(yhat)
probs_out.to_csv("predictions/baseline_probs.csv", index=False)