Merge pull request #1 from menouarazib/dev

Merge dev to master

.github/workflows/unit_tests.yml

@@ -15,7 +15,7 @@ jobs:
     strategy:
       matrix:
         os: [ ubuntu-latest, windows-latest, macos-latest ]
-        python-version: [ "3.9", "3.10" ]
+        python-version: [ "3.9", "3.10", "3.11" ]
 
     steps:
       - uses: actions/checkout@v3
@@ -35,9 +35,9 @@ jobs:
       - name: Lint with ruff
         run: |
           # stop the build if there are Python syntax errors or undefined names
-          ruff --format=github --select=E9,F63,F7,F82 --target-version=py37 .
+          ruff --output-format=github --select=E9,F63,F7,F82 --target-version=py37 .
           # default set of ruff rules with GitHub Annotations
-          ruff --format=github --target-version=py37 .
+          ruff --output-format=github --target-version=py37 .
         continue-on-error: true
       - name: List files in workspace
         run: |

README.md

@@ -29,8 +29,8 @@ Secondly, it `does not require labeled datasets` where each point is
 labeled; instead, it only requires reference events defined as time
 points or intervals of time. Thirdly, it is designed to be `robust`
 through the use of a stacked ensemble learning metamodel that
-combines deep learning models, including classic feed-forward
-neural networks (FFNs) and state-of-the-art architectures like
+combines deep learning models, from classic feed-forward
+neural networks (FFNs) to the state-of-the-art architectures like
 transformers. By leveraging the collective strengths of multiple
 models, this ensemble approach can mitigate individual model
 weaknesses and biases, resulting in more robust predictions.

eventdetector_ts/optimization/event_extraction_pipeline.py

@@ -1,6 +1,6 @@
 from datetime import timedelta
 from math import ceil
-from typing import Tuple
+from typing import Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -20,8 +20,8 @@ class OptimizationData:
         - time_unit (TimeUnit): Unit of time used in the dataset.
         - true_events (pd.DataFrame): DataFrame to store true events.
         - predicted_op (np.ndarray): Array to store predicted outcomes.
-        - delta (int | float): The maximum time tolerance used to determine the correspondence between a predicted event
-                    and its actual counterpart in the true events.
+        - delta Union[int, float]: The maximum time tolerance used to determine the correspondence between a predicted 
+            event and its actual counterpart in the true events.
         - s_h (float): A step parameter for the peak height threshold h.
         - s_s (int): Step size in time unit for overlapping the partition.
         - w_s (int): Size in time unit of the overlapping partition.
@@ -34,7 +34,7 @@ class OptimizationData:
 
     def __init__(self, t_max: float, w_s: int, s_s: int,
                  s_h: float,
-                 delta: int | float,
+                 delta: Union[int, float],
                  output_dir: str, time_unit: TimeUnit):
         """
         Initializes the OptimizationData object.
@@ -44,8 +44,8 @@ def __init__(self, t_max: float, w_s: int, s_s: int,
             w_s (int): Size in time unit of the overalapping partition.
             s_s (int): Step size in time unit for overalapping the partition.
             s_h (float): A step parameter for the peak height threshold h.
-            delta (int | float): The maximum time tolerance used to determine the correspondence between a predicted event
-                    and its actual counterpart in the true events.
+            delta Union[int, float]: The maximum time tolerance used to determine the correspondence between a predicted
+                event and its actual counterpart in the true events.
             output_dir (str): The parent directory.
             time_unit (TimeUnit): Unit of time used in the dataset.
         """

eventdetector_ts/plotter/plotter.py

@@ -165,7 +165,7 @@ def plot_predicted_events(self) -> None:
                                        style=STYLE_PREDICTED)
             ax.add_patch(rect1)
 
-        for _, test_date in self.true_events[MIDDLE_EVENT_LABEL].iteritems():
+        for _, test_date in self.true_events[MIDDLE_EVENT_LABEL].items():
             rect1 = event_to_rectangle(event=test_date, width_events_s=self.width_events_s, time_unit=self.time_unit,
                                        color=COLOR_TRUE,
                                        style=STYLE_TRUE)