# Concrete solutions to real problems¶

## An NLP workshop by Emmanuel Ameisen (@EmmanuelAmeisen), from Insight AI¶

While there exist a wealth of elaborate and abstract NLP techniques, clustering and classification should always be in our toolkit as the first techniques to use when dealing with this kind of data. In addition to being amongst some of the easiest to scale in production, their ease of use can quickly help business address a set of applied problems:

• How do you automatically make the distinction between different categories of sentences?
• How can you find sentences in a dataset that are most similar to a given one?
• How can you extract a rich and concise representation that can then be used for a range of other tasks?
• Most importantly, how do you find quickly whether these tasks are possible on your dataset at all?

While there is a vast amount of resources on classical Machine Learning, or Deep Learning applied to images, I've found that there is a lack of clear, simple guides as to what to do when one wants to find a meaningful representation for sentences (in order to classify them or group them together for examples). Here is my attempt below.

## It starts with data¶

### Our Dataset: Disasters on social media¶

Contributors looked at over 10,000 tweets retrieved with a variety of searches like “ablaze”, “quarantine”, and “pandemonium”, then noted whether the tweet referred to a disaster event (as opposed to a joke with the word or a movie review or something non-disastrous). Thank you Crowdflower.

### Why it matters¶

We will try to correctly predict tweets that are about disasters. This is a very relevant problem, because:

• It is actionable to anybody trying to get signal from noise (such as police departments in this case)
• It is tricky because relying on keywords is harder than in most cases like spam
In [251]:
import keras
import nltk
import pandas as pd
import numpy as np
import re
import codecs


### Sanitizing input¶

Let's make sure our tweets only have characters we want. We remove '#' characters but keep the words after the '#' sign because they might be relevant (eg: #disaster)

In [252]:
input_file = codecs.open("socialmedia_relevant_cols.csv", "r",encoding='utf-8', errors='replace')
output_file = open("socialmedia_relevant_cols_clean.csv", "w")

def sanitize_characters(raw, clean):
for line in input_file:
out = line
output_file.write(line)
sanitize_characters(input_file, output_file)


### Let's inspect the data¶

It looks solid, but we don't really need urls, and we would like to have our words all lowercase (Hello and HELLO are pretty similar for our task)

In [253]:
questions = pd.read_csv("socialmedia_relevant_cols_clean.csv")
questions.columns=['text', 'choose_one', 'class_label']

Out[253]:
text choose_one class_label
0 Just happened a terrible car crash Relevant 1
1 Our Deeds are the Reason of this #earthquake M... Relevant 1
2 Heard about #earthquake is different cities, s... Relevant 1
3 there is a forest fire at spot pond, geese are... Relevant 1
In [254]:
questions.tail()

Out[254]:
text choose_one class_label
10871 M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt... Relevant 1
10872 Police investigating after an e-bike collided ... Relevant 1
10873 The Latest: More Homes Razed by Northern Calif... Relevant 1
10874 MEG issues Hazardous Weather Outlook (HWO) htt... Relevant 1
10875 #CityofCalgary has activated its Municipal Eme... Relevant 1
In [255]:
questions.describe()

Out[255]:
class_label
count 10876.000000
mean 0.432604
std 0.498420
min 0.000000
25% 0.000000
50% 0.000000
75% 1.000000
max 2.000000

Let's use a few regular expressions to clean up pour data, and save it back to disk for future use

In [256]:
def standardize_text(df, text_field):
df[text_field] = df[text_field].str.replace(r"http\S+", "")
df[text_field] = df[text_field].str.replace(r"http", "")
df[text_field] = df[text_field].str.replace(r"@\S+", "")
df[text_field] = df[text_field].str.replace(r"[^A-Za-z0-9(),[email protected]\'\\"\_\n]", " ")
df[text_field] = df[text_field].str.replace(r"@", "at")
df[text_field] = df[text_field].str.lower()
return df

questions = standardize_text(questions, "text")

questions.to_csv("clean_data.csv")

Out[256]:
text choose_one class_label
0 just happened a terrible car crash Relevant 1
1 our deeds are the reason of this earthquake m... Relevant 1
2 heard about earthquake is different cities, s... Relevant 1
3 there is a forest fire at spot pond, geese are... Relevant 1
In [257]:
clean_questions = pd.read_csv("clean_data.csv")
clean_questions.tail()

Out[257]:
Unnamed: 0 text choose_one class_label
10871 10871 m1 94 01 04 utc ?5km s of volcano hawaii Relevant 1
10872 10872 police investigating after an e bike collided ... Relevant 1
10873 10873 the latest more homes razed by northern calif... Relevant 1
10874 10874 meg issues hazardous weather outlook (hwo) Relevant 1
10875 10875 cityofcalgary has activated its municipal eme... Relevant 1

### Data Overview¶

Let's look at our class balance.

In [258]:
clean_questions.groupby("class_label").count()

Out[258]:
Unnamed: 0 text choose_one
class_label
0 6187 6187 6187
1 4673 4673 4673
2 16 16 16

We can see our classes are pretty balanced, with a slight oversampling of the "Irrelevant" class.

### Our data is clean, now it needs to be prepared¶

Now that our inputs are more reasonable, let's transform our inputs in a way our model can understand. This implies:

• Tokenizing sentences to a list of separate words
• Creating a train test split
• Inspecting our data a little more to validate results
In [259]:
from nltk.tokenize import RegexpTokenizer

tokenizer = RegexpTokenizer(r'\w+')

clean_questions["tokens"] = clean_questions["text"].apply(tokenizer.tokenize)

Out[259]:
Unnamed: 0 text choose_one class_label tokens
0 0 just happened a terrible car crash Relevant 1 [just, happened, a, terrible, car, crash]
1 1 our deeds are the reason of this earthquake m... Relevant 1 [our, deeds, are, the, reason, of, this, earth...
2 2 heard about earthquake is different cities, s... Relevant 1 [heard, about, earthquake, is, different, citi...
3 3 there is a forest fire at spot pond, geese are... Relevant 1 [there, is, a, forest, fire, at, spot, pond, g...

### Inspecting our dataset a little more¶

In [260]:
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical

all_words = [word for tokens in clean_questions["tokens"] for word in tokens]
sentence_lengths = [len(tokens) for tokens in clean_questions["tokens"]]
VOCAB = sorted(list(set(all_words)))
print("%s words total, with a vocabulary size of %s" % (len(all_words), len(VOCAB)))
print("Max sentence length is %s" % max(sentence_lengths))

154724 words total, with a vocabulary size of 18101
Max sentence length is 34

In [261]:
import matplotlib.pyplot as plt

fig = plt.figure(figsize=(10, 10))
plt.xlabel('Sentence length')
plt.ylabel('Number of sentences')
plt.hist(sentence_lengths)
plt.show()


## On to the Machine Learning¶

Now that our data is clean and prepared, let's dive in to the machine learning part.

## Enter embeddings¶

Machine Learning on images can use raw pixels as inputs. Fraud detection algorithms can use customer features. What can NLP use?

A natural way to represent text for computers is to encode each character individually, this seems quite inadequate to represent and understand language. Our goal is to first create a useful embedding for each sentence (or tweet) in our dataset, and then use these embeddings to accurately predict the relevant category.

The simplest approach we can start with is to use a bag of words model, and apply a logistic regression on top. A bag of words just associates an index to each word in our vocabulary, and embeds each sentence as a list of 0s, with a 1 at each index corresponding to a word present in the sentence.

## Bag of Words Counts¶

In [262]:
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer

def cv(data):
count_vectorizer = CountVectorizer()

emb = count_vectorizer.fit_transform(data)

return emb, count_vectorizer

list_corpus = clean_questions["text"].tolist()
list_labels = clean_questions["class_label"].tolist()

X_train, X_test, y_train, y_test = train_test_split(list_corpus, list_labels, test_size=0.2,
random_state=40)

X_train_counts, count_vectorizer = cv(X_train)
X_test_counts = count_vectorizer.transform(X_test)


### Visualizing the embeddings¶

Now that we've created embeddings, let's visualize them and see if we can identify some structure. In a perfect world, our embeddings would be so distinct that are two classes would be perfectly separated. Since visualizing data in 20k dimensions is hard, let's project it down to 2.

In [263]:
from sklearn.decomposition import PCA, TruncatedSVD
import matplotlib
import matplotlib.patches as mpatches

def plot_LSA(test_data, test_labels, savepath="PCA_demo.csv", plot=True):
lsa = TruncatedSVD(n_components=2)
lsa.fit(test_data)
lsa_scores = lsa.transform(test_data)
color_mapper = {label:idx for idx,label in enumerate(set(test_labels))}
color_column = [color_mapper[label] for label in test_labels]
colors = ['orange','blue','blue']
if plot:
plt.scatter(lsa_scores[:,0], lsa_scores[:,1], s=8, alpha=.8, c=test_labels, cmap=matplotlib.colors.ListedColormap(colors))
red_patch = mpatches.Patch(color='orange', label='Irrelevant')
green_patch = mpatches.Patch(color='blue', label='Disaster')
plt.legend(handles=[red_patch, green_patch], prop={'size': 30})

fig = plt.figure(figsize=(16, 16))
plot_LSA(X_train_counts, y_train)
plt.show()


These embeddings don't look very cleanly separated. Let's see if we can still fit a useful model on them.

### Fitting a classifier¶

Starting with a logistic regression is a good idea. It is simple, often gets the job done, and is easy to interpret.

In [264]:
from sklearn.linear_model import LogisticRegression

clf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',
multi_class='multinomial', n_jobs=-1, random_state=40)
clf.fit(X_train_counts, y_train)

y_predicted_counts = clf.predict(X_test_counts)


### Evaluation¶

Let's start by looking at some metrics to see if our classifier performed well at all.

In [265]:
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report

def get_metrics(y_test, y_predicted):
# true positives / (true positives+false positives)
precision = precision_score(y_test, y_predicted, pos_label=None,
average='weighted')
# true positives / (true positives + false negatives)
recall = recall_score(y_test, y_predicted, pos_label=None,
average='weighted')

# harmonic mean of precision and recall
f1 = f1_score(y_test, y_predicted, pos_label=None, average='weighted')

# true positives + true negatives/ total
accuracy = accuracy_score(y_test, y_predicted)
return accuracy, precision, recall, f1

accuracy, precision, recall, f1 = get_metrics(y_test, y_predicted_counts)
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy, precision, recall, f1))

accuracy = 0.754, precision = 0.752, recall = 0.754, f1 = 0.753


### Inspection¶

A metric is one thing, but in order to make an actionnable decision, we need to actually inspect the kind of mistakes our classifier is making. Let's start by looking at the confusion matrix.

In [266]:
import numpy as np
import itertools
from sklearn.metrics import confusion_matrix

def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.winter):
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, fontsize=30)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, fontsize=20)
plt.yticks(tick_marks, classes, fontsize=20)

fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.

for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center",
color="white" if cm[i, j] < thresh else "black", fontsize=40)

plt.tight_layout()
plt.ylabel('True label', fontsize=30)
plt.xlabel('Predicted label', fontsize=30)

return plt

In [267]:
cm = confusion_matrix(y_test, y_predicted_counts)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')
plt.show()
print(cm)

[[970 251   3]
[274 670   1]
[  3   4   0]]


Our classifier never predicts class 3, which is not surprising, seeing as it is critically undersampled. This is not very important here, as the label is not very meaningful. Our classifier creates more false negatives than false positives (proportionally). Depending on the use case, this seems desirable (a false positive is quite a high cost for law enforcement for example).

### Further inspection¶

Let's look at the features our classifier is using to make decisions.

In [268]:
def get_most_important_features(vectorizer, model, n=5):
index_to_word = {v:k for k,v in vectorizer.vocabulary_.items()}

# loop for each class
classes ={}
for class_index in range(model.coef_.shape[0]):
word_importances = [(el, index_to_word[i]) for i,el in enumerate(model.coef_[class_index])]
sorted_coeff = sorted(word_importances, key = lambda x : x[0], reverse=True)
tops = sorted(sorted_coeff[:n], key = lambda x : x[0])
bottom = sorted_coeff[-n:]
classes[class_index] = {
'tops':tops,
'bottom':bottom
}
return classes

importance = get_most_important_features(count_vectorizer, clf, 10)

In [269]:
def plot_important_words(top_scores, top_words, bottom_scores, bottom_words, name):
y_pos = np.arange(len(top_words))
top_pairs = [(a,b) for a,b in zip(top_words, top_scores)]
top_pairs = sorted(top_pairs, key=lambda x: x[1])

bottom_pairs = [(a,b) for a,b in zip(bottom_words, bottom_scores)]
bottom_pairs = sorted(bottom_pairs, key=lambda x: x[1], reverse=True)

top_words = [a[0] for a in top_pairs]
top_scores = [a[1] for a in top_pairs]

bottom_words = [a[0] for a in bottom_pairs]
bottom_scores = [a[1] for a in bottom_pairs]

fig = plt.figure(figsize=(10, 10))

plt.subplot(121)
plt.barh(y_pos,bottom_scores, align='center', alpha=0.5)
plt.title('Irrelevant', fontsize=20)
plt.yticks(y_pos, bottom_words, fontsize=14)
plt.suptitle('Key words', fontsize=16)
plt.xlabel('Importance', fontsize=20)

plt.subplot(122)
plt.barh(y_pos,top_scores, align='center', alpha=0.5)
plt.title('Disaster', fontsize=20)
plt.yticks(y_pos, top_words, fontsize=14)
plt.suptitle(name, fontsize=16)
plt.xlabel('Importance', fontsize=20)

plt.show()

top_scores = [a[0] for a in importance[1]['tops']]
top_words = [a[1] for a in importance[1]['tops']]
bottom_scores = [a[0] for a in importance[1]['bottom']]
bottom_words = [a[1] for a in importance[1]['bottom']]

plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")


Our classifier correctly picks up on some patterns (hiroshima, massacre), but clearly seems to be overfitting on some irellevant terms (heyoo, x1392)

### TFIDF Bag of Words¶

Let's try a slightly more subtle approach. On top of our bag of words model, we use a TF-IDF (Term Frequency, Inverse Document Frequency) which means weighing words by how frequent they are in our dataset, discounting words that are too frequent, as they just add to the noise.

In [270]:
def tfidf(data):
tfidf_vectorizer = TfidfVectorizer()

train = tfidf_vectorizer.fit_transform(data)

return train, tfidf_vectorizer

X_train_tfidf, tfidf_vectorizer = tfidf(X_train)
X_test_tfidf = tfidf_vectorizer.transform(X_test)

In [271]:
fig = plt.figure(figsize=(16, 16))
plot_LSA(X_train_tfidf, y_train)
plt.show()


These embeddings look much more separated, let's see if it leads to better performance.

In [272]:
clf_tfidf = LogisticRegression(C=30.0, class_weight='balanced', solver='newton-cg',
multi_class='multinomial', n_jobs=-1, random_state=40)
clf_tfidf.fit(X_train_tfidf, y_train)

y_predicted_tfidf = clf_tfidf.predict(X_test_tfidf)

In [273]:
accuracy_tfidf, precision_tfidf, recall_tfidf, f1_tfidf = get_metrics(y_test, y_predicted_tfidf)
print("accuracy = %.3f, precision = %.3f, recall = %.3f, f1 = %.3f" % (accuracy_tfidf, precision_tfidf,
recall_tfidf, f1_tfidf))

accuracy = 0.762, precision = 0.760, recall = 0.762, f1 = 0.761


The results are a little better, let's see if they translate to an actual difference in our use case.

In [274]:
cm2 = confusion_matrix(y_test, y_predicted_tfidf)
fig = plt.figure(figsize=(10, 10))
plot = plot_confusion_matrix(cm2, classes=['Irrelevant','Disaster','Unsure'], normalize=False, title='Confusion matrix')
plt.show()
print("TFIDF confusion matrix")
print(cm2)
print("BoW confusion matrix")
print(cm)

TFIDF confusion matrix
[[974 249   1]
[261 684   0]
[  3   4   0]]
BoW confusion matrix
[[970 251   3]
[274 670   1]
[  3   4   0]]


Our False positives have decreased, as this model is more conservative about choosing the positive class.

# Looking at important coefficients for linear regression¶

Insert details here

In [275]:
importance_tfidf = get_most_important_features(tfidf_vectorizer, clf_tfidf, 10)

In [276]:
top_scores = [a[0] for a in importance_tfidf[1]['tops']]
top_words = [a[1] for a in importance_tfidf[1]['tops']]
bottom_scores = [a[0] for a in importance_tfidf[1]['bottom']]
bottom_words = [a[1] for a in importance_tfidf[1]['bottom']]

plot_important_words(top_scores, top_words, bottom_scores, bottom_words, "Most important words for relevance")


The words it picked up look much more relevant! Although our metrics on our held out validation set haven't increased much, we have much more confidence in the terms our model is using, and thus would feel more comfortable deploying it in a system that would interact with customers.

### Capturing semantic meaning¶

Our first models have managed to pick up on high signal words. However, it is unlikely that we will have a training set containing all relevant words. To solve this problem, we need to capture the semantic meaning of words. Meaning we need to understand that words like 'good' and 'positive' are closer than apricot and 'continent'.

### Enter word2vec¶

Word2vec is a model that was pre-trained on a very large corpus, and provides embeddings that map words that are similar close to each other. A quick way to get a sentence embedding for our classifier, is to average word2vec scores of all words in our sentence.

In [277]:
import gensim


In [278]:
def get_average_word2vec(tokens_list, vector, generate_missing=False, k=300):
if len(tokens_list)<1:
return np.zeros(k)
if generate_missing:
vectorized = [vector[word] if word in vector else np.random.rand(k) for word in tokens_list]
else:
vectorized = [vector[word] if word in vector else np.zeros(k) for word in tokens_list]
length = len(vectorized)
summed = np.sum(vectorized, axis=0)
averaged = np.divide(summed, length)
return averaged

def get_word2vec_embeddings(vectors, clean_questions, generate_missing=False):
embeddings = clean_questions['tokens'].apply(lambda x: get_average_word2vec(x, vectors,
generate_missing=generate_missing))
return list(embeddings)

In [279]:
embeddings = get_word2vec_embeddings(word2vec, clean_questions)
X_train_word2vec, X_test_word2vec, y_train_word2vec, y_test_word2vec = train_test_split(embeddings, list_labels,
test_size=0.2, random_state=40)

In [280]:
fig = plt.figure(figsize=(16, 16))
plot_LSA(embeddings, list_labels)
plt.show()
`