# -*- coding: utf-8 -*-
# Generated by Django 1.11.5 on 2017-11-27 09:42
from __future__ import unicode_literals

from django.db import migrations, models


class Migration(migrations.Migration):

    dependencies = [
        ('spam_filtering', '0002_auto_20171127_0816'),
    ]

    operations = [
        migrations.RemoveField(
            model_name='spamfilter',
            name='bow_ngram_range',
        ),
        migrations.AddField(
            model_name='spamfilter',
            name='bow_ngram_range_max',
            field=models.SmallIntegerField(default=1, help_text='The upper boundary of the range of n-values for different n-grams to be extracted (comma-separated). All values of n such that min_n <= n <= max_n will be used.', verbose_name='BoW n-gram Range - Max'),
        ),
        migrations.AddField(
            model_name='spamfilter',
            name='bow_ngram_range_min',
            field=models.SmallIntegerField(default=1, help_text='The lower boundary of the range of n-values for different n-grams to be extracted (comma-separated). All values of n such that min_n <= n <= max_n will be used.', verbose_name='BoW n-gram Range - Min'),
        ),
        migrations.AddField(
            model_name='spamfilter',
            name='bow_use_tf_idf',
            field=models.BooleanField(default=False, help_text='If True, all non zero counts are set to 1. This is useful for discrete probabilistic models that model binary events rather than integer counts.', verbose_name='BoW Binary'),
        ),
        migrations.AlterField(
            model_name='spamfilter',
            name='bow_stop_words',
            field=models.TextField(blank=True, default=None, help_text='If ‘english’, a built-in stop word list for English is used. If a comma-separated string, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if analyzer == ´word´. If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms.', null=True, verbose_name='BoW Stop Words'),
        ),
    ]
