Used absolute imports for sphinx build

Author: SamoraHunter

ml_grid/results_processing/filters.py

@@ -7,7 +7,7 @@
 import pandas as pd
 import numpy as np
 from typing import List, Dict, Optional, Union, Tuple, Any
-from .core import get_clean_data
+from ml_grid.results_processing.core import get_clean_data
 
 
 class ResultsFilter:

ml_grid/results_processing/plot_best_model.py

@@ -11,8 +11,8 @@
 import textwrap
 import ast
 
-from .core import get_clean_data
-from .plot_hyperparameters import HyperparameterAnalysisPlotter # To reuse parsing logic
+from ml_grid.results_processing.core import get_clean_data
+from ml_grid.results_processing.plot_hyperparameters import HyperparameterAnalysisPlotter # To reuse parsing logic
 
 # Limit on how many outcomes to plot automatically to avoid generating too many figures.
 MAX_OUTCOMES_TO_PLOT = 10

ml_grid/results_processing/plot_master.py

@@ -9,17 +9,17 @@
 from typing import List, Optional, NoReturn
 
 # Import all the individual plotter classes
-from .plot_algorithms import AlgorithmComparisonPlotter
-from .plot_distributions import DistributionPlotter
-from .plot_features import FeatureAnalysisPlotter
-from .plot_timeline import TimelineAnalysisPlotter
-from .plot_hyperparameters import HyperparameterAnalysisPlotter
-from .plot_feature_categories import FeatureCategoryPlotter
-from .plot_pipeline_parameters import PipelineParameterPlotter
-from .plot_global_importance import GlobalImportancePlotter
-from .plot_interactions import InteractionPlotter
-from .plot_best_model import BestModelAnalyzerPlotter
-from .summarize_results import ResultsSummarizer
+from ml_grid.results_processing.plot_algorithms import AlgorithmComparisonPlotter
+from ml_grid.results_processing.plot_distributions import DistributionPlotter
+from ml_grid.results_processing.plot_features import FeatureAnalysisPlotter
+from ml_grid.results_processing.plot_timeline import TimelineAnalysisPlotter
+from ml_grid.results_processing.plot_hyperparameters import HyperparameterAnalysisPlotter
+from ml_grid.results_processing.plot_feature_categories import FeatureCategoryPlotter
+from ml_grid.results_processing.plot_pipeline_parameters import PipelineParameterPlotter
+from ml_grid.results_processing.plot_global_importance import GlobalImportancePlotter
+from ml_grid.results_processing.plot_interactions import InteractionPlotter
+from ml_grid.results_processing.plot_best_model import BestModelAnalyzerPlotter
+from ml_grid.results_processing.summarize_results import ResultsSummarizer
 
 
 class MasterPlotter:

ml_grid/results_processing/summarize_results.py

@@ -8,7 +8,7 @@
 from sklearn.preprocessing import MultiLabelBinarizer
 import warnings
 
-from .core import get_clean_data
+from ml_grid.results_processing.core import get_clean_data
 
 class ResultsSummarizer:
     """Provides methods to summarize and transform results data into concise DataFrames."""