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make max-words a real, documented parameter

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Former-commit-id: 178a8b1494
This commit is contained in:
Robyn Speer 2016-03-24 14:10:02 -04:00
parent 6344b38194
commit f493d0eec4
2 changed files with 8 additions and 5 deletions
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7
wordfreq_builder/wordfreq_builder/cli/merge_counts.py
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@ -2,10 +2,10 @@ from wordfreq_builder.word_counts import read_values, merge_counts, write_wordli
import argparse
def merge_lists(input_names, output_name, cutoff=0):
def merge_lists(input_names, output_name, cutoff=0, max_size=1000000):
count_dicts = []
for input_name in input_names:
values, total = read_values(input_name, cutoff=cutoff, max_size=1000000)
values, total = read_values(input_name, cutoff=cutoff, max_size=max_size)
count_dicts.append(values)
merged = merge_counts(count_dicts)
write_wordlist(merged, output_name)
@ -15,7 +15,8 @@ if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-o', '--output', help='filename to write the output to', default='combined-counts.csv')
parser.add_argument('-c', '--cutoff', type=int, default=0, help='minimum count to read from an input file')
parser.add_argument('-m', '--max-words', type=int, default=1000000, help='maximum number of words to read from each list')
parser.add_argument('inputs', help='names of input files to merge', nargs='+')
args = parser.parse_args()
merge_lists(args.inputs, args.output, cutoff=args.cutoff)
merge_lists(args.inputs, args.output, cutoff=args.cutoff, max_size=args.max_words)

6
wordfreq_builder/wordfreq_builder/word_counts.py
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@ -42,9 +42,11 @@ def read_values(filename, cutoff=0, max_size=1e8, lang=None):
a dictionary of values and the total of all values.
Only words with a value greater than or equal to `cutoff` are returned.
In addition, only up to `max_size` words are read.
If `cutoff` is greater than 0, the csv file must be sorted by value
in descending order.
If `cutoff` is greater than 0 or `max_size` is smaller than the list,
the csv file must be sorted by value in descending order, so that the
most frequent words are kept.
If `lang` is given, it will apply language-specific tokenization to the
words that it reads.