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mr_cls_Attention.py
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mr_cls_Attention.py
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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 6 10:01:15 2020
@author: Venelin Kovatchev
The class for the BILSTM classifier for the "What is on your mind"
COLING2020 paper
The class uses tensor flow BILSTM as core model
Different methods take care of processing the data in a standardized way
"""
import pandas as pd
import numpy as np
import scipy
import nltk
import spacy
import gensim
import glob
import csv
import matplotlib
import matplotlib.pyplot as plt
import sklearn
from sklearn.model_selection import cross_val_score
import sklearn.model_selection
import sklearn.pipeline
import re
from sklearn import svm
from sklearn import *
from sklearn.feature_selection import SelectKBest, VarianceThreshold
from sklearn.feature_selection import chi2
from sklearn.model_selection import KFold
from sklearn.base import BaseEstimator, TransformerMixin
import gensim.models.wrappers.fasttext
from scipy import sparse
import tensorflow_datasets as tfds
import tensorflow as tf
import collections
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import LeaveOneOut,KFold,train_test_split
from sklearn.utils import shuffle
from tensorflow.keras.layers import Layer, Dense, Flatten, Activation, Permute
from tensorflow.keras.layers import Multiply, Lambda, Reshape, Dot, Concatenate, RepeatVector, \
TimeDistributed, Permute, Bidirectional
from tensorflow.keras.layers import Concatenate, Dense, Input, LSTM, Embedding, Dropout, Activation, GRU, Flatten
from tensorflow.keras.layers import Bidirectional, GlobalMaxPool1D
from tensorflow.keras.models import Model, Sequential
from tensorflow.keras.layers import Convolution1D
from tensorflow.keras import initializers, regularizers, constraints, optimizers, layers
# Custom imports
# Some of those functions can probably be incorporated as methods in the class
from mr_generic_scripts import *
# Pre built self attention class
# Taken from https://github.com/uzaymacar/attention-mechanisms
class SelfAttention(Layer):
"""
Layer for implementing self-attention mechanism. Weight variables were preferred over Dense()
layers in implementation because they allow easier identification of shapes. Softmax activation
ensures that all weights sum up to 1.
@param (int) size: a.k.a attention length, number of hidden units to decode the attention before
the softmax activation and becoming annotation weights
@param (int) num_hops: number of hops of attention, or number of distinct components to be
extracted from each sentence.
@param (bool) use_penalization: set True to use penalization, otherwise set False
@param (int) penalty_coefficient: the weight of the extra loss
@param (str) model_api: specify to use TF's Sequential OR Functional API, note that attention
weights are not outputted with the former as it only accepts single-output layers
"""
def __init__(self, size, num_hops=8, use_penalization=True,
penalty_coefficient=0.1, model_api='functional', **kwargs):
if model_api not in ['sequential', 'functional']:
raise ValueError("Argument for param @model_api is not recognized")
self.size = size
self.num_hops = num_hops
self.use_penalization = use_penalization
self.penalty_coefficient = penalty_coefficient
self.model_api = model_api
super(SelfAttention, self).__init__(**kwargs)
def get_config(self):
base_config = super(SelfAttention, self).get_config()
base_config['size'] = self.size
base_config['num_hops'] = self.num_hops
base_config['use_penalization'] = self.use_penalization
base_config['penalty_coefficient'] = self.penalty_coefficient
base_config['model_api'] = self.model_api
return base_config
def build(self, input_shape):
self.W1 = self.add_weight(name='W1',
shape=(self.size, input_shape[2]), # (size, H)
initializer='glorot_uniform',
trainable=True)
self.W2 = self.add_weight(name='W2',
shape=(self.num_hops, self.size), # (num_hops, size)
initializer='glorot_uniform',
trainable=True)
super(SelfAttention, self).build(input_shape)
def call(self, inputs): # (B, S, H)
# Expand weights to include batch size through implicit broadcasting
W1, W2 = self.W1[None, :, :], self.W2[None, :, :]
hidden_states_transposed = Permute(dims=(2, 1))(inputs) # (B, H, S)
attention_score = tf.matmul(W1, hidden_states_transposed) # (B, size, S)
attention_score = Activation('tanh')(attention_score) # (B, size, S)
attention_weights = tf.matmul(W2, attention_score) # (B, num_hops, S)
attention_weights = Activation('softmax')(attention_weights) # (B, num_hops, S)
embedding_matrix = tf.matmul(attention_weights, inputs) # (B, num_hops, H)
embedding_matrix_flattened = Flatten()(embedding_matrix) # (B, num_hops*H)
if self.use_penalization:
attention_weights_transposed = Permute(dims=(2, 1))(attention_weights) # (B, S, num_hops)
product = tf.matmul(attention_weights, attention_weights_transposed) # (B, num_hops, num_hops)
identity = tf.eye(self.num_hops, batch_shape=(inputs.shape[0],)) # (B, num_hops, num_hops)
frobenius_norm = tf.sqrt(tf.reduce_sum(tf.square(product - identity))) # distance
self.add_loss(self.penalty_coefficient * frobenius_norm) # loss
if self.model_api == 'functional':
return embedding_matrix_flattened, attention_weights
elif self.model_api == 'sequential':
return embedding_matrix_flattened
class MR_attention:
def __init__(self, text_cols, age_list, v_size, max_len):
# Initialize the core variables
# The current classifier
self.mr_c = None
# The current tokenizer
self.mr_tok = None
# Initialize model variables
self.mr_set_model_vars(text_cols, age_list, v_size, max_len)
# Function that sets model variables
# Input: list of questions, list of ages, size of vocabulary, max len of sentence
# Also includes certain pre-build variables for truncating
# Also includes certain pre-built variables for dataset creation (batch size, shuffle buffer)
def mr_set_model_vars(self, text_cols, age_list, v_size, max_len,
trunc_type = 'post', padding_type = 'post', oov_tok = '<OOV>',
batch_size = 4, shuffle_buffer_size = 100):
# List of questions
self.q_list = text_cols
# List of ages
self.age_list = age_list
# Size of the vocabulary
self.v_size = v_size
# Padding length
self.max_len = max_len
# Truncating type
self.trunc_type = trunc_type
# Padding type
self.padding_type = padding_type
# Token to replace OOV tokens
self.oov_tok = oov_tok
# Batch size for tf_dataset
self.batch_size = batch_size
# Shuffle buffer size
self.shuffle_buffer_size = shuffle_buffer_size
# Function that sets evaluation variables
def mr_set_eval_vars(self, eval_q, eval_age, return_err = False):
# Whether or not to perform evaluation by question
self.eval_q = eval_q
# Whether or not to perform evaluation by age
self.eval_age = eval_age
# Whether or not to return wrong predictions
self.return_err = return_err
# Convert the text from words to indexes and pad to a fixed length (needed for LSTM)
# Input - text
# Uses model variables for vocabulary size, token to be used for OOV, padding and truncating
def mr_t2f(self, inp_text):
# Check if a tokenizer already exists
# If it is None, this is the first time we run the function -> fit the tokenizer
if self.mr_tok == None:
# Initialize the tokenizer
self.mr_tok = Tokenizer(num_words = self.v_size, oov_token=self.oov_tok)
# Fit the tokenizer
self.mr_tok.fit_on_texts(inp_text)
# Convert the dataset
indexed_dataset = self.mr_tok.texts_to_sequences(inp_text)
# Pad to max length
padded_dataset = pad_sequences(indexed_dataset,
maxlen = self.max_len,
padding = self.padding_type,
truncating = self.trunc_type)
# Return the converted dataset
return padded_dataset
# Function that created a tensorflow dataset from X and Y
# Input: X and Y
def mr_tf_data(self, var_X, var_y):
# Convert the labels in proper format
y_arr = var_y.to_numpy().astype(int)
# Create the actual dataset and shuffle it
var_dataset = tf.data.Dataset.from_tensor_slices((var_X, y_arr))
var_dataset = var_dataset.shuffle(self.shuffle_buffer_size).batch(self.batch_size)
return var_dataset
# Function that converts a dataframe to a dataset
# Input - dataframe
def mr_to_dataset(self, cur_df):
# X is the answer column
cur_X = cur_df["Answer"]
# Y is the score column
cur_Y = cur_df["Score"]
# Convert X to a one-hot vector representation
# The vector is of a predefined fixed length and uses a fixed vocabulary size
X_idx = self.mr_t2f(cur_X)
# Create the dataset
cur_dataset = self.mr_tf_data(X_idx,cur_Y)
# Return everything
return(X_idx, cur_Y, cur_dataset)
# Function that trains the classifier
# Input - a train set, and a val set
def mr_train(self, train_df, val_df):
# Reset the tokenizer and the model at the start of each training
self.mr_c = None
self.mr_tok = None
# Convert dataframes to datasets
X_train_idx, y_train, train_dataset = self.mr_to_dataset(train_df)
X_val_idx, y_val, val_dataset = self.mr_to_dataset(val_df)
# Current shape var
inp_shape = np.shape(X_train_idx[0])[0]
# Define a vanilla self-attention model
model = tf.keras.Sequential([
# Input layer
tf.keras.layers.Input(shape=(inp_shape)),
# Word embedding layers, size of the vocabulary X 64 dimensions
tf.keras.layers.Embedding(self.v_size, 64),
# Self attention layer, size is sentence length
SelfAttention(size=self.max_len, num_hops=6, use_penalization=False,model_api='sequential'),
# Flatten the output
tf.keras.layers.Flatten(),
# Dense relu layer on top of the attention
tf.keras.layers.Dense(self.max_len, activation='relu'),
# Add dropout to reduce overfitting
tf.keras.layers.Dropout(.5),
# Softmax classification for 3 classes
tf.keras.layers.Dense(3,activation='softmax')
])
# Compile the model
model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
optimizer=tf.keras.optimizers.Adam(1e-4),
metrics=['accuracy'])
# Print the moodel setting
print(model.summary())
print('\n Training')
# Train
history = model.fit(train_dataset, epochs=20,
validation_data=val_dataset,
validation_steps=30)
# Update the current model variable
self.mr_c = model
# Function that evaluates the model on a test set
# Input - test set
def mr_test(self, test_df):
# Initialize output vars
acc_scores = []
f1_scores = []
# Convert the dataframe to a dataset
X_test_idx, y_test, test_dataset = self.mr_to_dataset(test_df)
print("Testing the model on the test set:")
# Run the model internal evaluation on the test set
test_loss, test_acc = self.mr_c.evaluate(test_dataset)
# Get the actual predictions of the model for the test set
y_pred = self.mr_c.predict_classes(X_test_idx)
# Calculate macro F1
macro_score = sklearn.metrics.f1_score(y_test.tolist(),
[float(ele) for ele in y_pred],
average='macro')
print('Test Macro F1: {} \n'.format(round(macro_score,2)))
# Add the results to the output
acc_scores.append(round(test_acc,2))
f1_scores.append(round(macro_score,2))
# Test by question (if requested)
# Add the scores to the output
# Otherwise add empty list
if self.eval_q:
qa_scores, qf_scores = self.mr_eval_col(test_df,"Question",self.q_list)
acc_scores.append(qa_scores)
f1_scores.append(qf_scores)
else:
acc_scores.append([])
f1_scores.append([])
# Test by age (if requested)
# Add the scores to the output
# Otherwise add empty list
if self.eval_age:
aa_scores, af_scores = self.mr_eval_col(test_df,"Age",self.age_list)
acc_scores.append(aa_scores)
f1_scores.append(af_scores)
else:
acc_scores.append([])
f1_scores.append([])
return(acc_scores,f1_scores)
# Function that evaluates the model by a specific column
# Can also return the actual wrong predictions
# Input - test set, column, values
def mr_eval_col(self, test_df, col_name, col_vals):
# Initialize output
acc_scores = []
f1_scores = []
# Initialize output for wrong predictions, if needed
if self.return_err:
wrong_pred = []
# Loop through all values
for col_val in col_vals:
# Initialize output for wrong predictions, if needed
if self.return_err:
cur_wrong = []
# Get only the entries for the current value
cur_q = test_df[test_df[col_name] == col_val].copy()
# Convert dataframe to dataset
X_test_idx, y_test, test_dataset = self.mr_to_dataset(cur_q)
print("Evaluating column {} with value {}".format(col_name,col_val))
# Print the internal evaluation
test_loss, test_acc = self.mr_c.evaluate(test_dataset)
# Get the actual predictions of the model for the test set
y_pred = self.mr_c.predict_classes(X_test_idx)
# Calculate macro F1
macro_score = sklearn.metrics.f1_score(y_test.tolist(),
[float(ele) for ele in y_pred],
average='macro')
print('Macro F1: {} \n'.format(round(macro_score,2)))
# Add the results to the output
acc_scores.append(round(test_acc,2))
f1_scores.append(round(macro_score,2))
if self.return_err:
# Loop through all predictions and keep the incorrect ones
# cur_q["Answer"], y_test, and y_pred are all matched, since they
# are not shuffled (shuffle only applies to the test_dataset)
for c_text,c_gold,c_pred in zip(cur_q["Answer"],y_test.tolist(),
[float(ele) for ele in y_pred]):
if c_pred != c_gold:
cur_wrong.append([c_text,c_gold,c_pred])
wrong_pred.append(cur_wrong)
# Return the output
if self.return_err:
return(acc_scores,f1_scores, wrong_pred)
else:
return(acc_scores, f1_scores)
# Function for a dummy one run on train-test
# Input - full df, ratio for splitting on train/val/test, return errors or not
def mr_one_train_test(self, full_df, test_r, val_r=0):
# Split train and test
train_df, test_df = train_test_split(full_df, test_size = test_r)
# Check if we also need val
if val_r > 0:
train_df, val_df = train_test_split(train_df, test_size = val_r)
else:
# If not, validation is same as test
val_df = test_df
# Train the classifier
self.mr_train(train_df, val_df)
# Test the classifier
return(self.mr_test(test_df))
# Function for a dummy one-run on a provided train-test split
# Input - train_df, test_df, ratio for splitting val
def mr_one_run_pre_split(self,train_df, test_df, val_r = 0):
# Check if we also need val
if val_r > 0:
train_df, val_df = train_test_split(train_df, test_size = val_r)
else:
# If not, validation is same as test
val_df = test_df
# Train the classifier
self.mr_train(train_df, val_df)
# Test the classifier
return(self.mr_test(test_df))
#Function for a dummy 10-fold cross validation
# Input - full df, ratio for splitting on train/val/test, number of runs
def mr_kfold_train_test(self, full_df, val_r=0.25, num_runs=10, r_state = 42):
# Initialize output
all_results = []
# Run k-fold split
kf = KFold(n_splits=num_runs, shuffle=True, random_state = r_state)
# Run different splits
for train_index, test_index in kf.split(full_df):
train_df = full_df.iloc[train_index]
test_df = full_df.iloc[test_index]
cur_acc, cur_f1 = self.mr_one_run_pre_split(train_df, test_df, val_r)
all_results.append((cur_acc, cur_f1))
return(all_results)