Merge pull request #259 from JiriPavela/lazy-loading

Add support for lazy loading and imports of some expensive subpackages and modules to speed up Perun startup time

.pre-commit-config.yaml

@@ -11,4 +11,4 @@ repos:
     rev: 24.8.0
     hooks:
     -   id: black
-        types: [file, python]
+        types_or: [python, pyi]

MANIFEST.in

@@ -0,0 +1,2 @@
+recursive-include perun *.pyi
+include perun/py.typed

docs/_static/templates/degradation_api.py

@@ -1,6 +1,6 @@
 """..."""
 
-from perun.utils.structs import DegradationInfo
+from perun.utils.structs.common_structs import DegradationInfo
 
 
 def my_degradation_checker(baseline_profile, target_profile):

docs/cli.rst

@@ -69,7 +69,7 @@ Perun Commands
 Collect Commands
 ----------------
 
-.. click:: perun.cli:collect
+.. click:: perun.cli_groups.collect_cli:collect
    :prog: perun collect
 
 .. _cli-collect-units-ref:

docs/degradation.rst

@@ -233,10 +233,10 @@ just small requirements and have to `yield` the reports about degradation as a i
 ``DegradationInfo`` objects specified as follows:
 
 .. currentmodule: perun.utils.structs
-.. autoclass::  perun.utils.structs.DegradationInfo
+.. autoclass::  perun.utils.structs.common_structs.DegradationInfo
    :members:
 
-.. autoclass::  perun.utils.structs.PerformanceChange
+.. autoclass::  perun.utils.structs.common_structs.PerformanceChange
    :members:
 
 You can register your new performance change checker as follows:

meson.build

@@ -18,6 +18,7 @@ perun_files = files(
     'LICENSE',
     'pyproject.toml',
     'tox.ini',
+    'MANIFEST.in',
 )
 
 perun_dir = 'perun'

perun/check/__init__.py

@@ -2,3 +2,7 @@
 
 Contains the actual methods in isolate modules, and then a factory module,
 containing helper and generic stuff."""
+
+import lazy_loader as lazy
+
+__getattr__, __dir__, __all__ = lazy.attach_stub(__name__, __file__)

perun/check/__init__.pyi

@@ -0,0 +1,21 @@
+from .detection_kit import (
+    create_filter_by_model as create_filter_by_model,
+    create_model_record as create_model_record,
+    get_filtered_best_models_of as get_filtered_best_models_of,
+    get_function_values as get_function_values,
+    general_detection as general_detection,
+)
+from .factory import (
+    pre_collect_profiles as pre_collect_profiles,
+    degradation_in_minor as degradation_in_minor,
+    degradation_in_history as degradation_in_history,
+    degradation_between_profiles as degradation_between_profiles,
+    run_degradation_check as run_degradation_check,
+    degradation_between_files as degradation_between_files,
+    is_rule_applicable_for as is_rule_applicable_for,
+    run_detection_with_strategy as run_detection_with_strategy,
+)
+from .nonparam_kit import (
+    classify_change as classify_change,
+    preprocess_nonparam_models as preprocess_nonparam_models,
+)

perun/check/detection_kit.py

@@ -21,7 +21,7 @@
 from perun.postprocess.regression_analysis import regression_models
 from perun.profile import query
 from perun.utils.common import common_kit
-from perun.utils.structs import (
+from perun.utils.structs.common_structs import (
     PerformanceChange,
     DegradationInfo,
     ModelRecord,

perun/check/factory.py

@@ -27,7 +27,7 @@
     polynomial_regression,
 )
 from perun.utils import decorators, log
-from perun.utils.structs import (
+from perun.utils.structs.common_structs import (
     DetectionChangeResult,
     DegradationInfo,
     PerformanceChange,
@@ -61,34 +61,6 @@ def __call__(
         """Call Function"""
 
 
-def get_supported_detection_models_strategies() -> list[str]:
-    """
-    Provides supported detection models strategies to execute
-    the degradation check between two profiles with different kinds
-    of models. The individual strategies represent the way of
-    executing the detection between profiles and their models:
-
-        - best-param: best parametric models from both profiles
-        - best-non-param: best non-parametric models from both profiles
-        - best-model: best models from both profiles
-        - all-param: all parametric models pair from both profiles
-        - all-non-param: all non-parametric models pair from both profiles
-        - all-models: all models pair from both profiles
-        - best-both: best parametric and non-parametric models from both profiles
-
-    :return: the names of all supported degradation models strategies
-    """
-    return [
-        "best-model",
-        "best-param",
-        "best-nonparam",
-        "all-param",
-        "all-nonparam",
-        "all-models",
-        "best-both",
-    ]
-
-
 def profiles_to_queue(
     minor_version: str,
 ) -> dict[tuple[str, str, str, str], ProfileInfo]:

perun/check/meson.build

@@ -2,6 +2,7 @@ perun_check_dir = perun_dir / 'check'
 
 perun_check_files = files(
     '__init__.py',
+    '__init__.pyi',
     'factory.py',
     'detection_kit.py',
     'nonparam_kit.py',

perun/check/methods/abstract_base_checker.py

@@ -16,7 +16,7 @@
 # Perun Imports
 if TYPE_CHECKING:
     from perun.profile.factory import Profile
-    from perun.utils.structs import DegradationInfo
+    from perun.utils.structs.common_structs import DegradationInfo
 
 
 class AbstractBaseChecker(ABC):

perun/check/methods/average_amount_threshold.py

@@ -46,7 +46,7 @@
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
 from perun.profile import convert
 from perun.utils.common import common_kit
-from perun.utils.structs import DegradationInfo, PerformanceChange
+from perun.utils.structs.common_structs import DegradationInfo, PerformanceChange
 
 if TYPE_CHECKING:
     from perun.profile.factory import Profile

perun/check/methods/best_model_order_equality.py

@@ -48,9 +48,9 @@
 # Third-Party Imports
 
 # Perun Imports
+from perun import check as check
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
-from perun.utils.structs import DegradationInfo, PerformanceChange
-import perun.check.detection_kit as detection
+from perun.utils.structs.common_structs import DegradationInfo, PerformanceChange
 
 if TYPE_CHECKING:
     from perun.profile.factory import Profile
@@ -81,10 +81,8 @@ def check(
         :param _: unification with other detection methods (unused in this method)
         :returns: tuple (degradation result, degradation location, degradation rate)
         """
-        best_baseline_models = detection.get_filtered_best_models_of(
-            baseline_profile, group="param"
-        )
-        best_target_models = detection.get_filtered_best_models_of(target_profile, group="param")
+        best_baseline_models = check.get_filtered_best_models_of(baseline_profile, group="param")
+        best_target_models = check.get_filtered_best_models_of(target_profile, group="param")
 
         for uid, best_model in best_target_models.items():
             best_baseline_model = best_baseline_models.get(uid)

perun/check/methods/exclusive_time_outliers.py

@@ -79,7 +79,7 @@
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
 from perun.logic import config
 from perun.profile import convert
-from perun.utils.structs import DegradationInfo, PerformanceChange
+from perun.utils.structs.common_structs import DegradationInfo, PerformanceChange
 
 if TYPE_CHECKING:
     from perun.profile.factory import Profile

perun/check/methods/fast_check.py

@@ -14,10 +14,10 @@
 import numpy as np
 
 # Perun Imports
+from perun import check as check
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
 from perun.logic import runner
-from perun.utils.structs import DegradationInfo, ClassificationMethod
-import perun.check.detection_kit as detect
+from perun.utils.structs.common_structs import DegradationInfo, ClassificationMethod
 
 if TYPE_CHECKING:
     from perun.profile.factory import Profile
@@ -35,7 +35,7 @@ def check(
         :param _: unification with other detection methods (unused in this method)
         :returns: tuple (degradation result, degradation location, degradation rate, confidence)
         """
-        return detect.general_detection(
+        return check.general_detection(
             baseline_profile, target_profile, ClassificationMethod.FastCheck
         )
 

perun/check/methods/integral_comparison.py

@@ -14,10 +14,10 @@
 
 # Perun Imports
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
-from perun.check import factory, nonparam_kit as nparam_helpers
+from perun import check as check
 from perun.postprocess.regression_analysis import regression_models
 from perun.utils.common import common_kit
-from perun.utils.structs import DegradationInfo, ModelRecord, DetectionChangeResult
+from perun.utils.structs.common_structs import DegradationInfo, ModelRecord, DetectionChangeResult
 
 if TYPE_CHECKING:
     from perun.profile.factory import Profile
@@ -95,7 +95,7 @@ def execute_analysis(
     :return: tuple with degradation info between a pair of models:
         (deg. result, deg. location, deg. rate, confidence type and rate, etc.)
     """
-    x_pts, baseline_y_pts, target_y_pts = nparam_helpers.preprocess_nonparam_models(
+    x_pts, baseline_y_pts, target_y_pts = check.preprocess_nonparam_models(
         uid, baseline_model, target_profile, target_model
     )
 
@@ -114,7 +114,7 @@ def execute_analysis(
         float(target_integral - baseline_integral), float(baseline_integral)
     )
 
-    change_info = nparam_helpers.classify_change(
+    change_info = check.classify_change(
         rel_error if np.isfinite(rel_error) else 0,
         _INTEGRATE_DIFF_NO_CHANGE,
         _INTEGRATE_DIFF_CHANGE,
@@ -142,7 +142,7 @@ def check(
         :param _: other kwgargs
         :returns: tuple - degradation result (structure DegradationInfo)
         """
-        for degradation_info in factory.run_detection_with_strategy(
+        for degradation_info in check.run_detection_with_strategy(
             execute_analysis, baseline_profile, target_profile, models_strategy
         ):
             yield degradation_info

perun/check/methods/linear_regression.py

@@ -14,11 +14,11 @@
 from scipy import stats
 
 # Perun Imports
-from perun.check import detection_kit as detect
+from perun import check as check
 from perun.check.methods import fast_check
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
 from perun.utils.common import common_kit
-from perun.utils.structs import DegradationInfo, ModelRecord, ClassificationMethod
+from perun.utils.structs.common_structs import DegradationInfo, ModelRecord, ClassificationMethod
 
 if TYPE_CHECKING:
     import numpy
@@ -40,7 +40,7 @@ def check(
         :returns: tuple (degradation result, degradation location, degradation rate, confidence)
         """
 
-        return detect.general_detection(
+        return check.general_detection(
             baseline_profile, target_profile, ClassificationMethod.LinearRegression
         )
 
@@ -107,15 +107,15 @@ def exec_linear_regression(
         uid, baseline_profile, baseline_x_pts, lin_abs_error
     )
     # obtaining the models (linear and quadratic) from the new regressed profile
-    quad_err_model = detect.get_filtered_best_models_of(
+    quad_err_model = check.get_filtered_best_models_of(
         std_err_profile,
         group="param",
-        model_filter=detect.create_filter_by_model("quadratic"),
+        model_filter=check.create_filter_by_model("quadratic"),
     )
-    linear_err_model = detect.get_filtered_best_models_of(
+    linear_err_model = check.get_filtered_best_models_of(
         std_err_profile,
         group="param",
-        model_filter=detect.create_filter_by_model("linear"),
+        model_filter=check.create_filter_by_model("linear"),
     )
 
     # check the last quadratic type of change
@@ -127,7 +127,7 @@ def exec_linear_regression(
 
     # We did not classify the change
     if not change_type:
-        std_err_model = detect.get_filtered_best_models_of(std_err_profile, group="param")
+        std_err_model = check.get_filtered_best_models_of(std_err_profile, group="param")
         change_type = std_err_model[uid].type
 
     return change_type

perun/check/methods/local_statistics.py

@@ -13,12 +13,11 @@
 from scipy import integrate
 
 # Perun Imports
-from perun.check import factory
+from perun import check as check
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
 from perun.profile.factory import Profile
 from perun.utils.common import common_kit
-from perun.utils.structs import DegradationInfo, ModelRecord, DetectionChangeResult
-import perun.check.nonparam_kit as nparam_helpers
+from perun.utils.structs.common_structs import DegradationInfo, ModelRecord, DetectionChangeResult
 
 if TYPE_CHECKING:
     import numpy.typing as npt
@@ -141,7 +140,7 @@ def classify_stats_diff(
     """
     # create vectorized functions which take a np.arrays as inputs and perform actions over it
     compare_diffs = np.vectorize(compare_diff_values)
-    classify_change = np.vectorize(nparam_helpers.classify_change)
+    classify_change = np.vectorize(check.classify_change)
     stat_no = len(baseline_stats.keys())
     stat_size = baseline_stats.get(list(baseline_stats.keys())[0])
 
@@ -227,7 +226,7 @@ def execute_analysis(
         original_x_pts,
         baseline_y_pts,
         target_y_pts,
-    ) = nparam_helpers.preprocess_nonparam_models(uid, baseline_model, target_profile, target_model)
+    ) = check.preprocess_nonparam_models(uid, baseline_model, target_profile, target_model)
 
     baseline_window_stats, _ = compute_window_stats(original_x_pts, baseline_y_pts)
     target_window_stats, x_pts = compute_window_stats(original_x_pts, target_y_pts)
@@ -238,7 +237,7 @@ def execute_analysis(
     x_pts_odd = x_pts[:, 1::2].reshape(-1, x_pts.size // 2)[0].round(2)
     partial_intervals = list(np.array((change_info, partial_rel_error, x_pts_even, x_pts_odd)).T)
 
-    change_info_enum = nparam_helpers.classify_change(
+    change_info_enum = check.classify_change(
         common_kit.safe_division(float(np.sum(partial_rel_error)), partial_rel_error.size),
         _STATS_DIFF_NO_CHANGE,
         _STATS_DIFF_CHANGE,
@@ -268,7 +267,7 @@ def check(
         :param models_strategy: detection model strategy for obtains the relevant kind of models
         :returns: tuple - degradation result
         """
-        for degradation_info in factory.run_detection_with_strategy(
+        for degradation_info in check.run_detection_with_strategy(
             execute_analysis, baseline_profile, target_profile, models_strategy
         ):
             yield degradation_info

perun/check/methods/polynomial_regression.py

@@ -13,9 +13,9 @@
 import numpy as np
 
 # Perun Imports
+from perun import check as check
 from perun.check.methods.abstract_base_checker import AbstractBaseChecker
-from perun.utils.structs import DegradationInfo, ClassificationMethod
-import perun.check.detection_kit as detect
+from perun.utils.structs.common_structs import DegradationInfo, ClassificationMethod
 
 if TYPE_CHECKING:
     import numpy.typing as npt
@@ -37,7 +37,7 @@ def check(
         :param _: unification with other detection methods (unused in this method)
         :returns: tuple (degradation result, degradation location, degradation rate, confidence)
         """
-        return detect.general_detection(
+        return check.general_detection(
             baseline_profile,
             target_profile,
             ClassificationMethod.PolynomialRegression,