From 581c0fdbe4b0cdc646c79572371a7a6a3cb965fa Mon Sep 17 00:00:00 2001 From: Alexander Fengler Date: Thu, 28 Nov 2024 18:27:34 -0500 Subject: [PATCH] Plot improvements (#598) * wip * bring plot_func_model in shape * some stylistic improvement to two choice version * n choices functional now * lots of fixes, adding in tests * drop stray files * fix tests * stray print * fix wrong tuple return * address comments and improve tests a bit * address comments and improve tests a bit * typing insanity * drop Dict import * fix indentation * address remaining comments * more mypy pain --- src/hssm/plotting/__init__.py | 7 +- src/hssm/plotting/model_cartoon.py | 1982 ++++++++++++++ src/hssm/plotting/posterior_predictive.py | 149 +- src/hssm/plotting/quantile_probability.py | 2 +- src/hssm/plotting/utils.py | 63 +- tests/conftest.py | 65 + tests/fixtures/data_race.csv | 3001 +++++++++++++++++++++ tests/fixtures/idata_cavanagh_cartoon.nc | Bin 0 -> 266820 bytes tests/fixtures/idata_race_cartoon.nc | Bin 0 -> 312973 bytes tests/fixtures/test_idata_race.nc | Bin 0 -> 319868 bytes tests/test_plotting.py | 20 +- tests/test_plotting_cartoon.py | 144 + 12 files changed, 5383 insertions(+), 50 deletions(-) create mode 100755 src/hssm/plotting/model_cartoon.py create mode 100644 tests/fixtures/data_race.csv create mode 100644 tests/fixtures/idata_cavanagh_cartoon.nc create mode 100644 tests/fixtures/idata_race_cartoon.nc create mode 100644 tests/fixtures/test_idata_race.nc create mode 100644 tests/test_plotting_cartoon.py diff --git a/src/hssm/plotting/__init__.py b/src/hssm/plotting/__init__.py index 9fbdd23f..f27a60c4 100644 --- a/src/hssm/plotting/__init__.py +++ b/src/hssm/plotting/__init__.py @@ -1,6 +1,11 @@ """Plotting functionalities for HSSM.""" +from .model_cartoon import plot_model_cartoon from .posterior_predictive import plot_posterior_predictive from .quantile_probability import plot_quantile_probability -__all__ = ["plot_posterior_predictive", "plot_quantile_probability"] +__all__ = [ + "plot_posterior_predictive", + "plot_quantile_probability", + "plot_model_cartoon", +] diff --git a/src/hssm/plotting/model_cartoon.py b/src/hssm/plotting/model_cartoon.py new file mode 100755 index 00000000..ba8bf648 --- /dev/null +++ b/src/hssm/plotting/model_cartoon.py @@ -0,0 +1,1982 @@ +"""Plotting functionalities for HSSM.""" + +import logging +from copy import deepcopy +from itertools import product +from typing import Any, Iterable, Literal, Protocol, cast + +import arviz as az +import matplotlib as mpl +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import pymc as pm +import seaborn as sns +import xarray as xr +from matplotlib.axes import Axes +from matplotlib.lines import Line2D +from ssms.basic_simulators.simulator import simulator + +# Original model cartoon plot from gui +from ..defaults import SupportedModels, default_model_config +from .posterior_predictive import _process_lines +from .utils import ( + _check_groups_and_groups_order, + _get_plotting_df, + _get_title, + # _hdi_to_interval, # TODO: We eventually want to add HDI plotting back in here + _subset_df, + _use_traces_or_sample, +) + +_logger = logging.getLogger("hssm") + +TRAJ_COLOR_DEFAULT_DICT = { + -1: "black", + 0: "black", + 1: "green", + 2: "blue", + 3: "red", + 4: "orange", + 5: "purple", + 6: "brown", +} + + +class PlotFunctionProtocol(Protocol): + """Protocol for plot functions.""" + + def __call__(self, *args, **kwargs) -> Axes: + """Plot function.""" + ... + + +def _plot_model_cartoon_1D( + data: pd.DataFrame, + model_name: str, + plot_data: bool = True, + plot_mean: bool = True, + plot_samples: bool = False, + colors: str | list[str] | None = None, + title: str | None = "Model Plots", + xlabel: str | None = "Response Time", + ylabel: str | None = "", + **kwargs, +) -> mpl.axes.Axes: + """Plot the posterior predictive distribution against the observed data. + + Check the `plot_model_cartoon` function below for docstring. + + Returns + ------- + mpl.Axes + A matplotlib Axes object containing the plot. + """ + if not (plot_mean or plot_samples): + raise ValueError("At least one of plot_mean or plot_samples must be True") + + if "color" in kwargs: + del kwargs["color"] + colors = colors or ["#ec205b", "#338fb8"] + + if plot_data and isinstance(colors, str): + raise ValueError("When `plot_data=True`, `colors` must be a list or dict.") + + if "ax" in kwargs: + ax = kwargs.pop("ax") + else: + ax = plt.gca() + + config_tmp = default_model_config[cast(SupportedModels, model_name)] + model_params = config_tmp["list_params"] + + n_choices = len(config_tmp["choices"]) + + is_pp_mean = data.source == "posterior_predictive_mean" + is_pp_samples = data.source == "posterior_predictive" + is_observed = data.observed == "observed" + is_predicted = data.observed == "predicted" + + data_posterior_predictive_mean = data.loc[ + is_pp_mean & is_predicted, + :, + ] + + if plot_mean: + data_observed = data.loc[is_pp_mean & is_observed, :] + + if plot_samples and (not plot_mean): + data_observed = data.loc[is_pp_samples & is_observed, :] + + data_posterior_predictive = data.loc[is_pp_samples & is_predicted, :] + + plot_function: PlotFunctionProtocol + if n_choices == 2: + plot_function = plot_func_model + elif n_choices > 2: + plot_function = plot_func_model_n + else: + raise NotImplementedError( + "The model plot works only for >2 choice models at the moment" + ) + + ax = plot_function( + model_name=model_name, + axis=ax, + theta_mean=( + data_posterior_predictive_mean.reset_index()[model_params] + if plot_mean + else None + ), + theta_posterior=( + data_posterior_predictive[model_params] if plot_samples else None + ), + data=(data_observed.reset_index() if plot_data else None), + **kwargs, + ) + + if title: + ax.set_title(title) + if xlabel: + ax.set_xlabel(xlabel) + if ylabel: + ax.set_ylabel(ylabel) + + return ax + + +def _plot_model_cartoon_2D( + data: pd.DataFrame, + model_name: str, + plot_data: bool = True, + plot_mean: bool = True, + plot_samples: bool = False, + row: str | None = None, + col: str | None = None, + col_wrap: int | None = None, + bins: int | np.ndarray | str | None = 100, + step: bool = False, + interval: tuple[float, float] | None = None, + colors: str | list[str] | None = None, + linestyles: str | list[str] = "-", + linewidths: float | list[float] = 1.25, + title: str | None = "Model Cartoon", + xlabel: str | None = "Response Time", + ylabel: str | None = "", + grid_kwargs: dict | None = None, + **kwargs, +) -> sns.FacetGrid: + """Plot the posterior predictive distribution against the observed data. + + Check the function below for docstring. + + Returns + ------- + sns.FacetGrid + A seaborn FacetGrid object containing the plot. + """ + g = sns.FacetGrid( + data=data, + col=col, + row=row, + col_wrap=col_wrap, + legend_out=True, + **(grid_kwargs or {}), + ) + + g.map_dataframe( + _plot_model_cartoon_1D, + model_name=model_name, + plot_data=plot_data, + plot_mean=plot_mean, + plot_samples=plot_samples, + bins=bins, + step=step, + interval=interval, + colors=colors, + linestyles=linestyles, + linewidths=linewidths, + title=None, + xlabel=xlabel, + ylabel=ylabel, + **kwargs, + ) + + if plot_data: + custom_lines = [ + Line2D([0], [0], color="blue", linestyle="-", lw=1.5), + Line2D([0], [0], color="black", linestyle="-", lw=1.5), + ] + + custom_labels = ["observed", "mean_pp"] + + g.add_legend( + dict(zip(custom_labels, custom_lines)), + title="", + label_order=["observed", "mean_pp"], + ) + + if title: + g.figure.subplots_adjust(top=0.9) + g.figure.suptitle(title) + + g.set_xlabels(xlabel) + g.set_ylabels(ylabel) + + return g + + +def compute_merge_necessary_deterministics(model, idata, inplace=True): + """Compute the necessary deterministic variables for the model.""" + # Get the list of deterministic variables + necessary_params = default_model_config[model.model_name]["list_params"] + deterministics_list = [] + idata_posterior_keys = list(idata["posterior"].keys()) + # Compute the deterministic variables + for param in necessary_params: + if param not in idata_posterior_keys: + if param in [ + deterministic.name for deterministic in model.pymc_model.deterministics + ]: + deterministics_list.append( + pm.compute_deterministics( + idata.posterior, model=model.pymc_model, var_names=[param] + ) + ) + + deterministics_idata = xr.merge(deterministics_list) + setattr(idata, "posterior", xr.merge([idata.posterior, deterministics_idata])) + return idata + + +def attach_trialwise_params_to_df(model, df, idata): + """Attach the trial-wise parameters to the dataframe.""" + necessary_params = default_model_config[model.model_name]["list_params"] + df[necessary_params] = 0.0 + + for chain_tmp, draw_tmp in {(x[0], x[1]) for x in list(df.index) if x[0] != -1}: + for param in necessary_params: + df.loc[(chain_tmp, draw_tmp, slice(None)), param] = ( + idata["posterior"].sel(chain=chain_tmp, draw=draw_tmp)[param].values + ) + return df + + +def _make_idata_mean_posterior(idata: az.InferenceData) -> az.InferenceData: + """Create a new InferenceData object containing only the posterior mean. + + Takes an InferenceData object and computes the mean across chains and draws, + then restructures it to have a single chain and draw. Removes posterior + predictive samples if present. + + Parameters + ---------- + idata : arviz.InferenceData + The InferenceData object to process + + Returns + ------- + arviz.InferenceData + A new InferenceData object containing only the posterior mean + """ + setattr(idata, "posterior", idata["posterior"].mean(dim=["chain", "draw"])) + setattr(idata, "posterior", idata["posterior"].assign_coords(chain=[0], draw=[0])) + for data_var in list(idata["posterior"].data_vars): + idata["posterior"][data_var] = idata["posterior"][data_var].expand_dims( + dim=["chain", "draw"], axis=[0, 1] + ) + + if "posterior_predictive" in idata: + del idata["posterior_predictive"] + return idata + + +def plot_model_cartoon( + model, + idata: az.InferenceData | None = None, + data: pd.DataFrame | None = None, + plot_data: bool = True, + n_samples: int | float | None = 20, + row: str | None = None, + col: str | None = None, + col_wrap: int | None = None, + groups: str | Iterable[str] | None = None, + groups_order: Iterable[str] | dict[str, Iterable[str]] | None = None, + bins: int | np.ndarray | str | None = 50, + step: bool = False, + plot_pp_mean: bool = True, + plot_pp_samples: bool = False, + colors: str | list[str] | None = None, + linestyles: str | list[str] | tuple[str] | dict[str, str] = "-", + linewidths: float | list[float] | tuple[float] | dict[str, float] = 1.25, + title: str | None = "Posterior Predictive Distribution", + xlabel: str | None = "Response Time", + ylabel: str | None = "", + grid_kwargs: dict | None = None, + **kwargs, +) -> Axes | sns.FacetGrid | list[sns.FacetGrid]: + """Plot the posterior predictive distribution against the observed data. + + Parameters + ---------- + model : hssm.HSSM + A fitted HSSM model. + idata : optional + The InferenceData object with posterior samples. If not provided, will use the + traces object stored inside the model. If posterior predictive samples are not + present in this object, will generate posterior predictive samples using the + this InferenceData object and the original data. + data : optional + The observed data. + + - If `data` is provided and the idata object does not contain a + `"posterior_predictive"` group, will generate posterior predictive samples using + covariate provided in this object. If the group does exist, it is assumed that + the posterior predictive samples are generated with the covariates provided in + this DataFrame. + - If `data` is not provided (i.e., `data=None`), the behavior depends on whether + "plot_data" is true or not. If `plot_data=True`, the plotting function will use + the data stored in the `model` object and proceed as the case above. If + `plot_data=False`, if posterior predictive samples are not present in the + `idata` object, the plotting function will generate posterior predictive samples + using the data stored in the `model` object. If posterior predictive samples + exist in the `idata` object, these samples will be used for plotting, but a + ValueError will be thrown if any of `col` or `row` is not None. + plot_data : optional + Whether to plot the observed data, by default True. + n_samples : optional + When idata is provided, the number or proportion of posterior predictive samples + randomly drawn to be used from each chain for plotting. When idata is not + provided, the number or proportion of posterior samples to be used to generate + posterior predictive samples. The number or proportion are defined as follows: + + - When an integer >= 1, the number of samples to be extracted from the draw + dimension. + - When a float between 0 and 1, the proportion of samples to be extracted from + the draw dimension. + - When None, all samples are extracted. + row : optional + Variables that define subsets of the data, which will be drawn on the row + dimension of the facets in the grid. When both row and col are None, one single + plot will be produced, by default None. + col : optional + Variables that define subsets of the data, which will be drawn on the column + dimension of the facets in the grid. When both row and col are None, one single + plot will be produced, by default None. + col_wrap : optional + “Wrap” the column variable at this width, so that the column facets span + multiple rows. Incompatible with a row facet., by default None. + groups : optional + Additional dimensions along which to plot different groups. This is useful when + there are 3 or more dimensions of covariates to plot against, by default None. + groups_order : optional + The order to plot the groups, by default None, in which case the order is the + order in which the groups appear in the data. Only when `groups` is a string, + this can be an iterable of strings. Otherwise, this is a dictionary mapping the + dimension name to the order of the groups in that dimension. + bins : optional + Specification of hist bins, by default 100. There are three options: + - A string describing the binning strategy (passed to `np.histogram_bin_edges`). + - A list-like defining the bin edges. + - An integer defining the number of bins to be used. + step : optional + Whether to plot the distributions as a step function or a smooth density plot, + by default False. + colors : optional + Colors to use for the different levels of the hue variable. When a `str`, the + color of posterior predictives, in which case an error will be thrown if + `plot_data` is `True`. When a length-2 iterable, indicates the colors in the + order of posterior predictives and observed data. The values must be + interpretable by matplotlib. When None, use default color palette, by default + None. + linestyles : optional + Linestyles to use for the different levels of the hue variable. When a `str`, + the linestyle of both distributions. When a length-2 iterable, indicates the + linestyles in the order of posterior predictives and observed data. The values + must be interpretable by matplotlib. When None, use solid lines, by default "-". + When dictionary, the keys must be 'predicted' and/or 'observed', and the values + must be interpretable by matplotlib. + linewidths : optional + Linewidths to use for the different levels of the hue variable. When a `float`, + the linewidth of both distributions. When a length-2 iterable, indicates the + linewidths in the order of posterior predictives and observed data, by default + 1.25. + title : optional + The title of the plot, by default "Posterior Predictive Distribution". Ignored + when `groups` is provided. + xlabel : optional + The label for the x-axis, by default "Response Time". + ylabel : optional + The label for the y-axis, by default "Density". + grid_kwargs : optional + Additional keyword arguments are passed to the [`sns.FacetGrid` constructor] + (https://seaborn.pydata.org/generated/seaborn.FacetGrid.html#seaborn.FacetGrid.__init__) + when any of row or col is provided. When producing a single plot, these + arguments are ignored. + kwargs : optional + Additional keyword arguments passed to ax.plot() functions. + + Returns + ------- + mpl.axes.Axes | sns.FacetGrid + The matplotlib `axis` or seaborn `FacetGrid` object containing the plot. + """ + if not (plot_pp_mean or plot_pp_samples): + raise ValueError("At least one of plot_pp_mean or plot_pp_samples must be True") + + # Process linestyles + linestyles_ = _process_lines(linestyles, mode="linestyles") + # Process linewidths + linewidths_ = _process_lines(linewidths, mode="linewidths") + + groups, groups_order = _check_groups_and_groups_order( + groups, groups_order, row, col + ) + + extra_dims = [e for e in [row, col] if e is not None] or None + if extra_dims is not None and groups is not None: + extra_dims += list(groups) + + if data is None: + if ( + (not extra_dims) + and (not plot_data) + and (idata is not None) + and ("posterior_predictive" in idata) + ): + # Allows data to be None only when plot_data=False and no extra_dims + # and posterior predictive samples are available + data = None + else: + data = model.data + + # Mean version of plot + plotting_df_mean = None + if plot_pp_mean: + idata_mean = _make_idata_mean_posterior( + deepcopy(model.traces if idata is None else idata) + ) + idata_mean, _ = _use_traces_or_sample(model, data, idata_mean, n_samples=None) + + # Get the plotting dataframe by chain and sample + plotting_df_mean = _get_plotting_df( + idata_mean, + data, + extra_dims=extra_dims, + n_samples=None, + response_str=model.response_str, + ) + + # Get plotting dataframe for posterior mean + # df by chain and sample + idata_mean = compute_merge_necessary_deterministics(model, idata_mean) + plotting_df_mean = attach_trialwise_params_to_df( + model, plotting_df_mean, idata_mean + ) + + # Flip the rt values if necessary + if np.any(plotting_df_mean["response"] == 0) and model.n_choices == 2: + plotting_df_mean["response"] = np.where( + plotting_df_mean["response"] == 0, -1, 1 + ) + if model.n_choices == 2: + plotting_df_mean["rt"] = ( + plotting_df_mean["rt"] * plotting_df_mean["response"] + ) + + plotting_df_mean["source"] = "posterior_predictive_mean" + + if plot_pp_samples: + idata, sampled = _use_traces_or_sample(model, data, idata, n_samples=n_samples) + + # Get the plotting dataframe by chain and sample + plotting_df = _get_plotting_df( + idata, + data, + extra_dims=extra_dims, + n_samples=None if sampled else n_samples, + response_str=model.response_str, + ) + + # Get plotting dataframe for posterior mean + # df by chain and sample + idata = compute_merge_necessary_deterministics(model, idata) + plotting_df = attach_trialwise_params_to_df(model, plotting_df, idata) + + # Flip the rt values if necessary + if np.any(plotting_df["response"] == 0) and model.n_choices == 2: + plotting_df["response"] = np.where(plotting_df["response"] == 0, -1, 1) + if model.n_choices == 2: + plotting_df["rt"] = plotting_df["rt"] * plotting_df["response"] + + plotting_df["source"] = "posterior_predictive" + else: + plotting_df = None + + if (plotting_df is not None) and (plotting_df_mean is not None): + plotting_df = pd.concat([plotting_df, plotting_df_mean]) + elif plotting_df_mean is not None: + plotting_df = plotting_df_mean + + # Then, plot the posterior predictive distribution against the observed data + # Determine whether we are producing a single plot or a grid of plots + + if not extra_dims: + ax = _plot_model_cartoon_1D( + data=plotting_df, + model_name=model.model_name, + plot_data=plot_data, + plot_mean=plot_pp_mean, + plot_samples=plot_pp_samples, + bins=bins, + step=step, + # interval=interval, + colors=colors, + linestyles=linestyles_, + linewidths=linewidths_, + title=title, + xlabel=xlabel, + ylabel=ylabel, + **kwargs, + ) + + custom_lines = [ + Line2D([0], [0], color="blue", linestyle="-", lw=1.5), + Line2D([0], [0], color="black", linestyle="-", lw=1.5), + ] + + custom_labels = ["observed", "mean_pp"] + ax.legend(custom_lines, custom_labels, title="", loc="upper right") + return ax + + # The multiple dimensions case + # If group is not provided, we are producing a grid of plots + if groups is None: + g = _plot_model_cartoon_2D( + data=plotting_df, + model_name=model.model_name, + plot_data=plot_data, + plot_mean=plot_pp_mean, + plot_samples=plot_pp_samples, + row=row, + col=col, + col_wrap=col_wrap, + bins=bins, + step=step, + # interval=interval, + colors=colors, + linestyles=linestyles_, + linewidths=linewidths_, + title=title, + xlabel=xlabel, + ylabel=ylabel, + grid_kwargs=grid_kwargs, + **kwargs, + ) + return g + + # The group dimension case + plots = [] + if not isinstance(groups_order, dict): + raise ValueError("groups_order must be a dictionary") + + orders = product( + *[groups_order.get(g, plotting_df[g].unique().tolist()) for g in groups] + ) + + for order in orders: + df = _subset_df(plotting_df, groups, order) + title = _get_title(groups, order) + if len(df) == 0: + _logger.warning( + "No posterior predictive samples found for the group %s." + + "Skipping this group.", + title, + ) + continue + g = _plot_model_cartoon_2D( + data=df, + model_name=model.model_name, + plot_data=plot_data, + plot_mean=plot_pp_mean, + plot_samples=plot_pp_samples, + row=row, + col=col, + col_wrap=col_wrap, + bins=bins, + step=step, + colors=colors, + linestyles=linestyles_, + linewidths=linewidths_, + title=title, + xlabel=xlabel, + ylabel=ylabel, + grid_kwargs=grid_kwargs, + **kwargs, + ) + + plots.append(g) + + return plots + + +# Original model cartoon plot from gui +def plot_func_model( + model_name: str, + axis: Axes, + theta_mean: pd.DataFrame | None = None, + theta_posterior: pd.DataFrame | None = None, + data: pd.DataFrame | None = None, + n_samples=10, + bin_size: float = 0.05, + n_trajectories: int = 0, + delta_t_model: float = 0.01, + random_state: int | None = None, + keep_slope: bool = True, + keep_boundary: bool = True, + keep_ndt: bool = True, + keep_starting_point: bool = True, + markersize_starting_point: float | int = 50, + markertype_starting_point: str = ">", + markershift_starting_point: float | int = 0, + linewidth_histogram: float | int = 1.5, + linewidth_model: float | int = 1.5, + color_data: str = "blue", + color_pp_mean: str = "black", + color_pp: str = "black", + alpha_pp: float = 0.05, + alpha_trajectories: float = 0.5, + **kwargs, +): + """Plot model cartoon with posterior predictive samples. + + Parameters + ---------- + model_name : str + Name of the model to plot. + axis : matplotlib.axes.Axes + Axis to plot into. + theta_mean : pd.DataFrame, optional + DataFrame containing posterior mean parameter values. + theta_posterior : pd.DataFrame, optional + DataFrame containing posterior samples of parameters. + data : pd.DataFrame, optional + DataFrame containing observed data to overlay. + n_samples : int, optional + Number of posterior samples to use. Defaults to 10. + bin_size : float, optional + Size of bins used for histograms. Defaults to 0.05. + n_trajectories : int, optional + Number of trajectories to plot. Defaults to 0. + delta_t_model : float, optional + Time step for model simulation. Defaults to 0.01. + random_state : int, optional + Random seed for reproducibility. + keep_slope : bool, optional + Whether to plot drift slopes. Defaults to True. + keep_boundary : bool, optional + Whether to plot decision boundaries. Defaults to True. + keep_ndt : bool, optional + Whether to plot non-decision time. Defaults to True. + keep_starting_point : bool, optional + Whether to plot starting point. Defaults to True. + markersize_starting_point : float or int, optional + Size of starting point marker. Defaults to 50. + markertype_starting_point : int, optional + Type of starting point marker. Defaults to 0. + markershift_starting_point : float or int, optional + Shift of starting point marker. Defaults to 0. + linewidth_histogram : float or int, optional + Width of histogram lines. Defaults to 0.5. + linewidth_model : float or int, optional + Width of model lines. Defaults to 0.5. + color_data : str, optional + Color for data histogram. Defaults to "blue". + color_pp_mean : str, optional + Color for posterior mean. Defaults to "black". + color_pp : str, optional + Color for posterior samples. Defaults to "black". + alpha_pp : float, optional + Transparency of posterior samples. Defaults to 0.05. + alpha_trajectories : float, optional + Transparency of trajectories. Defaults to 0.5. + **kwargs + Additional arguments passed to plotting functions. + + Returns + ------- + matplotlib.axes.Axes + The axis with the model cartoon plot. + """ + ylim_low, ylim_high = kwargs.get("ylims", (-3, 3)) + xlim_low, xlim_high = kwargs.get("xlims", (-0.05, 5)) + + # Extract some parameters from kwargs + bins = list(np.arange(xlim_low, xlim_high, bin_size)) + + # RUN SIMULATIONS + # ------------------------------- + + # Simulator Data from posterior mean + if random_state is not None: + np.random.seed(random_state) + + rand_int = np.random.randint(0, 400000000) + + if theta_mean is not None: + sim_out = simulator( + model=model_name, + theta=theta_mean.values, + n_samples=n_samples, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + ) + + # Simulate model without noise: posterior mean + # (this allows to extract the time-dynamics of the drift e.g.) + sim_out_no_noise = simulator( + model=model_name, + theta=theta_mean.loc[np.random.choice(theta_mean.shape[0], 1), :].values, + n_samples=1, + no_noise=True, + delta_t=delta_t_model, + bin_dim=None, + smooth_unif=False, + ) + + # Simulate model without noise: posterior samples + if theta_posterior is not None: + posterior_pred_no_noise = {} + posterior_pred_sims = {} + for i, (chain, draw) in enumerate( + list(theta_posterior.index.droplevel("obs_n").unique()) + ): + posterior_pred_no_noise[i] = simulator( + model=model_name, + theta=theta_posterior.loc[(chain, draw), :].values, + n_samples=1, + no_noise=True, + delta_t=delta_t_model, + bin_dim=None, + smooth_unif=False, + ) + + # Simulate model: posterior samples + posterior_pred_sims[i] = simulator( + model=model_name, + theta=theta_posterior.loc[(chain, draw), :].values, + n_samples=n_samples, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + ) + + # Simulate trajectories + sim_out_traj = {} + for i in range(n_trajectories): + if theta_mean is not None: + tmp_theta = theta_mean.loc[ + (np.random.choice(theta_mean.shape[0], 1)), : + ].values + elif theta_posterior is not None: + # wrap in max statement here + # because negative value are possible, + # however refer to data instead of posterior samples + random_index = tuple( + [ + np.random.choice(theta_posterior.index.get_level_values(name_)) + for name_ in ("chain", "draw", "obs_n") + ] + ) + tmp_theta = theta_posterior.loc[random_index, :].values + else: + raise ValueError("No theta values provided but n_trajectories is > 0") + + sim_out_traj[i] = simulator( + model=model_name, + theta=tmp_theta, + n_samples=1, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + smooth_unif=False, + ) + + # ADD DATA HISTOGRAMS + hist_bottom_high, hist_bottom_low = calculate_histogram_bounds( + theta_mean, + theta_posterior, + sim_out if (theta_mean is not None) else None, + posterior_pred_no_noise if (theta_posterior is not None) else None, + **kwargs, + ) + + hist_histtype = kwargs.get("hist_histtype", "step") + axis.set_xlim(xlim_low, xlim_high) + axis.set_ylim(ylim_low, ylim_high) + axis_twin_up: Axes = cast(Axes, axis.twinx()) + axis_twin_down: Axes = cast(Axes, axis.twinx()) + axis_twin_up.set_ylim(ylim_low, ylim_high) + axis_twin_up.set_yticks([]) + axis_twin_down.set_ylim(ylim_high, ylim_low) + axis_twin_down.set_yticks([]) + axis_twin_down.set_axis_off() + axis_twin_up.set_axis_off() + + # This ensures zorder across axis elements is correct + # for the sequence in which they are invoked. + axis.set_zorder(1) + axis.set_facecolor("none") + axis_twin_up.set_zorder(0) + axis_twin_down.set_zorder(0) + + if theta_mean is not None: + data_up = np.abs( + sim_out["rts"][(sim_out["rts"] != -999) & (sim_out["choices"] == 1)] + ) + data_down = np.abs( + sim_out["rts"][(sim_out["rts"] != -999) & (sim_out["choices"] != 1)] + ) + + add_histograms_to_twin_axes( + data_up=data_up, + data_down=data_down, + hist_bottom_high=hist_bottom_high, + hist_bottom_low=hist_bottom_low, + color_data=color_pp_mean, + linewidth_histogram=linewidth_histogram, + bins=bins, + axis_twin_up=axis_twin_up, + axis_twin_down=axis_twin_down, + hist_histtype=hist_histtype, + bin_size=bin_size, + zorder=-1, + ) + + if theta_posterior is not None: + # Add histograms for posterior samples: + for k, sim_out_tmp in posterior_pred_sims.items(): + data_up = np.abs( + sim_out_tmp["rts"][ + (sim_out_tmp["rts"] != -999) & (sim_out_tmp["choices"] == 1) + ] + ) + data_down = np.abs( + sim_out_tmp["rts"][ + (sim_out_tmp["rts"] != -999) & (sim_out_tmp["choices"] != 1) + ] + ) + + add_histograms_to_twin_axes( + data_up=data_up, + data_down=data_down, + hist_bottom_high=hist_bottom_high, + hist_bottom_low=hist_bottom_low, + color_data=color_pp, + linewidth_histogram=linewidth_histogram, + bins=bins, + axis_twin_up=axis_twin_up, + axis_twin_down=axis_twin_down, + hist_histtype=hist_histtype, + bin_size=bin_size, + zorder=-k - 1, + ) + + # Add histograms for real data + if data is not None: + data_up = data.query(f"rt != {-999} and response == {1}")["rt"].values + data_down = data.query(f"rt != {-999} and response != {1}")["rt"].values + add_histograms_to_twin_axes( + data_up=data_up, + data_down=data_down, + hist_bottom_high=hist_bottom_high, + hist_bottom_low=hist_bottom_low, + color_data=color_data, + linewidth_histogram=linewidth_histogram, + bins=bins, + axis_twin_up=axis_twin_up, + axis_twin_down=axis_twin_down, + hist_histtype=hist_histtype, + bin_size=bin_size, + ) + + z_cnt = 0 # controlling the order of elements in plot + + if theta_posterior is not None: + # ADD MODEL CARTOONS: + t_s = np.arange( + 0, posterior_pred_no_noise[0]["metadata"]["max_t"], delta_t_model + ) + + # Model cartoon for posterior samples + for j, sim_out_tmp in posterior_pred_no_noise.items(): + _add_model_cartoon_to_ax( + sample=sim_out_tmp, + axis=axis, + keep_slope=keep_slope, + keep_boundary=keep_boundary, + keep_ndt=keep_ndt, + keep_starting_point=keep_starting_point, + markersize_starting_point=markersize_starting_point, + markertype_starting_point=markertype_starting_point, + markershift_starting_point=markershift_starting_point, + delta_t_graph=delta_t_model, + alpha=alpha_pp, + lw_m=linewidth_model, + ylim_low=ylim_low, + ylim_high=ylim_high, + t_s=t_s, + color=color_pp, + zorder_cnt=z_cnt, + ) + + z_cnt += 1 + + if theta_mean is not None: + t_s = np.arange(0, sim_out_no_noise["metadata"]["max_t"], delta_t_model) + # Model cartoon for posterior mean + _add_model_cartoon_to_ax( + sample=sim_out_no_noise, + axis=axis, + keep_slope=keep_slope, + keep_boundary=keep_boundary, + keep_ndt=keep_ndt, + keep_starting_point=keep_starting_point, + markersize_starting_point=markersize_starting_point, + markertype_starting_point=markertype_starting_point, + markershift_starting_point=markershift_starting_point, + delta_t_graph=delta_t_model, + alpha=1, + lw_m=linewidth_model, + ylim_low=ylim_low, + ylim_high=ylim_high, + t_s=t_s, + color=color_pp, + zorder_cnt=z_cnt + 1, + ) + + # Add in trajectories + if n_trajectories > 0: + _add_trajectories( + axis=axis, + sample=sim_out_traj, + t_s=t_s, + delta_t_graph=delta_t_model, + n_trajectories=n_trajectories, + alpha_trajectories=alpha_trajectories, + **kwargs, + ) + return axis + + +def calculate_histogram_bounds( + theta_mean: pd.DataFrame | None, + theta_posterior: pd.DataFrame | None, + sim_out: dict[str, Any] | None, + posterior_pred_no_noise: dict[int, dict[str, Any]] | None, + **kwargs: Any, +) -> tuple[float, float]: + """Calculate the bounds for histograms in model cartoon plots. + + This function determines appropriate histogram bounds based on either + the posterior mean or posterior samples. If neither + is provided, it returns default values. + + Parameters + ---------- + theta_mean : pd.DataFrame | None + DataFrame containing posterior mean parameters. If None, will try to use + theta_posterior instead. + theta_posterior : pd.DataFrame | None + DataFrame containing posterior samples. Only used if theta_mean is None. + sim_out : dict[str, Any] | None + Dictionary containing simulation output for posterior mean. + posterior_pred_no_noise : dict[int, dict[str, Any]] | None + Dictionary containing simulation output for posterior samples without noise. + **kwargs : Any + Additional keyword arguments. Can include: + - hist_bottom_high: float - override for upper histogram bound + - hist_bottom_low: float - override for lower histogram bound + + Returns + ------- + tuple[float, float] + Upper and lower bounds for the histograms (hist_bottom_high, hist_bottom_low). + """ + theta_mean_is_none = theta_mean is None + theta_posterior_is_none = theta_posterior is None + b_high = 3 + b_low = 3 + if all([theta_mean_is_none, theta_posterior_is_none]): + _logger.warning( + 'No "theta_mean" provided. Using default values for histogram' + " location. Likely highly suboptimal choice!" + ) + return b_high, b_low + + if not theta_mean_is_none: + if sim_out is None: + raise ValueError("No sim_out provided but theta_mean is not None") + else: + boundary_data = sim_out["metadata"]["boundary"] + b_high = np.maximum(boundary_data, 0)[0] + b_low = np.minimum(-boundary_data, 0)[0] + elif not theta_posterior_is_none: + if posterior_pred_no_noise is None: + raise ValueError( + "No posterior_pred_no_noise provided but theta_posterior is not None" + ) + else: + all_boundaries = [ + posterior_pred_no_noise[key_]["metadata"]["boundary"] + for key_ in posterior_pred_no_noise + ] + b_high = np.max([np.maximum(boundary, 0)[0] for boundary in all_boundaries]) + b_low = np.min([np.minimum(-boundary, 0)[0] for boundary in all_boundaries]) + hist_bottom_high = kwargs.get("hist_bottom_high", b_high) + hist_bottom_low = kwargs.get("hist_bottom_low", -b_low) + return hist_bottom_high, hist_bottom_low + + +# AF-TODO: Add documentation for this function +def _add_trajectories( + axis: Axes, + sample: dict[int, Any], + t_s: np.ndarray, + delta_t_graph: float = 0.01, + n_trajectories: int = 10, + highlight_trajectory_rt_choice: bool = True, + markersize_trajectory_rt_choice: float | int = 50, + markertype_trajectory_rt_choice: str = "*", + markercolor_trajectory_rt_choice: str | list[str] | dict[str, str] = "red", + linewidth_trajectories: float | int = 1, + alpha_trajectories: float | int = 0.5, + color_trajectories: str | list[str] | dict[str, str] = "black", + **kwargs, +): + """Add simulated decision trajectories to a given matplotlib axis. + + Parameters + ---------- + axis : matplotlib.axes.Axes + The axis to add the trajectories to. + sample : dict + Dictionary containing simulation data including trajectories and metadata. + t_s : numpy.ndarray + Array of timepoints for plotting. + delta_t_graph : float, optional + Time step size for plotting, by default 0.01. + n_trajectories : int, optional + Number of trajectories to plot, by default 10. + highlight_trajectory_rt_choice : bool, optional + Whether to highlight the response time and choice with a marker, by default + True. + markersize_trajectory_rt_choice : float or int, optional + Size of marker for response time/choice, by default 50. + markertype_trajectory_rt_choice : str, optional + Marker style for response time/choice, by default "*". + markercolor_trajectory_rt_choice : str, int, list or dict, optional + Color(s) for response time/choice markers. Can be a single color, + list of colors, or dict mapping choices to colors. By default "red". + linewidth_trajectories : float or int, optional + Line width for trajectories, by default 1. + alpha_trajectories : float or int, optional + Opacity of trajectories, by default 0.5. + color_trajectories : str, list or dict, optional + Color(s) for trajectories. Can be a single color, list of colors, + or dict mapping choices to colors. By default "black". + **kwargs + Additional keyword arguments passed to plotting functions. + """ + # Check markercolor type + if isinstance(markercolor_trajectory_rt_choice, str): + markercolor_trajectory_rt_choice_dict = { + value_: markercolor_trajectory_rt_choice + for value_ in sample[0]["metadata"]["possible_choices"] + } + elif isinstance(markercolor_trajectory_rt_choice, list): + markercolor_trajectory_rt_choice_dict = { + value_: markercolor_trajectory_rt_choice[cnt] + for cnt, value_ in enumerate(sample[0]["metadata"]["possible_choices"]) + } + elif isinstance(markercolor_trajectory_rt_choice, dict): + markercolor_trajectory_rt_choice_dict = markercolor_trajectory_rt_choice + else: + raise ValueError( + "The `markercolor_trajectory_rt_choice`" + " argument must be a string, list, or dict." + ) + + # Check trajectory color type + if isinstance(color_trajectories, str): + color_trajectories_dict = {} + for value_ in sample[0]["metadata"]["possible_choices"]: + color_trajectories_dict[value_] = color_trajectories + elif isinstance(color_trajectories, list): + cnt = 0 + for value_ in sample[0]["metadata"]["possible_choices"]: + color_trajectories_dict[value_] = color_trajectories[cnt] + cnt += 1 + elif isinstance(color_trajectories, dict): + color_trajectories_dict = color_trajectories + else: + raise ValueError( + "The `color_trajectories` argument must be a string, list, or dict." + ) + + # Make bounds + (b_high, b_low) = ( + np.maximum(sample[0]["metadata"]["boundary"], 0), + np.minimum((-1) * sample[0]["metadata"]["boundary"], 0), + ) + + b_h_init = b_high[0] + b_l_init = b_low[0] + n_roll = int((sample[0]["metadata"]["t"][0] / delta_t_graph) + 1) + b_high = np.roll(b_high, n_roll) + b_high[:n_roll] = b_h_init + b_low = np.roll(b_low, n_roll) + b_low[:n_roll] = b_l_init + + # Trajectories + for i in range(n_trajectories): + metadata = sample[i]["metadata"] + tmp_traj = metadata["trajectory"] + tmp_traj_choice = float(sample[i]["choices"].flatten()) + maxid = np.minimum(np.argmax(np.where(tmp_traj > -999)), t_s.shape[0]) + + # Identify boundary value at timepoint of crossing + b_tmp = b_high[maxid + n_roll] if tmp_traj_choice > 0 else b_low[maxid + n_roll] + + axis.plot( + t_s[:maxid] + metadata["t"][0], # sample.t.values[0], + tmp_traj[:maxid], + color=color_trajectories_dict[tmp_traj_choice], + alpha=alpha_trajectories, + linewidth=linewidth_trajectories, + zorder=2000 + i, + ) + if highlight_trajectory_rt_choice: + axis.scatter( + t_s[maxid] + metadata["t"][0], + b_tmp, + markersize_trajectory_rt_choice, + color=markercolor_trajectory_rt_choice_dict[tmp_traj_choice], + alpha=1, + marker=markertype_trajectory_rt_choice, + zorder=2000 + i, + ) + + +def add_histograms_to_twin_axes( + data_up: np.ndarray, + data_down: np.ndarray, + hist_bottom_high: float, + hist_bottom_low: float, + color_data: str, + linewidth_histogram: float, + bins: list[float], + axis_twin_up: Axes, + axis_twin_down: Axes, + hist_histtype: Literal["bar", "barstacked", "step", "stepfilled"], + bin_size: float, + zorder: int = -1, +): + """Add histograms to a given matplotlib axis.""" + # Compute weights + weights_up_data = np.tile( + (1 / bin_size) / (data_up.shape[0] + data_down.shape[0]), + reps=data_up.shape[0], + ) + weights_down_data = np.tile( + (1 / bin_size) / (data_up.shape[0] + data_down.shape[0]), + reps=data_down.shape[0], + ) + + # Add histograms for simulated data + axis_twin_up.hist( + np.abs(data_up), + bins=bins, + weights=weights_up_data, + histtype=hist_histtype, + bottom=hist_bottom_high, + alpha=1, + color=color_data, + edgecolor=color_data, + linewidth=linewidth_histogram, + zorder=zorder, + ) + + axis_twin_down.hist( + np.abs(data_down), + bins=bins, + weights=weights_down_data, + histtype=hist_histtype, + bottom=hist_bottom_low, + alpha=1, + color=color_data, + edgecolor=color_data, + linewidth=linewidth_histogram, + zorder=zorder, + ) + + +def _add_model_cartoon_to_ax( + sample: dict, + axis: Axes, + t_s: np.ndarray, + ylim_low: float, + ylim_high: float, + keep_slope: bool = True, + keep_boundary: bool = True, + keep_ndt: bool = True, + keep_starting_point: bool = True, + markersize_starting_point: float | int = 80, + markertype_starting_point: str = ">", + markershift_starting_point: float | int = -0.05, + delta_t_graph: float | None = None, + alpha: float | None = None, + lw_m: float | None = None, + tmp_label: str | None = None, + zorder_cnt: int = 1, + color: str = "black", +): + """Add a model cartoon visualization to a matplotlib axis. + + Parameters + ---------- + sample : dict + Dictionary containing model metadata including boundary, + trajectory, time points, etc. + axis : Axes + Matplotlib axis to plot on. + keep_slope : bool, default=True + Whether to plot the trajectory slope. + keep_boundary : bool, default=True + Whether to plot decision boundaries. + keep_ndt : bool, default=True + Whether to plot non-decision time marker. + keep_starting_point : bool, default=True + Whether to plot starting point marker. + markersize_starting_point : float or int, default=80 + Size of starting point marker. + markertype_starting_point : float or int, default=1 + Marker type for starting point. + markershift_starting_point : float or int, default=-0.05 + Horizontal shift of starting point marker. + delta_t_graph : float, optional + Time step size for plotting. + alpha : float, optional + Opacity of plot elements. + lw_m : float, optional + Line width for plot elements. + tmp_label : str, optional + Label for plot legend. + ylim_low : float or int, optional + Lower y-axis limit. + ylim_high : float or int, optional + Upper y-axis limit. + t_s : numpy.ndarray, optional + Time points array. + zorder_cnt : int, default=1 + Base z-order for plot elements. + color : str, default="black" + Color for plot elements. + """ + # Make bounds + (b_high, b_low) = ( + np.maximum(sample["metadata"]["boundary"], 0), + np.minimum((-1) * sample["metadata"]["boundary"], 0), + ) + + # Set initial boundary value + b_h_init = b_high[0] + b_l_init = b_low[0] + + # Push boundary forward to accomodate non-decision time + # Initial boundary value applied from t = 0 to t = t_ndt + n_roll = int((sample["metadata"]["t"][0] / delta_t_graph) + 1) + b_high = np.roll(b_high, n_roll) + b_high[:n_roll] = b_h_init + b_low = np.roll(b_low, n_roll) + b_low[:n_roll] = b_l_init + + tmp_traj = sample["metadata"]["trajectory"] + maxid = np.minimum(np.argmax(np.where(tmp_traj > -999)), t_s.shape[0]) + + if keep_boundary: + # Upper bound + axis.plot( + t_s, + b_high[: t_s.shape[0]], + color=color, + alpha=alpha, + zorder=1000 + zorder_cnt, + linewidth=lw_m, + label=tmp_label, + ) + + # Lower bound + axis.plot( + t_s, + b_low[: t_s.shape[0]], + color=color, + alpha=alpha, + zorder=1000 + zorder_cnt, + linewidth=lw_m, + ) + + # Slope + if keep_slope: + axis.plot( + t_s[:maxid] + sample["metadata"]["t"][0], + tmp_traj[:maxid], + color=color, + alpha=alpha, + zorder=1000 + zorder_cnt, + linewidth=lw_m, + ) # TOOK AWAY LABEL + + # Non-decision time + if keep_ndt: + axis.axvline( + x=sample["metadata"]["t"][0], + ymin=ylim_low, + ymax=ylim_high, + color=color, + linestyle="--", + linewidth=lw_m, + zorder=1000 + zorder_cnt, + alpha=alpha, + ) + # Starting point + if keep_starting_point: + axis.scatter( + sample["metadata"]["t"][0] + markershift_starting_point, + b_low[0] + (sample["metadata"]["z"][0] * (b_high[0] - b_low[0])), + markersize_starting_point, + marker=markertype_starting_point, + color=color, + alpha=alpha, + zorder=1000 + zorder_cnt, + ) + + +def plot_func_model_n( + model_name: str, + axis: Axes, + theta_mean: pd.DataFrame | None = None, + theta_posterior: pd.DataFrame | None = None, + data: pd.DataFrame | None = None, + n_trajectories: int = 10, + bin_size: float = 0.05, + n_samples: int = 10, + linewidth_histogram: float | int = 0.5, + linewidth_model: float | int = 0.5, + legend_fontsize: int = 7, + legend_shadow: bool = True, + legend_location: str = "upper right", + delta_t_model: float = 0.01, + add_legend: bool = True, + alpha: float = 1.0, + alpha_pp: float = 0.05, + alpha_trajectories: float = 0.5, + keep_frame: bool = False, + random_state: int | None = None, + **kwargs, +): + """Calculate and plot posterior predictive for a model. + + Parameters + ---------- + model_name : str + Name of the model to simulate. + axis : matplotlib.axes.Axes + The axis to plot on. + theta_mean : pandas.DataFrame, optional + Mean parameter values for simulation. + theta_posterior : pandas.DataFrame, optional + Posterior samples of parameters. + data : pandas.DataFrame, optional + Observed data to plot. + n_trajectories : int, default=10 + Number of trajectories to plot. + bin_size : float, default=0.05 + Size of bins for histograms. + n_samples : int, default=10 + Number of posterior samples to use. + linewidth_histogram : float or int, default=0.5 + Line width for histogram elements. + linewidth_model : float or int, default=0.5 + Line width for model cartoon elements. + legend_fontsize : int, default=7 + Font size for legend. + legend_shadow : bool, default=True + Whether to add shadow to legend box. + legend_location : str, default="upper right" + Location of legend. + delta_t_model : float, default=0.01 + Time step size for model cartoon plotting. + add_legend : bool, default=True + Whether to add legend to plot. + alpha : float, default=1.0 + Transparency for main plot elements. + alpha_pp : float, default=0.05 + Transparency for posterior predictive samples. + alpha_trajectories : float, default=0.5 + Transparency for trajectory paths. + keep_frame : bool, default=False + Whether to keep the frame around the plot. + random_state : int, optional + Random seed for reproducibility. + **kwargs + Additional keyword arguments passed to plotting functions. + + Notes + ----- + This function visualizes model predictions by plotting simulated trajectories, + histograms of response times, and model cartoons. It can show both the mean + prediction and uncertainty from posterior samples. + """ + color_dict = { + -1: "black", + 0: "black", + 1: "green", + 2: "blue", + 3: "red", + 4: "orange", + 5: "purple", + 6: "brown", + } + + ylim_low, ylim_high = kwargs.get("ylims", (0, 5)) + xlim_low, xlim_high = kwargs.get("xlims", (0, 5)) + + # # Extract some parameters from kwargs + axis.set_xlim(xlim_low, xlim_high) + axis.set_ylim(ylim_low, ylim_high) + bins = list(np.arange(xlim_low, xlim_high, bin_size)) + + # ADD MODEL: + + # RUN SIMULATIONS + # ------------------------------- + + # Simulator Data + if random_state is not None: + np.random.seed(random_state) + + rand_int = np.random.randint(0, 400000000) + if theta_mean is not None: + sim_out = simulator( + model=model_name, + theta=theta_mean.values, + n_samples=n_samples, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + ) + + sim_out_no_noise = simulator( + model=model_name, + theta=theta_mean.loc[np.random.choice(theta_mean.shape[0], 1), :].values, + n_samples=1, + no_noise=True, + delta_t=delta_t_model, + bin_dim=None, + smooth_unif=False, + ) + + # Simulate model without noise: posterior samples + if theta_posterior is not None: + posterior_pred_no_noise = {} + posterior_pred_sims = {} + for i, (chain, draw) in enumerate( + list(theta_posterior.index.droplevel("obs_n").unique()) + ): + # Simulate model: no noise + posterior_pred_no_noise[i] = simulator( + model=model_name, + theta=theta_posterior.loc[(chain, draw), :].values, + n_samples=1, + no_noise=True, + delta_t=delta_t_model, + bin_dim=None, + smooth_unif=False, + ) + + # Simulate model: posterior samples + posterior_pred_sims[i] = simulator( + model=model_name, + theta=theta_posterior.loc[(chain, draw), :].values, + n_samples=1, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + ) + + # Simulate trajectories + sim_out_traj = {} + for i in range(n_trajectories): + if theta_mean is not None: + tmp_theta = theta_mean.loc[ + (np.random.choice(theta_mean.shape[0], 1)), : + ].values + elif theta_posterior is not None: + # wrap in max statement here + # because negative value are possible, + # however refer to data instead of posterior samples + random_index = tuple( + [ + np.random.choice(theta_posterior.index.get_level_values(name_)) + for name_ in ("chain", "draw", "obs_n") + ] + ) + + tmp_theta = theta_posterior.loc[random_index, :].values + else: + raise ValueError("No theta values provided but n_trajectories is > 0") + + sim_out_traj[i] = simulator( + model=model_name, + theta=tmp_theta, + n_samples=1, + no_noise=False, + delta_t=delta_t_model, + bin_dim=None, + random_state=rand_int, + smooth_unif=False, + ) + + # ADD HISTOGRAMS + # ------------------------------- + choices = default_model_config[cast(SupportedModels, model_name)]["choices"] + cnt_cumul = 0 + + # POSTERIOR MEAN BASED HISTOGRAM + if theta_mean is not None: + b = np.maximum(sim_out["metadata"]["boundary"], 0) + bottom = b[0] + + for i, choice in enumerate(choices): + tmp_label = None + + if add_legend and i == 0: + tmp_label = "PostPred" + + weights = np.tile( + (1 / bin_size) + / sim_out["rts"][sim_out["rts"] != -999].flatten().shape[0], + reps=sim_out["rts"][ + (sim_out["choices"] == choice) & (sim_out["rts"] != -999) + ] + .flatten() + .shape[0], + ) + + axis.hist( + np.abs( + sim_out["rts"][ + (sim_out["choices"] == choice) & (sim_out["rts"] != -999) + ] + ), + bins=bins, + bottom=bottom, + weights=weights, + histtype="step", + alpha=alpha, + color=color_dict[choice], + zorder=cnt_cumul, + label=tmp_label, + linewidth=linewidth_histogram, + linestyle="-.", + ) + cnt_cumul += 1 + + # POSTERIOR SAMPLE BASED HISTOGRAM + if theta_posterior is not None: + if theta_mean is None: + bottom = np.max( + [ + np.maximum( + posterior_pred_no_noise[key_]["metadata"]["boundary"], 0 + )[0] + for key_, _ in posterior_pred_no_noise.items() + ] + ) + + for k, sim_out_tmp in posterior_pred_sims.items(): + for i, choice in enumerate(choices): + tmp_label = None + + if add_legend and i == 0: + tmp_label = "PostPred" + + weights = np.tile( + (1 / bin_size) + / sim_out_tmp["rts"][sim_out_tmp["rts"] != -999].shape[0], + reps=sim_out_tmp["rts"][ + (sim_out_tmp["choices"] == choice) + & (sim_out_tmp["rts"] != -999) + ].shape[0], + ) + + axis.hist( + np.abs( + sim_out_tmp["rts"][ + (sim_out_tmp["choices"] == choice) + & (sim_out_tmp["rts"] != -999) + ] + ), + bins=bins, + bottom=bottom, + weights=weights, + histtype="step", + alpha=alpha_pp, + color=color_dict[choice], + zorder=cnt_cumul, + label=tmp_label, + linewidth=linewidth_histogram, + linestyle="-.", + ) + cnt_cumul += 1 + + # DATA BASED HISTOGRAM + if data is not None: + for i, choice in enumerate(choices): + tmp_label = None + + if add_legend and (i == 0): + tmp_label = "Data" + + data_tmp = data.query(f"rt != {-999} and response == {choice}")["rt"].values + weights = np.tile( + (1 / bin_size) / data.shape[0], + reps=data_tmp.shape[0], + ) + + axis.hist( + np.abs(data_tmp), + bins=bins, + bottom=bottom, + weights=weights, + histtype="step", + alpha=1, + color=color_dict[choice], + zorder=cnt_cumul, + label="Data", + linewidth=linewidth_histogram, + linestyle="-", + ) + cnt_cumul += 1 + + # ADD MODEL CARTOONS: + + tmp_label = None + z_cnt = 0 + if theta_posterior is not None: + for k, sim_out_tmp in posterior_pred_no_noise.items(): + t_s = np.arange(0, sim_out_tmp["metadata"]["max_t"], delta_t_model) + _add_model_n_cartoon_to_ax( + sample=sim_out_tmp, + axis=axis, + delta_t_graph=delta_t_model, + alpha=alpha_pp, + lw_m=linewidth_model, + tmp_label=tmp_label, + linestyle="-", + ylim=ylim_high, + t_s=t_s, + color_dict=color_dict, + zorder_cnt=z_cnt, + ) + z_cnt += 1 + + if theta_mean is not None: + t_s = np.arange(0, sim_out_no_noise["metadata"]["max_t"], delta_t_model) + _add_model_n_cartoon_to_ax( + sample=sim_out_no_noise, + axis=axis, + delta_t_graph=delta_t_model, + alpha=alpha, + lw_m=linewidth_model, + tmp_label=tmp_label, + linestyle="-", + ylim=ylim_high, + t_s=t_s, + color_dict=color_dict, + zorder_cnt=z_cnt + 1, + ) + + if (n_trajectories > 0) and ( + (theta_mean is not None) or (theta_posterior is not None) + ): + _add_trajectories_n( + axis=axis, + sample=sim_out_traj, + t_s=t_s, + delta_t_graph=delta_t_model, + n_trajectories=n_trajectories, + alpha_trajectories=alpha_trajectories, + **kwargs, + ) + elif n_trajectories > 0: + raise ValueError("Trajectories are requested but no theta values are provided.") + + if add_legend: + custom_elems = [ + Line2D([0], [0], color=color_dict[choice], lw=1) for choice in choices + ] + custom_titles = ["response: " + str(choice) for choice in choices] + + custom_elems.append(Line2D([0], [0], color="black", lw=1.0, linestyle="dashed")) + + axis.legend( + custom_elems, + custom_titles, + fontsize=legend_fontsize, + shadow=legend_shadow, + loc=legend_location, + ) + + # FRAME + if not keep_frame: + axis.set_frame_on(False) + + return axis + + +def _add_trajectories_n( + axis: Axes, + sample: dict[Any, Any], + t_s: np.ndarray, + delta_t_graph: float = 0.01, + n_trajectories: int = 10, + highlight_rt_choice: bool = True, + marker_size_rt_choice: float = 50, + marker_type_rt_choice: str = "*", + linewidth_trajectories: float = 1, + alpha_trajectories: float = 0.5, + color_trajectories: ( + str | list[str] | dict[str, str] | dict[int, str] + ) = TRAJ_COLOR_DEFAULT_DICT, + **kwargs, +): + """Add simulated decision trajectories to a given matplotlib axis. + + Parameters + ---------- + axis : matplotlib.axes.Axes, optional + The axis to plot on + sample : list of dict, optional + List of dictionaries containing simulated trajectories and metadata + t_s : numpy.ndarray, optional + Array of time points for plotting + delta_t_graph : float, default=0.01 + Time step size for plotting + n_trajectories : int, default=10 + Number of trajectories to plot + highlight_rt_choice : bool, default=True + Whether to highlight response time and choice with markers + marker_size_rt_choice : float, default=50 + Size of markers for response time/choice + marker_type_rt_choice : str, default="*" + Marker style for chosen response + linewidth_trajectories : float, default=1 + Line width for trajectory paths + alpha_trajectories : float, default=0.5 + Transparency of trajectory paths + color_trajectories : str or list or dict, default="black" + Color(s) for trajectories. Can be: + - str: Single color for all trajectories + - list: List of colors mapped to possible choices + - dict: Mapping of choices to colors + **kwargs + Additional keyword arguments passed to plotting functions + + Notes + ----- + This function visualizes multiple simulated decision paths, optionally highlighting + the response times and choices. Each trajectory shows the evidence accumulation + process leading to a decision. + """ + # Check trajectory color type + if isinstance(color_trajectories, str): + color_trajectories_dict = { + value_: color_trajectories + for value_ in sample[0]["metadata"]["possible_choices"] + } + elif isinstance(color_trajectories, list): + color_trajectories_dict = { + value_: color_trajectories[i] + for i, value_ in enumerate(sample[0]["metadata"]["possible_choices"]) + } + elif isinstance(color_trajectories, dict): + color_trajectories_dict = color_trajectories + else: + raise ValueError( + "The `color_trajectories` argument must be a string, list, or dict." + ) + + # Make bounds + b = np.maximum(sample[0]["metadata"]["boundary"], 0) + b_init = b[0] + n_roll = int((sample[0]["metadata"]["t"][0] / delta_t_graph) + 1) + b = np.roll(b, n_roll) + b[:n_roll] = b_init + + # Trajectories + for i in range(n_trajectories): + metadata = sample[i]["metadata"] + tmp_traj = metadata["trajectory"] + tmp_traj_choice = float(sample[i]["choices"].flatten()) + + for j in range(len(metadata["possible_choices"])): + tmp_maxid = np.minimum( + np.argmax(np.where(tmp_traj[:, j] > -999)), t_s.shape[0] + ) + + # Identify boundary value at timepoint of crossing + b_tmp = b[tmp_maxid + n_roll] + + axis.plot( + t_s[:tmp_maxid] + metadata["t"][0], + tmp_traj[:tmp_maxid, j], + color=color_trajectories_dict[j], + alpha=alpha_trajectories, + linewidth=linewidth_trajectories, + zorder=2000 + i, + ) + + if highlight_rt_choice and tmp_traj_choice == j: + axis.scatter( + t_s[tmp_maxid] + metadata["t"][0], + b_tmp, + marker_size_rt_choice, + color=color_trajectories_dict[tmp_traj_choice], + alpha=1, + marker=marker_type_rt_choice, + zorder=2000 + i, + ) + elif highlight_rt_choice and tmp_traj_choice != j: + axis.scatter( + t_s[tmp_maxid] + metadata["t"][0] + 0.05, + tmp_traj[tmp_maxid, j], + marker_size_rt_choice, + color=color_trajectories_dict[j], + alpha=1, + marker=marker_type_rt_choice, + zorder=2000 + i, + ) + + +def _add_model_n_cartoon_to_ax( + sample: dict, + axis: Axes, + delta_t_graph: float, + lw_m: float, + tmp_label: str | None, + linestyle: str, + ylim: float, + t_s: np.ndarray, + color_dict: dict, + zorder_cnt: int, + alpha: float | None = None, + keep_boundary: bool = True, + keep_slope: bool = True, + keep_starting_point: bool = True, +) -> None: + """Add model cartoon visualization to a matplotlib axis. + + Parameters + ---------- + sample : dict + Dictionary containing model metadata and trajectories + axis : matplotlib.axes.Axes + The axis to plot on + delta_t_graph : float + Time step size for plotting + lw_m : float + Line width for plotting model elements + tmp_label : str | None + Label for the plot legend + linestyle : str + Style of lines in plot + ylim : float + Y-axis limit value + t_s : np.ndarray + Array of time points + color_dict : dict + Dictionary mapping choice indices to colors + zorder_cnt : int + Base z-order for plot elements + alpha : float | None, optional + Transparency value, by default None + keep_boundary : bool, optional + Whether to plot decision boundaries, by default True + keep_slope : bool, optional + Whether to plot drift slopes, by default True + keep_starting_point : bool, optional + Whether to plot starting point marker, by default True + + Returns + ------- + None + Modifies the provided axis in place + """ + b = np.maximum(sample["metadata"]["boundary"], 0) + b_init = b[0] + n_roll = int((sample["metadata"]["t"][0] / delta_t_graph) + 1) + b = np.roll(b, n_roll) + b[:n_roll] = b_init + + # Upper bound + if keep_boundary: + axis.plot( + t_s, + b[: t_s.shape[0]], + color="black", + alpha=alpha, + zorder=1000 + zorder_cnt, + linewidth=lw_m, + linestyle=linestyle, + label=tmp_label, + ) + + # Starting point + if keep_starting_point: + axis.axvline( + x=sample["metadata"]["t"][0], + ymin=-ylim, + ymax=ylim, + color="black", + linestyle=linestyle, + linewidth=lw_m, + alpha=alpha, + ) + + # Slopes + if keep_slope: + tmp_traj = sample["metadata"]["trajectory"] + + for i in range(len(sample["metadata"]["possible_choices"])): + tmp_maxid = np.minimum( + np.argmax(np.where(tmp_traj[:, i] > -999)), t_s.shape[0] + ) + + # Slope + axis.plot( + t_s[:tmp_maxid] + sample["metadata"]["t"][0], + tmp_traj[:tmp_maxid, i], + color=color_dict[i], + linestyle=linestyle, + alpha=alpha, + zorder=1000 + zorder_cnt, + linewidth=lw_m, + ) diff --git a/src/hssm/plotting/posterior_predictive.py b/src/hssm/plotting/posterior_predictive.py index 57d84038..d5463b0d 100644 --- a/src/hssm/plotting/posterior_predictive.py +++ b/src/hssm/plotting/posterior_predictive.py @@ -2,7 +2,7 @@ import logging from itertools import product -from typing import Iterable +from typing import Iterable, Literal, cast, overload import arviz as az import matplotlib as mpl @@ -10,12 +10,14 @@ import numpy as np import pandas as pd import seaborn as sns +from matplotlib.axes import Axes from .utils import ( _check_groups_and_groups_order, _check_sample_size, _get_plotting_df, _get_title, + _hdi_to_interval, _subset_df, _use_traces_or_sample, ) @@ -189,14 +191,14 @@ def _plot_posterior_predictive_2D( if plot_data: g.add_legend( - dict(zip(["predicted", "observed"], g.fig.axes[0].get_lines())), + dict(zip(["predicted", "observed"], g.figure.axes[0].get_lines())), title="", label_order=["observed", "predicted"], ) if title: - g.fig.subplots_adjust(top=0.9) - g.fig.suptitle(title) + g.figure.subplots_adjust(top=0.9) + g.figure.suptitle(title) g.set_xlabels(xlabel) g.set_ylabels(ylabel) @@ -204,6 +206,98 @@ def _plot_posterior_predictive_2D( return g +@overload +def _process_lines( + line_attrs: str | Iterable[str] | tuple[str] | dict[str, str], + mode: Literal["linestyles"], +) -> list[str]: ... + + +@overload +def _process_lines( + line_attrs: float | Iterable[float] | tuple[float] | dict[str, float], + mode: Literal["linewidths"], +) -> list[float]: ... + + +def _process_lines( + line_attrs: ( + str + | Iterable[str] + | Iterable[float] + | float + | tuple[str] + | tuple[float] + | dict[str, str] + | dict[str, float] + ), + mode: Literal["linestyles", "linewidths"], +) -> list[str] | list[float]: + check_type: type[str | float] + if mode == "linestyles": + dict_defaults_ls: dict[str, str] = {"predicted": "-", "observed": "-"} + check_type = str + elif mode == "linewidths": + dict_defaults_lw: dict[str, float] = {"predicted": 1.25, "observed": 1.25} + check_type = float + else: + raise ValueError(f"Invalid mode: {mode}") + + if isinstance(line_attrs, check_type): + if check_type is str: + return [cast(str, line_attrs)] * 2 + elif check_type is float: + return [cast(float, line_attrs)] * 2 + else: + raise ValueError(f"Invalid type: {check_type}") + elif isinstance(line_attrs, (list, tuple)): + line_attrs = list(line_attrs) + if not all(isinstance(la, check_type) for la in line_attrs): + raise ValueError( + f"The `{mode}` argument must be a string or a list of strings." "or 2." + ) + elif len(line_attrs) in {1, 2}: + return line_attrs * 2 if len(line_attrs) == 1 else line_attrs + else: + raise ValueError( + f"The `{mode}` argument must be a string or a list of strings." + ) + elif isinstance(line_attrs, dict): + if not set(line_attrs.keys()).issubset({"predicted", "observed"}): + raise ValueError( + f"The keys of the `{mode}` dictionary must be 'predicted' and/or " + "'observed'." + ) + else: + if mode == "linestyles": + return [ + cast( + str, + line_attrs.get("predicted", dict_defaults_ls["predicted"]), + ), + cast( + str, + line_attrs.get("observed", dict_defaults_ls["observed"]), + ), + ] + elif mode == "linewidths": + return [ + cast( + float, + line_attrs.get("predicted", dict_defaults_lw["predicted"]), + ), + cast( + float, + line_attrs.get("observed", dict_defaults_lw["observed"]), + ), + ] + else: + raise ValueError( + f"The `{mode}` argument must be a string, a list of strings," + " or a dictionary." + ) + + def plot_posterior_predictive( model, idata: az.InferenceData | None = None, @@ -215,19 +309,19 @@ def plot_posterior_predictive( col_wrap: int | None = None, groups: str | Iterable[str] | None = None, groups_order: Iterable[str] | dict[str, Iterable[str]] | None = None, - bins: int | np.ndarray | str | None = 100, + bins: int | np.ndarray | str | None = 50, range: tuple[float, float] | None = None, step: bool = False, - interval: tuple[float, float] | None = None, + hdi: float | str | tuple[float, float] | None = None, colors: str | list[str] | None = None, - linestyles: str | list[str] = "-", - linewidths: float | list[float] = 1.25, + linestyles: str | list[str] | tuple[str] | dict[str, str] = "-", + linewidths: float | list[float] | tuple[float] | dict[str, float] = 1.25, title: str | None = "Posterior Predictive Distribution", xlabel: str | None = "Response Time", ylabel: str | None = "Density", grid_kwargs: dict | None = None, **kwargs, -) -> mpl.axes.Axes | sns.FacetGrid | list[sns.FacetGrid]: +) -> Axes | sns.FacetGrid | list[sns.FacetGrid]: """Plot the posterior predictive distribution against the observed data. Parameters @@ -293,19 +387,22 @@ def plot_posterior_predictive( - A list-like defining the bin edges. - An integer defining the number of bins to be used. range : optional - The lower and upper range of the bins. Lower and upper outliers are ignored. If - not provided, range is simply the minimum and the maximum of the data, by + The lower and upper range of the bins. Lower and upper outliers are ignored. + If not provided, range is simply the minimum and the maximum of the data, by default None. step : optional Whether to plot the distributions as a step function or a smooth density plot, by default False. - interval : optional - A two-tuple of floats indicating the interval of uncertainty, by default None. + hdi : optional + A two-tuple of floats indicating the hdi to plot, by default None. The values in the tuple should be between 0 and 1, indicating the percentiles used to compute the interval. For example, (0.05, 0.95) will compute the 90% interval. There should be at least 50 posterior predictive samples for each chain for this to work properly. A warning message will be displayed if there are fewer than 50 posterior samples. If None, no interval is plotted. + If a float, the interval the computed interval will be + ((1 - hdi) / 2, 1 - (1 - hdi) / 2). If a string, the format needs + to be e.g. '10%'. colors : optional Colors to use for the different levels of the hue variable. When a `str`, the color of posterior predictives, in which case an error will be thrown if @@ -318,6 +415,8 @@ def plot_posterior_predictive( the linestyle of both distributions. When a length-2 iterable, indicates the linestyles in the order of posterior predictives and observed data. The values must be interpretable by matplotlib. When None, use solid lines, by default "-". + When dictionary, the keys must be 'predicted' and/or 'observed', and the values + must be interpretable by matplotlib. linewidths : optional Linewidths to use for the different levels of the hue variable. When a `float`, the linewidth of both distributions. When a length-2 iterable, indicates the @@ -343,6 +442,14 @@ def plot_posterior_predictive( mpl.axes.Axes | sns.FacetGrid The matplotlib `axis` or seaborn `FacetGrid` object containing the plot. """ + # Process hdi + interval = _hdi_to_interval(hdi=hdi) if hdi is not None else None + + # Process linestyles + linestyles_ = _process_lines(linestyles, mode="linestyles") + # Process linewidths + linewidths_ = _process_lines(linewidths, mode="linewidths") + groups, groups_order = _check_groups_and_groups_order( groups, groups_order, row, col ) @@ -378,14 +485,13 @@ def plot_posterior_predictive( _check_sample_size(plotting_df) # Flip the rt values if necessary - if np.any(plotting_df["response"] == 0): + if np.any(plotting_df["response"] == 0) and model.n_choices == 2: plotting_df["response"] = np.where(plotting_df["response"] == 0, -1, 1) if model.n_choices == 2: plotting_df["rt"] = plotting_df["rt"] * plotting_df["response"] # Then, plot the posterior predictive distribution against the observed data # Determine whether we are producing a single plot or a grid of plots - if not extra_dims: ax = _plot_posterior_predictive_1D( data=plotting_df, @@ -395,8 +501,8 @@ def plot_posterior_predictive( step=step, interval=interval, colors=colors, - linestyles=linestyles, - linewidths=linewidths, + linestyles=linestyles_, + linewidths=linewidths_, title=title, xlabel=xlabel, ylabel=ylabel, @@ -406,7 +512,6 @@ def plot_posterior_predictive( return ax # The multiple dimensions case - # If group is not provided, we are producing a grid of plots if groups is None: g = _plot_posterior_predictive_2D( @@ -420,8 +525,8 @@ def plot_posterior_predictive( step=step, interval=interval, colors=colors, - linestyles=linestyles, - linewidths=linewidths, + linestyles=linestyles_, + linewidths=linewidths_, title=title, xlabel=xlabel, ylabel=ylabel, @@ -459,8 +564,8 @@ def plot_posterior_predictive( step=step, interval=interval, colors=colors, - linestyles=linestyles, - linewidths=linewidths, + linestyles=linestyles_, + linewidths=linewidths_, title=title, xlabel=xlabel, ylabel=ylabel, diff --git a/src/hssm/plotting/quantile_probability.py b/src/hssm/plotting/quantile_probability.py index f81f9da3..f4cd7aa9 100644 --- a/src/hssm/plotting/quantile_probability.py +++ b/src/hssm/plotting/quantile_probability.py @@ -344,7 +344,7 @@ def plot_quantile_probability( ) # Flip the rt values if necessary - if np.any(plotting_df["response"] == 0): + if np.any(plotting_df["response"] == 0) and model.n_choices == 2: plotting_df["response"] = np.where(plotting_df["response"] == 0, -1, 1) if model.n_choices == 2: plotting_df["rt"] = plotting_df["rt"] * plotting_df["response"] diff --git a/src/hssm/plotting/utils.py b/src/hssm/plotting/utils.py index a1bdfda3..22d8b66b 100644 --- a/src/hssm/plotting/utils.py +++ b/src/hssm/plotting/utils.py @@ -85,6 +85,36 @@ def _process_data(data: pd.DataFrame, extra_dims: list[str]) -> pd.DataFrame: return data +def _hdi_to_interval(hdi: str | float | tuple[float, float]) -> tuple[float, float]: + """Convert HDI to range.""" + if isinstance(hdi, tuple): + if not all(isinstance(i, float) for i in hdi): + raise ValueError("The HDI must be a tuple of floats.") + elif not all(0 <= i <= 1 for i in hdi): + raise ValueError("The HDI must be between 0 and 1.") + elif hdi[0] > hdi[1]: + raise ValueError( + "The lower bound of the HDI must be less than the upper bound." + ) + else: + return hdi + elif isinstance(hdi, str): + # check format of string (ends with %) + if not hdi.endswith("%"): + raise ValueError("HDI must be a percentage") + # check format of string (begins with two floats) + if not (hdi[0].isdigit() or hdi[1].isdigit()): + raise ValueError("HDI must begin with two floats") + # process + hdi = float(hdi.strip("%")) / 100 + elif isinstance(hdi, float): + if not 0 <= hdi <= 1: + raise ValueError("HDI must be between 0 and 1") + else: + raise ValueError("HDI must be a float, a string, or a tuple of two floats") + return ((1 - hdi) / 2, 1 - ((1 - hdi) / 2)) + + def _get_plotting_df( idata: az.InferenceData | None = None, data: pd.DataFrame | None = None, @@ -128,8 +158,10 @@ def _get_plotting_df( return data # get the posterior samples - idata_posterior = idata["posterior_predictive"][response_str] - posterior = _xarray_to_df(idata_posterior, n_samples=n_samples) + idata_posterior_predictive = idata["posterior_predictive"][response_str] + posterior_predictive = _xarray_to_df( + idata_posterior_predictive, n_samples=n_samples + ) if data is None: if extra_dims: @@ -138,10 +170,10 @@ def _get_plotting_df( + " HSSM requires a dataset to determine the values of the covariates" + " to plot these additional dimensions." ) - posterior.insert(0, "observed", "predicted") - return posterior + posterior_predictive.insert(0, "observed", "predicted") + return posterior_predictive - if extra_dims and idata_posterior["__obs__"].size != data.shape[0]: + if extra_dims and idata_posterior_predictive["__obs__"].size != data.shape[0]: raise ValueError( "The number of observations in the data and the number of posterior " + "samples are not equal." @@ -151,19 +183,19 @@ def _get_plotting_df( # merge the posterior samples with the data if extra_dims: - posterior_merged = posterior.merge( - data.loc[:, extra_dims], - left_on="obs_n", - right_on="obs_n", - how="left", - sort=True, + posterior_predictive = ( + posterior_predictive.reset_index() + .merge( + data.loc[:, extra_dims], + on="obs_n", + how="left", + ) + .set_index(["chain", "draw", "obs_n"]) ) - posterior_merged.index = posterior.sort_index().index - posterior = posterior_merged # concatenate the posterior samples with the data plotting_df = pd.concat( - {"predicted": posterior, "observed": data}, + {"predicted": posterior_predictive, "observed": data}, names=["observed", "chain", "draw", "obs_n"], ).reset_index("observed") @@ -316,7 +348,7 @@ def _check_groups_and_groups_order( groups_order: Iterable[str] | dict[str, Iterable[str]] | None, row: str | None, col: str | None, -) -> tuple[Iterable[str], dict[str, Iterable[str]]]: +) -> tuple[Iterable[str] | None, dict[str, Iterable[str]]]: """Check the validity of `groups` and `groups_order`.""" if groups is None: if groups_order is not None: @@ -388,7 +420,6 @@ def _use_traces_or_sample( inplace=True, draws=n_samples, ) - idata = model.traces sampled = True return cast(az.InferenceData, idata), sampled diff --git a/tests/conftest.py b/tests/conftest.py index 02bc13aa..fa12fecc 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -101,6 +101,71 @@ def cavanagh_test(): return pd.read_csv("tests/fixtures/cavanagh_theta_test.csv", index_col=None) +# Cartoon plot fixtures +@pytest.fixture +def cav_model_cartoon(cavanagh_test): + cav_model = hssm.HSSM( + model="ddm", + data=cavanagh_test, + include=[ + { + "name": "v", + "prior": { + "Intercept": {"name": "Normal", "mu": 0.0, "sigma": 1.5}, + }, + "formula": "v ~ 1 + stim", + "link": "identity", + }, + { + "name": "a", + "prior": { + "Intercept": {"name": "Normal", "mu": 1.5, "sigma": 0.5}, + }, + "formula": "a ~ 1 + (1|participant_id)", + "link": "identity", + }, + ], + p_outlier=0.00, + ) + + # Attach trace + idata_cav = az.from_netcdf("tests/fixtures/idata_cavanagh_cartoon.nc") + cav_model._inference_obj = idata_cav + return cav_model + + +@pytest.fixture +def race_model_cartoon(): + my_race_data = pd.read_csv("tests/fixtures/data_race.csv") + race_model = hssm.HSSM( + model="race_no_bias_angle_4", + data=my_race_data, + include=[ + { + "name": "v0", + "prior": { + "Intercept": {"name": "Normal", "mu": 0.0, "sigma": 1.5}, + }, + "formula": "v0 ~ 1 + stim", + "link": "identity", + }, + { + "name": "a", + "prior": { + "Intercept": {"name": "Normal", "mu": 1.5, "sigma": 0.5}, + }, + "formula": "a ~ 1 + (1|participant_id)", + "link": "identity", + }, + ], + p_outlier=0.00, + ) + # Attach trace + idata_race = az.from_netcdf("tests/fixtures/test_idata_race.nc") + race_model._inference_obj = idata_race + return race_model + + # Only useful if running tests serially def pytest_collection_modifyitems(config, items): slow_tests = [item for item in items if 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assert len(g1.fig.axes) == 5 * 2 - assert len(g1.fig.axes[0].get_lines()) == 2 + assert len(g1.figure.axes) == 5 * 2 + assert len(g1.figure.axes[0].get_lines()) == 2 g2 = _plot_posterior_predictive_2D( df, @@ -147,8 +147,8 @@ def test__plot_posterior_predictive_2D(cav_idata, cavanagh_test): row="participant_id", col="conf", ) - assert len(g2.fig.axes) == 5 * 2 - assert len(g2.fig.axes[0].get_lines()) == 1 + assert len(g2.figure.axes) == 5 * 2 + assert len(g2.figure.axes[0].get_lines()) == 1 def test_plot_posterior_predictive(cav_idata, cavanagh_test): @@ -194,8 +194,8 @@ def test_plot_posterior_predictive(cav_idata, cavanagh_test): ) assert len(plots) == 2 # Lengths might defer because of subsetting the data frame - assert len(plots[0].fig.axes) == 5 - assert len(plots[1].fig.axes) == 5 * 2 + assert len(plots[0].figure.axes) == 5 + assert len(plots[1].figure.axes) == 5 * 2 plots = plot_posterior_predictive( model, @@ -206,8 +206,8 @@ def test_plot_posterior_predictive(cav_idata, cavanagh_test): groups_order=["LC"], ) assert len(plots) == 1 - assert len(plots[0].fig.axes) == 5 - assert len(plots[0].fig.axes[0].get_lines()) == 1 + assert len(plots[0].figure.axes) == 5 + assert len(plots[0].figure.axes[0].get_lines()) == 1 with pytest.raises(ValueError): plot_posterior_predictive( @@ -279,14 +279,14 @@ def test__plot_quantile_probability_2D(cav_idata, cavanagh_test): ) g = _plot_quantile_probability_2D(df, cond="stim", col="participant_id", col_wrap=3) - assert len(g.fig.axes) == 10 + assert len(g.figure.axes) == 10 df = _get_plotting_df( cav_idata, cavanagh_test, extra_dims=["participant_id", "stim", "conf"] ) g = _plot_quantile_probability_2D(df, cond="stim", col="participant_id", row="conf") - assert len(g.fig.axes) == 5 * 4 + assert len(g.figure.axes) == 5 * 4 def test_plot_quantile_probability(cav_idata, cavanagh_test): diff --git a/tests/test_plotting_cartoon.py b/tests/test_plotting_cartoon.py new file mode 100644 index 00000000..dac5cb64 --- /dev/null +++ b/tests/test_plotting_cartoon.py @@ -0,0 +1,144 @@ +"""Test plotting module.""" + +import pytest + +import arviz as az +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import xarray as xr + +import hssm +from hssm.plotting.utils import ( + _get_plotting_df, + _xarray_to_df, + _get_title, + _subset_df, + _row_mask_with_error, + _process_df_for_qp_plot, +) +from hssm.plotting.posterior_predictive import ( + _plot_posterior_predictive_1D, + _plot_posterior_predictive_2D, + plot_posterior_predictive, +) +from hssm.plotting.quantile_probability import ( + _plot_quantile_probability_1D, + _plot_quantile_probability_2D, + plot_quantile_probability, +) + +hssm.set_floatX("float32") + + +# I want to parameter +@pytest.mark.parametrize( + ["n_trajectories", "groups", "plot_pp_mean", "plot_pp_samples", "row", "col"], + [ + (2, None, False, False, "participant_id", "stim"), + (2, None, True, True, "participant_id", "stim"), + (2, None, False, True, "participant_id", "stim"), + (0, None, False, True, "participant_id", "stim"), + (0, None, True, False, "participant_id", "stim"), + (2, ["dbs"], True, True, "participant_id", "stim"), + (2, None, True, False, "participant_id", None), + (2, None, True, False, "participant_id", "stim"), + (2, None, True, False, None, None), + ], +) +def test_plot_model_cartoon_2_choice( + cav_model_cartoon, n_trajectories, groups, plot_pp_mean, plot_pp_samples, row, col +): + """Test plot_model_cartoon for 2-choice data.""" + if (not plot_pp_mean) and (not plot_pp_samples): + with pytest.raises(ValueError): + ax = hssm.plotting.plot_model_cartoon( + cav_model_cartoon, + n_samples=10, + bins=30, + col=col, + row=row, + groups=groups, + plot_pp_mean=plot_pp_mean, + plot_pp_samples=plot_pp_samples, + alpha=0.025, + **{"n_trajectories": n_trajectories}, + ) + else: + ax = hssm.plotting.plot_model_cartoon( + cav_model_cartoon, + n_samples=10, + bins=30, + col=col, + row=row, + groups=groups, + plot_pp_mean=plot_pp_mean, + plot_pp_samples=plot_pp_samples, + alpha=0.025, + **{"n_trajectories": n_trajectories}, + ) + + if groups is None: + if row is not None: + assert np.all(ax.row_names == cav_model_cartoon.data[row].unique()) + if col is not None: + assert np.all(ax.col_names == cav_model_cartoon.data[col].unique()) + elif groups is ["dbs"]: + assert isinstance(ax, list) + assert len(ax) == len(cav_model_cartoon.data[groups[0]].unique()) + + +@pytest.mark.parametrize( + ["n_trajectories", "groups", "plot_pp_mean", "plot_pp_samples", "row", "col"], + [ + (2, None, False, False, "participant_id", "stim"), + (2, None, True, True, "participant_id", "stim"), + (2, None, False, True, "participant_id", "stim"), + (0, None, False, True, "participant_id", "stim"), + (0, None, True, False, "participant_id", "stim"), + (2, None, True, False, "participant_id", None), + (2, None, True, False, "participant_id", "stim"), + (2, None, True, False, None, None), + ], +) +def test_plot_model_cartoon_3_choice( + race_model_cartoon, n_trajectories, groups, plot_pp_mean, plot_pp_samples, row, col +): + """Test plot_model_cartoon for 3-choice data.""" + + if (not plot_pp_mean) and (not plot_pp_samples): + with pytest.raises(ValueError): + ax = hssm.plotting.plot_model_cartoon( + race_model_cartoon, + n_samples=10, + bins=30, + col=col, + row=row, + groups=groups, + plot_pp_mean=plot_pp_mean, + plot_pp_samples=plot_pp_samples, + alpha=0.025, + **{"n_trajectories": n_trajectories}, + ) + else: + ax = hssm.plotting.plot_model_cartoon( + race_model_cartoon, + n_samples=10, + bins=30, + col=col, + row=row, + groups=groups, + plot_pp_mean=plot_pp_mean, + plot_pp_samples=plot_pp_samples, + alpha=0.025, + **{"n_trajectories": n_trajectories}, + ) + + if groups is None: + if row is not None: + assert np.all(ax.row_names == race_model_cartoon.data[row].unique()) + if col is not None: + assert np.all(ax.col_names == race_model_cartoon.data[col].unique()) + else: + assert isinstance(ax, list) + assert len(ax) == len(race_model_cartoon.data[groups].unique())