Icon

kn_​example_​python_​lists_​sets_​distance

Python - work with lists and sets - also: distances between sets and comparison

Python - work with lists and sets - also: distances between sets and comparison

import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom itertools import combinations# Define a list of sets and their corresponding IDsset_data = [ {'ID': 'Set 1', 'Set': {'apple', 'banana', 'orange', 'pear'}}, {'ID': 'Set 2', 'Set': {'banana', 'cherry', 'pear'}}, {'ID': 'Set 3', 'Set': {'cherry', 'durian'}}, {'ID': 'Set 4', 'Set': {'durian', 'elderberry', 'fig'}}, {'ID': 'Set 5', 'Set': {'cherry', 'durian'}}]# Extract the sets and their IDs from the set_data listsets = [d['Set'] for d in set_data]set_ids = [d['ID'] for d in set_data]# Create a Pandas dataframe to store the setsset_df = pd.DataFrame(set_data)# Calculate the Jaccard distance and difference between each pair of setsdata = {'Set 1 ID': [], 'Set 1': [], 'Set 2 ID': [], 'Set 2': [], 'Jaccard Distance': [], 'Intersection': [], 'Set 1 Difference': [], 'Set 2 Difference': []}for i, s1 in enumerate(sets[:-1]): for j, s2 in enumerate(sets[i+1:], i+1): intersection = s1.intersection(s2) union = s1.union(s2) jaccard_distance = 1 - len(intersection) / len(union) s1_difference = s1.difference(s2) s2_difference = s2.difference(s1) data['Set 1 ID'].append(set_ids[i]) data['Set 1'].append(s1) data['Set 2 ID'].append(set_ids[j]) data['Set 2'].append(s2) data['Jaccard Distance'].append(jaccard_distance) data['Intersection'].append(intersection if intersection else None) data['Set 1 Difference'].append(s1_difference if s1_difference else None) data['Set 2 Difference'].append(s2_difference if s2_difference else None)df = pd.DataFrame(data)# Calculate the average Jaccard distance for all setsaverage_distance = df['Jaccard Distance'].mean()# Print the dataframes and the average Jaccard distanceprint("Set Data:")print(set_df)print("\nJaccard Distance Data:")print(df)print(f"\nAverage Jaccard Distance: {average_distance}")knio.output_tables[0] = knio.Table.from_pandas(set_df)knio.output_tables[1] = knio.Table.from_pandas(df) import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom itertools import combinationsinput_df = knio.input_tables[0].to_pandas()# Convert the 'Set' column to setsinput_df['Set'] = input_df['Set'].apply(set)# Convert sets to list format with IDsset_data = [{'ID': row['ID'], 'Set': row['Set']} for _, row in input_df.iterrows()]# Extract the sets and their IDs from the set_data listsets = [d['Set'] for d in set_data]set_ids = [d['ID'] for d in set_data]# Create a Pandas dataframe to store the setsset_df = pd.DataFrame(set_data)# Calculate the Jaccard distance and intersection between each pair of setsdata = {'Set 1 ID': [], 'Set 1': [], 'Set 2 ID': [], 'Set 2': [], 'Jaccard Distance': [], 'Intersection': []}for i, s1 in enumerate(sets[:-1]): for j, s2 in enumerate(sets[i+1:], i+1): intersection = s1.intersection(s2) union = s1.union(s2) jaccard_distance = 1 - len(intersection) / len(union) data['Set 1 ID'].append(set_ids[i]) data['Set 1'].append(s1) data['Set 2 ID'].append(set_ids[j]) data['Set 2'].append(s2) data['Jaccard Distance'].append(jaccard_distance) data['Intersection'].append(intersection if intersection else None)df = pd.DataFrame(data)# Calculate the average Jaccard distance for all setsaverage_distance = df['Jaccard Distance'].mean()# Print the dataframes and the average Jaccard distanceprint("Set Data:")print(set_df)print("\nJaccard Distance Data:")print(df)print(f"\nAverage Jaccard Distance: {average_distance}")knio.output_tables[0] = knio.Table.from_pandas(set_df)knio.output_tables[1] = knio.Table.from_pandas(df) Python - work with lists and sets - also: distances between sets and comparison compare tow sets 1compare tow sets 2set of setsList Collection of Stringsset of sets from List input asCollection of Stringscheck if the setcame thruunharmedresult_1.tableresult_2.tableset_1.table Python Script Python Script Python Script Python Script Table DifferenceFinder Table Writer Table Writer Table Writer import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom itertools import combinations# Define a list of sets and their corresponding IDsset_data = [ {'ID': 'Set 1', 'Set': {'apple', 'banana', 'orange', 'pear'}}, {'ID': 'Set 2', 'Set': {'banana', 'cherry', 'pear'}}, {'ID': 'Set 3', 'Set': {'cherry', 'durian'}}, {'ID': 'Set 4', 'Set': {'durian', 'elderberry', 'fig'}}, {'ID': 'Set 5', 'Set': {'cherry', 'durian'}}]# Extract the sets and their IDs from the set_data listsets = [d['Set'] for d in set_data]set_ids = [d['ID'] for d in set_data]# Create a Pandas dataframe to store the setsset_df = pd.DataFrame(set_data)# Calculate the Jaccard distance and difference between each pair of setsdata = {'Set 1 ID': [], 'Set 1': [], 'Set 2 ID': [], 'Set 2': [], 'Jaccard Distance': [], 'Intersection': [], 'Set 1 Difference': [], 'Set 2 Difference': []}for i, s1 in enumerate(sets[:-1]): for j, s2 in enumerate(sets[i+1:], i+1): intersection = s1.intersection(s2) union = s1.union(s2) jaccard_distance = 1 - len(intersection) / len(union) s1_difference = s1.difference(s2) s2_difference = s2.difference(s1) data['Set 1 ID'].append(set_ids[i]) data['Set 1'].append(s1) data['Set 2 ID'].append(set_ids[j]) data['Set 2'].append(s2) data['Jaccard Distance'].append(jaccard_distance) data['Intersection'].append(intersection if intersection else None) data['Set 1 Difference'].append(s1_difference if s1_difference else None) data['Set 2 Difference'].append(s2_difference if s2_difference else None)df = pd.DataFrame(data)# Calculate the average Jaccard distance for all setsaverage_distance = df['Jaccard Distance'].mean()# Print the dataframes and the average Jaccard distanceprint("Set Data:")print(set_df)print("\nJaccard Distance Data:")print(df)print(f"\nAverage Jaccard Distance: {average_distance}")knio.output_tables[0] = knio.Table.from_pandas(set_df)knio.output_tables[1] = knio.Table.from_pandas(df) import knime.scripting.io as knioimport numpy as npimport pandas as pdfrom itertools import combinationsinput_df = knio.input_tables[0].to_pandas()# Convert the 'Set' column to setsinput_df['Set'] = input_df['Set'].apply(set)# Convert sets to list format with IDsset_data = [{'ID': row['ID'], 'Set': row['Set']} for _, row in input_df.iterrows()]# Extract the sets and their IDs from the set_data listsets = [d['Set'] for d in set_data]set_ids = [d['ID'] for d in set_data]# Create a Pandas dataframe to store the setsset_df = pd.DataFrame(set_data)# Calculate the Jaccard distance and intersection between each pair of setsdata = {'Set 1 ID': [], 'Set 1': [], 'Set 2 ID': [], 'Set 2': [], 'Jaccard Distance': [], 'Intersection': []}for i, s1 in enumerate(sets[:-1]): for j, s2 in enumerate(sets[i+1:], i+1): intersection = s1.intersection(s2) union = s1.union(s2) jaccard_distance = 1 - len(intersection) / len(union) data['Set 1 ID'].append(set_ids[i]) data['Set 1'].append(s1) data['Set 2 ID'].append(set_ids[j]) data['Set 2'].append(s2) data['Jaccard Distance'].append(jaccard_distance) data['Intersection'].append(intersection if intersection else None)df = pd.DataFrame(data)# Calculate the average Jaccard distance for all setsaverage_distance = df['Jaccard Distance'].mean()# Print the dataframes and the average Jaccard distanceprint("Set Data:")print(set_df)print("\nJaccard Distance Data:")print(df)print(f"\nAverage Jaccard Distance: {average_distance}")knio.output_tables[0] = knio.Table.from_pandas(set_df)knio.output_tables[1] = knio.Table.from_pandas(df) Python - work with lists and sets - also: distances between sets and comparison compare tow sets 1compare tow sets 2set of setsList Collection of Stringsset of sets from List input asCollection of Stringscheck if the setcame thruunharmedresult_1.tableresult_2.tableset_1.table Python Script Python Script Python Script Python Script Table DifferenceFinder Table Writer Table Writer Table Writer

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: m_lauber
Created at: 2023-03-13
On NodePit since: 2023-03-14
Last update: 2024-11-21
Created with KNIME version: v4.7.1
Tags: ChatGPTjaccardPythonlistsetdistance

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!