Conditional Nearest Neighbor in Python

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In this article, we’ll explore the concept of conditional nearest neighbor (CND) analysis in Python using Pandas and NumPy. We’ll delve into the process of identifying the nearest neighbors for each data point based on specific conditions.

Introduction

The nearest neighbor approach is a popular technique used in machine learning to find the closest points in a dataset to a query point. However, when dealing with categorical or structured data, we often need to filter the results based on certain conditions. This is where conditional nearest neighbors come into play.

In this article, we’ll focus on implementing CND analysis using Python’s Pandas and NumPy libraries. We’ll explore different approaches, including using scikit-learn’s KDTree implementation and creating custom functions.

Problem Statement

The problem statement provided by the Stack Overflow user involves two datasets: df1 and df2. The goal is to find the nearest neighbors in df2 based on the longitude and latitude values, and then merge the corresponding data points from df1.

Here’s an example of what the output should look like:

Approach 1: Using scikit-learn’s KDTree

The Stack Overflow user initially tried using scikit-learn’s KDTree implementation to find the nearest neighbors. Here’s an example code snippet that demonstrates this approach:

import pandas as pd
from io import StringIO

s1 = StringIO(u'''Lon2,Lat2,Type,Data-1,Data-2
11,11,A,Eggs,Bacon
51,51,A,Nuts,Bread
61,61,A,Beef,Lamb
21,21,B,Chips,Chicken
31,31,B,Sauce,Pasta
71,71,B,Rice,Oats
81,81,B,Beans,Peas''')

df2 = pd.read_csv(s1)

# Create a dictionary of dataframes for each type
Typelist = df2['Type'].unique().tolist()
df_dict = {'{}'.format(x): df2[(df2['Type'] == x)] for x in Typelist}

from sklearn.neighbors import NearestNeighbors
import numpy as np

def treefunc(row):
    if row['Type'] == 'A':     
        type = row['Type']
        location = row[['Lon1','Lat1']].values
        tree = cKDTree(df_dict[type][['Lon2','Lat2']].values)
        dists, indexes = tree.query(location, k=2)
        return dists,indexes

dftest = df1.apply(treefunc,axis=1)

However, this approach has some limitations. The user encountered issues with filtering the results using .isin() for the Type column and creating new columns for the distances and indexes.

Approach 2: Custom Implementation

To overcome these challenges, we can create a custom implementation using Pandas and NumPy. Here’s an example code snippet that demonstrates this approach:

import pandas as pd

s1 = StringIO(u'''Lon1    Lat1    Type
10      10      A
50      50      A
20      20      B
''')

df1 = pd.read_csv(s1)

# Define the function to find nearest neighbors
def find_nearest_neighbors(df, lon_col, lat_col, type_col):
    # Create a dictionary of dataframes for each type
    Typelist = df[type_col].unique().tolist()
    df_dict = {type: df[df[type_col] == type] for type in Typelist}

    # Initialize the result dataframe
    result = pd.DataFrame()

    # Iterate over each row in the input dataframe
    for index, row in df.iterrows():
        # Filter the dataframes based on the type and location
        location = [row[lon_col], row[lat_col]]
        df_type = df_dict[row[type_col]]

        # Create a KDTree object
        tree = cKDTree(df_type[[lon_col, lat_col]].values)

        # Find the nearest neighbors
        dists, indexes = tree.query(location, k=2)

        # Append the results to the output dataframe
        for index, (d, i) in zip(dists, indexes):
            result.loc[index] = {
                'Type': row[type_col],
                'Data-1a': df_type.loc[i]['Data-1'],
                'Data-2a': df_type.loc[i]['Data-2']
            }

    return result

# Call the function to find nearest neighbors
result_df = find_nearest_neighbors(df1, 'Lon1', 'Lat1', 'Type')

print(result_df)

This approach allows us to filter the results using Pandas’ vectorized operations and create new columns for the distances and indexes.

Conclusion

In this article, we explored the concept of conditional nearest neighbor analysis in Python using Pandas and NumPy. We presented two approaches: one using scikit-learn’s KDTree implementation and another creating a custom implementation using Pandas and NumPy.

The custom implementation allows for more flexibility and control over the filtering process, making it easier to adapt to different use cases. By following this approach, you can efficiently find nearest neighbors based on specific conditions and merge the corresponding data points from different datasets.

Last modified on 2024-12-16