Nearest Neighbors

The Turi Create nearest neighbors toolkit is used to find the rows in a data table that are most similar to a query row. This is a two-stage process, analogous to many other Turi Create toolkits. First we create a NearestNeighborsModel, using a reference dataset contained in an SFrame. Next we query the model, using either the query or the similarity_graph method. Each of these methods is explained further below.

For this chapter we use an example dataset of house attributes and prices:

import turicreate as tc
sf.head(5)
+------+---------+------+--------+------+-------+
| tax  | bedroom | bath | price  | size |  lot  |
+------+---------+------+--------+------+-------+
| 590  |    2    | 1.0  | 50000  | 770  | 22100 |
| 1050 |    3    | 2.0  | 85000  | 1410 | 12000 |
|  20  |    3    | 1.0  | 22500  | 1060 |  3500 |
| 870  |    2    | 2.0  | 90000  | 1300 | 17500 |
| 1320 |    3    | 2.0  | 133000 | 1500 | 30000 |
+------+---------+------+--------+------+-------+
[5 rows x 6 columns]

Because the features in this dataset have very different scales (e.g. price is in the hundreds of thousands while the number of bedrooms is in the single digits), it is important to normalize the features. In this example we standardize so that each feature is measured in terms of standard deviations from the mean (see Wikipedia for more detail). In addition, both reference and query datasets may have a column with row labels, but for this example we let the model default to using row indices as labels.

for c in sf.column_names():
sf[c] = (sf[c] - sf[c].mean()) / sf[c].std()

First, we create a nearest neighbors model. We can list specific features to use in our distance computations, or default to using all features in the reference SFrame. In the model summary below the following code snippet, note that there are three features, because our second command specifies three numeric SFrame columns as features for the model. There are also three unpacked features, because each feature is in its own column.

model = tc.nearest_neighbors.create(sf)
model = tc.nearest_neighbors.create(sf, features=['bedroom', 'bath', 'size'])
model.summary()
Class                         : NearestNeighborsModel

Attributes
----------
Distance                      : euclidean
Method                        : ball tree
Number of examples            : 15
Number of feature columns     : 3
Number of unpacked features   : 3
Total training time (seconds) : 0.0091

Ball Tree Attributes
--------------------
Tree depth                    : 1
Leaf size                     : 1000

To retrieve the five closest neighbors for new data points or a subset of the original reference data, we query the model with the query method. Query points must also be contained in an SFrame, and must have columns with the same names as those used to construct the model (additional columns are allowed, but ignored). The result of the query method is an SFrame with four columns: query label, reference label, distance, and rank of the reference point among the query point's nearest neighbors.

knn = model.query(sf[:5], k=5)
knn.head()
+-------------+-----------------+----------------+------+
| query_label | reference_label |    distance    | rank |
+-------------+-----------------+----------------+------+
|      0      |        0        |      0.0       |  1   |
|      0      |        5        | 0.100742954001 |  2   |
|      0      |        7        | 0.805943632008 |  3   |
|      0      |        10       | 1.82070683014  |  4   |
|      0      |        2        | 1.83900997922  |  5   |
|      1      |        1        |      0.0       |  1   |
|      1      |        8        | 0.181337317202 |  2   |
|      1      |        4        | 0.181337317202 |  3   |
|      1      |        11       | 0.322377452803 |  4   |
|      1      |        12       | 0.705200678007 |  5   |
+-------------+-----------------+----------------+------+
[10 rows x 4 columns]

In some cases the query dataset is the reference dataset. For this task of constructing the similarity_graph on the reference data, the model's similarity_graph can be used. For brute force models it can be almost twice as fast, depending on the data sparsity and chosen distance function. By default, the similarity_graph method returns an SGraph whose vertices are the rows of the reference dataset and whose edges indicate a nearest neighbor match. Specifically, the destination vertex of an edge is a nearest neighbor of the source vertex. similarity_graph can also return results in the same form as the query method if so desired.

sim_graph = model.similarity_graph(k=3)

Distance functions

The most critical choice in computing nearest neighbors is the distance function that measures the dissimilarity between any pair of observations.

For numeric data, the options are euclidean, manhattan, cosine, and transformed_dot_product. For data in dictionary format (i.e. sparse data), jaccard and weighted_jaccard are also options, in addition to the numeric distances. For string features, use levenshtein distance, or use the text analytics toolkit's count_ngrams feature to convert strings to dictionaries of words or character shingles, then use Jaccard or weighted Jaccard distance. Leaving the distance parameter set to its default value of auto tells the model to choose the most reasonable distance based on the type of features in the reference data. In the following output cell, the second line of the model summary confirms our choice of Manhattan distance.

model = tc.nearest_neighbors.create(sf, features=['bedroom', 'bath', 'size'],
distance='manhattan')
model.summary()
Class                         : NearestNeighborsModel

Attributes
----------
Distance                      : manhattan
Method                        : ball tree
Number of examples            : 15
Number of feature columns     : 3
Number of unpacked features   : 3
Total training time (seconds) : 0.013

Ball Tree Attributes
--------------------
Tree depth                    : 1
Leaf size                     : 1000

Distance functions are also exposed in the turicreate.distances module. This allows us not only to specify the distance argument for a nearest neighbors model as a distance function (rather than a string), but also to use that function for any other purpose.

In the following snippet we use a nearest neighbors model to find the closest reference points to the first three rows of our dataset, then confirm the results by computing a couple of the distances manually with the Manhattan distance function.

model = tc.nearest_neighbors.create(sf, features=['bedroom', 'bath', 'size'],
distance=tc.distances.manhattan)
knn = model.query(sf[:3], k=3)
knn.print_rows()

sf_check = sf[['bedroom', 'bath', 'size']]
print("distance check 1:", tc.distances.manhattan(sf_check[2], sf_check[10]))
print("distance check 2:", tc.distances.manhattan(sf_check[2], sf_check[14]))
+-------------+-----------------+-----------------+------+
| query_label | reference_label |     distance    | rank |
+-------------+-----------------+-----------------+------+
|      0      |        0        |       0.0       |  1   |
|      0      |        5        |  0.100742954001 |  2   |
|      0      |        7        |  0.805943632008 |  3   |
|      1      |        1        |       0.0       |  1   |
|      1      |        8        |  0.181337317202 |  2   |
|      1      |        4        |  0.181337317202 |  3   |
|      2      |        2        |       0.0       |  1   |
|      2      |        10       | 0.0604457724006 |  2   |
|      2      |        14       |  1.61656820464  |  3   |
+-------------+-----------------+-----------------+------+
[9 rows x 4 columns

distance check 1: 0.0604457724006
distance check 2: 1.61656820464

Turi Create also allows composite distances, which allow the nearest neighbors tool (and other distance-based tools) to work with features that have different types. Specifically, a composite distance is a weighted sum of standard distances applied to subsets of features, specified in the form of a Python list. Each element of a composite distance list contains three things:

1. a list or tuple of feature names
2. a standard distance name
3. a multiplier for the standard distance.

In our house price dataset, for example, suppose we want to measure the difference between numbers of bedrooms and baths with Manhattan distance and the difference between house and lot sizes with Euclidean distance. In addition, we want the Euclidean component to carry twice as much weight. The composite distance for this would be:

my_dist = [[['bedroom', 'bath'], 'manhattan', 1],
[['size', 'lot'], 'euclidean', 2]]

This list can be passed to the distance parameter just like a standard distance function name or handle.

model = tc.nearest_neighbors.create(sf, distance=my_dist)
model.summary()
Class                         : NearestNeighborsModel

Attributes
----------
Method                        : brute_force
Number of distance components : 2
Number of examples            : 15
Number of feature columns     : 4
Number of unpacked features   : 4
Total training time (seconds) : 0.0017

If we specify the distance parameter as auto, a composite distance is created where each type of feature is paired with the most appropriate distance function. Please see the documentation for the Turi Create distances module for more on composite distances.

Search methods

Another important choice in model creation is the method. The brute_force method computes the distance between a query point and each of the reference points, with a run time linear in the number of reference points. Creating a model with the ball_tree method takes more time, but leads to much faster queries by partitioning the reference data into successively smaller balls and searching only those that are relatively close to the query. The default method is auto which chooses a reasonable method based on both the feature types and the selected distance function. The method parameter is also specified when the model is created. The third row of the model summary confirms our choice to use the ball tree in the next example.

model = tc.nearest_neighbors.create(sf, features=['bedroom', 'bath', 'size'],
method='ball_tree', leaf_size=5)
model.summary()
Class                         : NearestNeighborsModel

Attributes
----------
Method                        : ball_tree
Number of distance components : 1
Number of examples            : 15
Number of feature columns     : 3
Number of unpacked features   : 3
Total training time (seconds) : 0.0253

Ball Tree Attributes
--------------------
Tree depth                    : 3
Leaf size                     : 5

If the ball tree is used, it's important to choose an appropriate value for the 'leaf_size' parameter, which controls how many observations are stored in each leaf of the ball tree. By default, this is set so that the tree is no more than 12 levels deep, but larger or smaller values may lead to quicker queries depending on the shape and dimension of the data. Our houses example only has 15 rows, so the leaf_size parameter (and the ball_tree method for that matter) are not too useful, but for illustration purposes we set the leaf size to 5 above.

Missing data

The reference dataset that is used to create the nearest neighbors model cannot have missing data. Please use the SFrame.fillna and SFrame.dropna utilities to preprocess missing values before creating a nearest neighbors model.

On the other hand, data passed to the query method can have missing data. For numeric columns, missing values are imputed to be the mean of the corresponding column in the reference dataset used to create the model. Missing values in string columns are imputed as empty strings.