diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index f9a1cfb..8abfd5d 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -30,9 +30,9 @@ jobs: coverage run -m --source=iohinspector unittest discover coverage report -m - name: Upload coverage report - if: ${{ (matrix.python-version == 3.12) && (matrix.os == 'ubuntu-20.04') }} + if: ${{ (matrix.python-version == 3.12) && (matrix.os == 'ubuntu-22.04') }} env: CODACY_PROJECT_TOKEN: ${{ secrets.CODACY_PROJECT_TOKEN }} run: | coverage xml -o cobertura.xml - bash <(curl -Ls https://coverage.codacy.com/get.sh) report \ No newline at end of file + bash <(curl -Ls https://coverage.codacy.com/get.sh) report diff --git a/.gitignore b/.gitignore index 1aa7dc7..f612e04 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ __pycache__/ *.py[cod] *$py.class + # C extensions *.so @@ -161,5 +162,5 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ -data -aux/* \ No newline at end of file +data/ +aux/ \ No newline at end of file diff --git a/FUNCTION_REFERENCE.md b/FUNCTION_REFERENCE.md new file mode 100644 index 0000000..def6a2f --- /dev/null +++ b/FUNCTION_REFERENCE.md @@ -0,0 +1,1024 @@ +# IOHinspector Function Reference + +This document provides a comprehensive reference of all functions available in the IOHinspector package, organized by module. + +## Table of Contents +- [Metrics Functions](#metrics-functions) + - [AOCC (Area Over Convergence Curve)](#aocc) + - [Attractor Network](#attractor-network) + - [ECDF (Empirical Cumulative Distribution Function)](#ecdf) + - [EAF (Empirical Attainment Function)](#eaf) + - [Fixed Budget](#fixed-budget) + - [Fixed Target](#fixed-target) + - [Multi-Objective](#multi-objective) + - [Ranking](#ranking) + - [Single Run](#single-run) + - [Trajectory](#trajectory) + - [Utils](#metrics-utils) +- [Plotting Functions](#plotting-functions) + - [Attractor Network Plots](#attractor-network-plots) + - [ECDF Plots](#ecdf-plots) + - [EAF Plots](#eaf-plots) + - [Fixed Budget Plots](#fixed-budget-plots) + - [Fixed Target Plots](#fixed-target-plots) + - [Multi-Objective Plots](#multi-objective-plots) + - [Ranking Plots](#ranking-plots) + - [Single Run Plots](#single-run-plots) + - [Plot Utils](#plot-utils) + +--- + +## Metrics Functions + +### AOCC + +#### `get_aocc(data, eval_var="evaluations", fval_var="raw_y", eval_max=None, maximization=False)` +Calculate Area Over Convergence Curve (AOCC) for algorithm performance evaluation. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function values. Defaults to "raw_y". +- `eval_max (int, optional)`: Maximum evaluation bound for AOCC calculation. If None, uses data maximum. Defaults to None. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. + +**Returns:** +- `pl.DataFrame`: DataFrame with AOCC values calculated for each algorithm. + +--- + +### Attractor Network + +#### `get_attractor_network(data, coord_vars=["x0", "x1"], fval_var="raw_y", eval_var="evaluations", maximization=False, beta=40, epsilon=0.0001)` +Generate attractor network analysis from optimization algorithm trajectory data. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm trajectory data with position and performance information. +- `coord_vars (Iterable[str], optional)`: Which columns contain the decision variable coordinates. Defaults to ["x0", "x1"]. +- `fval_var (str, optional)`: Which column contains the fitness/objective values. Defaults to "raw_y". +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `beta (int, optional)`: Minimum stagnation length for attractor detection. Defaults to 40. +- `epsilon (float, optional)`: Distance threshold below which positions are considered identical. Defaults to 0.0001. + +**Returns:** +- `tuple[pd.DataFrame, pd.DataFrame]`: Two dataframes containing the nodes and edges of the attractor network. + +--- + +### ECDF + +#### `get_data_ecdf(data, fval_var="raw_y", eval_var="evaluations", free_vars=["algorithm_name"], maximization=False, f_min=None, f_max=None, scale_f_log=True, eval_values=None, eval_min=None, eval_max=None, scale_eval_log=True, turbo=True)` +Generate Empirical Cumulative Distribution Function (ECDF) data for performance analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `fval_var (str, optional)`: Which column contains the function/performance values. Defaults to "raw_y". +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables. Defaults to ["algorithm_name"]. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `f_min (float, optional)`: Minimum function value bound. If None, uses data minimum. Defaults to None. +- `f_max (float, optional)`: Maximum function value bound. If None, uses data maximum. Defaults to None. +- `scale_f_log (bool, optional)`: Whether function values should be log-scaled. Defaults to True. +- `eval_values (Iterable[int], optional)`: Specific evaluation points. If None, uses eval_min/eval_max. Defaults to None. +- `eval_min (int, optional)`: Minimum evaluation bound. If None, uses data minimum. Defaults to None. +- `eval_max (int, optional)`: Maximum evaluation bound. If None, uses data maximum. Defaults to None. +- `scale_eval_log (bool, optional)`: Whether evaluation axis should be log-scaled. Defaults to True. +- `turbo (bool, optional)`: Whether to use optimized computation. Defaults to True. + +**Returns:** +- `pd.DataFrame`: DataFrame containing ECDF data with evaluation points and cumulative probabilities. + +--- + +### EAF + +#### `get_discritized_eaf_single_objective(data, eval_var="evaluations", fval_var="raw_y", eval_min=1, eval_max=None, scale_eval_log=True, n_quantiles=100)` +Generate discretized EAF data for single-objective optimization analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing single-objective optimization trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function values. Defaults to "raw_y". +- `eval_min (int, optional)`: Minimum evaluation bound. Defaults to 1. +- `eval_max (int, optional)`: Maximum evaluation bound. If None, uses data maximum. Defaults to None. +- `scale_eval_log (bool, optional)`: Whether evaluations should be log-scaled. Defaults to True. +- `n_quantiles (int, optional)`: Number of quantile levels for discretization. Defaults to 100. + +**Returns:** +- `pl.DataFrame`: DataFrame with discretized EAF data for visualization. + +#### `get_eaf_data(data, eval_var="evaluations", eval_min=1, eval_max=None, scale_eval_log=True, return_as_pandas=True)` +Generate Empirical Attainment Function data for algorithm performance analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `eval_min (int, optional)`: Minimum evaluation bound. Defaults to 1. +- `eval_max (int, optional)`: Maximum evaluation bound. If None, uses data maximum. Defaults to None. +- `scale_eval_log (bool, optional)`: Whether evaluations should be log-scaled. Defaults to True. +- `return_as_pandas (bool, optional)`: Whether to return results as pandas DataFrame. Defaults to True. + +**Returns:** +- `pl.DataFrame | pd.DataFrame`: DataFrame containing EAF data with evaluation points and performance values. + +#### `get_eaf_pareto_data(data, obj1_var, obj2_var)` +Generate EAF data for multi-objective optimization in Pareto space. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `obj1_var (str)`: Which column contains the first objective values. +- `obj2_var (str)`: Which column contains the second objective values. + +**Returns:** +- `pd.DataFrame`: DataFrame containing EAF data in Pareto space with attainment probabilities. + +#### `get_eaf_diff_data(data1, data2, obj1_var, obj2_var)` +Calculate EAF differences between two algorithm datasets for comparative analysis. + +**Args:** +- `data1 (pl.DataFrame)`: Input dataframe containing trajectory data for the first algorithm. +- `data2 (pl.DataFrame)`: Input dataframe containing trajectory data for the second algorithm. +- `obj1_var (str)`: Which column contains the first objective values. +- `obj2_var (str)`: Which column contains the second objective values. + +**Returns:** +- `pd.DataFrame`: DataFrame containing EAF differences with statistical significance indicators. + +--- + +### Fixed Budget + +#### `aggregate_convergence(data, eval_var="evaluations", fval_var="raw_y", free_vars=["algorithm_name"], eval_min=None, eval_max=None, maximization=False)` +Aggregate algorithm performance data for fixed-budget convergence analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function/objective values. Defaults to "raw_y". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables. Defaults to ["algorithm_name"]. +- `eval_min (float, optional)`: Minimum evaluation bound. If None, uses data minimum. Defaults to None. +- `eval_max (float, optional)`: Maximum evaluation bound. If None, uses data maximum. Defaults to None. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. + +**Returns:** +- `pl.DataFrame`: DataFrame with aggregated convergence statistics including geometric mean, mean, median, min, max. + +--- + +### Fixed Target + +#### `aggregate_running_time(data, eval_var="evaluations", fval_var="raw_y", free_vars=["algorithm_name"], f_min=None, f_max=None, scale_f_log=True, eval_max=None, maximization=False)` +Aggregate Expected Running Time (ERT) data for fixed-target performance analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function/objective values. Defaults to "raw_y". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables. Defaults to ["algorithm_name"]. +- `f_min (float, optional)`: Minimum function value bound for target range. If None, uses data minimum. Defaults to None. +- `f_max (float, optional)`: Maximum function value bound for target range. If None, uses data maximum. Defaults to None. +- `scale_f_log (bool, optional)`: Whether function values should be log-scaled for target sampling. Defaults to True. +- `eval_max (int, optional)`: Maximum evaluation budget to consider. If None, uses data maximum. Defaults to None. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. + +**Returns:** +- `pl.DataFrame`: DataFrame with ERT statistics including Expected Running Time, mean, PAR-10, min, max. + +--- + +### Multi-Objective + +#### `get_pareto_front_2d(data, obj1_var="raw_y", obj2_var="F2")` +Extract 2D Pareto front data from multi-objective optimization results. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `obj1_var (str, optional)`: Which column contains the first objective values. Defaults to "raw_y". +- `obj2_var (str, optional)`: Which column contains the second objective values. Defaults to "F2". + +**Returns:** +- `pd.DataFrame`: DataFrame containing only the Pareto-optimal solutions for visualization. + +#### `get_indicator_over_time_data(data, indicator, obj_vars=["raw_y", "F2"], eval_min=1, eval_max=50_000, scale_eval_log=True, eval_steps=50)` +Calculate multi-objective quality indicator values over evaluation time. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `indicator (object)`: Quality indicator object from iohinspector.indicators module. +- `obj_vars (Iterable[str], optional)`: Which columns contain the objective values. Defaults to ["raw_y", "F2"]. +- `eval_min (int, optional)`: Minimum evaluation bound for the time axis. Defaults to 1. +- `eval_max (int, optional)`: Maximum evaluation bound for the time axis. Defaults to 50_000. +- `scale_eval_log (bool, optional)`: Whether the evaluation axis should be log-scaled. Defaults to True. +- `eval_steps (int, optional)`: Number of evaluation points to sample. Defaults to 50. + +**Returns:** +- `pd.DataFrame`: DataFrame with indicator values calculated at different evaluation points. + +--- + +### Ranking + +#### `get_tournament_ratings(data, alg_vars=["algorithm_name"], fid_vars=["function_name"], fval_var="raw_y", nrounds=25, maximization=False)` +Calculate ELO ratings from tournament-style algorithm competition. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance data across multiple problems. +- `alg_vars (Iterable[str], optional)`: Which columns contain the algorithm identifiers. Defaults to ["algorithm_name"]. +- `fid_vars (Iterable[str], optional)`: Which columns contain the problem/function identifiers. Defaults to ["function_name"]. +- `fval_var (str, optional)`: Which column contains the performance values. Defaults to "raw_y". +- `nrounds (int, optional)`: Number of tournament rounds to simulate. Defaults to 25. +- `maximization (bool, optional)`: Whether the performance should be maximized. Defaults to False. + +**Returns:** +- `pd.DataFrame`: DataFrame with ELO ratings and deviations for each algorithm. + +#### `get_robustrank_over_time(data, obj_vars, evals, indicator)` +Generate robust ranking data for algorithms at specific evaluation timesteps. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `obj_vars (Iterable[str])`: Which columns contain the objective values for ranking calculation. +- `evals (Iterable[int])`: Evaluation timesteps at which to compute rankings. +- `indicator (object)`: Quality indicator object from iohinspector.indicators module. + +**Returns:** +- `tuple`: Comparison and benchmark objects for robust ranking analysis. + +#### `get_robustrank_changes(data, obj_vars, evals, indicator)` +Calculate robust ranking changes between evaluation timesteps. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `obj_vars (Iterable[str])`: Which columns contain the objective values for ranking calculation. +- `evals (Iterable[int])`: Evaluation timesteps at which to compute ranking changes. +- `indicator (object)`: Quality indicator object from iohinspector.indicators module. + +**Returns:** +- `object`: Ranking comparisons data for trajectory analysis. + +--- + +### Single Run + +#### `get_heatmap_single_run_data(data, vars, eval_var="evaluations", var_mins=[-5], var_maxs=[5])` +Generate heatmap data for single algorithm run search space exploration analysis. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing trajectory data from a single algorithm run. +- `vars (Iterable[str])`: Which columns contain the decision/search space variables. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `var_mins (Iterable[float], optional)`: Minimum bounds for the search space variables. Defaults to [-5]. +- `var_maxs (Iterable[float], optional)`: Maximum bounds for the search space variables. Defaults to [5]. + +**Returns:** +- `pd.DataFrame`: DataFrame formatted for heatmap visualization of search space exploration. + +--- + +### Trajectory + +#### `get_trajectory(data, traj_length=None, min_fevals=1, evaluation_variable="evaluations", fval_variable="raw_y", free_variables=["algorithm_name"], maximization=False, return_as_pandas=True)` +Generate aligned performance trajectories for algorithm comparison over fixed evaluation sequences. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `traj_length (int, optional)`: Length of the trajectory to generate. If None, uses maximum evaluations from data. Defaults to None. +- `min_fevals (int, optional)`: Starting evaluation number for the trajectory. Defaults to 1. +- `evaluation_variable (str, optional)`: Which column contains the evaluation numbers. Defaults to "evaluations". +- `fval_variable (str, optional)`: Which column contains the function values. Defaults to "raw_y". +- `free_variables (Iterable[str], optional)`: Which columns to NOT aggregate over. Defaults to ["algorithm_name"]. +- `maximization (bool, optional)`: Whether the performance metric is being maximized. Defaults to False. +- `return_as_pandas (bool, optional)`: Whether to return results as pandas DataFrame. Defaults to True. + +**Returns:** +- `pl.DataFrame | pd.DataFrame`: DataFrame with aligned trajectory data where each row corresponds to a specific evaluation and performance value. + +--- + +### Metrics Utils + +#### `get_sequence(min, max, len, scale_log=False, cast_to_int=False)` +Create sequence of points, used for subselecting targets / budgets for alignment and data processing. + +**Args:** +- `min (float)`: Starting point of the range. +- `max (float)`: Final point of the range. +- `len (float)`: Number of steps in the sequence. +- `scale_log (bool, optional)`: Whether values should be scaled logarithmically. Defaults to False. +- `cast_to_int (bool, optional)`: Whether the values should be casted to integers. Defaults to False. + +**Returns:** +- `np.ndarray`: Array of evenly spaced values between min and max. + +#### `normalize_objectives(data, obj_vars=["raw_y"], bounds=None, log_scale=False, maximize=False, prefix="ert", keep_original=True)` +Normalize multiple objective columns in a dataframe using min-max normalization. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing the objective columns. +- `obj_vars (Iterable[str], optional)`: Which columns contain the objective values to normalize. Defaults to ["raw_y"]. +- `bounds (Optional[Dict[str, tuple]], optional)`: Optional manual bounds per column as (lower_bound, upper_bound). Defaults to None. +- `log_scale (Union[bool, Dict[str, bool]], optional)`: Whether to apply log10 scaling. Defaults to False. +- `maximize (Union[bool, Dict[str, bool]], optional)`: Whether to treat objective as maximization. Defaults to False. +- `prefix (str, optional)`: Prefix for normalized column names. Defaults to "ert". +- `keep_original (bool, optional)`: Whether to keep original objective column names. Defaults to True. + +**Returns:** +- `pl.DataFrame`: The original dataframe with new normalized objective columns added. + +#### `add_normalized_objectives(data, obj_vars, max_obj=None, min_obj=None)` +Add new normalized columns to provided dataframe based on the provided objective columns. + +**Args:** +- `data (pl.DataFrame)`: The original dataframe containing objective columns. +- `obj_vars (Iterable[str])`: Which columns contain the objective values to normalize. +- `max_obj (Optional[pl.DataFrame], optional)`: Maximum values for normalization. If None, uses data maximum. Defaults to None. +- `min_obj (Optional[pl.DataFrame], optional)`: Minimum values for normalization. If None, uses data minimum. Defaults to None. + +**Returns:** +- `pl.DataFrame`: The original DataFrame with new 'objI' columns added for each objective. + +#### `transform_fval(data, lb=1e-8, ub=1e8, scale_log=True, maximization=False, fval_var="raw_y")` +Helper function to transform function values using min-max normalization based on provided bounds and scaling. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing function values. +- `lb (float, optional)`: Lower bound for normalization. Defaults to 1e-8. +- `ub (float, optional)`: Upper bound for normalization. Defaults to 1e8. +- `scale_log (bool, optional)`: Whether to apply logarithmic scaling. Defaults to True. +- `maximization (bool, optional)`: Whether the problem is a maximization problem. Defaults to False. +- `fval_var (str, optional)`: Which column contains the function values to transform. Defaults to "raw_y". + +**Returns:** +- `pl.DataFrame`: The original dataframe with normalized function values in a new 'eaf' column. + +--- + +## Plotting Functions + +### Attractor Network Plots + +#### `plot_attractor_network(data, coord_vars=["x0", "x1"], fval_var="raw_y", eval_var="evaluations", maximization=False, beta=40, epsilon=0.0001, *, ax=None, file_name=None, plot_args=None)` +Plot an attractor network visualization from optimization algorithm data. + +Creates a network graph where nodes represent attractors (stable points) in the search space and edges represent transitions between them. Node sizes reflect visit frequency and colors represent fitness values. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `coord_vars (Iterable[str], optional)`: Which columns contain the decision variable coordinates. Defaults to ["x0", "x1"]. +- `fval_var (str, optional)`: Which column contains the fitness/objective values. Defaults to "raw_y". +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `beta (int, optional)`: Minimum stagnation length for attractor detection. Defaults to 40. +- `epsilon (float, optional)`: Distance threshold below which positions are considered identical. Defaults to 0.0001. +- `ax (matplotlib.axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (str, optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | AttractorNetworkPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, tuple[pd.DataFrame, pd.DataFrame]]`: The matplotlib axes object and a tuple containing two dataframes with the nodes and edges of the attractor network. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_attractor_network +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1], algorithms=['RandomSearch']).load(True, True) +ax, (nodes_df, edges_df) = plot_attractor_network( + df, + coord_vars=["x0", "x1"], + fval_var="raw_y", + file_name="example_plots/attractor_network.png" +) +``` + +**Generated Plot:** +
+Attractor Network Plot + +*Example attractor network visualization showing nodes (attractors) and edges (transitions) with node sizes representing visit frequency and colors indicating fitness values.* + +--- + +### ECDF Plots + +#### `plot_ecdf(data, fval_var="raw_y", eval_var="evaluations", free_vars=["algorithm_name"], maximization=False, f_min=None, f_max=None, scale_f_log=True, eval_values=None, eval_min=None, eval_max=None, scale_eval_log=True, *, ax=None, file_name=None, plot_args=None)` +Plot Empirical Cumulative Distribution Function (ECDF) based on Empirical Attainment Functions. + +Creates line plots showing the cumulative probability of achieving different performance levels at various evaluation budgets, allowing comparison between algorithms or configurations. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `fval_var (str, optional)`: Which column contains the function/performance values. Defaults to "raw_y". +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables for distinguishing between different lines in the plot. Defaults to ["algorithm_name"]. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `f_min (int, optional)`: Minimum function value bound. If None, uses data minimum. Defaults to None. +- `f_max (int, optional)`: Maximum function value bound. If None, uses data maximum. Defaults to None. +- `scale_f_log (bool, optional)`: Whether function values should be log-scaled before normalization. Defaults to True. +- `eval_values (Iterable[int], optional)`: Specific evaluation points to plot. If None, uses eval_min/eval_max with scale_eval_log to sample points. Defaults to None. +- `eval_min (int, optional)`: Minimum evaluation bound. If None, uses data minimum. Defaults to None. +- `eval_max (int, optional)`: Maximum evaluation bound. If None, uses data maximum. Defaults to None. +- `scale_eval_log (bool, optional)`: Whether the evaluation axis should be log-scaled. Defaults to True. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | LinePlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the processed dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_ecdf +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +ax, data = plot_ecdf( + df, + file_name="example_plots/ecdf.png" +) +``` + +**Generated Plot:** +
+ECDF Plot + +*Example ECDF plot showing cumulative distribution of performance across different algorithms at various evaluation budgets.* + +--- + +### EAF Plots + +#### `plot_eaf_single_objective(data, eval_var="evaluations", fval_var="raw_y", eval_min=None, eval_max=None, scale_eval_log=True, n_quantiles=100, *, ax=None, file_name=None, plot_args=None)` +Plot the Empirical Attainment Function (EAF) for single-objective optimization against budget. + +Creates a heatmap visualization showing the probability of attaining different function values at different evaluation budgets across multiple algorithm runs. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function values. Defaults to "raw_y". +- `eval_min (int, optional)`: Minimum evaluation bound for the plot. If None, uses data minimum. Defaults to None. +- `eval_max (int, optional)`: Maximum evaluation bound for the plot. If None, uses data maximum. Defaults to None. +- `scale_eval_log (bool, optional)`: Whether the evaluations should be log-scaled. Defaults to True. +- `n_quantiles (int, optional)`: Number of discrete probability levels in the EAF heatmap. Defaults to 100. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | HeatmapPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pl.DataFrame]`: The matplotlib axes object and the processed dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_eaf_single_objective +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1], algorithms=['HillClimber']).load(True, True) +ax, data = plot_eaf_single_objective( + df, + file_name="example_plots/eaf_single_objective.png" +) +``` + +**Generated Plot:** +
+EAF Single Objective Plot + +*Example EAF heatmap showing probability of attaining different function values at various evaluation budgets.* + +#### `plot_eaf_pareto(data, obj1_var, obj2_var, *, ax=None, file_name=None, plot_args=None)` +Plot the Empirical Attainment Function (EAF) for multi-objective optimization with two objectives. + +Creates a heatmap visualization showing the probability of attaining different combinations of objective values across multiple algorithm runs in the Pareto front space. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `obj1_var (str)`: Which column contains the first objective values. +- `obj2_var (str)`: Which column contains the second objective values. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | HeatmapPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the EAF dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_eaf_pareto, + add_normalized_objectives +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("MO_Data") + +df = manager.select(function_ids=[0], algorithms=['NSGA2']).load(False, False) +df = add_normalized_objectives(df, obj_vars = ['raw_y', 'F2']) + +ax, data = plot_eaf_pareto( + df, + obj1_var="obj1", + obj2_var="obj2", + file_name="example_plots/eaf_pareto.png" +) +``` + +**Generated Plot:** +
+EAF Pareto Plot + +*Example EAF plot in Pareto space showing attainment probabilities for multi-objective optimization.* + +#### `plot_eaf_diffs(data1, data2, obj1_var, obj2_var, *, ax=None, file_name=None, plot_args=None)` +Plot the Empirical Attainment Function (EAF) differences between two algorithms. + +Creates a heatmap visualization showing the statistical differences in attainment probabilities between two algorithms in the objective space, highlighting regions where one algorithm performs better than the other. + +**Args:** +- `data1 (pl.DataFrame)`: Input dataframe containing trajectory data for the first algorithm. +- `data2 (pl.DataFrame)`: Input dataframe containing trajectory data for the second algorithm. +- `obj1_var (str)`: Which column contains the first objective values. +- `obj2_var (str)`: Which column contains the second objective values. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | HeatmapPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the EAF differences dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_eaf_diffs, + add_normalized_objectives +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("MO_Data") + +df1 = manager.select(function_ids=[0], algorithms=['NSGA2']).load(False, False) +df1 = add_normalized_objectives(df1, obj_vars = ['raw_y', 'F2']) + +df2 = manager.select(function_ids=[0], algorithms=['SMS-EMOA']).load(False, False) +df2 = add_normalized_objectives(df2, obj_vars = ['raw_y', 'F2']) + +ax, data = plot_eaf_diffs( + df1, + df2, + obj1_var="obj1", + obj2_var="obj2", + file_name="example_plots/eaf_diffs.png" +) +``` + +**Generated Plot:** +
+EAF Differences Plot + +*Example EAF differences plot showing statistical significance of performance differences between two algorithms in objective space.* + +--- + +### Fixed Budget Plots + +#### `plot_single_function_fixed_budget(data, eval_var="evaluations", fval_var="raw_y", free_vars=["algorithm_name"], eval_min=None, eval_max=None, maximization=False, measures=["geometric_mean"], *, ax=None, file_name=None, plot_args=None)` +Create a fixed-budget convergence plot showing algorithm performance over evaluation budgets. + +Visualizes how different algorithms converge by plotting aggregate performance measures (geometric mean, median, etc.) against evaluation budgets, allowing direct comparison of convergence behavior across algorithms. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function/objective values. Defaults to "raw_y". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables for distinguishing between different lines in the plot. Defaults to ["algorithm_name"]. +- `eval_min (float, optional)`: Minimum evaluation bound for the plot. If None, uses data minimum. Defaults to None. +- `eval_max (float, optional)`: Maximum evaluation bound for the plot. If None, uses data maximum. Defaults to None. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `measures (Iterable[str], optional)`: Aggregate measures to plot. Valid options are "geometric_mean", "mean", "median", "min", "max". Defaults to ["geometric_mean"]. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (str, optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | LinePlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pl.DataFrame]`: The matplotlib axes object and the processed (melted/filtered) dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_single_function_fixed_budget +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +ax, data = plot_single_function_fixed_budget( + df, + file_name="example_plots/fixed_budget.png" +) +``` + +**Generated Plot:** +
+Fixed Budget Plot + +*Example fixed-budget convergence plot showing algorithm performance over evaluation budgets with geometric mean and median measures.* + +--- + +### Fixed Target Plots + +#### `plot_single_function_fixed_target(data, eval_var="evaluations", fval_var="raw_y", free_vars=["algorithm_name"], f_min=None, f_max=None, scale_f_log=True, eval_max=None, maximization=False, measures=["ERT"], *, ax=None, file_name=None, plot_args=None)` +Create a fixed-target plot showing Expected Running Time (ERT) analysis for algorithm performance. + +Visualizes how much computational budget (evaluations) algorithms need to reach specific target performance levels, allowing comparison of algorithm efficiency across different difficulty targets. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing optimization algorithm trajectory data. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `fval_var (str, optional)`: Which column contains the function/objective values. Defaults to "raw_y". +- `free_vars (Iterable[str], optional)`: Which columns contain the grouping variables for distinguishing between different lines in the plot. Defaults to ["algorithm_name"]. +- `f_min (float, optional)`: Minimum function value bound for target range. If None, uses data minimum. Defaults to None. +- `f_max (float, optional)`: Maximum function value bound for target range. If None, uses data maximum. Defaults to None. +- `scale_f_log (bool, optional)`: Whether function values should be log-scaled for target sampling. Defaults to True. +- `eval_max (int, optional)`: Maximum evaluation budget to consider. If None, uses data maximum. Defaults to None. +- `maximization (bool, optional)`: Whether the optimization problem is maximization. Defaults to False. +- `measures (Iterable[str], optional)`: Running time measures to plot. Valid options are "ERT", "mean", "PAR-10", "min", "max". Defaults to ["ERT"]. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (str, optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | LinePlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pl.DataFrame]`: The matplotlib axes object and the processed (melted/filtered) dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_single_function_fixed_target +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +ax, data = plot_single_function_fixed_target( + df, + file_name="example_plots/fixed_target.png" +) +``` + +**Generated Plot:** +
+Fixed Target Plot + +*Example fixed-target ERT plot showing expected running time to reach different performance targets for multiple algorithms.* + +--- + +### Multi-Objective Plots + +#### `plot_paretofronts_2d(data, obj1_var="raw_y", obj2_var="F2", free_var="algorithm_name", *, ax=None, file_name=None, plot_args=None)` +Visualize 2D Pareto fronts for multi-objective optimization algorithms. + +Creates a scatter plot showing the non-dominated solutions (Pareto fronts) achieved by different algorithms in a two-objective space, allowing visual comparison of algorithm performance and trade-off quality. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `obj1_var (str, optional)`: Which column contains the first objective values. Defaults to "raw_y". +- `obj2_var (str, optional)`: Which column contains the second objective values. Defaults to "F2". +- `free_var (str, optional)`: Which column contains the grouping variable for distinguishing between different algorithms/categories. Defaults to "algorithm_name". +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (str, optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | ScatterPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the Pareto front dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_paretofronts_2d +) +import os + +os.makedirs("example_plots", exist_ok=True) + +manager = DataManager() +manager.add_folder("MO_Data") + +df = manager.select().load(True, True) + +ax, data = plot_paretofronts_2d( + df, + obj1_var="raw_y", + obj2_var="F2", + file_name="example_plots/pareto_fronts.png" +) +``` + +**Generated Plot:** +
+Pareto Fronts 2D Plot + +*Example 2D Pareto fronts visualization showing non-dominated solutions achieved by different algorithms in objective space.* + +#### `plot_indicator_over_time(data, obj_vars=["raw_y", "F2"], indicator=None, free_var="algorithm_name", eval_min=1, eval_max=50_000, scale_eval_log=True, eval_steps=50, *, ax=None, file_name=None, plot_args=None)` +Plot the anytime performance of multi-objective quality indicators over evaluation budgets. + +Creates line plots showing how quality indicators (like hypervolume, IGD, etc.) evolve over the course of algorithm runs, enabling comparison of convergence behavior and solution quality improvement across different algorithms. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing multi-objective optimization trajectory data. +- `obj_vars (Iterable[str], optional)`: Which columns contain the objective values for indicator calculation. Defaults to ["raw_y", "F2"]. +- `indicator (object, optional)`: Quality indicator object from iohinspector.indicators module. Defaults to None. +- `free_var (str, optional)`: Which column contains the grouping variable for distinguishing between different algorithms. Defaults to "algorithm_name". +- `eval_min (int, optional)`: Minimum evaluation bound for the time axis. Defaults to 1. +- `eval_max (int, optional)`: Maximum evaluation bound for the time axis. Defaults to 50_000. +- `scale_eval_log (bool, optional)`: Whether the evaluation axis should be log-scaled. Defaults to True. +- `eval_steps (int, optional)`: Number of evaluation points to sample between eval_min and eval_max. Defaults to 50. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | LinePlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the indicator performance dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_indicator_over_time, + add_normalized_objectives, + get_reference_set, + IGDPlus +) + +manager = DataManager() +manager.add_folder("MO_Data") + +df = manager.select(function_ids=[1]).load(False, True) +df = add_normalized_objectives(df, obj_vars = ['raw_y', 'F2']) +ref_set = get_reference_set(df, ['obj1', 'obj2'], 1000) + +igdp_indicator = IGDPlus(reference_set = ref_set) + +ax, data = plot_indicator_over_time( + df, ['obj1', 'obj2'], igdp_indicator, + eval_min=10, eval_max=2000, eval_steps=50, free_var='algorithm_name', + file_name="example_plots/indicator_over_time.png" +) +``` + +**Generated Plot:** +
+Indicator Over Time Plot + +*Example plot showing hypervolume indicator evolution over evaluation time for multiple multi-objective algorithms.* + +--- + +### Ranking Plots + +#### `plot_tournament_ranking(data, alg_vars=["algorithm_name"], fid_vars=["function_name"], fval_var="raw_y", nrounds=25, maximization=False, *, ax=None, file_name=None, plot_args=None)` +Plot ELO ratings from tournament-style algorithm competition across multiple problems. + +Creates a point plot with error bars showing ELO ratings calculated from pairwise algorithm competitions. In each round, all algorithms compete against each other on every function, with performance samples determining winners and ELO rating updates. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `alg_vars (Iterable[str], optional)`: Which columns contain the algorithm identifiers that will compete. Defaults to ["algorithm_name"]. +- `fid_vars (Iterable[str], optional)`: Which columns contain the problem/function identifiers for competition. Defaults to ["function_name"]. +- `fval_var (str, optional)`: Which column contains the performance values. Defaults to "raw_y". +- `nrounds (int, optional)`: Number of tournament rounds to simulate. Defaults to 25. +- `maximization (bool, optional)`: Whether the performance should be maximized. Defaults to False. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (str, optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | BasePlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the ELO ratings dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_tournament_ranking +) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +ax, data = plot_tournament_ranking( + df, + file_name="example_plots/tournament_rankings.png" +) +``` + +**Generated Plot:** +
+Tournament Ranking Plot + +*Example tournament ranking plot showing ELO ratings with error bars for algorithms competing across multiple benchmark functions.* + +#### `plot_robustrank_over_time(data, obj_vars, evals, indicator, *, file_name=None)` +Plot robust ranking confidence intervals at distinct evaluation timesteps. + +Creates multiple subplots showing robust ranking analysis with confidence intervals for algorithm performance at different evaluation budgets, using statistical comparison methods to handle uncertainty in performance measurements. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. Must contain data for a single function only. +- `obj_vars (Iterable[str])`: Which columns contain the objective values for ranking calculation. +- `evals (Iterable[int])`: Evaluation timesteps at which to compute and plot rankings. +- `indicator (object)`: Quality indicator object from iohinspector.indicators module. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. + +**Returns:** +- `tuple[np.ndarray, tuple]`: Array of matplotlib axes objects and a tuple containing (comparison, benchmark) data used for the robust ranking analysis. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_robustrank_over_time, + IGDPlus, + get_reference_set, + add_normalized_objectives +) + +manager = DataManager() +manager.add_folder("MO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +df = add_normalized_objectives(df, obj_vars = ['raw_y', 'F2']) +ref_set = get_reference_set(df, ['obj1', 'obj2'], 1000) + +igdp_indicator = IGDPlus(reference_set = ref_set) +evals = [10,100,1000,2000] + +ax, (comparison, benchmark) = plot_robustrank_over_time( + df, + obj_vars=['obj1', 'obj2'], + evals=evals, + indicator=igdp_indicator, + file_name="example_plots/robustrank_over_time.png" +) +``` + +**Generated Plot:** +
+Robust Rank Over Time Plot + +*Example robust ranking analysis showing confidence intervals for algorithm performance at different evaluation timesteps.* + +#### `plot_robustrank_changes(data, obj_vars, evals, indicator, *, ax=None, file_name=None)` +Plot robust ranking changes over evaluation timesteps as connected line plots. + +Creates a line plot showing how algorithm rankings evolve over time, with lines connecting ranking positions across different evaluation budgets to visualize ranking stability and performance trajectory changes. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing algorithm performance trajectory data. +- `obj_vars (Iterable[str])`: Which columns contain the objective values for ranking calculation. +- `evals (Iterable[int])`: Evaluation timesteps at which to compute rankings and plot changes. +- `indicator (object)`: Quality indicator object from iohinspector.indicators module. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, object]`: The matplotlib axes object and the ranking comparisons data used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_robustrank_changes, + IGDPlus, + get_reference_set, + add_normalized_objectives +) + +manager = DataManager() +manager.add_folder("MO_Data") + +df = manager.select(function_ids=[1]).load(True, True) +df = add_normalized_objectives(df, obj_vars = ['raw_y', 'F2']) +ref_set = get_reference_set(df, ['obj1', 'obj2'], 1000) + +igdp_indicator = IGDPlus(reference_set = ref_set) +evals = [10,100,1000,2000] + +ax, comparison = plot_robustrank_changes( + df, + obj_vars=['obj1', 'obj2'], + evals=evals, + indicator=igdp_indicator, + file_name="example_plots/robustrank_changes.png" +) +``` + +**Generated Plot:** +
+Robust Rank Changes Plot + +*Example ranking changes plot showing how algorithm rankings evolve over evaluation time with connected line trajectories.* + +--- + +### Single Run Plots + +#### `plot_heatmap_single_run(data, vars, eval_var="evaluations", var_mins=[-5], var_maxs=[5], *, ax=None, file_name=None, plot_args=None)` +Create a heatmap visualization showing search space exploration patterns in a single algorithm run. + +Visualizes how an optimization algorithm explores the search space over time by showing the density of evaluations across different variable dimensions and evaluation budgets, revealing search patterns and exploration behavior. + +**Args:** +- `data (pl.DataFrame)`: Input dataframe containing trajectory data from a single algorithm run. Must contain data for exactly one run (unique data_id). +- `vars (Iterable[str])`: Which columns contain the decision/search space variables to visualize. +- `eval_var (str, optional)`: Which column contains the evaluation counts. Defaults to "evaluations". +- `var_mins (Iterable[float], optional)`: Minimum bounds for the search space variables. Should be same length as vars. Defaults to [-5]. +- `var_maxs (Iterable[float], optional)`: Maximum bounds for the search space variables. Should be same length as vars. Defaults to [5]. +- `ax (matplotlib.axes._axes.Axes, optional)`: Matplotlib axes to plot on. If None, creates new figure. Defaults to None. +- `file_name (Optional[str], optional)`: Path to save the plot. If None, plot is not saved. Defaults to None. +- `plot_args (dict | HeatmapPlotArgs, optional)`: Plot styling arguments. Defaults to None. + +**Returns:** +- `tuple[matplotlib.axes.Axes, pd.DataFrame]`: The matplotlib axes object and the processed heatmap dataframe used to create the plot. + +**Example:** +```python +from iohinspector import ( + DataManager, + plot_heatmap_single_run +) + +manager = DataManager() +manager.add_folder("SO_Data") + +df = manager.select(function_ids=[1], data_ids=[1], algorithms=["RandomSearch"]).load(True, True) + +ax, data = plot_heatmap_single_run( + df, + vars = ["x0","x1"], + var_mins=[-5,-5], + var_maxs=[5,5], + file_name="example_plots/heatmap_single_run.png" +) +``` + +**Generated Plot:** +
+Heatmap Single Run Plot + +*Example search space exploration heatmap showing algorithm evaluation density across decision variables and evaluation timesteps.* + +--- + +## Notes + +- All functions support both Polars and Pandas DataFrames as input/output +- Plot functions return both the matplotlib axes object and the processed data +- All plotting functions support customizable styling through plot_args parameters + +For more detailed usage examples and tutorials, see the examples directory in the repository. \ No newline at end of file diff --git a/example_plots/attractor_network.png b/example_plots/attractor_network.png new file mode 100644 index 0000000..06d7a19 Binary files /dev/null and b/example_plots/attractor_network.png differ diff --git a/example_plots/eaf_differences.png b/example_plots/eaf_differences.png new file mode 100644 index 0000000..69b8962 Binary files /dev/null and b/example_plots/eaf_differences.png differ diff --git a/example_plots/eaf_diffs.png 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index 56502eb..dcd4570 100644 --- a/examples/MO_Examples.ipynb +++ b/examples/MO_Examples.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -122,7 +122,7 @@ "└─────────┴───────────────┴───────────────┴──────────────┴───┴────────┴───────┴──────────┴─────────┘" ] }, - "execution_count": 3, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -140,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ " Function(id=0, name='pymoo_ZDT1', maximization=False)))" ] }, - "execution_count": 4, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -238,7 +238,7 @@ "└─────────┴───────────────┴───────────────┴──────────────┴───┴────────┴───────┴──────────┴─────────┘" ] }, - "execution_count": 6, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -269,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -299,7 +299,7 @@ "└─────────┴───────────────┴───────────────┴──────────────┴───┴────────┴───────┴──────────┴─────────┘" ] }, - "execution_count": 9, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +324,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -371,7 +371,7 @@ "└─────────┴─────────────┴────────────┴────────────┴───┴─────────┴────────────┴──────────┴──────────┘" ] }, - "execution_count": 10, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -397,12 +397,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 39, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -414,19 +414,21 @@ "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sbs\n", - "\n", + "import numpy as np\n", "popsize = 100\n", "funcname = 'pymoo_ZDT1'\n", "\n", "df = manager.select(function_ids=[0]).load(False, True)\n", "#Currently, this normalization function assumes that our function was already scaled to have all 0's as the ideal point.\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", + "# df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", "\n", "#The cast-to-int is there to handle data type differences and prevent duplicate values for function evaluation count\n", "evals = iohinspector.metrics.get_sequence(10, 2000, 10, cast_to_int=True, scale_log=True)\n", "\n", + "\n", "hv_indicator = iohinspector.indicators.anytime.HyperVolume(reference_point = [1.1, 1.1])\n", - "df_hv = iohinspector.indicators.add_indicator(df, hv_indicator, objective_columns = ['obj1', 'obj2'], evals = evals)\n", + "df_hv = iohinspector.indicators.add_indicator(df, hv_indicator, obj_vars = ['obj1', 'obj2'], evals = evals)\n", "\n", "plt.figure(figsize=(16,9))\n", "sbs.lineplot(df_hv.to_pandas(), x='evaluations', y=hv_indicator.var_name, hue='algorithm_name')\n", @@ -445,12 +447,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -461,12 +463,12 @@ ], "source": [ "df = manager.select(function_ids=[1]).load(False, True)\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", "hv_indicator = iohinspector.indicators.anytime.HyperVolume(reference_point = [1.1, 1.1])\n", "\n", - "df_hv = iohinspector.plot.plot_indicator_over_time(\n", + "df_hv = iohinspector.plots.plot_indicator_over_time(\n", " df, ['obj1', 'obj2'], hv_indicator, \n", - " evals_min=10, evals_max=2000, nr_eval_steps=50, free_variable='algorithm_name'\n", + " eval_min=10, eval_max=2000, eval_steps=50, free_var='algorithm_name'\n", ")" ] }, @@ -482,12 +484,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -498,14 +500,14 @@ ], "source": [ "df = manager.select(function_ids=[1]).load(False, True)\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", "ref_set = iohinspector.indicators.get_reference_set(df, ['obj1', 'obj2'], 1000)\n", "\n", "igdp_indicator = iohinspector.indicators.anytime.IGDPlus(reference_set = ref_set)\n", "\n", - "df_igdp = iohinspector.plot.plot_indicator_over_time(\n", + "df_igdp = iohinspector.plots.plot_indicator_over_time(\n", " df, ['obj1', 'obj2'], igdp_indicator, \n", - " evals_min=10, evals_max=2000, nr_eval_steps=50, free_variable='algorithm_name'\n", + " eval_min=10, eval_max=2000, eval_steps=50, free_var='algorithm_name'\n", ")" ] }, @@ -527,26 +529,26 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "df = manager.load(False, True)\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", "evals = iohinspector.metrics.get_sequence(10, 2000, 2000, cast_to_int=True, scale_log=False)\n", "hv_indicator = iohinspector.indicators.anytime.HyperVolume(reference_point = [1.1, 1.1])\n", - "df_hv = iohinspector.indicators.add_indicator(df, hv_indicator, objective_columns = ['obj1', 'obj2'], evals = evals)\n", + "df_hv = iohinspector.indicators.add_indicator(df, hv_indicator, obj_vars = ['obj1', 'obj2'], evals = evals)\n", "df_hv = df_hv.with_columns((pl.col('HyperVolume')/1.21).alias('eaf'))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 46, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -556,7 +558,7 @@ } ], "source": [ - "_ = iohinspector.plot.single_function_fixedbudget(df_hv, fval_variable='eaf', maximization=True)" + "_ = iohinspector.plots.plot_single_function_fixed_budget(df_hv, fval_var='eaf', maximization=True)" ] }, { @@ -572,12 +574,36 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 49, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "(,\n", + " obj1 obj2 eaf\n", + " 0 0.000000e+00 0.278460 0.2\n", + " 60 4.928938e-07 0.278460 0.4\n", + " 1 4.928938e-07 0.231651 0.2\n", + " 129 2.007482e-06 0.293080 0.6\n", + " 130 3.474061e-06 0.278460 0.6\n", + " .. ... ... ...\n", + " 192 9.822622e-01 0.015580 0.6\n", + " 128 9.832064e-01 0.004345 0.4\n", + " 59 9.938718e-01 0.000000 0.2\n", + " 277 9.949035e-01 0.027837 1.0\n", + " 193 9.974315e-01 0.009963 0.6\n", + " \n", + " [278 rows x 3 columns])" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" ] @@ -588,8 +614,8 @@ ], "source": [ "df = manager.select(function_ids=[0], algorithms=['NSGA2']).load(False, False)\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", - "iohinspector.plot.plot_eaf_pareto(df, 'obj1', 'obj2', scale_xlog=False, scale_ylog=False)" + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", + "iohinspector.plots.plot_eaf_pareto(df, 'obj1', 'obj2')" ] }, { @@ -603,36 +629,50 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 51, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", " warnings.warn(\"No results found. Start computations\")\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", " warnings.warn(f\"There are only {binom(2*num_instances, num_instances):.0f} unique samples possible, \"\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", " warnings.warn(\"No results found. Start computations\")\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", " warnings.warn(f\"There are only {binom(2*num_instances, num_instances):.0f} unique samples possible, \"\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", " warnings.warn(\"No results found. Start computations\")\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", " warnings.warn(f\"There are only {binom(2*num_instances, num_instances):.0f} unique samples possible, \"\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/abstract_comparison.py:45: UserWarning: No results found. Start computations\n", " warnings.warn(\"No results found. Start computations\")\n", - "/home/dinu/miniconda3/envs/iohi/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/robustranking/comparison/bootstrap_comparison.py:67: UserWarning: There are only 252 unique samples possible, which is less than the requested 1000 bootstrap samples. Duplicate samples are inevitable. Consider increasing the number of instances or reducing the number of bootstraps.\n", " warnings.warn(f\"There are only {binom(2*num_instances, num_instances):.0f} unique samples possible, \"\n" ] }, { "data": { - "image/png": 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"text/plain": [ - "
" + "(,\n", + " {'10': ,\n", + " '100': ,\n", + " '1000': ,\n", + " '2000': })" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" ] }, "metadata": {}, @@ -641,7 +681,7 @@ ], "source": [ "df = manager.select(function_ids=[0]).load(False, True)\n", - "df = iohinspector.metrics.add_normalized_objectives(df, obj_cols = ['raw_y', 'F2'])\n", + "df = iohinspector.metrics.add_normalized_objectives(df, obj_vars = ['raw_y', 'F2'])\n", "\n", "#The cast-to-int is there to handle data type differences and prevent duplicate values for function evaluation count\n", "evals = [10,100,1000,2000]\n", @@ -650,22 +690,21 @@ "\n", "igdp_indicator = iohinspector.indicators.anytime.IGDPlus(reference_set = ref_set)\n", "\n", - "\n", - "iohinspector.plot.plot_robustrank_changes(df, ['obj1', 'obj2'], evals, igdp_indicator)" + "iohinspector.plots.plot_robustrank_changes(df, ['obj1', 'obj2'], evals, igdp_indicator)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Further analysis\n", + "# Further metrics\n", "This tutorial is a work-in-progress, more examples will be added in future releases. " ] } ], "metadata": { "kernelspec": { - "display_name": "iohi", + "display_name": "iohinspector", "language": "python", "name": "python3" }, diff --git a/examples/SO_Examples.ipynb b/examples/SO_Examples.ipynb index aad801d..749471f 100644 --- a/examples/SO_Examples.ipynb +++ b/examples/SO_Examples.ipynb @@ -495,7 +495,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -505,8 +505,8 @@ } ], "source": [ - "#Note: we filter the data by function ID here. This is equivalent to doing the subselecting before loading the performance data. \n", - "data_singleft = iohinspector.plot.single_function_fixedtarget(df.filter(pl.col(\"function_id\") == 1))" + "#Note: we filter the data by function ID here. This is equivalent to doing the subselecting before loading the performance data. \n", + "data_singleft = iohinspector.plots.plot_single_function_fixed_target(df.filter(pl.col(\"function_id\") == 1))" ] }, { @@ -517,7 +517,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -526,7 +526,7 @@ }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -540,8 +540,8 @@ "import matplotlib.pyplot as plt\n", "\n", "fig, axs = plt.subplots(2,1, sharey=True, figsize=(16,14))\n", - "data_singleft1 = iohinspector.plot.single_function_fixedtarget(df.filter(pl.col(\"function_id\") == 1), ax=axs[0])\n", - "data_singleft2 = iohinspector.plot.single_function_fixedtarget(df.filter(pl.col(\"function_id\") == 2), ax=axs[1])\n", + "data_singleft1 = iohinspector.plots.plot_single_function_fixed_target(df.filter(pl.col(\"function_id\") == 1), ax=axs[0])\n", + "data_singleft2 = iohinspector.plots.plot_single_function_fixed_target(df.filter(pl.col(\"function_id\") == 2), ax=axs[1])\n", "axs[0].legend('') #Disable legend to avoid duplication" ] }, @@ -559,7 +559,7 @@ "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -569,7 +569,7 @@ } ], "source": [ - "data_singlefb = iohinspector.plot.single_function_fixedbudget(df.filter(pl.col(\"function_id\") == 1))" + "data_singlefb = iohinspector.plots.plot_single_function_fixed_budget(df.filter(pl.col(\"function_id\") == 1))" ] }, { @@ -586,9 +586,9 @@ "outputs": [ { "data": { - "image/png": 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", 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" + "
" ] }, "metadata": {}, @@ -596,7 +596,7 @@ } ], "source": [ - "df_eaf = iohinspector.plot.plot_eaf_singleobj(df)" + "df_eaf = iohinspector.plots.plot_eaf_single_objective(df)" ] }, { @@ -604,9 +604,17 @@ "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/dinu/miniconda3/envs/iohinspector/lib/python3.10/site-packages/iohinspector/align.py:109: UserWarning: Sortedness of columns cannot be checked when 'by' groups provided\n", + " result_df = x_vals.join_asof(\n" + ] + }, { "data": { - "image/png": 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" ] @@ -616,7 +624,7 @@ } ], "source": [ - "df_ecdf = iohinspector.plot.plot_ecdf(df)" + "df_ecdf = iohinspector.plots.plot_ecdf(df)" ] }, { @@ -633,9 +641,9 @@ "outputs": [ { "data": { - "image/png": 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bWmqq9NRTaYqKitb331dQYGDhfv4tatmgMDmf4/06t3fv3lyXqkpOTtaOHTuyPe7qcvDIyEhFRkbm+HyFChX03nvvqXfv3rm2kzVcS9KXX36Za31WpUqVyjVgR0REaOnSpXr44Yd15MgR2e12/fbbb9nqqlWrpi+++CJf4RoAAAAAkN0NGbCtUrlyZe3atUvr16/XunXrtGfPHp09e1axsbFKSUlRUFCQypUrp6ZNm6p79+7q06dPkRg9bdmypXbu3KnPPvtM33zzjQ4cOKDY2FiFhoaqVq1auv/++zVgwAAFBgYWdlcBAAAA4IZxQwbs3EaZ88Jms6l+/fqqX7++Hn/8cUvalKRrcVV+YGCghg8f7jDxGQAAAACg4DDJGQAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFvAq7A4AAAAAAJxLSMj4ySo1VUpKki5c8FR0tBQQkP244OCMH1xbBGwAAAAAKKJWr5Z++SX744GBkr9/mt5800MeTq5LvvNOqVevgu8fHBGwAQAAAKCIattWiojI/rjdnq6VKw+oQ4cK8vb2zPY8o9eFg4ANAAAAAEVUTpd62+1S6dIXVKmS5O197fsF55jkDAAAAAAACxCwAQAAAACwAAEbAAAAAAALELABAAAAALAAARsAAAAAAAtYOov4lClTrup4Dw8PBQUFqUSJEqpbt64iIiLk4+NjUe8AAAAAACg4lgbsSZMmyWazWdaej4+P7rnnHo0ePVqNGze2rF0AAAAAAKxm+SXihmE4/Dh7zN3nU1NT9dVXX6lFixZ68cUXre4qAAAAAACWsXQEe+LEiZKkxMREvffee7p06ZIMw1ClSpXUrFkzVaxYUYGBgUpOTtaxY8e0adMmHT16VJLk5+en4cOHy8/PT+fOndPOnTu1ceNGpaWl6fLly5o0aZICAwP17LPPWtllAAAAAAAsYXnAPnDggHr27KnU1FQ1bdpU06dPV5s2bXI8Zs2aNRozZow2bdqkn376SYsWLVLNmjUlSUePHtUzzzyjn3/+WYZh6IUXXtBDDz2ksLAwK7sNAAAAAMBVs/QS8ZSUFPXp00eHDx9Wjx49tGbNmlzDtSTdfvvt+vPPP9WzZ08dOnRI99xzjy5cuCBJqly5shYsWKDevXtLki5cuKCPP/7Yyi4DAAAAAGAJSwP23LlztW/fPvn5+Wnu3Lny9vZ26zhvb2/NmTNHfn5+2rt3r+bOnevw/FtvvSVPT09J0vLly63sMgAAAAAAlrA0YH/11Vey2Wxq166dSpUqladjS5UqpQ4dOsgwDM2fP9/huSpVqqhRo0YyDEP79++3sssAAAAAAFjC0oD9zz//SJLCw8PzdXzFihUd2smqTp06kqRz587ls3cAAAAAABQcSwN2XFycpPyH4MzjMtvJKjAwUJLMS8UBAAAAAChKLA3YZcqUkWEYWrVqlex2e56OtdvtWrVqldnOlRISEiQpz5eeAwAAAABwLVgasFu1aiUpYyT6+eefz9OxL7zwgmJjY2Wz2dSyZctsz//999+SpNKlS199RwEAAAAAsJilAXvw4MHm9vTp0zVixAglJibmesz58+f19NNP6/XXXzcfGzJkiENNbGysdu3aJZvNpltuucXKLgMAAAAAYAkvKxvr3LmzHn74YX3xxRey2Wx6//33NW/ePPXs2VPNmzdXeHi4ihUrppSUFB07dkwbN27Ur7/+qqSkJBmGIZvNpgcffFCdOnVyaPeLL77Q5cuXZbPZ1LFjRyu7DAAAAACAJSwN2JL0ySefKCUlRT/++KNsNpuSkpL0zTff6JtvvnFabxiGud27d+9sa2BLGSPYAwcOlCTdddddVncZAAAAAICrZnnA9vb21vfff6+PP/5YkyZN0vHjxx1CtDPly5fXpEmTsl0anmny5MlWdxMAAAAAAEtZHrAzPfbYY3r00Uf122+/adWqVdq+fbvOnDmjpKQkBQYGqlSpUrr11lvVvn17devWjeW3AAAAAADXtQIL2JLk4eGhHj16qEePHgV5GgAAAAAACp2ls4gDAAAAAHCzImADAAAAAGABAjYAAAAAABYo0Huw09PTtW/fPkVGRioxMVF2u93tYwcMGFCAPQMAAAAAwFoFErCPHj2qF198Ud9++62SkpLyfLzNZiNgAwAAAACuK5YH7EWLFumBBx5QSkqKy/WvAQAAAAC4UVgasKOionTffffpwoUL5mNly5ZVRESEQkND5e3tbeXpAAAAAAAoMiwN2NOnT9eFCxdks9lUvnx5zZ49mzWwAQAAAAA3BUsD9u+//57RqJeXli5dqrp161rZPAAAAAAARZaly3RFR0fLZrOpffv2hGsAAAAAwE3F0oCdeY911apVrWwWAAAAAIAiz9KAXblyZUlSYmKilc0CAAAAAFDkWRqwe/XqJcMwtH79eiubBQAAAACgyLM0YA8dOlRBQUGKiorS/PnzrWwaAAAAAIAizdKAXaFCBc2dO1ceHh568sknzVnFAQAAAAC40Vm6TFdUVJQaN26s999/X0899ZS6d++uO++8U/fdd58aNGig4OBg2Ww2t9qqVKmSlV0DAAAAAKBAWRqwq1Sp4hCgDcPQwoULtXDhwjy1Y7PZdPnyZSu7BgAAAABAgbL0EvFMhmFIkhm2DcPI88/VSEtL086dO/Xxxx9r2LBhatKkiXx8fGSz2cx1ut0VGRlpHufuT40aNfLU33379unf//63GjZsqJIlSyogIEC1atXSwIEDtXz58jy++gxnz57V9OnT1apVK5UrV05+fn6qXLmyevTooXnz5slut+erXQAAAACAc5aOYFeqVMntS8ALyoIFC/Twww8rJSWlUPvhrpdfflmTJ0/OFngPHjyogwcP6rPPPlO/fv00e/ZsFS9e3K02f/nlFw0ePFhnzpxxeDwqKkpRUVFavHix3nrrLc2fP1+1atWy7LUAAAAAwM3M0oAdGRlpZXP5Eh8fX2Dhunjx4howYIDLutKlS7vV3oQJE/Tiiy+a++XKlVObNm3k5+env/76S3v27JEkzZ8/X7Gxsfr111/l5ZX7r2zp0qXq06ePeYl9sWLFdMcdd6h06dI6dOiQVq9eLcMwtHXrVt1xxx3auHGjypcv71Z/AQAAAAA5szRgFyVhYWFq2rSp+bNkyRK9/fbbV9VmyZIl9e6771rSv+XLlzuE63//+9966aWX5OPjYz42f/58DR48WBcvXtTSpUs1depUTZgwIcc2Y2Nj9cADD5jh+o477tBXX32lUqVKmTU7duxQ7969FRUVpWPHjumRRx7J92XoAAAAAID/d8MF7G7duuno0aPZZiHfuHFjIfXIufHjx5vbDz74oKZNm5atpl+/fkpISNCwYcMkSdOnT9fw4cMdAnNWr732muLj4yVJ1atX188//6xixYo51ERERGjhwoVq0qSJ7Ha7VqxYoaVLl6pLly4WvTIAAAAAuDkVyCRnhals2bJFfomvzZs3a/PmzZIkDw8Pp+E605NPPqmaNWtKks6fP6958+Y5rbPb7frwww/N/SlTpmQL15kaNmyogQMHmvszZ87M82sAAAAAADi64QL29WDBggXmdqdOnRQeHp5jrc1mcwjDP/74o9O6VatWmaPXxYsXV9++fXPtw6BBg8ztpUuXKjk52XXHAQAAAAA5ImAXgpUrV5rb7iwZ1qFDB3N73bp1Sk1NzbXNli1bytfXN9c2mzVrZo5wX7x4UevXr3fZDwAAAABAzvJ8D7anp6e5bbPZzAm1rnzualzZblFx+fJl/f7779qyZYvOnj0rPz8/lSpVSk2aNFGzZs1chtpM+/btM7cbN27ssr5Ro0bmdlpamg4cOKAGDRpcVZve3t5q0KCBeW/6vn371KlTJ5fHAQAAAACcy3PANgxDNptNhmHk6bkbwfHjx3OcDCwkJETDhw/XuHHjFBgYmGMbp0+fNi/llqTKlSu7PK+/v79Kly5trmv9999/ZwvY+/fvz1ObUsa65ZkB+++//3brGAAAAACAc/maRTy3AH2jhmtX4uLi9PLLL+u7777Tzz//rFq1ajmti42NddgPCwtzq/2yZcuaAfvcuXO5tpuXNjM5azOr1NRUh0vTExMTJWVMrma32906X0HK7ENR6AsAAABQ0IrS59+i0IeiIs8BOz09PV/PXc8yJw3r1q2bGjVqpAoVKsjb21unT5/Whg0bNHv2bC1btkxSxkhyt27dtHHjRpUuXTpbW0lJSQ77/v7+bvUha92VbVz5mFVtZvXKK69o8uTJ2R5funRpjrOVF4bff/+9sLsAAAAAXDNF4fNvSkpKYXehyLjh1sG2Wrly5RQTE+P0su+KFSvq3nvv1b333qsPPvhAQ4cOlWEYOnLkiMaPH6+PPvoo2zEXL1502Pfx8XGrH1nv775w4UKu7VrVZlbjx4/XqFGjzP3ExESFh4erS5cuCgoKcut8Bclut+v3339X586d5e3tXdjdAQAAAApUUfr8m3l1KwjYLvn6+ro1edkTTzyho0ePaurUqZKkuXPn6uWXX852ubafn5/D/qVLl7I95kzWy7OdjVD7+fmZ3xxdunTJZXvutJlVTu+Dt7d3of+Dzqqo9QcAAAAoSEXh829hn78osXSZrqioKEVFRTlM4pUXCQkJZhvXo/Hjx5tBNS0tzenlGleOhLsaOXZW52w0PetjVrUJAAAAAHCfpQG7SpUqqlq1qiZMmJCv41966SVVrVpV1apVs7Jb10xgYKCaN29u7mddOitTaGiow/6pU6fcavvkyZPmdsmSJXNt16o2AQAAAADuszRgW8EwjOt6JvJy5cqZ22fPns32fJkyZVSiRAlz/+jRoy7bvHjxojmDuCTVqVMnW03t2rXz1KYkhysFnLUJAAAAAHBfkQvY17vk5GRzOyAgwGlN3bp1ze1t27a5bHPr1q3mtqenp9MlwPLa5uXLl7Vr1y6nxwMAAAAA8q5IBezMSbfcnQW7KMoabsuXL++0pkOHDub2qlWrXLb5xx9/mNutWrVyOtlY1jbXr1/vcqKzzZs3m5Oi+fn5qWXLli77AQAAAADIWZEK2Hv27JF0/d4PvGzZMkVHR5v77du3d1p39913Oxxz7NixXNudO3eu02Ozat++vYKDgyVlTJP/ww8/uN1m586dcxxtBwAAAAC4p9ADdlpamqKiovTGG29o1apVstlsql+/fmF3S1LGclfuLnl15swZDR061NyvW7euGjdu7LS2adOmatq0qaSM1z9u3Lgc2/3ggw904MABSVLx4sU1YMAAp3Xe3t56/PHHzf0JEybkOJv47t27HQL2iBEjcjw/AAAAAMA9+Q7Ynp6e2X6kjEnKZs6c6fR5Zz8+Pj6qWrWqxo4da05u1rdvX2te3VWKiYlR9erVNW3atBwnDjMMQ7/++quaNm2qQ4cOSZJsNpumT58uD4+c395XXnnF3P7iiy80btw42e12h5pvvvlGI0eONPfHjBmjUqVK5djmuHHjzAnUDh48qLvuukuxsbEONTt37lSvXr3MLw46dOigrl275tgmAAAAAMA9NiOfU3Z7eHjIZrNZPuN3u3bttGzZMjOw50ePHj0UExPj8NjJkyfN5asCAgJUo0aNbMctWrTI4b7pyMhIVa1a1dyvUqWKGjRooFKlSsnb21tnzpzRxo0bs53r9ddf15gxY1z284UXXtBLL71k7pcvX15t2rSRn5+f/vrrL+3evdt8rnPnzlq0aJG8vLxybXPJkiW68847dfnyZUlSsWLF1KlTJ5UuXVqHDh3SH3/8Yf7OKlSooE2bNuV4r3huEhMTFRwcrISEBAUFBeX5eKvZ7XYtWrRIPXr0YKF7AAAA3PCK0uffopYNClPuac0FK8K1n5+fQkNDVb9+ffXt21eDBg26qnAtSXv37s11qark5GTt2LEj2+OuLgePjIxUZGRkjs9XqFBB7733nnr37u1WP6dMmSJfX19NmTJFdrtdMTEx+vrrr7PVPfjgg5o9e7bLcC1JXbt21Q8//KDBgwfr7NmzSklJ0c8//5ytrlGjRpo/f36+wjUAAAAAILt8B+z09PRsj2WOao8YMUL/8z//c1UdKwoqV66sXbt2af369Vq3bp327Nmjs2fPKjY2VikpKQoKClK5cuXUtGlTde/eXX369MnTt0c2m03PP/+8+vbtq48++khLly5VdHS07Ha7ypUrp5YtW2rgwIHq1KlTnvrdq1cv7d27V3PmzNGPP/6ow4cPKz4+XmFhYbrlllvUr18/9evXr9C/6QIAAACAG8lVjWA7Y/Ul4/mR2yhzXmROuFa/fn2HCcSsVrduXb3xxhuWtlm6dGmNHTtWY8eOtbRdAAAAAIBzlgbslStXSsq4VBoAAAAAgJuJpQG7Xbt2VjYHAAAAAMB1o9DXwQYAAAAA4EZAwAYAAAAAwAKWT3KW1ZIlS7Rs2TJt375dZ8+e1fnz553OPn4lm82mQ4cOFWTXAAAAAACwVIEE7A0bNujRRx/VgQMHzMcyZxe32WzZHstks9lkGIZDDQAAAAAA1wPLA/ayZcvUs2dPXb58OccAfeVjUkbYLgpLfAEAAAAAkB+W3oOdnJysfv36yW63yzAMDR06VBs3btSAAQPMmiNHjmjnzp1auHCh/v3vf6tMmTIyDEOBgYH67LPPdOTIER0+fNjKbgEAAAAAUOAsDdgfffSRYmNjZbPZNGbMGL333ntq2rSpihcvbtZUrlxZ9evXV8+ePfXaa6/p8OHDevLJJ5WUlKTHHntMO3fuVOXKla3sFgAAAAAABc7SgL1kyRJJkp+fnyZMmODWMf7+/po1a5aGDBkiu92uQYMG6eTJk1Z2CwAAAACAAmdpwN61a5dsNptatGihwMBApzU53Wf9xhtvKCAgQPHx8ZozZ46V3QIAAAAAoMBZGrBjY2MlSVWrVnV43Mvr/+dSu3DhgtNjixcvrvbt28swDC1YsMDKbgEAAAAAUOAsDdiZo9M+Pj4Oj2e9B/vEiRM5Hl+uXDlJUlRUlJXdAgAAAACgwFkasEuWLClJSkpKcni8bNmy5va+fftyPP748eOSpLi4OCu7BQAAAABAgbM0YNeuXVuGYejo0aMOj0dERJjbv/zyi9NjExIStHHjRklSSEiIld0CAAAAAKDAWRqwmzZtKknas2ePw+PNmzdXqVKlZBiGPv30U61evdrhecMw9NRTT+ncuXOy2Wxq3ry5ld0CAAAAAKDAWRqw77jjDkkZl3j/9ddf5uNeXl568sknJUmXLl3SHXfcod69e+u///2vnn32WdWpU0dffvmlWf/EE09Y2S0AAAAAAAqcl+sS93Xs2FElS5bUuXPnNG/ePN12223mc//973/1yy+/aMeOHUpPT9evv/6qX3/9NVsbAwYMUI8ePazsFgAAAAAABc7SgO3l5aXt27crOTlZ/v7+Ds/5+flp5cqVGj58uL7++uts62EXK1ZMY8aM0YQJE6zsEgAAAAAA14SlAVuSKlasmONzJUqU0Jdffqnp06drxYoViomJkYeHh6pVq6aOHTuqRIkSVncHAAAAAIBrwvKA7Y7y5curf//+OT6fmJiooKCga9gjAAAAAACujqWTnF2t+Ph4TZgwQVWqVCnsrgAAAAAAkCeFMoJ9pdjYWL3xxhuaOXOmkpKSCrs7AAAAAADkWaEG7NOnT2vatGmaPXu2UlJSzInPbDZbYXYLAAAAAIA8u+pLxLdu3aqhQ4eqXr16KlGihPz8/FS5cmU98MADWrFihdNjEhIS9Nxzz6lq1aqaMWOGkpOTzecCAgI0evToq+0WAAAAAADX1FWNYI8ePVpvvfWWJDksuxUdHa1jx47pu+++06hRo/T666+bz3333XcaNmyYzp07J8MwzNHqgIAAjRgxQqNHj1apUqWuplsAAAAAAFxz+Q7YU6ZM0YwZMyTlfEm3YRh68803FRYWpjFjxuj555/XK6+84hCsAwMD9fTTT2vUqFEqWbJkfrsDAAAAAEChylfAjomJ0csvv+wQrDt06KBbb71V/v7+iomJ0fLlyxUdHS3DMDRt2jSFhoZq6tSp5jHFihXTyJEjNXr0aNa/BgAAAABc9/IVsD/99FPZ7XbZbDZVqlRJCxYsUEREhENNWlqaXnnlFU2YMEGxsbF68sknJWWManft2lUfffSRKlSocPWvAAAAAACAIiBfk5ytXr3a3J4/f362cC1Jnp6eev755/Xggw/KMAxdvnxZNptNDz30kBYtWkS4BgAAAADcUPIVsPfu3SubzaYGDRqoRYsWudYOHz7c3Pb19dWbb77JMlwAAAAAgBtOvgJ2XFycJDkdub5SZo3NZlOrVq1UpkyZ/JwSAAAAAIAiLV8BOykpSZIUHBzssrZ48eLmdqVKlfJzOgAAAAAAirx8Bez8CgwMvJanAwAAAADgmrmmARsAAAAAgBsVARsAAAAAAAvkax3sTJs2bdKUKVMKpH7ChAn57RYAAAAAANfcVQXszZs3a/PmzS7rMpflcrdeImADAAAAAK4vVxWwDcOwqh8OWCcbAAAAAHC9yVfAbtu2LSEYhSIhIePnSna7dOaMv6KiJG/v7M8HB2f8AAAAAEBByVfAXrVqlcXdANyzerX0yy+Oj6WlSWvXeiopqaXWrfNwGrDvvFPq1eva9BEAAADAzemqLhEHrrW2baWICMfHUlOlCRMMHT8er3//208BAZ7ZjmP0GgAAAEBBI2DjuuLsUu/UVCkwUPL3T1N4eMY2AAAAAFxrrIMNAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFbsiAnZaWpp07d+rjjz/WsGHD1KRJE/n4+Mhms8lms6l9+/aWnevnn3822838iYyMdPv4+Ph4vfvuu7rzzjsVHh6uYsWKydfXV2XKlNHtt9+ucePG6e+//85zv6KiojRp0iTddtttKl26tPz9/VW9enXde++9+vHHH2UYRp7bBAAAAADkzKuwO2C1BQsW6OGHH1ZKSkqBnysxMVHDhw/P9/Fff/21hg8frnPnzmV77syZMzpz5ozWrl2r119/XcOGDdOMGTPk7e3tst2PP/5YzzzzjJKTkx0eP3z4sA4fPqzvv/9enTp10rx581S2bNl89x8AAAAA8P9uuIAdHx9/TcK1JI0dO1bHjx/P17Fffvml+vfv7zCSfNttt6l69ery8/PTkSNHtGnTJqWmpio9PV0zZ87UiRMn9P333+fa7ieffKIhQ4aY+yVKlFDHjh0VHBys3bt3a/PmzZKkZcuWqWvXrlq7dq0CAwPz9RqKAsMwtC0qXjuOSecSbZqx7B/1bFxWjcJLyGazFXb3AAAAANxEbshLxCUpLCxMd955pyZPnqxFixbpmWeesbT9P//8Ux988IEk6aGHHsrTsXFxcXrqqafMcH3rrbdq8+bN2rJli77++mt9+umnWr16tQ4ePKh77rnHPO6HH37QDz/8kGO7+/fv19ChQ839hx9+WMeOHdP333+vTz75RJs2bdLy5csVEhIiSdq5c6f+9a9/5anvRcmBU+d118y1uv/9DdoTmaKYWB/N+vm0+sxcp7tmrtWBU+cLu4sAAAAAbiI3XMDu1q2bjh49qpMnT2rhwoWaMGGCunfvrhIlSlh2josXL2rIkCEyDEM1atTQCy+8kKfjf/rpJ8XFxUmS/P399euvv6pJkybZ6sLDw/XNN9+oYcOG5mNffPFFju2+8MILstvtkqTWrVvrs88+U0BAgENNx44dHdr47LPPtG/fvjz1vyg4cOq87p21Tlv+MhS3so5Sj5VUakyI4lbUU9zyutryl6F7Z60jZAMAAAC4Zm64gF22bFlVqlSpQM8xZcoUHThwQJI0a9Ys+fn55en4nTt3mtvt2rVT+fLlc6z19PTUgw8+aO5nnvdKp06dcrh8fNq0afLwcP7r7d69uzp16iQpY0K4999/P0/9L2yGYWjMtzt09mgxxa+tqUungyXPNNl87PLwu6TUk8GKX1tTZ48W07+/3cGEbgAAAACuiRsuYBe0HTt26PXXX5ck9e/f3wyqeZGUlGRuZ16unZuSJUua2+np6U5rfv75Z/O5WrVqqVWrVrm2OWjQIHN7wYIFLvtQlGyLjteO6AQl7aqgtBRveZdMks3TkM0mefimyTs0SWkp3kraXUHboxO0PTq+sLsMAAAA4CZAwM6DtLQ0DRkyRJcvX1bJkiX15ptv5qudrCPse/bscVm/e/duczsiIsJpzcqVK81td5Yh69Chg7kdFRWlf/75x+UxRcXve0/pclyA7LHF5VX8oq6cy8xmk7yKX5T9bHFdjgvQ0r2nCqejAAAAAG4qBOw8mDFjhrZs2SJJev3111W6dOl8tdO7d29ze+fOnfrqq69yrN27d6/mzJkjSbLZbBo2bJjTuqz3UTdu3NhlH8qXL6+wsDCnxxd1CRfsSk/1knHZQzbvy05rbN6XZVz2UHqqlxIu2K9xDwEAAADcjAjYbjp8+LAmTpwoSWrbtq0effTRfLfVsGFDh6W0+vfvr8GDB2vDhg06e/askpKStGvXLk2aNEktWrRQcnKybDabXnvtNbVp08Zpm/v37ze3K1eu7FY/so6k//333/l8NddesL+3PHwvy+aVLsPufKU5w+4lm1e6PHwvK9jf9drhAAAAAHC1brh1sAvKE088oZSUFPn4+Gj27NlXvcby7NmzFRQUpDfffFNpaWmaM2eOOVJ9pZYtW+qFF15Q9+7dnT5/4cIFXbhwwdzPOjKdm7Jly5rb586dy0PvC1fnemF6b+UheYeeV+rJYHmXTHZ43jCky+f95FsuQV4hyepSz733AwAAAACuBgHbDZ988omWL18uSRo3bpzq1Klz1W16eHjojTfe0JAhQzRixAiHe6izCg8P17333pvjyLXkOGmalLH0lzuy1l3ZxpVSU1OVmppq7icmJkqS7Ha7uTTYtVK/bIAaVgzSXw2O63JiMdnPBcpIs0kehtJTPXU5yU+exewKrH9cERWDdEvZgGveRwAAAKAgZX6+LQqfc4tCH4oKArYLp06d0pgxYyRlzM79n//8x7K2v/vuO/33v//VgQMH5OnpqWbNmql27dry9PTUoUOHtG7dOkVHR2v06NF688039d1336lFixbZ2rl48aLDvo+Pj1vn9/X1NbezjoA788orr2jy5MnZHl+6dKmKFSvm1vms1LOU9E95T6n1QZ3fUVEXEsrKuGxT+kUf+ZZLUGD94woun6AepdK0ePHia94/AAAA4Fr4/fffC7sLSklJKewuFBkEbBeeeuopxcXFScq4rDtrKL0azz33nKZNmyZJat26tebOnasaNWo41MTExGjo0KFauHChjh8/rm7dumnz5s2qWbOmQ92V63BfunTJrT5kHZF2Neo9fvx4jRo1ytxPTExUeHi4unTpoqCgILfOZ7W2bZL03I+7taPk30q/4CMjzUMlbj8g79JJiqgYpFf7tFbNsMBC6RsAAABQkOx2u37//Xd17txZ3t6FO+dQ5tWtIGDn6qefftJ3330nKWPdaHeWv3LHV199ZYbrunXrasmSJQoICMhWV758ef3www/q1KmT/vjjDyUkJOiZZ57RokWLHOoCAx1DpKvRaGd1V7ZxJV9fX6dfLnh7exfaP+h6FUP081O3a9M/8Xp6r3QuPlEDe5fRnY0b6tbwEld9nzwAAABQ1BXm5/GsfUAGZhHPQUpKioYPHy5JKlWqlKZPn25Z21kvtX7hhRechutMXl5eevnll8393377TSdOnHCo8ff3dxiBPnXKvXWfT548aW6XLFnSrWOKGpvNplsrhSiiYpCqBxl6tlMNNaoUQrgGAAAAcM0xgp2D06dPKyYmRlJGiOvZs2eOtVkvtZakPn36mKO9PXv21AsvvGA+d/ToUYclsTp27OiyLy1btpS/v78uXLggwzC0devWbP2pXbu2tm/fbp7DHVFRUea2FRO3AQAAAMDNjIDthjNnzujMmTNu12cGXSl7cD1+/LjDfmhoqMv2PDw8FBISYl7SnZCQkK2mbt265nm3bdvmss2YmBiHke66deu6PAYAAAAAkDMuEb/GrpxMzJ31p9PT0xUfH2/ulyhRIltNhw4dzO1Vq1a5bPOPP/4wtytVqpRtgjUAAAAAQN4QsHNQpUoVGYbh1s+RI0ccjj1y5Ij53Ny5cx2eCw8Pd7g/OKf1r7PasGGDw9T3zsJw79695eGR8evcv3+/NmzYkGubWft11113uewDAAAAACB3BOxrrFSpUoqIiDD3X3rppVxn/U5LS9Pzzz9v7lepUkW1atXKVhcWFqZ77rnH3B87dqwMw3Da5tKlS7V06VJJkqenp4YOHZrn1wEAAAAAcETALgRZ15PevXu3unXrpsOHD2erO3HihO69916HUe6xY8fm2O6LL75oTpH/559/auDAgUpOTnaoWblypR566CFzf8CAAapXr16+XwsAAAAAIMMNOclZjx49zBnAM2VdkmrLli269dZbsx23aNEilS9fvqC7p/79+2vhwoX69ttvJUmrV69WrVq11Lx5c9WuXVseHh46fPiw1q1b5zBDea9evfTEE0/k2G6dOnU0a9YsDRkyRJI0b948/fLLL+rYsaOCgoK0d+9ebdy40axv2LCh3n777QJ6lQAAAABwc7khA/bevXtzXaoqOTlZO3bsyPb4pUuXCrJbJpvNps8//1yVKlXSjBkzlJ6errS0NK1bt07r1q1zWj9ixAi9/vrr8vT0zLXtxx57TIZhaOTIkUpOTlZcXJy+//77bHV33HGH5s2bp+LFi1v2ugAAAADgZnZDBuzrgY+Pj6ZPn66nnnpKc+bM0Z9//ql9+/YpLi5OUsZM4TVr1lSbNm00ePDgPM3yPWTIEHXp0kUff/yxFi5cqKioKCUlJalcuXJq3Lix+vfvr7vvvtthsjUAAAAAwNWxGTnNhAXkIjExUcHBwUpISFBQUFCh9iU1VXrqqTRFRUXr++8rKDDQu1D7AwAAABQ0u92uRYsWqUePHuY8TIWlKGWDwsYkZwAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFjAq7A7AORFQkLGT1apqVJSknThgqeio6WAgOzHBQdn/AAAAABAQSFg47qyerX0yy/ZHw8MlPz90/Tmmx7ycHJdxp13Sr16FXz/AAAAANy8CNi4rrRtK0VEZH/cbk/XypUH1KFDBXl7e2Z7ntFrAAAAAAWNgI3rSk6XetvtUunSF1SpkuTtfe37BQAAAABMcgYAAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWMCrsDuA65NhGJKkxMTEQu5JBrvdrpSUFCUmJsrb27uwuwMAAAAUqKL0+TczE2RmhJsZARv5cv78eUlSeHh4IfcEAAAAQFFw/vx5BQcHF3Y3CpXN4GsG5EN6erpiYmJUvHhx2Wy2wu6OEhMTFR4erujoaAUFBRV2dwAAAIACVZQ+/xqGofPnz6t8+fLy8Li570JmBBv54uHhoYoVKxZ2N7IJCgoq9P/AAAAAANdKUfn8e7OPXGe6ub9eAAAAAADAIgRsAAAAAAAsQMDGDcHX11cTJ06Ur69vYXcFAAAAKHB8/i2amOQMAAAAAAALMIINAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2JAktW/fXjabTTabTatWrSrs7uAGFhkZaf6tValSpbC7AwAArmNVqlQxP1dERkY6rRk0aJBZM3fuXKc1c+fONWsGDRpUYP3NjVWvpShw57XcqG7ogJ01NDr7KV68uCpVqqRu3bpp6tSpOn78eGF3GYUoOjpaL7/8srp27arw8HAFBATI29tbJUqUUJ06ddSjRw/997//1cKFC5WUlFTY3QUAACjysn4enzRpUp6OnTRpknls+/btC6R/VoiKitKsWbN0zz33qF69eipTpox8fHwUEhKiGjVq6J577tFrr72mw4cPF3ZXcQ14FXYHClNSUpKSkpIUHR2tJUuWaNKkSfrvf/+rCRMmyGazFXb3cI1cvHhRzz//vN566y2lpaVlez4hIUEJCQnav3+/Fi9eLEny9vbWH3/8oZYtW17r7gIAAKAIiI6O1pQpUzR37lxdvnw52/Px8fGKj4/XoUOH9OOPP2rcuHHq2LGjpk6dqubNmxdCj3Et3DQBu2nTpmrWrJnDYwkJCdqxY4d27dolSbLb7Zo0aZLi4+M1Y8aMwugmrrFLly7prrvu0tKlS83HfHx81KRJE1WvXl3FihVTYmKiIiMjtX37dl24cEFSxt9KcnJyYXUbAAAAhWjlypXq27ev4uLizMdsNpsaNmyo6tWrKzQ0VOfPn9eJEye0ZcsW83PjihUr1KJFC23YsIGQfYO6aQJ2jx49crwsZd26derXr5+ioqIkSW+99ZYefvhhNWnS5Br2EIXh1VdfNcO1zWbT2LFjNW7cOJUoUSJbrd1u16pVq/TNN99o/vz517inAAAAuFJh3N+7cOFC9e3bV3a7XZIUEBCgUaNGacSIEQoLC8tWn5qaqmXLlunVV1/VmjVrJMkctMmruXPnFul7r3GD34PtrlatWumnn35yuCz8gw8+KMQe4Vqw2+0OVypMmTJFr776qtNwLWVcFt65c2d9+OGHOnbsmBo1anSNegoAAICi4PDhwxowYIAZritXrqy//vpLU6ZMcRquJcnX11c9e/bUn3/+qR9++EEhISHXssu4xgjY/+fWW291mDxh9erVhdcZXBObNm1SfHy8pIzw/Mwzz7h9bIkSJRQaGlpAPQMAAEBR9MQTT5ifHwMDA7VixQrVrl3b7eP79OmjLVu2KDw8vIB6iMJGwM7i1ltvNbdjYmJyrLPb7VqyZInGjh2rDh06qHz58vLz85O/v78qVqyo7t2766233nJrpumclizasmWLhgwZolq1aqlYsWIKCQlRs2bNNHXq1Dzd+5uenq5PP/1UnTt3VtmyZeXn56cqVarorrvu0oIFC9xu50pHjx7VhAkT1KJFC4WFhcnHx0dhYWFq0aKFJk6cqOjoaJdtrFq1yunMkL/88ovuueceValSRX5+fgoNDVX37t21aNEip6/vp59+0p133qmqVavKz89P5cqV03333acNGzbkev6ss8aXLFlSxYsXd/8NyIPNmzfr2Wef1a233qrSpUvLx8dHZcuWVbt27fTaa6853LuTm9OnT2vOnDkaOHCgGjVqpJIlSzrMcv7oo49qyZIlbrWVdVbOzFsnLly4oI8//lhdunRRpUqV5OPjI5vNpu3btzttY82aNXrmmWfUqFEjlSlTRt7e3goKClKDBg00cOBAzZ8/P0+XP1n1Nw8AAG4e13I5qC1btmj58uXm/tSpU1WtWrU8t1OtWjVVr149X31wZ5kuZ5/zLl68qNmzZ6t9+/YqV66cfHx8VLFiRQ0YMEB79+7N1kZSUpJmzpyp22+/XeXKlZOfn5+qV6+uESNG6NixY/nq+99//62RI0eqXr16CgoKUlBQkBo2bKjnn39eJ0+ezFNbhmHoxx9/1MCBA1WrVi0FBwfLz89P4eHhuvvuu/Xpp586nXguq5xy2Jo1azRkyBDVqVNHwcHBstlsGjlyZJ46d8Nq166dIcmQZEycONFl/X/+8x+z3tvb22lNVFSUERoaatbl9hMaGmosXbo013MeOXLErK9cubKRnp5uTJgwwfDw8Mix3apVqxqHDh1y+XpOnDhhNG/ePNc+9unTx0hMTHR4r1auXJlruy+99JLh5+eXa7t+fn7Gq6++mms7K1euNOvbtWtnJCcnGw8++GCu7Wb9PZ4+fdpo1apVjrU2m8145513cjz/t99+61CblJTk8j3Ni3Pnzhl9+/Z1+XdSokQJ49tvv821rbffftvw9PR06++uY8eOxtmzZ3Ntb+LEiQ7v6d69e41bbrnFaXvbtm1zODY6Otro3LmzW31p3rx5tnMX5N88AAAoWvL6eTyrrJ9X2rVr57SmcuXKZs2RI0ec1gwcONCsmTNnjtOaOXPmmDUDBw50WjN48GCzJjg42EhOTs7T63HFqtdy5ee8Q4cOGRERETl+zvL19TV+++038/hNmzYZFSpUyLE+KCjIWL9+fZ5eywcffGD4+vrm2GZISIjx008/ufU+7dixw7j11ltdfg6tXbu2sWfPnhzbufIzaWpqqvHkk086beuZZ55xq2+GYRg3zSRn7sg6ap3TPRTJycmKjY2VJIWEhOiWW25R5cqVFRgYqEuXLunIkSPasGGDLl68qNjYWPXo0UN//PGHWrVq5VYfJk+erClTpkjKGFFv0KCBvL29tX37dm3dulWSdOTIEd19993aunWrvLyc/wrj4+PVsWNH7du3z3ysatWqatmypXx9fbVnzx5t2rRJP/74ozw83L+Q4amnntLMmTPN/cDAQHXo0EFly5bVyZMntXLlSiUlJenixYsaN26cTp486faM7I899pi++uoreXl5qXXr1qpRo4ZSUlK0YsUKnTp1ynx/ateurbvvvltdunTR9u3b5efnp7Zt26pSpUqKj4/X8uXLFRcXJ8Mw9K9//Uu33Xab0+W0sn5zaBiGpk2bpsmTJ7v9XuTm5MmT2d7/W265RREREQoMDNTp06f1559/KjY2VvHx8br//vs1b948Pfzww07bi4mJMZcQq1atmurWravSpUvLz89P8fHx2rVrl/bs2SMpY3bKTp06acOGDfL19XXZ19jYWHXr1k1RUVHy8/PT7bffrsqVKyspKSnbVQB79uxR586ddeLECfOxMmXKqFWrVipdurQuXryoQ4cOadu2bbpw4YIuXrzo8vxW/c0DAAAUpBUrVpjbd911l4oVK1aIvXFPYmKiunfvrgMHDigoKEjt2rUzP7cvX75cKSkpSk1NVZ8+fbRr1y7Z7XZ16tRJiYmJKlWqlNq2bavQ0FBFRUVpxYoVstvtSkxM1N133639+/crODjYZR9++ukncwS4QoUKuv322xUYGKgDBw5o7dq1Sk9PV1xcnO69914tXLhQXbt2zbGt1atXq1evXkpMTJSUcZtn06ZNVbNmTXl7eysyMlJr1qzRxYsXtX//frVq1Urr169X3bp1Xfbz2Wef1ezZsyVJDRo0UEREhLy9vXXgwIE85SVGsP+P3W43wsPDzfp7773XaV1kZKTx9NNPGxs3bjTS0tKc1iQkJBijR48226pVq1aOtVm/OfHx8TFsNptRvXp1Y+PGjdlqv/nmG8Pb29us//TTT3N8PVm/YfPx8TE+/vjjbDUbN240v13y8fEx63Mawf76668dvskZNGiQkZCQkO219+/f36Hu+++/d9pe1hHszG+0WrVqlW2kMiUlxbjvvvvM2po1axpPP/20IWWMwJ86dcqh/ty5c0bbtm3N+g4dOjg9f3p6ulGlShWHUez+/fsb69evN9LT03N6a11KS0szOnToYLbbrFkzY+vWrdnqLly4YEyaNMmw2WyGJCMgIMA4fPiw0zY//vhj45133jGOHTuW43l37NhhNGnSxDzviy++mGNt1m82vby8zL/506dPZ3stly5dMgwj43dbs2ZN87hSpUoZX375pdP3Kikpyfjiiy+MRx99NNtzBfU3DwAAip4bZQQ7Ojra4fPtzJkz8/Ra3FEQI9iZn7Eff/xxIzEx0aEuOjraqFOnjsPrvu222wybzWZMmjTJSE1NdajfvXu3UbZsWbN+8uTJbr0WHx8fw8PDw3jjjTeyZaI9e/Y4XEVZtmxZ49y5c07bPHHihFGmTBmzdsCAAUZMTEy2upMnTxp9+vQx6xo0aGBcvnw5W13Wz6SZV4qGh4cbq1evzlZ78eLFHF/rlQjY/+fFF190+Efj6tJudwwdOtRsb9GiRU5rsv5ipYzLyo8fP55jm2PGjDFru3Xr5rRm//79ZmiTZMydOzfH9vbv328UK1bMoQ/OAnZaWppRtWpVs+a+++7LMYSmp6cbd911l1lbvXp1p18wZA3Y+r/LOHK6TDsxMdEoWbKkQ33Hjh1z/OIiMjLS/Idis9mMEydOOK377rvvnF4GEhoaavTo0cOYMGGCsXDhwhz/oTvz2Wefme20aNHCSElJybU+638Ehw4d6vZ5nImPjzf/w1euXDmn/zG58pySjC5duuT4Xmb673//a9YHBwcbf//9d776WBB/8wAAoGjK+nm8adOmxogRI9z+adq0aZEJ2H/++afD55dVq1bl7w3JRUEEbElG//79czznmjVrsn0Ozi03ff7552Zd3bp13XotknK9dfTEiRNGqVKlzNoXXnjBaV3WAcR//etfObZnGIZx+fJlo2PHjmb9V199la3mys+kxYoVM/bv359ru+64qQN2QkKCsXr1aqNfv34Ob+6zzz5ryfk3btxotjlq1CinNVf+Yt94441c29y7d69DMHFm7NixZk2zZs1c9jPrvec5BezFixc7fAuVU2DNdOzYMYeRx6z3dWS6MmD/+OOPubb5yCOPONRv37491/o2bdqYtQsXLsyx7qOPPnJ5T7nNZjOaNWtmvPPOO8aFCxdyPW/We0Jc9dEwMkayS5QoYQZXV0HXlWHDhpnn37lzp9OaK//Du3fv3lzbvHjxotlHScasWbPy3b+C+JsHAABFU9bP41fzU9gB+6effsrT59D8KIiA7ePjY5w8eTLX81aqVMmsDwsLyzZynVVKSop55avNZss2Ku7stVStWtWw2+259uF//ud/zPry5ctnG8g7ffq0ed6yZcu6/DxuGIaxfv16s81evXple/7Kz6Rjx4512aY7bpqbGSdPnuzy/trQ0FD9+9//1nPPPedWm3a7XRs3btSOHTt08uRJnT9/3mG2uvPnz5vbOc3EfKX77rsv1+fr1Kkjf39/XbhwQbGxsTp//ny22a9Xrlxpbj/yyCMuzzlw4EBNnTo115qs95z06NFDZcuWzbW+QoUK6tatmxYuXGj2Kbf7Kfz9/dWzZ89c22zQoIG5XaNGDUVERORaX79+ff3555+SMu7hzcljjz2mzp0767XXXtP8+fOdzuptGIY2bdqkTZs26bXXXtO8efMcZj7PdOLECfN3Xa9ePZd9lCQ/Pz+1bNlSixcvVkJCgnbv3q2GDRvmWH/69Glt2LBB+/btU1xcnJKTk2UYhvn8li1bzO3t27c7vG/ONGzY0OV9KRs2bDCXpChevLgGDhzo8nW5y4q/eQAAgIKU9XO9lDEP0fWgTZs2Oc4tlal+/fqKioqSJPXq1Us+Pj451vr7+6t69erat2+fDMNQZGSky8+aDz30kMs5dPr3769nn31WaWlpiomJ0f79+1WnTh3z+WXLlunSpUuSpHvuuUd+fn65tidJzZs3V0BAgJKTk7VmzRqX9Q8++KDLGnfcNAHbFU9PT7322mt67LHHXNZeuHBBU6dO1fvvv6+zZ8+61b47dcHBwS7XxLPZbAoJCTGXP0pMTHQIG4ZhaMeOHea+s8m9rlSrVi2VLFlS586dy7Fm27Zt5ra7E7a1bt3aDNiZk1Xl1gdvb+9ca0JCQsztW265xeX5S5YsaW5nToSQk0qVKmnmzJmaMWOGNm7cqD///FObN2/WX3/9lW3JsWPHjqlz58769ddf1aVLF4fn1q9fb25fuHBBTz31lMt+StKhQ4fM7ejoaKcBe+/evXruuee0ePFic8IzV9z5u7vttttc1mSd7KxFixby9/d36/yuWPE3DwAArg8TJ040l41yx6RJkyybgPZqXfnZw53leIuC+vXru6wpyM/Yknt5JCQkRLVr1zaXDNu2bZtDwM76GXvnzp1uf8bOlDkoFRAQ4PR5b29vl18UuOumCdhNmzZVs2bNzP2kpCRFRUVp3bp1Sk1NVVpamoYMGaLDhw/r5ZdfzrGduLg4dezY0e0R6UxXfuvljDuz8ElyCKJ2u93huYSEBPPbHSkjOLqjUqVKuQbsM2fOmNuVK1d2q82s68m5CnruvPas33zltf7K9yknPj4+atOmjdq0aWM+FhkZqe+++05vvfWWuXb25cuXNWDAAB0+fNhhBsmsM9EfOXLEYcZ1dzkbQV+yZInuuusupaam5qktd/7uSpcu7bImcxZ3Sfla7zEnVvzNAwAAFLSsoVKSeWVfUVcUPmPnJY9kBuys2UNy/Iy9Zs0at0akrxQXF5djwA4JCbFspZo8zDd+fevRo4feffdd82fu3LlasWKFIiMj1a9fP7Nu6tSp+uabb3JsZ8SIEWa49vHx0ZAhQ/TTTz/pwIED5iXiRsa97Q6XJaenp7vso81my/8L/D9Xfpvm7vIBOf2xOWvXVa2zOldBL6+v3Yr3yl1VqlTRmDFjtHfvXofLwk+dOqWvv/7aoTYhIeGqz5f1NgMp4z8wDzzwgBmuK1eurFdeeUVr1qxRTEyMUlJSlJ6ebv7dTZw40TzWnb87d0ajs/7+rLwk6lr+HgEAAPIr68CRJDMIFnVF4TN2fvLIldmhID5jZ2XV1ZnSTRSwc1K2bFl9/vnnDvcHDxs2zOko4vHjx/XVV19Jkjw8PPTbb7/pww8/VO/evVWzZk0FBgbK09PTrHdn9NBqV4aflJQUt45LTk52u11Xtc7qboRLeoOCgjRv3jyH33HmPd6Zsv6HoXfv3mbozcvPoEGDHNr88MMPzf+oREREaOfOnRo3bpxat26tcuXKyd/f3+E/hgXxd5f193e9XBIFAABglYoVKzpcxblx48ZC7M31JT955MrskPUz9ptvvpmvz9hXfklSUG76gC1lhOWPPvrI/MWdO3fO6aRfK1asMCeT6t69uzp06JBru0ePHrW+sy4EBwc7TEyQOWGBK1feZ3ylrJcRu9tmZGSkuV2qVCm3jinqKlas6HBvyokTJxyezzqJxMmTJy055/Lly83t559/XkFBQbnWF8TfXdbXlduEcQAAADeqjh07mts//fST28HxZpefPHJldiiIz9gFhYD9fypWrKiRI0ea+++++262X17Wa//duQl+9erVlvXPXTabzWHm6qyTU+Xk4MGDio2NzbWmUaNG5va6devc6kvWusaNG7t1zPUg66yFvr6+Ds81b97c3N6+fbvbo/25ycvfXVpamtauXXvV57xSixYtzO3169ebE44BAADcLIYNG2Zux8fH65NPPinE3lw/3Mkj8fHx+vvvv839K7ND1s/YBfFZ10oE7CxGjx5tXo5w8eJFTZs2zeF5D4//f7tcfWOVkpKizz77zPpOuiHryPrnn3/ust6dfmb9xm7RokU6ffp0rvUxMTFavHix0+OvZ6mpqQ7/+K+ctKFatWrmkleXLl3Sxx9/fNXnzMvf3YIFCwrkW70WLVqYM0yeP3++0P62AQAACkvTpk0dPtP+5z//cbhi012HDx92WEHmRjd//nyXK+B88cUXZk25cuVUu3Zth+e7du1qTkK2bt06h1WTihoCdhYhISF6+umnzf3Zs2c7zGCXdfbkRYsW5fqHMnr0aIeZl6+lrEuNbdiwIdeQ/c8//2jGjBku2+zSpYuqVq0qKSNkZh3tv5JhGHr66afNWQWrV6+uTp06udn7a2fjxo2aPn16ni7vmTZtmsNyBN26dctWk3Ud9eeff167du1yu31n4Tjr393PP/+c47FnzpzRs88+6/a58sLX11fDhw8395977jnt37+/QM4FAABQVH3wwQfm7Xrnz59Xx44ddfDgQbePX7BggZo0aeLy9swbyaFDh3LNG6dOndKUKVPM/cceeyzbZGsVKlRQ//79JWVkjQEDBri1RJiUMenvlbOSFyQC9hVGjRplTuiVkpKiN954w3yuY8eO5ix4//zzjwYOHJhtiv7ExEQ98cQTev/9992ebdtqtWrVcpgoa8iQIfr000+z1W3ZskWdO3dWcnJyrgvKSxmjqK+++qq5P3/+fD3++OPZJrw6f/68Hn30Uf3www/mY9OmTXMYhS0q4uLi9O9//1tVqlTRqFGjtHXrVvMe+yudPXtWzz77rCZMmGA+1qhRI6cBu3///ua3m+fPn9ftt9+u2bNnOyyfllViYqK++OILtW/f3uELnky9evUyt1955RWnX5hs3bpV7dq1U3R0dIH93Y0dO1bVq1eXlDGT4+23366vvvrK6XuWkpKi+fPna/DgwQXSFwAAgMJQvXp1ffrpp+Zo6pEjR9S4cWNNmjQpx8G11NRULVq0SG3btlWfPn2cTqZ8I/Px8dFzzz2nt99+O9sKN/v27VPnzp3Nq2PDwsJyHDB6+eWXVa5cOUkZa2E3a9ZMS5cuzfG8x44d04wZM1S7du1sK/8UpJtmHWx3hYaGasSIEXrttdckSTNnztTYsWNVsmRJhYSEaMyYMeY3LF988YUWL16s5s2bq0KFCjpx4oRWrVql5ORkeXl56b333tPAgQML5XW8+eabWr9+vfbv36/U1FQNGjRIU6ZMUcuWLeXr66s9e/Zo06ZNMgxD99xzj2JjY/XHH3/k2ub999+v1atXm2s7f/TRR/r666/VoUMHhYWF6fTp01q+fLlD6B45cqTuueeeAn2tV+vMmTOaMWOGZsyYoeDgYN12220qV66cihcvrqSkJB08eFB//fWXw9T+YWFh+uKLL5x+ceDp6alvvvlGnTt31rZt25SYmKihQ4dq7NixatmypSpUqCBPT0/FxcVp//792rdvn9l23759s7U3cOBAvfHGGzpw4IBSU1P1yCOPaOrUqYqIiJCfn592796tLVu2SMqYZbxr167Zbm+wQlBQkH744QfzP4Jnz55Vv379NHLkSLVq1UqlS5fWxYsXdejQIW3dulUXLlxwmA8AAADgRnD33Xdr8eLFuu+++xQfH6+kpCRNnjxZU6ZMUUREhKpXr67Q0FCdP39eJ06c0ObNmx3m5fHw8Ci0gbjCMG3aNI0cOVIjR47U9OnTdfvttyswMFAHDhzQmjVrzNDt5eWlTz75JNua45nKly+vn376ST169NDZs2e1f/9+de3aVRUqVFCzZs1UunRp2e12nT17Vrt37y60iXkJ2E6MHj1a7777rpKTk5WUlKQZM2boxRdflCRNmDBBkZGR5j2o586dc7jXWJJKlCihOXPm6NZbb73WXTeFhIRoxYoVuuuuu8zwdfjwYR0+fNihrnfv3po7d67DKGlu3n33XZUtW1YvvfSSUlNTdf78eaeXLfv5+WnChAkaP3781b+YAlK1alW1a9dOa9asMS/3T0hI0IoVK3I9rnv37po5c6Z5ybwzoaGhWrt2rUaNGqWPPvpIly9fVmJiopYsWZLjMf7+/rrtttuyPe7r66uFCxeqe/fu5u9v37592rdvn0Nd69at9fXXX+vDDz/Mtf9Xo2HDhtq0aZMGDBhgTuJ36tQp/fjjj07rrVwzGwAAoKjo1KmTduzYoUmTJumzzz5TWlqaDMPQ9u3btX37dqfHeHh4qHv37nr55ZdvqkGIu+66S76+vnrmmWd07Ngxc9njrEqUKKFPPvlEPXr0yLWtpk2basuWLXrsscfMlXaOHz+e42dRKWNgrGbNmlf3IvKAgO1E6dKlNWzYME2fPl2S9M4772j06NEqUaKEPD099emnn+q+++7TBx98oI0bNyouLk4hISGqVKmS7rrrLg0ePFjly5fP16QHVipfvrw2bNigzz77TF988YV27typhIQEhYWFKSIiQgMHDlTfvn3zvKD8888/r0ceeUQfffSRlixZoiNHjig+Pl4lSpRQtWrV1LVrVw0ZMiTbBGBFTe3atbVq1SqdPXtWq1at0po1a7Rr1y79888/io2N1cWLF1WsWDGFhISoTp06atasme6//363ZpCXMgLzrFmz9Nxzz+nzzz/XihUrdODAAcXGxio9PV3BwcGqVq2aIiIidMcdd6hbt245LsFVq1Ytbdu2TTNnztQPP/yg/fv369KlSypbtqwaNGighx56SPfff7/DGt0FpXLlyvrjjz+0fPlyffvtt/rzzz914sQJJSYmKiAgQJUrV9Ztt92mnj17qnfv3gXeHwAAgMJQqVIlffLJJ5o4caJ++eUXLVu2TPv27dPZs2eVkJCgwMBAlSpVShEREWrVqpXuv/9+VaxYsbC7XSiGDh2qNm3a6P3339eyZct07NgxSVKVKlXUq1cvPf300+bl365UrlxZy5Yt0/r16/Xtt99q9erVio6OVlxcnLy8vBQaGqqaNWuqSZMm6tKli9q3b29e0n8t2IycbjoFAAAAAABuK3ozTwEAAAAAcB0iYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAAAAAGABAjYAAAAAABYgYAMAAAAAYAECNgAAAAAAFiBgAwAAAABgAQI2AAAAAAAWIGADAArVpEmTZLPZZLPZNGnSpMLuTqFo3769+R6sWrXKsnYHDRpktjt37lzL2gUAAM4RsAEAAAAAsAABGwCA6wAj/QAAFH0EbAAAAAAALOBV2B0AAOBmZ+V91wAAoPAwgg0AAAAAgAUI2AAAAAAAWICADQDIs6NHj2rWrFnq16+f6tevr+DgYHl7eys0NFQNGjTQsGHDtGHDhgI597FjxzRu3Dg1aNBAQUFBCgoK0i233KJnn31Wf//9tyQpMjLSnBCsSpUqbrW7ZMkSDR48WLVq1VJQUJD8/f1VuXJl9enTR3PnzpXdbnfZhrNlseLj4/X222+rbdu2qlChgry8vGSz2RQfH28el9syXZnPTZ482Xxs8uTJZn3Wn0GDBrnsY0pKit577z3dfvvtCgsLk6+vr8LDw9WvXz+tXbvW5fHOJlu7ePGiZs+erfbt26tcuXLy8fFRxYoVNWDAAO3duzdbG0lJSZo5c6Zuv/12lStXTn5+fqpevbpGjBihY8eOueyDFbK+b5n279+vkSNHqm7dugoMDFRQUJAiIiI0fvx4nT171q12T58+rTlz5mjgwIFq1KiRSpYsKW9vb5UoUUJ16tTRo48+qiVLlrjVVlF7r5OTkzVr1iz16tVLlStXVrFixVS8eHHVrFlTgwcP1ooVK/LUHgDckAwAAPJgzJgxhs1mMyS5/HnwwQeN5OTkXNubOHGiWT9x4sRca+fPn28UL148x/P5+voaH374oXHkyBHzscqVK+fa5qlTp4w77rjD5WupWbOmsXnz5lzbGjhwoFk/Z84cY82aNUZ4eLjT9uLi4szj2rVrZz6+cuVKhzazPufqZ+DAgbn2Z8+ePUbdunVzbWPChAm5vsYrf1+HDh0yIiIicv2d/Pbbb+bxmzZtMipUqJBjfVBQkLF+/fpc+2CFrOc0DMOYNWuW4evrm2O/QkNDXf7+3377bcPT09Ot31XHjh2Ns2fP5tpeUXqvv/nmG6Ns2bIuX9edd95pxMfHu9UmANyImOQMAJAn0dHRMgxDNptNtWvXVu3atRUaGipvb2/FxsZq27ZtOnTokCTpq6++UmJion755ReHkcL8+PHHH9W/f3+lpaVJkjw9PdW6dWvVrFlTSUlJWrt2rY4dO6bHH39c77zzjlttnjp1Sq1btzb7K0nVq1dX8+bN5evrq71792rjxo2SpIMHD6pDhw767bff1Lp1a5dt//PPPxo5cqQSEhJUvHhxtW3bVuXLl1dcXJxWr17t9uvu06eP6tevr02bNmnz5s2SpKZNm6pZs2bZalu0aJFjOzExMerUqZNOnDihEiVKqE2bNipbtqzOnj2rFStWKCEhQZI0ZcoU1atXTw888IDLviUmJqp79+46cOCAgoKC1K5dO5UtW1YnT57U8uXLlZKSotTUVPXp00e7du2S3W5Xp06dlJiYqFKlSqlt27YKDQ1VVFSUVqxYIbvdrsTERN19993av3+/goOD3X6frsbcuXM1bNgwSVLt2rXVpEkT+fv76++//9batWtlGIZiY2PVu3dv7du3L8d+xcTEmH+f1apVU926dVW6dGn5+fkpPj5eu3bt0p49eyRJK1asUKdOnbRhwwb5+vq67GNhvtczZszQ6NGjZRiGJCkoKEgtW7ZUxYoVlZaWpj179mjLli0yDEO//PKL2rdvr7Vr16pYsWJ5+j0AwA2hcPM9AOB6M23aNGPOnDnGmTNncqxZvXq1UaNGDXNUa968eTnWujOCffr0aSM0NNSsa9SokXHgwAGHmvT0dON//ud/DE9PT4eRyNxGsLt3727WBQQEGPPnz89Ws3nzZqNatWpmXXh4uMPoc1ZZR4y9vLwMScaIESOM8+fPO9RdunTJSEtLM/dzG8HOy/uUW38y35Pnnnsu21UFsbGxRseOHc3aatWqGenp6S77kdnm448/biQmJjrURUdHG3Xq1HEYXb/tttsMm81mTJo0yUhNTXWo3717t8MI6eTJk916jfmVeZ7M11G6dGlj8eLF2er++OMPIygoyK1+ffzxx8Y777xjHDt2LMeaHTt2GE2aNDHbe/HFF3OsLQrv9bJlywwPDw9DkuHj42O8+uqrTq9K2bZtm1GvXj2zzWHDhuXYJgDcyAjYAIACceTIEcPPz8+QZDRr1izHOneC43PPPWfWlC9fPtdLa998802H8JRTwF6xYoVD3S+//JLrawkODnYZSLIGWknGkCFDcmwzq2sRsCUZ48ePz7H25MmTRkBAgFm7YcMGl/2QZPTv3z/HNtesWZPtEuLc+v7555+bdXXr1nXrNebXlQF7x44dOda+++67Zm2dOnWu+tzx8fFmwC1Xrpxx+fJlp3WF/V6npaUZNWvWNOt++OGHXF/XiRMnjLCwMEOS4e3tbURHR+daDwA3IiY5AwAUiCpVqqhDhw6SpM2bNysxMTFf7aSnp2vOnDnm/qRJkxQaGppj/b/+9S/VqFHDZbuzZ882t3v37q2ePXvmWFulShX95z//Mffff/9983LZnPj5+WnatGku+3GtlC5dWhMmTMjx+bCwMIf3YNOmTS7b9PHx0fTp03N8vnXr1qpUqZLDObK+j1e655575OPjI0n6+++/df78eZd9sMITTzyhhg0b5vj8gAED5OWVcVfd/v378/23nCk4OFh9+vSRJJ04ccLp5GRXKoz3euHChTp48KAk6e677zb7nJOyZctq5MiRkiS73a5vvvkm13oAuBFxDzYAIN+ioqK0adMmHThwQPHx8bpw4YJD8Dxy5IgkyTAM7dixQ23atMnzOfbt26fTp09Lkry8vFzeG+zp6al+/frpxRdfzLVu5cqV5vbgwYNd9uPRRx/V+PHjlZ6erhMnTmj//v2qU6dOjvVdunRRSEiIy3avlV69esnPzy/XmkaNGpmhKDIy0mWbbdq0UVhYWK419evXV1RUlNmHzFDnjL+/v6pXr659+/bJMAxFRkaqQYMGLvtxte67775cny9evLiqV6+u/fv3yzAMHT161GW/Tp8+rQ0bNmjfvn2Ki4tTcnKyw7+NLVu2mNvbt2932V5hvNeLFi0ytx966KFcz52pY8eO5vaaNWs0atQot44DgBsFARsAkGfr16/XuHHj9Oeff7ocyc3k7jJHV9q+fbu5XbduXQUFBbk8pnnz5rk+f/z4cTO0S1KrVq1ctlm6dGnVqlXLXAps69atuQbs2267zWWb15I7QTXrlQHujNLWr1/fZU3WLxluueUWl/UlS5bMUx+sYOV7s3fvXj333HNavHixOeGZK+782yiM93r9+vXm9vfff68//vjDZZuZk+VJGRMiAsDNhoANAMiTTz75REOGDHE7WGfK7+W+Z86cMbfDw8PdOqZixYput+nv76/SpUu71W6VKlXMgO0qFLnb5rXizozc3t7e5rY7636702bmpdX5qXenD1aw6r1ZsmSJ7rrrLqWmpubp/O782yiM9zomJsbc/vrrr122d6W4uLg8HwMA1zvuwQYAuG3v3r168sknzXB9yy236O2339amTZt06tQp8xLxzJ+BAweax6anp+frnElJSea2u8v+BAYGut1mQECA233JWusqFPn7+7vd7rVwtcukWdFmQfTBClb068yZM3rggQfMcF25cmW98sorWrNmjWJiYpSSkqL09HTz38bEiRPNY935t1EY73XW0ej8uHz58lX3AQCuN4xgAwDc9tZbb5kfmrt27aqff/451/s8rZikKmtYTklJceuY5ORkt9t0VZtTu8WLF3f7ONz4PvzwQzOQRkREaPXq1bneznCtJnC7GgEBAeZr2rp1qxo1alTIPQKAoo8RbACA25YvX25uv/TSS7mGa0k6evToVZ+zVKlS5vaxY8fcOsZVXdbLty9cuOD2/eFZJ/7K2i8g67+N559/3uVcAVb82yhoWSdVO3nyZCH2BACuHwRsAIDbst6T6WpiqISEBO3cufOqz3nrrbea2/v27XNr5M/VElMVKlRQmTJlzP1169a5bPPs2bM6cOCAud+4cWOXx1ipqF5ejQx5+beRlpamtWvXFnSXrlrWyQKvh/4CQFFAwAYAuM3D4///t+Hqcu2PPvrIkkmq6tWrZ4Zhd9bWTU9P1/z58122m7lGtyTNnTvXZf3cuXPNe2XLly+v2rVruzzGSlmX2LpWk3/BfXn5t7FgwYLrYkT4zjvvNLc/+eQTXbx4sRB7AwDXBwI2AMBt1apVM7d//vnnHOsOHjyoyZMnW3JODw8Ph8nSJk2apHPnzuVY/+677zqMNOfkySefNLd//PFHLVmyJMfao0eP6uWXX3Y49lqPKGddJur48ePX9Nxwzd1/G2fOnNGzzz57Lbp01fr27asaNWpIkk6cOKHhw4e7vXpAUlJSnuY3AIAbBQEbAOC2Xr16mdujRo1yGkqXL1+u9u3b6/z583maoTs3o0ePNtfsPXbsmLp27ap//vnHocYwDL333nsaNWqUfH19XbbZoUMHde/e3dy/99579e2332ar++uvv9SpUyfFx8dLylgq7F//+tdVvJr8yboO8tKlS696hmdYK+u/jVdeeUWff/55tpqtW7eqXbt2io6OtuzfRkHy9PTUrFmz5OnpKUmaM2eOevbsqX379uV4zPbt2/Xcc88pPDxcR44cuVZdBYAig1nEAQBuGzlypD766COdOXNG586dU7du3dS4cWPVq1dPNptNW7du1Z49eyRlzDJepkwZzZs376rPGxYWptmzZ+uBBx5Qenq6tmzZojp16qhNmzaqUaOGkpOTtWbNGkVHR0vKmO386aefluR46e6V5syZo9atW+vQoUNKSkrS/fffr5o1a6p58+by8fHR3r17tXHjRnPULiAgQPPnz1eJEiWu+jXlVbNmzRQeHq7o6GidOHFCderUUZcuXVSqVClzNL1p06Z64IEHrnnfIA0cOFBvvPGGDhw4oNTUVD3yyCOaOnWqIiIi5Ofnp927d2vLli2SMmYZ79q1q6ZNm1bIvXatU6dOmjVrloYNG6a0tDQtXrxYv/32m+rVq6eGDRsqKChIKSkpOnHihHbs2OGwxjwA3IwI2AAAt5UpU0Y//fSTevfubc68vXXrVm3dutWh7u6779bcuXP1zDPPWHbue++9V/PmzdOTTz6ppKQkpaWladWqVVq1apVZ4+vrq3feeUft27c3H8ttNuewsDCtXbtWDz30kFasWCEp4/L2gwcPZqutUaOGvvzySzVt2tSy15QXHh4eeu+999S3b19dunRJJ0+e1GeffeZQM3DgQAJ2IfH19dXChQvVvXt3HT58WFLGpHxXjva2bt1aX3/9tT788MPC6Ga+PP7446pRo4aefPJJHTx4UIZhaM+ePeaXac7ccsst5lUnAHAzIWADAPKkZcuW2rNnj9566y0tXLjQDBPlypXTbbfdpv79+ztcLmulhx56SG3atNE777yjX3/9VVFRUbLZbKpYsaK6dOmioUOHqk6dOtq4caN5jKvR5rCwMC1fvly//fabvv76a61Zs0YnT56U3W5XmTJl1KhRI919993q37+/vL29C+R1uevOO+/Uli1bNHPmTK1Zs0ZRUVFKSkpy+75YFKxatWpp27Ztmjlzpn744Qft379fly5dUtmyZdWgQQM99NBDuv/++81Lrq8nHTp00L59+7RgwQL9+uuv2rBhg06ePKnExEQVK1ZMYWFhqlOnjlq1aqXu3bs7zP4PADcTm8H/lQEAN5gPP/xQTzzxhCRp6NChmjVrViH3CAAA3AyY5AwAcMP5+uuvze3CuqQbAADcfBjBBgDcUH744Qf17dtXUsba0TExMQoJCSnkXgEAgJsBI9gAgOvCunXr9Pjjj2v79u1On09NTdVbb72lfv36mY898cQThGsAAHDNMIINALgurFq1Sh06dJCUsRb1rbfeqrCwMBmGoePHj2v9+vUOa0PXq1dPmzZtui7WGwYAADcGZhEHAFx3oqOjzTWvnenatau+/PJLwvUN4ty5c5owYcJVt/PMM8+oZs2aFvQIAADnGMEGAFwX0tPT9ccff2jRokXavHmzTpw4obNnzyoxMVFBQUEqX768br/9dj344INq165dYXcXFoqMjFTVqlWvup2VK1c6rJEOAIDVGMEGAFwXPDw81KFDB/MycQAAgKKGEWwAAAAAACzALOIAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWICADQAAAACABQjYAAAAAABYgIANAAAAAIAFCNgAAAAAAFiAgA0AAAAAgAUI2AAAAAAAWOB/AWcclbVx2jxHAAAAAElFTkSuQmCC", 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OWc+uruWWW24xMjIy8uzhX//6l1lfqVKlHBMqz549a543LCzMbU5kGIaxceNGc8wuXbrkeP3qrGT48OFux7SCRUP/pjFjxrhdHzckJESvvvqqXnvtNUtjZmRkaPPmzdq1a5dOnz6txMREp7s2JiYmmts7d+60NOajjz6a5+t16tSRr6+vUlNTdeHCBSUmJqps2bJONatXrza3n3zySbfn7NOnj8aNG5dnjePaPJ07d1ZYWFie9ZUrV1bHjh21ePFis6e81p3x9fXVAw88kOeY9erVM7dvvfVWRUZG5ll/xx13aO3atZIur8Gbm2effVbt2rXT+PHjNWvWLMXGxuaoMQxDW7Zs0ZYtWzR+/Hh99913atWqVY66U6dOmT/r2267zW2PkuTj46OmTZtq6dKlio+P1969e1W/fv1c68+ePatNmzbpwIEDio2NVXJysgzDMF/ftm2bub1z506n982V+vXru12/Z9OmTYqLi5MklS1bVn369HF7XVZdj888AAAAAAAlhWPeJF2+v9XNoHnz5rnes+yKO+64QzExMZKkLl26yMvLK9daX19f1ahRQwcOHJBhGIqOjnabgfTq1cvtvZl69+6tIUOGKCsrSydPntTBgwdVp04d8/UVK1bo0qVLkqQePXrIx8cnz/Ek6e6771aZMmWUnJysdevWua1//PHH3dZYQaBcwDw9PTV+/Hg9++yzbmtTU1M1btw4/fvf/9b58+ctjW+lLiAgQOHh4XnW2Gw2BQUFKTU1VdLlBc0dwzXDMLRr1y5z39XN6K5Wq1YtBQcH6+LFi7nW7Nixw9y2eoPBe++91wyUr9xcLa8e7HZ7njVBQUHm9u233+72/MHBweb2lQXSc1O1alVNmjRJEydO1ObNm7V27Vpt3bpV//vf/3Ts2DGn2uPHj6tdu3b6z3/+o/bt2zu9tnHjRnM7NTVVAwcOdNunJB0+fNjcPnbsmMtAef/+/Xrttde0dOlS8wZ97lj53N11111uaxxvznfPPffI19fX0vnduR6feQAAAAAA8mvUqFEaPXq05frRo0e7nahYWK7+N3FSUlIRdZI/d9xxh9uagsx+JGs5WVBQkGrXrq39+/dLupyJOQbKjtnP7t27LWc/V1yZHFimTBmXr9vtdrfBuFUEyn9T48aN1aRJE3M/KSlJMTEx2rBhg9LT05WVlaV+/frpyJEjeu+993IdJzY2Vm3atLE84/iKq789csXK3SglOQWvGRkZTq/Fx8eb35JIl4NSK6pWrZpnoHzu3Dlzu1q1apbGjIiIMLfdBZtWrt3xG6T81l/9PuXGy8tLzZs3V/Pmzc3noqOjNXfuXH366ac6ceKEJCkzM1NPPfWUjhw54nQn1ZMnT5rbR48e1aRJkyyd15GrGdLLli1Tt27dlJ6enq+xrHzuypcv77bmzJkz5nb16tXz1UNersdnHgAAAACAksQxRJVk/kXxje5GyH7yk5NdCZQdMzHJOftZt26dpRnHV4uNjc01UA4KCnI7i9oqj+sySgnWuXNnffHFF+Zj2rRpWrVqlaKjo9WzZ0+zbty4cfrpp59yHWfAgAFmmOzl5aV+/fpp4cKFOnTokLnkhXF5zWunZRays7Pd9miz2a79Av+fq7+Vcgw785Lbh9jVuO5qXdW5Czbze+3X472yKiIiQq+88or279/vtMzFmTNn9OOPPzrVxsfH/+3zOS6bIl3+xfWPf/zDDJOrVaum999/X+vWrdPJkyeVkpKi7Oxs83M3atQo81grnzsrs40df37X809pCvPnCAAAAABAceA4gU+SGXze6G6E7OdacrKrM62CyH4cXa+/CpcIlAtMWFiYvv/+e6f1ffv37+9yluiJEyc0e/ZsSZKHh4f++9//6uuvv1bXrl1Vs2ZN+fn5ydPT06y3Mjv0ers67EtJSbF0XHJysuVx3dW6qisOSxT4+/vru+++c/oZX1mj+QrHXzhdu3Y1Q978PPr27es05tdff23+soqMjNTu3bs1YsQI3XvvvapYsaJ8fX2dfskWxOfO8ed3s/wpDQAAAAAAxVGVKlWc/np88+bNRdjNzeVacrKrMy3H7OeTTz65puzn6i8FCgqBcgHy8PDQN998Y34gLl686PImdatWrTJvftapUye1bt06z3H/+uuv69+sGwEBAU4Lll9ZyNydq9cJvprjsghWx4yOjja3y5UrZ+mYG12VKlWc1vA5deqU0+uOi8ufPn36upxz5cqV5vZbb70lf3//POsL4nPneF153eAQAAAAAAAUvDZt2pjbCxcutByUlnTXkpNdnWkVRPZTUAiUC1iVKlU0ePBgc/+LL77I8aFwXCPFyuLYUVFR160/q2w2myIjI819x5up5eaPP/7QhQsX8qxp2LChub1hwwZLvTjW3XnnnZaOuRk43r3T29vb6bW7777b3N65c6fl2dx5yc/nLisrS+vXr//b57zaPffcY25v3LjRvEEeAAAAAAAofP379ze34+Li9O233xZhNzcPKzlZXFycfv/9d3P/6kzLMfspiAzmeiJQLgTDhg0zp7GnpaXpww8/dHrdw+P//zG4++YnJSVFM2bMuP5NWuA4c/r77793W2+lT8dvvpYsWaKzZ8/mWX/y5EktXbrU5fE3s/T0dKdfKlcv5l69enXVrVtXknTp0iVNmTLlb58zP5+7n3/+uUC+HbvnnnvMO60mJiYW2WcbAAAAAABIjRs3dspa3njjDae/FLfqyJEjOnz48HXs7MY2a9YsZWVl5Vkzc+ZMs6ZixYqqXbu20+sdOnQwb5q3YcMG7dq1q2CavQ4IlAtBUFCQXnrpJXN/8uTJTndyrF69urm9ZMmSPD+Aw4YN05kzZwqmUTeeffZZc3vTpk15hsp//vmnJk6c6HbM9u3b65ZbbpF0OVR1nM19NcMw9NJLL5l316xRo4batm1rsfvCs3nzZk2YMCFffxby4YcfKiEhwdzv2LFjjprXXnvN3H7rrbe0Z88ey+O7CoMdP3eLFi3K9dhz585pyJAhls+VH97e3nrxxRfN/ddee00HDx4skHMBAAAAAAD3vvrqK3NZzMTERLVp00Z//PGH5eN//vlnNWrUyO0yqMXJ4cOH88zBzpw5o7Fjx5r7zz77bI6bA1auXFm9e/eWdDkDe+qpp5yyorxkZ2c7ZY0FjUC5kAwdOtS8AV1KSoo+/vhj87U2bdqYd4P8888/1adPH8XFxTkdn5CQoOeff17//ve/nRbpLky1atVyurFbv379NH369Bx127ZtU7t27ZScnOy07rIrHh4e+uCDD8z9WbNm6bnnnstxg7bExEQ9/fTTmj9/vvnchx9+6DTL9kYRGxurV199VRERERo6dKi2b99urpF9tfPnz2vIkCEaOXKk+VzDhg1dBsq9e/c2vyVMTEzUfffdp8mTJ+vSpUsux05ISNDMmTPVqlUrpy80rujSpYu5/f7777v8gmD79u1q2bKljh07VmCfu+HDh6tGjRqSLt/R9L777tPs2bNdvmcpKSmaNWuWnnnmmQLpBQAAAACAkq5GjRqaPn26OVv26NGjuvPOOzV69OhcJzmmp6dryZIlatGihbp3767Y2NjCbLnIeXl56bXXXtNnn32m7Oxsp9cOHDigdu3amX+VHxoamuvEvffee08VK1aUJO3evVtNmjTR8uXLcz3v8ePHNXHiRNWuXVs//vjjdboa90oV2plKuJCQEA0YMEDjx4+XJE2aNEnDhw9XcHCwgoKC9Morr5jfVMycOVNLly7V3XffrcqVK+vUqVNas2aNkpOTVapUKf3f//2f+vTpUyTX8cknn2jjxo06ePCg0tPT1bdvX40dO1ZNmzaVt7e39u3bpy1btsgwDPXo0UMXLlzQb7/9lueYjz32mKKiojRp0iRJ0jfffKMff/xRrVu3VmhoqM6ePauVK1c6hcyDBw9Wjx49CvRa/65z585p4sSJmjhxogICAnTXXXepYsWKKlu2rJKSkvTHH3/of//7nzIzM81jQkNDNXPmTJdBuaenp3766Se1a9dOO3bsUEJCgl544QUNHz5cTZs2VeXKleXp6anY2FgdPHhQBw4cMMd++OGHc4zXp08fffzxxzp06JDS09P15JNPaty4cYqMjJSPj4/27t2rbdu2SZIiIyPVoUOHHMu1XA/+/v6aP3+++cv1/Pnz6tmzpwYPHqxmzZqpfPnySktL0+HDh7V9+3alpqY6recNAAAAAACur4ceekhLly7Vo48+qri4OCUlJWnMmDEaO3asIiMjVaNGDYWEhCgxMVGnTp3S1q1bne735OHhUWQTIovChx9+qMGDB2vw4MGaMGGC7rvvPvn5+enQoUNat26dGTKXKlVK3377rYKDg12OU6lSJS1cuFCdO3fW+fPndfDgQXXo0EGVK1dWkyZNVL58eWVkZOj8+fPau3evjh49WpiXaSJQLkTDhg3TF198oeTkZCUlJWnixIl65513JEkjR45UdHS0uYbsxYsXndYKlqTAwEBNnTpVDRo0KOzWTUFBQVq1apW6detmho1HjhzRkSNHnOq6du2qadOmOc2CzcsXX3yhsLAwvfvuu0pPT1diYqLLZRh8fHw0cuRIvf7663//YgrILbfcopYtW2rdunXm8iXx8fFatWpVnsd16tRJkyZNMpcAcSUkJETr16/X0KFD9c033ygzM1MJCQlatmxZrsf4+vrqrrvuyvG8t7e3Fi9erE6dOpk/vwMHDujAgQNOdffee69+/PFHff3113n2/3fUr19fW7Zs0VNPPWXedPLMmTNasGCBy/ors/0BAAAAAEDBaNu2rXbt2qXRo0drxowZysrKkmEY2rlzp3bu3OnyGA8PD3Xq1EnvvfdeiZoM1q1bN3l7e2vQoEE6fvy4Zs+enaMmMDBQ3377rTp37pznWI0bN9a2bdv07LPPauXKlZKkEydO5JqRSJcnKNasWfPvXUQ+ECgXovLly6t///6aMGGCJOnzzz/XsGHDFBgYKE9PT02fPl2PPvq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" ] }, "metadata": {}, @@ -643,7 +651,7 @@ } ], "source": [ - "rating_df = iohinspector.plot.plot_tournament_ranking(df)" + "rating_df = iohinspector.plots.plot_tournament_ranking(df)" ] }, { @@ -657,7 +665,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "iohinspector", "language": "python", "name": "python3" }, @@ -671,7 +679,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.10.18" } }, "nbformat": 4, diff --git a/image.png b/image.png deleted file mode 100644 index 7ba9c9f..0000000 Binary files a/image.png and /dev/null differ diff --git a/pyproject.toml b/pyproject.toml index edcc61f..4a17d8e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "iohinspector" -version = "0.0.5" +version = "0.1.0" authors = [ { name="Diederick Vermetten", email="d.vermetten@gmail.com" }, { name="Jacob de Nobel", email="jacobdenobel@gmail.com" }, @@ -26,7 +26,8 @@ dependencies = [ "pyarrow", "robustranking", "seaborn", - "skelo" + "skelo", + "networkx" ] [project.optional-dependencies] diff --git a/src/iohinspector/__init__.py b/src/iohinspector/__init__.py index 1b8e83b..a11311a 100644 --- a/src/iohinspector/__init__.py +++ b/src/iohinspector/__init__.py @@ -1,6 +1,6 @@ from .align import * from .data import * from .manager import * -from .metrics import * from .indicators import * -from .plot import * \ No newline at end of file +from .metrics import * +from .plots import * \ No newline at end of file diff --git a/src/iohinspector/indicators/__init__.py b/src/iohinspector/indicators/__init__.py index cf2e4fd..4c8842b 100644 --- a/src/iohinspector/indicators/__init__.py +++ b/src/iohinspector/indicators/__init__.py @@ -8,7 +8,7 @@ from .final import * def add_indicator( - df: pl.DataFrame, indicator: Callable, objective_columns: Iterable, **kwargs + df: pl.DataFrame, indicator: Callable, obj_vars: Iterable, **kwargs ) -> pl.DataFrame: """Adds an indicator to a Polars DataFrame. @@ -39,6 +39,6 @@ def add_indicator( group of data. """ indicator_callable = partial( - indicator, objective_columns=objective_columns, **kwargs + indicator, obj_vars=obj_vars, **kwargs ) return df.group_by("data_id").map_groups(indicator_callable) diff --git a/src/iohinspector/indicators/anytime.py b/src/iohinspector/indicators/anytime.py index 320b085..0745a5e 100644 --- a/src/iohinspector/indicators/anytime.py +++ b/src/iohinspector/indicators/anytime.py @@ -51,8 +51,8 @@ def _r2(weight_vec_set, ideal_point, point_set): class NonDominated: - def __call__(self, group: pl.DataFrame, objective_columns: Iterable): - objectives = np.array(group[objective_columns]) + def __call__(self, group: pl.DataFrame, obj_vars: Iterable): + objectives = np.array(group[obj_vars]) is_efficient = np.ones(objectives.shape[0], dtype=bool) for i, c in enumerate(objectives[1:]): if is_efficient[i + 1]: @@ -82,12 +82,12 @@ def minimize(self): return False def __call__( - self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int] + self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int] ) -> pl.DataFrame: """ Args: group (pl.DataFrame): The DataFrame on which the indicator will be added (should be 1 optimization run only) - objective_columns (Iterable): Which columns are the objectives + obj_vars (Iterable): Which columns are the objectives evals (Iterable[int]): At which evaluations the operation should be performed. Note that using more evaluations will make the code slower. @@ -95,7 +95,7 @@ def __call__( pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+ """ obj_vals = np.clip( - np.array(group[objective_columns]), None, self.reference_point + np.array(group[obj_vars]), None, self.reference_point ) evals_dt = group["evaluations"] hvs = [ @@ -111,7 +111,7 @@ def __call__( ) .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward") .fill_null(np.inf) - .drop(objective_columns) + .drop(obj_vars) ) @@ -140,12 +140,12 @@ def minimize(self): return True def __call__( - self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int] + self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int] ) -> pl.DataFrame: """ Args: group (pl.DataFrame): The DataFrame on which the indicator will be added (should be 1 optimization run only) - objective_columns (Iterable): Which columns are the objectives + obj_vars (Iterable): Which columns are the objectives evals (Iterable[int]): At which evaluations the operation should be performed. Note that using more evaluations will make the code slower. @@ -153,7 +153,7 @@ def __call__( pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+ """ obj_vals = np.clip( - np.array(group[objective_columns]), None, self.reference_point + np.array(group[obj_vars]), None, self.reference_point ) evals_dt = group["evaluations"] hvs = [ @@ -172,7 +172,7 @@ def __call__( ) .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward") .fill_null(np.inf) - .drop(objective_columns) + .drop(obj_vars) ) @@ -195,7 +195,7 @@ def var_name(self): return "IGD+" def __call__( - self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int] + self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int] ) -> pl.DataFrame: """ @@ -208,7 +208,7 @@ def __call__( Returns: pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+ """ - obj_vals = np.array(group[objective_columns]) + obj_vals = np.array(group[obj_vars]) evals_dt = group["evaluations"] igds = [ igd_plus( @@ -226,7 +226,7 @@ def __call__( ) .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward") .fill_null(np.inf) - .drop(objective_columns) + .drop(obj_vars) ) try: @@ -251,7 +251,7 @@ def minimize(self): return True def __call__( - self, group: pl.DataFrame, objective_columns: Iterable, evals: Iterable[int] + self, group: pl.DataFrame, obj_vars: Iterable, evals: Iterable[int] ) -> pl.DataFrame: """ @@ -264,7 +264,7 @@ def __call__( Returns: pl.DataFrame: a new DataFrame with columns of 'evals' and corresponding IGD+ """ - obj_vals = np.array(group[objective_columns]) + obj_vals = np.array(group[obj_vars]) evals_dt = group["evaluations"] igds = [ _r2( @@ -283,7 +283,7 @@ def __call__( ) .join_asof(group.sort("evaluations"), on="evaluations", strategy="backward") .fill_null(np.inf) - .drop(objective_columns) + .drop(obj_vars) ) except ImportError: diff --git a/src/iohinspector/indicators/final.py b/src/iohinspector/indicators/final.py index af25329..44a0c31 100644 --- a/src/iohinspector/indicators/final.py +++ b/src/iohinspector/indicators/final.py @@ -6,8 +6,8 @@ class NonDominated: - def __call__(self, group: pl.DataFrame, objective_columns: Iterable): - objectives = np.array(group[objective_columns]) + def __call__(self, group: pl.DataFrame, obj_vars: Iterable): + objectives = np.array(group[obj_vars]) return group.with_columns( pl.Series(name="final_nondominated", values=is_nondominated(objectives)) ) diff --git a/src/iohinspector/metrics.py b/src/iohinspector/metrics.py deleted file mode 100644 index f1de908..0000000 --- a/src/iohinspector/metrics.py +++ /dev/null @@ -1,630 +0,0 @@ -from functools import partial -from warnings import warn -from typing import Iterable, Callable, Optional - -import polars as pl -import numpy as np -import pandas as pd -from skelo.model.elo import EloEstimator - -from .align import align_data - - - - -def get_sequence( - min: float, - max: float, - len: float, - scale_log: bool = False, - cast_to_int: bool = False, -) -> np.ndarray: - """Create sequence of points, used for subselecting targets / budgets for allignment and data processing - - Args: - min (float): Starting point of the range - max (float): Final point of the range - len (float): Number of steps - scale_log (bool): Whether values should be scaled logarithmically. Defaults to False - version (str, optional): Whether the value should be casted to integers (e.g. in case of budget) or not. Defaults to False. - - Returns: - np.ndarray: Array of evenly spaced values - """ - transform = lambda x: x - if scale_log: - assert min > 0 - min = np.log10(min) - max = np.log10(max) - transform = lambda x: 10**x - values = transform( - np.arange( - min, - max + (max - min) / (2 * (len - 1)), - (max - min) / (len - 1), - dtype=float, - ) - ) - if cast_to_int: - return np.unique(np.array(values, dtype=int)) - return np.unique(values) - - -def _geometric_mean(series: pl.Series) -> float: - """Helper function for polars: geometric mean""" - return np.exp(np.log(series).mean()) - - -def aggegate_convergence( - data: pl.DataFrame, - evaluation_variable: str = "evaluations", - fval_variable: str = "raw_y", - free_variables: Iterable[str] = ["algorithm_name"], - x_min: int = None, - x_max: int = None, - custom_op: Callable[[pl.Series], float] = None, - maximization: bool = False, - return_as_pandas: bool = True, -): - """Function to aggregate performance on a fixed-budget perspective - - Args: - data (pl.DataFrame): The data object to use for getting the performance. Note that the fval, evaluation and free variables as defined in - this object determine the axes of the final performance (most data will have 'raw_y', 'evaluations' and ['algId'] as defaults) - evaluation_variable (str, optional): Column name for evaluation number. Defaults to "evaluations". - fval_variable (str, optional): Column name for function value. Defaults to "raw_y". - free_variables (Iterable[str], optional): Column name for free variables (variables over which performance should not be aggregated). Defaults to ["algorithm_name"]. - x_min (int, optional): Minimum evaulation value to use. Defaults to None (minimum present in data). - x_max (int, optional): Maximum evaulation value to use. Defaults to None (maximum present in data). - custom_op (Callable[[pl.Series], float], optional): Custom aggregation method for performance values. Defaults to None. - maximization (bool, optional): Whether performance metric is being maximized or not. Defaults to False. - return_as_pandas (bool, optional): Whether the data should be returned as Pandas (True) or Polars (False) object. Defaults to True. - - Returns: - DataFrame: Depending on 'return_as_pandas', a pandas or polars DataFrame with the aggregated performance values - """ - - # Getting alligned data (to check if e.g. limits should be args for this function) - if x_min is None: - x_min = data[evaluation_variable].min() - if x_max is None: - x_max = data[evaluation_variable].max() - x_values = get_sequence(x_min, x_max, 50, scale_log=True, cast_to_int=True) - group_variables = free_variables + [evaluation_variable] - data_aligned = align_data( - data.cast({evaluation_variable: pl.Int64}), - x_values, - group_cols=["data_id"] + free_variables, - x_col=evaluation_variable, - y_col=fval_variable, - maximization=maximization, - ) - - aggregations = [ - pl.mean(fval_variable).alias("mean"), - pl.min(fval_variable).alias("min"), - pl.max(fval_variable).alias("max"), - pl.median(fval_variable).alias("median"), - pl.std(fval_variable).alias("std"), - pl.col(fval_variable).log().mean().exp().alias("geometric_mean") - ] - - if custom_op is not None: - aggregations.append( - pl.col(fval_variable).apply(custom_op).alias(custom_op.__name__) - ) - dt_plot = data_aligned.group_by(*group_variables).agg(aggregations) - if return_as_pandas: - return dt_plot.sort(evaluation_variable).to_pandas() - return dt_plot.sort(evaluation_variable) - - -def transform_fval( - data: pl.DataFrame, - lb: float = 1e-8, - ub: float = 1e8, - scale_log: bool = True, - maximization: bool = False, - fval_col: str = "raw_y", -): - """Helper function to transform function values (min-max normalization based on provided bounds and scaling) - - Args: - data (pl.DataFrame): The data object to use for getting the performance. - lb (float, optional): Lower bound for scaling of function values. If None, it is the max value found in data. Defaults to 1e-8. - ub (float, optional): Upper bound for scaling of function values. If None, it is the max value found in data. Defaults to 1e8. - scale_log (bool, optional): Whether function values should be log-scaled before scaling. Defaults to True. - maximization (bool, optional): Whether function values is being maximized. Defaults to False. - fval_col (str, optional): Which column in data to use. Defaults to "raw_y". - - Returns: - _type_: a copy of the original data with a new column 'eaf' with the scaled function values (which is always to be maximized) - """ - if ub == None: - ub = data[fval_col].max() - if lb == None: - lb = data[fval_col].min() - if lb <= 0 and scale_log: - lb = 1e-8 - warnings.warn( - "If using logarithmic scaling, lb should be set to prevent errors in log-calculation. Lb is being overwritten to 1e-8 to avoid this." - ) - if scale_log: - lb = np.log10(lb) - ub = np.log10(ub) - res = data.with_columns( - ((pl.col(fval_col).log10() - lb) / (ub - lb)).clip(0, 1).alias("eaf") - ) - else: - res = data.with_columns( - ((pl.col(fval_col) - lb) / (ub - lb)).clip(0, 1).alias("eaf") - ) - if maximization: - return res - return res.with_columns((1 - pl.col("eaf")).alias("eaf")) - - -def _aocc(group: pl.DataFrame, max_budget: int, fval_col: str = "eaf"): - group = group.cast({"evaluations": pl.Int64}).filter( - pl.col("evaluations") <= max_budget - ) - new_row = pl.DataFrame( - { - "evaluations": [0, max_budget], - fval_col: [group[fval_col].min(), group[fval_col].max()], - } - ) - group = ( - pl.concat([group, new_row], how="diagonal") - .sort("evaluations") - .fill_null(strategy="forward") - .fill_null(strategy="backward") - ) - return group.with_columns( - ( - ( - pl.col("evaluations").diff(n=1, null_behavior="ignore") - * (pl.col(fval_col).shift(1)) - ) - / max_budget - ).alias("aocc_contribution") - ) - - -def get_aocc( - data: pl.DataFrame, - max_budget: int, - fval_col: str = "eaf", - group_cols: Iterable[str] = ["function_name", "algorithm_name"], -): - """Helper function for AOCC calculations - - Args: - data (pl.DataFrame): The data object to use for getting the performance. - max_budget (int): Maxium value of evaluations to use - fval_col (str, optional): Which data column specifies the performance value. Defaults to "eaf". - group_cols (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["function_name", "algorithm_name"]. - - Returns: - pl.DataFrame: a polars dataframe with the area under the EAF (=area over convergence curve) - """ - aocc_contribs = data.group_by(*["data_id"]).map_groups( - partial(_aocc, max_budget=max_budget, fval_col=fval_col) - ) - aoccs = aocc_contribs.group_by(["data_id"] + group_cols).agg( - pl.col("aocc_contribution").sum() - ) - return aoccs.group_by(group_cols).agg( - pl.col("aocc_contribution").mean().alias("AOCC") - ) - - -def get_tournament_ratings( - data: pl.DataFrame, - alg_vars: Iterable[str] = ["algorithm_name"], - fid_vars: Iterable[str] = ["function_name"], - perf_var: str = "raw_y", - nrounds: int = 25, - maximization: bool = False, -): - """Method to calculate ratings of a set of algorithm on a set of problems. - Calculated based on nrounds of competition, where in each round all algorithms face all others (pairwise) on every function. - For each round, a sampled performance value is taken from the data and used to determine the winner. - This function uses the ELO rating scheme, as opposed to the Glicko2 scheme used in the IOHanalyzer. Deviations are estimated based on the last 5% of rounds. - - Args: - data (pl.DataFrame): The data object to use for getting the performance. - alg_vars (Iterable[str], optional): Which variables specific the algortihms which will compete. Defaults to ["algorithm_name"]. - fid_vars (Iterable[str], optional): Which variables denote the problems on which will be competed. Defaults to ["function_name"]. - perf_var (str, optional): Which variable corresponds to the performance. Defaults to "raw_y". - nrounds (int, optional): How many round should be played. Defaults to 25. - maximization (bool, optional): Whether the performance metric is being maximized. Defaults to False. - - Returns: - pd.DataFrame: Pandas dataframe with rating, deviation and volatility for each 'alg_vars' combination - """ - fids = data[fid_vars].unique() - aligned_comps = data.pivot( - index=alg_vars, - columns=fid_vars, - values=perf_var, - aggregate_function=pl.element(), - ) - players = aligned_comps[alg_vars] - n_players = players.shape[0] - comp_arr = np.array(aligned_comps[aligned_comps.columns[len(alg_vars) :]]) - - rng = np.random.default_rng() - fids = [i for i in range(len(fids))] - lplayers = [i for i in range(n_players)] - records = [] - for r in range(nrounds): - for fid in fids: - for p1 in lplayers: - for p2 in lplayers: - if p1 == p2: - continue - s1 = rng.choice(comp_arr[p1][fid], 1)[0] - s2 = rng.choice(comp_arr[p2][fid], 1)[0] - if s1 == s2: - won = 0.5 - else: - won = abs(float(maximization) - float(s1 < s2)) - - records.append([r, p1, p2, won]) - - dt_comp = pd.DataFrame.from_records( - records, columns=["round", "p1", "p2", "outcome"] - ) - dt_comp = dt_comp.sample(frac=1).sort_values("round") - model = EloEstimator(key1_field="p1", key2_field="p2", timestamp_field="round").fit( - dt_comp, dt_comp["outcome"] - ) - model_dt = model.rating_model.to_frame() - ratings = np.array(model_dt[np.isnan(model_dt["valid_to"])]["rating"]) - deviations = ( - model_dt.query(f"valid_from >= {nrounds * 0.95}").groupby("key")["rating"].std() - ) - rating_dt_elo = pd.DataFrame( - [ - ratings, - deviations, - *players[players.columns], - ] - ).transpose() - rating_dt_elo.columns = ["Rating", "Deviation", *players.columns] - return rating_dt_elo - - -def aggegate_running_time( - data: pl.DataFrame, - evaluation_variable: str = "evaluations", - fval_variable: str = "raw_y", - free_variables: Iterable[str] = ["algorithm_name"], - f_min: float = None, - f_max: float = None, - scale_flog: bool = True, - max_budget: int = None, - maximization: bool = False, - custom_op: Callable[[pl.Series], float] = None, - return_as_pandas: bool = True, -): - """Function to aggregate performance on a fixed-target perspective - - Args: - data (pl.DataFrame): The data object to use for getting the performance. Note that the fval, evaluation and free variables as defined in - this object determine the axes of the final performance (most data will have 'raw_y', 'evaluations' and ['algId'] as defaults) - evaluation_variable (str, optional): Column name for evaluation number. Defaults to "evaluations". - fval_variable (str, optional): Column name for function value. Defaults to "raw_y". - free_variables (Iterable[str], optional): Column name for free variables (variables over which performance should not be aggregated). Defaults to ["algorithm_name"]. - f_min (int, optional): Minimum function value to use. Defaults to None (minimum present in data). - f_max (int, optional): Maximum function value to use. Defaults to None (maximum present in data). - scale_flog (bool): Whether or not function values should be scaled logarithmically for the x-axis. Defaults to True. - max_budget: If present, what budget value should be the maximum considered. Defaults to None. - custom_op (Callable[[pl.Series], float], optional): Custom aggregation method for performance values. Defaults to None. - maximization (bool, optional): Whether performance metric is being maximized or not. Defaults to False. - return_as_pandas (bool, optional): Whether the data should be returned as Pandas (True) or Polars (False) object. Defaults to True. - - Returns: - DataFrame: Depending on 'return_as_pandas', a pandas or polars DataFrame with the aggregated performance values - """ - - # Getting alligned data (to check if e.g. limits should be args for this function) - if f_min is None: - f_min = data[fval_variable].min() - if f_max is None: - f_max = data[fval_variable].max() - f_values = get_sequence(f_min, f_max, 50, scale_log=scale_flog) - group_variables = free_variables + [fval_variable] - data_aligned = align_data( - data, - f_values, - group_cols=["data_id"] + free_variables, - x_col=fval_variable, - y_col=evaluation_variable, - maximization=maximization, - ) - if max_budget is None: - max_budget = data[evaluation_variable].max()+1 - - data_aligned = data_aligned.with_columns( - pl.when(pl.col(evaluation_variable) < 1) - .then(1) - .when(pl.col(evaluation_variable) > max_budget) - .then(max_budget) - .otherwise(pl.col(evaluation_variable)) - .alias(f"{evaluation_variable}") - ) - - aggregations = [ - pl.col(evaluation_variable).mean().alias("mean"), - # pl.mean(evaluation_variable).alias("mean"), - pl.col(evaluation_variable).min().alias("min"), - pl.col(evaluation_variable).max().alias("max"), - pl.col(evaluation_variable) - .median() - .alias("median"), - pl.col(evaluation_variable).std().alias("std"), - (pl.col(evaluation_variable) < max_budget).mean().alias("success_ratio"), - (pl.col(evaluation_variable) < max_budget).sum().alias("success_count"), - ( - pl.col(evaluation_variable).sum() - / (pl.col(evaluation_variable) < max_budget).sum() - ).alias("ERT"), - ( - pl.col(evaluation_variable).sum() + pl.col(evaluation_variable).is_between(max_budget, np.inf).count() * max_budget * 9 - / pl.col(evaluation_variable).count() - ).alias("PAR-10"), - ] - - if custom_op is not None: - aggregations.append( - pl.col(evaluation_variable) - .apply(custom_op) - .alias(custom_op.__name__) - ) - dt_plot = data_aligned.group_by(*group_variables).agg(aggregations) - if return_as_pandas: - return dt_plot.sort(fval_variable).to_pandas() - return dt_plot.sort(fval_variable) - - -def add_normalized_objectives( - data: pl.DataFrame, obj_cols: Iterable[str], max_vals: Optional[pl.DataFrame] = None, min_vals: Optional[pl.DataFrame] = None -): - """Add new normalized columns to provided dataframe based on the provided objective columns - - Args: - data (pl.DataFrame): The original dataframe - obj_cols (Iterable[str]): The names of each objective column - max_vals (Optional[pl.DataFrame]): If provided, these values will be used as the maxima instead of the values found in `data` - min_vals (Optional[pl.DataFrame]): If provided, these values will be used as the minima instead of the values found in `data` - - Returns: - _type_: The original `data` DataFrame with a new column 'objI' added for each objective, for I=1...len(obj_cols) - """ - if type(max_vals) == pl.DataFrame: - data_max = [max_vals[colname].max() for colname in obj_cols] - else: - data_max = [data[colname].max() for colname in obj_cols] - if type(min_vals) == pl.DataFrame: - data_min = [min_vals[colname].min() for colname in obj_cols] - else: - data_min = [data[colname].min() for colname in obj_cols] - return data.with_columns( - [ - ((data[colname] - data_min[idx]) / (data_max[idx] - data_min[idx])).alias(f"obj{idx + 1}") - for idx, colname in enumerate(obj_cols) - ] - ) - - -def _get_nodeidx(xloc, yval, nodes, epsilon): - if len(nodes) == 0: - return -1 - candidates = nodes[np.isclose(nodes["y"], yval, atol=epsilon)] - if len(candidates) == 0: - return -1 - idxs = np.all( - np.isclose(np.array(candidates)[:, : len(xloc)], xloc, atol=epsilon), axis=1 - ) - if any(idxs): - return candidates[idxs].index[0] - return -1 - - -def get_attractor_network( - data, - coord_vars=["x1", "x2"], - fval_var: str = "raw_y", - eval_var: str = "evaluations", - maximization: bool = False, - beta=40, - epsilon=0.0001, - eval_max=None, -): - """Create an attractor network from the provided data - - Args: - data (pl.DataFrame): The original dataframe, should contain the performance and position information - coord_vars (Iterable[str], optional): Which columns correspond to position information. Defaults to ['x1', 'x2']. - fval_var (str, optional): Which column corresponds to performance. Defaults to 'raw_y'. - eval_var (str, optional): Which column corresponds to evaluations. Defaults to 'evaluations'. - maximization (bool, optional): Whether fval_var is to be maximized. Defaults to False. - beta (int, optional): Minimum stagnation lenght. Defaults to 40. - epsilon (float, optional): Radius below which positions should be considered identical in the network. Defaults to 0.0001. - eval_max (int, optional): Maximum evaluation number. Defaults to the maximum of eval_var if None. - Returns: - pd.DataFrame, pd.DataFrame: two dataframes containing the nodes and edges of the network respectively. - """ - - running_idx = 0 - running_edgeidx = 0 - nodes = pd.DataFrame(columns=[*coord_vars, "y", "count", "evals"]) - edges = pd.DataFrame(columns=["start", "end", "count", "stag_length_avg"]) - if eval_max is None: - eval_max = max(data[eval_var]) - - for run_id in data["data_id"].unique(): - dt_group = data.filter( - pl.col("data_id") == run_id, pl.col(eval_var) <= eval_max - ) - if maximization: - ys = np.maximum.accumulate(np.array(dt_group[fval_var])) - else: - ys = np.minimum.accumulate(np.array(dt_group[fval_var])) - xs = np.array(dt_group[coord_vars]) - - stopping_points = np.where(np.abs(np.diff(ys, prepend=np.inf)) > 0)[0] - evals = np.array(dt_group[eval_var]) - - stagnation_lengths = np.diff(evals[stopping_points], append=eval_max) - edge_lengths = stagnation_lengths[stagnation_lengths > beta] - real_idxs = [stopping_points[i] for i in np.where(stagnation_lengths > beta)[0]] - - xloc = xs[real_idxs[0]] - yval = ys[real_idxs[0]] - nodeidx = _get_nodeidx(xloc, yval, nodes, epsilon) - if nodeidx == -1: - nodes.loc[running_idx] = [*xloc, yval, 1, evals[real_idxs[0]]] - node1 = running_idx - running_idx += 1 - else: - nodes.loc[nodeidx, "evals"] += evals[real_idxs[0]] - nodes.loc[nodeidx, "count"] += 1 - node1 = nodeidx - - if len(real_idxs) == 1: - continue - - for i in range(len(real_idxs) - 1): - xloc = xs[real_idxs[i + 1]] - yval = ys[real_idxs[i + 1]] - nodeidx = _get_nodeidx(xloc, yval, nodes, epsilon) - if nodeidx == -1: - nodes.loc[running_idx] = [*xloc, yval, 1, evals[real_idxs[i + 1]]] - node2 = running_idx - running_idx += 1 - else: - nodes.loc[nodeidx, "evals"] += evals[real_idxs[i + 1]] - nodes.loc[nodeidx, "count"] += 1 - node2 = nodeidx - - edgelen = edge_lengths[i] - edge_idxs = edges.query(f"start == {node1} & end == {node2}").index - if len(edge_idxs) == 0: - edges.loc[running_edgeidx] = [node1, node2, 1, edgelen] - running_edgeidx += 1 - else: - curr_count = edges.loc[edge_idxs[0]]["count"] - curr_len = edges.loc[edge_idxs[0]]["stag_length_avg"] - edges.loc[edge_idxs[0], "stag_length_avg"] = ( - curr_len * curr_count + edgelen - ) / (curr_count + 1) - edges.loc[edge_idxs[0], "count"] += 1 - node1 = node2 - return nodes, edges - - -def get_data_ecdf( - data, - fval_var: str = "raw_y", - eval_var: str = "evaluations", - free_vars: Iterable[str] = ["algorithm_name"], - maximization: bool = False, - x_values: Iterable[int] = None, - x_min: int = None, - x_max: int = None, - scale_xlog: bool = True, - y_min: int = None, - y_max: int = None, - scale_ylog: bool = True, -): - """Function to plot empirical cumulative distribution function (Based on EAF) - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - eval_var (str, optional): Column in 'data' which corresponds to the number of evaluations. Defaults to "evaluations". - fval_var (str, optional): Column in 'data' which corresponds to the performance measure. Defaults to "raw_y". - free_vars (Iterable[str], optional): Columns in 'data' which correspond to groups over which data should not be aggregated. Defaults to ["algorithm_name"]. - maximization (bool, optional): Boolean indicating whether the 'fval_var' is being maximized. Defaults to False. - measures (Iterable[str], optional): List of measures which should be used in the plot. Valid options are 'geometric_mean', 'mean', 'median', 'min', 'max'. Defaults to ['geometric_mean']. - x_values (Iterable[int], optional): List of x-values at which to get the ECDF data. If not provided, the x_min, x_max and scale_xlog arguments will be used to sample these points. - scale_xlog (bool, optional): Should the x-samples be log-scaled. Defaults to True. - x_min (float, optional): Minimum value to use for the 'eval_var', if not present the min of that column will be used. Defaults to None. - x_max (float, optional): Maximum value to use for the 'eval_var', if not present the max of that column will be used. Defaults to None. - scale_ylog (bool, optional): Should the y-values be log-scaled before normalization. Defaults to True. - y_min (float, optional): Minimum value to use for the 'fval_var', if not present the min of that column will be used. Defaults to None. - y_max (float, optional): Maximum value to use for the 'fval_var', if not present the max of that column will be used. Defaults to None. - - Returns: - pd.DataFrame: pandas dataframe of the ECDF data. - """ - if x_values is None: - if x_min is None: - x_min = data[eval_var].min() - if x_max is None: - x_max = data[eval_var].max() - x_values = get_sequence( - x_min, x_max, 50, scale_log=scale_xlog, cast_to_int=True - ) - data_aligned = align_data( - data.cast({eval_var: pl.Int64}), - x_values, - group_cols=["data_id"], - x_col=eval_var, - y_col=fval_var, - maximization=maximization, - ) - dt_ecdf = ( - transform_fval( - data_aligned, - fval_col=fval_var, - maximization=maximization, - lb=y_min, - ub=y_max, - scale_log=scale_ylog, - ) - .group_by([eval_var] + free_vars) - .mean() - .sort(eval_var) - ).to_pandas() - return dt_ecdf - -def get_trajectory(data: pl.DataFrame, - traj_length: int = None, - min_fevals: int = 1, - evaluation_variable: str = "evaluations", - fval_variable: str = "raw_y", - free_variables: Iterable[str] = ["algorithm_name"], - maximization: bool = False -) -> pl.DataFrame: - """get the trajectory of the performance of the algorithms in the data - This function aligns the data to a fixed number of evaluations and returns the performance trajectory. - - Args: - data (pl.DataFrame): The DataFrame resulting from loading the data from a DataManager. - traj_length (int, optional): Length of the trajecotry. Defaults to None. - min_fevals (int, optional): Evaluation number from which to start the trajectory. Defaults to 1. - evaluation_variable (str, optional): Variable corresponding to evaluation count in `data`. Defaults to "evaluations". - fval_variable (str, optional): Variable corresponding to function value in `data`. Defaults to "raw_y". - free_variables (Iterable[str], optional): Free variables in `data`. Defaults to ["algorithm_name"]. - maximization (bool, optional): Whether the data is maximizing or not. Defaults to False. - - Returns: - pd.DataFrame: DataFrame: A polars DataFrame with the aligned data, where each row corresponds to a specific evaluation count and the performance value. - """ - if traj_length is None: - max_fevals = data[eval_var].max() - else: - max_fevals = traj_length + min_fevals - x_values = np.arange(min_fevals, max_fevals + 1) - data_aligned = align_data( - data.cast({evaluation_variable: pl.Int64}), - x_values, - group_cols=["data_id"] + free_variables, - x_col=evaluation_variable, - y_col=fval_variable, - maximization=maximization, - ) - return data_aligned \ No newline at end of file diff --git a/src/iohinspector/metrics/__init__.py b/src/iohinspector/metrics/__init__.py new file mode 100644 index 0000000..6ad6e10 --- /dev/null +++ b/src/iohinspector/metrics/__init__.py @@ -0,0 +1,10 @@ +from .utils import (get_sequence, normalize_objectives, add_normalized_objectives, transform_fval) +from .fixed_budget import (aggregate_convergence) +from .fixed_target import (aggregate_running_time) +from .aocc import (get_aocc) +from .ecdf import (get_data_ecdf) +from .eaf import (get_discritized_eaf_single_objective, get_eaf_data, get_eaf_pareto_data, get_eaf_diff_data) +from .ranking import (get_tournament_ratings,get_robustrank_over_time, get_robustrank_changes) +from .attractor_network import (get_attractor_network) +from .trajectory import (get_trajectory) +from .single_run import (get_heatmap_single_run_data) diff --git a/src/iohinspector/metrics/aocc.py b/src/iohinspector/metrics/aocc.py new file mode 100644 index 0000000..12c4e26 --- /dev/null +++ b/src/iohinspector/metrics/aocc.py @@ -0,0 +1,93 @@ +import polars as pl +import pandas as pd +from typing import Iterable, Callable +from functools import partial +import numpy as np +def _aocc( + group: pl.DataFrame, + eval_max: int, + fval_var: str = "eaf" +) -> pl.DataFrame: + """Internal helper function to calculate AOCC contribution for a single data group. + + Args: + group (pl.DataFrame): A single group DataFrame containing evaluation data for one run. + eval_max (int): Maximum value of evaluations to consider for AOCC calculation. + fval_var (str, optional): Which data column specifies the performance value. Defaults to "eaf". + + Returns: + pl.DataFrame: DataFrame with added 'aocc_contribution' column containing normalized area contributions. + """ + group = group.filter( + pl.col("evaluations") <= eval_max + ) + # Ensure consistent types for the new_row DataFrame + new_row = pl.DataFrame( + { + "evaluations": [0.0, float(eval_max)], + fval_var: [float(group[fval_var].min()), float(group[fval_var].max())], + } + ) + group = ( + pl.concat([group, new_row], how="diagonal") + .sort("evaluations") + .fill_null(strategy="forward") + .fill_null(strategy="backward") + ) + + return group.with_columns( + ( + ( + pl.col("evaluations").diff(n=1, null_behavior="ignore") + * (pl.col(fval_var).shift(1)) + ) + / eval_max + ).alias("aocc_contribution") + ) + + + +def get_aocc( + data: pl.DataFrame, + eval_max: int, + fval_var: str = "eaf", + free_vars: Iterable[str] = ["function_name", "algorithm_name"], + scale_eval_log: bool = False, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Calculate Area Over Convergence Curve (AOCC) metric for algorithm performance evaluation. + + Args: + data (pl.DataFrame): The data object containing performance evaluation data. + eval_max (int): Maximum value of evaluations to use for AOCC calculation. + fval_var (str, optional): Which data column specifies the performance value. Defaults to "eaf". + free_vars (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["function_name", "algorithm_name"]. + scale_eval_log (bool, optional): Whether to use logarithmic scaling for evaluations. Defaults to False. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A dataframe with the area under the EAF (=area over convergence curve). + """ + # Ensure consistent data types for evaluations + data = data.with_columns( + pl.col("evaluations").cast(pl.Float64) + ) + + if scale_eval_log: + data = data.with_columns( + pl.col("evaluations").log10().alias("evaluations") + ) + eval_max = np.log10(eval_max) + # Group by without strict=False (invalid argument for polars) + aocc_contribs = data.group_by(*["data_id"]).map_groups( + partial(_aocc, eval_max=eval_max, fval_var=fval_var) + ) + aoccs = aocc_contribs.group_by(["data_id"] + free_vars).agg( + pl.col("aocc_contribution").sum() + ) + final_df = aoccs.group_by(free_vars).agg( + pl.col("aocc_contribution").mean().alias("AOCC") + ) + if return_as_pandas: + return final_df.to_pandas() + return final_df diff --git a/src/iohinspector/metrics/attractor_network.py b/src/iohinspector/metrics/attractor_network.py new file mode 100644 index 0000000..08bee2c --- /dev/null +++ b/src/iohinspector/metrics/attractor_network.py @@ -0,0 +1,130 @@ +import numpy as np +import pandas as pd +import polars as pl +from typing import Iterable, Tuple + + +def _get_nodeidx( + xloc: np.ndarray, + yval: float, + nodes: pd.DataFrame, + epsilon: float +): + """Internal helper function to find existing node index based on position and function value. + + Args: + xloc (array-like): Position coordinates to search for in the network. + yval (float): Function value to match with existing nodes. + nodes (pd.DataFrame): DataFrame containing existing network nodes. + epsilon (float): Tolerance threshold for considering positions as identical. + + Returns: + int: Index of matching node if found, -1 otherwise. + """ + if len(nodes) == 0: + return -1 + candidates = nodes[np.isclose(nodes["y"], yval, atol=epsilon)] + if len(candidates) == 0: + return -1 + idxs = np.all( + np.isclose(np.array(candidates)[:, : len(xloc)], xloc, atol=epsilon), axis=1 + ) + if any(idxs): + return candidates[idxs].index[0] + return -1 + + +def get_attractor_network( + data: pl.DataFrame, + coord_vars: Iterable[str] = ["x1", "x2"], + fval_var: str = "raw_y", + eval_var: str = "evaluations", + maximization: bool = False, + beta: int = 40, + epsilon: float = 0.0001, + eval_max=None, +) -> Tuple[pd.DataFrame, pd.DataFrame]: + """Create an attractor network from optimization trajectory data. + + Args: + data (pl.DataFrame): The original dataframe containing performance and position information. + coord_vars (Iterable[str], optional): Which columns correspond to position information. Defaults to ["x1", "x2"]. + fval_var (str, optional): Which column corresponds to performance values. Defaults to "raw_y". + eval_var (str, optional): Which column corresponds to evaluation numbers. Defaults to "evaluations". + maximization (bool, optional): Whether fval_var is to be maximized. Defaults to False. + beta (int, optional): Minimum stagnation length threshold. Defaults to 40. + epsilon (float, optional): Radius below which positions should be considered identical in the network. Defaults to 0.0001. + eval_max (int, optional): Maximum evaluation number to consider. Defaults to the maximum of eval_var if None. + + Returns: + tuple[pd.DataFrame, pd.DataFrame]: Two DataFrames containing the nodes and edges of the network respectively. + """ + + running_idx = 0 + running_edgeidx = 0 + nodes = pd.DataFrame(columns=[*coord_vars, "y", "count", "evals"]) + edges = pd.DataFrame(columns=["start", "end", "count", "stag_length_avg"]) + if eval_max is None: + eval_max = max(data[eval_var]) + + for run_id in data["data_id"].unique(): + dt_group = data.filter( + pl.col("data_id") == run_id, pl.col(eval_var) <= eval_max + ) + if maximization: + ys = np.maximum.accumulate(np.array(dt_group[fval_var])) + else: + ys = np.minimum.accumulate(np.array(dt_group[fval_var])) + xs = np.array(dt_group[coord_vars]) + + stopping_points = np.where(np.abs(np.diff(ys, prepend=np.inf)) > 0)[0] + evals = np.array(dt_group[eval_var]) + + stagnation_lengths = np.diff(evals[stopping_points], append=eval_max) + edge_lengths = stagnation_lengths[stagnation_lengths > beta] + real_idxs = [stopping_points[i] for i in np.where(stagnation_lengths > beta)[0]] + if not real_idxs: + continue + + xloc = xs[real_idxs[0]] + yval = ys[real_idxs[0]] + nodeidx = _get_nodeidx(xloc, yval, nodes, epsilon) + if nodeidx == -1: + nodes.loc[running_idx] = [*xloc, yval, 1, evals[real_idxs[0]]] + node1 = running_idx + running_idx += 1 + else: + nodes.loc[nodeidx, "evals"] += evals[real_idxs[0]] + nodes.loc[nodeidx, "count"] += 1 + node1 = nodeidx + + if len(real_idxs) == 1: + continue + + for i in range(len(real_idxs) - 1): + xloc = xs[real_idxs[i + 1]] + yval = ys[real_idxs[i + 1]] + nodeidx = _get_nodeidx(xloc, yval, nodes, epsilon) + if nodeidx == -1: + nodes.loc[running_idx] = [*xloc, yval, 1, evals[real_idxs[i + 1]]] + node2 = running_idx + running_idx += 1 + else: + nodes.loc[nodeidx, "evals"] += evals[real_idxs[i + 1]] + nodes.loc[nodeidx, "count"] += 1 + node2 = nodeidx + + edgelen = edge_lengths[i] + edge_idxs = edges.query(f"start == {node1} & end == {node2}").index + if len(edge_idxs) == 0: + edges.loc[running_edgeidx] = [node1, node2, 1, edgelen] + running_edgeidx += 1 + else: + curr_count = edges.loc[edge_idxs[0]]["count"] + curr_len = edges.loc[edge_idxs[0]]["stag_length_avg"] + edges.loc[edge_idxs[0], "stag_length_avg"] = ( + curr_len * curr_count + edgelen + ) / (curr_count + 1) + edges.loc[edge_idxs[0], "count"] += 1 + node1 = node2 + return nodes, edges \ No newline at end of file diff --git a/src/iohinspector/metrics/eaf.py b/src/iohinspector/metrics/eaf.py new file mode 100644 index 0000000..b2c339e --- /dev/null +++ b/src/iohinspector/metrics/eaf.py @@ -0,0 +1,176 @@ + +from iohinspector.align import align_data +from iohinspector.metrics import transform_fval, get_sequence +import numpy as np +import pandas as pd +import polars as pl +from moocore import eaf, eafdiff + + +def get_discritized_eaf_single_objective( + data: pl.DataFrame, + fval_var: str = "raw_y", + eval_var: str = "evaluations", + eval_values = None, + eval_min = None, + eval_max = None, + eval_targets = 10, + scale_eval_log: bool = True, + f_min = 1e-8, + f_max = 1e2, + scale_f_log: bool = True, + f_targets = 101, + return_as_pandas: bool = True, +) -> pd.DataFrame | pl.DataFrame: + """Generate discretized EAF data for single-objective optimization problems. + + Args: + data (pl.DataFrame): The data object containing optimization trajectory data. + fval_var (str, optional): Which column contains the function values. Defaults to "raw_y". + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + eval_values (array-like, optional): Specific evaluation values to use. If None, generated from eval_min/max. + eval_min (int, optional): Minimum evaluation value. If None, uses minimum from data. + eval_max (int, optional): Maximum evaluation value. If None, uses maximum from data. + eval_targets (int, optional): Number of evaluation targets to generate. Defaults to 10. + scale_eval_log (bool, optional): Whether to use logarithmic scaling for evaluations. Defaults to True. + f_min (float, optional): Minimum function value for scaling. Defaults to 1e-8. + f_max (float, optional): Maximum function value for scaling. Defaults to 1e2. + scale_f_log (bool, optional): Whether to use logarithmic scaling for function values. Defaults to True. + f_targets (int, optional): Number of function value targets to generate. Defaults to 101. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame: A DataFrame with discretized EAF data for single-objective problems. + """ + + if eval_values is None: + if eval_min is None: + eval_min = data[eval_var].min() + if eval_max is None: + eval_max = data[eval_var].max() + eval_values = get_sequence( + eval_min, eval_max, eval_targets, scale_log=scale_eval_log, cast_to_int=True + ) + + dt_aligned = align_data( + data, + eval_values, + x_col=eval_var, + y_col=fval_var, + output="long" + ) + dt_aligned = transform_fval( + dt_aligned, + lb=f_min, + ub=f_max, + scale_log=scale_f_log, + fval_var=fval_var, + ) + targets = np.linspace(0, 1, f_targets) + dt_targets = pd.DataFrame(targets, columns=["eaf_target"]) + + dt_merged = dt_targets.merge(dt_aligned[[eval_var, 'eaf']].to_pandas(), how='cross') + dt_merged['ps'] = dt_merged['eaf_target'] <= dt_merged['eaf'] + dt_discr = dt_merged.pivot_table(index='eaf_target', columns=eval_var, values='ps') + if return_as_pandas: + return dt_discr + return pl.from_pandas(dt_discr) + + + +def get_eaf_data( + data: pl.DataFrame, + eval_var: str = "evaluations", + eval_min: int = None, + eval_max: int = None, + scale_eval_log: bool = True, + return_as_pandas: bool = True, + )-> pd.DataFrame | pl.DataFrame: + """Generate aligned EAF data for visualization and analysis. + + Args: + data (pl.DataFrame): The data object containing optimization trajectory data. + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + eval_min (int, optional): Minimum evaluation value. If None, uses minimum from data. + eval_max (int, optional): Maximum evaluation value. If None, uses maximum from data. + scale_eval_log (bool, optional): Whether to use logarithmic scaling for evaluations. Defaults to True. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame with aligned EAF data. + """ + + if eval_min is None: + eval_min = data[eval_var].min() + if eval_max is None: + eval_max = data[eval_var].max() + + evals = get_sequence(eval_min, eval_max, 50, scale_eval_log, True) + long = align_data(data, np.array(evals, "uint64"), ["data_id"], output="long") + + if return_as_pandas: + return long.to_pandas() + return long + + +def get_eaf_pareto_data( + data: pl.DataFrame, + obj1_var: str, + obj2_var: str, + return_as_pandas: bool = True, +)-> pd.DataFrame | pl.DataFrame: + """Generate EAF data for multi-objective optimization problems using Pareto fronts. + + Args: + data (pl.DataFrame): The data object containing multi-objective optimization data. + obj1_var (str): Name of the column containing first objective values. + obj2_var (str): Name of the column containing second objective values. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame with EAF data including objective values and EAF percentiles. + """ + data_to_process = np.array(data[[obj1_var, obj2_var, "data_id"]]) + eaf_data = eaf(data_to_process[:,:-1], data_to_process[:,-1] ) + eaf_data_df = pd.DataFrame(eaf_data) + eaf_data_df.columns = [obj1_var, obj2_var, "eaf"] + # scale EAF values from percentages to proportions + eaf_data_df["eaf"] = eaf_data_df["eaf"].astype(float) / 100.0 + if return_as_pandas: + return eaf_data_df + return pl.from_pandas(eaf_data_df) + + +def get_eaf_diff_data( + data1: pl.DataFrame, + data2: pl.DataFrame, + obj1_var: str, + obj2_var: str, + return_as_pandas: bool = True, +)-> pd.DataFrame | pl.DataFrame: + """Calculate EAF difference data between two multi-objective optimization datasets. + + Args: + data1 (pl.DataFrame): First dataset containing multi-objective optimization data. + data2 (pl.DataFrame): Second dataset containing multi-objective optimization data. + obj1_var (str): Name of the column containing first objective values. + obj2_var (str): Name of the column containing second objective values. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame with EAF difference rectangles and difference values. + """ + x = np.array(data1[[obj1_var, obj2_var, "data_id"]]) + y = np.array(data2[[obj1_var, obj2_var, "data_id"]]) + if np.array_equal(np.sort(x.view(np.void), axis=0), np.sort(y.view(np.void), axis=0)): + cols = ["x_min", "y_min", "x_max", "y_max", "eaf_diff"] + empty_df = pl.DataFrame({c: [] for c in cols}) + if return_as_pandas: + return empty_df.to_pandas() + return empty_df + eaf_diff_rect = eafdiff(x, y, rectangles=True) + eaf_diff_df = pl.DataFrame(eaf_diff_rect, schema=["x_min", "y_min", "x_max", "y_max", "eaf_diff"]) + + if return_as_pandas: + return eaf_diff_df.to_pandas() + return eaf_diff_df diff --git a/src/iohinspector/metrics/ecdf.py b/src/iohinspector/metrics/ecdf.py new file mode 100644 index 0000000..80a3240 --- /dev/null +++ b/src/iohinspector/metrics/ecdf.py @@ -0,0 +1,89 @@ +import polars as pl +import pandas as pd +from typing import Iterable +from .utils import get_sequence +from ..align import align_data, turbo_align +from .utils import transform_fval + + + + +def get_data_ecdf( + data: pl.DataFrame, + fval_var: str = "raw_y", + eval_var: str = "evaluations", + free_vars: Iterable[str] = ["algorithm_name"], + f_min: int = None, + f_max: int = None, + scale_f_log: bool = True, + eval_values: Iterable[int] = None, + eval_min: int = None, + eval_max: int = None, + scale_eval_log: bool = True, + maximization: bool = False, + turbo: bool = False, + return_as_pandas: bool = True, +) -> pd.DataFrame | pl.DataFrame: + """Generate empirical cumulative distribution function (ECDF) data based on EAF calculations. + + Args: + data (pl.DataFrame): The DataFrame containing the full performance trajectory data. + fval_var (str, optional): Which column contains the performance measure values. Defaults to "raw_y". + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + free_vars (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["algorithm_name"]. + f_min (int, optional): Minimum value for function value scaling. If None, uses minimum from data. Defaults to None. + f_max (int, optional): Maximum value for function value scaling. If None, uses maximum from data. Defaults to None. + scale_f_log (bool, optional): Whether to use logarithmic scaling for function values. Defaults to True. + eval_values (Iterable[int], optional): Specific evaluation values to use. If None, generated from eval_min/max. Defaults to None. + eval_min (int, optional): Minimum evaluation value. If None, uses minimum from data. Defaults to None. + eval_max (int, optional): Maximum evaluation value. If None, uses maximum from data. Defaults to None. + scale_eval_log (bool, optional): Whether to use logarithmic scaling for evaluations. Defaults to True. + maximization (bool, optional): Whether the performance measure is being maximized. Defaults to False. + turbo (bool, optional): Whether to use turbo alignment for faster processing. Defaults to False. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame containing the ECDF data with aligned evaluation points. + """ + if eval_values is None: + if eval_min is None: + eval_min = data[eval_var].min() + if eval_max is None: + eval_max = data[eval_var].max() + eval_values = get_sequence( + eval_min, eval_max, 50, scale_log=scale_eval_log, cast_to_int=True + ) + if turbo: + data_aligned = turbo_align( + data.cast({eval_var: pl.Int64}), + eval_values, + x_col=eval_var, + y_col=fval_var, + maximization=maximization, + ) + else: + data_aligned = align_data( + data.cast({eval_var: pl.Int64}), + eval_values, + group_cols=["data_id"], + x_col=eval_var, + y_col=fval_var, + maximization=maximization, + ) + dt_ecdf = ( + transform_fval( + data_aligned, + fval_var=fval_var, + maximization=maximization, + lb=f_min, + ub=f_max, + scale_log=scale_f_log, + ) + .group_by([eval_var] + free_vars) + .mean() + .sort(eval_var) + ) + + if return_as_pandas: + return dt_ecdf.to_pandas() + return dt_ecdf \ No newline at end of file diff --git a/src/iohinspector/metrics/fixed_budget.py b/src/iohinspector/metrics/fixed_budget.py new file mode 100644 index 0000000..572694c --- /dev/null +++ b/src/iohinspector/metrics/fixed_budget.py @@ -0,0 +1,71 @@ +import polars as pl +import pandas as pd +from typing import Iterable, Callable +from .utils import get_sequence +from ..align import align_data + +def aggregate_convergence( + data: pl.DataFrame, + eval_var: str = "evaluations", + fval_var: str = "raw_y", + free_vars: Iterable[str] = ["algorithm_name"], + eval_min: int = None, + eval_max: int = None, + custom_op: Callable[[pl.Series], float] = None, + maximization: bool = False, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Aggregate performance data from a fixed-budget perspective with multiple statistics. + + Args: + data (pl.DataFrame): The data object containing evaluation and performance data. + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + fval_var (str, optional): Which column contains the function values. Defaults to "raw_y". + free_vars (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["algorithm_name"]. + eval_min (int, optional): Minimum evaluation value to include. If None, uses minimum from data. Defaults to None. + eval_max (int, optional): Maximum evaluation value to include. If None, uses maximum from data. Defaults to None. + custom_op (Callable[[pl.Series], float], optional): Custom aggregation function to apply per group. Defaults to None. + maximization (bool, optional): Whether the objective is being maximized. Defaults to False. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A DataFrame with aggregated performance statistics (mean, min, max, median, std, geometric_mean). + """ + if(data.is_empty()): + raise ValueError("Data is empty, cannot aggregate convergence.") + + # Getting alligned data (to check if e.g. limits should be args for this function) + if eval_min is None: + eval_min = data[eval_var].min() + if eval_max is None: + eval_max = data[eval_var].max() + x_values = get_sequence(eval_min, eval_max, 50, scale_log=True, cast_to_int=True) + group_variables = free_vars + [eval_var] + data_aligned = align_data( + data.cast({eval_var: pl.Int64}), + x_values, + group_cols=["data_id"] + free_vars, + x_col=eval_var, + y_col=fval_var, + maximization=maximization, + ) + aggregations = [ + pl.mean(fval_var).alias("mean"), + pl.min(fval_var).alias("min"), + pl.max(fval_var).alias("max"), + pl.median(fval_var).alias("median"), + pl.std(fval_var).alias("std"), + pl.col(fval_var).log().mean().exp().alias("geometric_mean") + ] + + if custom_op is not None: + aggregations.append( + pl.col(fval_var).map_batches( + lambda s: custom_op(s), return_dtype=pl.Float64, returns_scalar=True + ).alias(custom_op.__name__) + ) + + dt_plot = data_aligned.group_by(*group_variables).agg(aggregations) + if return_as_pandas: + return dt_plot.sort(eval_var).to_pandas() + return dt_plot.sort(eval_var) \ No newline at end of file diff --git a/src/iohinspector/metrics/fixed_target.py b/src/iohinspector/metrics/fixed_target.py new file mode 100644 index 0000000..7d0292a --- /dev/null +++ b/src/iohinspector/metrics/fixed_target.py @@ -0,0 +1,91 @@ +import polars as pl +import pandas as pd +from typing import Iterable, Callable +from .utils import get_sequence +from ..align import align_data + +def aggregate_running_time( + data: pl.DataFrame, + eval_var: str = "evaluations", + fval_var: str = "raw_y", + free_vars: Iterable[str] = ["algorithm_name"], + f_min: float = None, + f_max: float = None, + scale_f_log: bool = True, + eval_max: int = None, + maximization: bool = False, + custom_op: Callable[[pl.Series], float] = None, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Aggregate performance data from a fixed-target perspective with running time statistics. + + Args: + data (pl.DataFrame): The data object containing performance and evaluation data. + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + fval_var (str, optional): Which column contains the function values. Defaults to "raw_y". + free_vars (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["algorithm_name"]. + f_min (float, optional): Minimum function value to use. If None, uses minimum from data. Defaults to None. + f_max (float, optional): Maximum function value to use. If None, uses maximum from data. Defaults to None. + scale_f_log (bool, optional): Whether to use logarithmic scaling for function values. Defaults to True. + eval_max (int, optional): Maximum evaluation value to consider. If None, uses maximum from data. Defaults to None. + maximization (bool, optional): Whether the performance metric is being maximized. Defaults to False. + custom_op (Callable[[pl.Series], float], optional): Custom aggregation function to apply per group. Defaults to None. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A DataFrame with aggregated running time statistics (mean, min, max, median, std, success_ratio, ERT, PAR-10). + """ + + # Getting alligned data (to check if e.g. limits should be args for this function) + if f_min is None: + f_min = data[fval_var].min() + if f_max is None: + f_max = data[fval_var].max() + f_values = get_sequence(f_min, f_max, 50, scale_log=scale_f_log) + group_variables = free_vars + [fval_var] + data_aligned = align_data( + data, + f_values, + group_cols=["data_id"] + free_vars, + x_col=fval_var, + y_col=eval_var, + maximization=maximization, + ) + + if eval_max is None: + eval_max = data[eval_var].max() + + aggregations = [ + pl.col(eval_var).mean().alias("mean"), + pl.col(eval_var).min().alias("min"), + pl.col(eval_var).max().alias("max"), + pl.col(eval_var).median().alias("median"), + pl.col(eval_var).std().alias("std"), + pl.col(eval_var).is_finite().mean().alias("success_ratio"), + pl.col(eval_var).is_finite().sum().alias("success_count"), + ( + pl.when(pl.col(eval_var).is_finite()) + .then(pl.col(eval_var)) + .otherwise(eval_max) + .sum() + /pl.col(eval_var).is_finite().sum() + ).alias("ERT"), + ( + pl.when(pl.col(eval_var).is_finite()) + .then(pl.col(eval_var)) + .otherwise(10 * eval_max) + .sum() + / pl.col(eval_var).count() + ).alias("PAR-10"), + ] + + if custom_op is not None: + aggregations.append( + pl.col(eval_var) + .map_batches(lambda s: custom_op(s), return_dtype=pl.Float64, returns_scalar=True) + .alias(custom_op.__name__) + ) + dt_plot = data_aligned.group_by(*group_variables).agg(aggregations) + if return_as_pandas: + return dt_plot.sort(fval_var).to_pandas() + return dt_plot.sort(fval_var) \ No newline at end of file diff --git a/src/iohinspector/metrics/multi_objective.py b/src/iohinspector/metrics/multi_objective.py new file mode 100644 index 0000000..b7f5988 --- /dev/null +++ b/src/iohinspector/metrics/multi_objective.py @@ -0,0 +1,70 @@ + +from typing import Iterable +import polars as pl +import pandas as pd +from iohinspector.indicators import final, add_indicator +from iohinspector.metrics import get_sequence + + +def get_pareto_front_2d( + data: pl.DataFrame, + obj1_var: str = "raw_y", + obj2_var: str = "F2", + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Extract the Pareto front from a 2D multi-objective optimization dataset. + + Args: + data (pl.DataFrame): The data object containing multi-objective optimization data. + obj1_var (str, optional): Which column contains the first objective values. Defaults to "raw_y". + obj2_var (str, optional): Which column contains the second objective values. Defaults to "F2". + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A DataFrame containing only the non-dominated Pareto front points. + """ + df = add_indicator(data, final.NonDominated(), [obj1_var, obj2_var]) + df = df.filter(pl.col("final_nondominated") == True) + if return_as_pandas: + return df.to_pandas() + return df + + + +def get_indicator_over_time_data( + data: pl.DataFrame, + indicator: object = None, + obj_vars: Iterable[str] = ["raw_y", "F2"], + eval_min: int = 1, + eval_max: int = 50_000, + scale_eval_log: bool = True, + eval_steps: int = 50, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Calculate multi-objective indicator values over time for performance analysis. + + Args: + data (pl.DataFrame): The data object containing multi-objective optimization trajectory data. + indicator (object, optional): The indicator object to calculate over time. Defaults to None. + obj_vars (Iterable[str], optional): Which columns contain the objective values. Defaults to ["raw_y", "F2"]. + eval_min (int, optional): Minimum evaluation value to consider. Defaults to 1. + eval_max (int, optional): Maximum evaluation value to consider. Defaults to 50_000. + scale_eval_log (bool, optional): Whether to use logarithmic scaling for evaluations. Defaults to True. + eval_steps (int, optional): Number of evaluation steps to generate. Defaults to 50. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A DataFrame with indicator values calculated over the specified evaluation timeline. + """ + + + evals = get_sequence( + eval_min, eval_max, eval_steps, cast_to_int=True, scale_log=scale_eval_log + ) + df = add_indicator( + data, indicator, obj_vars=obj_vars, evals=evals + ) + + if return_as_pandas: + return df.to_pandas() + return df \ No newline at end of file diff --git a/src/iohinspector/metrics/ranking.py b/src/iohinspector/metrics/ranking.py new file mode 100644 index 0000000..1b7fe9a --- /dev/null +++ b/src/iohinspector/metrics/ranking.py @@ -0,0 +1,178 @@ +from iohinspector.indicators import add_indicator +from skelo.model.elo import EloEstimator +import numpy as np +import pandas as pd +import polars as pl +from typing import Iterable + + + + +def get_tournament_ratings( + data: pl.DataFrame, + alg_vars: Iterable[str] = ["algorithm_name"], + fid_vars: Iterable[str] = ["function_name"], + fval_var: str = "raw_y", + nrounds: int = 25, + maximization: bool = False, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Calculate ELO tournament ratings for algorithms competing on multiple problems. + + Args: + data (pl.DataFrame): The data object containing algorithm performance data. + alg_vars (Iterable[str], optional): Which columns specify the algorithms that will compete. Defaults to ["algorithm_name"]. + fid_vars (Iterable[str], optional): Which columns denote the problems on which competition occurs. Defaults to ["function_name"]. + fval_var (str, optional): Which column contains the performance values. Defaults to "raw_y". + nrounds (int, optional): Number of tournament rounds to play. Defaults to 25. + maximization (bool, optional): Whether the performance metric is being maximized. Defaults to False. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame with ELO ratings, deviations, and algorithm identifiers. + """ + fids = data[fid_vars].unique() + aligned_comps = data.pivot( + index=alg_vars, + columns=fid_vars, + values=fval_var, + aggregate_function=pl.element(), + ) + players = aligned_comps[alg_vars] + n_players = players.shape[0] + comp_arr = np.array(aligned_comps[aligned_comps.columns[len(alg_vars) :]]) + + rng = np.random.default_rng() + fids = [i for i in range(len(fids))] + lplayers = [i for i in range(n_players)] + records = [] + for r in range(nrounds): + for fid in fids: + for p1 in lplayers: + for p2 in lplayers: + if p1 == p2: + continue + s1 = rng.choice(comp_arr[p1][fid], 1)[0] + s2 = rng.choice(comp_arr[p2][fid], 1)[0] + if s1 == s2: + won = 0.5 + else: + won = abs(float(maximization) - float(s1 < s2)) + + records.append([r, p1, p2, won]) + dt_comp = pd.DataFrame.from_records( + records, columns=["round", "p1", "p2", "outcome"] + ) + dt_comp = dt_comp.sample(frac=1).sort_values("round") + model = EloEstimator(key1_field="p1", key2_field="p2", timestamp_field="round").fit( + dt_comp, dt_comp["outcome"] + ) + model_dt = model.rating_model.to_frame() + ratings = np.array(model_dt[np.isnan(model_dt["valid_to"])]["rating"]) + deviations = ( + model_dt.query(f"valid_from >= {nrounds * 0.95}").groupby("key")["rating"].std() + ) + + rating_dt_elo = pd.DataFrame( + [ + ratings, + deviations, + *players[players.columns], + ] + ).transpose() + rating_dt_elo.columns = ["Rating", "Deviation", *players.columns] + if return_as_pandas: + return rating_dt_elo + else: + rating_dt_elo_pl = pl.from_pandas(rating_dt_elo) + return rating_dt_elo_pl + + +def get_robustrank_over_time( + data: pl.DataFrame, + obj_vars: Iterable[str], + evals: Iterable[int], + indicator: object, + +): + """Calculate robust ranking data over multiple time points for multi-objective optimization. + + Args: + data (pl.DataFrame): The data object containing multi-objective optimization trajectory data. + obj_vars (Iterable[str]): Which columns correspond to the objective values. + evals (Iterable[int]): Evaluation time points at which to calculate rankings. + indicator (object): Indicator object from iohinspector.indicators for performance measurement. + + Returns: + tuple: A tuple containing (comparison, benchmark) objects for robust ranking analysis. + """ + from robustranking import Benchmark + from robustranking.comparison import MOBootstrapComparison + + df = add_indicator( + data, indicator, obj_vars=obj_vars, evals=evals + ).to_pandas() + df_part = df[["evaluations", indicator.var_name, "algorithm_name", "run_id"]] + dt_pivoted = pd.pivot( + df_part, + index=["algorithm_name", "run_id"], + columns=["evaluations"], + values=[indicator.var_name], + ).reset_index() + dt_pivoted.columns = ["algorithm_name", "run_id"] + evals + benchmark = Benchmark() + benchmark.from_pandas(dt_pivoted, "algorithm_name", "run_id", evals) + comparison = MOBootstrapComparison( + benchmark, + alpha=0.05, + minimise=indicator.minimize, + bootstrap_runs=1000, + aggregation_method=np.mean, + ) + + return comparison, benchmark + + +def get_robustrank_changes( + data: pl.DataFrame, + obj_vars: Iterable[str], + evals: Iterable[int], + indicator: object, + ): + """Calculate robust ranking changes across multiple evaluation time points. + + Args: + data (pl.DataFrame): The data object containing multi-objective optimization trajectory data. + obj_vars (Iterable[str]): Which columns correspond to the objective values. + evals (Iterable[int]): Evaluation time points at which to calculate ranking changes. + indicator (object): Indicator object from iohinspector.indicators for performance measurement. + + Returns: + dict: A dictionary of comparison objects for each evaluation time point showing ranking changes. + """ + from robustranking import Benchmark + from robustranking.comparison import BootstrapComparison + + df = add_indicator( + data, indicator, obj_vars=obj_vars, evals=evals + ).to_pandas() + df_part = df[["evaluations", indicator.var_name, "algorithm_name", "run_id"]] + dt_pivoted = pd.pivot( + df_part, + index=["algorithm_name", "run_id"], + columns=["evaluations"], + values=[indicator.var_name], + ).reset_index() + dt_pivoted.columns = ["algorithm_name", "run_id"] + evals + + comparisons = { + f"{eval}": BootstrapComparison( + Benchmark().from_pandas(dt_pivoted, "algorithm_name", "run_id", eval), + alpha=0.05, + minimise=indicator.minimize, + bootstrap_runs=1000, + ) + for eval in evals + } + + return comparisons \ No newline at end of file diff --git a/src/iohinspector/metrics/single_run.py b/src/iohinspector/metrics/single_run.py new file mode 100644 index 0000000..b3eb06b --- /dev/null +++ b/src/iohinspector/metrics/single_run.py @@ -0,0 +1,37 @@ +import numpy as np +import polars as pl +import pandas as pd +from typing import Iterable, Optional + + + +def get_heatmap_single_run_data( + data: pl.DataFrame, + vars: Iterable[str], + eval_var: str = "evaluations", + var_mins: Iterable[float] = [-5], + var_maxs: Iterable[float] = [5], + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Generate normalized heatmap data showing search space points evaluated in a single optimization run. + + Args: + data (pl.DataFrame): The data object containing single-run optimization trajectory data. + vars (Iterable[str]): Which columns correspond to the search space variable values. + eval_var (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + var_mins (Iterable[float], optional): Minimum bounds for normalization of variables. Defaults to [-5]. + var_maxs (Iterable[float], optional): Maximum bounds for normalization of variables. Defaults to [5]. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pd.DataFrame or pl.DataFrame: A DataFrame with normalized variable values arranged for heatmap visualization. + """ + assert data["data_id"].n_unique() == 1 + dt = data[vars].transpose().to_pandas() + dt.columns = list(data[eval_var]) + var_mins_arr = np.array(var_mins) + var_maxs_arr = np.array(var_maxs) + dt = (dt.subtract(var_mins_arr, axis=0)).divide(var_maxs_arr - var_mins_arr, axis=0) + if return_as_pandas: + return dt + return pl.from_pandas(dt) diff --git a/src/iohinspector/metrics/trajectory.py b/src/iohinspector/metrics/trajectory.py new file mode 100644 index 0000000..c559432 --- /dev/null +++ b/src/iohinspector/metrics/trajectory.py @@ -0,0 +1,49 @@ +import numpy as np +import polars as pl +import pandas as pd +from typing import Iterable +from iohinspector.align import align_data + + + + +def get_trajectory(data: pl.DataFrame, + traj_length: int = None, + min_fevals: int = 1, + evaluation_variable: str = "evaluations", + fval_variable: str = "raw_y", + free_variables: Iterable[str] = ["algorithm_name"], + maximization: bool = False, + return_as_pandas: bool = True, +) -> pl.DataFrame | pd.DataFrame: + """Generate aligned performance trajectories for algorithm comparison over fixed evaluation sequences. + + Args: + data (pl.DataFrame): The data object containing algorithm performance trajectory data. + traj_length (int, optional): Length of the trajectory to generate. If None, uses maximum evaluations from data. Defaults to None. + min_fevals (int, optional): Starting evaluation number for the trajectory. Defaults to 1. + evaluation_variable (str, optional): Which column contains the evaluation numbers. Defaults to "evaluations". + fval_variable (str, optional): Which column contains the function values. Defaults to "raw_y". + free_variables (Iterable[str], optional): Which columns to NOT aggregate over. Defaults to ["algorithm_name"]. + maximization (bool, optional): Whether the performance metric is being maximized. Defaults to False. + return_as_pandas (bool, optional): Whether to return results as pandas DataFrame. Defaults to True. + + Returns: + pl.DataFrame or pd.DataFrame: A DataFrame with aligned trajectory data where each row corresponds to a specific evaluation and performance value. + """ + if traj_length is None: + max_fevals = data[evaluation_variable].max() + else: + max_fevals = traj_length + min_fevals + x_values = np.arange(min_fevals, max_fevals + 1) + data_aligned = align_data( + data.cast({evaluation_variable: pl.Int64}), + x_values, + group_cols=["data_id"] + free_variables, + x_col=evaluation_variable, + y_col=fval_variable, + maximization=maximization, + ) + if return_as_pandas: + data_aligned = data_aligned.to_pandas() + return data_aligned \ No newline at end of file diff --git a/src/iohinspector/metrics/utils.py b/src/iohinspector/metrics/utils.py new file mode 100644 index 0000000..0a114a1 --- /dev/null +++ b/src/iohinspector/metrics/utils.py @@ -0,0 +1,197 @@ +import numpy as np +import polars as pl +import warnings +from typing import Iterable, Optional, Union, Dict + +from moocore import ( + filter_dominated, +) + +def get_sequence( + min: float, + max: float, + len: float, + scale_log: bool = False, + cast_to_int: bool = False, +) -> np.ndarray: + """Create sequence of points, used for subselecting targets / budgets for alignment and data processing. + + Args: + min (float): Starting point of the range. + max (float): Final point of the range. + len (float): Number of steps in the sequence. + scale_log (bool, optional): Whether values should be scaled logarithmically. Defaults to False. + cast_to_int (bool, optional): Whether the values should be casted to integers (e.g. in case of budget) or not. Defaults to False. + + Returns: + np.ndarray: Array of evenly spaced values between min and max. + """ + transform = lambda x: x + if scale_log: + assert min > 0 + min = np.log10(min) + max = np.log10(max) + transform = lambda x: 10**x + if len == 1: + values =np.array([min]) + else: + if(max == min): + values = np.ones(len) * min + else: + values = np.arange( + min, + max + (max - min) / (2 * (len - 1)), + (max - min) / (len - 1), + dtype=float, + ) + + values = transform(values) + if cast_to_int: + return np.unique(np.array(values, dtype=int)) + return np.unique(values) + + + + +def normalize_objectives( + data: pl.DataFrame, + obj_vars: Iterable[str] = ["raw_y"], + bounds: Optional[Dict[str, tuple[Optional[float], Optional[float]]]] = None, + log_scale: Union[bool, Dict[str, bool]] = False, + maximize: Union[bool, Dict[str, bool]] = False, + only_nondominated: bool = False, + prefix: str = "ert", + keep_original: bool = True +) -> pl.DataFrame: + """Normalize multiple objective columns in a dataframe using min-max normalization. + + Args: + data (pl.DataFrame): Input dataframe containing the objective columns. + obj_vars (Iterable[str], optional): Which columns contain the objective values to normalize. Defaults to ["raw_y"]. + bounds (Optional[Dict[str, tuple[Optional[float], Optional[float]]]], optional): Optional manual bounds per column as (lower_bound, upper_bound). Defaults to None. + log_scale (Union[bool, Dict[str, bool]], optional): Whether to apply log10 scaling. Can be a single bool or a dict per column. Defaults to False. + maximize (Union[bool, Dict[str, bool]], optional): Whether to treat objective as maximization. Can be a single bool or dict per column. Defaults to False. + only_nondominated (bool, optional): Whether to only consider non-dominated objectives in computing bounds. Defaults to False. + prefix (str, optional): Prefix for normalized column names. Defaults to "ert". + keep_original (bool, optional): Whether to keep original objective column names. Defaults to True. + + Returns: + pl.DataFrame: The original dataframe with new normalized objective columns added. + """ + result = data.clone() + n_objectives = len(obj_vars) + + ndpoints = None + if only_nondominated and len(obj_vars) > 1: + obj_vals = np.array(result[obj_vars]) + ndpoints = filter_dominated(obj_vals) + + + for i, col in enumerate(obj_vars): + # Determine log scaling + use_log = log_scale[col] if isinstance(log_scale, dict) else log_scale + is_max = maximize[col] if isinstance(maximize, dict) else maximize + + # Get bounds + lb, ub = None, None + if bounds and col in bounds: + lb, ub = bounds[col] + if lb is None: + lb = result[col].min() if ndpoints is None else ndpoints[:,i].min() + if ub is None: + ub = result[col].max() if ndpoints is None else ndpoints[:,i].max() + # Log scale if needed + if use_log: + if lb <= 0: + warnings.warn( + f"Lower bound for column '{col}' <= 0; resetting to 1e-8 for log-scaling." + ) + lb = 1e-8 + lb, ub = np.log10(lb), np.log10(ub) + norm_expr = ((pl.col(col).log10() - lb) / (ub - lb)).clip(0, 1) + else: + norm_expr = ((pl.col(col) - lb) / (ub - lb)).clip(0, 1) + + # Reverse if minimization + if not is_max: + norm_expr = 1 - norm_expr + # Add normalized column with appropriate name + if n_objectives > 1: + if keep_original: + norm_expr = norm_expr.alias(f"{prefix}_{col}") + else: + idx = list(obj_vars).index(col) + 1 + norm_expr = norm_expr.alias(f"{prefix}{idx}") + else: + # If only one objective, use the prefix directly + norm_expr = norm_expr.alias(prefix) + result = result.with_columns(norm_expr) + + return result + + +def add_normalized_objectives( + data: pl.DataFrame, + obj_vars: Iterable[str], + max_obj: Optional[pl.DataFrame] = None, + min_obj: Optional[pl.DataFrame] = None, + only_nondominated: bool = False, +) -> pl.DataFrame: + """Add new normalized columns to provided dataframe based on the provided objective columns. + + Args: + data (pl.DataFrame): The original dataframe containing objective columns. + obj_vars (Iterable[str]): Which columns contain the objective values to normalize. + max_obj (Optional[pl.DataFrame], optional): If provided, these values will be used as the maxima instead of the values found in `data`. Defaults to None. + min_obj (Optional[pl.DataFrame], optional): If provided, these values will be used as the minima instead of the values found in `data`. Defaults to None. + only_nondominated (bool, optional): Whether to only consider non-dominated points for the normalization bounds. Defaults to False.) + Returns: + pl.DataFrame: The original `data` DataFrame with a new column 'objI' added for each objective, for I=1...len(obj_vars). + """ + + return normalize_objectives( + data, + obj_vars=obj_vars, + bounds={ + col: (min_obj[col][0] if min_obj is not None else None, + max_obj[col][0] if max_obj is not None else None) + for col in obj_vars + }, + maximize=True, + only_nondominated=only_nondominated, + prefix="obj", + keep_original=False + ) + + +def transform_fval( + data: pl.DataFrame, + lb: float = 1e-8, + ub: float = 1e8, + scale_log: bool = True, + maximization: bool = False, + fval_var: str = "raw_y", +) -> pl.DataFrame: + """Helper function to transform function values using min-max normalization based on provided bounds and scaling. + + Args: + data (pl.DataFrame): Input dataframe containing function values. + lb (float, optional): Lower bound for normalization. Defaults to 1e-8. + ub (float, optional): Upper bound for normalization. Defaults to 1e8. + scale_log (bool, optional): Whether to apply logarithmic scaling. Defaults to True. + maximization (bool, optional): Whether the problem is a maximization problem. Defaults to False. + fval_var (str, optional): Which column contains the function values to transform. Defaults to "raw_y". + + Returns: + pl.DataFrame: The original dataframe with normalized function values in a new 'eaf' column. + """ + bounds = {fval_var: (lb, ub)} + res = normalize_objectives( + data, + obj_vars=[fval_var], + bounds=bounds, + log_scale=scale_log, + maximize=maximization, + prefix="eaf" + ) + return res \ No newline at end of file diff --git a/src/iohinspector/plot.py b/src/iohinspector/plot.py deleted file mode 100644 index fba3424..0000000 --- a/src/iohinspector/plot.py +++ /dev/null @@ -1,868 +0,0 @@ -from typing import Iterable, Optional -import polars as pl -import numpy as np -import pandas as pd -from moocore import eaf, eafdiff - -import matplotlib -import matplotlib.pyplot as plt -from matplotlib.patches import Polygon, Rectangle -import seaborn as sbs - -from .metrics import ( - aggegate_running_time, - get_sequence, - aggegate_convergence, - get_tournament_ratings, - get_attractor_network, - transform_fval, -) -from .align import align_data, turbo_align -from .indicators import add_indicator, final - - -matplotlib.rcParams["pdf.fonttype"] = 42 -matplotlib.rcParams["ps.fonttype"] = 42 -font = {"size": 24} -plt.rc("font", **font) - - -# tradeoff between simple (few parameters) and flexible. Maybe many parameter but everything with clear defaults? -# Can also make sure any useful function for data processing is available separately for more flexibility - - -def single_function_fixedtarget( - data: pl.DataFrame, - evaluation_variable: str = "evaluations", - fval_variable: str = "raw_y", - free_variables: Iterable[str] = ["algorithm_name"], - f_min: float = None, - f_max: float = None, - max_budget: int = None, - maximization: bool = False, - measures: Iterable[str] = ["ERT"], - scale_xlog: bool = True, - scale_ylog: bool = True, - ax: matplotlib.axes._axes.Axes = None, - file_name: str = None, -): - """Create a fixed-target plot for a given set of performance data. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - evaluation_variable (str, optional): Column in 'data' which corresponds to the number of evaluations. Defaults to "evaluations". - fval_variable (str, optional): Column in 'data' which corresponds to the performance measure. Defaults to "raw_y". - free_variables (Iterable[str], optional): Columns in 'data' which correspond to the variables which will be used to distinguish between lines in the plot. Defaults to ["algorithm_name"]. - f_min (float, optional): Minimum value to use for the 'fval_variable', if not present the min of that column will be used. Defaults to None. - f_max (float, optional): Maximum value to use for the 'fval_variable', if not present the max of that column will be used. Defaults to None. - max_budget (int, optional): Maximum value to use for the 'evaluation_variable', if not present the max of that column will be used. Defaults to None. - maximization (bool, optional): Boolean indicating whether the 'fval_variable' is being maximized. Defaults to False. - measures (Iterable[str], optional): List of measures which should be used in the plot. Valid options are 'ERT', 'mean', 'PAR-10', 'min', 'max'. Defaults to ['ERT']. - scale_xlog (bool, optional): Should the x-axis be log-scaled. Defaults to True. - scale_ylog (bool, optional): Should the y-axis be log-scaled. Defaults to True. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - - Returns: - pd.DataFrame: The final dataframe which was used to create the plot - """ - dt_agg = aggegate_running_time( - data, - evaluation_variable=evaluation_variable, - fval_variable=fval_variable, - free_variables=free_variables, - f_min=f_min, - f_max=f_max, - scale_flog=scale_xlog, - max_budget=max_budget, - maximization=maximization, - ) - - dt_molt = dt_agg.melt(id_vars=[fval_variable] + free_variables) - dt_plot = dt_molt[dt_molt["variable"].isin(measures)].sort_values(free_variables) - - if ax is None: - fig, ax = plt.subplots(1, 1, figsize=(16, 9)) - sbs.lineplot( - dt_plot, - x=fval_variable, - y="value", - style="variable", - hue=dt_plot[free_variables].apply(tuple, axis=1), - ax=ax, - ) - if scale_xlog: - ax.set_xscale("log") - if scale_ylog: - ax.set_yscale("log") - - if not maximization: - ax.set_xlim(ax.get_xlim()[::-1]) - - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - - return dt_plot - - -def single_function_fixedbudget( - data: pl.DataFrame, - evaluation_variable: str = "evaluations", - fval_variable: str = "raw_y", - free_variables: Iterable[str] = ["algorithm_name"], - x_min: float = None, - x_max: float = None, - maximization: bool = False, - measures: Iterable[str] = ["geometric_mean"], - scale_xlog: bool = True, - scale_ylog: bool = True, - ax: matplotlib.axes._axes.Axes = None, - file_name: str = None, -): - """Create a fixed-budget plot for a given set of performance data. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - evaluation_variable (str, optional): Column in 'data' which corresponds to the number of evaluations. Defaults to "evaluations". - fval_variable (str, optional): Column in 'data' which corresponds to the performance measure. Defaults to "raw_y". - free_variables (Iterable[str], optional): Columns in 'data' which correspond to the variables which will be used to distinguish between lines in the plot. Defaults to ["algorithm_name"]. - x_min (float, optional): Minimum value to use for the 'evaluation_variable', if not present the min of that column will be used. Defaults to None. - x_max (float, optional): Maximum value to use for the 'evaluation_variable', if not present the max of that column will be used. Defaults to None. - maximization (bool, optional): Boolean indicating whether the 'fval_variable' is being maximized. Defaults to False. - measures (Iterable[str], optional): List of measures which should be used in the plot. Valid options are 'geometric_mean', 'mean', 'median', 'min', 'max'. Defaults to ['geometric_mean']. - scale_xlog (bool, optional): Should the x-axis be log-scaled. Defaults to True. - scale_ylog (bool, optional): Should the y-axis be log-scaled. Defaults to True. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - - Returns: - pd.DataFrame: The final dataframe which was used to create the plot - """ - dt_agg = aggegate_convergence( - data, - evaluation_variable=evaluation_variable, - fval_variable=fval_variable, - free_variables=free_variables, - x_min=x_min, - x_max=x_max, - maximization=maximization, - ) - - dt_molt = dt_agg.melt(id_vars=[evaluation_variable] + free_variables) - dt_plot = dt_molt[dt_molt["variable"].isin(measures)].sort_values(free_variables) - if ax is None: - fig, ax = plt.subplots(1, 1, figsize=(16, 9)) - sbs.lineplot( - dt_plot, - x=evaluation_variable, - y="value", - style="variable", - hue=dt_plot[free_variables].apply(tuple, axis=1), - ax=ax, - ) - if scale_xlog: - ax.set_xscale("log") - if scale_ylog: - ax.set_yscale("log") - - - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - - return dt_plot - - -def heatmap_single_run( - data: pl.DataFrame, - var_cols: Iterable[str], - eval_col: str = "evaluations", - scale_xlog: bool = True, - x_mins: Iterable[float] = [-5], - x_maxs: Iterable[float] = [5], - ax: matplotlib.axes._axes.Axes = None, - file_name: Optional[str] = None, -): - """Create a heatmap showing the search space points evaluated in a single run - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - var_cols (Iterable[str]): The variables which correspond to the searchspace variable columns - eval_col (str): The variable corresponding to evaluations. Defaults to 'evaluations' - scale_xlog (bool, optional): Whether the evaluations should be log-scaled. Defaults to True. - x_mins (Iterable[float], optional): Minimum bound for the variables. Should be of the same length as 'var_cols'. Defaults to [-5]. - x_maxs (Iterable[float], optional): Maximum bound for the variables. Should be of the same length as 'var_cols'.. Defaults to [5]. - ax (matplotlib.axes._axes.Axes, optional): Axis on which to create the plot. Defaults to None. - file_name (Optional[str], optional): If ax is not given, filename to save the plot. Defaults to None. - - Returns: - pd.DataFrame: pandas dataframe of the exact data used to create the plot - """ - assert data["data_id"].n_unique() == 1 - dt_plot = data[var_cols].transpose().to_pandas() - dt_plot.columns = list(data["evaluations"]) - dt_plot = (dt_plot - x_mins) / (x_maxs - x_mins) - if ax is None: - fig, ax = plt.subplots(figsize=(32, 9)) - sbs.heatmap(dt_plot, cmap="viridis", vmin=0, vmax=1, ax=ax) - if scale_xlog: - ax.set_xscale("log") - ax.set_xlim(1, len(data)) - - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - return dt_plot - - -def plot_eaf_singleobj( - data: pl.DataFrame, - min_budget: int = None, - max_budget: int = None, - scale_xlog: bool = True, - n_quantiles: int = 100, - eval_var: str = "evaluations", - fval_var: str = "raw_y", - ax: matplotlib.axes._axes.Axes = None, - file_name: Optional[str] = None, -): - """Plot the EAF for a single objective column agains budget. For the EAF-plot for multiple objective - columns, see 'plot_eaf_pareto'. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - n_quantiles (int, optional): Number of discrete levels in the EAF. Defaults to 100. - eval_var (str, optional): The variable corresponding to evaluations. Defaults to 'evaluations' - fval_var (str, optional): The variable corresponding to function values. Defaults to "raw_y". - scale_xlog (bool, optional): Whether the evaluations should be log-scaled. Defaults to True. - min_budget (Iterable[float], optional): Minimum bound for the variables. Should be of the same length as 'var_cols'. Defaults to [-5]. - max_budget (Iterable[float], optional): Maximum bound for the variables. Should be of the same length as 'var_cols'.. Defaults to [5]. - ax (matplotlib.axes._axes.Axes, optional): Axis on which to create the plot. Defaults to None. - file_name (Optional[str], optional): If ax is not given, filename to save the plot. Defaults to None. - - Returns: - pd.DataFrame: pandas dataframe of the exact data used to create the plot - """ - if min_budget is None: - min_budget = data[eval_var].min() - if max_budget is None: - max_budget = data[eval_var].max() - evals = get_sequence(min_budget, max_budget, 50, scale_xlog, True) - long = align_data(data, np.array(evals, "uint64"), ["data_id"], output="long") - - quantiles = np.arange(0, 1 + 1 / ((n_quantiles - 1) * 2), 1 / (n_quantiles - 1)) - if ax is None: - fig, ax = plt.subplots(figsize=(16, 9)) - colors = sbs.color_palette("viridis", n_colors=len(quantiles)) - for quant, color in zip(quantiles, colors[::-1]): - poly = np.array( - long.group_by(eval_var).quantile(quant).sort(eval_var)[eval_var, fval_var] - ) - poly = np.append( - poly, np.array([[max(poly[:, 0]), long[fval_var].max()]]), axis=0 - ) - poly = np.append( - poly, np.array([[min(poly[:, 0]), long[fval_var].max()]]), axis=0 - ) - poly2 = np.repeat(poly, 2, axis=0) - poly2[2::2, 1] = poly[:, 1][:-1] - ax.add_patch(Polygon(poly2, facecolor=color)) - ax.set_ylim(long[fval_var].min(), long[fval_var].max()) - ax.set_xlim(min(evals), max(evals)) - ax.set_axisbelow(True) - ax.grid(which="both", zorder=100) - ax.set_yscale("log") - if scale_xlog: - ax.set_xscale("log") - - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - return long - - -def plot_eaf_pareto( - data: pl.DataFrame, - x_column: str, - y_column: str, - min_y: float = 0, - max_y: float = 1, - scale_xlog: bool = False, - scale_ylog: bool = False, - ax: matplotlib.axes._axes.Axes = None, - filename_fig: Optional[str] = None, -): - """Plot the EAF for two arbitrary data columns. For the EAF-plot for single-objective - optimization runs, the 'plot_eaf_singleobj' provides a simpler interface. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - x_column (str, optional): The variable corresponding to the first objective. - y_column (str, optional): The variable corresponding to the second objective. - min_y (float): Minimum value for the second objective. - max_y (float): Maximum value for the second objective. - scale_xlog (bool, optional): Whether the first objective should be log-scaled. Defaults to False. - scale_ylog (bool, optional): Whether the second objective should be log-scaled. Defaults to False. - ax (matplotlib.axes._axes.Axes, optional): Axis on which to create the plot. Defaults to None. - file_name (Optional[str], optional): If ax is not given, filename to save the plot. Defaults to None. - """ - data_to_process = np.array(data[[x_column, y_column, "data_id"]]) - eaf_data = eaf(data_to_process[:,:-1], data_to_process[:,-1] ) - eaf_data_df = pd.DataFrame(eaf_data) - if ax is None: - fig, ax = plt.subplots(figsize=(16, 9)) - colors = sbs.color_palette("viridis", n_colors=eaf_data_df[2].nunique()) - eaf_data_df = eaf_data_df.sort_values(0) - min_x = np.min(eaf_data_df[0]) - max_x = np.max(eaf_data_df[0]) - if min_y is None: - min_y = np.min(eaf_data_df[1]) - if max_y is None: - max_y = np.max(eaf_data_df[1]) - for i, color in zip(eaf_data_df[2].unique(), colors[::-1]): - poly = np.array(eaf_data_df[eaf_data_df[2] == i][[0, 1]]) - # poly = np.append(poly, np.array([[max(poly[:, 0]), max(poly[:, 1])]]), axis=0) - # poly = np.append(poly, np.array([[min(poly[:, 0]), max(poly[:, 1])]]), axis=0) - poly = np.append(poly, np.array([[max_x, max_y]]), axis=0) - poly = np.append(poly, np.array([[min(poly[:, 0]), max_y]]), axis=0) - poly2 = np.repeat(poly, 2, axis=0) - poly2[2::2, 1] = poly[:, 1][:-1] - ax.add_patch(Polygon(poly2, facecolor=color)) - # ax.add_colorbar() - ax.set_ylim(min_y, max_y) - ax.set_xlim(min_x, max_x) - ax.set_axisbelow(True) - sm = plt.cm.ScalarMappable(cmap="viridis", norm=plt.Normalize(vmin=0, vmax=1)) - sm.set_array([]) - plt.colorbar(sm, ax=ax) - if scale_ylog: - ax.set_yscale("log") - if scale_xlog: - ax.set_xscale("log") - ax.grid(which="both", zorder=100) - if filename_fig: - fig.tight_layout() - fig.savefig(filename_fig) - - -def eaf_diffs( - data1: pl.DataFrame, - data2: pl.DataFrame, - x_column: str, - y_column: str, - min_y: float = 0, - max_y: float = 1, - scale_xlog: bool = False, - scale_ylog: bool = False, - ax: matplotlib.axes._axes.Axes = None, - filename_fig: Optional[str] = None, -): - """Plot the EAF differences between two datasets. - - Args: - data1 (pl.DataFrame): The DataFrame which contains the full performance trajectory for algorithm 1. Should be generated from a DataManager. - data2 (pl.DataFrame): The DataFrame which contains the full performance trajectory for algorithm 2. Should be generated from a DataManager. - x_column (str, optional): The variable corresponding to the first objective. - y_column (str, optional): The variable corresponding to the second objective. - min_y (float): Minimum value for the second objective. - max_y (float): Maximum value for the second objective. - scale_xlog (bool, optional): Whether the first objective should be log-scaled. Defaults to False. - scale_ylog (bool, optional): Whether the second objective should be log-scaled. Defaults to False. - ax (matplotlib.axes._axes.Axes, optional): Axis on which to create the plot. Defaults to None. - file_name (Optional[str], optional): If ax is not given, filename to save the plot. Defaults to None. - """ - # TODO: add an approximation version to speed up plotting - x = np.array(data1[[x_column, y_column, "data_id"]]) - y = np.array(data2[[x_column, y_column, "data_id"]]) - eaf_diff_rect = eafdiff(x, y, rectangles=True) - color_dict = { - k: v - for k, v in zip( - np.unique(eaf_diff_rect[:, -1]), - sbs.color_palette("viridis", n_colors=len(np.unique(eaf_diff_rect[:, -1]))), - ) - } - if ax is None: - fig, ax = plt.subplots(figsize=(16, 9)) - for rect in eaf_diff_rect: - ax.add_patch( - Rectangle( - (rect[0], rect[1]), - rect[2] - rect[0], - rect[3] - rect[1], - facecolor=color_dict[rect[-1]], - ) - ) - if min_y is None: - min_y = np.min(x[1]) - if max_y is None: - max_y = np.max(x[1]) - ax.set_ylim(min_y, max_y) - if scale_ylog: - ax.set_yscale("log") - if scale_xlog: - ax.set_xscale("log") - if filename_fig: - fig.tight_layout() - fig.savefig(filename_fig) - - -def plot_ecdf( - data, - fval_var: str = "raw_y", - eval_var: str = "evaluations", - free_vars: Iterable[str] = ["algorithm_name"], - scale_xlog: bool = True, - x_min: int = None, - x_max: int = None, - x_values: Iterable[int] = None, - y_min: int = None, - y_max: int = None, - scale_ylog: bool = True, - maximization: bool = False, - ax: matplotlib.axes._axes.Axes = None, - file_name: Optional[str] = None, -): - """Function to plot empirical cumulative distribution function (Based on EAF) - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - eval_var (str, optional): Column in 'data' which corresponds to the number of evaluations. Defaults to "evaluations". - fval_var (str, optional): Column in 'data' which corresponds to the performance measure. Defaults to "raw_y". - free_vars (Iterable[str], optional): Columns in 'data' which correspond to the variables which will be used to distinguish between lines in the plot. Defaults to ["algorithm_name"]. - x_min (float, optional): Minimum value to use for the 'eval_var', if not present the min of that column will be used. Defaults to None. - x_max (float, optional): Maximum value to use for the 'eval_var', if not present the max of that column will be used. Defaults to None. - x_values (Iterable[int], optional): List of x-values at which to plot the ECDF. If not provided, the x_min, x_max and scale_xlog arguments will be used to sample these points. - scale_xlog (bool, optional): Should the x-axis be log-scaled. Defaults to True. - y_min (float, optional): Minimum value to use for the 'fval_var', if not present the min of that column will be used. Defaults to None. - y_max (float, optional): Maximum value to use for the 'fval_var', if not present the max of that column will be used. Defaults to None. - scale_ylog (bool, optional): Should the y-values be log-scaled before normalization. Defaults to True. - maximization (bool, optional): Boolean indicating whether the 'fval_var' is being maximized. Defaults to False. - measures (Iterable[str], optional): List of measures which should be used in the plot. Valid options are 'geometric_mean', 'mean', 'median', 'min', 'max'. Defaults to ['geometric_mean']. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - - Returns: - pd.DataFrame: pandas dataframe of the exact data used to create the plot - """ - if x_min is None: - x_min = data[eval_var].min() - if x_max is None: - x_max = data[eval_var].max() - x_values = get_sequence(x_min, x_max, 50, scale_log=scale_xlog, cast_to_int=True) - - data_aligned = turbo_align( - data, - x_values, - x_col=eval_var, - y_col=fval_var, - maximization=maximization, - ) - dt_plot = ( - transform_fval(data_aligned, fval_col=fval_var, maximization=maximization) - .group_by([eval_var] + free_vars) - .mean() - .sort(eval_var) - ).to_pandas() - - if ax is None: - fig, ax = plt.subplots(figsize=(16, 9)) - if len(free_vars) == 1: - hue_arg = free_vars[0] - style_arg = free_vars[0] - else: - style_arg = free_vars[0] - hue_arg = dt_plot[free_vars[1:]].apply(tuple, axis=1) - - sbs.lineplot( - dt_plot, - x="evaluations", - y="eaf", - style=style_arg, - hue=hue_arg, - ax=ax, - ) - if scale_xlog: - ax.set_xscale("log") - ax.grid() - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - return dt_plot - - -def multi_function_fixedbudget(): - # either just loop over function column(s), or more advanced - raise NotImplementedError - - -def multi_function_fixedtarget(): - raise NotImplementedError - - -def plot_tournament_ranking( - data, - alg_vars: Iterable[str] = ["algorithm_name"], - fid_vars: Iterable[str] = ["function_name"], - perf_var: str = "raw_y", - nrounds: int = 25, - maximization: bool = False, - ax: matplotlib.axes._axes.Axes = None, - file_name: str = None, -): - """Method to plot ELO ratings of a set of algorithm on a set of problems. - Calculated based on nrounds of competition, where in each round all algorithms face all others (pairwise) on every function. - For each round, a sampled performance value is taken from the data and used to determine the winner. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - alg_vars (Iterable[str], optional): Which variables specific the algortihms which will compete. Defaults to ["algorithm_name"]. - fid_vars (Iterable[str], optional): Which variables denote the problems on which will be competed. Defaults to ["function_name"]. - perf_var (str, optional): Which variable corresponds to the performance. Defaults to "raw_y". - nrounds (int, optional): How many round should be played. Defaults to 25. - maximization (bool, optional): Whether the performance should be maximized. Defaults to False. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - - Returns: - pd.DataFrame: pandas dataframe of the exact data used to create the plot - """ - # candlestick plot based on average and volatility - dt_elo = get_tournament_ratings( - data, alg_vars, fid_vars, perf_var, nrounds, maximization - ) - if ax is None: - _, ax = plt.subplots(1, 1, figsize=(10, 5)) - - sbs.pointplot(data=dt_elo, x=alg_vars[0], y="Rating", linestyle="none", ax=ax) - - ax.errorbar( - dt_elo[alg_vars[0]], - dt_elo["Rating"], - yerr=dt_elo["Deviation"], - fmt="o", - color="blue", - alpha=0.6, - capsize=5, - elinewidth=1.5, - ) - ax.grid() - - if file_name: - plt.tight_layout() - plt.savefig(file_name) - return dt_elo - - -def robustranking(): - # to decide which plot(s) to use and what exact interface to define - raise NotImplementedError() - - -def stats_comparison(): - # heatmap or graph of statistical comparisons - raise NotImplementedError() - - -def winnning_fraction_heatmap(): - # nevergrad-like heatmap - raise NotImplementedError() - - -def plot_paretofronts_2d( - data: pl.DataFrame, - obj_vars: Iterable[str] = ["raw_y", "F2"], - free_vars: Iterable[str] = ["algorithm_name"], - ax: matplotlib.axes._axes.Axes = None, - file_name: str = None, -): - """Very basic plot to visualize pareto fronts - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - obj_vars (Iterable[str], optional): Which variables (length should be 2) to use for plotting. Defaults to ["raw_y", "F2"]. - free_vars (Iterable[str], optional): Which varialbes should be used to distinguish between categories. Defaults to ["algorithm_name"]. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - - Returns: - pd.DataFrame: pandas dataframe of the exact data used to create the plot - """ - assert len(obj_vars) == 2 - - df = add_indicator(data, final.NonDominated(), obj_vars) - - if ax is None: - fig, ax = plt.subplots(figsize=(16, 9)) - sbs.scatterplot(df, x=data.obj_vars[0], y=data.obj_vars[1], hue=free_vars, ax=ax) - if ax is None and file_name: - fig.tight_layout() - fig.savefig(file_name) - return df - - -def plot_indicator_over_time( - data: pl.DataFrame, - obj_columns: Iterable[str], - indicator: object, - eval_column: str = "evaluations", - evals_min: int = 0, - evals_max: int = 50_000, - nr_eval_steps: int = 50, - free_variable: str = "algorithm_name", - ax: matplotlib.axes._axes.Axes = None, - filename_fig: Optional[str] = None, -): - """Convenience function to plot the anytime performance of a single indicator. - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - obj_columns (Iterable[str], optional): Which columns in 'data' correspond to the objectives. - indicator (object): Indicator object from iohinspector.indicators - eval_column (Iterable[str], optional): Which columns in 'data' correspond to the objectives. Defaults to 'evaluations'. - evals_min (int, optional): Lower bound for eval_column. Defaults to 0. - evals_max (int, optional): Upper bound for eval_column. Defaults to 50_000. - nr_eval_steps (int, optional): Number of steps between lower and upper bounds of eval_column. Defaults to 50. - free_variable (str, optional): Variable which corresponds to category to differentiate in the plot. Defaults to 'algorithm_name'. - ax (matplotlib.axes._axes.Axes, optional): Existing matplotlib axis object to draw the plot on. - file_name (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - """ - - evals = get_sequence( - evals_min, evals_max, nr_eval_steps, cast_to_int=True, scale_log=True - ) - df = add_indicator( - data, indicator, objective_columns=obj_columns, evals=evals - ).to_pandas() - if ax is None: - fig, ax = plt.subplots(1, 1, figsize=(16, 9)) - sbs.lineplot( - df, - x=eval_column, - y=indicator.var_name, - hue=free_variable, - palette=sbs.color_palette(n_colors=len(np.unique(data[free_variable]))), - ax=ax, - ) - ax.set_xlabel(eval_column) - ax.set_xlim(evals_min, evals_max) - ax.set_xscale("log") - ax.grid() - if filename_fig: - fig.tight_layout() - fig.savefig(filename_fig) - - return df - - -def plot_robustrank_over_time( - data: pl.DataFrame, - obj_columns: Iterable[str], - evals: Iterable[int], - indicator: object, - filename_fig: Optional[str] = None, -): - """Plot robust ranking at distinct timesteps - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - obj_columns (Iterable[str], optional): Which columns in 'data' correspond to the objectives. - evals (Iterable[int]): Timesteps at which to get the rankings - indicator (object): Indicator object from iohinspector.indicators - filename_fig (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - """ - from robustranking import Benchmark - from robustranking.comparison import MOBootstrapComparison, BootstrapComparison - from robustranking.utils.plots import plot_ci_list, plot_line_ranks - - df = add_indicator( - data, indicator, objective_columns=obj_columns, evals=evals - ).to_pandas() - df_part = df[["evaluations", indicator.var_name, "algorithm_name", "run_id"]] - dt_pivoted = pd.pivot( - df_part, - index=["algorithm_name", "run_id"], - columns=["evaluations"], - values=[indicator.var_name], - ).reset_index() - dt_pivoted.columns = ["algorithm_name", "run_id"] + evals - benchmark = Benchmark() - benchmark.from_pandas(dt_pivoted, "algorithm_name", "run_id", evals) - - comparison = MOBootstrapComparison( - benchmark, - alpha=0.05, - minimise=indicator.minimize, - bootstrap_runs=1000, - aggregation_method=np.mean, - ) - fig, axs = plt.subplots(1, 4, figsize=(16, 9), sharey=True) - for ax, runtime in zip(axs.ravel(), benchmark.objectives): - plot_ci_list(comparison, objective=runtime, ax=ax) - if runtime != evals[0]: - ax.set_ylabel("") - if runtime != evals[-1]: - ax.get_legend().remove() - ax.set_title(runtime) - - plt.tight_layout() - if filename_fig: - plt.savefig(filename_fig) - plt.close() - - -def plot_robustrank_changes( - data: pl.DataFrame, - obj_columns: Iterable[str], - evals: Iterable[int], - indicator: object, - filename_fig: Optional[str] = None, -): - """Plot robust ranking changes at distinct timesteps - - Args: - data (pl.DataFrame): The DataFrame which contains the full performance trajectory. Should be generated from a DataManager. - obj_columns (Iterable[str], optional): Which columns in 'data' correspond to the objectives. - evals (Iterable[int]): Timesteps at which to get the rankings - indicator (object): Indicator object from iohinspector.indicators - filename_fig (str, optional): Where should the resulting plot be stored. Defaults to None. If existing axis is provided, this functionality is disabled. - """ - from robustranking import Benchmark - from robustranking.comparison import MOBootstrapComparison, BootstrapComparison - from robustranking.utils.plots import plot_ci_list, plot_line_ranks - - df = add_indicator( - data, indicator, objective_columns=obj_columns, evals=evals - ).to_pandas() - df_part = df[["evaluations", indicator.var_name, "algorithm_name", "run_id"]] - dt_pivoted = pd.pivot( - df_part, - index=["algorithm_name", "run_id"], - columns=["evaluations"], - values=[indicator.var_name], - ).reset_index() - dt_pivoted.columns = ["algorithm_name", "run_id"] + evals - - comparisons = { - f"{eval}": BootstrapComparison( - Benchmark().from_pandas(dt_pivoted, "algorithm_name", "run_id", eval), - alpha=0.05, - minimise=indicator.minimize, - bootstrap_runs=1000, - ) - for eval in evals - } - - fig, ax = plt.subplots(1, 1, figsize=(16, 9)) - plot_line_ranks(comparisons, ax=ax) - - plt.tight_layout() - if filename_fig: - plt.savefig(filename_fig) - plt.close() - - -def plot_attractor_network( - data, - coord_vars: Iterable[str] = ["x1", "x2"], - fval_var: str = "raw_y", - eval_var: str = "evaluations", - maximization: bool = False, - beta=40, - epsilon=0.0001, -): - """Plot an attractor network from the provided data - - Args: - data (pl.DataFrame): The original dataframe, should contain the performance and position information - coord_vars (Iterable[str], optional): Which columns correspond to position information. Defaults to ['x1', 'x2']. - fval_var (str, optional): Which column corresponds to performance. Defaults to 'raw_y'. - eval_var (str, optional): Which column corresponds to evaluations. Defaults to 'evaluations'. - maximization (bool, optional): Whether fval_var is to be maximized. Defaults to False. - beta (int, optional): Minimum stagnation lenght. Defaults to 40. - epsilon (float, optional): Radius below which positions should be considered identical in the network. Defaults to 0.0001. - - Returns: - pd.DataFrame, pd.DataFrame: two dataframes containing the nodes and edges of the network respectively. - """ - try: - import networkx as nx - except: - print("NetworkX is required to use this plot type") - return - from sklearn.decomposition import MDS - - nodes, edges = get_attractor_network( - data, maximization, coord_vars, fval_var, eval_var, beta, epsilon - ) - network = nx.DiGraph() - for idx, row in nodes.iterrows(): - network.add_node( - idx, - decision=np.array(row)[: len(coord_vars)], - fitness=row["y"], - hitcount=row["count"], - evals=row["evals"] / row["count"], - ) - - for _, row in edges.iterrows(): - network.add_edge( - row["start"], - row["end"], - weight=row["count"], - evaldiff=row["stag_length_avg"], - ) - network.remove_edges_from(nx.selfloop_edges(network)) - - decision_matrix = [network.nodes[node]["decision"] for node in network.nodes()] - mds = MDS(n_components=1, random_state=0) - x_positions = mds.fit_transform( - decision_matrix - ).flatten() # Flatten to get 1D array for x-axis - y_positions = [network.nodes[node]["fitness"] for node in network.nodes()] - pos = { - node: (x, y) for node, x, y in zip(network.nodes(), x_positions, y_positions) - } - - hitcounts = [network.nodes[node]["hitcount"] for node in network.nodes()] - if len(hitcounts) > 1: - min_hitcount = min(hitcounts) - max_hitcount = max(hitcounts) - # Node sizes and colors based on fitness values (as in your original code) - if len(hitcounts) > 1 and np.std(hitcounts) > 0: - node_sizes = [ - 100 - + ( - 400 - * (network.nodes[node]["hitcount"] - min_hitcount) - / (max_hitcount - min_hitcount) - ) - for node in network.nodes() - ] - else: - node_sizes = [500] * len(hitcounts) - fitness_values = y_positions # Reuse y_positions as they represent 'fitness' - norm = plt.Normalize(min(fitness_values), max(fitness_values)) - node_colors = plt.cm.viridis(norm(fitness_values)) - - # Draw the graph - if ax is None: - fig, ax = plt.subplots(figsize=(10, 6)) - nx.draw( - network, - pos=pos, - with_labels=False, - node_size=node_sizes, - node_color=node_colors[:, :3], - edge_color="gray", - width=2, - ax=ax, - ) - - # Add colorbar for fitness values - sm = plt.cm.ScalarMappable(cmap="viridis", norm=norm) - sm.set_array(fitness_values) - plt.xlabel("MDS-reduced decision vector") - plt.ylabel("fitness") - plt.tight_layout() diff --git a/src/iohinspector/plots/__init__.py b/src/iohinspector/plots/__init__.py new file mode 100644 index 0000000..dfc4a5f --- /dev/null +++ b/src/iohinspector/plots/__init__.py @@ -0,0 +1,17 @@ +import matplotlib +import matplotlib.pyplot as plt +matplotlib.rcParams["pdf.fonttype"] = 42 +matplotlib.rcParams["ps.fonttype"] = 42 +font = {"size": 24} +plt.rc("font", **font) + + +from .fixed_target import plot_single_function_fixed_target +from .fixed_budget import plot_single_function_fixed_budget +from .ecdf import plot_ecdf +from .eaf import plot_eaf_single_objective, plot_eaf_pareto, plot_eaf_diffs +from .multi_objective import plot_paretofronts_2d, plot_indicator_over_time +from .ranking import plot_tournament_ranking, plot_robustrank_over_time, plot_robustrank_changes +from .attractor_network import plot_attractor_network +from .single_run import plot_heatmap_single_run +from .utils import BasePlotArgs, LinePlotArgs \ No newline at end of file diff --git a/src/iohinspector/plots/attractor_network.py b/src/iohinspector/plots/attractor_network.py new file mode 100644 index 0000000..f2038a7 --- /dev/null +++ b/src/iohinspector/plots/attractor_network.py @@ -0,0 +1,204 @@ +from dataclasses import dataclass +import numpy as np +import pandas as pd +import polars as pl +from typing import Iterable, Tuple +import matplotlib +import matplotlib.pyplot as plt +from iohinspector.metrics import get_attractor_network +from iohinspector.plots.utils import BasePlotArgs, _create_plot_args, _save_fig + +@dataclass +class AttractorNetworkPlotArgs(BasePlotArgs): + color_map: str = "viridis" + + def as_dict(self): + """Convert the attractor network plot arguments to a dictionary representation. + + Returns: + Dict[str, Any]: Dictionary containing all attractor network plot configuration parameters including color map. + """ + results = super().as_dict() + results["color_map"] = self.color_map + return results + + def apply(self, ax): + """Apply attractor network plot properties to a matplotlib Axes object. + + Args: + ax: matplotlib Axes instance to apply the attractor network plot properties to. + + Returns: + ax: The modified matplotlib Axes object with attractor network plot properties applied. + """ + return super().apply(ax) + + def override(self, other): + """Update attractor network plot arguments in place with values from another source. + + Args: + other: Attractor network plot arguments to override current values with. + """ + return super().override(other) + + +def plot_attractor_network( + data: pl.DataFrame, + coord_vars: Iterable[str] = ["x0", "x1"], + fval_var: str = "raw_y", + eval_var: str = "evaluations", + maximization: bool = False, + beta: int = 40, + epsilon: float = 0.0001, + *, + ax: matplotlib.axes.Axes = None, + file_name: str = None, + plot_args: dict | AttractorNetworkPlotArgs = None, + +): + """Plot an attractor network visualization from optimization algorithm data. + + Creates a network graph where nodes represent attractors (stable points) in the search space + and edges represent transitions between them. Node sizes reflect visit frequency and colors + represent fitness values. + + Args: + data (pl.DataFrame): Input dataframe containing optimization algorithm trajectory data. + coord_vars (Iterable[str], optional): Which columns contain the decision variable coordinates. + Defaults to ["x0", "x1"]. + fval_var (str, optional): Which column contains the fitness/objective values. Defaults to "raw_y". + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + maximization (bool, optional): Whether the optimization problem is maximization. Defaults to False. + beta (int, optional): Minimum stagnation length for attractor detection. Defaults to 40. + epsilon (float, optional): Distance threshold below which positions are considered identical. + Defaults to 0.0001. + ax (matplotlib.axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. + Defaults to None. + file_name (str, optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | AttractorNetworkPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Attractor Network". + - xlabel (str): X-axis label. Defaults to "MDS-reduced decision vector". + - ylabel (str): Y-axis label. Defaults to "fitness". + - color_map (str): Colormap for node colors based on fitness. Defaults to "viridis". + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other BasePlotArgs parameters (xlim, ylim, xscale, yscale, grid, legend, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame, pd.DataFrame]: The matplotlib axes object + and two dataframes with the nodes and edges of the attractor network. + """ + try: + import networkx as nx + except: + print("NetworkX is required to use this plot type") + return + from sklearn.manifold import MDS + + nodes, edges = get_attractor_network( + data = data, + coord_vars = coord_vars, + fval_var = fval_var, + eval_var= eval_var, + maximization = maximization, + beta = beta, + epsilon = epsilon + ) + + plot_args = _create_plot_args( + AttractorNetworkPlotArgs( + title="Attractor Network", + xlabel="MDS-reduced decision vector", + ylabel="fitness", + color_map="viridis" + ), + plot_args + ) + + + network = nx.DiGraph() + for idx, row in nodes.iterrows(): + network.add_node( + idx, + decision=np.array(row)[: len(coord_vars)], + fitness=row["y"], + hitcount=row["count"], + evals=row["evals"] / row["count"], + ) + + for _, row in edges.iterrows(): + network.add_edge( + row["start"], + row["end"], + weight=row["count"], + evaldiff=row["stag_length_avg"], + ) + network.remove_edges_from(nx.selfloop_edges(network)) + + decision_matrix = [network.nodes[node]["decision"] for node in network.nodes()] + mds = MDS(n_components=1, random_state=0) + x_positions = mds.fit_transform( + decision_matrix + ).flatten() # Flatten to get 1D array for x-axis + y_positions = [network.nodes[node]["fitness"] for node in network.nodes()] + pos = { + node: (x, y) for node, x, y in zip(network.nodes(), x_positions, y_positions) + } + + hitcounts = [network.nodes[node]["hitcount"] for node in network.nodes()] + if len(hitcounts) > 1: + min_hitcount = min(hitcounts) + max_hitcount = max(hitcounts) + + if len(hitcounts) > 1 and np.std(hitcounts) > 0: + node_sizes = [ + 100 + + ( + 400 + * (network.nodes[node]["hitcount"] - min_hitcount) + / (max_hitcount - min_hitcount) + ) + for node in network.nodes() + ] + else: + node_sizes = [500] * len(hitcounts) + fitness_values = y_positions # Reuse y_positions as they represent 'fitness' + + if(plot_args.yscale == "log"): + norm = matplotlib.colors.LogNorm(min(fitness_values), max(fitness_values)) + else: + norm = plt.Normalize(min(fitness_values), max(fitness_values)) + + # Safely get colormap name or default to 'viridis' if not present on plot_args + cmap_name = getattr(plot_args, "color_map", "viridis") + cmap = plt.get_cmap(cmap_name) + node_colors = cmap(norm(fitness_values)) + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + nx.draw( + network, + pos=pos, + with_labels=True, + node_size=node_sizes, + node_color=node_colors[:, :3], + edge_color="gray", + width=2, + ax=ax, + ) + # ensure the axis frame, ticks and grid are visible + ax.set_axis_on() + ax.set_aspect("auto") + + # Add colorbar for fitness values + sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) + sm.set_array(fitness_values) + plt.colorbar(sm, ax=ax) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args) + + return ax, nodes, edges \ No newline at end of file diff --git a/src/iohinspector/plots/eaf.py b/src/iohinspector/plots/eaf.py new file mode 100644 index 0000000..dad9b82 --- /dev/null +++ b/src/iohinspector/plots/eaf.py @@ -0,0 +1,337 @@ + + +import numpy as np +import polars as pl +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt +from matplotlib.patches import Polygon, Rectangle +import seaborn as sbs +from typing import Optional, Iterable +from iohinspector.metrics import get_eaf_data, get_eaf_pareto_data, get_eaf_diff_data +from iohinspector.plots.utils import HeatmapPlotArgs, _create_plot_args, _save_fig +from moocore import eaf, eafdiff + +def plot_eaf_single_objective( + data: pl.DataFrame, + eval_var: str = "evaluations", + fval_var: str = "raw_y", + eval_min: int = None, + eval_max: int = None, + scale_eval_log: bool = True, + n_quantiles: int = 100, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | HeatmapPlotArgs = None +): + """Plot the Empirical Attainment Function (EAF) for single-objective optimization against budget. + + Creates a heatmap visualization showing the probability of attaining different function values + at different evaluation budgets across multiple algorithm runs. + + Args: + data (pl.DataFrame): Input dataframe containing optimization algorithm trajectory data. + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + fval_var (str, optional): Which column contains the function values. Defaults to "raw_y". + eval_min (int, optional): Minimum evaluation bound for the plot. If None, uses data minimum. Defaults to None. + eval_max (int, optional): Maximum evaluation bound for the plot. If None, uses data maximum. Defaults to None. + scale_eval_log (bool, optional): Whether the evaluations should be log-scaled. Defaults to True. + n_quantiles (int, optional): Number of discrete probability levels in the EAF heatmap. Defaults to 100. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | HeatmapPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "EAF". + - xlabel (str): X-axis label. Defaults to eval_var value. + - ylabel (str): Y-axis label. Defaults to fval_var value. + - xscale (str): X-axis scale ("log" or "linear"). Defaults to "log" if scale_eval_log=True. + - yscale (str): Y-axis scale. Defaults to "log". + - xlim (Tuple[float, float]): X-axis limits. Defaults to (eval_min, eval_max). + - ylim (Tuple[float, float]): Y-axis limits. Defaults to (1e-8, 1e2). + - heatmap_palette (str): Colormap name. Defaults to "viridis_r". + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other HeatmapPlotArgs parameters. + + Returns: + tuple[matplotlib.axes.Axes, pl.DataFrame]: The matplotlib axes object and the processed + dataframe used to create the plot. + """ + df = get_eaf_data( + data, + eval_var=eval_var, + eval_min=eval_min, + eval_max=eval_max, + scale_eval_log=scale_eval_log, + return_as_pandas=False, + ) + eval_min = df[eval_var].min() + eval_max = df[eval_var].max() + + plot_args = _create_plot_args( + HeatmapPlotArgs( + xlabel= eval_var, + ylabel= fval_var, + title= "EAF", + xscale= "log" if scale_eval_log else "linear", + yscale= "log", + xlim= (eval_min, eval_max), + ylim= (10**-8,10**2), + heatmap_palette= "viridis_r", + ), + plot_args + ) + f_min, f_max = plot_args.ylim + + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + quantiles = np.arange(0, 1 + 1 / ((n_quantiles - 1) * 2), 1 / (n_quantiles - 1)) + cmap = plt.get_cmap(plot_args.heatmap_palette) + norm = plt.Normalize( + vmin=0, + vmax=1 + ) + colors = [cmap(norm(quant)) for quant in quantiles] + if(not plot_args.use_background_color): + ax.add_patch( + Rectangle( + (eval_min, f_min), + eval_max - eval_min, + f_max - f_min, + facecolor=cmap(norm(0)), + zorder=0, + ) + ) + + for quant, color in zip(quantiles,colors): + poly = np.array( + df.group_by(eval_var).quantile(quant).sort(eval_var)[eval_var, fval_var] + ) + poly = np.append( + poly, np.array([[max(poly[:, 0]), f_max]]), axis=0 + ) + poly = np.append( + poly, np.array([[min(poly[:, 0]), f_max]]), axis=0 + ) + poly2 = np.repeat(poly, 2, axis=0) + poly2[2::2, 1] = poly[:, 1][:-1] + ax.add_patch(Polygon(poly2, facecolor=color)) + + sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) + sm.set_array([]) + plt.colorbar(sm, ax=ax) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args) + + return ax, df + + +def plot_eaf_pareto( + data: pl.DataFrame, + obj1_var: str, + obj2_var: str, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | HeatmapPlotArgs = None +): + """Plot the Empirical Attainment Function (EAF) for multi-objective optimization with two objectives. + + Creates a heatmap visualization showing the probability of attaining different combinations + of objective values across multiple algorithm runs in the Pareto front space. + + Args: + data (pl.DataFrame): Input dataframe containing multi-objective optimization trajectory data. + obj1_var (str): Which column contains the first objective values. + obj2_var (str): Which column contains the second objective values. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | HeatmapPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Pareto EAF". + - xlabel (str): X-axis label. Defaults to obj1_var value. + - ylabel (str): Y-axis label. Defaults to obj2_var value. + - xlim (Tuple[float, float]): X-axis limits. Defaults to data range. + - ylim (Tuple[float, float]): Y-axis limits. Defaults to data range. + - heatmap_palette (str): Colormap name. Defaults to "viridis_r". + - use_background_color (bool): Whether to use background color. Defaults to True. + - background_color (str): Background color. Defaults to "white". + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other HeatmapPlotArgs parameters. + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the EAF + dataframe used to create the plot. + """ + + eaf_data_df = get_eaf_pareto_data(data, obj1_var, obj2_var) + + x_max = eaf_data_df[obj1_var].max() + x_min = eaf_data_df[obj1_var].min() + y_max = eaf_data_df[obj2_var].max() + y_min = eaf_data_df[obj2_var].min() + + min_eaf = eaf_data_df["eaf"].min() + + plot_args = _create_plot_args( + HeatmapPlotArgs( + xlabel= obj1_var, + ylabel= obj2_var, + title= "Pareto EAF", + xlim= (x_min, x_max), + ylim= (y_min, y_max), + heatmap_palette= "viridis_r", + ), + plot_args + ) + + x_min, x_max = plot_args.xlim + y_min, y_max = plot_args.ylim + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + eaf_data_df = eaf_data_df.sort_values(obj1_var) + + cmap = plt.get_cmap(plot_args.heatmap_palette) + norm = plt.Normalize( + vmin=(min_eaf if plot_args.use_background_color else 0), + vmax=1 + ) + _unique_eafs = eaf_data_df["eaf"].unique() + colors = [cmap(norm(v)) for v in _unique_eafs] + + + ax.add_patch( + Rectangle( + (x_min, y_min), + x_max - x_min, + y_max - y_min, + facecolor= (plot_args.background_color if plot_args.use_background_color else cmap(norm(0))), + zorder=0, + ) + ) + + for i, color in zip(eaf_data_df["eaf"].unique(), colors): + poly = np.array(eaf_data_df[eaf_data_df["eaf"] == i][[obj1_var, obj2_var]]) + poly = np.append(poly, np.array([[x_max, y_max]]), axis=0) + poly = np.append(poly, np.array([[min(poly[:, 0]), y_max]]), axis=0) + poly2 = np.repeat(poly, 2, axis=0) + poly2[2::2, 1] = poly[:, 1][:-1] + ax.add_patch(Polygon(poly2, facecolor=color)) + + sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) + sm.set_array([]) + plt.colorbar(sm, ax=ax) + # set a background rectangle behind the EAF polygons + + ax.set_facecolor("white") + ax = plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args) + + return ax, eaf_data_df + +def plot_eaf_diffs( + data1: pl.DataFrame, + data2: pl.DataFrame, + obj1_var: str, + obj2_var: str, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | HeatmapPlotArgs = None +): + """Plot the Empirical Attainment Function (EAF) differences between two algorithms. + + Creates a heatmap visualization showing the statistical differences in attainment probabilities + between two algorithms in the objective space, highlighting regions where one algorithm + performs better than the other. + + Args: + data1 (pl.DataFrame): Input dataframe containing trajectory data for the first algorithm. + data2 (pl.DataFrame): Input dataframe containing trajectory data for the second algorithm. + obj1_var (str): Which column contains the first objective values. + obj2_var (str): Which column contains the second objective values. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | HeatmapPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "EAF Differences". + - xlabel (str): X-axis label. Defaults to obj1_var value. + - ylabel (str): Y-axis label. Defaults to obj2_var value. + - xlim (Tuple[float, float]): X-axis limits. Defaults to data range. + - ylim (Tuple[float, float]): Y-axis limits. Defaults to data range. + - heatmap_palette (str): Colormap name. Defaults to "viridis". + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other HeatmapPlotArgs parameters. + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the EAF + differences dataframe used to create the plot. + + Note: + The plot shows regions where data1 performs better (positive differences) and regions + where data2 performs better (negative differences) in different colors. + """ + # TODO: add an approximation version to speed up plotting + eaf_diff_rect_data = get_eaf_diff_data( + data1, + data2, + obj1_var, + obj2_var, + ) + x_min = eaf_diff_rect_data["x_min"].replace([np.inf, -np.inf], np.nan).min() + x_max = eaf_diff_rect_data["x_max"].replace([np.inf, -np.inf], np.nan).max() + y_min = eaf_diff_rect_data["y_min"].replace([np.inf, -np.inf], np.nan).min() + y_max = eaf_diff_rect_data["y_max"].replace([np.inf, -np.inf], np.nan).max() + + plot_args = _create_plot_args( + HeatmapPlotArgs( + xlabel= obj1_var, + ylabel= obj2_var, + title= "EAF Differences", + xlim= (x_min, x_max), + ylim= (y_min, y_max), + ), + plot_args + ) + eaf_min_diff = eaf_diff_rect_data["eaf_diff"].min() + eaf_max_diff = eaf_diff_rect_data["eaf_diff"].max() + + color_dict = { + k: v + for k, v in zip( + np.unique(eaf_diff_rect_data["eaf_diff"]), + sbs.color_palette(plot_args.heatmap_palette, n_colors=len(np.unique(eaf_diff_rect_data["eaf_diff"]))), + ) + } + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + for rect in eaf_diff_rect_data.itertuples(index=False): + ax.add_patch( + Rectangle( + (rect.x_min, rect.y_min), + rect.x_max - rect.x_min, + rect.y_max - rect.y_min, + facecolor=color_dict[rect.eaf_diff], + ) + ) + sm = plt.cm.ScalarMappable(cmap=plot_args.heatmap_palette, norm=plt.Normalize(vmin=eaf_min_diff, vmax=eaf_max_diff)) + sm.set_array([]) + plt.colorbar(sm, ax=ax) + ax = plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args) + + + return ax, eaf_diff_rect_data \ No newline at end of file diff --git a/src/iohinspector/plots/ecdf.py b/src/iohinspector/plots/ecdf.py new file mode 100644 index 0000000..73c7459 --- /dev/null +++ b/src/iohinspector/plots/ecdf.py @@ -0,0 +1,119 @@ +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +import polars as pl +from typing import Iterable, Optional +from iohinspector.metrics import get_data_ecdf +from iohinspector.plots.utils import LinePlotArgs, _create_plot_args, _save_fig + +def plot_ecdf( + data: pl.DataFrame, + fval_var: str = "raw_y", + eval_var: str = "evaluations", + free_vars: Iterable[str] = ["algorithm_name"], + maximization: bool = False, + f_min: int = None, + f_max: int = None, + scale_f_log: bool = True, + eval_values: Iterable[int] = None, + eval_min: int = None, + eval_max: int = None, + scale_eval_log: bool = True, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | LinePlotArgs = None, +): + """Plot Empirical Cumulative Distribution Function (ECDF) based on Empirical Attainment Functions. + + Creates line plots showing the cumulative probability of achieving different performance levels + at various evaluation budgets, allowing comparison between algorithms or configurations. + + Args: + data (pl.DataFrame): Input dataframe containing optimization algorithm trajectory data. + fval_var (str, optional): Which column contains the function/performance values. Defaults to "raw_y". + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + free_vars (Iterable[str], optional): Which columns contain the grouping variables for distinguishing + between different lines in the plot. Defaults to ["algorithm_name"]. + maximization (bool, optional): Whether the optimization problem is maximization. Defaults to False. + f_min (int, optional): Minimum function value bound. If None, uses data minimum. Defaults to None. + f_max (int, optional): Maximum function value bound. If None, uses data maximum. Defaults to None. + scale_f_log (bool, optional): Whether function values should be log-scaled before normalization. Defaults to True. + eval_values (Iterable[int], optional): Specific evaluation points to plot. If None, uses eval_min/eval_max + with scale_eval_log to sample points. Defaults to None. + eval_min (int, optional): Minimum evaluation bound. If None, uses data minimum. Defaults to None. + eval_max (int, optional): Maximum evaluation bound. If None, uses data maximum. Defaults to None. + scale_eval_log (bool, optional): Whether the evaluation axis should be log-scaled. Defaults to True. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | LinePlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "ECDF". + - xlabel (str): X-axis label. Defaults to eval_var value. + - ylabel (str): Y-axis label. Defaults to "eaf". + - xscale (str): X-axis scale ("log" or "linear"). Defaults to "log" if scale_eval_log=True. + - yscale (str): Y-axis scale ("log" or "linear"). Defaults to "log" if scale_f_log=True. + - line_colors (Sequence[str]): Colors for different lines. Defaults to seaborn palette. + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other LinePlotArgs parameters (xlim, ylim, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the processed + dataframe used to create the plot. + """ + + + dt_plot = get_data_ecdf( + data, + fval_var=fval_var, + eval_var=eval_var, + free_vars=free_vars, + maximization=maximization, + f_min=f_min, + f_max=f_max, + scale_f_log=scale_f_log, + eval_values=eval_values, + eval_max=eval_max, + eval_min=eval_min, + scale_eval_log=scale_eval_log, + turbo=True + ) + + plot_args = _create_plot_args( + LinePlotArgs( + xlabel= eval_var, + ylabel= "eaf", + title= "ECDF", + xscale= "log" if scale_eval_log else "linear", + yscale= "log" if scale_f_log else "linear", + ), + plot_args + ) + + + dt_plot.sort_values(free_vars) + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + + if len(free_vars) == 1: + hue_arg = free_vars[0] + style_arg = free_vars[0] + else: + style_arg = free_vars[0] + hue_arg = dt_plot[free_vars[1:]].apply(tuple, axis=1) + + + sbs.lineplot( + dt_plot, + x= eval_var, + y="eaf", + style=style_arg, + hue=hue_arg, + palette=plot_args.line_colors, + ax=ax, + ) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args=plot_args) + + return ax, dt_plot \ No newline at end of file diff --git a/src/iohinspector/plots/fixed_budget.py b/src/iohinspector/plots/fixed_budget.py new file mode 100644 index 0000000..537ce9e --- /dev/null +++ b/src/iohinspector/plots/fixed_budget.py @@ -0,0 +1,104 @@ +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +import polars as pl +from typing import Iterable +from iohinspector.metrics.fixed_budget import aggregate_convergence +from iohinspector.plots.utils import LinePlotArgs, _save_fig, _create_plot_args +import matplotlib + +def plot_single_function_fixed_budget( + data: pl.DataFrame, + eval_var: str = "evaluations", + fval_var: str = "raw_y", + free_vars: Iterable[str] = ["algorithm_name"], + eval_min: float = None, + eval_max: float = None, + maximization: bool = False, + measures: Iterable[str] = ["geometric_mean"], + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: str = None, + plot_args: dict | LinePlotArgs = None, +): + """Create a fixed-budget convergence plot showing algorithm performance over evaluation budgets. + + Visualizes how different algorithms converge by plotting aggregate performance measures + (geometric mean, median, etc.) against evaluation budgets, allowing direct comparison + of convergence behavior across algorithms. + + Args: + data (pl.DataFrame): Input dataframe containing optimization algorithm trajectory data. + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + fval_var (str, optional): Which column contains the function/objective values. Defaults to "raw_y". + free_vars (Iterable[str], optional): Which columns contain the grouping variables for distinguishing + between different lines in the plot. Defaults to ["algorithm_name"]. + eval_min (float, optional): Minimum evaluation bound for the plot. If None, uses data minimum. Defaults to None. + eval_max (float, optional): Maximum evaluation bound for the plot. If None, uses data maximum. Defaults to None. + maximization (bool, optional): Whether the optimization problem is maximization. Defaults to False. + measures (Iterable[str], optional): Aggregate measures to plot. Valid options are "geometric_mean", + "mean", "median", "min", "max". Defaults to ["geometric_mean"]. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (str, optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | LinePlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Fixed-Budget Plot". + - xlabel (str): X-axis label. Defaults to eval_var value. + - ylabel (str): Y-axis label. Defaults to fval_var value. + - xscale (str): X-axis scale. Defaults to "log". + - yscale (str): Y-axis scale. Defaults to "log". + - line_colors (Sequence[str]): Colors for different lines. Defaults to seaborn palette. + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other LinePlotArgs parameters (xlim, ylim, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pl.DataFrame]: The matplotlib axes object and the processed + (melted/filtered) dataframe used to create the plot. + """ + dt_agg = aggregate_convergence( + data, + eval_var=eval_var, + fval_var=fval_var, + free_vars=free_vars, + eval_min=eval_min, + eval_max=eval_max, + maximization=maximization, + ) + dt_molt = dt_agg.melt(id_vars=[eval_var] + free_vars) + dt_plot = dt_molt[dt_molt["variable"].isin(measures)].sort_values(free_vars) + + plot_args = _create_plot_args( + LinePlotArgs( + xlabel=eval_var, + ylabel=fval_var, + title="Fixed-Budget Plot", + xscale="log", + yscale="log", + ), + plot_args + ) + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize) + else: + fig = None + + sbs.lineplot( + dt_plot, + x=eval_var, + y="value", + style="variable", + hue=dt_plot[free_vars].apply(tuple, axis=1), + palette=plot_args.line_colors, + ax=ax, + ) + + + ax = plot_args.apply(ax=ax) + + _save_fig(fig, file_name, plot_args=plot_args) + + return ax, dt_plot + + + +def plot_multi_function_fixed_budget(): + raise NotImplementedError \ No newline at end of file diff --git a/src/iohinspector/plots/fixed_target.py b/src/iohinspector/plots/fixed_target.py new file mode 100644 index 0000000..e7aeef1 --- /dev/null +++ b/src/iohinspector/plots/fixed_target.py @@ -0,0 +1,116 @@ +import polars as pl +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +from typing import Iterable +from iohinspector.metrics.fixed_target import aggregate_running_time +from iohinspector.plots.utils import LinePlotArgs, _save_fig, _create_plot_args + +def plot_single_function_fixed_target( + data: pl.DataFrame, + eval_var: str = "evaluations", + fval_var: str = "raw_y", + free_vars: Iterable[str] = ["algorithm_name"], + f_min: float = None, + f_max: float = None, + scale_f_log: bool = True, + eval_max: int = None, + maximization: bool = False, + measures: Iterable[str] = ["ERT"], + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: str = None, + plot_args: dict | LinePlotArgs = None, +): + """Create a fixed-target plot showing Expected Running Time (ERT) analysis for algorithm performance. + + Visualizes how much computational budget (evaluations) algorithms need to reach specific target + performance levels, allowing comparison of algorithm efficiency across different difficulty targets. + + Args: + data (pl.DataFrame): Input dataframe containing optimization algorithm trajectory data. + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + fval_var (str, optional): Which column contains the function/objective values. Defaults to "raw_y". + free_vars (Iterable[str], optional): Which columns contain the grouping variables for distinguishing + between different lines in the plot. Defaults to ["algorithm_name"]. + f_min (float, optional): Minimum function value bound for target range. If None, uses data minimum. Defaults to None. + f_max (float, optional): Maximum function value bound for target range. If None, uses data maximum. Defaults to None. + scale_f_log (bool, optional): Whether function values should be log-scaled for target sampling. Defaults to True. + eval_max (int, optional): Maximum evaluation budget to consider. If None, uses data maximum. Defaults to None. + maximization (bool, optional): Whether the optimization problem is maximization. Defaults to False. + measures (Iterable[str], optional): Running time measures to plot. Valid options are "ERT" (Expected Running Time), + "mean", "PAR-10", "min", "max". Defaults to ["ERT"]. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (str, optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | LinePlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Fixed-Target Plot". + - xlabel (str): X-axis label. Defaults to fval_var value. + - ylabel (str): Y-axis label. Defaults to "value". + - xscale (str): X-axis scale. Defaults to "log". + - yscale (str): Y-axis scale ("log" or "linear"). Defaults to "log" if scale_f_log=True. + - reverse_xaxis (bool): Whether to reverse x-axis. Defaults to True for minimization, False for maximization. + - line_colors (Sequence[str]): Colors for different lines. Defaults to seaborn palette. + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other LinePlotArgs parameters (xlim, ylim, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pl.DataFrame]: The matplotlib axes object and the processed + (melted/filtered) dataframe used to create the plot. + """ + + + dt_agg = aggregate_running_time( + data, + eval_var=eval_var, + fval_var=fval_var, + free_vars=free_vars, + f_min=f_min, + f_max=f_max, + scale_f_log=scale_f_log, + eval_max=eval_max, + maximization=maximization, + ) + + dt_molt = dt_agg.melt(id_vars=[fval_var] + free_vars) + dt_plot = dt_molt[dt_molt["variable"].isin(measures)].sort_values(free_vars) + + plot_args = _create_plot_args( + LinePlotArgs( + xlabel= fval_var, + title= "Fixed-Target Plot", + xscale= "log", + yscale= "log" if scale_f_log else "linear", + reverse_xaxis= not maximization + ), + plot_args + ) + + + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize) + else: + fig = None + + sbs.lineplot( + dt_plot, + x=fval_var, + y="value", + style="variable", + hue=dt_plot[free_vars].apply(tuple, axis=1), + palette=plot_args.line_colors, + ax=ax, + ) + + + + plot_args.apply(ax) + + + _save_fig(fig, file_name, plot_args=plot_args) + + return ax, dt_plot + + +def plot_multi_function_fixed_target(): + # either just loop over function column(s), or more advanced + raise NotImplementedError \ No newline at end of file diff --git a/src/iohinspector/plots/multi_objective.py b/src/iohinspector/plots/multi_objective.py new file mode 100644 index 0000000..950088d --- /dev/null +++ b/src/iohinspector/plots/multi_objective.py @@ -0,0 +1,164 @@ +from typing import Iterable, Optional, cast +from iohinspector.metrics.multi_objective import get_pareto_front_2d, get_indicator_over_time_data +import numpy as np +import polars as pl +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +from iohinspector.plots.utils import ScatterPlotArgs, LinePlotArgs, _save_fig, _create_plot_args + +def plot_paretofronts_2d( + data: pl.DataFrame, + obj1_var: str = "raw_y", + obj2_var: str = "F2", + free_var: str = "algorithm_name", + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: str = None, + plot_args: dict | ScatterPlotArgs = None +): + """Visualize 2D Pareto fronts for multi-objective optimization algorithms. + + Creates a scatter plot showing the non-dominated solutions (Pareto fronts) achieved by + different algorithms in a two-objective space, allowing visual comparison of algorithm + performance and trade-off quality. + + Args: + data (pl.DataFrame): Input dataframe containing multi-objective optimization trajectory data. + obj1_var (str, optional): Which column contains the first objective values. Defaults to "raw_y". + obj2_var (str, optional): Which column contains the second objective values. Defaults to "F2". + free_var (str, optional): Which column contains the grouping variable for distinguishing + between different algorithms/categories. Defaults to "algorithm_name". + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (str, optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | ScatterPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Pareto Fronts". + - xlabel (str): X-axis label. Defaults to obj1_var value. + - ylabel (str): Y-axis label. Defaults to obj2_var value. + - point_colors (Sequence[str]): Colors for different algorithm points. Defaults to seaborn palette. + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other ScatterPlotArgs parameters (xlim, ylim, xscale, yscale, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the Pareto front + dataframe used to create the plot. + """ + df = get_pareto_front_2d( + data, obj1_var=obj1_var, obj2_var=obj2_var + ) + + plot_args = _create_plot_args( + ScatterPlotArgs( + xlabel= obj1_var, + ylabel= obj2_var, + title= "Pareto Fronts", + ), + plot_args + ) + + df.sort_values(free_var) + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + sbs.scatterplot( + df, + x=obj1_var, + y=obj2_var, + hue=free_var, + palette= plot_args.point_colors, + ax=ax + ) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args=plot_args) + + return ax,df + + +def plot_indicator_over_time( + data: pl.DataFrame, + obj_vars: Iterable[str] = ["raw_y", "F2"], + indicator: object = None, + free_var: str = "algorithm_name", + eval_min: int = 1, + eval_max: int = 50_000, + scale_eval_log: bool = True, + eval_steps: int = 50, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | LinePlotArgs = None +): + """Plot the anytime performance of multi-objective quality indicators over evaluation budgets. + + Creates line plots showing how quality indicators (like hypervolume, IGD, etc.) evolve + over the course of algorithm runs, enabling comparison of convergence behavior and + solution quality improvement across different algorithms. + + Args: + data (pl.DataFrame): Input dataframe containing multi-objective optimization trajectory data. + obj_vars (Iterable[str], optional): Which columns contain the objective values for indicator calculation. + Defaults to ["raw_y", "F2"]. + indicator (object, optional): Quality indicator object from iohinspector.indicators module. Defaults to None. + free_var (str, optional): Which column contains the grouping variable for distinguishing + between different algorithms. Defaults to "algorithm_name". + eval_min (int, optional): Minimum evaluation bound for the time axis. Defaults to 1. + eval_max (int, optional): Maximum evaluation bound for the time axis. Defaults to 50_000. + scale_eval_log (bool, optional): Whether the evaluation axis should be log-scaled. Defaults to True. + eval_steps (int, optional): Number of evaluation points to sample between eval_min and eval_max. Defaults to 50. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | LinePlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Anytime Performance: {indicator.var_name}". + - xlabel (str): X-axis label. Defaults to "evaluations". + - ylabel (str): Y-axis label. Defaults to indicator.var_name value. + - xscale (str): X-axis scale ("log" or "linear"). Defaults to "log" if scale_eval_log=True. + - line_colors (Sequence[str]): Colors for different algorithm lines. Defaults to seaborn palette. + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other LinePlotArgs parameters (xlim, ylim, yscale, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the indicator + performance dataframe used to create the plot. + """ + df = get_indicator_over_time_data( + data, + indicator=indicator, + obj_vars=obj_vars, + eval_min=eval_min, + eval_max=eval_max, + scale_eval_log=scale_eval_log, + eval_steps=eval_steps, + ) + + plot_args = _create_plot_args( + LinePlotArgs( + xlabel= "evaluations", + ylabel= indicator.var_name, + title= f"Anytime Performance: {indicator.var_name}", + xscale= "log" if scale_eval_log else "linear", + ), + plot_args + ) + + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize) + else: + fig = None + sbs.lineplot( + df, + x="evaluations", + y=indicator.var_name, + hue=free_var, + palette=sbs.color_palette(n_colors=len(np.unique(data[free_var]))), + ax=ax, + ) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args=plot_args) + return ax, df diff --git a/src/iohinspector/plots/ranking.py b/src/iohinspector/plots/ranking.py new file mode 100644 index 0000000..1dd243d --- /dev/null +++ b/src/iohinspector/plots/ranking.py @@ -0,0 +1,224 @@ +from typing import Iterable, Optional +from iohinspector.metrics.ranking import get_robustrank_changes, get_robustrank_over_time +from iohinspector.plots.utils import BasePlotArgs, _create_plot_args, _save_fig +import polars as pl +import numpy as np +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +from iohinspector.metrics import get_tournament_ratings +from iohinspector.indicators import add_indicator + + +def plot_tournament_ranking( + data, + alg_vars: Iterable[str] = ["algorithm_name"], + fid_vars: Iterable[str] = ["function_name"], + fval_var: str = "raw_y", + nrounds: int = 25, + maximization: bool = False, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: str = None, + plot_args: dict | BasePlotArgs = None, +): + """Plot ELO ratings from tournament-style algorithm competition across multiple problems. + + Creates a point plot with error bars showing ELO ratings calculated from pairwise algorithm + competitions. In each round, all algorithms compete against each other on every function, + with performance samples determining winners and ELO rating updates. + + Args: + data (pl.DataFrame): Input dataframe containing algorithm performance trajectory data. + alg_vars (Iterable[str], optional): Which columns contain the algorithm identifiers that will compete. + Defaults to ["algorithm_name"]. + fid_vars (Iterable[str], optional): Which columns contain the problem/function identifiers for competition. + Defaults to ["function_name"]. + fval_var (str, optional): Which column contains the performance values. Defaults to "raw_y". + nrounds (int, optional): Number of tournament rounds to simulate. Defaults to 25. + maximization (bool, optional): Whether the performance should be maximized. Defaults to False. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (str, optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | BasePlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. Defaults to "Tournament Ranking". + - xlabel (str): X-axis label. Defaults to "Algorithms". + - ylabel (str): Y-axis label. Defaults to "ELO Rating". + - figsize (Tuple[float, float]): Figure size. Defaults to (16, 9). + - All other BasePlotArgs parameters (xlim, ylim, xscale, yscale, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the ELO ratings + dataframe used to create the plot. + """ + # candlestick plot based on average and volatility + dt_elo = get_tournament_ratings( + data, alg_vars, fid_vars, fval_var, nrounds, maximization + ) + + plot_args = _create_plot_args( + BasePlotArgs( + title= "Tournament Ranking", + xlabel="Algorithms", + ylabel="ELO Rating", + grid= True + ), + plot_args + ) + + + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize) + else: + fig = None + + sbs.pointplot(data=dt_elo, x=alg_vars[0], y="Rating", linestyle="none", ax=ax) + + ax.errorbar( + dt_elo[alg_vars[0]], + dt_elo["Rating"], + yerr=dt_elo["Deviation"], + fmt="o", + color="blue", + alpha=0.6, + capsize=5, + elinewidth=1.5, + ) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args) + + + return ax, dt_elo + + +def robustranking(): + # to decide which plot(s) to use and what exact interface to define + raise NotImplementedError() + + +def stats_comparison(): + # heatmap or graph of statistical comparisons + raise NotImplementedError() + + +def winnning_fraction_heatmap(): + # nevergrad-like heatmap + raise NotImplementedError() + + + + +def plot_robustrank_over_time( + data: pl.DataFrame, + obj_vars: Iterable[str], + evals: Iterable[int], + indicator: object, + *, + file_name: Optional[str] = None, +): + """Plot robust ranking confidence intervals at distinct evaluation timesteps. + + Creates multiple subplots showing robust ranking analysis with confidence intervals + for algorithm performance at different evaluation budgets, using statistical comparison + methods to handle uncertainty in performance measurements. + + Args: + data (pl.DataFrame): Input dataframe containing algorithm performance trajectory data. + Must contain data for a single function only. + obj_vars (Iterable[str]): Which columns contain the objective values for ranking calculation. + evals (Iterable[int]): Evaluation timesteps at which to compute and plot rankings. + indicator (object): Quality indicator object from iohinspector.indicators module. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + + Returns: + tuple[np.ndarray, tuple]: Array of matplotlib axes objects and a tuple containing + (comparison, benchmark) data used for the robust ranking analysis. + + Raises: + ValueError: If data contains multiple functions (function_id has more than one unique value). + """ + from robustranking.utils.plots import plot_ci_list + + if(data["function_id"].n_unique() > 1): + raise ValueError("Robust ranking over time plot can only be generated for a single function at a time.") + + comparison, benchmark = get_robustrank_over_time( + data=data, + obj_vars=obj_vars, + evals=evals, + indicator=indicator, + ) + + plot_args =BasePlotArgs( + figsize=(5*len(evals), 5), + ) + + + fig, axs = plt.subplots(1, len(evals), figsize=plot_args.figsize, sharey=True) + + for ax, runtime in zip(axs.ravel(), benchmark.objectives): + plot_ci_list(comparison, objective=runtime, ax=ax) + if runtime != evals[0]: + ax.set_ylabel("") + if runtime != evals[-1]: + ax.get_legend().remove() + ax.set_title(runtime) + + _save_fig(fig, file_name, plot_args) + + return axs, comparison, benchmark + +def plot_robustrank_changes( + data: pl.DataFrame, + obj_vars: Iterable[str], + evals: Iterable[int], + indicator: object, + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, +): + """Plot robust ranking changes over evaluation timesteps as connected line plots. + + Creates a line plot showing how algorithm rankings evolve over time, with lines + connecting ranking positions across different evaluation budgets to visualize + ranking stability and performance trajectory changes. + + Args: + data (pl.DataFrame): Input dataframe containing algorithm performance trajectory data. + obj_vars (Iterable[str]): Which columns contain the objective values for ranking calculation. + evals (Iterable[int]): Evaluation timesteps at which to compute rankings and plot changes. + indicator (object): Quality indicator object from iohinspector.indicators module. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + + Returns: + tuple[matplotlib.axes.Axes, object]: The matplotlib axes object and the ranking + comparisons data used to create the plot. + """ + from robustranking.utils.plots import plot_line_ranks + + comparisons = get_robustrank_changes( + data=data, + obj_vars=obj_vars, + evals=evals, + indicator=indicator, + ) + + plot_args = BasePlotArgs( + figsize=(max(5 * len(evals), 16), 5), + ) + + + if ax is None: + fig, ax = plt.subplots(1, 1, figsize=plot_args.figsize) + else: + fig = None + + plot_line_ranks(comparisons, ax=ax) + + plot_args.apply(ax) + _save_fig(fig, file_name, plot_args) + + return ax, comparisons diff --git a/src/iohinspector/plots/single_run.py b/src/iohinspector/plots/single_run.py new file mode 100644 index 0000000..650d6d7 --- /dev/null +++ b/src/iohinspector/plots/single_run.py @@ -0,0 +1,82 @@ +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sbs +import polars as pl +from typing import Iterable, Optional +import numpy as np +from iohinspector.plots.utils import HeatmapPlotArgs, _create_plot_args, _save_fig +from iohinspector.metrics.single_run import get_heatmap_single_run_data + +def plot_heatmap_single_run( + data: pl.DataFrame, + vars: Iterable[str], + eval_var: str = "evaluations", + var_mins: Iterable[float] = [-5], + var_maxs: Iterable[float] = [5], + *, + ax: matplotlib.axes._axes.Axes = None, + file_name: Optional[str] = None, + plot_args: dict | HeatmapPlotArgs = None +): + """Create a heatmap visualization showing search space exploration patterns in a single algorithm run. + + Visualizes how an optimization algorithm explores the search space over time by showing + the density of evaluations across different variable dimensions and evaluation budgets, + revealing search patterns and exploration behavior. + + Args: + data (pl.DataFrame): Input dataframe containing trajectory data from a single algorithm run. + Must contain data for exactly one run (unique data_id). + vars (Iterable[str]): Which columns contain the decision/search space variables to visualize. + eval_var (str, optional): Which column contains the evaluation counts. Defaults to "evaluations". + var_mins (Iterable[float], optional): Minimum bounds for the search space variables. + Should be same length as vars. Defaults to [-5]. + var_maxs (Iterable[float], optional): Maximum bounds for the search space variables. + Should be same length as vars. Defaults to [5]. + ax (matplotlib.axes._axes.Axes, optional): Matplotlib axes to plot on. If None, creates new figure. Defaults to None. + file_name (Optional[str], optional): Path to save the plot. If None, plot is not saved. Defaults to None. + plot_args (dict | HeatmapPlotArgs, optional): Plot styling arguments. Can include: + - title (str): Plot title. No default title set. + - xlabel (str): X-axis label. Defaults to eval_var value. + - ylabel (str): Y-axis label. Defaults to "Variables". + - figsize (Tuple[float, float]): Figure size. Defaults to (32, 9). + - heatmap_palette (str): Colormap for the heatmap. Defaults to "viridis". + - All other HeatmapPlotArgs parameters (xlim, ylim, xscale, yscale, grid, legend, fontsize, etc.). + + Returns: + tuple[matplotlib.axes.Axes, pd.DataFrame]: The matplotlib axes object and the processed + heatmap dataframe used to create the plot. + + Raises: + AssertionError: If data contains multiple runs (data_id has more than one unique value). + """ + assert data["data_id"].n_unique() == 1 + + dt_plot = get_heatmap_single_run_data( + data = data, + vars = vars, + eval_var=eval_var, + var_mins=var_mins, + var_maxs=var_maxs, + ) + + plot_args = _create_plot_args( + HeatmapPlotArgs( + figsize= (32, 9), + xlabel= eval_var, + ylabel= "Variables", + ), + plot_args + ) + + if ax is None: + fig, ax = plt.subplots(figsize=plot_args.figsize) + else: + fig = None + + sbs.heatmap(dt_plot, cmap=plot_args.heatmap_palette, vmin=0, vmax=1, ax=ax) + + plot_args.apply(ax) + + _save_fig(fig, file_name, plot_args=plot_args) + return ax, dt_plot diff --git a/src/iohinspector/plots/utils.py b/src/iohinspector/plots/utils.py new file mode 100644 index 0000000..fd750b2 --- /dev/null +++ b/src/iohinspector/plots/utils.py @@ -0,0 +1,338 @@ +from dataclasses import dataclass, field +from typing import Optional, Tuple, Sequence, Union, Dict, Any +from dataclasses import fields +from typing import TypeVar, Generic + +T = TypeVar('T', bound='BasePlotArgs') + +@dataclass +class BasePlotArgs: + title: Optional[str] = None + xlabel: Optional[str] = None + ylabel: Optional[str] = None + + xlim: Optional[Tuple[float, float]] = None + ylim: Optional[Tuple[float, float]] = None + + xscale: str = None + yscale: str = None + + figsize: Optional[Tuple[float, float]] = (16,9) + dpi: Optional[int] = None + + grid: Union[bool, str] = False + legend: bool = False + legend_loc: str = "best" + legend_kwargs: Dict[str, Any] = field(default_factory=dict) + + fontsize: Optional[Union[int, str]] = None + title_fontsize: Optional[Union[int, str]] = None + tick_params: Dict[str, Any] = field(default_factory=dict) + + xticks: Optional[Sequence[float]] = None + yticks: Optional[Sequence[float]] = None + + reverse_xaxis: bool = False + reverse_yaxis: bool = False + + tight_layout: bool = True + + def __post_init__(self) -> None: + if self.xlim is not None and not isinstance(self.xlim, tuple): + self.xlim = tuple(self.xlim) # type: ignore + if self.ylim is not None and not isinstance(self.ylim, tuple): + self.ylim = tuple(self.ylim) # type: ignore + if self.xticks is not None and not isinstance(self.xticks, tuple): + self.xticks = tuple(self.xticks) # type: ignore + if self.yticks is not None and not isinstance(self.yticks, tuple): + self.yticks = tuple(self.yticks) # type: ignore + + def as_dict(self) -> Dict[str, Any]: + """Convert the plot arguments to a dictionary representation. + + Returns: + Dict[str, Any]: Dictionary containing all plot configuration parameters. + """ + return { + "title": self.title, + "xlabel": self.xlabel, + "ylabel": self.ylabel, + "xlim": self.xlim, + "ylim": self.ylim, + "xscale": self.xscale, + "yscale": self.yscale, + "figsize": self.figsize, + "dpi": self.dpi, + "grid": self.grid, + "legend": self.legend, + "legend_loc": self.legend_loc, + "legend_kwargs": dict(self.legend_kwargs), + "fontsize": self.fontsize, + "title_fontsize": self.title_fontsize, + "tick_params": dict(self.tick_params), + "xticks": self.xticks, + "yticks": self.yticks, + "tight_layout": self.tight_layout, + } + def apply(self, ax): + """Apply stored plot properties to a matplotlib Axes object. + + Args: + ax: matplotlib Axes instance to apply the properties to. + + Returns: + ax: The modified matplotlib Axes object with properties applied. + + Raises: + RuntimeError: If matplotlib is not available. + """ + try: + import matplotlib.pyplot as plt + except Exception as exc: + raise RuntimeError("matplotlib is required to apply plot properties") from exc + + + # Title and labels with fontsize handling + if self.title is not None: + if self.title_fontsize is not None: + ax.set_title(self.title, fontsize=self.title_fontsize) + elif self.fontsize is not None: + ax.set_title(self.title, fontsize=self.fontsize) + else: + ax.set_title(self.title) + + if self.xlabel is not None: + if self.fontsize is not None: + ax.set_xlabel(self.xlabel, fontsize=self.fontsize) + else: + ax.set_xlabel(self.xlabel) + + if self.ylabel is not None: + if self.fontsize is not None: + ax.set_ylabel(self.ylabel, fontsize=self.fontsize) + else: + ax.set_ylabel(self.ylabel) + + + + # Ticks + if self.xticks is not None: + ax.set_xticks(list(self.xticks)) + if self.yticks is not None: + ax.set_yticks(list(self.yticks)) + + # Limits + if self.xlim is not None: + ax.set_xlim(*self.xlim) + if self.ylim is not None: + ax.set_ylim(*self.ylim) + + # Scales + if self.xscale: + ax.set_xscale(self.xscale) + if self.yscale: + ax.set_yscale(self.yscale) + + + # Grid + if isinstance(self.grid, bool): + ax.grid(self.grid) + elif isinstance(self.grid, str): + ax.grid(True, which=self.grid) + + # Legend + if self.legend: + kwargs = dict(self.legend_kwargs or {}) + if "loc" not in kwargs: + kwargs["loc"] = self.legend_loc + # Only attempt to create legend if there are labeled artists + try: + ax.legend(**kwargs) + except Exception: + # fallback: call without kwargs + ax.legend() + + # Tick params (includes labelsize if provided) + if self.tick_params: + ax.tick_params(**self.tick_params) + elif self.fontsize is not None: + ax.tick_params(labelsize=self.fontsize) + + # Reverse axes if requested + if self.reverse_xaxis: + ax.invert_xaxis() + if self.reverse_yaxis: + ax.invert_yaxis() + + return ax + + + def override(self, other: Optional[Union["BasePlotArgs", Dict[str, Any]]]): + """Update plot arguments in place with values from another source. + + Args: + other (Optional[Union[BasePlotArgs, Dict[str, Any]]]): Plot arguments to override current values with. + Can be either a BasePlotArgs instance or a dictionary. Values from `other` override those + from `self` when they are not None. Dictionary fields (legend_kwargs, tick_params) are merged + with `other` taking precedence for overlapping keys. + + Note: + Works with inheritance - handles fields from both base and derived classes. + For sequence-like fields (xlim, ylim, xticks, yticks) lists/tuples from `other` are converted to tuples. + """ + if other is None: + return + + is_dict = isinstance(other, dict) + + # Use self.__class__ to get fields from the actual class (including subclass fields) + for f in fields(self.__class__): + name = f.name + v2 = other.get(name, None) if is_dict else getattr(other, name, None) + + if v2 is not None: + setattr(self, name, v2) + + + +@dataclass +class LinePlotArgs(BasePlotArgs): + line_colors: Optional[Sequence[str]] = None + + def as_dict(self): + """Convert the line plot arguments to a dictionary representation. + + Returns: + Dict[str, Any]: Dictionary containing all line plot configuration parameters including line colors. + """ + results = super().as_dict() + results["line_colors"] = self.line_colors + return results + + + def apply(self, ax): + """Apply line plot properties to a matplotlib Axes object. + + Args: + ax: matplotlib Axes instance to apply the line plot properties to. + + Returns: + ax: The modified matplotlib Axes object with line plot properties applied. + """ + return super().apply(ax) + + def override(self, other): + """Update line plot arguments in place with values from another source. + + Args: + other: Line plot arguments to override current values with. + """ + return super().override(other) + + +@dataclass +class HeatmapPlotArgs(BasePlotArgs): + heatmap_palette: Optional[str] = "viridis" + use_background_color: bool = True + background_color: str = "white" + + def as_dict(self): + """Convert the heatmap plot arguments to a dictionary representation. + + Returns: + Dict[str, Any]: Dictionary containing all heatmap plot configuration parameters including palette settings. + """ + results = super().as_dict() + results["heatmap_palette"] = self.heatmap_palette + return results + + + def apply(self, ax): + """Apply heatmap plot properties to a matplotlib Axes object. + + Args: + ax: matplotlib Axes instance to apply the heatmap plot properties to. + + Returns: + ax: The modified matplotlib Axes object with heatmap plot properties applied. + """ + return super().apply(ax) + + def override(self, other): + """Update heatmap plot arguments in place with values from another source. + + Args: + other: Heatmap plot arguments to override current values with. + """ + return super().override(other) + + +@dataclass +class ScatterPlotArgs(BasePlotArgs): + point_colors: Optional[Sequence[str]] = None + + def as_dict(self): + """Convert the scatter plot arguments to a dictionary representation. + + Returns: + Dict[str, Any]: Dictionary containing all scatter plot configuration parameters including point colors. + """ + results = super().as_dict() + results["point_colors"] = self.point_colors + return results + + + def apply(self, ax): + """Apply scatter plot properties to a matplotlib Axes object. + + Args: + ax: matplotlib Axes instance to apply the scatter plot properties to. + + Returns: + ax: The modified matplotlib Axes object with scatter plot properties applied. + """ + return super().apply(ax) + + def override(self, other): + """Update scatter plot arguments in place with values from another source. + + Args: + other: Scatter plot arguments to override current values with. + """ + return super().override(other) + +def _save_fig(fig = None, file_name: str=None, plot_args: BasePlotArgs=None): + """Save a matplotlib figure to file with optional plot arguments. + + Args: + fig: matplotlib Figure object to save. Defaults to None. + file_name (str, optional): Path where to save the figure. Defaults to None. + plot_args (BasePlotArgs, optional): Plot arguments containing DPI and layout settings. Defaults to None. + """ + if fig and file_name: + if plot_args.tight_layout: + fig.tight_layout() + fig.savefig(file_name, dpi=plot_args.dpi) + + +def _create_plot_args( + defaults: T, + overrides: Optional[Union[T, Dict[str, Any]]] = None, +) -> T: + """Create plot properties by merging defaults with overrides, preserving the exact type of the defaults object. + + Args: + defaults (T): Default properties object (any BasePlotArgs subclass). + overrides (Optional[Union[T, Dict[str, Any]]], optional): Properties to override (dict or same type as defaults). Defaults to None. + + Returns: + T: New properties object of the same type as defaults with overrides applied. + """ + if overrides is None: + return defaults + + # Create a copy to avoid mutating the input + import copy + result = copy.deepcopy(defaults) + result.override(overrides) + return result \ No newline at end of file diff --git a/tests/test_align.py b/tests/test_align.py new file mode 100644 index 0000000..e687a74 --- /dev/null +++ b/tests/test_align.py @@ -0,0 +1,141 @@ +import unittest +import numpy as np +import polars as pl +from iohinspector.align import align_data +from iohinspector.align import turbo_align + + +class TestAlignData(unittest.TestCase): + + def test_align_data_minimization_long(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "evaluations": [1, 2, 5, 1, 4, 5], + "raw_y": [10, 8, 6, 20, 18, 16] + }) + + evals = [1, 2, 3, 4, 5] + result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False) + expected = pl.DataFrame({ + "evaluations": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], + "raw_y": [10, 8, 8, 8, 6, 20, 20, 20, 18, 16], + "data_id": [1, 1, 1, 1, 1, 2, 2, 2, 2, 2] + }) + result_sorted = result.sort(["data_id", "evaluations"]) + expected_sorted = expected.sort(["data_id", "evaluations"]) + self.assertEqual(result_sorted.to_dicts(), expected_sorted.to_dicts()) + + def test_align_data_maximization_long(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1], + "evaluations": [1, 2, 3], + "raw_y": [5, 7, 6] + }) + evals = [1, 2, 3] + result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=True) + expected = pl.DataFrame({ + "evaluations": [1, 2, 3], + "raw_y": [5, 7, 7], + "data_id": [1, 1, 1] + }) + result_sorted = result.sort(["data_id", "evaluations"]) + expected_sorted = expected.sort(["data_id", "evaluations"]) + self.assertEqual(result_sorted.to_dicts(), expected_sorted.to_dicts()) + + def test_align_data_wide_output(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "evaluations": [1, 2, 5, 1, 4, 5], + "raw_y": [10, 8, 6, 20, 18, 16] + }) + evals = [1, 2, 3, 4, 5] + + result = align_data(df, evals, group_cols=("data_id",), x_col="evaluations", y_col="raw_y", output="wide", maximization=False) + # Should pivot to wide format + self.assertIn("1", result.columns) + self.assertIn("2", result.columns) + self.assertIn("evaluations", result.columns) + self.assertEqual(result.shape[0], 5) # 3 evals + + + def test_align_data_custom_group_col(self): + df = pl.DataFrame({ + "exp_id": [1, 1, 2, 2], + "evaluations": [1, 2, 1, 2], + "raw_y": [5, 3, 7, 6] + }) + evals = [1, 2] + result = align_data(df, evals, group_cols=("exp_id",), x_col="evaluations", y_col="raw_y", output="long", maximization=False) + self.assertTrue(set(result["exp_id"].to_list()) == {1, 2}) + + def test_align_data_non_default_x_col(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1], + "steps": [10, 20, 30], + "score": [100, 90, 80] + }) + evals = [10, 20, 30] + result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False) + self.assertTrue(result["steps"].to_list() == [10, 20, 30]) + +class TestTurboAlignData(unittest.TestCase): + def test_turbo_align_minimization_long(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "evaluations": [1, 2, 5, 1, 4, 5], + "raw_y": [10, 8, 6, 20, 18, 16] + }) + evals = [1, 2, 3, 4, 5] + result = turbo_align(df, evals, x_col="evaluations", y_col="raw_y", output="long", maximization=False) + expected = pl.DataFrame({ + "evaluations": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], + "data_id": [1, 1, 1, 1, 1, 2, 2, 2, 2, 2], + "raw_y": [10, 8, 8, 8, 6, 20, 20, 20, 18, 16] + }) + result_sorted = result.sort(["data_id", "evaluations"]) + expected_sorted = expected.sort(["data_id", "evaluations"]) + self.assertEqual(result_sorted.to_dicts(), expected_sorted.to_dicts()) + + + def test_turbo_align_maximization_long(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1], + "evaluations": [1, 2, 5], + "raw_y": [5, 7, 8] + }) + evals = [1, 2, 3, 4, 5] + result = turbo_align(df, evals, x_col="evaluations", y_col="raw_y", output="long", maximization=True) + expected = pl.DataFrame({ + "evaluations": [1, 2, 3, 4, 5], + "data_id": [1, 1, 1, 1, 1], + "raw_y": [5, 7, 7, 7, 8] + }) + result_sorted = result.sort(["data_id", "evaluations"]) + expected_sorted = expected.sort(["data_id", "evaluations"]) + self.assertEqual(result_sorted.to_dicts(), expected_sorted.to_dicts()) + + def test_turbo_align_wide_output(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "evaluations": [1, 2, 5, 1, 4, 5], + "raw_y": [10, 8, 6, 20, 18, 16] + }) + evals = [1, 2, 3, 4, 5] + result = turbo_align(df, evals, x_col="evaluations", y_col="raw_y", output="wide", maximization=False) + self.assertIn("1", result.columns) + self.assertIn("2", result.columns) + self.assertIn("evaluations", result.columns) + self.assertEqual(result.shape[0], 5) + + def test_turbo_align_non_default_x_col(self): + df = pl.DataFrame({ + "data_id": [1, 1, 1], + "steps": [10, 20, 30], + "score": [100, 90, 80] + }) + evals = [10, 20, 30] + result = align_data(df, evals, group_cols=("data_id",), x_col="steps", y_col="score", output="long", maximization=False) + self.assertTrue(result["steps"].to_list() == [10, 20, 30]) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_data.py b/tests/test_data.py index 805dcf2..af51c8c 100644 --- a/tests/test_data.py +++ b/tests/test_data.py @@ -105,7 +105,7 @@ def test_plot_ecdf(self): selection = manager.select(function_ids=[1], algorithms = ['algorithm_A', 'algorithm_B']) df = selection.load(monotonic=True, include_meta_data=True) - dt = plot_ecdf(df) + ax, dt = plot_ecdf(df) self.assertEqual(dt.shape, (66, 14)) diff --git a/tests/test_manager.py b/tests/test_manager.py new file mode 100644 index 0000000..9c02cf1 --- /dev/null +++ b/tests/test_manager.py @@ -0,0 +1,138 @@ +import unittest +import polars as pl +import os +import tempfile +from typing import List +from iohinspector.manager import DataManager +from iohinspector.data import Dataset, Function, Algorithm, METADATA_SCHEMA + +BASE_DIR = os.path.dirname(__file__) +DATA_DIR = os.path.realpath(os.path.join(BASE_DIR, "test_data")) +COCO_DATA_DIR = os.path.realpath(os.path.join(BASE_DIR, "test_coco_data")) + +class TestDataManager(unittest.TestCase): + def setUp(self): + self.data_folders = [os.path.join(DATA_DIR, x) for x in sorted(os.listdir(DATA_DIR))] + self.data_dir = self.data_folders[0] + self.json_files = sorted( + [ + fname + for f in os.listdir(self.data_dir) + if os.path.isfile((fname := os.path.join(self.data_dir, f))) + ] + ) + + def test_add_folder_file_not_found(self): + m = DataManager() + with self.assertRaises(FileNotFoundError): + m.add_folder("nonexistent_folder") + + def test_add_folder_no_json_or_coco(self): + with tempfile.TemporaryDirectory() as tmpdir: + m = DataManager() + with self.assertRaises(FileNotFoundError): + m.add_folder(tmpdir) + + def test_add_folder_json(self): + manager = DataManager() + manager.add_folder(self.data_dir) + self.assertEqual(len(manager.functions), 1) + self.assertEqual(len(manager.algorithms), 1) + self.assertEqual(len(manager.data_sets), 1) + + def test_add_folder_coco(self): + manager = DataManager() + manager.add_folder(COCO_DATA_DIR) + self.assertEqual(len(manager.functions), 1) + self.assertEqual(len(manager.algorithms), 1) + self.assertEqual(len(manager.data_sets), 1) + + def test_add_json(self): + manager = DataManager() + manager.add_json(self.json_files[0]) + self.assertEqual(len(manager.functions), 1) + self.assertEqual(len(manager.algorithms), 1) + self.assertEqual(len(manager.data_sets), 1) + + def test_add_coco_info(self): + coco_file = os.path.join(COCO_DATA_DIR, "BFGS-scipy-2019_Varelas/BFGS-scipy-2019_bbob_Varelas_Dahito/minimize_on_bbob_budget100000xD/bbobexp_f1_i1.info") + manager = DataManager() + manager.add_coco_info(coco_file) + self.assertEqual(len(manager.functions), 1) + self.assertEqual(len(manager.algorithms), 1) + self.assertEqual(len(manager.data_sets), 1) + + + def test_add_coco_info_file_not_found(self): + m = DataManager() + with self.assertRaises(FileNotFoundError): + m.add_coco_info("missing.info") + + def test_select_by_data_ids(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(data_ids=[1]) + self.assertTrue(selected.any) + self.assertLessEqual(len(selected.data_sets), 2) + + def test_select_by_function_ids(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(function_ids=[1]) + self.assertEqual(len(selected.data_sets), 1) + self.assertEqual(selected.data_sets[0].function.id, 1) + + def test_select_by_algorithms(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(algorithms=["algorithm_A"]) + self.assertEqual(len(selected.data_sets), 1) + self.assertEqual(selected.data_sets[0].algorithm.name, "algorithm_A") + + def test_select_by_data_attributes(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(data_attributes=["evaluations", "raw_y"]) + self.assertEqual(len(selected.data_sets), 1) + + + def test_select_by_dimensions(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(dimensions=[2]) + self.assertEqual(len(selected.data_sets), 1) + + def test_select_by_instances(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select(instances=[1]) + self.assertEqual(len(selected.data_sets), 1) + + def test_select_indexes(self): + m = DataManager() + m.add_folder(self.data_dir) + selected = m.select_indexes([0]) + self.assertEqual(len(selected.data_sets), 1) + self.assertEqual(selected.data_sets[0].file.split("/")[-1], "IOHprofiler_f1_Sphere.json") + + def test_load(self): + m = DataManager() + m.add_folder(self.data_dir) + df = m.load() + self.assertIsInstance(df, pl.DataFrame) + self.assertIn("raw_y", df.columns) + df2 = m.load(include_meta_data=True) + self.assertIsInstance(df2, pl.DataFrame) + self.assertIn("algorithm_name", df2.columns) + df3 = m.load(include_columns=["algorithm_name"]) + self.assertIn("algorithm_name", df3.columns) + df4 = m.load(x_values=[1]) + self.assertIsInstance(df4, pl.DataFrame) + + def test_load_empty(self): + m = DataManager() + df = m.load() + self.assertEqual(len(df), 0) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/__init__.py b/tests/test_metrics/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_metrics/test_aocc.py b/tests/test_metrics/test_aocc.py new file mode 100644 index 0000000..99243ae --- /dev/null +++ b/tests/test_metrics/test_aocc.py @@ -0,0 +1,99 @@ +import unittest +import polars as pl +import pandas as pd +import numpy as np +from iohinspector.metrics import get_aocc + +class TestAOCC(unittest.TestCase): + def setUp(self): + # Simple dataset with two groups and two data_ids + self.df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "function_name": ["f1", "f1", "f1", "f1", "f1", "f1"], + "algorithm_name": ["alg1", "alg1", "alg1", "alg1", "alg1", "alg1"], + "evaluations": [0, 5, 10, 0, 5, 10], + "eaf": [10.0, 7.0, 4.0, 12.0, 9.0, 6.0], + }) + + def test_basic_aocc(self): + # AOCC should be computed for the group + result = get_aocc(self.df, eval_max=10) + self.assertIsInstance(result, pd.DataFrame) + + self.assertIn("AOCC", result.columns) + aocc_val = result["AOCC"][0] + self.assertTrue(aocc_val == 6.5) + + + result = get_aocc(self.df, eval_max=10, return_as_pandas=False) + self.assertIsInstance(result, pl.DataFrame) + + def test_multiple_groups(self): + # Add a second group + df = self.df.with_columns([ + pl.Series("function_name", ["f1", "f1", "f1", "f2", "f2", "f2"]) + ]) + + result = get_aocc(df, eval_max=10) + self.assertIn("AOCC", result.columns) + aocc_f1_val = result[result["function_name"] == "f1"]["AOCC"].iloc[0] + aocc_f2_val = result[result["function_name"] == "f2"]["AOCC"].iloc[0] + self.assertTrue(aocc_f1_val == 5.5) + self.assertTrue(aocc_f2_val == 7.5) + + def test_custom_fval_col(self): + # Use a different column for fval_var + df = self.df.rename({"eaf": "custom_col"}) + result = get_aocc(df, eval_max=10, fval_var="custom_col") + self.assertIn("AOCC", result.columns) + aocc_val = result["AOCC"][0] + self.assertTrue(aocc_val == 6.5) + + def test_custom_free_vars(self): + # Use only algorithm_name as free var + result = get_aocc(self.df, eval_max=10, free_vars=["algorithm_name"]) + aocc_val = result["AOCC"][0] + self.assertTrue(aocc_val == 6.5) + + def test_aocc_with_missing_evaluations(self): + # Remove some evaluation steps to test fill_null + df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2], + "function_name": ["f1", "f1", "f1", "f1", "f1"], + "algorithm_name": ["alg1", "alg1", "alg1", "alg1", "alg1"], + "evaluations": [0, 5, 10, 0, 10], + "eaf": [10.0, 8.0, 4.0, 12.0, 6.0], + }) + result = get_aocc(df, eval_max=10) + + self.assertIn("AOCC", result.columns) + aocc_val = result["AOCC"][0] + self.assertTrue(aocc_val == 6) + + def test_aocc_zero_budget(self): + # Test with max_budget=0 (should handle gracefully) + df = self.df + result = get_aocc(df, eval_max=0) + self.assertIn("AOCC", result.columns) + # AOCC should be nan or 0 + aocc_val = result["AOCC"][0] + self.assertTrue(np.isnan(aocc_val) or aocc_val == 0) + + + def test_aocc_log(self): + self.df = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "function_name": ["f1", "f1", "f1", "f1", "f1", "f1"], + "algorithm_name": ["alg1", "alg1", "alg1", "alg1", "alg1", "alg1"], + "evaluations": [1, 10, 100, 1, 10, 100], + "eaf": [10.0, 7.0, 4.0, 12.0, 9.0, 6.0], + }) + result = get_aocc(self.df, eval_max=100, scale_eval_log=True) + aocc_val = result["AOCC"][0] + self.assertTrue(aocc_val == 6.5) + + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_attractor_network.py b/tests/test_metrics/test_attractor_network.py new file mode 100644 index 0000000..8adad4d --- /dev/null +++ b/tests/test_metrics/test_attractor_network.py @@ -0,0 +1,52 @@ +import unittest +import polars as pl +import numpy as np +from iohinspector.metrics import get_attractor_network + +class TestGetAttractorNetwork(unittest.TestCase): + def test_basic(self): + data = pl.DataFrame({ + "x1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + "x2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + "raw_y": [35, 33, 31, 29, 27, 23, 18, 16, 14, 12, 10, 9, 6], + "evaluations": [1,42, 81,121,161,201,241,281,321,361,401,442,481], + "data_id": [1]*13 + }) + nodes, edges = get_attractor_network( + data, + coord_vars=["x1", "x2"], + fval_var="raw_y", + eval_var="evaluations", + ) + # Check nodes DataFrame shape and content + self.assertEqual(nodes.shape[1], 5) # x1, x2, y, count, evals + self.assertGreaterEqual(nodes.shape[0], 1) + # Check that node coordinates and y values are as expected + self.assertIn("x1", nodes.columns) + self.assertIn("x2", nodes.columns) + self.assertIn("y", nodes.columns) + self.assertIn("count", nodes.columns) + self.assertIn("evals", nodes.columns) + # Check that the first node matches the first stagnation point + self.assertEqual(nodes.iloc[0]["x1"], 0) + self.assertEqual(nodes.iloc[0]["x2"], 0) + self.assertEqual(nodes.iloc[0]["y"], 35) + self.assertEqual(nodes.iloc[-1]["x1"], 10) + self.assertEqual(nodes.iloc[-1]["x2"], 10) + self.assertEqual(nodes.iloc[-1]["y"], 10) + # Check that counts and evals are positive + self.assertTrue((nodes["count"] > 0).all()) + self.assertTrue((nodes["evals"] > 0).all()) + + # Check edges DataFrame shape and content + self.assertEqual(edges.shape[1], 4) # start, end, count, stag_length_avg + self.assertTrue((edges["count"] > 0).all()) + self.assertTrue((edges["stag_length_avg"] > 0).all()) + # Check that start and end refer to valid node indices + self.assertTrue(edges["start"].isin(nodes.index).all()) + self.assertTrue(edges["end"].isin(nodes.index).all()) + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_eaf.py b/tests/test_metrics/test_eaf.py new file mode 100644 index 0000000..2bcec24 --- /dev/null +++ b/tests/test_metrics/test_eaf.py @@ -0,0 +1,285 @@ +import unittest +import polars as pl +import pandas as pd +import numpy as np +from iohinspector.metrics.eaf import ( + get_discritized_eaf_single_objective, + get_eaf_data, + get_eaf_pareto_data, + get_eaf_diff_data +) + +class TestGetDiscritizedEAF(unittest.TestCase): + def setUp(self): + # Create a simple polars DataFrame for testing + self.data = pl.DataFrame({ + "evaluations": [1, 10, 100, 1000], + "raw_y": [1.0, 0.1, 0.01, 0.001], + "data_id": [1, 1, 1, 1] + }) + + self.multi_data = pl.DataFrame({ + "evaluations": [1, 10, 100, 1000, 1, 10, 100, 1000], + "raw_y": [1.0, 0.1, 0.01, 0.001, 1.5, 0.15, 0.015, 0.0015], + "data_id": [1, 1, 1, 1, 2, 2, 2, 2] + }) + + def test_basic_single_data_id(self): + result = get_discritized_eaf_single_objective(self.data) + + self.assertIsInstance(result, pd.DataFrame) + + self.assertIn('eaf_target', result.index.names) + self.assertTrue(len(result.columns) == 10) # default x_targets + self.assertEqual(result.shape[0], 101) # default y_targets + # Assert all values are 1 or 0 + self.assertTrue(result[self.data["evaluations"].to_list()].applymap(lambda x: x in [1, 0]).all().all()) + self.assertEqual(result[1].tolist()[-1], 0) + self.assertEqual(result[1000].tolist()[0], 1) + + result = get_discritized_eaf_single_objective(self.data, return_as_pandas=False) + self.assertIsInstance(result, pl.DataFrame) + + def test_basic_multi_data_id(self): + result = get_discritized_eaf_single_objective(self.multi_data) + self.assertIn('eaf_target', result.index.names) + self.assertTrue(len(result.columns) == 10) # default x_targets + self.assertEqual(result.shape[0], 101) # default y_targets + # Assert all values are 1, 0.5 or 0 + self.assertTrue(result[self.multi_data["evaluations"].to_list()].applymap(lambda x: x in [1, 0.5, 0]).all().all()) + self.assertEqual(result[1].tolist()[-1], 0) + self.assertEqual(result[1000].tolist()[0], 1) + + def test_custom_eval_values(self): + eval_values = [1, 3, 5] + result = get_discritized_eaf_single_objective(self.data, eval_values=eval_values) + self.assertTrue(all(x in result.columns for x in eval_values)) + + def test_custom_eval_min_max(self): + result = get_discritized_eaf_single_objective(self.data, eval_min=2, eval_max=4, eval_targets=2) + self.assertTrue(all(x in result.columns for x in [2, 4])) + + def test_custom_f_min_max_targets(self): + result = get_discritized_eaf_single_objective(self.data, f_min=0.0, f_max=1.0, f_targets=5) + self.assertEqual(result.shape[0], 5) + self.assertAlmostEqual(result.index.min(), 0.0) + self.assertAlmostEqual(result.index.max(), 1.0) + + def test_scale_eval_log_and_f_log(self): + result = get_discritized_eaf_single_objective(self.data, scale_f_log=False, scale_eval_log=False) + # Check that all values except the last row are 1, and the last row is 0 + values = result.values + self.assertTrue(np.all(values[:-1] == 1)) + self.assertTrue(np.all(values[-1] == 0)) + + self.budgets = result.columns.to_list() + np.testing.assert_allclose(self.budgets, np.linspace(1, 1000, 10)) + + +class TestGetEAFData(unittest.TestCase): + def setUp(self): + # Simple predictable data: constant improvement + self.simple_data = pl.DataFrame({ + "evaluations": [1, 2, 3, 4, 5], + "raw_y": [5.0, 4.0, 3.0, 2.0, 1.0], # Decreasing linearly + "data_id": [1, 1, 1, 1, 1] + }) + + # Two identical runs for predictable EAF + self.dual_data = pl.DataFrame({ + "evaluations": [1, 2, 3, 1, 2, 3], + "raw_y": [10.0, 5.0, 1.0, 10.0, 5.0, 1.0], # Same values for both runs + "data_id": [1, 1, 1, 2, 2, 2] + }) + + + def test_basic_with_simple_data(self): + """Test with simple predictable data""" + result = get_eaf_data(self.simple_data, eval_min=1, eval_max=5, scale_eval_log=False) + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("evaluations", result.columns) + self.assertIn("raw_y", result.columns) + self.assertIn("data_id", result.columns) + + # Check that we have the expected number of rows (should be same as input) + self.assertEqual(len(result), len(self.simple_data)) + + # Check that data_id is preserved + self.assertEqual(result["data_id"].unique().tolist(), [1]) + + # Check evaluation values are within expected range + self.assertTrue((result["evaluations"] >= 1).all()) + self.assertTrue((result["evaluations"] <= 5).all()) + + def test_dual_runs_predictable_eaf(self): + result = get_eaf_data(self.dual_data, eval_min=1, eval_max=3, scale_eval_log=False) + + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("evaluations", result.columns) + self.assertIn("raw_y", result.columns) + self.assertIn("data_id", result.columns) + + # Should have both data_ids + self.assertEqual(len(result["data_id"].unique()), 2) + self.assertEqual(set(result["data_id"].unique()), {1, 2}) + + # Should have expected number of rows + self.assertEqual(len(result), len(self.dual_data)) + + def test_return_types(self): + """Test different return types""" + # Pandas return + result_pd = get_eaf_data(self.simple_data, return_as_pandas=True) + self.assertIsInstance(result_pd, pd.DataFrame) + + # Polars return + result_pl = get_eaf_data(self.simple_data, return_as_pandas=False) + self.assertIsInstance(result_pl, pl.DataFrame) + + +class TestGetEAFParetoData(unittest.TestCase): + def setUp(self): + # Simple predictable Pareto front data + # Run 1: Points that clearly dominate each other + # Run 2: Same structure but slightly worse + self.simple_mo_data = pl.DataFrame({ + "obj1": [3.0, 2.0, 1.0, 3.5, 2.5, 0.5], # Minimization objective + "obj2": [1.0, 2.0, 3.0, 1.5, 2.5, 3.0], # Minimization objective + "data_id": [1, 1, 1, 2, 2, 2] + }) + + self.simple_results = pl.DataFrame({ + "obj1": [3.0, 2.0, 1.0, 3.5, 2.5, 0.5], + "obj2": [1.0, 2.0, 3.0, 1.5, 2.5, 3.0], + "eaf": [0.5, 0.5, 1.0, 1.0, 1.0, 0.5] + }) + + # Identical runs for predictable EAF = 50% + self.identical_mo_data = pl.DataFrame({ + "obj1": [1.0, 2.0, 3.0, 1.0, 2.0, 3.0], + "obj2": [3.0, 2.0, 1.0, 3.0, 2.0, 1.0], + "data_id": [1, 1, 1, 2, 2, 2] + }) + + def test_simple_pareto_fronts(self): + """Test with simple, predictable Pareto front data""" + result = get_eaf_pareto_data(self.simple_mo_data, "obj1", "obj2") + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("eaf", result.columns) + self.assertIn("obj1", result.columns) + self.assertIn("obj2", result.columns) + # Should have some data points + self.assertGreater(len(result), 0) + + for row in result.itertuples(): + obj1 = row.obj1 + obj2 = row.obj2 + eaf_value = row.eaf + expected_eaf = self.simple_results.filter( + (pl.col("obj1") == obj1) & (pl.col("obj2") == obj2) + )["eaf"].to_list()[0] + + self.assertAlmostEqual(eaf_value, expected_eaf) + + + + def test_identical_runs_eaf(self): + result = get_eaf_pareto_data(self.identical_mo_data, "obj1", "obj2") + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("eaf", result.columns) + self.assertIn("obj1", result.columns) + self.assertIn("obj2", result.columns) + + # Should have some data points + self.assertGreater(len(result), 0) + + max_per_pair = result.groupby(["obj1", "obj2"])["eaf"].max() + self.assertTrue(np.allclose(max_per_pair.values, 1.0)) + + def test_return_types(self): + """Test different return types""" + # Pandas return (default) + result_pd = get_eaf_pareto_data(self.simple_mo_data, "obj1", "obj2") + self.assertIsInstance(result_pd, pd.DataFrame) + + # Polars return + result_pl = get_eaf_pareto_data(self.simple_mo_data, "obj1", "obj2", return_as_pandas=False) + self.assertIsInstance(result_pl, pl.DataFrame) + + +class TestGetEAFDiffData(unittest.TestCase): + def setUp(self): + # Dataset 1: Better performance (lower values for minimization) + self.better_data = pl.DataFrame({ + "obj1": [1.0, 2.0, 3.0], + "obj2": [3.0, 2.0, 1.0], + "data_id": [1, 1, 1] + }) + + # Dataset 2: Worse performance (higher values) + self.worse_data = pl.DataFrame({ + "obj1": [2.0, 3.0, 4.0], + "obj2": [4.0, 3.0, 2.0], + "data_id": [1, 1, 1] + }) + + # Identical datasets for predictable diff = 0 + self.identical_data1 = pl.DataFrame({ + "obj1": [1.0, 2.0], + "obj2": [2.0, 1.0], + "data_id": [1, 1] + }) + + self.identical_data2 = pl.DataFrame({ + "obj1": [2.0, 1.0], + "obj2": [1.0, 2.0], + "data_id": [1, 1] + }) + + def test_clear_performance_difference(self): + """Test with clearly better vs worse datasets""" + result = get_eaf_diff_data(self.better_data, self.worse_data, "obj1", "obj2") + + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("eaf_diff", result.columns) + self.assertIn("x_min", result.columns) + self.assertIn("y_min", result.columns) + self.assertIn("x_max", result.columns) + self.assertIn("y_max", result.columns) + + # Should have some rectangles with differences + self.assertGreater(len(result), 0) + + # Check that rectangle coordinates are valid + self.assertTrue((result["x_min"] <= result["x_max"]).all()) + self.assertTrue((result["y_min"] <= result["y_max"]).all()) + + # Check for no NaN values + self.assertFalse(result.isna().any().any()) + + # Since better_data dominates worse_data, should have positive differences + self.assertGreater(result["eaf_diff"].max(), 0) + + def test_identical_datasets_zero_diff(self): + """Test with identical datasets - should get minimal or no differences""" + result = get_eaf_diff_data(self.identical_data1, self.identical_data2, "obj1", "obj2") + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("eaf_diff", result.columns) + + # Result should be either empty or contain only very small differences + self.assertTrue(len(result) == 0 or abs(result["eaf_diff"]).max() < 0.1) + + def test_return_types(self): + """Test different return types""" + # Pandas return (default) + result_pd = get_eaf_diff_data(self.better_data, self.worse_data, "obj1", "obj2") + self.assertIsInstance(result_pd, pd.DataFrame) + + # Polars return + result_pl = get_eaf_diff_data(self.better_data, self.worse_data, "obj1", "obj2", return_as_pandas=False) + self.assertIsInstance(result_pl, pl.DataFrame) + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_ecdf.py b/tests/test_metrics/test_ecdf.py new file mode 100644 index 0000000..92dc37a --- /dev/null +++ b/tests/test_metrics/test_ecdf.py @@ -0,0 +1,80 @@ +import unittest +import polars as pl +import numpy as np +from iohinspector.metrics.ecdf import get_data_ecdf +import iohinspector + +class TestGetDataECDF(unittest.TestCase): + def setUp(self): + # Create a simple synthetic dataset + self.df = pl.DataFrame({ + "evaluations": [1, 2, 3, 4, 5, 1, 2, 3, 4, 5], + "raw_y": [10, 8, 6, 4, 2, 18, 16, 14, 12, 10], + "algorithm_name": ["algo1"] * 5 + ["algo2"] * 5, + "data_id": [1] * 5 + [2] * 5, + }) + + def test_basic_ecdf(self): + result = get_data_ecdf(self.df, scale_eval_log=False, scale_f_log=False) + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + algo1_eaf.sort() + np.testing.assert_allclose(algo1_eaf, [0.5, 0.625, 0.75, 0.875, 1]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + algo2_eaf.sort() + np.testing.assert_allclose(algo2_eaf, [0, 0.125, 0.25, 0.375, 0.5]) + + def test_ecdf_with_custom_eval_values(self): + eval_values = [2, 4] + result = get_data_ecdf(self.df, eval_values=eval_values, scale_eval_log=False, scale_f_log=False) + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + algo1_eaf.sort() + np.testing.assert_allclose(algo1_eaf, [2/3, 1]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + algo2_eaf.sort() + np.testing.assert_allclose(algo2_eaf, [0, 1/3]) + + def test_ecdf_with_maximization(self): + result = get_data_ecdf(self.df, maximization=True) + # eaf_raw_y should be between 0 and 1 + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + # Assert that all values in algo1_eaf are 0 and the array is not empty + np.testing.assert_allclose(algo1_eaf, [0, 0, 0, 0, 0]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + np.testing.assert_allclose(algo2_eaf, [1, 1, 1, 1, 1]) + + + def test_ecdf_with_custom_bounds(self): + result = get_data_ecdf(self.df, f_min=0, f_max=100, scale_eval_log=False, scale_f_log=False) + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + algo1_eaf.sort() + np.testing.assert_allclose(algo1_eaf, [90/100, 92/100, 94/100, 96/100, 98/100]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + algo2_eaf.sort() + np.testing.assert_allclose(algo2_eaf, [82/100, 84/100, 86/100, 88/100, 90/100]) + + def test_ecdf_with_eval_min_eval_max(self): + result = get_data_ecdf(self.df, eval_min=2, eval_max=4, scale_eval_log=False, scale_f_log=False) + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + algo1_eaf.sort() + np.testing.assert_allclose(algo1_eaf, [2/3, 5/6, 1]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + algo2_eaf.sort() + np.testing.assert_allclose(algo2_eaf, [0, 1/6, 1/3]) + + def test_basic_ecdf_turbo(self): + result = get_data_ecdf(self.df, scale_eval_log=False, scale_f_log=False, turbo=True) + algo1_eaf = result[result["algorithm_name"] == "algo1"]["eaf"].to_numpy() + algo1_eaf.sort() + np.testing.assert_allclose(algo1_eaf, [0.5, 0.625, 0.75, 0.875, 1]) + + algo2_eaf = result[result["algorithm_name"] == "algo2"]["eaf"].to_numpy() + algo2_eaf.sort() + np.testing.assert_allclose(algo2_eaf, [0, 0.125, 0.25, 0.375, 0.5]) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_fixed_budget.py b/tests/test_metrics/test_fixed_budget.py new file mode 100644 index 0000000..d78c4ba --- /dev/null +++ b/tests/test_metrics/test_fixed_budget.py @@ -0,0 +1,80 @@ +import unittest +import polars as pl +import numpy as np +from typing import Callable +from iohinspector.metrics.fixed_budget import aggregate_convergence + +class TestFixedBudget(unittest.TestCase): + def setUp(self): + # Create a simple test DataFrame + self.df = pl.DataFrame({ + "evaluations": [1, 2, 3, 1, 2, 3, 1,3, 1,3], + "raw_y": [30, 20, 10, 35, 25, 15, 40, 30, 20, 10], + "algorithm_name": ["A", "A", "A", "A", "A", "A", "B", "B", "B", "B"], + "data_id": [0, 0, 0, 1, 1, 1, 2, 2, 3, 3] + }) + + def test_basic_aggregation(self): + result = aggregate_convergence(self.df, return_as_pandas=True) + # Should contain columns for mean, min, max, median, std, geometric_mean + for col in ["mean", "min", "max", "median", "std", "geometric_mean"]: + self.assertIn(col, result.columns) + # Should have 6 rows (3 evals x 2 algs) + self.assertEqual(len(result), 6) + # Check that means are correct for one group + mean_a = result[(result["algorithm_name"] == "A")]["mean"].values + np.testing.assert_allclose(mean_a, [32.5, 22.5, 12.5]) + mean_b = result[(result["algorithm_name"] == "B")]["mean"].values + np.testing.assert_allclose(mean_b, [30,30,20]) + + def test_custom_op(self): + def custom_sum(s): + return s.sum() # Sum the Series and return as float + result = aggregate_convergence(self.df, custom_op=custom_sum, return_as_pandas=True) + self.assertIn("custom_sum", result.columns) + + # Check that custom_sum is correct for one group + sum_a = result[(result["algorithm_name"] == "A")]["custom_sum"].values + np.testing.assert_allclose(sum_a, [65, 45, 25]) + sum_a = result[(result["algorithm_name"] == "B")]["custom_sum"].values + np.testing.assert_allclose(sum_a, [60, 60, 40]) + + def test_maximization(self): + # Should not affect aggregation, but test for code path + result = aggregate_convergence(self.df, maximization=True, return_as_pandas=True) + self.assertIn("mean", result.columns) + + def test_eval_min_eval_max(self): + # Limit to a subset of evaluations + result = aggregate_convergence(self.df, eval_min=2, eval_max=3, return_as_pandas=True) + self.assertTrue((result["evaluations"] >= 2).all()) + self.assertTrue((result["evaluations"] <= 3).all()) + + def test_return_polars(self): + result = aggregate_convergence(self.df, return_as_pandas=False) + self.assertIsInstance(result, pl.DataFrame) + + def test_free_variables(self): + # Use a different free variable + df = self.df.with_columns(pl.lit("foo").alias("other_var")) + result = aggregate_convergence(df, free_vars=["other_var"], return_as_pandas=True) + self.assertIn("other_var", result.columns) + + def test_empty_data(self): + empty_df = self.df.filter(pl.col("evaluations") > 100) + with self.assertRaises(ValueError): + aggregate_convergence(empty_df, return_as_pandas=True) + + def test_single_row(self): + single_df = pl.DataFrame({ + "evaluations": [1], + "raw_y": [42], + "algorithm_name": ["A"], + "data_id": [0] + }) + result = aggregate_convergence(single_df, return_as_pandas=True) + self.assertEqual(len(result), 1) + self.assertAlmostEqual(result["mean"].iloc[0], 42.0) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_fixed_target.py b/tests/test_metrics/test_fixed_target.py new file mode 100644 index 0000000..10a968a --- /dev/null +++ b/tests/test_metrics/test_fixed_target.py @@ -0,0 +1,92 @@ +import unittest +import polars as pl +import math +from iohinspector.metrics.fixed_target import aggregate_running_time + +class TestFixedTarget(unittest.TestCase): + + def setUp(self): + self.df = pl.DataFrame({ + "evaluations": [1, 10, 20, 1, 15, 26], + "raw_y": [1.0, 0.7, 0.1, 0.9, 0.3, 0.2], + "algorithm_name": ["A", "A", "A", "B", "B", "B"], + "data_id": [1, 1, 1, 2, 2, 2] + }) + + + def test_basic_aggregation(self): + result = aggregate_running_time(self.df, return_as_pandas=False) + self.assertIn("mean", result.columns) + self.assertIn("ERT", result.columns) + self.assertIn("PAR-10", result.columns) + self.assertTrue(result.height > 0) + + # Assert the value of success_count for A is 1 and for B is 0 + # You can use filter as shown, or use row indexing with .row or .to_dicts() + a_success_count = result.filter( + (pl.col("algorithm_name") == "A") & (pl.col("raw_y") == 0.1) + )["success_count"].to_list()[0] + b_success_count = result.filter( + (pl.col("algorithm_name") == "B") & (pl.col("raw_y") == 0.1) + )["success_count"].to_list()[0] + + + self.assertEqual(a_success_count, 1) + self.assertEqual(b_success_count, 0) + + def test_return_as_pandas(self): + result = aggregate_running_time(self.df, return_as_pandas=True) + self.assertTrue(hasattr(result, "to_numpy")) # pandas DataFrame + + def test_custom_op(self): + def my_sum(s): + return float(s.sum()) + result = aggregate_running_time(self.df, custom_op=my_sum, return_as_pandas=False) + self.assertIn("my_sum", result.columns) + + def test_maximization(self): + # Should not raise error + df = pl.DataFrame({ + "evaluations": [1, 10, 20, 1, 15, 26], + "raw_y": [0.1, 0.7, 1.0, 0.2, 0.3, 0.9], + "algorithm_name": ["A", "A", "A", "B", "B", "B"], + "data_id": [1, 1, 1, 2, 2, 2] + }) + + result = aggregate_running_time(df, maximization=True, return_as_pandas=False) + self.assertTrue(result.height > 0) + + a_success_count = result.filter( + (pl.col("algorithm_name") == "A") & (pl.col("raw_y") >= 0.9) + )["success_count"].to_list()[0] + b_success_count = result.filter( + (pl.col("algorithm_name") == "B") & (pl.col("raw_y") >= 0.9) + )["success_count"].to_list()[0] + + + self.assertEqual(a_success_count, 1) + self.assertEqual(b_success_count, 0) + + def test_with_f_min_f_max(self): + result = aggregate_running_time(self.df, f_min=0.2, f_max=0.5, return_as_pandas=False) + self.assertTrue(result["raw_y"].min() >= 0.2) + self.assertTrue(result["raw_y"].max() <= 0.5) + + def test_with_different_free_variables(self): + result = aggregate_running_time(self.df, free_vars=["algorithm_name", "data_id"], return_as_pandas=False) + self.assertIn("algorithm_name", result.columns) + self.assertIn("data_id", result.columns) + + + def test_success_ratio_and_count(self): + # Add a non-finite value + df = self.df.with_columns([ + pl.when(pl.col("evaluations") == 3).then(float("nan")).otherwise(pl.col("evaluations")).alias("evaluations") + ]) + result = aggregate_running_time(df, return_as_pandas=False) + self.assertIn("success_ratio", result.columns) + self.assertIn("success_count", result.columns) + self.assertTrue((result["success_ratio"] <= 1).all()) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_multi_objective.py b/tests/test_metrics/test_multi_objective.py new file mode 100644 index 0000000..aa72b77 --- /dev/null +++ b/tests/test_metrics/test_multi_objective.py @@ -0,0 +1,165 @@ +import unittest +from iohinspector.indicators.anytime import HyperVolume, Epsilon, IGDPlus +import polars as pl +import pandas as pd +import numpy as np +from iohinspector.metrics.multi_objective import ( + get_pareto_front_2d, + get_indicator_over_time_data +) + + +class TestGetParetoFront2D(unittest.TestCase): + def setUp(self): + self.df = pl.DataFrame({ + # For minimization, Pareto front = non-dominated points with lowest values in both objectives + # A: Only (0.1, 0.9) is non-dominated for A + # B: (0.2, 0.8) and (0.4, 0.6) are non-dominated for B + # C: (0.3, 0.7), (0.6, 0.4), (0.7, 0.3) are all non-dominated for C + "raw_y": [0.1, 0.5, 0.9, 0.2, 0.5, 0.9, 0.3, 0.6, 0.9], + "F2": [0.2, 0.5, 0.8, 0.8, 0.2, 0.9, 0.7, 0.4, 0.1], + "algorithm_name": ["A", "A", "A", "B", "B", "B", "C", "C", "C"], + "evaluations": [1, 2, 3, 1, 2, 3, 1, 2, 3], + "data_id": [1, 1, 1, 2, 2, 2, 3, 3, 3] + }) + + + def test_basic_call(self): + result = get_pareto_front_2d( + self.df, + return_as_pandas=False + ) + self.assertIn("final_nondominated", result.columns) + + expected = { + ("A", 0.1, 0.2): True, + ("B", 0.2, 0.8): True, + ("B", 0.5, 0.2): True, + ("C", 0.3, 0.7): True, + ("C", 0.6, 0.4): True, + ("C", 0.9, 0.1): True, + } + for row in result.iter_rows(named=True): + key = (row["algorithm_name"], row["raw_y"], row["F2"]) + self.assertEqual(row["final_nondominated"], expected[key]) + + + def test_custom_obj_vars(self): + # Test with custom objective variable names + df_custom = self.df.rename({"raw_y": "obj1", "F2": "obj2"}) + result = get_pareto_front_2d( + df_custom, + obj1_var="obj1", + obj2_var="obj2", + return_as_pandas=False + ) + self.assertIn("final_nondominated", result.columns) + # Check that the correct points are marked as non-dominated for each algorithm, point by point + expected = { + ("A", 0.1, 0.2): True, + ("B", 0.2, 0.8): True, + ("B", 0.5, 0.2): True, + ("C", 0.3, 0.7): True, + ("C", 0.6, 0.4): True, + ("C", 0.9, 0.1): True, + } + for row in result.iter_rows(named=True): + key = (row["algorithm_name"], row["obj1"], row["obj2"]) + self.assertEqual(row["final_nondominated"], expected[key]) + + +class TestGetIndicatorOverTimeData(unittest.TestCase): + def setUp(self): + # Minimal DataFrame with two objectives and a single algorithm + # All points belong to algorithm "A" with 10 evaluations + # The points are constructed to simulate a progression towards the Pareto front + self.df = pl.DataFrame({ + "raw_y": [0.9, 0.7, 0.5, 0.3, 0.1], + "F2": [0.8, 0.6, 0.4, 0.2, 0.1], + "algorithm_name": ["A"] * 5, + "evaluations": [1,10,100, 1000, 10000], + "data_id": [1] * 5 + }) + # Create a dict mapping evaluation to (raw_y, F2) point + self.eval_points = dict(zip(self.df["evaluations"], zip(self.df["raw_y"], self.df["F2"]))) + + def test_plot_indicator_over_time_hypervolume(self): + # Use a simple indicator and check output DataFrame + indicator = HyperVolume(reference_point=[1.0, 1.0]) + result = get_indicator_over_time_data( + self.df, + indicator=indicator, + eval_steps=5, + eval_min=1, + eval_max=10_000, + scale_eval_log=True, + obj_vars=["raw_y", "F2"], + ) + # Make a dict of {evaluation: hypervolume} + hv_dict = dict(zip(result["evaluations"], result["HyperVolume"])) + + for eval in [1,10,100,1000,10000]: + point = self.eval_points[eval] + hv = (1.0 - point[0]) * (1.0 - point[1]) # Since we minimize both objectives + self.assertAlmostEqual(hv_dict[eval], hv, places=5) + + def test_plot_indicator_over_time_epsilon_additive(self): + # Use a simple indicator and check output DataFrame + indicator = Epsilon(reference_point=[1.0, 1.0]) + result = get_indicator_over_time_data( + self.df, + indicator=indicator, + eval_steps=5, + eval_min=1, + eval_max=10_000, + scale_eval_log=True, + obj_vars=["raw_y", "F2"], + ) + # Make a dict of {evaluation: hypervolume} + ae = dict(zip(result["evaluations"], result["Epsilon_Additive"])) + for eval in [1,10,100,1000,10000]: + point = self.eval_points[eval] + eps = max(point[0]-1.0, point[1]-1.0) # Since we minimize both objectives + self.assertAlmostEqual(ae[eval], eps, places=5) + + + def test_plot_indicator_over_time_epsilon_multiplicative(self): + # Use a simple indicator and check output DataFrame + indicator = Epsilon(reference_point=[1.0, 1.0], version="multiplicative") + result = get_indicator_over_time_data( + self.df, + indicator=indicator, + eval_steps=5, + eval_min=1, + eval_max=10_000, + scale_eval_log=True, + obj_vars=["raw_y", "F2"], + ) + # Make a dict of {evaluation: hypervolume} + ae = dict(zip(result["evaluations"], result["Epsilon_Mult"])) + for eval in [1,10,100,1000,10000]: + point = self.eval_points[eval] + eps = max(point[0]/1.0, point[1]/1.0) # Since we minimize both objectives + self.assertAlmostEqual(ae[eval], eps, places=5) + + def test_plot_indicator_over_time_igd_plus(self): + # Use a simple indicator and check output DataFrame + indicator = IGDPlus(reference_set=[[0.0, 0.0]]) + result = get_indicator_over_time_data( + self.df, + indicator=indicator, + eval_steps=5, + eval_min=1, + eval_max=10_000, + scale_eval_log=True, + obj_vars=["raw_y", "F2"], + ) + # Make a dict of {evaluation: hypervolume} + ae = dict(zip(result["evaluations"], result["IGD+"])) + for eval in [1,10,100,1000,10000]: + point = self.eval_points[eval] + idg_plus = np.sqrt((point[0]-0.0)**2 + (point[1]-0.0)**2) # Since we minimize both objectives + self.assertAlmostEqual(ae[eval], idg_plus, places=5) + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_metrics/test_ranking.py b/tests/test_metrics/test_ranking.py new file mode 100644 index 0000000..b845722 --- /dev/null +++ b/tests/test_metrics/test_ranking.py @@ -0,0 +1,175 @@ +import unittest +import numpy as np +import polars as pl +import pandas as pd +from iohinspector.metrics import get_tournament_ratings, get_robustrank_over_time, get_robustrank_changes +from iohinspector.indicators import HyperVolume + +class TestGetTournamentRatings(unittest.TestCase): + def setUp(self): + # Create a simple polars DataFrame for testing + self.data = pl.DataFrame({ + "algorithm_name": ["A", "A", "A", "B", "B", "B", "C", "C", "C"], + "function_name": ["f1", "f2", "f3", "f1", "f2", "f3", "f1", "f2", "f3"], + "raw_y": [1.0, 2.0, 1.7, 1.5, 2.8, 2.1, 0.9, 0.5, 1.6] + }) + + def test_basic(self): + result = get_tournament_ratings(self.data) + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("Rating", result.columns) + self.assertIn("Deviation", result.columns) + self.assertIn("algorithm_name", result.columns) + self.assertEqual(len(result), 3) # Three algorithms + # Check that algorithms are ordered by rating: C, A, B + sorted_algos = result.sort_values("Rating", ascending=False)["algorithm_name"].tolist() + self.assertEqual(sorted_algos, ["C", "A", "B"]) + + def test_basic_maximisation(self): + result = get_tournament_ratings(self.data, maximization=True) + self.assertIsInstance(result, pd.DataFrame) + self.assertIn("Rating", result.columns) + self.assertIn("Deviation", result.columns) + self.assertIn("algorithm_name", result.columns) + self.assertEqual(len(result), 3) # Three algorithms + # Check that algorithms are ordered by rating: C, A, B + sorted_algos = result.sort_values("Rating", ascending=False)["algorithm_name"].tolist() + self.assertEqual(sorted_algos, ["B", "A", "C"]) + + + def test_single_function(self): + data = pl.DataFrame({ + "algorithm_name": ["A", "B"], + "function_name": ["f1", "f1"], + "raw_y": [1.0, 2.0] + }) + result = get_tournament_ratings(data, nrounds=25) + self.assertEqual(len(result), 2) + self.assertTrue(set(result["algorithm_name"]) == {"A", "B"}) + + +class TestGetRobustRankOverTime(unittest.TestCase): + def setUp(self): + # Create simple polars DataFrame with different targets and ranks + self.data = pl.DataFrame({ + "algorithm_name": ["A"] * 9 + ["B"] * 9 + ["C"] * 9, + "evaluations": [1, 10, 100] * 9, + "f1": [ + # A: best at eval 1, B: best at eval 10, A: best at eval 100 (for run 1) + 0.8, 1.5, 0.7, # A, run 1 + 1.0, 1.6, 0.9, # A, run 2 + 0.9, 1.4, 0.8, # A, run 3 + + 1.0, 0.7, 1.2, # B, run 1 + 1.2, 0.8, 1.3, # B, run 2 + 1.1, 0.6, 1.1, # B, run 3 + + 1.5, 1.5, 0.1, # C, run 1 + 1.6, 1.6, 0.2, # C, run 2 + 1.4, 1.4, 0.3 # C, run 3 + ], + "f2": [ + 1.0, 2.0, 0.9, # A, run 1 + 1.2, 2.1, 1.1, # A, run 2 + 1.1, 2.2, 1.0, # A, run 3 + + 1.3, 0.8, 1.4, # B, run 1 + 1.5, 0.9, 1.5, # B, run 2 + 1.4, 0.7, 1.3, # B, run 3 + + 2.0, 2.0, 0.1, # C, run 1 + 2.1, 2.1, 0.2, # C, run 2 + 1.9, 1.9, 0.3 # C, run 3 + ], + "f3": [ + 2.0, 3.0, 1.8, # A, run 1 + 2.2, 3.1, 2.0, # A, run 2 + 2.1, 3.2, 1.9, # A, run 3 + + 2.3, 1.2, 2.4, # B, run 1 + 2.5, 1.3, 2.5, # B, run 2 + 2.4, 1.1, 2.3, # B, run 3 + + 3.0, 3.0, 0.1, # C, run 1 + 3.1, 3.1, 0.3, # C, run 2 + 2.9, 2.9, 0.2 # C, run 3 + ], + "data_id": [1]*3 + [2]*3 + [3]*3 + [4]*3 + [5]*3 + [6]*3 + [7]*3 + [8]*3 + [9]*3, + "run_id": [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3, + }) + + def test_basic(self): + evals = [1, 10, 100] + comparison, benchmark = get_robustrank_over_time( + self.data, + obj_vars=["f1","f2", "f3"], + evals=evals, + indicator=HyperVolume(reference_point=[5.0,5.0,5.0]), + ) + + +class TestGetRobustRankChanges(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "algorithm_name": ["A"] * 9 + ["B"] * 9 + ["C"] * 9, + "evaluations": [1, 10, 100] * 9, + "f1": [ + # A: best at eval 1, B: best at eval 10, A: best at eval 100 (for run 1) + 0.8, 1.5, 0.7, # A, run 1 + 1.0, 1.6, 0.9, # A, run 2 + 0.9, 1.4, 0.8, # A, run 3 + + 1.0, 0.7, 1.2, # B, run 1 + 1.2, 0.8, 1.3, # B, run 2 + 1.1, 0.6, 1.1, # B, run 3 + + 1.5, 1.5, 0.1, # C, run 1 + 1.6, 1.6, 0.2, # C, run 2 + 1.4, 1.4, 0.3 # C, run 3 + ], + "f2": [ + 1.0, 2.0, 0.9, # A, run 1 + 1.2, 2.1, 1.1, # A, run 2 + 1.1, 2.2, 1.0, # A, run 3 + + 1.3, 0.8, 1.4, # B, run 1 + 1.5, 0.9, 1.5, # B, run 2 + 1.4, 0.7, 1.3, # B, run 3 + + 2.0, 2.0, 0.1, # C, run 1 + 2.1, 2.1, 0.2, # C, run 2 + 1.9, 1.9, 0.3 # C, run 3 + ], + "f3": [ + 2.0, 3.0, 1.8, # A, run 1 + 2.2, 3.1, 2.0, # A, run 2 + 2.1, 3.2, 1.9, # A, run 3 + + 2.3, 1.2, 2.4, # B, run 1 + 2.5, 1.3, 2.5, # B, run 2 + 2.4, 1.1, 2.3, # B, run 3 + + 3.0, 3.0, 0.1, # C, run 1 + 3.1, 3.1, 0.3, # C, run 2 + 2.9, 2.9, 0.2 # C, run 3 + ], + "data_id": [1]*3 + [2]*3 + [3]*3 + [4]*3 + [5]*3 + [6]*3 + [7]*3 + [8]*3 + [9]*3, + "run_id": [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3, + }) + + def test_basic(self): + evals = [1, 10, 100] + result = get_robustrank_changes( + self.data, + obj_vars=["f1","f2", "f3"], + evals=evals, + indicator=HyperVolume(reference_point=[5.0,5.0,5.0]), + + ) + for eval in evals: + self.assertIn(str(eval), result.keys()) + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_single_run.py b/tests/test_metrics/test_single_run.py new file mode 100644 index 0000000..ef6369f --- /dev/null +++ b/tests/test_metrics/test_single_run.py @@ -0,0 +1,76 @@ +import unittest +import polars as pl +import numpy as np +import matplotlib.pyplot as plt +from iohinspector.metrics.single_run import get_heatmap_single_run_data + + +class TestPlotHeatmapSingleRun(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "data_id": [1]*5, + "evaluations": [1,2,3,4,5], + "x1": np.linspace(-5, 5, 5), + "x2": np.linspace(-5, 5, 5)[::-1], + }) + self.vars = ["x1", "x2"] + self.var_mins = np.array([-5, -5]) + self.var_maxs = np.array([5, 5]) + + def test_basic(self): + dt_plot = get_heatmap_single_run_data( + data=self.data, + vars=self.vars, + eval_var="evaluations", + var_mins=self.var_mins, + var_maxs=self.var_maxs, + ) + self.assertEqual(dt_plot.shape, (2, 5)) + self.assertAlmostEqual(dt_plot.values.min(), 0) + self.assertAlmostEqual(dt_plot.values.max(), 1) + self.assertTrue(np.all((dt_plot.values >= 0) & (dt_plot.values <= 1))) + + def test_asserts_on_multiple_data_ids(self): + data = pl.DataFrame({ + "data_id": [1, 2], + "evaluations": [1, 2], + "x1": [0, 1], + }) + with self.assertRaises(AssertionError): + get_heatmap_single_run_data(data, ["x1"]) + + def test_single_variable(self): + data = pl.DataFrame({ + "data_id": [1]*3, + "evaluations": [1, 2, 3], + "x1": [-5, 0, 5], + }) + dt_plot = get_heatmap_single_run_data( + data=data, + vars=["x1"], + eval_var="evaluations", + var_mins=[-5], + var_maxs=[5], + ) + self.assertEqual(dt_plot.shape, (1, 3)) + np.testing.assert_allclose(dt_plot.values, [[0, 0.5, 1]]) + + def test_non_default_eval_col(self): + data = pl.DataFrame({ + "data_id": [1]*4, + "evals": [1, 2, 3, 4], + "x1": [0, 1, 2, 3], + "x2": [3, 2, 1, 0], + }) + dt_plot = get_heatmap_single_run_data( + data=data, + vars=["x1", "x2"], + eval_var="evals", + var_mins=[0, 0], + var_maxs=[3, 3], + ) + self.assertEqual(dt_plot.shape, (2, 4)) + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_trajectory.py b/tests/test_metrics/test_trajectory.py new file mode 100644 index 0000000..a775ac6 --- /dev/null +++ b/tests/test_metrics/test_trajectory.py @@ -0,0 +1,66 @@ +import unittest +import polars as pl +import numpy as np +from iohinspector.metrics import get_trajectory + +class TestGetTrajectory(unittest.TestCase): + def setUp(self): + # Example data with two algorithms, two data_ids, and three evaluations each + self.data = pl.DataFrame({ + "data_id": [1, 1, 1, 2, 2, 2], + "algorithm_name": ["A", "A", "A", "B", "B", "B"], + "evaluations": [1, 10, 20, 1, 10, 20], + "raw_y": [0.5, 0.4, 0.3, 1.0, 0.9, 0.7] + }) + + def test_basic_trajectory(self): + result = get_trajectory(self.data, return_as_pandas=False) + self.assertIsInstance(result, pl.DataFrame) + # Should have as many rows as input (since all evaluations present) + self.assertEqual(result.shape[0], 40) # 2 algorithms * 20 evaluations + self.assertIn("evaluations", result.columns) + self.assertIn("raw_y", result.columns) + # Check that all evaluation points are present + for algo in self.data["algorithm_name"].unique(): + evals = result.filter(pl.col("algorithm_name") == algo)["evaluations"].to_list() + self.assertEqual(set(evals), set(range(1, 21))) + # Check that raw_y is non-increasing for each algorithm + for algo in self.data["algorithm_name"].unique(): + raw_y_values = result.filter(pl.col("algorithm_name") == algo).sort("evaluations")["raw_y"].to_list() + self.assertTrue(all(x >= y for x, y in zip(raw_y_values, raw_y_values[1:]))) + + def test_traj_length(self): + # Only first two evaluations should be present + result = get_trajectory(self.data, traj_length=1, return_as_pandas=False) + for algo in self.data["algorithm_name"].unique(): + evals = result.filter(pl.col("algorithm_name") == algo)["evaluations"].to_list() + self.assertEqual(set(evals), set(range(1, 3))) + + result = get_trajectory(self.data, traj_length=10, return_as_pandas=False) + for algo in self.data["algorithm_name"].unique(): + evals = result.filter(pl.col("algorithm_name") == algo)["evaluations"].to_list() + self.assertEqual(set(evals), set(range(1, 12))) + + def test_min_fevals(self): + # Start from evaluation 2 + result = get_trajectory(self.data, min_fevals=2, return_as_pandas=False) + for algo in self.data["algorithm_name"].unique(): + evals = result.filter(pl.col("algorithm_name") == algo)["evaluations"].to_list() + self.assertEqual(set(evals), set(range(2, 21))) + + + def test_custom_free_variables(self): + # Use only data_id as free variable + result = get_trajectory(self.data, free_variables=[], return_as_pandas=False) + self.assertIn("data_id", result.columns) + self.assertIn("raw_y", result.columns) + + def test_maximization(self): + result = get_trajectory(self.data, maximization=True, return_as_pandas=False) + + for algo in self.data["algorithm_name"].unique(): + raw_y_values = result.filter(pl.col("algorithm_name") == algo).sort("evaluations")["raw_y"].to_list() + self.assertTrue(all(x <= y for x, y in zip(raw_y_values, raw_y_values[1:]))) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_metrics/test_utils.py b/tests/test_metrics/test_utils.py new file mode 100644 index 0000000..b22f3cc --- /dev/null +++ b/tests/test_metrics/test_utils.py @@ -0,0 +1,236 @@ +import unittest +import numpy as np +from iohinspector.metrics.utils import get_sequence +import polars as pl +from iohinspector.metrics import normalize_objectives, add_normalized_objectives, transform_fval +import warnings + +class TestGetSequence(unittest.TestCase): + """ + Unit tests for the `get_sequence` function, covering various scenarios: + + - Linear and logarithmic sequences with both float and integer outputs. + - Edge cases such as minimum equals maximum, single-length sequences, and negative or reversed ranges. + - Validation of output types, uniqueness when casting to int, and handling of float precision. + - Ensures proper error handling when invalid parameters are provided (e.g., log scale with zero minimum). + - Tests for correct sequence generation with large lengths and duplicate handling when casting to int. + + Each test verifies that the output matches expected values and types using NumPy's testing utilities and standard unittest assertions. + """ + def test_linear_float(self): + seq = get_sequence(0, 10, 5, scale_log=False, cast_to_int=False) + expected = np.array([0., 2.5, 5., 7.5, 10.]) + np.testing.assert_allclose(seq, expected) + self.assertEqual(seq.dtype, float) + + def test_linear_int(self): + seq = get_sequence(0, 10, 5, scale_log=False, cast_to_int=True) + self.assertTrue(np.issubdtype(seq.dtype, np.integer)) + self.assertEqual(seq[0], 0) + self.assertEqual(seq[-1], 10) + self.assertGreaterEqual(len(seq), 3) + + def test_log_float(self): + seq = get_sequence(1, 1000, 4, scale_log=True, cast_to_int=False) + expected = np.array([1., 10., 100., 1000.]) + np.testing.assert_allclose(seq, expected, rtol=1e-6) + + def test_log_int(self): + seq = get_sequence(1, 1000, 4, scale_log=True, cast_to_int=True) + expected = np.array([1, 10, 100, 1000]) + np.testing.assert_array_equal(seq, expected) + + def test_min_equals_max(self): + seq = get_sequence(5, 5, 1, scale_log=False, cast_to_int=False) + np.testing.assert_array_equal(seq, np.array([5.])) + + + def test_len_one(self): + seq = get_sequence(2, 8, 1, scale_log=False, cast_to_int=False) + np.testing.assert_array_equal(seq, np.array([2.])) + + def test_log_min_zero_raises(self): + with self.assertRaises(AssertionError): + get_sequence(0, 10, 5, scale_log=True) + + def test_cast_to_int_uniqueness(self): + seq = get_sequence(0, 1, 100, scale_log=False, cast_to_int=True) + np.testing.assert_array_equal(seq, np.array([0, 1])) + + def test_negative_range(self): + seq = get_sequence(-5, 5, 3, scale_log=False, cast_to_int=False) + expected = np.array([-5., 0., 5.]) + np.testing.assert_allclose(seq, expected) + + def test_large_len(self): + seq = get_sequence(0, 1, 1000, scale_log=False, cast_to_int=False) + self.assertEqual(len(seq), 1000) + self.assertAlmostEqual(seq[0], 0) + self.assertAlmostEqual(seq[-1], 1) + + def test_log_scale_non_integer_len(self): + seq = get_sequence(1, 100, 3, scale_log=True, cast_to_int=False) + expected = np.array([1., 10., 100.]) + np.testing.assert_allclose(seq, expected, rtol=1e-6) + + def test_cast_to_int_with_duplicates(self): + seq = get_sequence(0, 0.9, 10, scale_log=False, cast_to_int=True) + np.testing.assert_array_equal(seq, np.array([0])) + + +class TestNormalizeObjectives(unittest.TestCase): + def setUp(self): + self.df = pl.DataFrame({ + "raw_y": [1.0, 2.0, 3.0, 4.0, 5.0], + "other": [10, 20, 30, 40, 50] + }) + + def test_basic_normalization(self): + normed = normalize_objectives(self.df, obj_vars=["raw_y"]) + self.assertIn("ert", normed.columns) + arr = normed["ert"].to_numpy() + np.testing.assert_allclose(arr, [1, 0.75, 0.5, 0.25, 0]) + + def test_maximization(self): + normed = normalize_objectives(self.df, obj_vars=["raw_y"], maximize=True) + arr = normed["ert"].to_numpy() + np.testing.assert_allclose(arr, [0, 0.25, 0.5, 0.75, 1]) + + def test_bounds(self): + bounds = {"raw_y": (0, 10)} + normed = normalize_objectives(self.df, obj_vars=["raw_y"], bounds=bounds) + arr = normed["ert"].to_numpy() + np.testing.assert_allclose(arr, [0.9, 0.8, 0.7, 0.6, 0.5]) + + def test_log_scale(self): + df = pl.DataFrame({"raw_y": [1, 10, 100, 1000, 10000]}) + normed = normalize_objectives(df, obj_vars=["raw_y"], log_scale=True) + arr = normed["ert"].to_numpy() + np.testing.assert_allclose(arr, [1, 0.75, 0.5, 0.25, 0]) + + def test_log_scale_with_zero_warns(self): + df = pl.DataFrame({"raw_y": [0, 1, 10]}) + with warnings.catch_warnings(record=True) as w: + warnings.simplefilter("always") + normed = normalize_objectives(df, obj_vars=["raw_y"], log_scale=True) + self.assertTrue(any("Lower bound" in str(warn.message) for warn in w)) + arr = normed["ert"].to_numpy() + self.assertTrue(np.all((arr >= 0) & (arr <= 1))) + + def test_multiple_objectives(self): + df = pl.DataFrame({ + "raw_y": [1, 2, 3], + "other": [10, 20, 30] + }) + normed = normalize_objectives(df, obj_vars=["raw_y", "other"]) + arr_raw_y = normed["ert_raw_y"].to_numpy() + np.testing.assert_allclose(arr_raw_y, [1.0, 0.5, 0.0]) + arr_other = normed["ert_other"].to_numpy() + np.testing.assert_allclose(arr_other, [1.0, 0.5, 0.0]) + + + def test_column_prefix(self): + normed = normalize_objectives(self.df, obj_vars=["raw_y"], prefix="normed") + self.assertIn("normed", normed.columns) + + def test_dict_log_and_maximize(self): + df = pl.DataFrame({"a": [1, 10, 100], "b": [3, 2, 1]}) + normed = normalize_objectives( + df, + obj_vars=["a", "b"], + log_scale={"a": True, "b": False}, + maximize={"a": True, "b": False} + ) + arr_raw_y = normed["ert_a"].to_numpy() + np.testing.assert_allclose(arr_raw_y, [0.0, 0.5, 1.0]) + arr_other = normed["ert_b"].to_numpy() + np.testing.assert_allclose(arr_other, [0.0, 0.5, 1.0]) + # a is maximized and log scaled, b is minimized and linear + + def test_add_normalized_objectives_basic(self): + df = pl.DataFrame({ + "raw_y": [1.0, 2.0, 3.0, 4.0, 5.0], + "other": [10, 20, 30, 40, 50] + }) + normed = add_normalized_objectives(df, obj_vars=["raw_y", "other"]) + self.assertIn("obj1", normed.columns) + self.assertIn("obj2", normed.columns) + arr_obj1 = normed["obj1"].to_numpy() + arr_obj2 = normed["obj2"].to_numpy() + np.testing.assert_allclose(arr_obj1, [0, 0.25, 0.5, 0.75, 1]) + np.testing.assert_allclose(arr_obj2, [0, 0.25, 0.5, 0.75, 1]) + + def test_add_normalized_objectives_with_bounds(self): + df = pl.DataFrame({ + "raw_y": [1.0, 2.0, 3.0], + "other": [10, 20, 30] + }) + min_obj = pl.DataFrame({"raw_y": [0.0], "other": [0]}) + max_obj = pl.DataFrame({"raw_y": [10.0], "other": [40]}) + normed = add_normalized_objectives(df, obj_vars=["raw_y", "other"], min_obj=min_obj, max_obj=max_obj) + arr_obj1 = normed["obj1"].to_numpy() + arr_obj2 = normed["obj2"].to_numpy() + np.testing.assert_allclose(arr_obj1, [0.1, 0.2, 0.3]) + np.testing.assert_allclose(arr_obj2, [0.25, 0.5, 0.75]) + + def test_add_normalized_objectives_single_objective(self): + df = pl.DataFrame({"raw_y": [1, 2, 3]}) + normed = add_normalized_objectives(df, obj_vars=["raw_y"]) + self.assertIn("obj", normed.columns) + arr = normed["obj"].to_numpy() + np.testing.assert_allclose(arr, [0, 0.5, 1]) + + def test_add_normalized_objectives_no_min_max(self): + df = pl.DataFrame({"raw_y": [5, 10, 15]}) + normed = add_normalized_objectives(df, obj_vars=["raw_y"]) + arr = normed["obj"].to_numpy() + np.testing.assert_allclose(arr, [0, 0.5, 1]) + + def test_transform_fval_basic(self): + df = pl.DataFrame({"raw_y": [1e-8, 1e-4, 1e-2, 1, 1e8]}) + res = transform_fval(df) + arr = res["eaf"].to_numpy() + # log10(1e-8) = -8, log10(1e8) = 8 + # normalized = (log10(x) - (-8)) / (8 - (-8)) = (log10(x) + 8) / 16 + expected = [np.abs((np.log10(x) - 8) / 16) for x in [1e-8, 1e-4, 1e-2, 1, 1e8]] + np.testing.assert_allclose(arr, expected) + + def test_transform_fval_maximization(self): + df = pl.DataFrame({"raw_y": [1e-8, 1e-4, 1e-2, 1, 1e8]}) + res = transform_fval(df, maximization=True) + arr = res["eaf"].to_numpy() + expected = [(np.log10(x) + 8) / 16 for x in [1e-8, 1e-4, 1e-2, 1, 1e8]] + + np.testing.assert_allclose(arr, expected) + + def test_transform_fval_minimization(self): + df = pl.DataFrame({"raw_y": [1e-8, 1e-4, 1e-2, 1, 1e8]}) + res = transform_fval(df, maximization=False) + arr = res["eaf"].to_numpy() + expected = [1 - ((np.log10(x) + 8) / 16) for x in [1e-8, 1e-4, 1e-2, 1, 1e8]] + np.testing.assert_allclose(arr, expected) + + def test_transform_fval_linear_scale(self): + df = pl.DataFrame({"raw_y": [1e-8, 1e-4, 1e-2, 1, 1e8]}) + res = transform_fval(df, scale_log=False) + arr = res["eaf"].to_numpy() + expected = [1-(x - 1e-8) / (1e8 - 1e-8) for x in [1e-8, 1e-4, 1e-2, 1, 1e8]] + np.testing.assert_allclose(arr, expected) + + def test_transform_fval_custom_bounds(self): + df = pl.DataFrame({"raw_y": [0, 5, 10]}) + res = transform_fval(df, lb=0, ub=10, scale_log=False) + arr = res["eaf"].to_numpy() + # For minimization, 0 maps to 1, 10 maps to 0 + expected = [1 - (x / 10) for x in [0, 5, 10]] + np.testing.assert_allclose(arr, expected) + + def test_transform_fval_varumn_name(self): + df = pl.DataFrame({"score": [1, 10, 100]}) + res = transform_fval(df, lb=1, ub=100, scale_log=True, fval_var="score") + arr = res["eaf"].to_numpy() + expected = [1- (np.log10(x) - np.log10(1)) / (np.log10(100) - np.log10(1)) for x in [1, 10, 100]] + np.testing.assert_allclose(arr, expected) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/__init__.py b/tests/test_plots/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_plots/test_attractor_network.py b/tests/test_plots/test_attractor_network.py new file mode 100644 index 0000000..4069c6d --- /dev/null +++ b/tests/test_plots/test_attractor_network.py @@ -0,0 +1,36 @@ +import unittest +import polars as pl +import numpy as np +import matplotlib +from iohinspector.plots import plot_attractor_network + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + + +class TestPlotAttractorNetwork(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "x1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + "x2": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], + "raw_y": [35, 33, 31, 29, 27, 23, 18, 16, 14, 12, 10, 9, 6], + "evaluations": [1,42, 81,121,161,201,241,281,321,361,401,442,481], + "data_id": [1]*13 + }) + + def test_basic_call_returns_axes_and_data(self): + ax, nodes, edges = plot_attractor_network( + self.data, + coord_vars=["x1", "x2"], + fval_var="raw_y", + eval_var="evaluations", + ) + + self.assertIsNotNone(ax) + self.assertIsNotNone(nodes) + self.assertIsNotNone(edges) + + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_eaf.py b/tests/test_plots/test_eaf.py new file mode 100644 index 0000000..29c27bd --- /dev/null +++ b/tests/test_plots/test_eaf.py @@ -0,0 +1,59 @@ +import unittest +import polars as pl +import numpy as np +import matplotlib +from pathlib import Path +from iohinspector.plots import plot_eaf_single_objective, plot_eaf_pareto, plot_eaf_diffs + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + + +class TestPlotEAFSingleObjective(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "raw_y": [10, 8, 6, 20, 18, 16], + "evaluations": [1, 2, 5, 1, 4, 5], + "data_id": [1, 1, 1, 2, 2, 2] + }) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_eaf_single_objective(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + + +class TestPlotEAFPareto(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "x": [1, 2, 3, 1, 2, 3], + "y": [10, 8, 6, 20, 18, 16], + "data_id": [1, 1, 1, 2, 2, 2] + }) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_eaf_pareto(self.data, obj1_var="x", obj2_var="y") + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + + +class TestPlotEAFDiffs(unittest.TestCase): + def setUp(self): + self.data1 = pl.DataFrame({ + "x": [1, 2, 3], + "y": [10, 8, 6], + "data_id": [1, 1, 1] + }) + self.data2 = pl.DataFrame({ + "x": [1, 2, 3], + "y": [9, 7, 5], + "data_id": [2, 2, 2] + }) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_eaf_diffs(self.data1, self.data2, obj1_var="x", obj2_var="y") + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_ecdf.py b/tests/test_plots/test_ecdf.py new file mode 100644 index 0000000..124ef21 --- /dev/null +++ b/tests/test_plots/test_ecdf.py @@ -0,0 +1,30 @@ +import unittest +import polars as pl +import matplotlib +import os +from iohinspector.plots import plot_ecdf +from iohinspector.manager import DataManager + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + +BASE_DIR = os.path.dirname(__file__) +DATA_DIR = os.path.realpath(os.path.join(BASE_DIR, "..", "test_data")) + + +class TestPlotECDF(unittest.TestCase): + + def setUp(self): + data_folders = [os.path.join(DATA_DIR, x) for x in sorted(os.listdir(DATA_DIR))] + data_dir = data_folders[0] + manager = DataManager() + manager.add_folder(data_dir) + self.data = manager.load(monotonic=True, include_meta_data=True) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_ecdf(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_fixed_budget.py b/tests/test_plots/test_fixed_budget.py new file mode 100644 index 0000000..283b965 --- /dev/null +++ b/tests/test_plots/test_fixed_budget.py @@ -0,0 +1,30 @@ +import unittest +import polars as pl +import matplotlib +import os +from iohinspector.plots import plot_single_function_fixed_budget +from iohinspector.manager import DataManager + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + +BASE_DIR = os.path.dirname(__file__) +DATA_DIR = os.path.realpath(os.path.join(BASE_DIR, "..", "test_data")) + + +class TestPlotSingleFunctionFixedBudget(unittest.TestCase): + + def setUp(self): + data_folders = [os.path.join(DATA_DIR, x) for x in sorted(os.listdir(DATA_DIR))] + data_dir = data_folders[0] + manager = DataManager() + manager.add_folder(data_dir) + self.data = manager.load(monotonic=True, include_meta_data=True) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_single_function_fixed_budget(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_fixed_target.py b/tests/test_plots/test_fixed_target.py new file mode 100644 index 0000000..95c2c6f --- /dev/null +++ b/tests/test_plots/test_fixed_target.py @@ -0,0 +1,30 @@ +import unittest +import polars as pl +import matplotlib +import os +from iohinspector.plots import plot_single_function_fixed_target +from iohinspector.manager import DataManager + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + +BASE_DIR = os.path.dirname(__file__) +DATA_DIR = os.path.realpath(os.path.join(BASE_DIR, "..", "test_data")) + + +class TestPlotSingleFunctionFixedTarget(unittest.TestCase): + + def setUp(self): + data_folders = [os.path.join(DATA_DIR, x) for x in sorted(os.listdir(DATA_DIR))] + data_dir = data_folders[0] + manager = DataManager() + manager.add_folder(data_dir) + self.data = manager.load(monotonic=True, include_meta_data=True) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_single_function_fixed_target(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_multi_objective.py b/tests/test_plots/test_multi_objective.py new file mode 100644 index 0000000..16d9423 --- /dev/null +++ b/tests/test_plots/test_multi_objective.py @@ -0,0 +1,59 @@ +import unittest +import polars as pl +import numpy as np +import matplotlib +from iohinspector.plots import plot_paretofronts_2d, plot_indicator_over_time +import tempfile, os +from iohinspector.indicators import HyperVolume, Epsilon, IGDPlus + + +matplotlib.use("Agg") # Use non-interactive backend for testing +import matplotlib.pyplot as plt + + +class TestPlotParetoFronts2D(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "raw_y": [0.1, 0.5, 0.9, 0.2, 0.5, 0.9, 0.3, 0.6, 0.9], + "F2": [0.2, 0.5, 0.8, 0.8, 0.2, 0.9, 0.7, 0.4, 0.1], + "algorithm_name": ["A", "A", "A", "B", "B", "B", "C", "C", "C"], + "evaluations": [1, 2, 3, 1, 2, 3, 1, 2, 3], + "data_id": [1, 1, 1, 2, 2, 2, 3, 3, 3] + }) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_paretofronts_2d(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + +class TestPlotIndicatorOverTime(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "raw_y": [0.9, 0.7, 0.5, 0.3, 0.1], + "F2": [0.8, 0.6, 0.4, 0.2, 0.1], + "algorithm_name": ["A"] * 5, + "evaluations": [1,10,100, 1000, 10000], + "data_id": [1] * 5 + }) + # Create a dict mapping evaluation to (raw_y, F2) point + self.eval_points = dict(zip(self.data["evaluations"], zip(self.data["raw_y"], self.data["F2"]))) + + def test_basic_call_returns_axes_and_data(self): + # Use a simple indicator and check output DataFrame + indicator = HyperVolume(reference_point=[1.0, 1.0]) + ax, data = plot_indicator_over_time( + self.data, + indicator=indicator, + eval_steps=5, + eval_min=1, + eval_max=10_000, + scale_eval_log=True, + obj_vars=["raw_y", "F2"], + free_var="algorithm_name" + ) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/tests/test_plots/test_ranking.py b/tests/test_plots/test_ranking.py new file mode 100644 index 0000000..f8e0880 --- /dev/null +++ b/tests/test_plots/test_ranking.py @@ -0,0 +1,149 @@ +import unittest +import polars as pl +import matplotlib +from iohinspector.plots import plot_robustrank_over_time,plot_tournament_ranking, plot_robustrank_changes +from iohinspector.indicators import HyperVolume + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + + +class TestPlotTournamentRanking(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "algorithm_name": ["A", "A", "A", "B", "B", "B", "C", "C", "C"], + "function_name": ["f1", "f2", "f3", "f1", "f2", "f3", "f1", "f2", "f3"], + "raw_y": [1.0, 2.0, 1.7, 1.5, 2.8, 2.1, 0.9, 0.5, 1.6] + }) + def test_basic_call_returns_axes_and_data(self): + ax, dt = plot_tournament_ranking(self.data) + self.assertIsNotNone(ax) + self.assertIsNotNone(dt) + +class TestPlotRobustRankOverTime(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "algorithm_name": ["A"] * 9 + ["B"] * 9 + ["C"] * 9, + "evaluations": [1, 10, 100] * 9, + "f1": [ + # A: best at eval 1, B: best at eval 10, A: best at eval 100 (for run 1) + 0.8, 1.5, 0.7, # A, run 1 + 1.0, 1.6, 0.9, # A, run 2 + 0.9, 1.4, 0.8, # A, run 3 + + 1.0, 0.7, 1.2, # B, run 1 + 1.2, 0.8, 1.3, # B, run 2 + 1.1, 0.6, 1.1, # B, run 3 + + 1.5, 1.5, 0.1, # C, run 1 + 1.6, 1.6, 0.2, # C, run 2 + 1.4, 1.4, 0.3 # C, run 3 + ], + "f2": [ + 1.0, 2.0, 0.9, # A, run 1 + 1.2, 2.1, 1.1, # A, run 2 + 1.1, 2.2, 1.0, # A, run 3 + + 1.3, 0.8, 1.4, # B, run 1 + 1.5, 0.9, 1.5, # B, run 2 + 1.4, 0.7, 1.3, # B, run 3 + + 2.0, 2.0, 0.1, # C, run 1 + 2.1, 2.1, 0.2, # C, run 2 + 1.9, 1.9, 0.3 # C, run 3 + ], + "f3": [ + 2.0, 3.0, 1.8, # A, run 1 + 2.2, 3.1, 2.0, # A, run 2 + 2.1, 3.2, 1.9, # A, run 3 + + 2.3, 1.2, 2.4, # B, run 1 + 2.5, 1.3, 2.5, # B, run 2 + 2.4, 1.1, 2.3, # B, run 3 + + 3.0, 3.0, 0.1, # C, run 1 + 3.1, 3.1, 0.3, # C, run 2 + 2.9, 2.9, 0.2 # C, run 3 + ], + "data_id": [1]*3 + [2]*3 + [3]*3 + [4]*3 + [5]*3 + [6]*3 + [7]*3 + [8]*3 + [9]*3, + "run_id": [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3, + "function_id": [1]*9 + [1]*9 + [1]*9 + }) + + def test_basic_call_returns_axes_and_data(self): + evals = [1, 10, 100] + axs, comparison, benchmark = plot_robustrank_over_time( + self.data, + obj_vars=["f1", "f2", "f3"], + evals=evals, + indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]), + ) + self.assertIsNotNone(axs) + self.assertIsNotNone(comparison) + self.assertIsNotNone(benchmark) + + +class TestPlotRobustRankChanges(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "algorithm_name": ["A"] * 9 + ["B"] * 9 + ["C"] * 9, + "evaluations": [1, 10, 100] * 9, + "f1": [ + # A: best at eval 1, B: best at eval 10, A: best at eval 100 (for run 1) + 0.8, 1.5, 0.7, # A, run 1 + 1.0, 1.6, 0.9, # A, run 2 + 0.9, 1.4, 0.8, # A, run 3 + + 1.0, 0.7, 1.2, # B, run 1 + 1.2, 0.8, 1.3, # B, run 2 + 1.1, 0.6, 1.1, # B, run 3 + + 1.5, 1.5, 0.1, # C, run 1 + 1.6, 1.6, 0.2, # C, run 2 + 1.4, 1.4, 0.3 # C, run 3 + ], + "f2": [ + 1.0, 2.0, 0.9, # A, run 1 + 1.2, 2.1, 1.1, # A, run 2 + 1.1, 2.2, 1.0, # A, run 3 + + 1.3, 0.8, 1.4, # B, run 1 + 1.5, 0.9, 1.5, # B, run 2 + 1.4, 0.7, 1.3, # B, run 3 + + 2.0, 2.0, 0.1, # C, run 1 + 2.1, 2.1, 0.2, # C, run 2 + 1.9, 1.9, 0.3 # C, run 3 + ], + "f3": [ + 2.0, 3.0, 1.8, # A, run 1 + 2.2, 3.1, 2.0, # A, run 2 + 2.1, 3.2, 1.9, # A, run 3 + + 2.3, 1.2, 2.4, # B, run 1 + 2.5, 1.3, 2.5, # B, run 2 + 2.4, 1.1, 2.3, # B, run 3 + + 3.0, 3.0, 0.1, # C, run 1 + 3.1, 3.1, 0.3, # C, run 2 + 2.9, 2.9, 0.2 # C, run 3 + ], + "data_id": [1]*3 + [2]*3 + [3]*3 + [4]*3 + [5]*3 + [6]*3 + [7]*3 + [8]*3 + [9]*3, + "run_id": [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3 + [1]*3 + [2]*3 + [3]*3, + "function_id": [1]*9 + [1]*9 + [1]*9 + }) + + def test_basic_call_returns_axes_and_data(self): + evals = [1, 10, 100] + ax, dt = plot_robustrank_changes( + self.data, + obj_vars=["f1","f2", "f3"], + evals=evals, + indicator=HyperVolume(reference_point=[5.0, 5.0, 5.0]), + ) + self.assertIsNotNone(ax) + self.assertIsNotNone(dt) + + +if __name__ == "__main__": + unittest.main() diff --git a/tests/test_plots/test_single_run.py b/tests/test_plots/test_single_run.py new file mode 100644 index 0000000..a76a34f --- /dev/null +++ b/tests/test_plots/test_single_run.py @@ -0,0 +1,36 @@ +import unittest +import polars as pl +import numpy as np +import matplotlib +from iohinspector.plots.single_run import plot_heatmap_single_run + +matplotlib.use("Agg") # Use non-interactive backend for tests +import matplotlib.pyplot as plt + + +class TestPlotHeatmapSingleRun(unittest.TestCase): + def setUp(self): + self.data = pl.DataFrame({ + "data_id": [1]*5, + "evaluations": [1,2,3,4,5], + "x1": np.linspace(-5, 5, 5), + "x2": np.linspace(-5, 5, 5)[::-1], + }) + self.vars = ["x1", "x2"] + self.var_mins = np.array([-5, -5]) + self.var_maxs = np.array([5, 5]) + + def test_basic_call_returns_axes_and_data(self): + ax, data = plot_heatmap_single_run( + data=self.data, + vars=self.vars, + eval_var="evaluations", + var_mins=self.var_mins, + var_maxs=self.var_maxs, + ) + self.assertIsNotNone(ax) + self.assertIsNotNone(data) + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file