Adjusting the Nearest Neighbor Code to Return Multiple Neighbors

In this article, we will explore how to adjust the given code to return not only the nearest neighbor but also the second and third nearest neighbors. We will delve into the NearestNeighbors class from scikit-learn and explain its usage.

Introduction to NearestNeighbors

The NearestNeighbors class is a powerful tool in machine learning that allows us to find the k-nearest neighbors of a point in n-dimensional space. This class is particularly useful when working with data that has a large number of features, as it enables efficient nearest neighbor search and retrieval.

Understanding the Current Code

The provided code snippet demonstrates how to calculate the average distance of nearest neighbors using pandas DataFrames and scikit-learn’s NearestNeighbors class. The code first creates a DataFrame containing time series data for cars on the road. It then uses the NearestNeighbors class to find the nearest neighbor for each group of points with the same ’time’ value.

The output is a new DataFrame (nn_df) that contains information about the index and distance of each nearest neighbor, as well as the corresponding ‘car’ values from the original DataFrame.

Adjusting the Code for Multiple Nearest Neighbors

To adjust the code to return not only the nearest neighbor but also the second and third nearest neighbors, we need to modify the n_neighbors parameter in the NearestNeighbors class.

Modifying the n_neighbors Parameter

By default, the n_neighbors parameter is set to 2, which means that the algorithm will find the two nearest neighbors for each point. However, to return multiple nearest neighbors (in this case, three), we need to change this value to 4:

# Argument
n_neighbor = 4

# Indices
[point_itself, neighbor_1, neighbor_2, neighbor_3]

# Distances
[ 0, distance_1, distance_2, distance_3]

In the code snippet above, we set n_neighbors to 4. This will return the point itself as the first nearest neighbor (index 0), followed by the next three nearest neighbors (neighbor_1, neighbor_2, and neighbor_3). The distances associated with each of these neighbors will be returned in descending order.

Implementing Multiple Nearest Neighbors

To implement this change, we can modify our code to use a higher value for n_neighbors when calling the kneighbors method:

def nn(x):
    nbrs = NearestNeighbors(
        n_neighbors=4,
        algorithm='auto',
        metric='euclidean'
    ).fit(x)
    distances, indices = nbrs.kneighbors(x)
    return distances, indices

time = [0, 0, 0, 1, 1, 2, 2]
x = [216, 218, 217, 280, 290, 130, 132]
y = [13, 12, 12, 110, 109, 3, 56] 
car = [1, 2, 3, 1, 3, 4, 5]
df = pd.DataFrame({'time': time, 'x': x, 'y': y, 'car': car})

nns = df.drop('car', 1).groupby('time').apply(lambda x: nn(x.as_matrix()))

Returning Multiple Nearest Neighbors

We can modify the existing code to return multiple nearest neighbors by creating new columns in the nn_df DataFrame:

nn_rows = []
for i, nn_set in enumerate(nns):
    group = groups.get_group(i)
    for j, tup in enumerate(zip(nn_set[0], nn_set[1])):
        # Append row with all three nearest neighbor information
        if j < 3:
            nn_rows.append({
                'time': i,
                'car': group.iloc[j]['car'],
                'nearest_neighbour_1': group.iloc[tup[1][1]]['car'],
                'nearest_neighbour_2': group.iloc[tup[1][2]]['car'],
                'nearest_neighbour_3': group.iloc[tup[1][3]]['car'],
                # Append distance columns if needed
            })

nn_df = pd.DataFrame(nn_rows)

In the modified code snippet above, we create a new list nn_rows that contains dictionaries with information about each nearest neighbor. The first three indices in this dictionary correspond to the first three nearest neighbors (including itself). We can add additional distance columns as needed.

Conclusion

By adjusting the n_neighbors parameter and modifying our code, we have successfully implemented multiple nearest neighbors using scikit-learn’s NearestNeighbors class. This modification enables efficient nearest neighbor search and retrieval in high-dimensional space data.

Last modified on 2024-03-01