Improve feedback from the algorithm training process.

README.md

@@ -88,7 +88,6 @@ The module requires installation of [python 3.10](https://www.python.org/downloa
 |:--------:|:---------:|
 |  numpy   | ≥ 1.22.4  |
 |  scipy   |  ≥ 1.8.1  |
-|   tqdm   | ≥ 4.64.1  |
 |  numba   | ≥ 0.59.0  |
 
 </div>

aisp/base/core/_base.py

@@ -45,6 +45,6 @@ def get_params(self, deep: bool = True) -> dict:  # pylint: disable=W0613
         """
         return {
             key: getattr(self, key)
-            for key, _ in signature(self.__init__).parameters.items()
+            for key, _ in signature(type(self).__init__).parameters.items()
             if key != "self" and not key.startswith("_") and hasattr(self, key)
         }

aisp/csa/_ai_recognition_sys.py

@@ -9,12 +9,12 @@
 import numpy as np
 import numpy.typing as npt
 from scipy.spatial.distance import pdist
-from tqdm import tqdm
 
 from ._artificial_recognition_ball import _ARB
 from ..base import BaseClassifier
 from ..base.immune.cell import BCell
 from ..exceptions import ModelNotFittedError
+from ..utils.display import ProgressBar
 from ..utils.distance import hamming, compute_metric_distance, get_metric_code
 from ..utils.multiclass import predict_knn_affinity
 from ..utils.random import set_seed_numba
@@ -200,15 +200,14 @@ def fit(
 
         self.classes = np.unique(y)
         sample_index = self._slice_index_list_by_class(y)
-        progress = tqdm(
-            total=len(y),
-            postfix="\n",
-            disable=not verbose,
-            bar_format="{desc} ┇{bar}┇ {n}/{total} memory cells for each aᵢ",
+        progress = ProgressBar(
+            len(y),
+            "memory cells for each aᵢ",
+            verbose=verbose,
         )
         pool_cells_classes = {}
         for _class_ in self.classes:
-            progress.set_description_str(
+            progress.set_description(
                 f"Generating the memory cells for the {_class_} class:"
             )
 
@@ -238,9 +237,9 @@ def fit(
 
         progress.set_description(
             f"\033[92m✔ Set of memory cells for classes ({', '.join(map(str, self.classes))}) "
-            f"successfully generated\033[0m"
+            f"successfully generated\033[0m",
         )
-        progress.close()
+        progress.finish()
         self._cells_memory = pool_cells_classes
         self._all_class_cell_vectors = [
             (class_name, cell.vector)

aisp/ina/_ai_network.py

@@ -8,7 +8,6 @@
 import numpy.typing as npt
 from scipy.sparse.csgraph import minimum_spanning_tree, connected_components
 from scipy.spatial.distance import squareform, pdist, cdist
-from tqdm import tqdm
 
 from ..base import BaseClusterer
 from ..base.immune.cell import Cell
@@ -19,6 +18,7 @@
 )
 from ..base.immune.populations import generate_random_antibodies
 from ..exceptions import ModelNotFittedError
+from ..utils.display import ProgressBar
 from ..utils.distance import hamming, compute_metric_distance, get_metric_code
 from ..utils.multiclass import predict_knn_affinity
 from ..utils.random import set_seed_numba
@@ -235,11 +235,10 @@ def fit(self, X: Union[npt.NDArray, list], verbose: bool = True) -> AiNet:
         """
         X = self._prepare_features(X)
 
-        progress = tqdm(
-            total=self.max_iterations,
-            postfix="\n",
-            disable=not verbose,
-            bar_format="{desc} ┇{bar}┇ {n}/{total} total training iterations",
+        progress = ProgressBar(
+            self.max_iterations,
+            "total training iterations",
+            verbose=verbose
         )
 
         population_p = self._init_population_antibodies()
@@ -273,7 +272,7 @@ def fit(self, X: Union[npt.NDArray, list], verbose: bool = True) -> AiNet:
 
         parts.append(f"Antibody population size: {len(self._population_antibodies)}")
         progress.set_description(f"\033[92m{' | '.join(parts)}\033[0m")
-        progress.close()
+        progress.finish()
 
         return self
 

aisp/nsa/_binary_negative_selection.py

@@ -6,11 +6,11 @@
 
 import numpy as np
 import numpy.typing as npt
-from tqdm import tqdm
 
 from ._base import check_detector_bnsa_validity, bnsa_class_prediction
 from ..base import BaseClassifier
 from ..exceptions import MaxDiscardsReachedError, ModelNotFittedError
+from ..utils.display import ProgressBar
 from ..utils.sanitizers import sanitize_seed, sanitize_param
 from ..utils.validation import (
     check_array_type,
@@ -174,17 +174,16 @@ def fit(
         list_detectors_by_class: dict = {}
         sample_index: dict = self._slice_index_list_by_class(y)
 
-        progress = tqdm(
-            total=int(self.N * (len(self.classes))),
-            bar_format="{desc} ┇{bar}┇ {n}/{total} detectors",
-            postfix="\n",
-            disable=not verbose,
+        progress = ProgressBar(
+            int(self.N * (len(self.classes))),
+            "detectors",
+            verbose=verbose,
         )
 
         for _class_ in self.classes:
             valid_detectors_set: list = []
             discard_count: int = 0
-            progress.set_description_str(
+            progress.set_description(
                 f"Generating the detectors for the {_class_} class:"
             )
             x_class = X[sample_index[_class_]]
@@ -208,7 +207,7 @@ def fit(
             f"\033[92m✔ Non-self detectors for classes ({', '.join(map(str, self.classes))}) "
             f"successfully generated\033[0m"
         )
-        progress.close()
+        progress.finish()
         self._detectors = list_detectors_by_class
         self._detectors_stack = np.array(
             [np.stack(self._detectors[class_name]) for class_name in self.classes]

aisp/nsa/_negative_selection.py

@@ -6,12 +6,12 @@
 
 import numpy as np
 import numpy.typing as npt
-from tqdm import tqdm
 
 from ._base import check_detector_rnsa_validity
 from ..base import BaseClassifier
 from ..base.immune.cell import Detector
 from ..exceptions import MaxDiscardsReachedError, ModelNotFittedError
+from ..utils.display import ProgressBar
 from ..utils.distance import (
     min_distance_to_class_vectors,
     get_metric_code,
@@ -215,18 +215,17 @@ def fit(
         list_detectors_by_class = {}
         sample_index = self._slice_index_list_by_class(y)
 
-        progress = tqdm(
-            total=int(self.N * (len(self.classes))),
-            bar_format="{desc} ┇{bar}┇ {n}/{total} detectors",
-            postfix="\n",
-            disable=not verbose,
+        progress = ProgressBar(
+            int(self.N * (len(self.classes))),
+            "detectors",
+            verbose=verbose
         )
         for _class_ in self.classes:
             valid_detectors_set: List[Detector] = []
             discard_count = 0
             x_class = X[sample_index[_class_]]
 
-            progress.set_description_str(
+            progress.set_description(
                 f"Generating the detectors for the {_class_} class:"
             )
 
@@ -255,7 +254,7 @@ def fit(
             f"\033[92m✔ Non-self detectors for classes ({', '.join(map(str, self.classes))}) "
             f"successfully generated\033[0m"
         )
-        progress.close()
+        progress.finish()
 
         self._detectors = list_detectors_by_class
         return self

aisp/utils/display.py

@@ -73,7 +73,7 @@ def _border(
             f"{nl}{left}{middle.join(line * w for w in self.headers.values())}{right}"
         )
 
-    def get_header(self):
+    def get_header(self) -> str:
         """
         Generate the table header, including the top border, column headings, and separator line.
 
@@ -100,7 +100,7 @@ def get_header(self):
 
         return top + titles + sep
 
-    def get_row(self, values: Mapping[str, Union[str, int, float]]):
+    def get_row(self, values: Mapping[str, Union[str, int, float]]) -> str:
         """
         Generate a formatted row for the table data.
 
@@ -113,7 +113,7 @@ def get_row(self, values: Mapping[str, Union[str, int, float]]):
         Returns
         -------
         str
-        Formatted string of the table row.
+            Formatted string of the table row.
         """
         border = "|" if self._ascii_only else "│"
         row = (
@@ -126,7 +126,7 @@ def get_row(self, values: Mapping[str, Union[str, int, float]]):
 
         return row
 
-    def get_bottom(self, new_line: bool = False):
+    def get_bottom(self, new_line: bool = False) -> str:
         """
         Generate the table's bottom border.
 
@@ -192,3 +192,113 @@ def finish(self) -> None:
 
         print(self.get_bottom())
         print(f"Total time: {time.perf_counter() - self._start:.6f} seconds")
+
+
+class ProgressBar:
+    """Display a console progress bar to track an algorithm's progress.
+
+    This class builds a progress bar in the format `{description}{bar*slots}{suffix}` and,
+    when calling the `finish` method, print the final progress bar along with the total time
+    elapsed since the class was instance.
+
+    Parameters
+    ----------
+    total : int
+        Total number of iterations.
+    suffix : str, default=''
+        Optional text displayed after the progress bar.
+    description : str, default=''
+        Optional text displayed before the progress bar.
+    slots : int, default=10
+        Number of character slots used to represent the bar.
+    verbose : bool, default=True
+        If False, prints nothing to the terminal.
+
+    Raises
+    ------
+    ValueError
+        If the total or slots is less than or equal to zero.
+    """
+
+    def __init__(
+        self,
+        total: int,
+        suffix: str = '',
+        description: str = '',
+        slots: int = 10,
+        verbose: bool = True
+    ) -> None:
+        if slots <= 0:
+            raise ValueError("'slots' must be greater than zero.")
+        if total <= 0:
+            raise ValueError("'total' must be greater than zero.")
+
+        self.verbose: bool = verbose
+        self.suffix: str = suffix
+        self.slots: int = slots
+
+        self._fill_char, self._edge_char = (
+            ('█', '┇') if _supports_box_drawing() else ('#', '|')
+        )
+
+        self._total: int = total
+        self._current: int = 0
+        self._description: str = description
+
+        if self.verbose:
+            self._start: float = time.perf_counter()
+
+    def _print_bar(self) -> None:
+        """Print the current progress bar."""
+        sys.stdout.write(
+            f"\r\033[K{self._description} {self._get_bar()} {self.suffix}"
+        )
+        sys.stdout.flush()
+
+    def _get_bar(self) -> str:
+        """Build string representation of the progress bar."""
+        slot_quant = self.slots
+        edge = self._edge_char
+        filled = int((self._current / self._total) * slot_quant)
+        progress_bar = self._fill_char * filled + ' ' * (slot_quant - filled)
+        return f'{edge}{progress_bar}{edge} {self._current} / {self._total}'
+
+    def set_description(self, description: str) -> None:
+        """Update the description before the progress bar.
+
+        Parameters
+        ----------
+        description : str
+            New description.
+        """
+        self._description = description
+
+    def update(self, increment: int = 1) -> None:
+        """Increment the progress bar.
+
+        Parameters
+        ----------
+        increment : int, default=1
+            Number of completed iterations to add to the current progress.
+
+        Raises
+        ------
+        ValueError
+            If the increment is negative.
+        """
+        if increment < 0:
+            raise ValueError("'increment' must be non-negative.")
+
+        self._current = min(self._current + increment, self._total)
+        if not self.verbose:
+            return
+
+        self._print_bar()
+
+    def finish(self) -> None:
+        """End the progress display and print the total elapsed time."""
+        if not self.verbose:
+            return
+
+        self._print_bar()
+        print(f"\nTotal time: {time.perf_counter() - self._start:.6f} seconds")

docs/en/api/utils/display/README.md

@@ -21,3 +21,4 @@ Utility functions for displaying algorithm information.
 |------------------------------------------|-----------------------------------------------------------------------------|
 | [`TableFormatter`](./table-formatter.md) | Format tabular data into strings for display in the console.                |
 | [`ProgressTable`](./progress-table.md)   | Display a formatted table in the console to track the algorithm's progress. |
+| [`ProgressTable`](./progress-table.md)   | Display a console progress bar to track an algorithm's progress.            |