kaggle

Useful Seaborn plots for data exploration

Types of plots:

  1. Multiple features histogram in single chart
  2. Diagonal Correlation Matrix
  3. Missing values Heat Map

Boston Housing prices dataset is used for 1, 2. Titanic Dataset for item 3.

Basic Python module import

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
% matplotlib inline

from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
df = pd.DataFrame(X, columns= boston.feature_names)

Multiple Histogram plots of numeric features

  • Stack the dataframe with all the features together. May consume significant memory if dataset have large number of features and observations.
numeric_features= df.select_dtypes(exclude=["object","bool"])
numeric_features = numeric_features.stack().reset_index().rename(columns = {"level_1":"Features",0:"Value"})
g = sns.FacetGrid(data =numeric_features, col="Features",  col_wrap=5, sharex=False, sharey=False)
g = g.map(sns.distplot, "Value", color ='blue')
plt.subplots_adjust(top=0.9)
plt.suptitle("Histograms of various features")

multiplehist

Diagonal Heat Map of Correlation Matrix

Reference: seaborn.pydata.org. Utilize the Seaborn heat map with masking of the upper diagonal.

f, ax = plt.subplots(figsize=(12, 12))
corr = df.select_dtypes(exclude=["object","bool"]).corr()

# TO display diagonal matrix instead of full matrix.
mask = np.zeros_like(corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True

# Generate a custom diverging colormap.
cmap = sns.diverging_palette(220, 10, as_cmap=True)

# Draw the heatmap with the mask and correct aspect ratio.
g = sns.heatmap(corr, mask=mask, cmap=cmap, vmax=1, center=0, annot=True, fmt='.2f',\
square=True, linewidths=.5, cbar_kws={"shrink": .5})

# plt.subplots_adjust(top=0.99)
plt.title("Diagonal Correlation HeatMap")

heatmap

Missing values Heat Map

Reference: Robin Kiplang’at github

dataset ='https://gist.githubusercontent.com/michhar/2dfd2de0d4f8727f873422c5d959fff5/raw/ff414a1bcfcba32481e4d4e8db578e55872a2ca1/titanic.csv'

titanic_df = pd.read_csv(dataset, sep='\t')
sns.heatmap(titanic_df.isnull(), yticklabels=False, cbar = False, cmap = 'viridis')

plt.title("Titanic Dataset Missing Data")

missingdata

 

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Google Colab import data, Specs, link Gsheets & link with Kaggle

Importing data to colab

  1. Direct import
    • from google.colab import files
      uploaded = files.upload()
    • import io
      df = pd.read_csv(io.BytesIO(uploaded[‘target.csv’]))
  2. Setup to use file from google drives
    • from google.colab import drive
      drive.mount(‘/content/drive’)
    • View list of files:
    • !ls “/content/drive/My Drive”
    • Note: In the notebook, click on the charcoal > on the top left of the notebook and click on Files, select the file and right click to “copy path”. Note the path must begin with “/content/xxx”

Hardware Spec for Colab

See link.

Linking with Google Sheets (reference from source)

# Step 1
!pip install --upgrade --quiet gspread

# Step 2
from google.colab import auth
auth.authenticate_user()

import gspread
from oauth2client.client import GoogleCredentials
gc = gspread.authorize(GoogleCredentials.get_application_default())

# Step 3
sh = gc.create('My spreadsheet')

worksheet = gc.open('My spreadsheet').sheet1

cell_list = worksheet.range('A1:C2')

import random
for cell in cell_list:
  cell.value = random.randint(1, 10)

worksheet.update_cells(cell_list)

Note: The google sheets is at the starting page of google Drive. Still figuring out the way to specify target directory.

Linking with Kaggle (eg. direct download and import Kaggle dataset)

  1. Retrieve API token from Kaggle (Kaggle–> accounts –> under AP,  hit “Create New API Token.”
  2. Save the token.json in Google Drive
  3. Run the following on colab to link with Kaggle
!pip install kaggle
!mkdir .kaggle
from googleapiclient.discovery import build
import io, os
from googleapiclient.http import MediaIoBaseDownload
from google.colab import auth

auth.authenticate_user()

drive_service = build('drive', 'v3')
results = drive_service.files().list(
        q="name = 'kaggle.json'", fields="files(id)").execute()
kaggle_api_key = results.get('files', [])

filename = "/content/.kaggle/kaggle.json"
os.makedirs(os.path.dirname(filename), exist_ok=True)

request = drive_service.files().get_media(fileId=kaggle_api_key[0]['id'])
fh = io.FileIO(filename, 'wb')
downloader = MediaIoBaseDownload(fh, request)
done = False
while done is False:
    status, done = downloader.next_chunk()
    print("Download %d%%." % int(status.progress() * 100))
os.chmod(filename, 600)

source: https://colab.research.google.com/drive/1JG6d49pAWpn4kF92c0Ko16gQV6hptAro#scrollTo=CSKDTkuLuTY3

!cp /content/.kaggle/kaggle.json ~/.kaggle/kaggle.json
!kaggle config set -n path -v{/content}

Testing

!kaggle datasets list

Downloading particular data set from Kaggle

  1. Under particular Kaggle competition, look under Data and get the API commands
  2. Eg. Some competition data set from Kaggle
  3. Commands copied from API: kaggle competitions download -c ndsc-advanced
  4. Modify the Command and run in Colab:
    • !kaggle competitions download -c ndsc-advanced -p /content
  5. Unzip the files:
    • !unzip \*.zip
  6. Open file with pandas:
    • import pandas as pd
      d = pd.read_csv(‘beauty_data_info_val_competition.csv’)

References:

  1. Setting Up Kaggle in Google Colab

Predict Product Attributes from Product Listings Part 2 – Pipelines & GridSearch

Further improvement on the Product Attributes Text Classifier

This is part 2 of the extracting attributes from product title with the following improvements or add on.

  1. Creating a more generic text cleaning function.
  2. Adding GridSearch for hyper parameters tuning.

Text Cleaning Function

I created a more generic text cleaning function that can accommodate various text data sets. This can use as a base function for text related problem set. The function, if enabled all options, will be able to perform the following:

  1. Converting all text to lowercase.
  2. Stripping html tags especially if data is scrapped from web.
  3. Replacing accented characters with closest English alphabets/characters.
  4. Removing special characters which includes punctuation. Digits may or may not be excluded depending on context. (Digits are not removed for this data set)
  5. Removing stop-words (simple vs detailed. If detailed, will tokenize words before removal else will use simple word replacement.
  6. Removing extra white spaces and newlines.
  7. Normalize text. This either refer to stemming or lemmatizing.

In this example, we only turn on:

  1. converting text to lowercase
  2. remove special characters (need to keep digits) and white spaces,
  3. do a simple stop words removal.

As mentioned in previous post, it is likely a seller would not include much stop words and will try to keep the title as concise as possible given the limited characters and also to make the title more relevant to search engine. As the text length is not too long, will skip normalizing text to save time.

# Text pre-processing modules
from bs4 import BeautifulSoup
import unidecode
import spacy, en_core_web_sm
nlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer
STOPWORDS = set(stopwords.words('english')) 

# Compile regular expression
SPEC_CHARS_REPLACE_BY_SPACE = re.compile('[/(){}\[\]\|@,;]')
SPEC_CHARS = re.compile(r'[^a-zA-z0-9\s]')
SPEC_CHARS_INCLUDE_DIGITS = re.compile(r'[^a-zA-z\s]')
EXTRA_NEWLINES = re.compile(r'[\r|\n|\r\n]+')

## Functions for text preprocessing, cleaning

def strip_htmltags(text):
    soup = BeautifulSoup(text,"lxml")
    return soup.get_text()

def replace_accented_chars(text):
    return unidecode.unidecode(text)

def stem_text(text):
    ps = PorterStemmer()
    modified_txt = ' '.join([ps.stem(word) for word in text.split()])
    return modified_txt    

def lemmatize(text):
    modified_text = nlp(text)
    return ' '.join([word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in modified_text])

def normalize(text, method='stem'):
    """ Text normalization to generate the root form of the inflected words.
        This is done by either "stem" or "lemmatize" the text as defined by the 'method' arguments.
        Note that using "lemmatize" will take much longer to run compared to "stem".
    """
    if method == 'stem':
        return stem_text(text)
    if method == 'lemmatize':
        return lemmatize(text)
    print('Please choose either "stem" or "lemmatize" method to normalize.')
    return text

def rm_special_chars(text, rm_digits=False):
    # remove & replace below special chars with space
    modified_txt = SPEC_CHARS_REPLACE_BY_SPACE.sub(' ', text)

    # remove rest of special chars, no replacing with space
    if rm_digits:
        return SPEC_CHARS_INCLUDE_DIGITS.sub('', modified_txt)
    else:
        return SPEC_CHARS.sub('', modified_txt)

def rm_extra_newlines_and_whitespace(text):
    # rm extra newlines
    modified_txt =  EXTRA_NEWLINES.sub(' ', text)

    # rm extra whitespaces
    return re.sub(r'\s+', ' ', modified_txt)

def rm_stopwords(text, simple=True):
    """ Remove stopwords using either the simple model with replacement.
        or using nltk.tokenize to split the words and replace each words. This will incur speed penalty.
    """
    if simple:
        return ' '.join(word for word in text.split() if word not in STOPWORDS)
    else:
        tokens = word_tokenize(text)
        tokens = [token.strip() for token in tokens]
        return ' '.join(word for word in tokens if word not in STOPWORDS)

def clean_text(raw_text, strip_html = True, replace_accented = True,
                normalize_text = True, normalize_methd = 'stem',
                remove_special_chars = True, remove_digits = True,
                remove_stopwords = True, rm_stopwords_simple_mode = True):

    """ The combined function for all the various preprocessing method.
        Keyword args:
            strip_html               : Remove html tags.
            replace_accented         : Convert accented characters to closest English characters.
            normalize_text           : Normalize text based on normalize_methd.
            normalize_methd          : "stem" or "lemmatize". Default "stem".
            remove_special_chars     : Remove special chars.
            remove_digits            : Remove digits/numeric as special characters.
            remove_stopwords         : Stopwords removal basedon NLTK corpus.
            rm_stopwords_simple_mode : skip tokenize before stopword removal. Speed up time.
    """

    text = raw_text.lower()

    if strip_html:
        text = strip_htmltags(text)
    if replace_accented:
        text = replace_accented_chars(text)
    if remove_special_chars:
        text = rm_special_chars(text, remove_digits)
    if normalize_text:
        text = normalize(text, normalize_methd)
    if remove_stopwords:
        text = rm_stopwords(text, rm_stopwords_simple_mode)

    text = rm_extra_newlines_and_whitespace(text)  

    return text

Grid Search for Hyper Parameters Tuning

Using pipelines, it is easy to incorporate the sklearn grid search to sweep through the various the hyper parameters and select the best value. Two main parameters tuning are:

  1. ngram range in CountVectorizer:
    • In the first part, we only looking a unigram or single word but there are some attributes that are identified by more than one word alone (eg 4G network, 32GB Memory etc) therefore we will sweep the ngram range to find the optimal range.
    • The larger the ngram range the more feature columns will be generated so it will be more memory consuming.
  2. alpha in SGDClassifier
    • This will affect the regularization term and the learning rate of the training model.

With the ngram range and alpha parameters sweep and the best value selected, we can see quite a significant improvement to the accuracy to all the attribute prediction compared to the first version. Most of the improvement comes from the ngram adjusted to (1,3), meaning account for trigram. This is within expectation as more attributes are described by more than one word.

# Prepare model -- Drop na and keep those with values
def get_X_Y_data(x_col, y_col):
    sub_df =  df[[x_col, y_col]]
    sub_df.head()
    sub_df = sub_df.dropna()
    return sub_df[x_col], sub_df[y_col]

# Model training & GridSearch
def generate_model(X, y, verbose = 1):

    text_vect_pipe = Pipeline([
                            ('vect', CountVectorizer()),
                            ('tfidf', TfidfTransformer())
                            ])

    pred_model = Pipeline([
                ('process', text_vect_pipe),
                ('clf', SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42, max_iter=5, tol=None))
               ])

    parameters = {}
    parameters['process__vect__ngram_range'] = [(0,1),(1,2),(1,3)]
    parameters['clf__loss'] = ["hinge"]
    parameters['clf__alpha'] = [5e-6,1e-5]

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state = 42)

    CV = GridSearchCV(pred_model, parameters)
    CV.fit(X_train, y_train)
    y_pred = CV.predict(X_test)

    print('accuracy %s' % accuracy_score(y_pred, y_test))
    print("=="*18)
    print()
    print("Details of GridSearch")

    if verbose:
        print('Best score and parameter combination = ')
        print(CV.best_score_)
        print(CV.best_params_)
        print()
        print("Grid scores on development set:")
        means = CV.cv_results_['mean_test_score']
        stds = CV.cv_results_['std_test_score']
        for mean, std, params in zip(means, stds, CV.cv_results_['params']):
            print("%0.3f (+/-%0.03f) for %r"
                  % (mean, std * 2, params))
        print("=="*18)
        print()

    return CV

X, y = get_X_Y_data('title1', 'Brand')
brand_model = generate_model(X, y)
print('='*29)

The full script is as below. The text cleaning function takes a large part of the code. Excluding the function, the additional of few lines of code for the grid search and pipeline can can bring a relatively significant accuracy improvement.

Next Actions

So far only text features are considered, the next part we will try adding numeric features to see if further improvement can be made.

See Also

  1. Predict Product Attributes from Product Listing Title — Text Feature Extraction and Classification

 

Predict Product Attributes From Product Listing Title — Text Feature Extraction and Classification

Extracting Attributes from Product Title and Image

This is a National (Singapore) Data Science Challenge organised by Shopee hosted on Kaggle. In the advanced category, the tasks is to extract a list of attributes from each product listing given product title and the accompanied image (a text and a image input). Training sets and full instructions are available in the Kaggle link. This is a short attempt of the problem which include the basic data exploration, data cleaning, feature extraction and classification.

Basic Data Exploration

While the project requirement have 3 main product categories, Beauty, Mobile, & Fashion, I will just focus on the Mobile data set. The two other categories will follow the same approach. For the mobile data set, the requirement is to extract the following attributes such as Brand, Phone Model, Camera, Phone Screen Size, Color Family.  A brief exploration of the training data set observed.

  1. Only title (text) & image (pic) available to predict the several attributes
    of the product.
  2. The attributes are already label-encoded.
  3. There are a lot of missing values particularly like Network Connections etc have more than 80% of data missing. This is quite expected as sellers unlikely to put some of these more obscured attributes in the title description while attributes like Brand and Model should have less missing data.

From seller’s perspective, seller will try to include as much information as possible in a
concise manner especially attributes like brands, models etc to make their posting relevant to search and stand out to the buyers. Using only image to extract attributes such as Brand and model might be difficult especially for mobile category where it is difficult to differentiate from pic even with human eye.

From the exploration, I planned the following steps.

  1. Using title (text) as main classification input and ignore images.
  2. Train and predict each attribute at a time.

Basic Data and Text Cleaning

There are some attributes Network Connections, Warranty Period which have large proportion of missing data. However, those attributes have majority of the observations having a certain attribute. In this case, those missing values are assigned with the mode of the training population (e.g. it is likely for Network Connections , most phones should be 4G etc). The attributes are also converted to integer for training purpose.

For the title, before extracting the numeric features, we perform cleaning on the data set. Since most users would highlight the most important feature in the product tile to make their product stand out and relevant, they would generally have omitted most of the stop words, most punctuation. and white spaces Hence for this data set, I will try minimal cleaning: change the title to lowercase and remove special characters. This can reduce a significant amount of time in text cleaning especially for large data set.

Data Cleaning and pipelines

For the advanced data extraction, I chose the Bag-Of-Word (BOW) model to generate the features from the text columns. In the BOW model, I use TF-IDF approach which computes the weighted frequency of each word in each title. For classification, SVM is chosen as the classifier. Pipe-lining makes it easy to streamline the whole text processing and attributes classification making it run on all the different attributes.

Below is the complete code running from extraction, cleaning to classification.

Further Improvement

This is the starting point of the project and take only a few lines of code to get it up and running for quick analysis.  I will improve the existing code by incorporate gridsearch for hyperparameters and expanding on the pipelines and features in the subsequent posts.

See Also

  1. Predict Product Attributes from Product Listings Part 2 – Pipelines & GridSearch