diff --git a/CHANGELOG.md b/CHANGELOG.md index 2886081..eb16efd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,7 +5,7 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [0.26.0.dev0] - 2026-09-28 +## [0.26.0.dev1] - 2026-10-05 ### Added @@ -18,6 +18,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added `cpm.models.learning.SARSATrace`, a SARSA learning rule with an eligibility trace - Added test units for `HybridMBMF` and `SARSATrace` - Added a two-step task example replicating Smid et al. (2022), with its data in `cpm.datasets.load_two_step_data` +- Added `cpm.brainexplorer`, which computes descriptive statistics and applies the exclusion criteria for the BrainExplorer games Space Observer, Scavenger, Treasure Hunt, and Milky Way and Pirate Market, with a page in the API reference +- Added test units for `cpm.brainexplorer` +- Added `cpm.datasets.load_model_based_model_free`, two-step task data from the BrainExplorer game Goblin Heist - Added an example that fits causal ratings in a blocking experiment, recreating Figures 1 and 2 of Spicer et al. (2021), with its data in `cpm.datasets.load_blocking_data`; based on an earlier version by @chotong - Added `cpm.generators.SessionWrapper`, a `Wrapper` for models that compute all trials of a participant at once - Added `cpm.models.kernels`, the formulas of the `cpm.models` classes as functions that numba can compile, used by the classes and the built-in applications @@ -49,6 +52,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Fixed +- Fixed the optimisers and `cpm.applications.signal_detection.EstimatorMetaD` raising `UnboundLocalError` for a pandas DataFrame without `ppt_identifier`; they now raise a `ValueError` that explains what to pass, or a `KeyError` that lists the columns if `ppt_identifier` is not one of them, and take `ppt_identifier` from the grouping of data grouped by a single column, such as `data.groupby("ppt")`, so that the fits record the participants - Fixed `cpm.hierarchical.EmpiricalBayes` and `cpm.hierarchical.VariationalBayes` discarding the estimated population priors after the first evaluation of each fit; hierarchical results change - Fixed the random starting priors of later chains of `EmpiricalBayes` and `VariationalBayes` being length-one arrays - Fixed `numpy.random.seed` not reproducing the later chains of `EmpiricalBayes` and `VariationalBayes`, whose starting priors now also respect the lower bound and work with infinite bounds diff --git a/cpm/__init__.py b/cpm/__init__.py index e8ecf09..009c706 100644 --- a/cpm/__init__.py +++ b/cpm/__init__.py @@ -21,5 +21,6 @@ from . import hierarchical from . import utils from . import datasets +from . import brainexplorer del os, sys, warnings, logging diff --git a/cpm/__version__.py b/cpm/__version__.py index 38fe9be..1289812 100644 --- a/cpm/__version__.py +++ b/cpm/__version__.py @@ -1 +1 @@ -__version__ = "0.26.0.dev0" +__version__ = "0.26.0.dev1" diff --git a/cpm/applications/signal_detection.py b/cpm/applications/signal_detection.py index a66eb82..e151082 100644 --- a/cpm/applications/signal_detection.py +++ b/cpm/applications/signal_detection.py @@ -11,7 +11,7 @@ from scipy.stats import norm, multivariate_normal from scipy.special import ndtr -from cpm.core.optimisers import numerical_hessian, prepare_data +from cpm.core.optimisers import group_identifier, numerical_hessian, prepare_data from cpm.core.data import detailed_pandas_compiler, decompose from cpm.core.parallel import detect_cores, execute_parallel from cpm.utils.metad import count_trials, bin_ratings @@ -428,7 +428,7 @@ class EstimatorMetaD: * 3 : display progress during iterations (more complete report). ppt_identifier : str, optional - Identifier for participants in the data. If None, the default identifier will be used. + The column that identifies the participants in the data. Required if `data` is a pandas.DataFrame. If `data` is grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. ignore_invalid : bool, default False If True, invalid confidence ratings will be ignored during binning. If False, an error will be raised if invalid ratings are found. We recommend setting this to False (Default). **kwargs : additional keyword arguments @@ -464,7 +464,7 @@ def __init__( ): self.data = data self.bins = bins - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/brainexplorer/__init__.py b/cpm/brainexplorer/__init__.py new file mode 100644 index 0000000..12ae622 --- /dev/null +++ b/cpm/brainexplorer/__init__.py @@ -0,0 +1,4 @@ +from cpm.brainexplorer import bandits +from cpm.brainexplorer import perceptual_decision_making +from cpm.brainexplorer import risky_decision_making +from cpm.brainexplorer import information_gathering diff --git a/cpm/brainexplorer/_utils.py b/cpm/brainexplorer/_utils.py new file mode 100644 index 0000000..3fd13b6 --- /dev/null +++ b/cpm/brainexplorer/_utils.py @@ -0,0 +1,70 @@ +"""Helpers shared by the BrainExplorer task classes.""" + +import os +import warnings +from contextlib import contextmanager + +import numpy as np +import pandas as pd + + +def load_data(source, name, **kwargs): + """ + Read the data of a task from a CSV file, or copy a DataFrame. + + `source` is a pandas DataFrame, or the path to a CSV file. Keyword arguments + are passed on to :func:`pandas.read_csv`. + """ + if source is None: + raise ValueError(f"{name} needs data: a pandas DataFrame or the path to a CSV file.") + if isinstance(source, pd.DataFrame): + return source.copy() + return pd.read_csv(os.fspath(source), header=0, **kwargs) + + +def require(data, columns, name): + """Raise a `KeyError` that names every required column missing from `data`.""" + missing = [column for column in columns if column not in data.columns] + if missing: + raise KeyError(f"{name} needs the columns {missing}, which are missing from the data.") + + +def time_of_day(hour): + """Name the time of day of an hour: night (0-6h), morning (6-12h), afternoon (12-18h) or evening (18-24h).""" + if hour < 6: + return "night" + if hour < 12: + return "morning" + if hour < 18: + return "afternoon" + return "evening" + + +def session_time(date): + """The date, day of the week, clock time and time of day of the first trial of a session.""" + date = pd.Timestamp(pd.to_datetime(date, format="ISO8601")) if isinstance(date, str) else pd.Timestamp(date) + return { + "date": date, + "day_of_week": date.day_name(), + "time": date.time(), + "time_of_day": time_of_day(date.hour), + } + + +@contextmanager +def quiet_empty_slices(): + """Silence the warnings numpy raises for the mean, median or SD of an empty selection, which are NaN by design.""" + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", message="Mean of empty slice") + warnings.filterwarnings("ignore", message="All-NaN slice encountered") + warnings.filterwarnings("ignore", message="Degrees of freedom <= 0") + warnings.filterwarnings("ignore", message="invalid value encountered") + yield + + +def proportion(condition, mask): + """The proportion of trials in `mask` for which `condition` is true, or NaN if `mask` selects no trials.""" + mask = np.asarray(mask, dtype=bool) + if not mask.any(): + return np.nan + return float(np.mean(np.asarray(condition, dtype=bool)[mask])) diff --git a/cpm/brainexplorer/bandits.py b/cpm/brainexplorer/bandits.py new file mode 100644 index 0000000..f9be173 --- /dev/null +++ b/cpm/brainexplorer/bandits.py @@ -0,0 +1,243 @@ +import numpy as np +import pandas as pd + +from ._utils import load_data, quiet_empty_slices, require, session_time + +__all__ = ["MilkyWay"] + +COLUMNS = ["userID", "trial_type", "run", "date", "correct", "outchosen", "obt_min_forg", "rep", "WSLS_v1", "WSLS_v2"] + +## the two games, by their trial_type in the data: (name, suffix of the metrics) +GAMES = {"reward": ("Milky Way", "MW"), "punish": ("Pirate Market", "PM")} + +## the metrics that are compared between the games: (name of the difference, metric without its suffix) +DIFFERENCES = [ + ("accuracy_diff", "accuracy"), + ("outcome_diff", "mean_outcome"), + ("reward_diff_obt_forg_diff", "reward_diff_obt_forg"), + ("prop_same_choice_diff", "prop_same_choice"), + ("prop_WSLS_1_diff", "prop_WSLS_1"), + ("prop_WSLS_2_diff", "prop_WSLS_2"), +] + + +class MilkyWay: + """ + Compute descriptive statistics from the two-armed bandit tasks in BrainExplorer, *Milky Way* (reward) and *Pirate Market* (punishment). + + The metrics of the two games are computed separately, and :meth:`difference_metrics` compares them within participants. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. + + Attributes + ---------- + MW_data, PM_data : pandas.DataFrame + The Milky Way and Pirate Market trials that pass the trial-level exclusion criteria. + results_MW, results_PM : pandas.DataFrame + The metrics of each participant in each game, filled by :meth:`metrics`. + results_diff : pandas.DataFrame + The differences between the games, filled by :meth:`difference_metrics`. + cleanedresults_MW, cleanedresults_PM, cleanedresults_diff : pandas.DataFrame + The results of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants_MW, deleted_participants_PM, deleted_participants_diff : int + The number of participants that :meth:`clean_data` excluded from each table. + codebook : dict + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.bandits import MilkyWay + >>> milky_way = MilkyWay("2025-02-20_MilkyWay_Data.csv") + >>> results_MW, results_PM = milky_way.metrics() + >>> differences = milky_way.difference_metrics() + >>> cleaned_MW, cleaned_PM, cleaned_diff = milky_way.clean_data() + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``trial_type``: the game, ``"reward"`` for Milky Way or ``"punish"`` for Pirate Market. + - ``run``: the attempt number of the participant. + - ``date``: the date and time of the trial. + - ``correct``: whether the choice was correct (1) or incorrect (0). + - ``outchosen``: the outcome of the chosen option. + - ``obt_min_forg``: the obtained minus the foregone outcome. + - ``rep``: whether the choice repeated the previous one (1) or not (0). + - ``WSLS_v1``, ``WSLS_v2``: whether the choice was a win-stay/lose-shift choice, in two versions of the definition. + + Only the first attempt of each participant in each game is kept, which is not necessarily ``run`` 1 for Pirate Market. + + Response times are not analysed. They would have to be corrected for the average time between the previous choice and the presentation of the new stimuli, 4200 ms, which can make them negative. + """ + + def __init__(self, filepath=None): + data = load_data(filepath, "MilkyWay") + require(data, COLUMNS, "MilkyWay") + self.data = data + self.MW_data = _first_attempt(data[data["trial_type"] == "reward"]) + self.PM_data = _first_attempt(data[data["trial_type"] == "punish"]) + + self.results_MW = pd.DataFrame() + self.results_PM = pd.DataFrame() + self.results_diff = pd.DataFrame() + + self.codebook = { + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "trial_type": "Type of trial (Milky Way or Pirate Market)", + "date": "Date and time of the first trial", + "day_of_week": "Day of the week of the first trial", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the first trial (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + } + for name, suffix in GAMES.values(): + self.codebook.update( + { + f"accuracy_{suffix}": f"Mean accuracy for {name} trials", + f"mean_outcome_{suffix}": f"Mean outcome for {name} trials", + f"reward_diff_obt_forg_{suffix}": f"Mean difference between obtained and foregone reward for {name} trials", + f"prop_same_choice_{suffix}": f"Proportion of same choice in {name} trials", + f"prop_WSLS_1_{suffix}": f"Proportion of win-stay/lose-shift choices (version 1) for {name} trials", + f"prop_WSLS_2_{suffix}": f"Proportion of win-stay/lose-shift choices (version 2) for {name} trials", + } + ) + self.codebook.update( + { + "accuracy_diff": "Difference in accuracy: Milky Way minus Pirate Market", + "outcome_diff": "Difference in mean outcome: Milky Way minus Pirate Market", + "reward_diff_obt_forg_diff": "Difference in the mean obtained minus foregone reward: Milky Way minus Pirate Market", + "prop_same_choice_diff": "Difference in the proportion of same choices: Milky Way minus Pirate Market", + "prop_WSLS_1_diff": "Difference in the proportion of win-stay/lose-shift choices (version 1): Milky Way minus Pirate Market", + "prop_WSLS_2_diff": "Difference in the proportion of win-stay/lose-shift choices (version 2): Milky Way minus Pirate Market", + } + ) + + def metrics(self): + """ + Compute the metrics of each participant in each game. + + Returns + ------- + tuple of pandas.DataFrame + The metrics of Milky Way and of Pirate Market, one row per participant, also stored in `results_MW` and `results_PM`. The columns are described in `codebook`. + """ + self.results_MW = _game_metrics(self.MW_data, *GAMES["reward"]) + self.results_PM = _game_metrics(self.PM_data, *GAMES["punish"]) + return self.results_MW, self.results_PM + + def difference_metrics(self): + """ + Compute the difference between the metrics of Milky Way and Pirate Market, for each participant who played both. + + Runs :meth:`metrics` first if it has not been run yet. + + Returns + ------- + pandas.DataFrame + The differences, Milky Way minus Pirate Market, one row per participant, also stored in `results_diff`. The columns are described in `codebook`. + """ + if self.results_MW.empty and self.results_PM.empty: + self.metrics() + columns = ["userID"] + [name for name, _ in DIFFERENCES] + if self.results_MW.empty or self.results_PM.empty: + self.results_diff = pd.DataFrame(columns=columns) + return self.results_diff + both = self.results_MW.merge(self.results_PM, on="userID", sort=False) + for name, metric in DIFFERENCES: + both[name] = both[f"{metric}_MW"] - both[f"{metric}_PM"] + self.results_diff = both[columns].reset_index(drop=True) + return self.results_diff + + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. + + Runs :meth:`metrics` and :meth:`difference_metrics` first if they have not been run yet. + + Returns + ------- + tuple of pandas.DataFrame + The metrics of Milky Way, of Pirate Market, and their differences, for the participants that pass the criteria, also stored in `cleanedresults_MW`, `cleanedresults_PM` and `cleanedresults_diff`. + + Notes + ----- + A participant is excluded from the metrics of a game if + + - they made the same choice on at least 95% of the trials, + - their accuracy is missing, + - they played more than 72 trials of the game (due to a technical error). + + The differences are kept only for participants who pass the criteria in both games. + """ + if self.results_MW.empty and self.results_PM.empty: + self.metrics() + if self.results_diff.empty: + self.difference_metrics() + + self.cleanedresults_MW = _clean_game(self.results_MW, self.MW_data, "MW") + self.cleanedresults_PM = _clean_game(self.results_PM, self.PM_data, "PM") + self.deleted_participants_MW = _n_users(self.results_MW) - _n_users(self.cleanedresults_MW) + self.deleted_participants_PM = _n_users(self.results_PM) - _n_users(self.cleanedresults_PM) + + both = set(_users(self.cleanedresults_MW)) & set(_users(self.cleanedresults_PM)) + self.cleanedresults_diff = self.results_diff[self.results_diff["userID"].isin(both)].copy() + self.deleted_participants_diff = _n_users(self.results_diff) - _n_users(self.cleanedresults_diff) + + return self.cleanedresults_MW, self.cleanedresults_PM, self.cleanedresults_diff + + def get_codebook(self): + """ + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. + """ + return self.codebook + + +def _first_attempt(data): + """The trials of the first attempt of each participant, which is their lowest `run`.""" + first = data.groupby("userID")["run"].transform("min") + return data[data["run"] == first].reset_index(drop=True) + + +def _game_metrics(data, name, suffix): + """The metrics of each participant in one game.""" + rows = [] + with quiet_empty_slices(): + for user_id, user_data in data.groupby("userID"): + row = {"userID": user_id, "n_trials": len(user_data), "trial_type": name} + row.update(session_time(user_data["date"].iloc[0])) + row[f"accuracy_{suffix}"] = np.nanmean(user_data["correct"]) + row[f"mean_outcome_{suffix}"] = np.nanmean(user_data["outchosen"]) + row[f"reward_diff_obt_forg_{suffix}"] = np.nanmean(user_data["obt_min_forg"]) + row[f"prop_same_choice_{suffix}"] = np.nanmean(user_data["rep"]) + row[f"prop_WSLS_1_{suffix}"] = np.nanmean(user_data["WSLS_v1"]) + row[f"prop_WSLS_2_{suffix}"] = np.nanmean(user_data["WSLS_v2"]) + rows.append(row) + return pd.DataFrame(rows) + + +def _clean_game(results, data, suffix): + """The metrics of one game, for the participants that pass the exclusion criteria.""" + if results.empty: + return results.copy() + trials = data.groupby("userID").size() + keep = results["userID"].isin(trials.index[trials <= 72]) + keep &= results[f"prop_same_choice_{suffix}"] < 0.95 + keep &= results[f"accuracy_{suffix}"].notna() + return results[keep].copy() + + +def _users(results): + return results["userID"].unique() if "userID" in results else [] + + +def _n_users(results): + return len(_users(results)) diff --git a/cpm/brainexplorer/information_gathering.py b/cpm/brainexplorer/information_gathering.py new file mode 100644 index 0000000..1fa7c5b --- /dev/null +++ b/cpm/brainexplorer/information_gathering.py @@ -0,0 +1,167 @@ +import re + +import numpy as np +import pandas as pd + +from ._utils import load_data, quiet_empty_slices, require, session_time + +__all__ = ["TreasureHunt"] + +COLUMNS = ["userID", "date", "run", "outcome", "choseCurEv", "confidence", "draws", "median_diffRT", "confidenceRT"] + + +class TreasureHunt: + """ + Compute descriptive statistics from the information-gathering task in BrainExplorer, *Treasure Hunt*. + + On each trial, participants draw samples of evidence until they decide to stop and choose an option, and then rate their confidence in their choice. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. + + Attributes + ---------- + data : pandas.DataFrame + The trials that pass the trial-level exclusion criteria. + results : pandas.DataFrame + The metrics of each participant, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. + codebook : dict + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.information_gathering import TreasureHunt + >>> treasure_hunt = TreasureHunt("2025-02-20_TreasureHunt_Data.csv") + >>> results = treasure_hunt.metrics() + >>> cleaned = treasure_hunt.clean_data() + >>> treasure_hunt.get_codebook()["mean_n_draws"] + 'Mean number of stimulus draws before making a decision' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``date``: the date and time of the trial. + - ``run``: the attempt number of the participant. + - ``outcome``: the points received on the trial. + - ``confidence``: the confidence rating. + - ``confidenceRT``: the response time of the confidence rating, in ms. + - ``draws``: the number of draws on the trial. + - ``choseCurEv``: whether the choice was in line with the current evidence (1) or not (0). + - ``median_diffRT``: the median response time between the actions of the trial, in ms. + + If the data contain a column ``ev``, the evidence after each draw as a list of integers such as ``"[1 2 -1]"``, it is converted to lists of integers. + + Only the first attempt of each participant (``run`` equal to 1) is kept. + """ + + def __init__(self, filepath=None): + data = load_data(filepath, "TreasureHunt") + require(data, COLUMNS, "TreasureHunt") + data = data[data["run"] == 1].copy() # only keep the first attempt + if "ev" in data: + data["ev"] = data["ev"].apply(_parse_evidence) + + self.data = data + self.results = pd.DataFrame() + self.codebook = { + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "date": "Date and time of the first trial", + "day_of_week": "Day of the week of the first trial", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the first trial (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + "mean_points": "Mean points received across all trials", + "accuracy": "Mean accuracy (proportion of choices in line with the current evidence)", + "mean_confidence": "Mean confidence rating across all trials", + "sd_confidence": "Standard deviation of confidence ratings across all trials", + "mean_n_draws": "Mean number of stimulus draws before making a decision", + "sd_n_draws": "Standard deviation of the number of stimulus draws before making a decision", + "median_RT_between_actions": "Median reaction time between actions (ms)", + "median_confidenceRT": "Median confidence reaction time across all trials (ms)", + "unique_draws": "Number of unique values in number of draws", + } + + def metrics(self): + """ + Compute the metrics of each participant. + + Returns + ------- + pandas.DataFrame + The metrics, one row per participant, also stored in `results`. The columns are described in `codebook`. + """ + rows = [] + with quiet_empty_slices(): + for user_id, user_data in self.data.groupby("userID"): + row = {"userID": user_id, "n_trials": len(user_data)} + row.update(session_time(user_data["date"].iloc[0])) + row["mean_points"] = np.nanmean(user_data["outcome"]) + row["accuracy"] = np.nanmean(user_data["choseCurEv"]) + row["mean_confidence"] = np.nanmean(user_data["confidence"]) + row["sd_confidence"] = np.nanstd(user_data["confidence"]) + row["mean_n_draws"] = np.nanmean(user_data["draws"]) + row["sd_n_draws"] = np.nanstd(user_data["draws"]) + row["median_RT_between_actions"] = np.nanmedian(user_data["median_diffRT"]) + row["median_confidenceRT"] = np.nanmedian(user_data["confidenceRT"]) + row["unique_draws"] = user_data["draws"].nunique() + rows.append(row) + self.results = pd.DataFrame(rows) + return self.results + + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. + + Runs :meth:`metrics` first if it has not been run yet. + + Returns + ------- + pandas.DataFrame + The metrics of the participants that pass the criteria, also stored in `cleanedresults`. + + Notes + ----- + A participant is excluded if + + - the mean number of draws is below 2 or above 23, + - at least 20% of their choices were not in line with the current evidence (an accuracy of 0.8 or below), + - the number of draws took fewer than 3 different values. + """ + if self.results.empty: + self.metrics() + results = self.results + n_before = results["userID"].nunique() + + keep = results["mean_n_draws"].between(2, 23) + keep &= results["accuracy"] > 0.8 + keep &= results["unique_draws"] >= 3 + + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() + return self.cleanedresults + + def get_codebook(self): + """ + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. + """ + return self.codebook + + +def _parse_evidence(value): + """Turn evidence such as ``"[1 2 -1]"`` or ``"[1, 2, -1]"`` into a list of integers; other values are kept.""" + if isinstance(value, str): + return [int(number) for number in re.findall(r"-?\d+", value)] + return value diff --git a/cpm/brainexplorer/perceptual_decision_making.py b/cpm/brainexplorer/perceptual_decision_making.py new file mode 100644 index 0000000..44bb774 --- /dev/null +++ b/cpm/brainexplorer/perceptual_decision_making.py @@ -0,0 +1,263 @@ +import numpy as np +import pandas as pd + +from ._utils import load_data, quiet_empty_slices, require, session_time + +__all__ = ["SpaceObserver"] + +COLUMNS = ["userID", "run", "Date", "accuracy", "RT_choice", "confidence", "confidenceRT", "stimulus_intensity"] + + +class SpaceObserver: + """ + Compute descriptive statistics from the perceptual decision-making task with confidence judgements in BrainExplorer, *Space Observer*. + + On each trial, participants decide which of two types of alien is more numerous, and rate their confidence in their choice. + For a more complete description of the task, see Dome, Moses-Payne, and Hauser (in prep.) or Marzuki et al. (2025). + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. + + Attributes + ---------- + data_raw : pandas.DataFrame + The trials that pass the trial-level exclusion criteria. + data_processed : pandas.DataFrame + The metrics of each session, one row per participant and session, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the sessions that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. + codebook : dict + The description of each column of the metrics. + + Examples + -------- + >>> from cpm.brainexplorer.perceptual_decision_making import SpaceObserver + >>> observer = SpaceObserver("2025-02-20_SpaceObserver_Data.csv") + >>> results = observer.metrics() + >>> cleaned = observer.clean_data() + >>> observer.get_codebook()["accuracy"] + 'Mean accuracy across all trials (proportion correct)' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``run``: the session number of the participant. + - ``Date``: the date and time of the trial. + - ``accuracy``: whether the choice was correct (1) or incorrect (0). + - ``RT_choice``: the response time of the choice, in ms. + - ``confidence``: the confidence rating. + - ``confidenceRT``: the response time of the confidence rating, in ms. + - ``stimulus_intensity``: the evidence strength, the difference in evidence between the two types of stimuli. + + Trials are excluded if the response time of the choice or of the confidence rating is below 150 ms or above 10000 ms, or if they have no confidence rating, since those are practice trials. + For participant-level exclusions, see :meth:`clean_data`, and Dome, Moses-Payne, and Hauser (in prep.). + + References + ---------- + Dome, L., Moses-Payne, M. E., & Hauser, T. U. (in prep.). Age-related shifts in metacognition reveals converging confidence for correct and error judgments across the lifespan. + + Marzuki, A., Kosina, L., Dome, L., Hewitt, S., & Hauser, T. (2025). Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD. *Research Square*. https://doi.org/10.21203/rs.3.rs-7544256/v1 + """ + + def __init__(self, filepath=None): + data = load_data(filepath, "SpaceObserver", na_values=["NaN", "nan"]) + require(data, COLUMNS, "SpaceObserver") + + data = data[data["RT_choice"].between(150, 10000)] + data = data[data["confidenceRT"].between(150, 10000)] + # trials without confidence ratings are practice trials + confidence = data["confidence"].replace(["NaN", "nan", "NAN", ""], np.nan) + data = data[confidence.notna()].copy() + data["confidence"] = pd.to_numeric(data["confidence"], errors="coerce") + + self.data_raw = data + self.data_processed = pd.DataFrame() + self.codebook = { + # --- Participant info --- + "userID": "Unique identifier for each participant", + "run": "Session number for the participant", + "date": "Date and time of the first trial in the session", + "n_trials": "Number of trials completed by the participant", + "day_of_week": "Day of the week the session started", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the session (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + # --- Accuracy --- + "accuracy": "Mean accuracy across all trials (proportion correct)", + # --- Choice reaction time --- + "mean_RT": "Mean choice response time across all trials (ms)", + "median_RT": "Median choice response time across all trials (ms)", + "median_RT_correct": "Median choice response time for correct trials (ms)", + "median_RT_incorrect": "Median choice response time for incorrect trials (ms)", + "diff_median_RT_correct_incorrect": "Difference in median choice RT: correct minus incorrect (ms)", + # --- Confidence --- + "mean_confidence": "Mean confidence rating across all trials", + "sd_confidence": "Standard deviation of confidence ratings across all trials", + "median_confidence": "Median confidence rating across all trials", + "mean_confidence_correct": "Mean confidence rating for correct trials", + "mean_confidence_incorrect": "Mean confidence rating for incorrect trials", + "median_confidence_correct": "Median confidence rating for correct trials", + "median_confidence_incorrect": "Median confidence rating for incorrect trials", + "diff_median_conf_correct_incorrect": "Difference in median confidence: correct minus incorrect trials", + "sd_confidence_correct": "Standard deviation of confidence ratings for correct trials", + "sd_confidence_incorrect": "Standard deviation of confidence ratings for incorrect trials", + "diff_sd_confidence_correct_incorrect": "Difference in confidence SD: correct minus incorrect trials", + # --- Confidence reaction time --- + "mean_confidenceRT": "Mean confidence response time across all trials (ms)", + "median_confidenceRT": "Median confidence response time across all trials (ms)", + "median_confidenceRT_correct": "Median confidence response time for correct trials (ms)", + "median_confidenceRT_incorrect": "Median confidence response time for incorrect trials (ms)", + "diff_median_confidenceRT_correct_incorrect": "Difference in median confidence RT: correct minus incorrect (ms)", + # --- Confidence percentiles --- + "confidence_10": "10th percentile of confidence ratings across all trials", + "confidence_25": "25th percentile of confidence ratings across all trials", + "confidence_75": "75th percentile of confidence ratings across all trials", + "confidence_90": "90th percentile of confidence ratings across all trials", + # --- Evidence strength --- + "evidence_strength_mean": "Mean evidence strength (stimulus intensity) across all trials", + "evidence_strength_correct_mean": "Mean evidence strength for correct trials", + "evidence_strength_incorrect_mean": "Mean evidence strength for incorrect trials", + "diff_evidence_strength_correct_incorrect": "Difference in mean evidence strength: correct minus incorrect trials", + "median_ES": "Median evidence strength across all trials", + # --- Evidence strength bins (trial-order quarters) --- + "ES_bin_1": "Mean evidence strength for the first quarter of trials", + "ES_bin_2": "Mean evidence strength for the second quarter of trials", + "ES_bin_3": "Mean evidence strength for the third quarter of trials", + "ES_bin_4": "Mean evidence strength for the fourth quarter of trials", + } + + def metrics(self): + """ + Compute the metrics of each session of each participant. + + Returns + ------- + pandas.DataFrame + The metrics, one row per participant and session, also stored in `data_processed`. The columns are described in `codebook`. + + Notes + ----- + Correct trials are those with an ``accuracy`` of 1, and incorrect trials those with an ``accuracy`` of 0. + Metrics of an empty selection, such as the median response time of incorrect trials when every choice was correct, are NaN. + The evidence strength bins split the trials of a session, in the order they were played, into four parts of (nearly) equal size. + """ + rows = [] + with quiet_empty_slices(): + for (user_id, run), user_data in self.data_raw.groupby(["userID", "run"]): + rows.append({"userID": user_id, "run": run, **_session_metrics(user_data)}) + self.data_processed = pd.DataFrame(rows) + return self.data_processed + + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. + + Runs :meth:`metrics` first if it has not been run yet. + + Returns + ------- + pandas.DataFrame + The metrics of the sessions that pass the criteria, also stored in `cleanedresults`. + + Notes + ----- + A session is excluded if + + - the mean accuracy is below 0.5, + - the median response time of the choices is above 3000 ms, + - the median response time of the confidence ratings is above 3000 ms, + - the median evidence strength is above 25, + - the median confidence is below 3 or above 97, + - the 10th and 25th percentiles of confidence are equal, and so are the 75th and 90th percentiles. + + Participants with more than 80 trials in total, across all their sessions, are excluded entirely (due to a technical error). + """ + if self.data_processed.empty: + self.metrics() + results = self.data_processed + n_before = results["userID"].nunique() + + trials = self.data_raw.groupby("userID").size() + keep = results["userID"].isin(trials.index[trials <= 80]) + keep &= results["accuracy"] >= 0.5 + keep &= results["median_RT"] <= 3000 + keep &= results["median_confidenceRT"] <= 3000 + keep &= results["median_ES"] <= 25 + keep &= results["median_confidence"].between(3, 97) + flat = (results["confidence_10"] == results["confidence_25"]) & ( + results["confidence_75"] == results["confidence_90"] + ) + keep &= ~flat + + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() + return self.cleanedresults + + def get_codebook(self): + """ + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. + """ + return self.codebook + + +def _session_metrics(data): + """The metrics of one session.""" + correct = (data["accuracy"] == 1).to_numpy() + incorrect = (data["accuracy"] == 0).to_numpy() + rt = data["RT_choice"].to_numpy(dtype=float) + confidence = data["confidence"].to_numpy(dtype=float) + confidence_rt = data["confidenceRT"].to_numpy(dtype=float) + evidence = data["stimulus_intensity"].to_numpy(dtype=float) + + out = {"n_trials": len(data), **session_time(data["Date"].iloc[0])} + out["accuracy"] = np.nanmean(data["accuracy"]) + + out["mean_RT"] = np.nanmean(rt) + out["median_RT"] = np.nanmedian(rt) + out["median_RT_correct"] = np.nanmedian(rt[correct]) + out["median_RT_incorrect"] = np.nanmedian(rt[incorrect]) + out["diff_median_RT_correct_incorrect"] = out["median_RT_correct"] - out["median_RT_incorrect"] + + out["mean_confidence"] = np.nanmean(confidence) + out["sd_confidence"] = np.nanstd(confidence) + out["median_confidence"] = np.nanmedian(confidence) + out["mean_confidence_correct"] = np.nanmean(confidence[correct]) + out["mean_confidence_incorrect"] = np.nanmean(confidence[incorrect]) + out["median_confidence_correct"] = np.nanmedian(confidence[correct]) + out["median_confidence_incorrect"] = np.nanmedian(confidence[incorrect]) + out["diff_median_conf_correct_incorrect"] = out["median_confidence_correct"] - out["median_confidence_incorrect"] + out["sd_confidence_correct"] = np.nanstd(confidence[correct]) + out["sd_confidence_incorrect"] = np.nanstd(confidence[incorrect]) + out["diff_sd_confidence_correct_incorrect"] = out["sd_confidence_correct"] - out["sd_confidence_incorrect"] + + out["mean_confidenceRT"] = np.nanmean(confidence_rt) + out["median_confidenceRT"] = np.nanmedian(confidence_rt) + out["median_confidenceRT_correct"] = np.nanmedian(confidence_rt[correct]) + out["median_confidenceRT_incorrect"] = np.nanmedian(confidence_rt[incorrect]) + out["diff_median_confidenceRT_correct_incorrect"] = ( + out["median_confidenceRT_correct"] - out["median_confidenceRT_incorrect"] + ) + + for percentile in (10, 25, 75, 90): + out[f"confidence_{percentile}"] = np.nanpercentile(confidence, percentile) + + out["evidence_strength_mean"] = np.nanmean(evidence) + out["evidence_strength_correct_mean"] = np.nanmean(evidence[correct]) + out["evidence_strength_incorrect_mean"] = np.nanmean(evidence[incorrect]) + out["diff_evidence_strength_correct_incorrect"] = ( + out["evidence_strength_correct_mean"] - out["evidence_strength_incorrect_mean"] + ) + for i, part in enumerate(np.array_split(evidence, 4), start=1): + out[f"ES_bin_{i}"] = np.nanmean(part) if len(part) > 0 else np.nan + out["median_ES"] = np.nanmedian(evidence) + return out diff --git a/cpm/brainexplorer/risky_decision_making.py b/cpm/brainexplorer/risky_decision_making.py new file mode 100644 index 0000000..cd63082 --- /dev/null +++ b/cpm/brainexplorer/risky_decision_making.py @@ -0,0 +1,408 @@ +import numpy as np +import pandas as pd + +from ._utils import load_data, proportion, quiet_empty_slices, require, session_time + +__all__ = ["Scavenger"] + +COLUMNS = [ + "userID", + "run", + "date", + "RT", + "chosen", + "ambiguousTrial", + "abovechance", + "EV_diff_chosen", + "safe_EV", + "outcome", + "nbcorrect_gain", + "resp_rep", + "resp_rep_sr", +] + + +class Scavenger: + """ + Compute descriptive statistics from the risky decision-making task in BrainExplorer, *Scavenger*. + + On each trial, participants choose between two options: the safe option returns its outcome with 100% probability, while the risky option returns one of two outcomes with 50% probability each. + In loss trials, the expected value of the safe option is negative. + In ambiguous trials, the probabilities of the outcomes of the risky option are only partly shown. + + Parameters + ---------- + filepath : str, os.PathLike or pandas.DataFrame + The data, as a DataFrame or the path to a CSV file. The column names must follow the convention in Notes. + + Attributes + ---------- + data : pandas.DataFrame + The trials that pass the trial-level exclusion criteria, with an added column ``risky``, 1 for risky and 0 for safe choices. + results : pandas.DataFrame + The metrics of each participant, filled by :meth:`metrics`. + cleanedresults : pandas.DataFrame + The metrics of the participants that pass the participant-level exclusion criteria, filled by :meth:`clean_data`. + deleted_participants : int + The number of participants that :meth:`clean_data` excluded. + codebook : dict + The description of each column of the metrics. + + Raises + ------ + ValueError + If ``chosen`` contains values other than 1 (safe) and 2 (risky). + + Examples + -------- + >>> from cpm.brainexplorer.risky_decision_making import Scavenger + >>> scavenger = Scavenger("2025-01-10_Scavenger.csv") + >>> results = scavenger.metrics() + >>> cleaned = scavenger.clean_data() + >>> scavenger.get_codebook()["risky_choices"] + 'Proportion of risky choices overall' + + Notes + ----- + The data must contain the following columns: + + - ``userID``: the unique identifier of the participant. + - ``run``: the attempt number of the participant. + - ``date``: the date and time of the trial. + - ``RT``: the response time, in ms. + - ``chosen``: the chosen option, 1 for safe and 2 for risky. + - ``ambiguousTrial``: whether the trial was ambiguous (1) or not (0). + - ``abovechance``: whether the choice was above chance level (1) or not (0). + - ``EV_diff_chosen``: the expected value of the chosen minus the unchosen option. A choice is rational if it is 0 or more. + - ``safe_EV``: the expected value of the safe option; trials where it is negative are loss trials, the others win trials. + - ``outcome``: the points received on the trial. + - ``nbcorrect_gain``: whether the choice in a no-brainer gain (catch) trial was correct (1) or not (0). + - ``resp_rep``: whether the left/right response repeated the previous one (1) or not (0). + - ``resp_rep_sr``: whether the safe/risky choice repeated the previous one (1) or not (0). + - ``config_id``: the version of the game. Only needed by :meth:`clean_data`. + + Trials are excluded if the response time is below 150 ms or above 10000 ms, and only the first attempt of each participant (``run`` equal to 1) is kept. + + References + ---------- + Habicht, J., Dubois, M., Michely, J., & Hauser, T. U. (2022). Do propranolol and amisulpride modulate confidence in risk-taking? *Wellcome Open Research*, 7, 23. + """ + + def __init__(self, filepath=None): + data = load_data(filepath, "Scavenger") + require(data, COLUMNS, "Scavenger") + data = data[data["run"] == 1] # only keep the first attempt + data = data[data["RT"].between(150, 10000)].copy() + + codes = set(data["chosen"].dropna().unique()) + if not codes <= {1, 2}: + raise ValueError( + f"Scavenger expects 'chosen' to be 1 (safe) or 2 (risky), but the data contain {sorted(codes)}." + ) + data["risky"] = data["chosen"] - 1 + + self.data = data + self.results = pd.DataFrame() + self.codebook = { + # --- Participant info --- + "userID": "Unique identifier for each participant", + "n_trials": "Number of trials completed by the participant", + "date": "Date and time of the first trial", + "day_of_week": "Day of the week the session started", + "time": "Clock time of the first trial", + "time_of_day": "Time of day of the session (night: 0-6h, morning: 6-12h, afternoon: 12-18h, evening: 18-24h)", + # --- Reaction time --- + "mean_RT": "Mean reaction time across all trials (ms)", + "median_RT": "Median reaction time across all trials (ms)", + "median_RT_safe": "Median reaction time for safe choices (ms)", + "median_RT_risky": "Median reaction time for risky choices (ms)", + "median_RT_win": "Median reaction time for win trials (ms)", + "median_RT_loss": "Median reaction time for loss trials (ms)", + "median_RT_ambg": "Median reaction time for ambiguous trials (ms)", + "median_RT_NoAmbg": "Median reaction time for non-ambiguous trials (ms)", + "median_RT_win_ambg": "Median reaction time for win trials that were ambiguous (ms)", + "median_RT_win_NoAmbg": "Median reaction time for win trials that were non-ambiguous (ms)", + "median_RT_loss_ambg": "Median reaction time for loss trials that were ambiguous (ms)", + "median_RT_loss_NoAmbg": "Median reaction time for loss trials that were non-ambiguous (ms)", + "diff_RT_win_amg_win_noAmbg": "Difference in median RT: win-ambiguous minus win-non-ambiguous (ms)", + "diff_RT_loss_amg_loss_noAmbg": "Difference in median RT: loss-ambiguous minus loss-non-ambiguous (ms)", + "diff_RT_win_loss_ambg": "Difference in median RT: win minus loss in ambiguous trials (ms)", + "diff_RT_win_loss_noAmbg": "Difference in median RT: win minus loss in non-ambiguous trials (ms)", + "diff_RT_win_ambg_loss_noAmbg": "Difference in median RT: win-ambiguous minus loss-non-ambiguous (ms)", + "diff_RT_loss_ambg_win_noAmbg": "Difference in median RT: loss-ambiguous minus win-non-ambiguous (ms)", + # --- Points / outcomes --- + "total_rewards": "Total points received across all trials", + "mean_points": "Mean points received across all trials", + "mean_points_safe": "Mean points received when choosing safe", + "mean_points_risky": "Mean points received when choosing risky", + "mean_points_ambg": "Mean points received in ambiguous trials", + "mean_points_NoAmbg": "Mean points received in non-ambiguous trials", + "mean_points_win_ambg": "Mean points received in win trials that were ambiguous", + "mean_points_win_NoAmbg": "Mean points received in win trials that were non-ambiguous", + "mean_points_loss_ambg": "Mean points received in loss trials that were ambiguous", + "mean_points_loss_NoAmbg": "Mean points received in loss trials that were non-ambiguous", + "diff_points_win_amg_win_noAmbg": "Difference in mean points: win-ambiguous minus win-non-ambiguous", + "diff_points_loss_amg_loss_noAmbg": "Difference in mean points: loss-ambiguous minus loss-non-ambiguous", + "diff_points_win_loss_ambg": "Difference in mean points: win minus loss in ambiguous trials", + "diff_points_win_loss_noAmbg": "Difference in mean points: win minus loss in non-ambiguous trials", + "diff_points_win_ambg_loss_noAmbg": "Difference in mean points: win-ambiguous minus loss-non-ambiguous", + "diff_points_loss_ambg_win_noAmbg": "Difference in mean points: loss-ambiguous minus win-non-ambiguous", + # --- Choice stickiness --- + "choice_stickiness": "Number of consecutive trial pairs where the same option (safe or risky) was chosen", + # --- Risky choice rates --- + "risky_choices": "Proportion of risky choices overall", + "win_risk": "Proportion of risky choices in win trials", + "loss_risk": "Proportion of risky choices in loss trials", + "diff_loss_win_risk": "Difference in risky choice rate: loss minus win trials", + "risk_Ambg": "Proportion of risky choices in ambiguous trials", + "risk_NoAmbg": "Proportion of risky choices in non-ambiguous trials", + "diff_Ambg_NoAmbg_risk": "Difference in risky choice rate: ambiguous minus non-ambiguous trials", + "win_risk_abg": "Proportion of risky choices in win-ambiguous trials", + "win_risk_NoAbg": "Proportion of risky choices in win-non-ambiguous trials", + "loss_risk_abg": "Proportion of risky choices in loss-ambiguous trials", + "loss_risk_NoAbg": "Proportion of risky choices in loss-non-ambiguous trials", + "diff_Ambg_NoAmbg_win_risk": "Difference in risky win rate: ambiguous minus non-ambiguous", + "diff_Ambg_NoAmbg_loss_risk": "Difference in risky loss rate: ambiguous minus non-ambiguous", + "diff_Ambg_win_loss_risk": "Difference in risky choice rate: win minus loss in ambiguous trials", + "diff_NoAmbg_win_loss_risk": "Difference in risky choice rate: win minus loss in non-ambiguous trials", + "diff_amg_win_nonAmg_loss_risk": "Difference in risky choice rate: win-ambiguous minus loss-non-ambiguous", + "diff_amg_loss_nonAmg_win_risk": "Difference in risky choice rate: loss-ambiguous minus win-non-ambiguous", + # --- Rational choices (overall, win/loss, ambiguity) --- + "rational_all": "Proportion of rational choices across all trials", + "rational_win": "Proportion of rational choices in win trials", + "rational_loss": "Proportion of rational choices in loss trials", + "diff_loss_win_rational": "Difference in rational choice rate: loss minus win trials", + "rational_abg": "Proportion of rational choices in ambiguous trials", + "rational_NoAbg": "Proportion of rational choices in non-ambiguous trials", + "diff_Ambg_NoAmbg_rational": "Difference in rational choice rate: ambiguous minus non-ambiguous", + "rational_win_abg": "Proportion of rational choices in win-ambiguous trials", + "rational_win_NoAbg": "Proportion of rational choices in win-non-ambiguous trials", + "rational_loss_abg": "Proportion of rational choices in loss-ambiguous trials", + "rational_loss_NoAbg": "Proportion of rational choices in loss-non-ambiguous trials", + "diff_rational_Ambg_NoAmbg_win": "Difference in rational win rate: ambiguous minus non-ambiguous", + "diff_rational_Ambg_NoAmbg_loss": "Difference in rational loss rate: ambiguous minus non-ambiguous", + "diff_rational_Ambg_win_loss": "Difference in rational choice rate: win minus loss in ambiguous trials", + "diff_rational_NoAmbg_win_loss": "Difference in rational choice rate: win minus loss in non-ambiguous trials", + "diff_rational_Ambg_win_NoAmbg_loss": "Difference in rational choice rate: win-ambiguous minus loss-non-ambiguous", + "diff_rational_Ambg_loss_NoAmbg_win": "Difference in rational choice rate: loss-ambiguous minus win-non-ambiguous", + # --- Rational choices (safe/risky) --- + "rational_safe": "Proportion of rational choices when choosing safe", + "rational_risky": "Proportion of rational choices when choosing risky", + "diff_rational_safe_risky": "Difference in rational choice rate: safe minus risky choices", + "rational_safe_win": "Proportion of rational choices for safe choices in win trials", + "rational_safe_loss": "Proportion of rational choices for safe choices in loss trials", + "rational_risky_win": "Proportion of rational choices for risky choices in win trials", + "rational_risky_loss": "Proportion of rational choices for risky choices in loss trials", + "diff_rational_safe_win_risky_win": "Difference in rational win rate: safe minus risky choices", + "diff_rational_safe_loss_risky_loss": "Difference in rational loss rate: safe minus risky choices", + "diff_rational_safe_win_risky_loss": "Difference in rational choice rate: safe-win minus risky-loss", + "diff_rational_safe_loss_risky_win": "Difference in rational choice rate: safe-loss minus risky-win", + "rational_safe_abg": "Proportion of rational choices for safe choices in ambiguous trials", + "rational_safe_NoAbg": "Proportion of rational choices for safe choices in non-ambiguous trials", + "rational_risky_abg": "Proportion of rational choices for risky choices in ambiguous trials", + "rational_risky_NoAbg": "Proportion of rational choices for risky choices in non-ambiguous trials", + "diff_rational_safe_Ambg_NoAmbg": "Difference in rational safe rate: ambiguous minus non-ambiguous", + "diff_rational_risky_Ambg_NoAmbg": "Difference in rational risky rate: ambiguous minus non-ambiguous", + "diff_rational_safe_risky_Ambg": "Difference in rational choice rate: safe minus risky in ambiguous trials", + "diff_rational_safe_risky_NoAmbg": "Difference in rational choice rate: safe minus risky in non-ambiguous trials", + # --- Quality / exclusion metrics --- + "nb_incorrect_gain": "Number of incorrect choices in no-brainer gain catch trials", + "above_chance": "Proportion of trials where choice was above chance level", + "chosen_prop_LeftRight": "Proportion of trials where the same left/right response was repeated", + "chosen_prop_SafeRisky": "Proportion of trials where the same safe/risky choice was repeated", + } + + def metrics(self): + """ + Compute the metrics of each participant: response times, points, and the proportions of risky and rational choices, overall and by win/loss and ambiguous/non-ambiguous trials. + + Returns + ------- + pandas.DataFrame + The metrics, one row per participant, also stored in `results`. The columns are described in `codebook`. + + Notes + ----- + Metrics of an empty selection, such as the proportion of risky choices in ambiguous loss trials for a participant who played none, are NaN. + """ + rows = [] + with quiet_empty_slices(): + for user_id, user_data in self.data.groupby("userID"): + rows.append({"userID": user_id, **_participant_metrics(user_data)}) + self.results = pd.DataFrame(rows) + return self.results + + def clean_data(self): + """ + Exclude participants with the participant-level exclusion criteria. + + Runs :meth:`metrics` first if it has not been run yet. + + Returns + ------- + pandas.DataFrame + The metrics of the participants that pass the criteria, also stored in `cleanedresults`. + + Raises + ------ + KeyError + If the data have no ``config_id`` column. + + Notes + ----- + A participant is excluded if + + - they repeated the same left/right response on at least 95% of the trials, + - they failed 2 or more of the no-brainer catch trials, + - they played more than 40 trials (due to a technical error), + - they played the version of the game with 60 trials (``config_id`` 242). + """ + require(self.data, ["config_id"], "Scavenger.clean_data") + if self.results.empty: + self.metrics() + results = self.results + n_before = results["userID"].nunique() + + trials = self.data["userID"].value_counts() + long_version = self.data.loc[self.data["config_id"] == 242, "userID"].unique() + keep = results["userID"].isin(trials.index[trials <= 40]) + keep &= ~results["userID"].isin(long_version) + keep &= results["nb_incorrect_gain"] < 2 + keep &= results["chosen_prop_LeftRight"] < 0.95 + + self.cleanedresults = results[keep].copy() + self.deleted_participants = n_before - self.cleanedresults["userID"].nunique() + return self.cleanedresults + + def get_codebook(self): + """ + Return the codebook, which describes each column of the metrics. + + Returns + ------- + dict + The description of each column, keyed by column name. + """ + return self.codebook + + +def _participant_metrics(data): + """The metrics of one participant.""" + rt = data["RT"].to_numpy(dtype=float) + points = data["outcome"].to_numpy(dtype=float) + risky = data["risky"].to_numpy() == 1 + safe = data["risky"].to_numpy() == 0 + ambiguous = data["ambiguousTrial"].to_numpy(dtype=bool) + loss = (data["safe_EV"] < 0).to_numpy() + win = ~loss + rational = data["EV_diff_chosen"].to_numpy(dtype=float) >= 0 + + out = {"n_trials": len(data), **session_time(data["date"].iloc[0])} + + out["mean_RT"] = np.nanmean(rt) + out["median_RT"] = np.nanmedian(rt) + for name, mask in [ + ("safe", safe), + ("risky", risky), + ("win", win), + ("loss", loss), + ("ambg", ambiguous), + ("NoAmbg", ~ambiguous), + ("win_ambg", win & ambiguous), + ("win_NoAmbg", win & ~ambiguous), + ("loss_ambg", loss & ambiguous), + ("loss_NoAmbg", loss & ~ambiguous), + ]: + out[f"median_RT_{name}"] = np.nanmedian(rt[mask]) + out["diff_RT_win_amg_win_noAmbg"] = out["median_RT_win_ambg"] - out["median_RT_win_NoAmbg"] + out["diff_RT_loss_amg_loss_noAmbg"] = out["median_RT_loss_ambg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_win_loss_ambg"] = out["median_RT_win_ambg"] - out["median_RT_loss_ambg"] + out["diff_RT_win_loss_noAmbg"] = out["median_RT_win_NoAmbg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_win_ambg_loss_noAmbg"] = out["median_RT_win_ambg"] - out["median_RT_loss_NoAmbg"] + out["diff_RT_loss_ambg_win_noAmbg"] = out["median_RT_loss_ambg"] - out["median_RT_win_NoAmbg"] + + out["total_rewards"] = np.nansum(points) + out["mean_points"] = np.nanmean(points) + for name, mask in [ + ("safe", safe), + ("risky", risky), + ("ambg", ambiguous), + ("NoAmbg", ~ambiguous), + ("win_ambg", win & ambiguous), + ("win_NoAmbg", win & ~ambiguous), + ("loss_ambg", loss & ambiguous), + ("loss_NoAmbg", loss & ~ambiguous), + ]: + out[f"mean_points_{name}"] = np.nanmean(points[mask]) + out["diff_points_win_amg_win_noAmbg"] = out["mean_points_win_ambg"] - out["mean_points_win_NoAmbg"] + out["diff_points_loss_amg_loss_noAmbg"] = out["mean_points_loss_ambg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_win_loss_ambg"] = out["mean_points_win_ambg"] - out["mean_points_loss_ambg"] + out["diff_points_win_loss_noAmbg"] = out["mean_points_win_NoAmbg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_win_ambg_loss_noAmbg"] = out["mean_points_win_ambg"] - out["mean_points_loss_NoAmbg"] + out["diff_points_loss_ambg_win_noAmbg"] = out["mean_points_loss_ambg"] - out["mean_points_win_NoAmbg"] + + chosen = data["chosen"].to_numpy() + out["choice_stickiness"] = int(np.sum(chosen[1:] == chosen[:-1])) + + every = np.ones(len(data), dtype=bool) + out["risky_choices"] = proportion(risky, every) + out["win_risk"] = proportion(risky, win) + out["loss_risk"] = proportion(risky, loss) + out["diff_loss_win_risk"] = out["loss_risk"] - out["win_risk"] + out["risk_Ambg"] = proportion(risky, ambiguous) + out["risk_NoAmbg"] = proportion(risky, ~ambiguous) + out["diff_Ambg_NoAmbg_risk"] = out["risk_Ambg"] - out["risk_NoAmbg"] + out["win_risk_abg"] = proportion(risky, win & ambiguous) + out["win_risk_NoAbg"] = proportion(risky, win & ~ambiguous) + out["loss_risk_abg"] = proportion(risky, loss & ambiguous) + out["loss_risk_NoAbg"] = proportion(risky, loss & ~ambiguous) + out["diff_Ambg_NoAmbg_win_risk"] = out["win_risk_abg"] - out["win_risk_NoAbg"] + out["diff_Ambg_NoAmbg_loss_risk"] = out["loss_risk_abg"] - out["loss_risk_NoAbg"] + out["diff_Ambg_win_loss_risk"] = out["win_risk_abg"] - out["loss_risk_abg"] + out["diff_NoAmbg_win_loss_risk"] = out["win_risk_NoAbg"] - out["loss_risk_NoAbg"] + out["diff_amg_win_nonAmg_loss_risk"] = out["win_risk_abg"] - out["loss_risk_NoAbg"] + out["diff_amg_loss_nonAmg_win_risk"] = out["loss_risk_abg"] - out["win_risk_NoAbg"] + + out["rational_all"] = proportion(rational, every) + out["rational_win"] = proportion(rational, win) + out["rational_loss"] = proportion(rational, loss) + out["diff_loss_win_rational"] = out["rational_loss"] - out["rational_win"] + out["rational_abg"] = proportion(rational, ambiguous) + out["rational_NoAbg"] = proportion(rational, ~ambiguous) + out["diff_Ambg_NoAmbg_rational"] = out["rational_abg"] - out["rational_NoAbg"] + out["rational_win_abg"] = proportion(rational, win & ambiguous) + out["rational_win_NoAbg"] = proportion(rational, win & ~ambiguous) + out["rational_loss_abg"] = proportion(rational, loss & ambiguous) + out["rational_loss_NoAbg"] = proportion(rational, loss & ~ambiguous) + out["diff_rational_Ambg_NoAmbg_win"] = out["rational_win_abg"] - out["rational_win_NoAbg"] + out["diff_rational_Ambg_NoAmbg_loss"] = out["rational_loss_abg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_win_loss"] = out["rational_win_abg"] - out["rational_loss_abg"] + out["diff_rational_NoAmbg_win_loss"] = out["rational_win_NoAbg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_win_NoAmbg_loss"] = out["rational_win_abg"] - out["rational_loss_NoAbg"] + out["diff_rational_Ambg_loss_NoAmbg_win"] = out["rational_loss_abg"] - out["rational_win_NoAbg"] + + out["rational_safe"] = proportion(rational, safe) + out["rational_risky"] = proportion(rational, risky) + out["diff_rational_safe_risky"] = out["rational_safe"] - out["rational_risky"] + out["rational_safe_win"] = proportion(rational, safe & win) + out["rational_safe_loss"] = proportion(rational, safe & loss) + out["rational_risky_win"] = proportion(rational, risky & win) + out["rational_risky_loss"] = proportion(rational, risky & loss) + out["diff_rational_safe_win_risky_win"] = out["rational_safe_win"] - out["rational_risky_win"] + out["diff_rational_safe_loss_risky_loss"] = out["rational_safe_loss"] - out["rational_risky_loss"] + out["diff_rational_safe_win_risky_loss"] = out["rational_safe_win"] - out["rational_risky_loss"] + out["diff_rational_safe_loss_risky_win"] = out["rational_safe_loss"] - out["rational_risky_win"] + out["rational_safe_abg"] = proportion(rational, safe & ambiguous) + out["rational_safe_NoAbg"] = proportion(rational, safe & ~ambiguous) + out["rational_risky_abg"] = proportion(rational, risky & ambiguous) + out["rational_risky_NoAbg"] = proportion(rational, risky & ~ambiguous) + out["diff_rational_safe_Ambg_NoAmbg"] = out["rational_safe_abg"] - out["rational_safe_NoAbg"] + out["diff_rational_risky_Ambg_NoAmbg"] = out["rational_risky_abg"] - out["rational_risky_NoAbg"] + out["diff_rational_safe_risky_Ambg"] = out["rational_safe_abg"] - out["rational_risky_abg"] + out["diff_rational_safe_risky_NoAmbg"] = out["rational_safe_NoAbg"] - out["rational_risky_NoAbg"] + + out["nb_incorrect_gain"] = int((data["nbcorrect_gain"] == 0).sum()) + out["above_chance"] = np.nanmean(data["abovechance"]) + out["chosen_prop_LeftRight"] = np.nanmean(data["resp_rep"]) + out["chosen_prop_SafeRisky"] = np.nanmean(data["resp_rep_sr"]) + return out diff --git a/cpm/core/optimisers.py b/cpm/core/optimisers.py index d5aa3fc..64beefe 100644 --- a/cpm/core/optimisers.py +++ b/cpm/core/optimisers.py @@ -9,6 +9,7 @@ "normalise_metrics", "fit_extras", "evaluate_fit", + "group_identifier", "prepare_data", "numerical_hessian", ] @@ -281,6 +282,34 @@ def evaluate_fit(pars, function, data, loss, prior=False, metrics=None): return output +def group_identifier(data, identifier): + """ + The name of the participant identifier, taken from the grouping of the data if it is not given. + + Parameters + ---------- + data : pd.DataFrame, pd.DataFrameGroupBy, list + The data that is used to fit the model. + identifier : str or None + The name of the participant identifier given by the user. + + Returns + ------- + str or None + `identifier` if it is given. Otherwise, if `data` is grouped by a single + named column, such as ``data.groupby("ppt")``, the name of that column, and + None in all other cases. + """ + if identifier is not None or not isinstance(data, pd.api.typing.DataFrameGroupBy): + return identifier + keys = data.keys + if isinstance(keys, list) and len(keys) == 1: + keys = keys[0] + if isinstance(keys, pd.Series): + keys = keys.name + return keys if isinstance(keys, str) else None + + def prepare_data(data, identifier): """ The function extracts variables from data and converts the data to a right structure before fitting. @@ -291,23 +320,47 @@ def prepare_data(data, identifier): The `data` parameter is the data that is used to fit the model. identifier : str The `identifier` parameter is the column name that is used to group the data. + It is required if `data` is a pd.DataFrame. + Returns ------- The data, participants, groups and __pandas__ are being returned. + + Raises + ------ + ValueError + If `data` is a pd.DataFrame and `identifier` is None. + KeyError + If `data` is a pd.DataFrame without a column named `identifier`. + TypeError + If `data` is none of the supported types. """ + if isinstance(data, pd.DataFrame): + if identifier is None: + raise ValueError( + "The data is a pandas DataFrame, but no ppt_identifier was given. " + "Pass the name of the column that identifies the participants as ppt_identifier, " + "or group the data yourself, for example data.groupby('ppt')." + ) + if identifier not in data.columns: + raise KeyError( + f"The ppt_identifier {identifier!r} is not a column of the data, so the data cannot be grouped by participant. " + f"The columns are {list(data.columns)}." + ) + data = data.groupby(identifier) if isinstance(data, pd.api.typing.DataFrameGroupBy): groups = list(data.groups.keys()) participants = data.get_group(groups[0]) __pandas__ = True - if isinstance(data, list): + elif isinstance(data, list): participants = data[0] groups = None __pandas__ = False - if isinstance(data, pd.DataFrame) and identifier is not None: - data = data.groupby(identifier) - groups = list(data.groups.keys()) - participants = data.get_group(groups[0]) - __pandas__ = True + else: + raise TypeError( + "The data must be a pandas DataFrame, a pandas DataFrameGroupBy or a list of dictionaries, " + f"not {type(data).__name__}." + ) return data, participants, groups, __pandas__ diff --git a/cpm/datasets/__init__.py b/cpm/datasets/__init__.py index 83e91d9..9f15aed 100644 --- a/cpm/datasets/__init__.py +++ b/cpm/datasets/__init__.py @@ -1,6 +1,7 @@ from .base import load_bandit_data from .base import load_risky_choices +from .base import load_csv +from .base import load_model_based_model_free from .base import load_metacognition_data from .base import load_two_step_data from .base import load_blocking_data -from .base import load_csv diff --git a/cpm/datasets/base.py b/cpm/datasets/base.py index b278190..b088d13 100644 --- a/cpm/datasets/base.py +++ b/cpm/datasets/base.py @@ -59,6 +59,7 @@ def load_bandit_data(): """ return load_csv("bandit_small.csv") + def load_risky_choices(): """ Load the risky choices dataset from a CSV file. @@ -119,6 +120,55 @@ def load_risky_choices(): return load_csv("risky_choices.csv") +def load_model_based_model_free(): + """ + Load the two-step task dataset from BrainExplorer. + + Returns + ------- + pd.DataFrame + A pandas DataFrame with one row per trial and participant. + + Examples + -------- + >>> data = load_model_based_model_free() + >>> print(data.head()) + + Notes + ----- + The data are from 93 participants who played the two-step task in the BrainExplorer app (https://brainexplorer.net/), the game called "*Goblin Heist*", in up to two sessions of 80 trials. + On each trial, participants choose one of two spaceships, which takes them to one of two planets, where they receive a reward. + Like the task of Kool et al. (2016), on which it is based, it has two starting states with two spaceships each, deterministic transitions, and a stake of 1 or 5 on each trial. + The task uses the original coding, so the columns need converting before they can be passed to :class:`~cpm.applications.reinforcement_learning.HybridMBMF`. + + The dataset includes the following columns: + + - ``userID``: the participant number. + - ``trial``: the trial number. + - ``block``: the block number. + - ``stimuli``: the two spaceships shown on the trial, numbered 1 to 4 across both starting states, in the order they were displayed, such as ``"[1, 2]"``. + - ``s``: the starting state and the planet reached, such as ``"[1, 2]"``. + - ``rews``: the reward values of the two planets recorded by the game on the trial, from 0 to 100, such as ``"[40, 50]"``. They are not always the reward received; that is ``points``. + - ``choice``: the chosen spaceship, numbered 1 to 4. + - ``stake``: the stake of the trial (1 or 5). + - ``points``: the reward received, from 0 to 9. + - ``points_real``: the points shown to the participant, ``points`` times 10 times ``stake``. + - ``planet``: the planet reached (1 or 2). + - ``theor_best_choice``: the spaceship that leads to the planet with the higher reward. + - ``RT``: the response time of the choice, in ms. + + See Also + -------- + cpm.applications.reinforcement_learning.HybridMBMF : hybrid model-based / model-free reinforcement learning model of the two-step task. + load_two_step_data : the two-step task data of Smid et al. (2022). + + References + ---------- + Kool, W., Cushman, F. A., & Gershman, S. J. (2016). When does model-based control pay off? PLoS Computational Biology, 12(8), e1005090. + """ + return load_csv("model_based_model_free.csv") + + def load_metacognition_data(): """ Load the metacognition dataset from a CSV file. @@ -135,7 +185,7 @@ def load_metacognition_data(): Notes ----- - + The data are from an experiment combining a perceptual decision making task with an additional post-decision component assessing participants' confidence in their choices (Marzuki et al., 2025; Moses-Payne et al., 2021). The experiment is implemented as a gamified smartphone-compatible application available in the BrainExplorer platform (https://brainexplorer.net/) as the game "*Space Observer*". The task is designed to assess aspects of metacognition, i.e., the extent to which one can accurately reflect on and evaluate their own performance, specifically how well one’s confidence is calibrated to their actual performance. @@ -143,17 +193,17 @@ def load_metacognition_data(): Briefly, in each trial of the game, participants are presented with a planet in the centre of the screen. On top of the planet, participants are shown 68 differently coloured aliens for 250 ms, overlaying the planet. The aliens are drawn from two distinct categories (S1 and S2), which differ in colour, but are identical in shape. - After the 68 aliens disappear, participants are presented with an example of the two differently coloured aliens and asked to choose which of the two had been more abundant on that trial. + After the 68 aliens disappear, participants are presented with an example of the two differently coloured aliens and asked to choose which of the two had been more abundant on that trial. After each of the trials, they are asked to indicate how confident they are about their decision on a visual analogue scale ranging from “totally guessing” (0) to “totally certain” (100). The scale is not labelled with numbers and participants are not informed about the numerical values associated with their confidence ratings. A staircase procedure (Cornsweet, 1962; García-Pérez, 1998; Levitt, 1971) is used throughout the task to dynamically adjust the stimulus intensity, defined as the difference in aliens required to keep participants’ performance at approximately 70%. - + The dataset includes the following columns: - `participant`: the participant number. - `signal`: which alien is more abundant (0 = S1, 1 = S2). - - `stimulus_intensity`: absolute difference in number of aliens (i.e., evidence strength). + - `stimulus_intensity`: absolute difference in number of aliens (i.e., evidence strength). - `response`: participant's choice (0 = S1, 1 = S2). - `confidence`: participant's confidence rating for their response. - `accuracy`: accuracy of the participant's response (0 = incorrect, 1 = correct). @@ -168,9 +218,9 @@ def load_metacognition_data(): Chen, Y., Daly, H. R., Pitt, M. A., & Van Zandt, T. (2024). Assessing the distortions introduced when calculating d’: A simulation approach. *Behavior Research Methods*. https://doi.org/10.3758/s13428-024-02447-8 Cornsweet, T. (1962). The staircase-method in psychophysics. Am. J. Psychol. 75, 485491. https://doi.org/10.2307/1419876 - + García-Pérez M.A. (1998). Forced-choice staircases with fixed step sizes: asymptotic and small-sample properties. Vision Res., 38(12), 1861-81. https://doi.org/10.1016/s0042-6989(97)00340-4. - + Levitt, H. (1971). Transformed up-down methods in psychoacoustics. Journal of the Acoustical Society of America, 49, 467–477. https://doi.org/10.1121/1.1912375 Marzuki, A., Kosina, L., Dome, L., Hewitt, S., & Hauser, T. (2025). Metacognitive antecedents to states of mental ill-health: Drops in confidence precede symptoms of OCD. *Research Square*. https://doi.org/10.21203/rs.3.rs-7544256/v1 diff --git a/cpm/datasets/data/model_based_model_free.csv b/cpm/datasets/data/model_based_model_free.csv new file mode 100644 index 0000000..678f199 --- /dev/null +++ b/cpm/datasets/data/model_based_model_free.csv @@ -0,0 +1,8002 @@ +userID,trial,stimuli,rews,s,block,stake,choice,points_real,points,planet,theor_best_choice,RT +1,1,"[1, 2]","[40, 50]","[1, 2]",1,5,2,250,5,2,2,4961 +1,2,"[4, 3]","[40, 50]","[2, 2]",1,5,4,200,4,2,4,2056 +1,3,"[2, 1]","[30, 70]","[1, 1]",1,1,1,70,7,1,2,1930 +1,4,"[3, 4]","[70, 70]","[2, 1]",1,5,3,350,7,1,3,2433 +1,5,"[3, 4]","[80, 40]","[2, 1]",1,1,3,80,8,1,3,1852 +1,6,"[1, 2]","[80, 50]","[1, 1]",1,1,1,80,8,1,1,2016 +1,7,"[2, 1]","[40, 90]","[1, 1]",1,1,1,90,9,1,2,1991 +1,8,"[2, 1]","[10, 60]","[1, 1]",1,1,1,60,6,1,2,3077 +1,9,"[4, 3]","[0, 40]","[2, 1]",1,5,3,200,4,1,4,1826 +1,10,"[2, 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class Bads: libraries : list, optional The libraries required for the parallel processing with `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the `pybads.bads `__ documentation for what is supported. @@ -140,7 +141,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/fmin.py b/cpm/optimisation/fmin.py index f8b2271..6f445d9 100644 --- a/cpm/optimisation/fmin.py +++ b/cpm/optimisation/fmin.py @@ -4,6 +4,7 @@ evaluate_fit, fit_extras, numerical_hessian, + group_identifier, prepare_data, ) from ..core.data import detailed_pandas_compiler, decompose @@ -96,7 +97,7 @@ class Fmin: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.fmin` documentation for what is supported. @@ -139,7 +140,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) @@ -368,7 +369,7 @@ class FminBound: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]`. ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.fmin_l_bfgs_b` documentation for what is supported. The solver's own `disp` and `iprint` options, which `display` also sets, were removed in SciPy 1.18.0 and are ignored there. @@ -411,7 +412,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/free.py b/cpm/optimisation/free.py index c56394b..39eaf51 100644 --- a/cpm/optimisation/free.py +++ b/cpm/optimisation/free.py @@ -4,6 +4,7 @@ evaluate_fit, fit_extras, numerical_hessian, + group_identifier, prepare_data, ) from ..core.data import detailed_pandas_compiler, decompose @@ -46,7 +47,7 @@ class Minimize: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]` ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.minimize` documentation for what is supported. @@ -90,7 +91,7 @@ def __init__( ): self.model = copy.deepcopy(model) self.data = data - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.data, self.participants, self.groups, self.__pandas__ = prepare_data( data, self.ppt_identifier ) diff --git a/cpm/optimisation/genetic.py b/cpm/optimisation/genetic.py index 4a3a7b0..baf3a02 100644 --- a/cpm/optimisation/genetic.py +++ b/cpm/optimisation/genetic.py @@ -8,7 +8,7 @@ from . import minimise from ..core.data import decompose, detailed_pandas_compiler, extract_params_from_fit from ..generators import Simulator, Wrapper -from ..core.optimisers import objective, evaluate_fit, fit_extras, prepare_data +from ..core.optimisers import objective, evaluate_fit, fit_extras, group_identifier, prepare_data from ..core.parallel import detect_cores, execute_parallel @@ -36,7 +36,7 @@ class DifferentialEvolution: libraries : list, optional The libraries to import for parallel processing for `ipyparallel` with the IPython kernel. Default is `["numpy", "pandas"]` ppt_identifier : str - The key in the participant data dictionary that contains the participant identifier. Default is `None`. Returned in the optimization details. + The column (or the key in the participant data dictionaries) that contains the participant identifier, returned in the optimization details. Required if `data` is a pd.DataFrame. If `data` is a pd.DataFrameGroupBy grouped by a single column, such as `data.groupby("ppt")`, it defaults to the name of that column. Default is `None`. **kwargs : dict Additional keyword arguments. See the :func:`scipy.optimize.differential_evolution` documentation for what is supported. @@ -72,7 +72,7 @@ def __init__( self.parameters = [] self.display = display - self.ppt_identifier = ppt_identifier + self.ppt_identifier = group_identifier(data, ppt_identifier) self.prior = prior self.metrics = metrics diff --git a/docs/api/brainexplorer.rst b/docs/api/brainexplorer.rst new file mode 100644 index 0000000..6cda4ad --- /dev/null +++ b/docs/api/brainexplorer.rst @@ -0,0 +1,20 @@ +cpm.brainexplorer +================= + +.. currentmodule:: cpm.brainexplorer + +Descriptive statistics and exclusion criteria for the games of +`BrainExplorer `__. Each class reads the data of +one game, from a pandas DataFrame or a CSV file, computes the metrics +of each participant with ``metrics()``, applies the participant-level +exclusion criteria with ``clean_data()``, and describes every metric in its +codebook, ``get_codebook()``. + +.. autosummary:: + :toctree: generated/ + :nosignatures: + + bandits.MilkyWay + information_gathering.TreasureHunt + perceptual_decision_making.SpaceObserver + risky_decision_making.Scavenger diff --git a/docs/api/datasets.rst b/docs/api/datasets.rst index f5851c2..56fa7a1 100644 --- a/docs/api/datasets.rst +++ b/docs/api/datasets.rst @@ -12,6 +12,7 @@ and examples. load_bandit_data load_two_step_data + load_model_based_model_free load_blocking_data load_risky_choices load_metacognition_data diff --git a/docs/api/index.rst b/docs/api/index.rst index 1e63c9e..ecf15f7 100644 --- a/docs/api/index.rst +++ b/docs/api/index.rst @@ -37,6 +37,12 @@ To learn how the pieces fit together, start with the :doc:`/tutorials/index`. Ready-made models for reinforcement learning, decision making and metacognition. + .. grid-item-card:: :fas:`gamepad` BrainExplorer + :link: brainexplorer + :link-type: doc + + Descriptive statistics and exclusion criteria for the BrainExplorer games. + .. grid-item-card:: :fas:`database` Datasets :link: datasets :link-type: doc @@ -57,5 +63,6 @@ To learn how the pieces fit together, start with the :doc:`/tutorials/index`. optimisation hierarchical applications + brainexplorer datasets utils diff --git a/docs/scripts/check_api_coverage.py b/docs/scripts/check_api_coverage.py index 4915a6c..dde1faa 100644 --- a/docs/scripts/check_api_coverage.py +++ b/docs/scripts/check_api_coverage.py @@ -30,6 +30,10 @@ "cpm.applications.reinforcement_learning", "cpm.applications.decision_making", "cpm.applications.signal_detection", + "cpm.brainexplorer.bandits", + "cpm.brainexplorer.information_gathering", + "cpm.brainexplorer.perceptual_decision_making", + "cpm.brainexplorer.risky_decision_making", "cpm.datasets", "cpm.utils.data", "cpm.utils.metad", diff --git a/test/brainexplorer/test_bandits.py b/test/brainexplorer/test_bandits.py new file mode 100644 index 0000000..286957c --- /dev/null +++ b/test/brainexplorer/test_bandits.py @@ -0,0 +1,161 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.bandits import MilkyWay + + +def _game_rows( + user_id, + trial_type, + correct, + rep=None, + run=1, + outcomes=None, + date="2025-02-20 09:00:00.000", +): + n = len(correct) + rep = rep if rep is not None else [1, 0] * (n // 2) + [1] * (n % 2) + outcomes = outcomes if outcomes is not None else [10] * n + return [ + { + "userID": user_id, + "trial_type": trial_type, + "run": run, + "date": date, + "correct": c, + "outchosen": outcome, + "obt_min_forg": outcome - 5, + "rep": r, + "WSLS_v1": c, + "WSLS_v2": 1 - c, + } + for c, r, outcome in zip(correct, rep, outcomes) + ] + + +def _both_games(user_id, **kwargs): + return _game_rows(user_id, "reward", [1, 1, 0, 1], **kwargs) + _game_rows(user_id, "punish", [1, 0, 0, 1], **kwargs) + + +def test_milkyway_splits_games_and_keeps_the_first_attempt(): + rows = _game_rows("u1", "reward", [1, 1], run=1) + rows += _game_rows("u1", "reward", [0, 0], run=2) + # the first Pirate Market attempt is not necessarily run 1 + rows += _game_rows("u1", "punish", [1, 0], run=2) + rows += _game_rows("u1", "punish", [0, 0], run=3) + milky_way = MilkyWay(pd.DataFrame(rows)) + assert set(milky_way.MW_data["run"]) == {1} + assert set(milky_way.PM_data["run"]) == {2} + + +def test_milkyway_metrics_computes_expected_values(): + rows = _game_rows("u1", "reward", [1, 1, 0, 1], rep=[1, 1, 0, 0], outcomes=[10, 20, 30, 40]) + rows += _game_rows("u1", "punish", [1, 0, 0, 0], rep=[1, 0, 0, 0], outcomes=[-10, -20, -30, -40]) + milky_way = MilkyWay(pd.DataFrame(rows)) + results_MW, results_PM = milky_way.metrics() + + mw = results_MW.iloc[0] + assert mw["trial_type"] == "Milky Way" + assert mw["n_trials"] == 4 + assert np.isclose(mw["accuracy_MW"], 0.75) + assert np.isclose(mw["mean_outcome_MW"], 25) + assert np.isclose(mw["reward_diff_obt_forg_MW"], 20) + assert np.isclose(mw["prop_same_choice_MW"], 0.5) + assert np.isclose(mw["prop_WSLS_1_MW"], 0.75) + assert np.isclose(mw["prop_WSLS_2_MW"], 0.25) + + pm = results_PM.iloc[0] + assert pm["trial_type"] == "Pirate Market" + assert np.isclose(pm["accuracy_PM"], 0.25) + assert np.isclose(pm["mean_outcome_PM"], -25) + assert np.isclose(pm["prop_same_choice_PM"], 0.25) + assert results_MW is milky_way.results_MW and results_PM is milky_way.results_PM + + +def test_milkyway_results_contain_the_session_time(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1", date="2025-02-20 03:30:00.000"))) + for results in milky_way.metrics(): + row = results.iloc[0] + assert row["day_of_week"] == "Thursday" + assert str(row["time"]) == "03:30:00" + assert row["time_of_day"] == "night" + + +def test_milkyway_difference_metrics_only_for_participants_who_played_both(): + rows = _game_rows("both", "reward", [1, 1, 1, 1], outcomes=[10, 10, 10, 10]) + rows += _game_rows("both", "punish", [1, 0, 1, 0], outcomes=[-10, -10, -10, -10]) + rows += _game_rows("reward_only", "reward", [1, 0, 1, 0]) + milky_way = MilkyWay(pd.DataFrame(rows)) + differences = milky_way.difference_metrics() # also runs metrics + + assert list(differences["userID"]) == ["both"] + row = differences.iloc[0] + assert np.isclose(row["accuracy_diff"], 0.5) + assert np.isclose(row["outcome_diff"], 20) + assert np.isclose(row["reward_diff_obt_forg_diff"], 20) + assert np.isclose(row["prop_WSLS_1_diff"], 0.5) + assert np.isclose(row["prop_WSLS_2_diff"], -0.5) + + +def test_milkyway_difference_metrics_without_pirate_market(): + milky_way = MilkyWay(pd.DataFrame(_game_rows("u1", "reward", [1, 0]))) + differences = milky_way.difference_metrics() + assert differences.empty + assert "accuracy_diff" in differences.columns + + +def test_milkyway_metrics_can_be_run_twice(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1"))) + first_MW, first_PM = (results.copy() for results in milky_way.metrics()) + second_MW, second_PM = milky_way.metrics() + pd.testing.assert_frame_equal(first_MW, second_MW) + pd.testing.assert_frame_equal(first_PM, second_PM) + first_diff = milky_way.difference_metrics().copy() + pd.testing.assert_frame_equal(first_diff, milky_way.difference_metrics()) + + +def test_milkyway_clean_data_excludes_invalid_participants(): + rows = _both_games("keep") + # same choice on at least 95% of Milky Way trials + rows += _game_rows("drop_MW_same", "reward", [1] * 20, rep=[1] * 20) + rows += _game_rows("drop_MW_same", "punish", [1, 0, 0, 1]) + # more than 72 Pirate Market trials + rows += _game_rows("drop_PM_trials", "reward", [1, 1, 0, 1]) + rows += _game_rows("drop_PM_trials", "punish", [1, 0] * 37) + # missing Milky Way accuracy + rows += _game_rows("drop_MW_accuracy", "reward", [np.nan, np.nan]) + rows += _game_rows("drop_MW_accuracy", "punish", [1, 0, 0, 1]) + + milky_way = MilkyWay(pd.DataFrame(rows)) + cleaned_MW, cleaned_PM, cleaned_diff = milky_way.clean_data() # also runs metrics and difference_metrics + + assert set(cleaned_MW["userID"]) == {"keep", "drop_PM_trials"} + assert set(cleaned_PM["userID"]) == {"keep", "drop_MW_same", "drop_MW_accuracy"} + assert set(cleaned_diff["userID"]) == {"keep"} + assert milky_way.deleted_participants_MW == 2 + assert milky_way.deleted_participants_PM == 1 + assert milky_way.deleted_participants_diff == 3 + + +def test_milkyway_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + MilkyWay() + data = pd.DataFrame(_both_games("u1")).drop(columns=["obt_min_forg"]) + with pytest.raises(KeyError, match="obt_min_forg"): + MilkyWay(data) + + +def test_milkyway_codebook_describes_every_metric(): + milky_way = MilkyWay(pd.DataFrame(_both_games("u1"))) + results_MW, results_PM = milky_way.metrics() + differences = milky_way.difference_metrics() + columns = set(results_MW.columns) | set(results_PM.columns) | set(differences.columns) + assert columns == set(milky_way.get_codebook()) + + +def test_milkyway_reads_csv(tmp_path): + filepath = tmp_path / "milky_way.csv" + pd.DataFrame(_both_games("u1")).to_csv(filepath, index=False) + results_MW, results_PM = MilkyWay(filepath).metrics() + assert len(results_MW) == 1 and len(results_PM) == 1 diff --git a/test/brainexplorer/test_brainexplorer_utils.py b/test/brainexplorer/test_brainexplorer_utils.py new file mode 100644 index 0000000..2854913 --- /dev/null +++ b/test/brainexplorer/test_brainexplorer_utils.py @@ -0,0 +1,56 @@ +import datetime + +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer._utils import load_data, proportion, require, session_time, time_of_day + + +@pytest.mark.parametrize( + "hour, expected", + [(0, "night"), (5, "night"), (6, "morning"), (11, "morning"), (12, "afternoon"), (17, "afternoon"), (18, "evening"), (23, "evening")], +) +def test_time_of_day(hour, expected): + assert time_of_day(hour) == expected + + +@pytest.mark.parametrize( + "date", + ["2025-02-20 19:05:30.250", "2025-02-20 19:05:30", "2025-02-20T19:05:30.250", pd.Timestamp("2025-02-20 19:05:30.250")], +) +def test_session_time(date): + out = session_time(date) + assert out["date"].floor("s") == pd.Timestamp("2025-02-20 19:05:30") + assert out["day_of_week"] == "Thursday" + assert out["time"].replace(microsecond=0) == datetime.time(19, 5, 30) + assert out["time_of_day"] == "evening" + + +def test_load_data_copies_dataframes(): + data = pd.DataFrame({"a": [1, 2]}) + loaded = load_data(data, "Task") + loaded.loc[0, "a"] = 10 + assert data.loc[0, "a"] == 1 + + +def test_load_data_reads_csv(tmp_path): + filepath = tmp_path / "data.csv" + pd.DataFrame({"a": [1, 2]}).to_csv(filepath, index=False) + pd.testing.assert_frame_equal(load_data(filepath, "Task"), pd.DataFrame({"a": [1, 2]})) + pd.testing.assert_frame_equal(load_data(str(filepath), "Task"), pd.DataFrame({"a": [1, 2]})) + + +def test_load_data_needs_a_source(): + with pytest.raises(ValueError, match="Task needs data"): + load_data(None, "Task") + + +def test_require_names_every_missing_column(): + with pytest.raises(KeyError, match="'b', 'c'"): + require(pd.DataFrame({"a": [1]}), ["a", "b", "c"], "Task") + + +def test_proportion(): + assert proportion([True, False, True, True], [True, True, True, False]) == pytest.approx(2 / 3) + assert np.isnan(proportion([True, False], [False, False])) diff --git a/test/brainexplorer/test_information_gathering.py b/test/brainexplorer/test_information_gathering.py new file mode 100644 index 0000000..5d7bcc7 --- /dev/null +++ b/test/brainexplorer/test_information_gathering.py @@ -0,0 +1,154 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.information_gathering import TreasureHunt + + +def _participant_rows( + user_id, + draws, + chose_current_evidence, + outcomes=None, + confidences=None, + run=1, + date="2025-02-20 09:00:00.000", +): + n = len(draws) + outcomes = outcomes if outcomes is not None else [10] * n + confidences = confidences if confidences is not None else [50] * n + return [ + { + "userID": user_id, + "date": date, + "run": run, + "outcome": outcome, + "confidence": confidence, + "RT": 1000, + "confidenceRT": 400 + 10 * i, + "draws": draw, + "choseCurEv": chose, + "median_diffRT": 200 + 10 * i, + "ev": "[1 2 -1]", + } + for i, (draw, chose, outcome, confidence) in enumerate(zip(draws, chose_current_evidence, outcomes, confidences)) + ] + + +def _keep_rows(user_id="keep", **kwargs): + return _participant_rows(user_id, draws=[3, 5, 7, 9, 11], chose_current_evidence=[1, 1, 1, 1, 1], **kwargs) + + +def test_treasurehunt_keeps_only_the_first_attempt(): + rows = _keep_rows() + _keep_rows(run=2) + hunt = TreasureHunt(pd.DataFrame(rows)) + assert len(hunt.data) == 5 + assert (hunt.data["run"] == 1).all() + + +def test_treasurehunt_metrics_computes_expected_values(): + rows = _participant_rows( + "u1", + draws=[2, 4, 6, 8], + chose_current_evidence=[1, 0, 1, 1], + outcomes=[10, 20, 30, 40], + confidences=[20, 40, 60, 80], + date="2025-02-20 14:30:00.000", + ) + hunt = TreasureHunt(pd.DataFrame(rows)) + result = hunt.metrics().iloc[0] + + assert result["n_trials"] == 4 + assert result["day_of_week"] == "Thursday" + assert result["time_of_day"] == "afternoon" + assert np.isclose(result["mean_points"], 25) + assert np.isclose(result["accuracy"], 0.75) + assert np.isclose(result["mean_confidence"], 50) + assert np.isclose(result["sd_confidence"], np.std([20, 40, 60, 80])) + assert np.isclose(result["mean_n_draws"], 5) + assert np.isclose(result["sd_n_draws"], np.std([2, 4, 6, 8])) + assert np.isclose(result["median_RT_between_actions"], 215) # median([200, 210, 220, 230]) + assert np.isclose(result["median_confidenceRT"], 415) # median([400, 410, 420, 430]) + assert result["unique_draws"] == 4 + + +@pytest.mark.parametrize( + "date, expected", + [ + ("2025-02-20 02:00:00.000", "night"), + ("2025-02-20 06:00:00.000", "morning"), + ("2025-02-20 12:00:00.000", "afternoon"), + ("2025-02-20 23:59:00.000", "evening"), + ], +) +def test_treasurehunt_time_of_day(date, expected): + hunt = TreasureHunt(pd.DataFrame(_keep_rows(date=date))) + assert hunt.metrics().iloc[0]["time_of_day"] == expected + + +def test_treasurehunt_median_confidence_rt_ignores_missing_values(): + rows = _keep_rows() + rows[0]["confidenceRT"] = np.nan + result = TreasureHunt(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isclose(result["median_confidenceRT"], 425) # median([410, 420, 430, 440]) + + +@pytest.mark.parametrize("evidence", ["[1 2 -1]", "[1 2 -1]", "[1, 2, -1]", " [1 2 -1] "]) +def test_treasurehunt_parses_evidence(evidence): + rows = _keep_rows() + for row in rows: + row["ev"] = evidence + hunt = TreasureHunt(pd.DataFrame(rows)) + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + hunt.metrics() + hunt.metrics() # the parsed evidence must survive a second run + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + + +def test_treasurehunt_evidence_column_is_optional(): + data = pd.DataFrame(_keep_rows()).drop(columns=["ev"]) + assert len(TreasureHunt(data).metrics()) == 1 + + +def test_treasurehunt_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + TreasureHunt() + data = pd.DataFrame(_keep_rows()).drop(columns=["median_diffRT"]) + with pytest.raises(KeyError, match="median_diffRT"): + TreasureHunt(data) + + +def test_treasurehunt_metrics_can_be_run_twice(): + hunt = TreasureHunt(pd.DataFrame(_keep_rows())) + first = hunt.metrics().copy() + pd.testing.assert_frame_equal(first, hunt.metrics()) + + +def test_treasurehunt_clean_data_excludes_invalid_participants(): + rows = _keep_rows() + # mean number of draws < 2 + rows += _participant_rows("drop_few_draws", draws=[1, 1, 1, 2, 3], chose_current_evidence=[1] * 5) + # mean number of draws > 23 + rows += _participant_rows("drop_many_draws", draws=[22, 24, 26, 28, 30], chose_current_evidence=[1] * 5) + # 20% of choices not in line with the evidence + rows += _participant_rows("drop_accuracy", draws=[3, 5, 7, 9, 11], chose_current_evidence=[1, 1, 1, 1, 0]) + # fewer than 3 unique numbers of draws + rows += _participant_rows("drop_unique", draws=[4, 4, 6, 6, 6], chose_current_evidence=[1] * 5) + + hunt = TreasureHunt(pd.DataFrame(rows)) + cleaned = hunt.clean_data() # also runs metrics + assert set(cleaned["userID"]) == {"keep"} + assert hunt.deleted_participants == 4 + + +def test_treasurehunt_codebook_describes_every_metric(): + hunt = TreasureHunt(pd.DataFrame(_keep_rows())) + assert set(hunt.metrics().columns) == set(hunt.get_codebook()) + + +def test_treasurehunt_reads_csv(tmp_path): + filepath = tmp_path / "treasure_hunt.csv" + pd.DataFrame(_keep_rows()).to_csv(filepath, index=False) + hunt = TreasureHunt(str(filepath)) + assert hunt.data["ev"].iloc[0] == [1, 2, -1] + assert len(hunt.metrics()) == 1 diff --git a/test/brainexplorer/test_perceptual_decision_making.py b/test/brainexplorer/test_perceptual_decision_making.py new file mode 100644 index 0000000..c254351 --- /dev/null +++ b/test/brainexplorer/test_perceptual_decision_making.py @@ -0,0 +1,407 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.perceptual_decision_making import SpaceObserver + + +def _write_space_observer_csv(tmp_path, rows): + filepath = tmp_path / "space_observer.csv" + pd.DataFrame(rows).to_csv(filepath, index=False) + return filepath + + +def _participant_rows( + user_id, + run, + accuracies, + rt_choices, + confidences, + confidence_rts, + stimulus_intensities, + date="2025-02-20 09:00:00.000", +): + rows = [] + for accuracy, rt_choice, confidence, confidence_rt, intensity in zip( + accuracies, + rt_choices, + confidences, + confidence_rts, + stimulus_intensities, + ): + rows.append( + { + "userID": user_id, + "run": run, + "Date": date, + "accuracy": accuracy, + "RT_choice": rt_choice, + "confidence": confidence, + "confidenceRT": confidence_rt, + "stimulus_intensity": intensity, + } + ) + return rows + + +def test_spaceobserver_init_filters_invalid_trials(tmp_path): + rows = [ + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 200, + "confidence": "50", + "confidenceRT": 250, + "stimulus_intensity": 2, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 120, + "confidence": "40", + "confidenceRT": 250, + "stimulus_intensity": 3, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 250, + "confidence": "40", + "confidenceRT": 120, + "stimulus_intensity": 3, + }, + { + "userID": "u1", + "run": 1, + "Date": "2025-02-20 10:30:00.000", + "accuracy": 1, + "RT_choice": 250, + "confidence": "", + "confidenceRT": 250, + "stimulus_intensity": 3, + }, + ] + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + + assert len(observer.data_raw) == 1 + assert observer.data_raw.iloc[0]["RT_choice"] == 200 + assert observer.data_raw.iloc[0]["confidenceRT"] == 250 + assert observer.data_raw.iloc[0]["confidence"] == 50 + assert pd.api.types.is_numeric_dtype(observer.data_raw["confidence"]) + + +def test_spaceobserver_time_of_day(tmp_path): + base_row = { + "userID": "u1", + "run": 1, + "accuracy": 1, + "RT_choice": 300, + "confidence": "50", + "confidenceRT": 300, + "stimulus_intensity": 5, + } + cases = [ + ("2025-02-20 02:00:00.000", "night"), + ("2025-02-20 09:00:00.000", "morning"), + ("2025-02-20 14:00:00.000", "afternoon"), + ("2025-02-20 20:00:00.000", "evening"), + ] + for date_str, expected_tod in cases: + rows = [{**base_row, "Date": date_str}] + filepath = tmp_path / f"tod_{expected_tod}.csv" + pd.DataFrame(rows).to_csv(filepath, index=False) + observer = SpaceObserver(str(filepath)) + results = observer.metrics() + assert results.iloc[0]["time_of_day"] == expected_tod, ( + f"Expected time_of_day={expected_tod!r} for {date_str}, " + f"got {results.iloc[0]['time_of_day']!r}" + ) + + +def test_spaceobserver_metrics_computes_expected_values(tmp_path): + rows = _participant_rows( + user_id="u1", + run=1, + accuracies=[1, 0, 1, 0], + rt_choices=[200, 300, 400, 500], + confidences=[20, 40, 60, 80], + confidence_rts=[250, 260, 270, 280], + stimulus_intensities=[1, 2, 3, 4], + date="2025-02-20 10:30:00.000", + ) + rows += _participant_rows( + user_id="u1", + run=2, + accuracies=[1, 1], + rt_choices=[600, 700], + confidences=[90, 95], + confidence_rts=[300, 310], + stimulus_intensities=[10, 12], + date="2025-02-20 18:30:00.000", + ) + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + results = observer.metrics().sort_values(["userID", "run"]).reset_index(drop=True) + + assert len(results) == 2 + run1 = results[results["run"] == 1].iloc[0] + run2 = results[results["run"] == 2].iloc[0] + + # run 1 basic metrics + assert run1["n_trials"] == 4 + assert run1["time_of_day"] == "morning" + assert np.isclose(run1["accuracy"], 0.5) + + # RT metrics + assert np.isclose(run1["mean_RT"], 350) + assert np.isclose(run1["median_RT"], 350) + assert np.isclose(run1["median_RT_correct"], 300) # median([200, 400]) + assert np.isclose(run1["median_RT_incorrect"], 400) # median([300, 500]) + assert np.isclose(run1["diff_median_RT_correct_incorrect"], -100) + + # confidence metrics + assert np.isclose(run1["mean_confidence"], 50) + assert np.isclose(run1["median_confidence"], 50) + assert np.isclose(run1["mean_confidence_correct"], 40) # mean([20, 60]) + assert np.isclose(run1["mean_confidence_incorrect"], 60) # mean([40, 80]) + assert np.isclose(run1["diff_median_conf_correct_incorrect"], -20) + assert np.isclose(run1["sd_confidence"], np.std([20, 40, 60, 80])) + + # confidence percentiles + assert np.isclose(run1["confidence_10"], np.percentile([20, 40, 60, 80], 10)) + assert np.isclose(run1["confidence_25"], np.percentile([20, 40, 60, 80], 25)) + assert np.isclose(run1["confidence_75"], np.percentile([20, 40, 60, 80], 75)) + assert np.isclose(run1["confidence_90"], np.percentile([20, 40, 60, 80], 90)) + + # confidenceRT metrics + assert np.isclose(run1["median_confidenceRT"], 265) # median([250,260,270,280]) + assert np.isclose(run1["median_confidenceRT_correct"], 260) # median([250, 270]) + assert np.isclose(run1["median_confidenceRT_incorrect"], 270) # median([260, 280]) + assert np.isclose(run1["diff_median_confidenceRT_correct_incorrect"], -10) + + # evidence strength metrics + assert np.isclose(run1["evidence_strength_mean"], 2.5) + assert np.isclose(run1["evidence_strength_correct_mean"], 2.0) # mean([1, 3]) + assert np.isclose(run1["evidence_strength_incorrect_mean"], 3.0) # mean([2, 4]) + assert np.isclose(run1["diff_evidence_strength_correct_incorrect"], -1.0) + assert np.isclose(run1["median_ES"], 2.5) + bins = [run1[f"ES_bin_{i}"] for i in range(1, 5)] + np.testing.assert_allclose(bins, [1.0, 2.0, 3.0, 4.0]) + + # run 2 has time_of_day == evening (18:30) + assert run2["time_of_day"] == "evening" + assert run2["n_trials"] == 2 + assert np.isclose(run2["accuracy"], 1.0) + + +def test_spaceobserver_clean_data_excludes_invalid_participants(tmp_path): + rows = [] + rows += _participant_rows( + user_id="keep", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # accuracy < 0.5 + rows += _participant_rows( + user_id="drop_acc", + run=1, + accuracies=[0, 0, 1, 0], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # flat confidence (10th==25th and 75th==90th) + rows += _participant_rows( + user_id="drop_flat_conf", + run=1, + accuracies=[1, 1, 1, 1], + rt_choices=[400, 500, 600, 700], + confidences=[50, 50, 50, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # more than 80 trials + rows += _participant_rows( + user_id="drop_trials", + run=1, + accuracies=[1] * 81, + rt_choices=[500] * 81, + confidences=[60] * 81, + confidence_rts=[500] * 81, + stimulus_intensities=[12] * 81, + ) + # median RT > 3000 ms + rows += _participant_rows( + user_id="drop_slow_rt", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[3200, 3100, 3300, 3400], + confidences=[30, 40, 50, 60], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # median confidenceRT > 3000 ms + rows += _participant_rows( + user_id="drop_slow_conf_rt", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[30, 40, 50, 60], + confidence_rts=[3100, 3200, 3300, 3400], + stimulus_intensities=[10, 12, 14, 16], + ) + # median evidence strength > 25 + rows += _participant_rows( + user_id="drop_high_es", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[30, 40, 50, 60], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[26, 28, 30, 32], + ) + # median confidence < 3 + rows += _participant_rows( + user_id="drop_low_conf", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[1, 2, 1, 2], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + # median confidence > 97 + rows += _participant_rows( + user_id="drop_high_conf", + run=1, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[97, 99, 97, 99], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + ) + + filepath = _write_space_observer_csv(tmp_path, rows) + observer = SpaceObserver(str(filepath)) + observer.metrics() + cleaned = observer.clean_data() + + remaining_users = set(cleaned["userID"].unique()) + assert remaining_users == {"keep"} + assert observer.deleted_participants == 8 + + +def _keep_rows(user_id="keep", run=1, date="2025-02-20 09:00:00.000"): + return _participant_rows( + user_id=user_id, + run=run, + accuracies=[1, 1, 0, 1], + rt_choices=[400, 500, 600, 700], + confidences=[20, 30, 40, 50], + confidence_rts=[500, 550, 600, 650], + stimulus_intensities=[10, 12, 14, 16], + date=date, + ) + + +def test_spaceobserver_accepts_dataframe_and_path(tmp_path): + rows = _keep_rows() + filepath = _write_space_observer_csv(tmp_path, rows) + from_path = SpaceObserver(filepath).metrics() + from_frame = SpaceObserver(pd.DataFrame(rows)).metrics() + pd.testing.assert_frame_equal(from_path, from_frame) + + +def test_spaceobserver_does_not_change_the_input_dataframe(): + data = pd.DataFrame(_keep_rows()) + original = data.copy() + SpaceObserver(data).metrics() + pd.testing.assert_frame_equal(data, original) + + +def test_spaceobserver_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + SpaceObserver() + data = pd.DataFrame(_keep_rows()).drop(columns=["confidenceRT"]) + with pytest.raises(KeyError, match="confidenceRT"): + SpaceObserver(data) + + +def test_spaceobserver_metrics_can_be_run_twice(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + first = observer.metrics().copy() + second = observer.metrics() + pd.testing.assert_frame_equal(first, second) + assert len(second) == 1 + + +def test_spaceobserver_dates_without_fractional_seconds(): + observer = SpaceObserver(pd.DataFrame(_keep_rows(date="2025-02-22 23:15:00"))) + result = observer.metrics().iloc[0] + assert result["day_of_week"] == "Saturday" + assert result["time_of_day"] == "evening" + assert str(result["time"]) == "23:15:00" + + +def test_spaceobserver_all_correct_session_is_nan_for_incorrect_trials(recwarn): + rows = _participant_rows( + user_id="u1", + run=1, + accuracies=[1, 1, 1], + rt_choices=[300, 400, 500], + confidences=[50, 60, 70], + confidence_rts=[300, 300, 300], + stimulus_intensities=[5, 6, 7], + ) + result = SpaceObserver(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isnan(result["median_RT_incorrect"]) + assert np.isnan(result["mean_confidence_incorrect"]) + assert np.isnan(result["diff_median_RT_correct_incorrect"]) + assert not [w for w in recwarn if issubclass(w.category, RuntimeWarning)] + + +def test_spaceobserver_clean_data_runs_metrics_first(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + cleaned = observer.clean_data() + assert list(cleaned["userID"]) == ["keep"] + assert observer.deleted_participants == 0 + + +def test_spaceobserver_trial_limit_counts_all_sessions(): + # 4 sessions of 21 trials: no session has more than 80 trials, but the participant has 84 + rows = [] + for run in range(1, 5): + rows += _participant_rows( + user_id="many_sessions", + run=run, + accuracies=[1, 0, 1] * 7, + rt_choices=[400, 500, 600] * 7, + confidences=[20, 50, 80] * 7, + confidence_rts=[500, 550, 600] * 7, + stimulus_intensities=[10, 12, 14] * 7, + ) + rows += _keep_rows() + observer = SpaceObserver(pd.DataFrame(rows)) + cleaned = observer.clean_data() + assert set(cleaned["userID"]) == {"keep"} + assert observer.deleted_participants == 1 + + +def test_spaceobserver_codebook_describes_every_metric(): + observer = SpaceObserver(pd.DataFrame(_keep_rows())) + results = observer.metrics() + assert set(results.columns) == set(observer.get_codebook()) diff --git a/test/brainexplorer/test_risky_decision_making.py b/test/brainexplorer/test_risky_decision_making.py new file mode 100644 index 0000000..2173b46 --- /dev/null +++ b/test/brainexplorer/test_risky_decision_making.py @@ -0,0 +1,200 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.brainexplorer.risky_decision_making import Scavenger + + +def _trial(user_id, chosen, loss=False, ambiguous=False, ev_diff=1.0, rt=500, outcome=10, **extra): + row = { + "userID": user_id, + "run": 1, + "date": "2025-01-10 15:00:00.000", + "RT": rt, + "chosen": chosen, + "ambiguousTrial": int(ambiguous), + "abovechance": 1, + "EV_diff_chosen": ev_diff, + "safe_EV": -5 if loss else 5, + "outcome": outcome, + "nbcorrect_gain": np.nan, + "resp_rep": 0, + "resp_rep_sr": 0, + "config_id": 1, + } + row.update(extra) + return row + + +def _example(user_id="u1"): + """Five trials, worked out by hand in the tests below; chosen is 1 for safe and 2 for risky.""" + return [ + _trial(user_id, chosen=2, ev_diff=5, rt=300, outcome=10), + _trial(user_id, chosen=1, ambiguous=True, ev_diff=-2, rt=600, outcome=5), + _trial(user_id, chosen=1, loss=True, ev_diff=0, rt=900, outcome=-5), + _trial(user_id, chosen=2, loss=True, ambiguous=True, ev_diff=3, rt=400, outcome=0), + _trial(user_id, chosen=2, ev_diff=1, rt=500, outcome=20), + ] + + +def _keep_rows(user_id="keep"): + return [_trial(user_id, chosen=1 + i % 2, loss=i % 4 >= 2, ambiguous=i % 3 == 0, resp_rep=i % 2) for i in range(12)] + + +def test_scavenger_recodes_safe_and_risky_choices(): + scavenger = Scavenger(pd.DataFrame(_example())) + assert list(scavenger.data["risky"]) == [1, 0, 0, 1, 1] + + +@pytest.mark.parametrize("codes", [[0, 1], [1, 3]]) +def test_scavenger_rejects_other_codings(codes): + rows = [_trial("u1", chosen=code) for code in codes] + with pytest.raises(ValueError, match="1 \\(safe\\) or 2 \\(risky\\)"): + Scavenger(pd.DataFrame(rows)) + + +def test_scavenger_trial_level_exclusions(): + rows = _example() + rows.append(_trial("u1", chosen=2, rt=100)) # too fast + rows.append(_trial("u1", chosen=2, rt=12000)) # too slow + rows.append(_trial("u1", chosen=2, run=2)) # second attempt + scavenger = Scavenger(pd.DataFrame(rows)) + assert len(scavenger.data) == 5 + + +def test_scavenger_choice_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + assert result["n_trials"] == 5 + assert result["time_of_day"] == "afternoon" + assert np.isclose(result["risky_choices"], 0.6) + assert np.isclose(result["win_risk"], 2 / 3) + assert np.isclose(result["loss_risk"], 0.5) + assert np.isclose(result["diff_loss_win_risk"], 0.5 - 2 / 3) + assert np.isclose(result["risk_Ambg"], 0.5) + assert np.isclose(result["risk_NoAmbg"], 2 / 3) + assert np.isclose(result["win_risk_abg"], 0) + assert np.isclose(result["win_risk_NoAbg"], 1) + assert np.isclose(result["loss_risk_abg"], 1) + assert np.isclose(result["loss_risk_NoAbg"], 0) + assert result["choice_stickiness"] == 2 # chosen 2, 1, 1, 2, 2 + + +def test_scavenger_rational_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + # rational: EV_diff_chosen >= 0, which is all but the second trial + assert np.isclose(result["rational_all"], 0.8) + assert np.isclose(result["rational_win"], 2 / 3) + assert np.isclose(result["rational_loss"], 1) + assert np.isclose(result["rational_abg"], 0.5) + assert np.isclose(result["rational_NoAbg"], 1) + assert np.isclose(result["rational_safe"], 0.5) + assert np.isclose(result["rational_risky"], 1) + # safe & win is only the second trial, which was irrational + assert np.isclose(result["rational_safe_win"], 0) + assert np.isclose(result["rational_safe_loss"], 1) + assert np.isclose(result["rational_risky_win"], 1) + assert np.isclose(result["rational_risky_loss"], 1) + assert np.isclose(result["diff_rational_safe_win_risky_win"], -1) + assert np.isclose(result["rational_safe_abg"], 0) + assert np.isclose(result["rational_safe_NoAbg"], 1) + assert np.isclose(result["rational_risky_abg"], 1) + assert np.isclose(result["rational_risky_NoAbg"], 1) + + +def test_scavenger_rt_and_points_metrics(): + result = Scavenger(pd.DataFrame(_example())).metrics().iloc[0] + + assert np.isclose(result["mean_RT"], 540) + assert np.isclose(result["median_RT_safe"], 750) + assert np.isclose(result["median_RT_risky"], 400) + assert np.isclose(result["median_RT_win"], 500) + assert np.isclose(result["median_RT_loss"], 650) + assert np.isclose(result["median_RT_ambg"], 500) + assert np.isclose(result["median_RT_NoAmbg"], 500) + assert np.isclose(result["median_RT_loss_ambg"], 400) + assert np.isclose(result["diff_RT_loss_amg_loss_noAmbg"], 400 - 900) + + assert np.isclose(result["total_rewards"], 30) + assert np.isclose(result["mean_points"], 6) + assert np.isclose(result["mean_points_safe"], 0) + assert np.isclose(result["mean_points_risky"], 10) + assert np.isclose(result["mean_points_win_NoAmbg"], 15) + assert np.isclose(result["mean_points_loss_ambg"], 0) + + +def test_scavenger_quality_metrics(): + rows = _example() + rows[0].update(nbcorrect_gain=0, resp_rep=1, resp_rep_sr=1, abovechance=0) + rows[1].update(nbcorrect_gain=1, resp_rep=1) + result = Scavenger(pd.DataFrame(rows)).metrics().iloc[0] + assert result["nb_incorrect_gain"] == 1 + assert np.isclose(result["above_chance"], 0.8) + assert np.isclose(result["chosen_prop_LeftRight"], 0.4) + assert np.isclose(result["chosen_prop_SafeRisky"], 0.2) + + +def test_scavenger_metrics_of_missing_conditions_are_nan(recwarn): + rows = [_trial("u1", chosen=1 + i % 2) for i in range(4)] # win, non-ambiguous trials only + result = Scavenger(pd.DataFrame(rows)).metrics().iloc[0] + assert np.isnan(result["loss_risk"]) + assert np.isnan(result["rational_loss_abg"]) + assert np.isnan(result["median_RT_ambg"]) + assert not [w for w in recwarn if issubclass(w.category, RuntimeWarning)] + + +def test_scavenger_metrics_can_be_run_twice(): + scavenger = Scavenger(pd.DataFrame(_example())) + first = scavenger.metrics().copy() + pd.testing.assert_frame_equal(first, scavenger.metrics()) + + +def test_scavenger_clean_data_excludes_invalid_participants(): + rows = _keep_rows() + # failed 2 catch trials + catch = _keep_rows("drop_catch") + catch[0]["nbcorrect_gain"] = 0 + catch[1]["nbcorrect_gain"] = 0 + rows += catch + # repeated the same left/right response on at least 95% of trials + rows += [{**row, "resp_rep": 1} for row in _keep_rows("drop_left_right")] + # more than 40 trials + rows += [_trial("drop_trials", chosen=1 + i % 2) for i in range(41)] + # the 60-trial version of the game + rows += [{**row, "config_id": 242} for row in _keep_rows("drop_config")] + + scavenger = Scavenger(pd.DataFrame(rows)) + cleaned = scavenger.clean_data() # also runs metrics + assert set(cleaned["userID"]) == {"keep"} + assert scavenger.deleted_participants == 4 + + +def test_scavenger_clean_data_keeps_one_failed_catch_trial(): + rows = _keep_rows() + rows[0]["nbcorrect_gain"] = 0 + assert list(Scavenger(pd.DataFrame(rows)).clean_data()["userID"]) == ["keep"] + + +def test_scavenger_requires_data_and_columns(): + with pytest.raises(ValueError, match="needs data"): + Scavenger() + data = pd.DataFrame(_example()).drop(columns=["safe_EV"]) + with pytest.raises(KeyError, match="safe_EV"): + Scavenger(data) + # config_id is only needed to clean the data + scavenger = Scavenger(pd.DataFrame(_example()).drop(columns=["config_id"])) + assert len(scavenger.metrics()) == 1 + with pytest.raises(KeyError, match="config_id"): + scavenger.clean_data() + + +def test_scavenger_codebook_describes_every_metric(): + scavenger = Scavenger(pd.DataFrame(_example())) + assert set(scavenger.metrics().columns) == set(scavenger.get_codebook()) + + +def test_scavenger_reads_csv(tmp_path): + filepath = tmp_path / "scavenger.csv" + pd.DataFrame(_example()).to_csv(filepath, index=False) + assert np.isclose(Scavenger(str(filepath)).metrics().iloc[0]["risky_choices"], 0.6) diff --git a/test/generators/test_parameters.py b/test/generators/test_parameters.py index 07bc536..af89c67 100644 --- a/test/generators/test_parameters.py +++ b/test/generators/test_parameters.py @@ -251,3 +251,12 @@ def test_parameters_callable_is_instance_scoped(): if __name__ == "__main__": pytest.main() + + +def test_parameters_pdf_outside_the_support_is_impossible(): + # a value outside the support must get the lowest log prior, never a value near 0 (probability 1) + params = Parameters( + a=Value(value=2.0, lower=0, upper=1, prior="truncated_normal", args={"mean": 0.5, "sd": 0.25}) + ) + assert params.PDF(log=True) == np.finfo(np.float64).min + assert params.PDF(log=False) == np.finfo(np.float64).tiny diff --git a/test/optimisation/test_ppt_identifier.py b/test/optimisation/test_ppt_identifier.py new file mode 100644 index 0000000..3e57b1a --- /dev/null +++ b/test/optimisation/test_ppt_identifier.py @@ -0,0 +1,81 @@ +import numpy as np +import pandas as pd +import pytest + +from cpm.core.optimisers import group_identifier, prepare_data +from cpm.generators import Parameters, Value, Wrapper +from cpm.optimisation import FminBound, minimise + + +def simple_model(parameters, trial): + return {"dependent": np.array([trial["stimulus"] * parameters.alpha])} + + +def make_data(): + return pd.DataFrame( + { + "ppt": np.repeat([1, 2], 3), + "stimulus": np.tile([1.0, 2.0, 3.0], 2), + "observed": np.tile([0.1, 0.2, 0.3], 2), + } + ) + + +def fit(data, **kwargs): + parameters = Parameters(alpha=Value(value=0.5, lower=0.0, upper=1.0, prior="uniform")) + model = Wrapper(model=simple_model, data=make_data().query("ppt == 1"), parameters=parameters) + optimiser = FminBound(model=model, data=data, minimisation=minimise.Distance.SSE, approx_grad=True, **kwargs) + optimiser.optimise(display=False) + return optimiser + + +@pytest.mark.parametrize( + "grouping, expected", + [ + (lambda d: d.groupby("ppt"), "ppt"), + (lambda d: d.groupby(["ppt"]), "ppt"), + (lambda d: d.groupby(d["ppt"]), "ppt"), + (lambda d: d.groupby(["ppt", "stimulus"]), None), + (lambda d: d.groupby(d["ppt"].to_numpy()), None), + ], +) +def test_group_identifier_reads_a_single_named_grouping(grouping, expected): + assert group_identifier(grouping(make_data()), None) == expected + + +def test_group_identifier_keeps_a_given_identifier(): + assert group_identifier(make_data().groupby("ppt"), "participant") == "participant" + assert group_identifier(make_data(), "ppt") == "ppt" + assert group_identifier([{"observed": [1]}], None) is None + + +def test_prepare_data_needs_an_identifier_for_a_dataframe(): + with pytest.raises(ValueError, match="ppt_identifier"): + prepare_data(make_data(), None) + + +def test_prepare_data_needs_the_identifier_column(): + with pytest.raises(KeyError, match="'subject' is not a column of the data"): + prepare_data(make_data(), "subject") + + +def test_prepare_data_groups_a_dataframe_by_the_identifier(): + data, participants, groups, pandas = prepare_data(make_data(), "ppt") + assert pandas and groups == [1, 2] + assert list(participants["ppt"]) == [1, 1, 1] + + +def test_prepare_data_rejects_other_types(): + with pytest.raises(TypeError, match="ndarray"): + prepare_data(np.zeros(3), None) + + +def test_fit_on_grouped_data_reports_the_participants(): + optimiser = fit(make_data().groupby("ppt")) + assert optimiser.ppt_identifier == "ppt" + assert list(optimiser.export()["ppt"]) == [1, 2] + + +def test_fit_on_a_dataframe_without_identifier_explains_the_error(): + with pytest.raises(ValueError, match="no ppt_identifier was given"): + fit(make_data())