Source code for nlp_primitives.part_of_speech_count

import nltk
import numpy as np
import pandas as pd
from featuretools.primitives.base import TransformPrimitive
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Double, NaturalLanguage

from nlp_primitives.utilities import clean_tokens


[docs]class PartOfSpeechCount(TransformPrimitive): """Calculates the occurences of each different part of speech. Description: Given a list of strings, categorize each word in the string as a different part of speech, and return the total count for each of 15 different categories of speech. If a string is missing, return `NaN`. Examples: >>> x = ['He was eating cheese', ''] >>> part_of_speech_count = PartOfSpeechCount() >>> part_of_speech_count(x).tolist() [[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [1.0, 0.0], [0.0, 0.0]] """ name = "part_of_speech_count" input_types = [ColumnSchema(logical_type=NaturalLanguage)] return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"}) default_value = 0
[docs] def __init__(self): self.number_output_features = 15 self.n = 15
def get_function(self): types = [ "C", "D", "E", "F", "I", "J", "L", "M", "N", "P", "R", "T", "U", "V", "W", ] def part_of_speech_count(x): li = [] for el in x: if pd.isnull(el): li.append([np.nan] * 15) else: tags = nltk.pos_tag(clean_tokens(el)) fd = nltk.FreqDist([b[0] for (a, b) in tags]) li.append([float(fd[i]) for i in types]) li = (np.array(li).T).tolist() return pd.Series(li) return part_of_speech_count