Implemented Ball Tree using Haversine distance as mentioned in abstra…

…ctqqq#237

Thie implmentation does not utilize the SpatialQueries Trait. But can
be modified after somce discussion on how to handle the differeing
parameters
This does implement all the function utilized in the query_knn.py
There is demo code at the botton of basics.ipynb

There are also some Rust tests to test the functioning of the ball tree.
This ball tree was influenced by [this repo](https://github.com/grantslatton/ball-tree).

Cargo.toml

@@ -12,7 +12,9 @@ crate-type = ["cdylib"]
 [dependencies]
 pyo3 = {version = "*", features = ["abi3-py38", "extension-module"]}
 pyo3-polars = {version = "0.15", features = ["derive"]}
-polars = {version = "0.41.3", features = ["performant", "cse", "lazy", "parquet", "dtype-array", "diff", "array_count", "abs", "cross_join", "rank", "ndarray", "log", "cum_agg", "round_series", "nightly"]}
+polars = {version = "0.41.3", features = ["performant", "cse", "lazy", 
+"parquet", "dtype-array", "diff", "array_count", "abs", "cross_join", "rank", "ndarray", "log", 
+"cum_agg", "round_series", "nightly", "dtype-struct"]}
 num = "0.4.1"
 faer = {version = "0.19", features = ["nightly"]}
 faer-ext = {version = "0.2.0", features = ["ndarray"]}
@@ -43,3 +45,8 @@ jemallocator = { version = "0.5", features = ["disable_initial_exec_tls"] }
 codegen-units = 1
 strip = "symbols" 
 # lto = "fat"
+
+
+[dev-dependencies]
+rand = "0.8"
+rand_chacha = "0.3"

python/polars_ds/__init__.py

@@ -11,6 +11,7 @@
 from polars_ds.features import *  # noqa: F403
 from polars_ds.query_knn import *  # noqa: F403
 from polars_ds.query_linear import *  # noqa: F403
+from polars_ds.query_balltree import *  # noqa: F403
 
 
 __version__ = "0.6.0"