diff --git a/data/tutorial/toySDT.ipynb b/data/tutorial/toySDT.ipynb new file mode 100644 index 0000000..c86805a --- /dev/null +++ b/data/tutorial/toySDT.ipynb @@ -0,0 +1,246 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Tutorial\n", + "## Basic Signal Detection Theory Model\n", + "\n", + "We use Gaussian distributions to model the noise and signal+noise distributions in a basic Signal Detection Theory (SDT) framework.\n", + "\n", + "This tutorial demonstrates how to create a basic SDT model, visualize the distributions, ROC curve, d' and pAUC." + ], + "id": "53a7cd26ed8862e3" + }, + { + "metadata": { + "collapsed": true, + "ExecuteTime": { + "end_time": "2025-12-13T00:01:45.574074Z", + "start_time": "2025-12-13T00:01:43.720693Z" + } + }, + "cell_type": "code", + "source": [ + "from pyWitness.BasicSDTModel import (\n", + " BasicSDTModel,\n", + " plot_gaussians_with_criterion,\n", + " plot_roc,\n", + " plot_dprime_and_pauc,\n", + ")\n", + "import matplotlib.pyplot as plt" + ], + "id": "7a2547d704bec6a5", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pyWitness 1.4.7.dev33+gb1fcbe3d3.d20251212 : Authorship - https://lmickes.github.io/pyWitness/authorship.html\n", + "pyWitness : Runtime warnings are suppressed\n", + "pyWitness : to reinstate (import warnings; warnings.simplefilter(\"\") \n" + ] + } + ], + "execution_count": 1 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Build the model", + "id": "acaa392dcf4df010" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-13T00:01:46.755154Z", + "start_time": "2025-12-13T00:01:46.752147Z" + } + }, + "cell_type": "code", + "source": [ + "model = BasicSDTModel(mu_noise=0.0, sigma_noise=1.0,\n", + " mu_signal=2, sigma_signal=1.0)\n", + "criterion = 0.0" + ], + "id": "cfd1e91323c1564", + "outputs": [], + "execution_count": 2 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "### Plot distributions and ROC curve", + "id": "d92affb4055c0cdc" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-13T00:01:48.919971Z", + "start_time": "2025-12-13T00:01:48.484854Z" + } + }, + "cell_type": "code", + "source": [ + "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n", + "plot_gaussians_with_criterion(model, criterion, ax=axes[0])\n", + "plot_roc(model, criterion, ax=axes[1])\n", + "plt.tight_layout()" + ], + "id": "f200631a299e078e", + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" 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" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "execution_count": 5 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Interactive demo", + "id": "90602f0cac71fcf4" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-13T00:03:04.023612Z", + "start_time": "2025-12-13T00:03:04.020236Z" + } + }, + "cell_type": "code", + "source": "from pyWitness.BasicSDTModel import interactive_sdt_demo", + "id": "b4450a4c51b695be", + "outputs": [], + "execution_count": 6 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Build the model\n", + "The lure distribution is standard normal by default." + ], + "id": "64a46aa34bd44d62" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-13T00:03:05.806211Z", + "start_time": "2025-12-13T00:03:05.803191Z" + } + }, + "cell_type": "code", + "source": "model = BasicSDTModel(mu_signal=2.0, sigma_noise=1.0)", + "id": "1b161ff83c232462", + "outputs": [], + "execution_count": 7 + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "### Run the interactive demo\n", + "You can set the initial criterion position and its range." + ], + "id": "fe2e77ee376859bf" + }, + { + "metadata": { + "ExecuteTime": { + "end_time": "2025-12-13T00:03:07.592371Z", + "start_time": "2025-12-13T00:03:07.173329Z" + } + }, + "cell_type": "code", + "source": "interactive_sdt_demo(model, c_init=0.5, c_min=-4.0, c_max=4.0)", + "id": "17ca15a58a1332a7", + "outputs": [ + { + "data": { + "text/plain": [ + "interactive(children=(FloatSlider(value=0.5, description='Criterion c', max=4.0, min=-4.0), Output()), _dom_cl…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "f329f30a94b94e49867f7b47d169eb1c" + } + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "._update(c)>" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 8 + } + ], + "metadata": { + "kernelspec": { + "display_name": "pwai", + "language": "python", + "name": "pwai" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/pyWitness/BasicSDTModel.py b/src/pyWitness/BasicSDTModel.py new file mode 100644 index 0000000..c6de492 --- /dev/null +++ b/src/pyWitness/BasicSDTModel.py @@ -0,0 +1,318 @@ +import numpy as _np +import matplotlib.pyplot as _plt +from scipy.stats import norm +from scipy import integrate + +class BasicSDTModel: + """ + Simple SDT with two Gaussians: + - Noise: X_N ~ N(mu_noise, sigma_noise) + - Signal: X_S ~ N(mu_signal, sigma_signal) + + In classic SDT toy models we usually set: + mu_noise = 0, sigma_noise = 1 + mu_signal = dprime, sigma_signal = 1 + + Methods provide: + - hit_far_rate(criterion) + - roc_curve() + - dprime_from_rates(HR, FAR) + - dprime_curve(criterion_grid) + - pauc(xmax) : integrate ROC over FAR from 0 to xmax + - pauc_at_criterion(c): x_max = FAR(c), so pAUC is a function of c + """ + def __init__(self, + mu_noise: float = 0.0, + mu_signal: float = 1.5, + sigma_noise: float = 1.0, + sigma_signal: float = 1.0): + self.mu_noise = mu_noise + self.mu_signal = mu_signal + self.sigma_noise = sigma_noise + self.sigma_signal = sigma_signal + + @property + def dprime_analytic(self) -> float: + """ + Analytic d' for equal-variance SDT: + d' = (mu_signal - mu_noise) / sigma_noise + If variances differ, this is still a useful 'effective' separation. + """ + return (self.mu_signal - self.mu_noise) / self.sigma_signal + + def hit_far_rate(self, criterion: float): + """ + Compute hit rate and false alarm rate for a given decision criterion. + HR = P(x_signal > criterion) + FAR = P(x_noise > criterion) + """ + hit_rate = 1 - norm.cdf(criterion, loc=self.mu_signal, scale=self.sigma_signal) + far_rate = 1 - norm.cdf(criterion, loc=self.mu_noise, scale=self.sigma_noise) + return hit_rate, far_rate + + def roc_curve(self, n_points: int = 200, span: float = 1.0): + """ + Construct ROC curve by sweeping criterion across the distributions. + n_points: number of points to compute on the ROC curve. + span: fraction of the way from mu_noise to mu_signal to cover with criteria. + """ + # Cover a range that comfortably includes both distributions + lower = min(self.mu_noise, self.mu_signal) - span * max(self.sigma_noise, self.sigma_signal) + upper = max(self.mu_noise, self.mu_signal) + span * max(self.sigma_noise, self.sigma_signal) + criteria = _np.linspace(lower, upper, n_points) + + HRs, FARs = [], [] + for c in criteria: + hr, fa = self.hit_far_rate(c) + HRs.append(hr) + FARs.append(fa) + HRs = _np.array(HRs) + FARs =_np.array(FARs) + + # Sort by FAR (0 -> 1) just to be safe + order = _np.argsort(FARs) + return FARs[order], HRs[order], criteria[order] + + @staticmethod + def dprime_from_rates(HR: float, FAR: float): + """ + Classic SDT definition: d' = z(HR) - z(FAR) + Handles edge cases by clipping probabilities. + """ + eps = 1e-6 + HR = _np.clip(HR, eps, 1 - eps) + FAR = _np.clip(FAR, eps, 1 - eps) + zHR = norm.ppf(HR) + zFAR = norm.ppf(FAR) + return zHR - zFAR + + def dprime_from_criterion(self, criterion: float) -> float: + HR, FAR = self.hit_far_rate(criterion) + return self.dprime_from_rates(HR, FAR) + + def dprime_curve(self, criteria: _np.ndarray) -> _np.ndarray: + dvals = [] + for c in criteria: + dvals.append(self.dprime_from_criterion(c)) + return _np.array(dvals) + + def _roc_given_far(self, far: float) -> float: + """ + ROC in parametric form: given FAR, return HR. + Steps: + 1) Find criterion c such that FAR(c) = far. + For the noise distribution: + FAR = P(X_N >= c) = 1 - Phi((c - mu_N)/sigma_N) + => 1 - far = Phi((c - mu_N)/sigma_N) + => (c - mu_N)/sigma_N = Phi^-1(1 - far) + => c = mu_N + sigma_N * Phi^-1(1 - far) + 2) Use the same c to compute HR(c). + """ + eps = 1e-9 + far = float(_np.clip(far, eps, 1 - eps)) + z = norm.ppf(1.0 - far) + c = self.mu_noise + self.sigma_noise * z + HR, _ = self.hit_far_rate(c) + return HR + + def pauc(self, xmax: float) -> float: + """ + Partial AUC from FAR=0 to FAR=xmax: + pAUC(xmax) = ∫_0^xmax ROC(far) dfar + """ + xmax = float(_np.clip(xmax, 0.0, 1.0)) + + def integrand(fa): + return self._roc_given_far(fa) + + val, _ = integrate.quad(integrand, 0.0, xmax) + return val + + def pauc_at_criterion(self, criterion: float) -> float: + """ + Define pAUC as a function of threshold by: + xmax = FAR(criterion) + pAUC(criterion) = ∫_0^{FAR(c)} ROC(far) dfar + This matches how your pyWitness pAUC integrates from 0 to some xmax. + """ + _, FAR = self.hit_far_rate(criterion) + return self.pauc(FAR) + + +def plot_gaussians_with_criterion(model: BasicSDTModel, criterion: float, ax=None): + if ax is None: + ax = _plt.gca() + + # x-range + span = 4.0 + lower = min(model.mu_noise, model.mu_signal) - span * max(model.sigma_noise, model.sigma_signal) + upper = max(model.mu_noise, model.mu_signal) + span * max(model.sigma_noise, model.sigma_signal) + xs = _np.linspace(lower, upper, 400) + + noise_pdf = norm.pdf((xs - model.mu_noise) / model.sigma_noise) / model.sigma_noise + signal_pdf = norm.pdf((xs - model.mu_signal) / model.sigma_signal) / model.sigma_signal + + ax.plot(xs, noise_pdf, label="Noise", linestyle="-") + ax.plot(xs, signal_pdf, label="Signal", linestyle="--") + ax.axvline(criterion, color="k", linestyle=":", label="Criterion") + + ax.text( + 0.98, 0.72, # x,y in axes coordinates (0~1) + f"c = {criterion:.2f}", + transform=ax.transAxes, + ha="right", va="top", + fontsize=10 + ) + + ax.set_xlabel("Internal response x") + ax.set_ylabel("Density") + ax.set_title("Signal vs Noise Distributions") + ax.legend() + ax.grid(False) + +def plot_roc(model: BasicSDTModel, criterion: float, ax=None, shade_pauc=True): + if ax is None: + ax = _plt.gca() + + FARs, HRs, criteria = model.roc_curve(n_points=400) + ax.plot(FARs, HRs, label="ROC", color="C0") + + HR_c, FAR_c = model.hit_far_rate(criterion) + ax.scatter([FAR_c], [HR_c], color="red", zorder=3, + label="Current c") + + if shade_pauc: + mask = FARs <= FAR_c + x_fill = FARs[mask] + y_fill = HRs[mask] + + if x_fill[0] > 0: + x_fill = _np.insert(x_fill, 0, 0.0) + y_fill = _np.insert(y_fill, 0, 0.0) + + ax.fill_between(x_fill, 0, y_fill, alpha=0.25, + color="C0", label="pAUC region") + + ax.text( + 0.98, 0.26, + f"FAR(c) = {FAR_c:.2f}\nHR(c) = {HR_c:.2f}", + transform=ax.transAxes, + ha="right", va="bottom", fontsize=10 + ) + + ax.plot([0, 1], [0, 1], "k--", linewidth=1) + + ax.set_xlabel("False Alarm Rate (FAR)") + ax.set_ylabel("Hit Rate (HR)") + ax.set_title("ROC Curve") + ax.legend() + ax.set_xlim(-0.01, 1.01) + ax.set_ylim(-0.01, 1.01) + ax.set_aspect("equal", adjustable="box") + +def plot_dprime_and_pauc(model: BasicSDTModel, ax1=None, ax2=None, current_c=None): + """ + Plot d'(c) and pAUC(c) as functions of criterion. + If current_c is not None, mark that position with a vertical line + marker. + """ + span = 4.0 + lower = min(model.mu_noise, model.mu_signal) - span * max(model.sigma_noise, model.sigma_signal) + upper = max(model.mu_noise, model.mu_signal) + span * max(model.sigma_noise, model.sigma_signal) + criteria = _np.linspace(lower, upper, 200) + + dvals = model.dprime_curve(criteria) + pauc_vals = _np.array([model.pauc_at_criterion(c) for c in criteria]) + + if ax1 is None or ax2 is None: + fig, (ax1, ax2) = _plt.subplots(1, 2, figsize=(10, 4)) + + # d'(c) curve + ax1.plot(criteria, dvals, label="d'(c)") + ax1.axhline(model.dprime_analytic, color="k", linestyle="--", label="Analytic d'") + ax1.set_xlabel("Criterion c") + ax1.set_ylabel("d'(c)") + ax1.set_title("d' as a function of criterion") + + if current_c is not None: + d_now = model.dprime_from_criterion(current_c) + ax1.axvline(current_c, color="red", linestyle=":", alpha=0.8) + ax1.scatter([current_c], [d_now], color="red") + ax1.legend() + + # pAUC(c) curve + ax2.plot(criteria, pauc_vals, label="pAUC(c)") + ax2.set_xlabel("Criterion c") + ax2.set_ylabel("pAUC(c)") + ax2.set_title("pAUC as a function of criterion") + + if current_c is not None: + pauc_now = model.pauc_at_criterion(current_c) + ax2.axvline(current_c, color="red", linestyle=":", alpha=0.8) + ax2.scatter([current_c], [pauc_now], color="red") + ax2.legend() + + _plt.tight_layout() + + +try: + from ipywidgets import interact, FloatSlider +except ImportError: + interact = None + FloatSlider = None + +def interactive_sdt_demo(model, + c_init=0.5, + c_min=-4.0, + c_max=4.0, + c_step=0.1): + """ + Launch an interactive SDT demo for a given model. + + Parameters + ---------- + model : BasicSDTModel + c_init, c_min, c_max, c_step : float + Slider settings for the decision criterion c. + """ + if interact is None or FloatSlider is None: + raise ImportError( + "ipywidgets is required for interactive_sdt_demo. " + "Install with `pip install ipywidgets` and enable it in Jupyter." + ) + + def _update(c): + # robustly convert c to float + if hasattr(c, "value"): + c_val = float(c.value) + else: + c_val = float(c) + + fig, axes = _plt.subplots(1, 3, figsize=(14, 4)) + + plot_gaussians_with_criterion(model, c_val, ax=axes[0]) + plot_roc(model, c_val, ax=axes[1]) + + d_val = model.dprime_from_criterion(c_val) + pauc_val = model.pauc_at_criterion(c_val) + + axes[2].axis("off") + text = ( + f"Criterion c = {c_val:.2f}\n" + f"d'(c) = {d_val:.3f}\n" + f"Analytic d' = {model.dprime_analytic:.3f}\n\n" + # f"The point on ROC is defined by a set of FAR(c) and HR(c).\n" + # f"pAUC(c) is Calculated by integrating ROC from FAR=0 to FAR(c).\n" + f"pAUC(c) = {pauc_val:.3f}" + ) + axes[2].text(0.01, 0.01, text, va="bottom", ha="left", fontsize=11) + _plt.tight_layout() + + slider = FloatSlider( + value=c_init, + min=c_min, + max=c_max, + step=c_step, + description="Criterion c" + ) + + return interact(_update, c=slider)