feat: add augurs-clustering crate with DBSCAN algorithm #100
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| Report | Wed, August 21, 2024 at 19:35:57 UTC |
| Project | augurs |
| Branch | 100/merge |
| Testbed | ubuntu-latest |
Click to view all benchmark results
| Benchmark | Latency | Latency Results nanoseconds (ns) | (Δ%) | Latency Upper Boundary nanoseconds (ns) | (%) |
|---|---|---|---|
| auto_fit/air_passengers | ✅ (view plot) | 1,893,600.00 (-0.91%) | 1,965,151.35 (96.36%) |
| dbscan | ✅ (view plot) | 1,659,300.00 (+17.75%) | 1,796,559.48 (92.36%) |
| distance_euclidean/None | ✅ (view plot) | 202,120.00 (-0.01%) | 202,704.63 (99.71%) |
| distance_euclidean/Some(10) | ✅ (view plot) | 15,607.00 (-2.05%) | 17,127.70 (91.12%) |
| distance_euclidean/Some(2) | ✅ (view plot) | 3,587.10 (+2.41%) | 3,588.48 (99.96%) |
| distance_euclidean/Some(20) | ✅ (view plot) | 31,401.00 (-0.39%) | 31,951.78 (98.28%) |
| distance_euclidean/Some(5) | ✅ (view plot) | 7,783.00 (-0.57%) | 7,920.91 (98.26%) |
| distance_euclidean/Some(50) | ✅ (view plot) | 75,451.00 (-0.20%) | 76,535.21 (98.58%) |
| distance_matrix_euclidean/window: Some(10), parallelize: false | ✅ (view plot) | 2,994,900,000.00 (-0.21%) | 3,016,233,475.48 (99.29%) |
| distance_matrix_euclidean/window: Some(10), parallelize: true | ✅ (view plot) | 2,994,900,000.00 (+31.90%) | 3,760,216,959.23 (79.65%) |
| distance_matrix_euclidean/window: Some(2), parallelize: false | ✅ (view plot) | 537,280,000.00 (+0.30%) | 543,438,068.04 (98.87%) |
| distance_matrix_euclidean/window: Some(2), parallelize: true | ✅ (view plot) | 536,900,000.00 (+27.25%) | 651,343,075.99 (82.43%) |
| fit/air_passengers | ✅ (view plot) | 423,880.00 (-2.18%) | 448,634.01 (94.48%) |
| forecast/air_passengers | ✅ (view plot) | 1,360.30 (-2.53%) | 1,466.70 (92.75%) |
| season_eight | ✅ (view plot) | 22,023.00 (-0.48%) | 22,858.72 (96.34%) |
| vic_elec | ✅ (view plot) | 39,214,000.00 (+0.62%) | 39,905,454.04 (98.27%) |
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Sequence Diagram(s)sequenceDiagram
participant User
participant Clustering
participant DBSCAN
participant DistanceMatrix
User->>Clustering: Create Dbscan instance
Clustering->>DBSCAN: Initialize with parameters
User->>Clustering: Call fit with DistanceMatrix
Clustering->>DistanceMatrix: Process input distance matrix
DBSCAN->>DistanceMatrix: Perform clustering
DBSCAN-->>Clustering: Return cluster assignments
Clustering-->>User: Output cluster results
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Actionable comments posted: 1
Outside diff range, codebase verification and nitpick comments (5)
crates/augurs-clustering/CHANGELOG.md (1)
9-9: Consider using "Others" instead of "Other".The LanguageTool suggests that the plural noun "others" might fit better in this context.
### Other - Add `augurs-clustering` crate + - Add `augurs-clustering` crateTools
LanguageTool
[misspelling] ~9-~9: It seems that the plural noun “others” fits better in this context.
Context: ...pec/v2.0.0.html). ## [Unreleased] ### Other - Addaugurs-clusteringcrate(OTHER_OTHERS)
crates/augurs-clustering/benches/dbscan.rs (1)
18-19: Consider parameterizing the DBSCAN parameters.The parameters
10.0and3are hardcoded. Consider parameterizing them to allow flexibility in benchmarking different configurations.let eps = 10.0; let min_points = 3; Dbscan::new(eps, min_points).fit(&distance_matrix);crates/augurs-clustering/README.md (3)
4-4: Add a comma for clarity.Consider adding a comma after "time series" for better readability.
Use this diff to improve the sentence:
This crate contains algorithms for clustering time series. -So far only DBSCAN is implemented, and the distance matrix must be passed directly. +So far, only DBSCAN is implemented, and the distance matrix must be passed directly.Tools
LanguageTool
[typographical] ~4-~4: It seems that a comma is missing.
Context: ...algorithms for clustering time series. So far only DBSCAN is implemented, and the dis...(SO_COMMA)
30-30: Correct the phrase for clarity.The phrase "based heavily on to the implementation" should be corrected to "based heavily on the implementation."
Use this diff to correct the phrase:
This implementation based heavily on to the implementation in [`linfa-clustering`] and [`scikit-learn`]. -This implementation based heavily on to the implementation in [`linfa-clustering`] and [`scikit-learn`]. +This implementation is based heavily on the implementation in [`linfa-clustering`] and [`scikit-learn`].Tools
LanguageTool
[uncategorized] ~30-~30: “to the” seems less likely than “the”.
Context: ...s This implementation based heavily on to the implementation in [linfa-clustering] ...(AI_HYDRA_LEO_CP_TO_THE_THE)
31-31: Correct the verb agreement.The verb "is" should be changed to "are" to match the plural subject "these."
Use this diff to correct the verb agreement:
The main difference between these is that we operate directly on the distance matrix rather than calculating -The main difference between these is that we operate directly on the distance matrix rather than calculating +The main difference between these are that we operate directly on the distance matrix rather than calculatingTools
LanguageTool
[grammar] ~31-~31: The verb ‘is’ is singular. Did you mean: “this is” or “these are”?
Context: ...it-learn`]. The main difference between these is that we operate directly on the distanc...(SINGULAR_VERB_AFTER_THESE_OR_THOSE)
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Files ignored due to path filters (1)
crates/augurs-clustering/data/dist.csvis excluded by!**/*.csv
Files selected for processing (18)
- .github/workflows/run_benchmarks.yaml (1 hunks)
- Cargo.toml (1 hunks)
- README.md (2 hunks)
- crates/augurs-clustering/CHANGELOG.md (1 hunks)
- crates/augurs-clustering/Cargo.toml (1 hunks)
- crates/augurs-clustering/LICENSE-APACHE (1 hunks)
- crates/augurs-clustering/LICENSE-MIT (1 hunks)
- crates/augurs-clustering/README.md (1 hunks)
- crates/augurs-clustering/benches/dbscan.rs (1 hunks)
- crates/augurs-clustering/src/lib.rs (1 hunks)
- crates/augurs-js/Cargo.toml (1 hunks)
- crates/augurs-js/src/clustering.rs (1 hunks)
- crates/augurs-js/src/dtw.rs (1 hunks)
- crates/augurs-js/src/lib.rs (1 hunks)
- crates/pyaugurs/Cargo.toml (1 hunks)
- crates/pyaugurs/src/clustering.rs (1 hunks)
- crates/pyaugurs/src/lib.rs (2 hunks)
- justfile (1 hunks)
Files skipped from review due to trivial changes (4)
- crates/augurs-clustering/Cargo.toml
- crates/augurs-clustering/LICENSE-APACHE
- crates/augurs-clustering/LICENSE-MIT
- justfile
Additional context used
LanguageTool
crates/augurs-clustering/CHANGELOG.md
[misspelling] ~9-~9: It seems that the plural noun “others” fits better in this context.
Context: ...pec/v2.0.0.html). ## [Unreleased] ### Other - Addaugurs-clusteringcrate(OTHER_OTHERS)
crates/augurs-clustering/README.md
[typographical] ~4-~4: It seems that a comma is missing.
Context: ...algorithms for clustering time series. So far only DBSCAN is implemented, and the dis...(SO_COMMA)
[uncategorized] ~30-~30: “to the” seems less likely than “the”.
Context: ...s This implementation based heavily on to the implementation in [linfa-clustering] ...(AI_HYDRA_LEO_CP_TO_THE_THE)
[grammar] ~31-~31: The verb ‘is’ is singular. Did you mean: “this is” or “these are”?
Context: ...it-learn`]. The main difference between these is that we operate directly on the distanc...(SINGULAR_VERB_AFTER_THESE_OR_THOSE)
Additional comments not posted (21)
crates/pyaugurs/Cargo.toml (1)
19-19: Dependency addition is consistent with workspace structure.The addition of
augurs-clustering.workspace = truealigns with the integration of the new crate and the workspace configuration.crates/augurs-js/Cargo.toml (1)
22-22: Addition ofaugurs-clusteringdependency approved.The inclusion of
augurs-clusteringas a dependency aligns with the PR objectives of integrating clustering functionality into the project.crates/augurs-js/src/clustering.rs (1)
1-49: Implementation of DBSCAN bindings approved.The implementation of the DBSCAN algorithm with JavaScript bindings is well-structured and aligns with the PR objectives. The use of
serde,tsify_next, andwasm_bindgenis appropriate for handling data and exposing Rust functionality to JavaScript.However, ensure that the
DistanceMatrixclass is correctly implemented and compatible with the DBSCAN algorithm.Run the following script to verify the implementation of
DistanceMatrix:Verification successful
DistanceMatrix Implementation Verified
The
DistanceMatrixstruct is correctly implemented and compatible with the DBSCAN algorithm. It provides necessary methods and is used consistently across the codebase.
DistanceMatrixis defined incrates/augurs-js/src/dtw.rsand wrapsaugurs_core::DistanceMatrix.- Methods like
inner()ensure compatibility with DBSCAN.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the implementation of `DistanceMatrix`. # Test: Search for the implementation of `DistanceMatrix`. Expect: Correct implementation and compatibility with DBSCAN. ast-grep --lang rust --pattern 'struct DistanceMatrix { $$$ }'Length of output: 64
Script:
#!/bin/bash # Search for the definition of the `DistanceMatrix` struct in the codebase. ast-grep --lang rust --pattern 'struct DistanceMatrix { $$$ }' # Search for any implementations of methods or traits for `DistanceMatrix`. ast-grep --lang rust --pattern 'impl DistanceMatrix { $$$ }' # Search for any usages of `DistanceMatrix` to understand its context and compatibility. rg 'DistanceMatrix' -A 5Length of output: 23945
Cargo.toml (1)
25-25: Dependency Addition Approved.The addition of
augurs-clusteringas a dependency aligns with the PR objectives and enhances the project's functionality..github/workflows/run_benchmarks.yaml (1)
40-40: Benchmark Command Enhancement Approved.The inclusion of
--all-featuresin the benchmarking command is a beneficial change, ensuring a comprehensive performance assessment.crates/augurs-js/src/lib.rs (1)
17-17: New Module Addition Approved.The addition of the
clusteringmodule expands the library's capabilities and aligns with the PR objectives.crates/pyaugurs/src/clustering.rs (3)
11-19: LGTM: Flexible input representation.The
InputDistanceMatrixenum provides a flexible way to represent distance matrices, supporting lists, numpy arrays, and augurs core distance matrices.
21-41: LGTM: Robust conversion implementation.The
TryFromimplementation effectively converts different input types into anaugurs_core::DistanceMatrix, with proper error handling.
50-92: LGTM: Well-structuredDbscanclass.The
Dbscanclass is well-implemented, providing clear methods for initialization and clustering. Ensure that the integration with the rest of the codebase is verified.Run the following script to verify the integration:
Verification successful
Dbscan class is well-integrated across the codebase.
The
Dbscanclass is utilized in various modules, including tests and benchmarks, and is part of both Python and JavaScript bindings. This indicates that it is effectively integrated and its functionality is being verified across different environments.
- Locations:
crates/augurs-clustering/src/lib.rs: Implementation and tests.crates/pyaugurs/src/clustering.rs: Python bindings.crates/augurs-js/src/clustering.rs: JavaScript bindings.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the integration of the `Dbscan` class in the codebase. # Test: Search for the usage of the `Dbscan` class. Expect: Proper integration and usage. rg --type rust -A 5 $'Dbscan'Length of output: 11195
crates/pyaugurs/src/lib.rs (2)
17-17: LGTM: New clustering module added.The
clusteringmodule has been successfully added, enhancing the library's functionality.
117-117: LGTM:Dbscanclass added to Python module.The
Dbscanclass is correctly added to the Python module, expanding the library's capabilities in clustering.README.md (1)
24-24: LGTM: Documentation foraugurs-clusteringadded.The README update clearly describes the new
augurs-clusteringmodule, enhancing the project's documentation.crates/augurs-js/src/dtw.rs (4)
83-84: Change toinnerfield type is appropriate.The change from
Vec<Vec<f64>>toaugurs_core::DistanceMatrixlikely enhances performance or functionality.
86-89: Addition ofinnermethod is appropriate.This method provides necessary encapsulation for accessing the underlying
augurs_core::DistanceMatrix.
93-94: Simplification offrommethod is appropriate.Directly assigning the
innerfield simplifies the conversion process.
100-100: Update tofrommethod is appropriate.Calling
into_inner()on theinnerfield reflects the new structure and ensures proper conversion.crates/augurs-clustering/src/lib.rs (5)
13-18: Definition ofDbscanstruct is appropriate.The fields
epsilonandmin_cluster_sizeare well-defined and relevant for the DBSCAN algorithm.
20-33: Initialization methodnewis appropriate.The method correctly initializes the
Dbscanstruct with the provided parameters.
47-99: Implementation offitmethod is robust.The method effectively implements the DBSCAN clustering algorithm, handling clustering and noise identification.
101-111: Implementation offind_neighboursmethod is efficient.The method efficiently identifies neighbors within the specified
epsilondistance.
114-192: Test module is comprehensive.The tests cover various scenarios for the DBSCAN algorithm, ensuring robustness.
This PR adds a new crate,
augurs-clustering, which adds time series clustering functionality using the DBSCAN algorithm.Summary by CodeRabbit
New Features
Documentation
augurs-clusteringmodule and its functionality.augurs-clusteringcrate.Chores