NLP Feature Engineering

Solution for submission 148134

A detailed solution for submission 148134 submitted for challenge NLP Feature Engineering


Solution for NLP Feature Engineering LB: 0.803

This solution consists utilises a count vectorizer a TF IDF and a stopword filter as feature engineering.

AIcrowd Runtime Configuration 🧷

Define configuration parameters. Please include any files needed for the notebook to run under ASSETS_DIR. We will copy the contents of this directory to your final submission file πŸ™‚

The dataset is available under /data on the workspace.

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import os

# Please use the absolute for the location of the dataset.
# Or you can use relative path with `os.getcwd() + "test_data/test.csv"`
AICROWD_DATASET_PATH = os.getenv("DATASET_PATH", os.getcwd()+"/data/data.csv")
AICROWD_ASSETS_DIR = os.getenv("ASSETS_DIR", "assets")

Install packages 🗃

We are going to use sklearn to do Count Vectorization and TF IDF.

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!pip install --upgrade scikit-learn gensim
!pip install -q -U aicrowd-cli
Requirement already up-to-date: scikit-learn in /usr/local/lib/python3.7/dist-packages (0.24.2)
Requirement already up-to-date: gensim in /usr/local/lib/python3.7/dist-packages (4.0.1)
Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.19.5)
Requirement already satisfied, skipping upgrade: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (2.1.0)
Requirement already satisfied, skipping upgrade: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.0.1)
Requirement already satisfied, skipping upgrade: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-learn) (1.4.1)
Requirement already satisfied, skipping upgrade: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim) (5.1.0)

Define preprocessing code 💻

The code that is common between the training and the prediction sections should be defined here. During evaluation, we completely skip the training section. Please make sure to add any common logic between the training and prediction sections here.

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from glob import glob
import os
import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, accuracy_score
import sklearn

Training phase ⚙️

You can define your training code here. This sections will be skipped during evaluation.

For this solution approach there is no training needed! πŸ™‚

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# Downloading the Dataset
!mkdir data

Prediction phase 🔎

Generating the features in test dataset.

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test_dataset = pd.read_csv(AICROWD_DATASET_PATH)

Count Vectorizer 🔢

A count vectorizer outputs a text as a matrix of counts of the related word.
It has a vocabulary that includes every word that is present in the data. When it converts a text into a vector, it first counts all the words.
For example, if the first digit of the vector contains the word "hello" and "hello" is counted 2 times in the text, then the number 2 will be in this position.
The advantage of this method is that the vector always has the same size and is therefore independent of the input.

TFidf 📐

Here is a very in-depth explanation:


Stopwords are words which have little meaning. If they are removed it should improve the compression of the text data into smaller vectors.

Somehow to submit something the output needs to be integers otherwise the evaluation will fail.

In [ ]:
from gensim.parsing.preprocessing import remove_stopwords
from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer(max_features = 512)
X_train_counts = count_vect.fit_transform([remove_stopwords(i) for i in test_dataset.text.tolist()])

from sklearn.feature_extraction.text import TfidfTransformer
tf_transformer = TfidfTransformer(use_idf=True).fit(X_train_counts)
X_train_tf = tf_transformer.transform(X_train_counts)
X_train_tf = np.round(X_train_tf.toarray()*6).astype(int) # Multiply by 5 is better than 100

test_dataset.feature = [str(i) for i in X_train_tf.tolist()]
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# Saving the sample submission
test_dataset.to_csv(os.path.join(AICROWD_OUTPUTS_PATH,'submission.csv'), index=False)

Submit to AIcrowd 🚀

Note : Please save the notebook before submitting it (Ctrl + S)

In [ ]:
aicrowd -v notebook submit \
    --assets-dir $AICROWD_ASSETS_DIR \
    --challenge nlp-feature-engineering

Congratulations πŸŽ‰!

Now you have an understanding of how to do simple feature engineering in NLP.
If you liked it please leave a like.

PS: The original notebook I copied it from is the getting-stated notebook by Shubhamaicrowd.

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