diff --git a/carl/distributions/histogram.py b/carl/distributions/histogram.py index 6981585..263fdfb 100644 --- a/carl/distributions/histogram.py +++ b/carl/distributions/histogram.py @@ -6,9 +6,9 @@ from astropy.stats import bayesian_blocks from itertools import product +from scipy.interpolate import interp1d from sklearn.utils import check_random_state from sklearn.utils import check_array -from scipy.interpolate import interp1d from .base import DistributionMixin @@ -41,7 +41,7 @@ def __init__(self, bins=10, range=None, interpolation=None, self.interpolation = interpolation self.variable_width = variable_width - def pdf(self, X, **kwargs): + def pdf(self, X, return_std=False, **kwargs): X = check_array(X) if self.ndim_ == 1 and self.interpolation: @@ -58,10 +58,27 @@ def pdf(self, X, **kwargs): indices[X[:, j] == self.edges_[j][-2]] -= 1 all_indices.append(indices) - return self.histogram_[all_indices] + p = self.histogram_[all_indices] + + if return_std: + return p, self.errors_[all_indices] + else: + return p + + def nll(self, X, return_std=False, **kwargs): + if not return_std: + p = self.pdf(X, **kwargs) + else: + p, std = self.pdf(X, return_std=True, **kwargs) + + if not return_std: + return -np.log(p) + else: + s = std / p + s[~np.isfinite(s)] = 0 + + return -np.log(p), s - def nll(self, X, **kwargs): - return -np.log(self.pdf(X, **kwargs)) def rvs(self, n_samples, random_state=None, **kwargs): rng = check_random_state(random_state) @@ -88,8 +105,12 @@ def rvs(self, n_samples, random_state=None, **kwargs): def fit(self, X, sample_weight=None, **kwargs): # Checks X = check_array(X) + if sample_weight is not None and len(sample_weight) != len(X): raise ValueError + if (self.bins == "blocks" or + self.variable_width) and (X.shape[1] != 1): + raise ValueError # Compute histogram and edges if self.bins == "blocks": @@ -97,6 +118,8 @@ def fit(self, X, sample_weight=None, **kwargs): range_ = self.range[0] if self.range else None h, e = np.histogram(X.ravel(), bins=bins, range=range_, weights=sample_weight, normed=False) + counts = h + volumes = e[1:] - e[:-1] e = [e] elif self.variable_width: @@ -107,19 +130,30 @@ def fit(self, X, sample_weight=None, **kwargs): h, e = np.histogram(X.ravel(), bins=ticks, range=range_, normed=False, weights=sample_weight) h, e = h.astype(float), e.astype(float) - widths = e[1:] - e[:-1] - h = h / widths / h.sum() + counts = h + volumes = e[1:] - e[:-1] e = [e] else: bins = self.bins h, e = np.histogramdd(X, bins=bins, range=self.range, - weights=sample_weight, normed=True) + weights=sample_weight, normed=False) + counts = h + volumes = np.ones_like(h) + for e_i in np.meshgrid(*[e_i[1:] - e_i[:-1] for e_i in e], + indexing="ij"): + volumes *= e_i + + # Histogram and bin uncertainties + h = counts / counts.sum() / volumes + errors = np.sqrt(counts) / counts.sum() / volumes # Poisson errors # Add empty bins for out of bound samples for j in range(X.shape[1]): h = np.insert(h, 0, 0., axis=j) h = np.insert(h, h.shape[j], 0., axis=j) + errors = np.insert(errors, 0, 0., axis=j) + errors = np.insert(errors, errors.shape[j], 0., axis=j) e[j] = np.insert(e[j], 0, -np.inf) e[j] = np.insert(e[j], len(e[j]), np.inf) @@ -135,6 +169,8 @@ def fit(self, X, sample_weight=None, **kwargs): self.histogram_ = h self.edges_ = e + self.counts_ = counts + self.errors_ = errors self.ndim_ = X.shape[1] return self diff --git a/carl/learning/base.py b/carl/learning/base.py index c72e2d4..c2d5d37 100644 --- a/carl/learning/base.py +++ b/carl/learning/base.py @@ -60,16 +60,23 @@ def predict(self, X): self.classes_[1], self.classes_[0]) - def predict_proba(self, X): + def predict_proba(self, X, return_std=False): X = check_array(X) - df = self.regressor_.predict(X) + if not return_std: + df = self.regressor_.predict(X) + else: + df, std = self.regressor_.predict(X, return_std=True) + df = np.clip(df, 0., 1.) probas = np.zeros((len(X), 2)) probas[:, 0] = 1. - df probas[:, 1] = df - return probas + if not return_std: + return probas + else: + return probas, std def score(self, X, y): return self.regressor_.score(X, y) diff --git a/carl/learning/calibration.py b/carl/learning/calibration.py index 8a48dcf..77e080c 100644 --- a/carl/learning/calibration.py +++ b/carl/learning/calibration.py @@ -33,7 +33,7 @@ class CalibratedClassifierCV(BaseEstimator, ClassifierMixin): """ def __init__(self, base_estimator, method="histogram", bins="auto", - interpolation=None, variable_width=False, cv=1): + range=None, interpolation=None, variable_width=False, cv=1): """Constructor. Parameters @@ -51,6 +51,10 @@ def __init__(self, base_estimator, method="histogram", bins="auto", * `bins` [int, default="auto"]: The number of bins, if `method` is `"histogram"`. + * `range` [(lower, upper), optional]: + The lower and upper bounds. If `None`, bounds are automatically + inferred from the data. Used only if `method` is `"histogram"`. + * `interpolation` [string, optional] Specifies the kind of interpolation between bins as a string (`"linear"`, `"nearest"`, `"zero"`, `"slinear"`, `"quadratic"`, @@ -75,6 +79,7 @@ def __init__(self, base_estimator, method="histogram", bins="auto", self.base_estimator = base_estimator self.method = method self.bins = bins + self.range = range self.interpolation = interpolation self.variable_width = variable_width self.cv = cv @@ -109,7 +114,8 @@ def fit(self, X, y, sample_weight=None): # Calibrator if self.method == "histogram": base_calibrator = HistogramCalibrator( - bins=self.bins, interpolation=self.interpolation, + bins=self.bins, range=self.range, + interpolation=self.interpolation, variable_width=self.variable_width) elif self.method == "kde": base_calibrator = KernelDensityCalibrator() @@ -205,7 +211,7 @@ def predict(self, X): self.classes_[1], self.classes_[0]) - def predict_proba(self, X): + def predict_proba(self, X, return_std=False): """Predict the posterior probabilities of classification for `X`. Parameters @@ -218,15 +224,31 @@ def predict_proba(self, X): * `probas` [array, shape=(n_samples, n_classes)]: The predicted probabilities. """ + if return_std and self.method != "histogram": + raise ValueError + p = np.zeros((len(X), 2)) + std = np.zeros(len(X)) for clf, calibrator in zip(self.classifiers_, self.calibrators_): - p[:, 1] += calibrator.predict(clf.predict_proba(X)[:, 1]) + if not return_std: + p[:, 1] += calibrator.predict(clf.predict_proba(X)[:, 1]) + + else: + p_, std_ = calibrator.predict(clf.predict_proba(X)[:, 1], + return_std=True) + p[:, 1] += p_ + std += std_ ** 2 p[:, 1] /= len(self.classifiers_) p[:, 0] = 1. - p[:, 1] + std = (1. / len(self.classifiers_) ** 2 * std) ** 0.5 + # assume independence? ok for cv==2 - return p + if not return_std: + return p + else: + return p, std def _clone(self): estimator = clone(self, original=True) @@ -311,6 +333,7 @@ def fit(self, T, y, sample_weight=None): t_min = max(0, min(np.min(t0), np.min(t1)) - self.eps) t_max = min(1, max(np.max(t0), np.max(t1)) + self.eps) range = [(t_min, t_max)] + # Fit self.calibrator0 = Histogram(bins=bins, range=range, interpolation=self.interpolation, @@ -324,7 +347,7 @@ def fit(self, T, y, sample_weight=None): return self - def predict(self, T): + def predict(self, T, return_std=False): """Calibrate data. Parameters @@ -332,19 +355,46 @@ def predict(self, T): * `T` [array-like, shape=(n_samples,)]: Data to calibrate. + * `return_std` [boolean] + If True, return the error associated with + `p(T|y=1)/(p(T|y=0)+p(T|y=1))`. + Returns ------- * `Tt` [array, shape=(n_samples,)]: Calibrated data. """ T = column_or_1d(T).reshape(-1, 1) - num = self.calibrator1.pdf(T) - den = self.calibrator0.pdf(T) + self.calibrator1.pdf(T) - p = num / den - p[den == 0] = 0.5 + if not return_std: + num = self.calibrator1.pdf(T) + den = self.calibrator0.pdf(T) + self.calibrator1.pdf(T) - return p + p = num / den + p[den == 0] = 0.5 + + return p + + else: + p1, std1 = self.calibrator1.pdf(T, return_std=True) + p0, std0 = self.calibrator0.pdf(T, return_std=True) + + num = p1 + den = p0 + p1 + p = num / den + p[den == 0] = 0.5 + + std_num = std1 + std_den = (std0 ** 2 + std1 ** 2) ** 0.5 + std_p = (p ** 2 * ((std_num / num) ** 2 + + (std_den / den) ** 2 - + 2 * std_num ** 2 / (num * den))) ** 0.5 + # nb: cov(a, a+b) = var(a) when a and b are independent + + std_p[den == 0] = 0 + std_p[~np.isfinite(std_p)] = 0 + + return p, std_p class KernelDensityCalibrator(BaseEstimator, RegressorMixin): diff --git a/carl/ratios/base.py b/carl/ratios/base.py index d471f1f..fcf493f 100644 --- a/carl/ratios/base.py +++ b/carl/ratios/base.py @@ -55,7 +55,7 @@ def fit(self, X=None, y=None, numerator=None, """ return self - def predict(self, X, log=False, **kwargs): + def predict(self, X, return_std=False, log=False, **kwargs): """Predict the density ratio `r(x_i)` for all `x_i` in `X`. Parameters @@ -73,7 +73,7 @@ def predict(self, X, log=False, **kwargs): """ raise NotImplementedError - def nllr(self, X, **kwargs): + def nllr(self, X, return_std=False, **kwargs): """Negative log-likelihood ratio. This method is a shortcut for `-ratio.predict(X, log=True).sum()`. @@ -88,13 +88,20 @@ def nllr(self, X, **kwargs): * `nllr` [float]: The negative log-likelihood ratio. """ - ratios = self.predict(X, log=True, **kwargs) + if not return_std: + ratios = self.predict(X, log=True, **kwargs) + else: + ratios, std = self.predict(X, log=True, return_std=True, **kwargs) + mask = np.isfinite(ratios) if mask.sum() < len(ratios): warnings.warn("r(X) contains non-finite values.") - return -np.sum(ratios[mask]) + if not return_std: + return -np.sum(ratios[mask]) + else: + return -np.sum(ratios[mask]), (std[mask] ** 2).sum() ** 0.5 def score(self, X, y, finite_only=True, **kwargs): """Negative MSE between predicted and known ratios. diff --git a/carl/ratios/classifier.py b/carl/ratios/classifier.py index b4175bb..27abe4b 100644 --- a/carl/ratios/classifier.py +++ b/carl/ratios/classifier.py @@ -132,7 +132,7 @@ def fit(self, X=None, y=None, sample_weight=None, return self - def predict(self, X, log=False, **kwargs): + def predict(self, X, return_std=False, log=False, **kwargs): """Predict the density ratio `r(x_i)` for all `x_i` in `X`. Parameters @@ -149,15 +149,44 @@ def predict(self, X, log=False, **kwargs): The predicted ratio `r(X)`. """ if self.identity_: - if log: - return np.zeros(len(X)) + if not return_std: + if log: + return np.zeros(len(X)) + else: + return np.ones(len(X)) else: - return np.ones(len(X)) + if log: + return np.zeros(len(X)), np.zeros(len(X)) + else: + return np.ones(len(X)), np.zeros(len(X)) else: - p = self.classifier_.predict_proba(X) + if not return_std: + p = self.classifier_.predict_proba(X) + + if log: + return np.log(p[:, 0]) - np.log(p[:, 1]) + else: + return np.divide(p[:, 0], p[:, 1]) - if log: - return np.log(p[:, 0]) - np.log(p[:, 1]) else: - return np.divide(p[:, 0], p[:, 1]) + p, std_p = self.classifier_.predict_proba(X, return_std=True) + + r = np.divide(p[:, 0], p[:, 1]) + std_r = (r ** 2 * ((std_p / p[:, 0]) ** 2 + + (std_p / p[:, 1]) ** 2 - + 2 * (-std_p ** 2) / (p[:, 0] * + p[:, 1]))) ** 0.5 + # nb: cov(p, 1-p) = -var(p) = -std^2(p) + #std_r = std_p / (p[:, 1] ** 2) + + std_r[~np.isfinite(std_r)] = 0 + + if not log: + return r, std_r + + else: + std_r = std_r / r + std_r[~np.isfinite(std_r)] = 0 + + return np.log(p[:, 0]) - np.log(p[:, 1]), std_r diff --git a/examples/Composing and fitting distributions.ipynb b/examples/Composing and fitting distributions.ipynb index d7adf27..901667f 100644 --- a/examples/Composing and fitting distributions.ipynb +++ b/examples/Composing and fitting distributions.ipynb @@ -91,7 +91,7 @@ "data": { "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -189,13 +189,13 @@ { "data": { "text/plain": [ - "array([[-0.03168252],\n", - " [ 1.19909686],\n", - " [-1.61576704],\n", + "array([[-0.04404857],\n", + " [ 1.26847754],\n", + " [-0.52666604],\n", " ..., \n", - " [ 1.32691464],\n", - " [ 0.52905262],\n", - " [ 2.2252217 ]])" + " [ 0.43813995],\n", + " [ 0.78295175],\n", + " [-1.00249389]])" ] }, "execution_count": 8, @@ -277,7 +277,7 @@ { "data": { "text/plain": [ - "{a, sigma}" + "{sigma, a}" ] }, "execution_count": 11, @@ -342,7 +342,7 @@ { "data": { "text/plain": [ - "{sigma, b, c}" + "{b, c, sigma}" ] }, "execution_count": 14, @@ -430,9 +430,9 @@ "outputs": [ { "data": { - "image/png": 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h8593IRolmJnx4dOfjkOWt7duthxU2rgginbivZsBO+lWktMtZnjzqU99Cp/6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\nOjT/H4IubCXZ1FR/AAAAAElFTkSuQmCC\n", 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h8593IRolmJnx4dOfjkOWt7duthxU2rgginbivZsBO+lWktMtZnjzqU99Cp/6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\nJ598QmRkJPHx8d4Op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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -503,7 +503,7 @@ { "data": { "text/plain": [ - "{mu, g, sigma, mu, sigma, sigma, mu}" + "{g, mu, sigma, sigma, mu, sigma, mu}" ] }, "execution_count": 20, @@ -526,7 +526,7 @@ "data": { "image/png": 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bb2bXJqpAEYBRo2DtWhg9OupKRFJXoka57AecAXQDDgbmmtlcd/8wQe8vWWzuXPjLX2DB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\nrAhqSBoz6wOsdfe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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -637,7 +637,7 @@ { "data": { "text/plain": [ - "{w, mu}" + "{mu, w}" ] }, "execution_count": 25, @@ -692,7 +692,7 @@ { "data": { "text/plain": [ - "array(0.9858563937730002)" + "array(1.0020688324851592)" ] }, "execution_count": 27, @@ -714,7 +714,7 @@ { "data": { "text/plain": [ - "array([ 0.6894848, 0.3105152])" + "array([ 0.6925866, 0.3074134])" ] }, "execution_count": 28, @@ -735,9 +735,9 @@ "outputs": [ { "data": { - "image/png": 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kgoIKkmxPUGSeY2ziWKvj+LwwE87bN/+NKUtv4eP+dxBBT2L7RvB/vzxO0dEi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\nbbX1mUIvIqnAdcC5xphzgd9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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -769,7 +769,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/tests/distributions/test_histogram.py b/tests/distributions/test_histogram.py index f8a0a98..5e09377 100644 --- a/tests/distributions/test_histogram.py +++ b/tests/distributions/test_histogram.py @@ -7,7 +7,9 @@ from numpy.testing import assert_raises from numpy.testing import assert_array_almost_equal from numpy.testing import assert_almost_equal +from sklearn.utils import check_random_state +from carl.distributions import Normal from carl.distributions import Histogram @@ -66,3 +68,50 @@ def test_histogram_variable_width(): integral = h.histogram_ * (h.edges_[0][1:] - h.edges_[0][:-1]) integral = integral[1:-1].sum() assert_almost_equal(integral, 1.) + + +def test_histogram_normalization(): + rng = check_random_state(1) + X = rng.rand(100, 1) + + for h in [Histogram(bins=10), + Histogram(bins=10, variable_width=True), + Histogram(bins="blocks")]: + h = Histogram(bins=10) + h.fit(X) + + volume = ((h.edges_[0][2:-1] - + h.edges_[0][1:-2]) * h.histogram_[1:-1]).sum() + assert_almost_equal(volume, 1.0) + + +def test_histogram_std(): + X = np.array([0.1, 0.1, 0.1, 0.1, 0.1, 0.9]).reshape(-1, 1) + h = Histogram(bins=5, range=[(0, 1)]) + h.fit(X) + p, std = h.pdf(X, return_std=True) + assert np.abs(std[0] - 5 ** 0.5 / 6 / 0.2) < 10e-5 + assert np.abs(std[-1] - 1 ** 0.5 / 6 / 0.2) < 10e-5 + + nll, std_nll = h.nll(X, return_std=True) + assert std_nll[0] == std[0] / p[0] + assert std_nll[-1] == std[-1] / p[-1] + + X = np.array([[0.5]]) + p, std = h.pdf(X, return_std=True) + assert std[0] == 0.0 + + # check that estimates are not too far off + h = Histogram(bins=10, range=[(-2, 2)]) + p = Normal(mu=0) + + pdfs, stds = [], [] + for i in range(1000): + X = p.rvs(100, random_state=i) + h.fit(X) + pdf, std = h.pdf([[0.]], return_std=True) + + pdfs.append(pdf[0]) + stds.append(std[0]) + + assert np.abs(np.mean(stds) - np.std(pdfs)) < 0.01 diff --git a/tests/learning/test_calibration.py b/tests/learning/test_calibration.py index be302ff..33b5d14 100644 --- a/tests/learning/test_calibration.py +++ b/tests/learning/test_calibration.py @@ -2,6 +2,7 @@ # under the terms of the Revised BSD License; see LICENSE file for # more details. +import numpy as np from numpy.testing import assert_array_almost_equal from numpy.testing import assert_raises from sklearn.utils.testing import assert_greater @@ -10,7 +11,9 @@ from sklearn.naive_bayes import MultinomialNB from sklearn.metrics import brier_score_loss +from carl.distributions import Normal from carl.learning import CalibratedClassifierCV +from carl.learning.calibration import HistogramCalibrator def check_calibration(method): @@ -74,3 +77,50 @@ def test_calibration(): for method in ["isotonic", "sigmoid", "histogram", "kde", "interpolated-isotonic"]: yield check_calibration, method + + +def test_calibration_histogram_std(): + p0 = Normal(mu=0.1, sigma=0.3) + probas0 = p0.rvs(10000) + probas0 = probas0[(probas0[:, 0] >= 0.0) & (probas0[:, 0] <= 1.0)] + p1 = Normal(mu=0.9, sigma=0.3) + probas1 = p1.rvs(10000) + probas1 = probas1[(probas1[:, 0] >= 0.0) & (probas1[:, 0] <= 1.0)] + + X = np.vstack([probas0, probas1]) + y = np.zeros(len(X)) + y[len(probas0):] = 1 + + h = HistogramCalibrator(bins=10) + h.fit(X.ravel() / 2 + 0.25, y) + + xs = np.linspace(0.25, 0.75, 101).reshape(-1, 1) + p, std = h.predict(xs.ravel(), return_std=True) + + assert std[50] > std[0] # uncertainty should be higher near the boundary + assert np.abs(std[0] - std[-1]) < 10e-3 # uncertainties should be similar + + +def test_calibration_clf_std(): + p0 = Normal(mu=0.1, sigma=0.3) + probas0 = p0.rvs(10000) + probas0 = probas0[(probas0[:, 0] >= 0.0) & (probas0[:, 0] <= 1.0)] + p1 = Normal(mu=0.9, sigma=0.3) + probas1 = p1.rvs(10000) + probas1 = probas1[(probas1[:, 0] >= 0.0) & (probas1[:, 0] <= 1.0)] + + X = np.vstack([probas0, probas1]) + y = np.zeros(len(X)) + y[len(probas0):] = 1 + + from sklearn.ensemble import ExtraTreesClassifier + clf = CalibratedClassifierCV(ExtraTreesClassifier(max_leaf_nodes=5, + n_estimators=100), + method="histogram", cv=3) + clf.fit(X, y) + + xs = np.linspace(0, 1, 101).reshape(-1, 1) + p, std = clf.predict_proba(xs, return_std=True) + + assert std[50] > std[0] # uncertainty should be higher near the boundary + assert np.abs(std[0] - std[-1]) < 10e-3 # uncertainties should be similar