Updated CI.yml

Author: Fek04

.github/workflows/CI.yml

@@ -158,7 +158,7 @@ jobs:
   publish:
     name: Publish to PyPI
     runs-on: ubuntu-latest
-    if: ${{ startsWith(github.ref, 'refs/tags/v') || github.event_name == 'workflow_dispatch' }}
+    if: ${{ startsWith(github.ref, 'refs/tags/v') || github.event_name == 'workflow_dispatch' || github.event_name == 'release' }}
     needs: [linux_wheels, macos_wheels, windows_wheels, sdist, rust_tests, python_tests, docs]
     steps:
       - uses: actions/download-artifact@v4
@@ -234,12 +234,12 @@ jobs:
 
   read_the_docs:
     name: Read the Docs trigger
-    needs: [docs, publish]
+    needs: [docs]
     runs-on: ubuntu-latest
     steps:
       - name: Trigger Read the Docs build
         run: |
-          curl -X POST -d "branches=main" -d "token=${{ secrets.READTHEDOCS_TOKEN }}" https://app.readthedocs.org/api/v2/webhook/ratspy/314741/
+          curl -X POST -d "branches=main" -d "token=${{ secrets.READTHEDOCS_TOKEN }}" https://app.readthedocs.org/api/v2/webhook/ratspy/314743/
 
   rust_tests:
     name: Build and test rats

cluster_tests/__pycache__/utils.cpython-39.pyc

@@ -0,0 +1,57 @@
+import pandas as pd
+from pathlib import Path
+from aeon.datasets import get_dataset_meta_data
+
+# Base results folder
+base = Path("results")
+
+all_data = []
+
+all_metadata = get_dataset_meta_data()
+all_metadata = all_metadata[all_metadata["Channels"] == 1]
+
+# Loop through each dataset folder
+for dataset_dir in sorted(base.iterdir()):
+    if not dataset_dir.is_dir():
+        continue
+    idx = int(dataset_dir.name)  # e.g. "0", "1", ...
+    
+    # File paths
+    time_file = dataset_dir / f"{idx}_time_benchmark.csv"
+    mem_file = dataset_dir / f"{idx}_memory_benchmark.csv"
+    
+    if time_file.is_file() is False or mem_file.is_file() is False:
+        print(f"Skipping {idx}, missing CSV files")
+        continue
+    
+    # Read the two CSVs
+    df_time = pd.read_csv(time_file)
+    df_mem = pd.read_csv(mem_file)
+    
+    # Merge time and memory by Augmenter
+    df = pd.merge(df_time, df_mem, on="Augmenter", how="outer")
+    
+    n_samples = all_metadata.iloc[idx]["TrainSize"] + all_metadata.iloc[idx]["TestSize"]
+    sample_length = all_metadata.iloc[idx]["Length"]
+    
+    # Flatten into single-row with column names indicating augmenter + metric
+    row = {"Index": idx, "Dataset": all_metadata.iloc[idx]["Dataset"], "NumSamples": n_samples, "SampleLength": sample_length}
+    for _, r in df.iterrows():
+        aug = r["Augmenter"]
+        for col in r.index:
+            if col == "Augmenter":
+                continue
+            row[f"{aug}_{col}"] = r[col]
+    
+    all_data.append(row)
+
+# Combine all datasets
+df_all = pd.DataFrame(all_data).sort_values("Index").reset_index(drop=True)
+
+df_all.drop(columns=["Index"], inplace=True)
+
+# Optionally save to CSV
+df_all.to_csv("aggregated_results.csv", index=False)
+
+print("Aggregated dataframe shape:", df_all.shape)
+print("Example columns:", df_all.columns[:10].tolist())

cluster_tests/dataframe.py

@@ -0,0 +1,48 @@
+{
+    "problemname": "adiac",
+    "timestamps": false,
+    "missing": false,
+    "univariate": true,
+    "equallength": true,
+    "classlabel": true,
+    "targetlabel": false,
+    "class_values": [
+        "1",
+        "2",
+        "3",
+        "4",
+        "5",
+        "6",
+        "7",
+        "8",
+        "9",
+        "10",
+        "11",
+        "12",
+        "13",
+        "14",
+        "15",
+        "16",
+        "17",
+        "18",
+        "19",
+        "20",
+        "21",
+        "22",
+        "23",
+        "24",
+        "25",
+        "26",
+        "27",
+        "28",
+        "29",
+        "30",
+        "31",
+        "32",
+        "33",
+        "34",
+        "35",
+        "36",
+        "37"
+    ]
+}

cluster_tests/results/0/0_meta.json

@@ -0,0 +1,5 @@
+Total datasets available: 150
+ Shape of X =  (781, 1, 176)
+ Meta data =  {'problemname': 'adiac', 'timestamps': False, 'missing': False, 'univariate': True, 'equallength': True, 'classlabel': True, 'targetlabel': False, 'class_values': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', '32', '33', '34', '35', '36', '37']}
+Saved dataset to ./data/0/0.csv
+Saved metadata to ./results/0/0_meta.json

cluster_tests/results/0/dataLoader.log

@@ -0,0 +1,29 @@
+Loading data from data/0/0.csv
+Augmenters:   0%|          | 0/17 [00:00<?, ?it/s]Repeat: RATSpy 245.07 MB, tsaug N/A MB
+Augmenters:  12%|█▏        | 2/17 [00:00<00:01, 13.03it/s]Scaling: RATSpy 134.32 MB, tsaug N/A MB
+Rotation: RATSpy 135.82 MB, tsaug N/A MB
+Augmenters:  24%|██▎       | 4/17 [00:00<00:01, 12.11it/s]Jittering: RATSpy 136.57 MB, tsaug 136.56640625 MB
+Drop: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Augmenters:  35%|███▌      | 6/17 [00:00<00:01, 10.13it/s]Crop: RATSpy 137.32 MB, tsaug 137.31640625 MB
+AmplitudePhasePerturbation: RATSpy 137.32 MB, tsaug N/A MB
+Augmenters:  47%|████▋     | 8/17 [00:00<00:00, 11.61it/s]FrequencyMask: RATSpy 137.32 MB, tsaug N/A MB
+Quantize: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Augmenters:  59%|█████▉    | 10/17 [00:00<00:00,  9.70it/s]Resize: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Reverse: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Augmenters:  71%|███████   | 12/17 [00:01<00:00, 10.73it/s]Permutate: RATSpy 137.32 MB, tsaug N/A MB
+AddNoise: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Augmenters:  82%|████████▏ | 14/17 [00:01<00:00, 11.04it/s]RandomTimeWarpAugmenter: RATSpy 137.32 MB, tsaug N/A MB
+Pool: RATSpy 137.32 MB, tsaug 137.31640625 MB
+Augmenters:  94%|█████████▍| 16/17 [00:01<00:00, 10.58it/s]Drift: RATSpy 137.32 MB, tsaug 138.06640625 MB
+Augmenters: 100%|██████████| 17/17 [00:01<00:00, 10.85it/s]
+Convolve: RATSpy 138.07 MB, tsaug 138.06640625 MB
+Running FFT...
+fft: RATSpy 138.07 MB, tsaug N/A
+Running IFFT...
+ifft: RATSpy 138.07 MB, tsaug N/A
+Running compare_within_tolerance...
+compare_within_tolerance: RATSpy 138.07 MB, tsaug N/A
+Running Batch Pipeline
+Pipeline_with_tsaug:   0%|          | 0/17 [00:00<?, ?it/s]Pipeline_with_tsaug: 100%|██████████| 17/17 [00:00<00:00, 7952.62it/s]
+Pipeline_with_tsaug: RATSpy 138.07 MB, tsaug 138.81640625 MB
+Benchmark results saved to results/0/0_memory_benchmark.csv

cluster_tests/results/0/memory.log

@@ -0,0 +1 @@
+Plots saved as results/0/plots/0_time_benchmark_bar.png, results/0/plots/0_memory_benchmark_bar.png, and results/0/plots/0_time_vs_memory_scatter.png

cluster_tests/results/0/plots.log

@@ -0,0 +1,29 @@
+Loading data from data/0/0.csv
+Running individual augmenter benchmarks:   0%|          | 0/17 [00:00<?, ?it/s]Running individual augmenter benchmarks: 100%|██████████| 17/17 [00:00<00:00, 255.19it/s]
+Repeat: RATSpy 0.0029s, tsaug N/A
+Scaling: RATSpy 0.0010s, tsaug N/A
+Rotation: RATSpy 0.0005s, tsaug N/A
+Jittering: RATSpy 0.0006s, tsaug 0.004163403995335102
+Drop: RATSpy 0.0008s, tsaug 0.0022167512215673923
+Crop: RATSpy 0.0007s, tsaug 0.0011537191458046436
+AmplitudePhasePerturbation: RATSpy 0.0115s, tsaug N/A
+FrequencyMask: RATSpy 0.0016s, tsaug N/A
+Quantize: RATSpy 0.0009s, tsaug 0.0022608120925724506
+Resize: RATSpy 0.0008s, tsaug 0.0011304216459393501
+Reverse: RATSpy 0.0008s, tsaug 0.0005421922542154789
+Permutate: RATSpy 0.0008s, tsaug N/A
+AddNoise: RATSpy 0.0008s, tsaug 0.003565932624042034
+RandomTimeWarpAugmenter: RATSpy 0.0009s, tsaug N/A
+Pool: RATSpy 0.0018s, tsaug 0.0025146109983325005
+Drift: RATSpy 0.0009s, tsaug 0.0034319711849093437
+Convolve: RATSpy 0.0010s, tsaug 0.002801113296300173
+Running FFT...
+fft: RATSpy 0.0008s, tsaug N/A
+Running IFFT...
+ifft: RATSpy 0.0006s, tsaug N/A
+Validating FFT and IFFT...
+compare_within_tolerance: RATSpy 0.0003s, max_diff=1.1102230246251565e-15, all_within=True
+Running Batch Pipeline_with_tsaug
+Pipeline_with_tsaug:   0%|          | 0/17 [00:00<?, ?it/s]Pipeline_with_tsaug: 100%|██████████| 17/17 [00:00<00:00, 8176.03it/s]
+Pipeline_with_tsaug: RATSpy 0.0048s, tsaug 0.02040749602019787
+Benchmark results saved to results/0/0_time_benchmark.csv

cluster_tests/results/0/time.log

@@ -0,0 +1,14 @@
+{
+    "problemname": "arrowhead",
+    "timestamps": false,
+    "missing": false,
+    "univariate": true,
+    "equallength": true,
+    "classlabel": true,
+    "targetlabel": false,
+    "class_values": [
+        "0",
+        "1",
+        "2"
+    ]
+}

cluster_tests/results/1/1_meta.json

@@ -0,0 +1,5 @@
+Total datasets available: 150
+ Shape of X =  (211, 1, 251)
+ Meta data =  {'problemname': 'arrowhead', 'timestamps': False, 'missing': False, 'univariate': True, 'equallength': True, 'classlabel': True, 'targetlabel': False, 'class_values': ['0', '1', '2']}
+Saved dataset to ./data/1/1.csv
+Saved metadata to ./results/1/1_meta.json