Text Compare¶
Documentation¶
- Class name:
Text Compare - Category:
WAS Suite/Text/Search - Output node:
False
The Text Compare node is designed to compare two text strings, evaluating their similarity or difference based on a specified mode and tolerance level. It abstractly measures how closely the texts match or diverge, providing a boolean result, a numerical score, and a comparison text as outputs.
Input types¶
Required¶
text_a- The first text string to be compared. Its content is crucial for the comparison process, influencing the outcome of the similarity or difference evaluation.
- Comfy dtype:
STRING - Python dtype:
str
text_b- The second text string to be compared against the first. The comparison results are directly affected by its content, playing a key role in determining the similarity or difference.
- Comfy dtype:
STRING - Python dtype:
str
mode- Specifies the mode of comparison: 'similarity' to evaluate how similar the texts are, or 'difference' to assess how they diverge. This choice dictates the nature of the comparison.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
tolerance- A threshold value that defines the level of tolerance for differences between the texts. It influences the sensitivity of the comparison, affecting the similarity or difference score.
- Comfy dtype:
FLOAT - Python dtype:
float
Output types¶
TEXT_A_PASS- Comfy dtype:
STRING - The text string from 'text_a' that is evaluated in the comparison process.
- Python dtype:
str
- Comfy dtype:
TEXT_B_PASS- Comfy dtype:
STRING - The text string from 'text_b' that is evaluated in the comparison process.
- Python dtype:
str
- Comfy dtype:
BOOLEAN- Comfy dtype:
BOOLEAN - A boolean value indicating whether the texts are considered similar or different according to the specified mode and tolerance.
- Python dtype:
bool
- Comfy dtype:
SCORE_NUMBER- Comfy dtype:
NUMBER - A numerical score representing the degree of similarity or difference between the two text strings.
- Python dtype:
float
- Comfy dtype:
COMPARISON_TEXT- Comfy dtype:
STRING - A text output providing detailed comparison results or insights.
- Python dtype:
str
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class WAS_Text_Compare:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text_a": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
"text_b": (TEXT_TYPE, {"forceInput": (True if TEXT_TYPE == 'STRING' else False)}),
"mode": (["similarity","difference"],),
"tolerance": ("FLOAT", {"default":0.0,"min":0.0,"max":1.0,"step":0.01}),
}
}
RETURN_TYPES = (TEXT_TYPE,TEXT_TYPE,"BOOLEAN","NUMBER",TEXT_TYPE)
RETURN_NAMES = ("TEXT_A_PASS","TEXT_B_PASS","BOOLEAN","SCORE_NUMBER","COMPARISON_TEXT")
FUNCTION = "text_compare"
CATEGORY = "WAS Suite/Text/Search"
def text_compare(self, text_a='', text_b='', mode='similarity', tolerance=0.0):
boolean = ( 1 if text_a == text_b else 0 )
sim = self.string_compare(text_a, text_b, tolerance, ( True if mode == 'difference' else False ))
score = float(sim[0])
sim_result = ' '.join(sim[1][::-1])
sim_result = ' '.join(sim_result.split())
return (text_a, text_b, bool(boolean), score, sim_result)
def string_compare(self, str1, str2, threshold=1.0, difference_mode=False):
m = len(str1)
n = len(str2)
if difference_mode:
dp = [[0 for x in range(n+1)] for x in range(m+1)]
for i in range(m+1):
for j in range(n+1):
if i == 0:
dp[i][j] = j
elif j == 0:
dp[i][j] = i
elif str1[i-1] == str2[j-1]:
dp[i][j] = dp[i-1][j-1]
else:
dp[i][j] = 1 + min(dp[i][j-1], # Insert
dp[i-1][j], # Remove
dp[i-1][j-1]) # Replace
diff_indices = []
i, j = m, n
while i > 0 and j > 0:
if str1[i-1] == str2[j-1]:
i -= 1
j -= 1
else:
diff_indices.append(i-1)
i, j = min((i, j-1), (i-1, j))
diff_indices.reverse()
words = []
start_idx = 0
for i in diff_indices:
if str1[i] == " ":
words.append(str1[start_idx:i])
start_idx = i+1
words.append(str1[start_idx:m])
difference_score = 1 - ((dp[m][n] - len(words)) / max(m, n))
return (difference_score, words[::-1])
else:
dp = [[0 for x in range(n+1)] for x in range(m+1)]
similar_words = set()
for i in range(m+1):
for j in range(n+1):
if i == 0:
dp[i][j] = j
elif j == 0:
dp[i][j] = i
elif str1[i-1] == str2[j-1]:
dp[i][j] = dp[i-1][j-1]
if i > 1 and j > 1 and str1[i-2] == ' ' and str2[j-2] == ' ':
word1_start = i-2
word2_start = j-2
while word1_start > 0 and str1[word1_start-1] != " ":
word1_start -= 1
while word2_start > 0 and str2[word2_start-1] != " ":
word2_start -= 1
word1 = str1[word1_start:i-1]
word2 = str2[word2_start:j-1]
if word1 in str2 or word2 in str1:
if word1 not in similar_words:
similar_words.add(word1)
if word2 not in similar_words:
similar_words.add(word2)
else:
dp[i][j] = 1 + min(dp[i][j-1], # Insert
dp[i-1][j], # Remove
dp[i-1][j-1]) # Replace
if dp[i][j] <= threshold and i > 0 and j > 0:
word1_start = max(0, i-dp[i][j])
word2_start = max(0, j-dp[i][j])
word1_end = i
word2_end = j
while word1_start > 0 and str1[word1_start-1] != " ":
word1_start -= 1
while word2_start > 0 and str2[word2_start-1] != " ":
word2_start -= 1
while word1_end < m and str1[word1_end] != " ":
word1_end += 1
while word2_end < n and str2[word2_end] != " ":
word2_end += 1
word1 = str1[word1_start:word1_end]
word2 = str2[word2_start:word2_end]
if word1 in str2 or word2 in str1:
if word1 not in similar_words:
similar_words.add(word1)
if word2 not in similar_words:
similar_words.add(word2)
if(max(m,n) == 0):
similarity_score = 1
else:
similarity_score = 1 - (dp[m][n]/max(m,n))
return (similarity_score, list(similar_words))