NLP

Tokenization

• Splitting a string into segments and storing them in a list.

• Tokenization based on white space, a hyphen, or any punctuation.

Bag of words representation

• Count the numnber of times a particular token appears = bag of workds

• It counts the number of times a word was pulled out of t he bag.

• This approach discard information about word order.

A better approach is using n-grams.

# Import CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer

# Create the token pattern: TOKENS_ALPHANUMERIC
TOKENS_ALPHANUMERIC = '[A-Za-z0-9]+(?=\\s+)'

# Fill missing values in df.Position_Extra
df.Position_Extra.fillna('', inplace=True)

# Instantiate the CountVectorizer: vec_alphanumeric
vec_alphanumeric = CountVectorizer(token_pattern=TOKENS_ALPHANUMERIC)

# Fit to the data
vec_alphanumeric.fit(df.Position_Extra)

# Print the number of tokens and first 15 tokens
msg = "There are {} tokens in Position_Extra if we split on non-alpha numeric"
print(msg.format(len(vec_alphanumeric.get_feature_names())))
print(vec_alphanumeric.get_feature_names()[:15])

In this exercise, you'll complete the function definition combine_text_columns(). When completed, this function will convert all training text data in your DataFrame to a single string per row that can be passed to the vectorizer object and made into a bag-of-words using the .fit_transform() method.

# Define combine_text_columns()
def combine_text_columns(data_frame, to_drop=NUMERIC_COLUMNS + LABELS):
    """ converts all text in each row of data_frame to single vector """
    
    # Drop non-text columns that are in the df
    to_drop = set(to_drop) & set(data_frame.columns.tolist())
    text_data = data_frame.drop(to_drop, axis=1)
    
    # Replace nans with blanks
    text_data.fillna('', inplace=True)
    
    # Join all text items in a row that have a space in between
    return text_data.apply(lambda x: " ".join(x), axis=1)

Now you will use combine_text_columns to convert all training text data in your DataFrame to a single vector that can be passed to the vectorizer object and made into a bag-of-words using the .fit_transform()method.

# Import the CountVectorizer
from sklearn.feature_extraction.text import CountVectorizer

# Create the basic token pattern
TOKENS_BASIC = '\\S+(?=\\s+)'

# Create the alphanumeric token pattern
TOKENS_ALPHANUMERIC = '[A-Za-z0-9]+(?=\\s+)'

# Instantiate basic CountVectorizer: vec_basic
vec_basic = CountVectorizer(token_pattern=TOKENS_BASIC)

# Instantiate alphanumeric CountVectorizer: vec_alphanumeric
vec_alphanumeric = CountVectorizer(token_pattern=TOKENS_ALPHANUMERIC)

# Create the text vector
text_vector = combine_text_columns(df)

# Fit and transform vec_basic
vec_basic.fit_transform(text_vector)

# Print number of tokens of vec_basic
print("There are {} tokens in the dataset".format(len(vec_basic.get_feature_names())))

# Fit and transform vec_alphanumeric
vec_alphanumeric.fit_transform(text_vector)