From 06bbf6ac9d5f7dcccb08cf8c18c6f4a2fdd8c671 Mon Sep 17 00:00:00 2001 From: SebastienMelo Date: Fri, 27 Feb 2026 13:25:45 +0100 Subject: [PATCH] commit residual estimator and erase previous one --- examples/example.ipynb | 962 +++++++++++---------- examples/example_advanced.ipynb | 143 ++++ glest/__init__.py | 4 +- glest/core.py | 1420 ++++++++++++++++--------------- glest/helpers.py | 629 -------------- glest/plot.py | 213 +++-- 6 files changed, 1497 insertions(+), 1874 deletions(-) create mode 100644 examples/example_advanced.ipynb delete mode 100644 glest/helpers.py diff --git a/examples/example.ipynb b/examples/example.ipynb index 3a570d7..b4f1978 100644 --- a/examples/example.ipynb +++ b/examples/example.ipynb @@ -4,90 +4,83 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Estimator of the Grouping Loss\n", + "## Epistemic Uncertainty Quantification\n", "\n", - "This package aims at estimating the grouping loss of a probabilistic classifier following the estimation procedure defined in [1].\n", - "\n", - "[1] Alexandre Perez-Lebel, Marine Le Morvan, and Gaël Varoquaux. \"Beyond calibration: estimating the grouping loss of modern neural networks.\" (ICLR 2023).\n", - "https://doi.org/10.48550/arxiv.2210.16315" + "This package aims at estimating the grouping loss (and more broadly the epistemic loss) of probabilistic classifers, using the method described in CITER PAPIER." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Load a probabilistic classifier\n", + "### Load a probabilistic classifier\n", "\n", "As an example, we fit a logistic regression on a binary classification dataset from scikit-learn. The classifier implements a `predict_proba` method to estimate the posterior probabilities of the classes." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.12020287, 0.76298063, 0.03892254, ..., 0.03135261, 0.91760703,\n", - " 0.04667157])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from sklearn.datasets import make_classification\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.model_selection import train_test_split\n", "\n", - "X, y = make_classification(n_samples=20000, n_informative=10, random_state=42)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n", + "X, y = make_classification(\n", + " n_samples=200000, random_state=42, n_features=20, n_informative=20, n_redundant=0\n", + ")\n", "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ "est = LogisticRegression()\n", "est.fit(X_train, y_train)\n", - "est.predict_proba(X_test)[:, 1]\n" + "S_test = est.predict_proba(X_test)[:, 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## " + "### Calibration error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Calibration\n", + "We note $c\\circ f(X)=\\mathbb{E}[Y\\mid f(X)]$ the calibrated probability\n", + "\n", + "From the decomposition of scoring rules, we have that $\\begin{equation}\n", + " \\underbrace{\\mathbb{E}[d_\\phi(f(X),Y)]}_{\\text{Expected Loss}} =\\overbrace{\\underbrace{\\mathbb{E}[d_\\phi(f(X),c \\circ f(X))]}_{\\text{Calibration Loss (CL)}} + \\underbrace{\\mathbb{E}[d_\\phi(c\\circ f(X),f^*(X)]}_{\\text{Grouping Loss (GL)}}}^{\\text{Epistemic loss (EL)}} + \\underbrace{\\mathbb{E}[d_\\phi(f^*(X),Y)]}_{\\text{Aleatoric Loss}}.\n", + "\\end{equation}$\n", + "\n", + "The calibration loss thus correponds to the distance between $f$ and the calibrated probabilities $c\\circ f$ and is the first part of the epistemic loss.\n", "\n", - "We first evaluate the calibration of the classifier. We observe below that the classifier is pretty well calibrated out of the box." + "\n", + "### Computation of the calibration loss\n", + "\n", + "Empirically, we regroup the samples by binning them according to their confidence score value $f(X)$. We discretize the $[0,1]$ in disjoint, consecutive intervals $[a,b]$ interval and estimate $\\mathbb{E}[Y\\mid f(X)\\in [a,b]]\\approx c \\circ f(X)$. From this, we see that the Logistic Regression is correcly calibrated out of the box." ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "image/png": 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", "text/plain": [ - "[Text(0.5, 1.0, 'Calibration curve')]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
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" ] }, "metadata": {}, @@ -95,39 +88,45 @@ } ], "source": [ - "from sklearn.calibration import CalibrationDisplay\n", "from sklearn.calibration import calibration_curve\n", + "import matplotlib.pyplot as plt\n", "\n", + "# Compute calibration curve\n", + "prob_true, prob_pred = calibration_curve(y_test, S_test, n_bins=15)\n", "\n", - "y_prob_test = est.predict_proba(X_test)[:, 1]\n", - "prob_true, prob_pred = calibration_curve(y_test, y_prob_test, n_bins=15, strategy=\"quantile\")\n", - "disp = CalibrationDisplay(prob_true, prob_pred, y_prob_test, estimator_name=est.__class__.__name__)\n", - "disp.plot(color=\"tab:red\")\n", - "disp.ax_.set(\n", - " title=\"Calibration curve\"\n", - ")\n", - "\n" + "# Plot calibration curve\n", + "plt.figure(figsize=(4, 4))\n", + "plt.plot(prob_pred, prob_true, marker=\"o\", label=\"Calibration curve\")\n", + "plt.plot([0, 1], [0, 1], linestyle=\"--\", label=\"Perfectly calibrated\")\n", + "plt.xlabel(\"Mean predicted probability\")\n", + "plt.ylabel(\"Fraction of positives\")\n", + "plt.title(\"Calibration Curve\")\n", + "plt.legend()\n", + "plt.grid()\n", + "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "However, errors on individual posterior probabilities remains, but are blind to calibration estimation. This is revealed by the grouping loss estimation we investigate below." + "### Grouping loss estimation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Estimation of the grouping loss of the classifier\n", + "#### Basic use\n", + "\n", + "By default, we can compute the grouping loss from a predefined partioning estimate without specifying its parameters.\n", "\n", - "### Use pre-defined partitioner" + "All of the objects require to have access to the score predictions $f(X)$ to be fitted. Thus, different from the `sklearn` API, the fitting and predicting methods require these values." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -135,65 +134,57 @@ "output_type": "stream", "text": [ "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0534\n", - " ↳ Uncorrected GL : 0.0685\n", - " ↳ Bias : 0.0143\n", - " ↳ Binning induced: 0.0008\n", + " Scoring Rule : Brier: 0.1403\n", + " Grouping loss : 0.0885\n", + " Calibration Loss : 0.0025\n", + " Epistemic Loss : 0.0910\n", "\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ "from glest.core import GLEstimator\n", + "from sklearn.tree import DecisionTreeRegressor\n", "\n", - "glest = GLEstimator(est, partitioner=\"decision_tree\", train_size=0.5, random_state=0)\n", - "glest.fit(X_test, y_test)\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "gle = GLEstimator()\n", + "gle.fit(X_test, S_test, y_test)\n", + "gle.estimate()\n", + "print(gle)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Customize binning\n", - "To allow more flexibility on the binning (number of bins and binning strategy), use the Partitioner.from_name classmethod to specify the number of bins and binning strategy to use." + "#### Grouping diagram\n", + "\n", + "Visually, we can illustrate all of the groups created by the paritioning estimate." ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0750\n", - " ↳ Uncorrected GL : 0.0876\n", - " ↳ Bias : 0.0105\n", - " ↳ Binning induced: 0.0021\n", - "\n" - ] + "data": { + "text/plain": [ + "GLEstimator()\n", + " Scoring Rule : Brier: 0.1403\n", + " Grouping loss : 0.0885\n", + " Calibration Loss : 0.0025\n", + " Epistemic Loss : 0.0910" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] }, "metadata": {}, @@ -201,235 +192,120 @@ } ], "source": [ - "from glest.core import Partitioner\n", - "\n", - "partitioner = Partitioner.from_name(\"decision_tree\", n_bins=10, strategy=\"quantile\")\n", - "\n", - "glest = GLEstimator(est, partitioner=partitioner, train_size=0.5, random_state=0)\n", - "glest.fit(X_test, y_test)\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "gle.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Customization of the partitioner\n", - "You can also specify a custom estimator to partition the feature space. You must specify the method that should be called to retrieve the partition assignments." + "### 0-1 Risk estimation" ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0761\n", - " ↳ Uncorrected GL : 0.0888\n", - " ↳ Bias : 0.0107\n", - " ↳ Binning induced: 0.0021\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "from sklearn.tree import DecisionTreeClassifier\n", - "\n", - "partitioner_est = DecisionTreeClassifier(max_depth=10, random_state=0)\n", - "partitioner = Partitioner(partitioner_est, n_bins=10, strategy=\"quantile\", predict_method=\"apply\")\n", - "\n", - "glest = GLEstimator(est, partitioner=partitioner, train_size=0.5, random_state=0)\n", - "glest.fit(X_test, y_test)\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "The risk estimator can predict the risk for new samples. Thus it has to be trained on a separate split." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0099\n", - " ↳ Uncorrected GL : 0.0134\n", - " ↳ Bias : 0.0014\n", - " ↳ Binning induced: 0.0021\n", - "\n" - ] - }, { "data": { - "image/png": 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", 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" + "" ] }, + "execution_count": 10, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from sklearn.cluster import KMeans\n", + "from glest.core import RiskEstimator\n", "\n", - "partitioner_est = KMeans(n_clusters=4, random_state=0, n_init=\"auto\")\n", - "partitioner = Partitioner(partitioner_est, n_bins=10, strategy=\"quantile\", predict_method=\"predict\")\n", "\n", - "glest = GLEstimator(est, partitioner=partitioner, train_size=0.5, random_state=0)\n", - "glest.fit(X_test, y_test)\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "X_test, X_risk, y_test, y_risk = train_test_split(\n", + " X_test, y_test, test_size=0.5, random_state=0\n", + ")\n", + "\n", + "\n", + "S_test = est.predict_proba(X_test)[:, 1]\n", + "risk = RiskEstimator()\n", + "risk.fit(X_test, S_test, y_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Custom split\n", - "\n", - "By default, the estimator split the data internally to avoid overfitting the partition and the evaluation based on the `train_size` argument. You can customize how this split is made by passing directly the test data with the `test_data` argument of `GLEstimator.fit`. For example with `glest.fit(X1, y1, test_data=(X2, y2))`, the partitions will be fitted on `(X1, y1)` and the grouping loss will be evaluated on `(X2, y2)`." + "The prediction can be made individually, giving both $\\widehat{\\mathcal{R}}^{CL}_{f}(X)$ and $\\widehat{\\mathcal{R}}_{f}(X)$. For predictions, the optimal threshold $t^*=\\frac{\\Lambda_{0,0}-\\Lambda_{1,0}}{\\Lambda_{0,0}-\\Lambda_{1,0}+\\Lambda_{1,1}-\\Lambda_{0,1}}$ must be given to account for misclassification costs. By default, the cost-insensitive threshold is used (i.e. $t^*=0.5$). Results are given normalized from the difference in risks $\\Lambda_{\\Delta}$.\n" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0869\n", - " ↳ Uncorrected GL : 0.1040\n", - " ↳ Bias : 0.0148\n", - " ↳ Binning induced: 0.0022\n", - "\n" - ] - }, { "data": { - "image/png": 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", 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" + "(array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)),\n", + " array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)))" ] }, + "execution_count": 11, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "from sklearn.model_selection import train_test_split\n", - "\n", - "X1, X2, y1, y2 = train_test_split(X_test, y_test, random_state=0)\n", - "\n", - "partitioner_est = DecisionTreeClassifier(max_depth=10, random_state=0)\n", - "partitioner = Partitioner(partitioner_est, n_bins=10, strategy=\"quantile\", predict_method=\"apply\")\n", - "\n", - "glest = GLEstimator(est, partitioner=partitioner, random_state=0)\n", - "glest.fit(X1, y1, test_data=(X2, y2))\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "risk.predict(X_risk, est.predict_proba(X_risk)[:, 1].reshape(-1, 1), t=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Pass the estimated probabilities manually\n", - "\n", - "In some situations, the probabilistic classifier you want to evaluate is not easily accessible under the form of a class with the `predict_proba` method. For example, when working with large models and data, running `predict_proba` each time is too resource-intensive. A solution would be to do a single pass on the data and to store the probabilities on disk. To still be able to use this package and evaluate the grouping loss, the GLEstimator accepts an array of probabilities `y_proba` as the `fitted_estimator` argument. The only constraint is that the `GLEstimator.fit` method should be called on the corresponding `(X, y)` data that generated the `y_proba` array. The estimator checks for shape mismatches, but it is the responsibility of the user to ensure both are matching: i.e. `y_proba[i]` is the output of the classifier for `X[i]`." + "The prediction can also be averaged, to make post processing decisions from it." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0690\n", - " ↳ Uncorrected GL : 0.0932\n", - " ↳ Bias : 0.0234\n", - " ↳ Binning induced: 0.0008\n", - "\n" - ] - }, { "data": { - "image/png": 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", "text/plain": [ - "
" + "(np.float64(0.00298), np.float64(0.007059932401632107))" ] }, + "execution_count": 12, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "\n", - "y_proba = est.predict_proba(X_test)\n", - "\n", - "X1, X2, y1, y2, y_proba1, y_proba2 = train_test_split(X_test, y_test, y_proba, random_state=0)\n", - "glest = GLEstimator(y_proba1, partitioner=\"decision_tree\", random_state=0)\n", - "glest.fit(X1, y1, test_data=(X2, y2, y_proba2))\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "risk.predict_total(X_risk, est.predict_proba(X_risk)[:, 1].reshape(-1, 1), t=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Manual partitioning\n", - "Similarly, the partitions can be set explicitly using the `partition` argument of the `GLEstimator.fit` method. This is useful for example if the groups are not defined from a fit/predict procedure. For example, one can use an explicit feature of the covariates (eg. socio-demographics)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", + "### Using custom honest tree partitioning estimates\n", "\n", - "def quantile_partition(x, n):\n", - " quantiles = np.quantile(x, np.linspace(0, 1, n+1))\n", - " quantile_ids = np.digitize(x, quantiles, right=False)\n", - " quantile_ids[quantile_ids == n] = n - 1\n", - " return quantile_ids\n", - "\n", - "partition = quantile_partition(X[:, 0], n=10)\n" + "The parameters of the honest tree partitioning estimate can be selected manually for both `GLEstimator` and `RiskEstimator` objects." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -437,335 +313,487 @@ "output_type": "stream", "text": [ "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0066\n", - " ↳ Uncorrected GL : 0.0091\n", - " ↳ Bias : 0.0019\n", - " ↳ Binning induced: 0.0007\n", + " Scoring Rule : Brier: 0.1399\n", + " Grouping loss : 0.0691\n", + " Calibration Loss : 0.0028\n", + " Epistemic Loss : 0.0719\n", "\n" ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ - "glest = GLEstimator(est, None)\n", - "glest.fit(X, y, partition=partition)\n", - "glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is still possible to adjust the binning strategy using an empty Partitioner." + "tree = DecisionTreeRegressor(max_depth=None, min_samples_leaf=50)\n", + "\n", + "gle_custom = GLEstimator(partitioning_estimate=tree)\n", + "gle_custom.fit(X_test, S_test, y_test)\n", + "gle_custom.estimate()\n", + "\n", + "print(gle_custom)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0052\n", - " ↳ Uncorrected GL : 0.0082\n", - " ↳ Bias : 0.0009\n", - " ↳ Binning induced: 0.0022\n", - "\n" - ] - }, { "data": { - 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", "text/plain": [ - "
" + "(array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)),\n", + " array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)))" ] }, + "execution_count": 14, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "partitioner = Partitioner.from_name(None, n_bins=10, strategy=\"quantile\")\n", - "glest = GLEstimator(est, partitioner)\n", - "glest.fit(X, y, partition=partition)\n", - "glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" + "risk = RiskEstimator(partitioning_estimate=tree)\n", + "risk.fit(X_test, S_test, y_test)\n", + "risk.predict(X_risk, est.predict_proba(X_risk)[:, 1].reshape(-1, 1), t=0.5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Cross-validate the estimation\n", + "### It all comes down to a tree\n", "\n", - "To account for variance in the estimation procedure, use `GLEstimatorCV`. Set the `cv` parameter to the desired splitting method. One GLEstimator will be fitted per split and stored in `GLEstimatorCV.glests_`." + "All of the objects are based on a partitioning estimate, which is fitted when the `fit` method is called. The partitioning estimate in itself is also callable through the `PartitioningEstimate` class. It allows to make local predictions of the heterogeneity and possesses the same method as the `GLEstimator` and `RiskEstimator` classes for plotting." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Split 1/5\n", - "Split 2/5\n", - "Split 3/5\n", - "Split 4/5\n", - "Split 5/5\n", - "GLEstimatorCV()\n", - " Scoring rule : brier\n", - " Grouping loss : 0.0824 (0.0086)\n", - " ↳ Uncorrected GL : 0.1020 (0.0084)\n", - " ↳ Bias : 0.0188 (0.0009)\n", - " ↳ Binning induced: 0.0008 (0.0001)\n", - "\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "from glest.core import GLEstimatorCV\n", + "from glest.core import PartitioningEstimate\n", "\n", - "glest_cv = GLEstimatorCV(est, \"decision_tree\", verbose=1, random_state=0).fit(X, y)\n", - "print(glest_cv)\n" + "part = PartitioningEstimate(\n", + " estimator=DecisionTreeRegressor(max_depth=5, min_samples_leaf=15)\n", + ")\n", + "\n", + "part.fit(X_test, S_test, y_test)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0927\n", - " ↳ Uncorrected GL : 0.1115\n", - " ↳ Bias : 0.0180\n", - " ↳ Binning induced: 0.0008\n", - "\n", - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0927\n", - " ↳ Uncorrected GL : 0.1129\n", - " ↳ Bias : 0.0194\n", - " ↳ Binning induced: 0.0008\n", - "\n", - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0736\n", - " ↳ Uncorrected GL : 0.0932\n", - " ↳ Bias : 0.0189\n", - " ↳ Binning induced: 0.0007\n", - "\n", - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0785\n", - " ↳ Uncorrected GL : 0.0970\n", - " ↳ Bias : 0.0177\n", - " ↳ Binning induced: 0.0008\n", - "\n", - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.0745\n", - " ↳ Uncorrected GL : 0.0953\n", - " ↳ Bias : 0.0201\n", - " ↳ Binning induced: 0.0007\n", - "\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "for glest in glest_cv.glests_:\n", - " print(glest)\n" + "part.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Change proper scoring rule\n", + "### Auditing a model\n", "\n", - "The grouping loss is given relative to the choice of a proper scoring rule. By default the Brier score is used. This can be changed in several ways shown below. However, the debiasing procedure is only implemented for the Brier score.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: GL bias computation is only available for \"brier\" psr.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/alexandreperez/dev/rep/inr-phd-glest_dev/glest/helpers.py:206: RuntimeWarning: divide by zero encountered in log\n", - " return lambda x: -(x * np.log(x) + (1 - x) * np.log(1 - x))\n", - "/Users/alexandreperez/dev/rep/inr-phd-glest_dev/glest/helpers.py:206: RuntimeWarning: invalid value encountered in multiply\n", - " return lambda x: -(x * np.log(x) + (1 - x) * np.log(1 - x))\n" - ] - }, - { - "data": { - "text/plain": [ - "{'psr': 'log',\n", - " 'GL': nan,\n", - " 'GL_induced': 0.0025744195295229924,\n", - " 'GL_uncorrected': 0.07747296805265232,\n", - " 'GL_bias': nan}" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "glest.metrics(\"log\")\n" + "From our fitted partitioner, we can have access to the group definitions. If the features have an interpretation/meaning, we therefore have an interpretation of the heterogeneity groups created. Group definitions are given in the form of a dictionnary of dictionnaries\n", + "\n", + " `group_definitions[leaf_id] = {\n", + " 'rules': combined_rules,\n", + " 'n_samples': n_eval_samples_in_leaf,\n", + " 'sample_indices': samples_indices_in_eval_set,\n", + " 'heterogeneity': r_j\n", + " }`.\n" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Warning: GL bias computation is only available for \"brier\" psr.\n", - "GLEstimator()\n", - " Scoring Rule : log\n", - " Grouping loss : nan\n", - " ↳ Uncorrected GL : 0.0775\n", - " ↳ Bias : nan\n", - " ↳ Binning induced: 0.0026\n", - "\n" + "================================================================================\n", + "GROUP DEFINITIONS\n", + "================================================================================\n", + "\n", + "Group 5:\n", + " Heterogeneity detected: 0.5677\n", + " Number of samples: 116\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 <= -2.3\n", + " • X_13 <= -1.3\n", + " • X_17 <= -1.6\n", + "\n", + "Group 6:\n", + " Heterogeneity detected: 0.3848\n", + " Number of samples: 137\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 <= -2.3\n", + " • X_13 <= -1.3\n", + " • X_17 > -1.6\n", + "\n", + "Group 8:\n", + " Heterogeneity detected: 0.0961\n", + " Number of samples: 93\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 <= -4.4\n", + " • X_13 > -1.3\n", + "\n", + "Group 9:\n", + " Heterogeneity detected: 0.0465\n", + " Number of samples: 238\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • -4.4 < X_8 <= -2.3\n", + " • X_13 > -1.3\n", + "\n", + "Group 12:\n", + " Heterogeneity detected: 0.1066\n", + " Number of samples: 381\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 > -2.3\n", + " • X_11 <= -1.8\n", + "\n", + "Group 13:\n", + " Heterogeneity detected: 0.0169\n", + " Number of samples: 659\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 > -2.3\n", + " • -1.8 < X_11 <= 2.3\n", + "\n", + "Group 15:\n", + " Heterogeneity detected: -0.0318\n", + " Number of samples: 85\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 > -2.3\n", + " • X_11 > 2.3\n", + " • X_2 <= -0.3\n", + "\n", + "Group 16:\n", + " Heterogeneity detected: -0.2657\n", + " Number of samples: 71\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 <= -3.2\n", + " • X_8 > -2.3\n", + " • X_11 > 2.3\n", + " • X_2 > -0.3\n", + "\n", + "Group 20:\n", + " Heterogeneity detected: 0.1440\n", + " Number of samples: 155\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 <= -1.7\n", + " • X_2 <= 0.9\n", + " • X_6 <= -2.8\n", + "\n", + "Group 21:\n", + " Heterogeneity detected: -0.0347\n", + " Number of samples: 1623\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 <= -1.7\n", + " • X_2 <= 0.9\n", + " • X_6 > -2.8\n", + "\n", + "Group 23:\n", + " Heterogeneity detected: 0.3050\n", + " Number of samples: 236\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 <= -1.7\n", + " • X_2 > 0.9\n", + " • X_17 <= -2.1\n", + "\n", + "Group 24:\n", + " Heterogeneity detected: 0.0781\n", + " Number of samples: 385\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 <= -1.7\n", + " • X_2 > 0.9\n", + " • X_17 > -2.1\n", + "\n", + "Group 27:\n", + " Heterogeneity detected: -0.0636\n", + " Number of samples: 92\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 > -1.7\n", + " • X_13 <= -4.2\n", + " • X_4 <= -1.6\n", + "\n", + "Group 28:\n", + " Heterogeneity detected: -0.3430\n", + " Number of samples: 253\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 > -1.7\n", + " • X_13 <= -4.2\n", + " • X_4 > -1.6\n", + "\n", + "Group 30:\n", + " Heterogeneity detected: 0.0399\n", + " Number of samples: 2131\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 > -1.7\n", + " • X_13 > -4.2\n", + " • X_4 <= -0.8\n", + "\n", + "Group 31:\n", + " Heterogeneity detected: -0.0678\n", + " Number of samples: 3326\n", + " Rules:\n", + " • X_15 <= -0.7\n", + " • X_12 > -3.2\n", + " • X_10 > -1.7\n", + " • X_13 > -4.2\n", + " • X_4 > -0.8\n", + "\n", + "Group 36:\n", + " Heterogeneity detected: 0.3346\n", + " Number of samples: 157\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 <= 1.0\n", + " • X_12 <= -1.8\n", + " • X_13 <= -0.0\n", + "\n", + "Group 37:\n", + " Heterogeneity detected: -0.0680\n", + " Number of samples: 219\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 <= 1.0\n", + " • X_12 <= -1.8\n", + " • X_13 > -0.0\n", + "\n", + "Group 39:\n", + " Heterogeneity detected: -0.0726\n", + " Number of samples: 924\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 <= 1.0\n", + " • X_12 > -1.8\n", + " • X_7 <= 3.7\n", + "\n", + "Group 40:\n", + " Heterogeneity detected: 0.2222\n", + " Number of samples: 61\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 <= 1.0\n", + " • X_12 > -1.8\n", + " • X_7 > 3.7\n", + "\n", + "Group 43:\n", + " Heterogeneity detected: 0.0852\n", + " Number of samples: 31\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 > 1.0\n", + " • X_7 <= -0.7\n", + " • X_17 <= -3.2\n", + "\n", + "Group 44:\n", + " Heterogeneity detected: 0.0134\n", + " Number of samples: 106\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 > 1.0\n", + " • X_7 <= -0.7\n", + " • X_17 > -3.2\n", + "\n", + "Group 46:\n", + " Heterogeneity detected: -0.0045\n", + " Number of samples: 59\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 > 1.0\n", + " • X_7 > -0.7\n", + " • X_16 <= -3.3\n", + "\n", + "Group 47:\n", + " Heterogeneity detected: 0.5269\n", + " Number of samples: 423\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 <= -3.0\n", + " • X_11 > 1.0\n", + " • X_7 > -0.7\n", + " • X_16 > -3.3\n", + "\n", + "Group 51:\n", + " Heterogeneity detected: -0.0550\n", + " Number of samples: 2331\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • X_11 <= -2.3\n", + " • X_13 <= 1.7\n", + " • X_10 <= 1.5\n", + "\n", + "Group 52:\n", + " Heterogeneity detected: 0.2118\n", + " Number of samples: 477\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • X_11 <= -2.3\n", + " • X_13 <= 1.7\n", + " • X_10 > 1.5\n", + "\n", + "Group 54:\n", + " Heterogeneity detected: 0.2077\n", + " Number of samples: 277\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • -3.0 < X_8 <= 0.9\n", + " • X_11 <= -2.3\n", + " • X_13 > 1.7\n", + "\n", + "Group 55:\n", + " Heterogeneity detected: 0.5765\n", + " Number of samples: 232\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > 0.9\n", + " • X_11 <= -2.3\n", + " • X_13 > 1.7\n", + "\n", + "Group 58:\n", + " Heterogeneity detected: -0.0412\n", + " Number of samples: 1219\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • X_11 > -2.3\n", + " • X_6 <= -1.6\n", + " • X_16 <= 0.4\n", + "\n", + "Group 59:\n", + " Heterogeneity detected: 0.1364\n", + " Number of samples: 877\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • X_11 > -2.3\n", + " • X_6 <= -1.6\n", + " • X_16 > 0.4\n", + "\n", + "Group 61:\n", + " Heterogeneity detected: -0.0566\n", + " Number of samples: 6310\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • -2.3 < X_11 <= 2.3\n", + " • X_6 > -1.6\n", + "\n", + "Group 62:\n", + " Heterogeneity detected: -0.1206\n", + " Number of samples: 1316\n", + " Rules:\n", + " • X_15 > -0.7\n", + " • X_8 > -3.0\n", + " • X_11 > 2.3\n", + " • X_6 > -1.6\n" ] } ], "source": [ - "print(f\"{glest:log}\")\n" + "groups = part.groups()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Apart from the preset choices \"brier\" and \"log\", any proper scoring rule can be used. This is done by passing the entropy of the chosen scoring rule." + "If some groups are of special interest, we can make them appear more clearly on the grouping diagram." ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: GL bias computation is only available for \"brier\" psr.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/qp/nt593bhx31z2cd1nbxhrg0ym0000gn/T/ipykernel_18013/3458350935.py:3: RuntimeWarning: divide by zero encountered in log\n", - " def custom_psr(x): return -x*np.log(x) - (1-x)*np.log(1-x)\n", - "/var/folders/qp/nt593bhx31z2cd1nbxhrg0ym0000gn/T/ipykernel_18013/3458350935.py:3: RuntimeWarning: invalid value encountered in multiply\n", - " def custom_psr(x): return -x*np.log(x) - (1-x)*np.log(1-x)\n" - ] - }, { "data": { + "image/png": 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", "text/plain": [ - "{'psr': ,\n", - " 'GL': nan,\n", - " 'GL_induced': 0.0025744195295229924,\n", - " 'GL_uncorrected': 0.07747296805265232,\n", - " 'GL_bias': nan}" + "
" ] }, - "execution_count": 16, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# Define the entropy of the custom scoring rule\n", - "# Here we take the entropy of the log scoring rule.\n", - "def custom_psr(x): return -x*np.log(x) - (1-x)*np.log(1-x)\n", - "glest.metrics(custom_psr)\n" + "part.plot(groups=[5, 59, 23])" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GLEstimator()\n", - " Scoring Rule : brier\n", - " Grouping loss : 0.1112\n", - " ↳ Uncorrected GL : 0.1325\n", - " ↳ Bias : 0.0192\n", - " ↳ Binning induced: 0.0021\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "partitioner = Partitioner.from_name(\"decision_tree\", n_bins=10, strategy=\"quantile\", binwise_fit=False)\n", - "\n", - "glest = GLEstimator(est, partitioner=partitioner, train_size=0.5, random_state=0)\n", - "glest.fit(X_test, y_test)\n", - "fig = glest.plot(fig_kw=dict(figsize=(4, 4)))\n", - "print(glest)\n" - ] + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "inr-phd-gl_estimation_draft", + "display_name": "testglest", "language": "python", "name": "python3" }, @@ -779,7 +807,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.0" + "version": "3.13.5" } }, "nbformat": 4, diff --git a/examples/example_advanced.ipynb b/examples/example_advanced.ipynb new file mode 100644 index 0000000..1b6b9e7 --- /dev/null +++ b/examples/example_advanced.ipynb @@ -0,0 +1,143 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Advanced functionalities\n", + "\n", + "In the example notebook, we learned how to measure the grouping loss and grouping risks with partitioning estimates fitted on the residuals. We can also use broader models such as **Histogram Gradient Boosting** or **Random Forests** directly fitted on the residuals to provide estimates. \n", + "\n", + "\n", + "> **⚠️ Warning:** These estimates come with no theoretical guarantee in our work." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import make_classification\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "X, y = make_classification(\n", + " n_samples=200000, random_state=42, n_features=20, n_informative=20, n_redundant=0\n", + ")\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n", + "\n", + "est = LogisticRegression()\n", + "est.fit(X_train, y_train)\n", + "S_test = est.predict_proba(X_test)[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To indicate that we aren't using a partitioning estimator, we use the `residual_estimator` option." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GLEstimator()\n", + " Scoring Rule : Brier: 0.1403\n", + " Grouping loss : 0.1313\n", + " Calibration Loss : 0.0025\n", + " Epistemic Loss : 0.1338\n", + "\n" + ] + } + ], + "source": [ + "from glest.core import GLEstimator\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "\n", + "gle_custom = GLEstimator(\n", + " residual_estimator=RandomForestRegressor(n_estimators=100, random_state=42)\n", + ")\n", + "gle_custom.fit(X_test, S_test, y_test)\n", + "gle_custom.estimate()\n", + "\n", + "print(gle_custom)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The same goes for the RiskEstimator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)),\n", + " array([0., 0., 0., ..., 0., 0., 0.], shape=(50000,)))" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from glest.core import RiskEstimator\n", + "\n", + "X_test, X_risk, y_test, y_risk = train_test_split(\n", + " X_test, y_test, test_size=0.5, random_state=0\n", + ")\n", + "\n", + "\n", + "S_test = est.predict_proba(X_test)[:, 1]\n", + "\n", + "risk = RiskEstimator(\n", + " residual_estimator=RandomForestRegressor(n_estimators=100, random_state=42)\n", + ")\n", + "risk.fit(X_test, S_test, y_test)\n", + "risk.predict(X_risk, est.predict_proba(X_risk)[:, 1].reshape(-1, 1), t=0.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "glestest", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/glest/__init__.py b/glest/__init__.py index 83549e0..bb373bd 100644 --- a/glest/__init__.py +++ b/glest/__init__.py @@ -1,3 +1,3 @@ -__version__ = "0.0.1-alpha.1" +__version__ = "0.0.2-alpha.1" -from .core import GLEstimator, GLEstimatorCV, Partitioner +from .core import GLEstimator, PartitioningEstimate, RiskEstimator diff --git a/glest/core.py b/glest/core.py index e90ecf1..1757421 100644 --- a/glest/core.py +++ b/glest/core.py @@ -1,64 +1,45 @@ -import matplotlib.pyplot as plt import numpy as np from sklearn.cluster import KMeans -from sklearn.model_selection import StratifiedShuffleSplit from sklearn.tree import DecisionTreeRegressor -from sklearn.utils.multiclass import check_classification_targets, type_of_target -from sklearn.utils.validation import check_is_fitted, check_X_y, column_or_1d -from sklearn.model_selection._split import check_cv -from .helpers import ( - CEstimator, - scores_to_bin_ids, - compute_GL_induced, - compute_GL_bias, - list_list_to_array, - bins_from_strategy, - compute_GL_uncorrected, - filter_valid_counts, -) +from sklearn.model_selection._split import train_test_split from .plot import grouping_diagram -from sklearn.utils.validation import indexable -from sklearn.base import clone -from typing import List +from sklearn.linear_model import LogisticRegression +from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor +from sklearn.neural_network import MLPRegressor +from sklearn.metrics import brier_score_loss -class Partitioner: - """A class for partitionning the feature space, stratified by level sets - of predicted probabilities. +class PartitioningEstimate: + """A class for partitioning-based estimation with honest splitting. + + This class fits a partitioning estimator to predict residuals from calibrated + scores, enabling grouping loss estimation and risk analysis. Parameters ---------- - estimator : object - An estimator to create the partition within level sets of predicted - probabilities. It must implement a fit method. In each bin, the - estimator is fitted using the fit method. Then, region assignments - are retrieved through the method defined with the `predict_method` - argument. The estimator must either support `sklearn.base.clone` - method (e.g. deriving from `sklearn.base.BaseEstimator`), - or implementing a `clone` method. - predict_method : str, default=None - The name of the method to call on `estimator` to get the class - assignments. If estimator is not None, `predict_method` should be set. - n_bins : int, default=15 - The number of bins to split the probability space [0, 1] into. - strategy : {"uniform", "quantile"}, default="uniform" - The binning strategy used to create the bins. With uniform, same-width - bins are created. With quantile, same-mass bins are created. - binwise_fit : bool, default=True - When True, fits one partitioner per bin. Otherwise, fits one - partitioner on the whole feature space at once. - raise_on_fit_error : bool, default=False - Whether to raise an error when the estimator fails to fit on a bin. - If False, no partition is created on the failing bin and all samples - within this bin are assigned the same label. If True, raises an error. - verbose : int, default=0 - Whether to print progress. - - Raises - ------ - Exception - When `raise_on_fit_error` is True and the estimator fails to fit on a - bin. + estimator : object or str + The estimator to use for partitioning (e.g., DecisionTreeRegressor, KMeans). + Can also be a string name like "decision_tree", "decision_stump", or "kmeans". + predict_method : str, optional + The method to call on the estimator to get partition assignments + (e.g., "apply" for trees, "predict" for KMeans). Default is None. + verbose : int, default=1 + Controls verbosity of output during fitting and evaluation. + + Attributes + ---------- + calibrator : LogisticRegression + The fitted calibrator for probability scores. + tree : callable + Function mapping features to residual predictions. + r_j : ndarray + Mean residuals for each partition. + v_j : ndarray + Variance of residuals for each partition. + n_j : ndarray + Number of samples in each partition. + group_definitions : dict + Human-readable definitions of each partition/group. """ @@ -66,53 +47,34 @@ def __init__( self, estimator, predict_method: str = None, - n_bins: int = 15, - strategy: str = "uniform", - binwise_fit: bool = True, - raise_on_fit_error: bool = False, verbose: int = 0, ) -> None: self.estimator = estimator - self.n_bins = n_bins - self.strategy = strategy - self.binwise_fit = binwise_fit self.predict_method = predict_method - self.raise_on_fit_error = raise_on_fit_error self.verbose = verbose @classmethod def from_name( cls, name: str, - n_bins: int = 15, - strategy: str = "uniform", - binwise_fit: bool = True, - raise_on_fit_error: bool = False, verbose: int = 0, random_state: int = None, ): - """Load a predefined Partitioner instance from a name. + """Load a predefined partitioning estimator from a name. Parameters ---------- - name : {"decision_tree", "decision_stump", "kmean", None} - The predefined estimator to use to partition the bins. - n_bins : int, default=15 - The number of bins to split the probability space [0, 1] into. - strategy : {"uniform", "quantile"}, default="uniform" - The binning strategy used to create the bins. With uniform, same-width - bins are created. With quantile, same-mass bins are created. - binwise_fit : bool, default=True - When True, fits one partitioner per bin. Otherwise, fits one - partitioner on the whole feature space at once. - raise_on_fit_error : bool, default=False - Whether to raise an error when the estimator fails to fit on a bin. - If False, no partition is created on the failing bin and all samples - within this bin are assigned the same label. If True, raises an error. + name : {"decision_tree", "decision_stump", "kmeans", None} + The predefined estimator to use for partitioning. verbose : int, default=0 - Whether to print progress. - random_state : int, default=none - Controls the randomness of the estimator used in the partitioner. + Controls verbosity of output. + random_state : int, default=None + Controls the randomness of the estimator. + + Returns + ------- + estimator : object or None + The configured estimator instance. """ available_names = [ @@ -131,6 +93,7 @@ def from_name( estimator = DecisionTreeRegressor( max_depth=10, random_state=random_state, + min_samples_leaf=15, ) predict_method = "apply" @@ -150,584 +113,731 @@ def from_name( estimator = None predict_method = None - return cls( - estimator=estimator, - n_bins=n_bins, - strategy=strategy, - binwise_fit=binwise_fit, - raise_on_fit_error=raise_on_fit_error, - verbose=verbose, - predict_method=predict_method, - ) - - def fit_bins(self, y_scores=None): - """Create bins from strategy, number of bins and proba distribution - if necessary. + return estimator, predict_method + def fit(self, X, y_scores, y_true, seed: int = 42): + """ + Fit the partitioning estimator with honest splitting. Parameters ---------- - y_scores : array-like - The probabilities from which to derive the bins if - strategy="quantile". - + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Fitted estimator. """ - self.bins_ = bins_from_strategy(self.n_bins, self.strategy, y_scores) + if self.verbose > 0: + print("Starting fit process...") - def transform_bins(self, y_scores): - """Convert probabilities to their bin assignment. + y_scores = y_scores.reshape(-1, 1) + X_train, X_test, y_scores_train, y_scores_test, y_true_train, y_true_test = ( + train_test_split(X, y_scores, y_true, test_size=0.5, random_state=seed) + ) + X_train, X_cal, y_scores_train, y_scores_cal, y_true_train, y_true_cal = ( + train_test_split( + X_train, y_scores_train, y_true_train, test_size=0.2, random_state=seed + ) + ) + + if self.verbose > 0: + print(f"Calibration set size: {len(X_cal)}") + print(f"Train set size: {len(X_train)}") + print(f"Test set size: {len(X_test)}") + + self.calibrate(y_scores_cal, y_true_cal) + self.train(X_train, y_scores_train, y_true_train) + self.evaluate(X_test, y_scores_test, y_true_test) + + if hasattr(X_test, "columns"): + feature_names = X_test.columns.tolist() + else: + feature_names = None + self.get_group_definitions(X_test, feature_names=feature_names) + + if self.verbose > 0: + print("Fit process completed.") + + return self + + def calibrate(self, y_scores, y_true): + """ + Calibrate the predicted scores using logistic regression. Parameters ---------- - y_scores : array-like of shape (n,) - The probabilities from which to derive the assignments. - + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. Returns ------- - array-like of shape (n,) - The array of bin indices each probability falls into. - + self : object + Fitted calibrator. """ - if not hasattr(self, "bins_"): - raise ValueError("fit_bins must have been called before transform_bins.") - y_bins, _ = scores_to_bin_ids(y_scores, self.bins_, None) - return y_bins + if self.verbose > 1: + print("Calibrating scores...") + + calibrator = LogisticRegression() + calibrator.fit(y_scores, y_true) + self.calibrator = calibrator - def fit(self, X, y_scores, y_true=None): - """Fit the partitioner. + if self.verbose > 1: + print("Calibration completed.") + + return self + def train(self, X, y_scores, y_true): + """ + Train the partitioning estimator on residuals. Parameters ---------- - X : array-like of shape (n, d) - The features. - y_scores : array-like of shape (n,) - The probabilities of each sample. - y_true : array-like of shape (n,), optional - The true labels. Used by some partitioner to find the best - partitions, by default None - + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. Returns ------- - Partitioner - Returns the current instance. - + self : object + Fitted partitioning estimator. """ - y_scores = GLEstimator._validate_scores(y_scores) + if self.verbose > 1: + print("Training partitioning estimator...") - if self.estimator is None: - raise ValueError( - "A Partitioner with estimator=None cannot be fitted. To use " - "predefined partitions, use the partition argument of " - "GLEstimator.fit instead." + if isinstance(self.estimator, str): + self.estimator, self.predict_method = PartitioningEstimate.from_name( + self.estimator ) - if not hasattr(self.estimator, "fit"): - raise AttributeError( - f'partitioner {self.estimator} must implement a "fit" method.' - ) + residuals_train = y_true - self.calibrator.predict(y_scores) + self.estimator.fit(X, residuals_train) - if not hasattr(self.estimator, self.predict_method): - raise AttributeError( - f'"{self.estimator.__class__.__name__}" object has no ' - f'attribute "{self.predict_method}". Make sure `predict_method` ' - f'is set accordingly to the estimator "{self.estimator}".' - ) + if self.verbose > 1: + print("Training completed.") - self.fit_bins(y_scores) # bins are stored in self.bins_ + return self - if self.binwise_fit: - y_bins = self.transform_bins(y_scores) - n_bins = len(self.bins_) - 1 - else: - y_bins = np.zeros_like(y_scores) - n_bins = 1 - - fitted_partitioners_ = [] - for i in range(n_bins): - if self.verbose > 0: - print(f"Bin {i+1}/{n_bins}: partitioning.") - bin_idx = y_bins == i - X_bin = X[bin_idx, :] - n_samples_bin = X_bin.shape[0] - - if n_samples_bin > 0: - try: - partitioner_bin = clone(self.estimator) - except TypeError: - if not hasattr(self.estimator, "clone"): - raise AttributeError( - f'Estimator "{self.estimator}" must either support ' - f"sklearn.base.clone, or implement a `clone` method " - f"itself." - ) - partitioner_bin = self.estimator.clone() - try: - if y_true is None: - partitioner_bin.fit(X_bin) - else: - partitioner_bin.fit(X_bin, y_true[bin_idx]) - except Exception as e: - if self.raise_on_fit_error: - raise e - else: - if self.verbose: - print( - f"WARNING: No partition created in bin #{i}: " - f"estimator {self.estimator} failed to fit. " - f'"{e}"' - ) - partitioner_bin = None - else: - partitioner_bin = None - fitted_partitioners_.append(partitioner_bin) + def evaluate(self, X, y_scores, y_true): + """ + Evaluate the partitioning estimator on a test set. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Evaluated partitioning estimator with computed statistics. + """ + if self.verbose > 1: + print("Evaluating on test set...") + + self.y_scores = y_scores + self.y_true = y_true + self.X = X + leaf_indices = self.estimator.apply(X) + + c_hat = self.calibrator.predict_proba(y_scores)[:, 1] + + v_j = np.zeros(max(leaf_indices) + 1) + r_j = np.zeros(max(leaf_indices) + 1) + n_j = np.zeros(max(leaf_indices) + 1) + # Vectorized computation using bincount for better performance + n_j = np.bincount(leaf_indices, minlength=max(leaf_indices) + 1) + # Compute residuals once + residuals = y_true - c_hat + + # Vectorized computation of means and variances + r_j = np.divide( + np.bincount(leaf_indices, weights=residuals), + n_j, + out=np.zeros_like(n_j, dtype=float), + where=n_j > 0, + ) + # Compute variance using E[X^2] - E[X]^2 formula + residuals_sq = residuals**2 + mean_sq = np.divide( + np.bincount(leaf_indices, weights=residuals_sq), + n_j, + out=np.zeros_like(n_j, dtype=float), + where=n_j > 0, + ) + v_j = mean_sq - r_j**2 - self.fitted_partitioners_ = fitted_partitioners_ - return self + # Apply Bessel's correction (ddof=1) + v_j *= n_j / (n_j - 1) + v_j = np.where(n_j > 1, v_j, 0) + + def r(X): + leaf_indices = self.estimator.apply(X) + return r_j[leaf_indices] + + self.cal_err = np.mean(np.square(y_scores.flatten() - c_hat)) + self.tree = r + self.r_j = r_j + self.v_j = v_j + self.n_j = n_j + + if self.verbose > 0: + print(f"Evaluation completed. Found {len(np.unique(leaf_indices))} groups.") + print(f"Calibration error: {self.cal_err:.4f}") - def predict(self, X, y_scores): - """Get the region assignments. + return self + def predict(self, X): + """ + Predict honest residuals for new data points. Parameters ---------- - X : array-like of shape (n, d) - The features. - y_scores : array-like of shape (n,) - The probabilities of each sample. - + X : array-like of shape (n_samples, n_features) + The input features. Returns ------- - array-like of shape (n,) - The assignments of each sample to the partition. + r_hat : array-like of shape (n_samples,) + The predicted residuals. """ - check_is_fitted(self) + return self.tree(X) + + def apply(self, X): + return self.estimator.apply(X) + + def plot(self, groups="all"): + # check_is_fitted(self) + leaf_ids = self.apply(self.X) + n_in_leaf = self.n_j[leaf_ids] + grouping_diagram( + c_hat=self.calibrator.predict_proba(self.y_scores)[:, 1], + r_hat=self.predict(self.X), + n_in_leaf=n_in_leaf, + f=self.y_scores.flatten(), + leaf_ids=leaf_ids, + groups=groups, + ) + + def get_group_definitions(self, X, feature_names=None): + """ + Extract human-readable decision rules for each partition/group. - labels = np.full((X.shape[0], 2), np.nan) - y_bins, _ = scores_to_bin_ids(y_scores, self.bins_, None) + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input features used to traverse the tree. + feature_names : list of str, optional + Names of features for readable output. If None, uses X_0, X_1, etc. - for i in range(len(self.bins_) - 1): - if self.verbose > 0: - print(f"Bin {i+1}/{len(self.bins_)-1}: assigning.") - bin_idx = y_bins == i # restrict to samples belonging to bin i - X_bin = X[bin_idx, :] - n_samples_bin = X_bin.shape[0] - partitioner = self.fitted_partitioners_[i if self.binwise_fit else 0] + Returns + ------- + group_definitions : dict + Dictionary mapping leaf IDs to group information including rules, + sample counts, and heterogeneity measures. + """ + tree = self.estimator + # Convert to numpy array if pandas DataFrame + X_array = X.values if hasattr(X, "values") else np.asarray(X) + + # Get unique leaf IDs + leaf_ids = tree.apply(X_array) + unique_leaves = np.unique(leaf_ids) + + group_definitions = {} + if feature_names is None: + feature_names = [f"X_{i}" for i in range(X_array.shape[1])] + elif all(isinstance(f, int) for f in feature_names): + feature_names = [f"X_{i}" for i in feature_names] + + for leaf_id in unique_leaves: + # Get samples in this leaf + samples_in_leaf = X_array[leaf_ids == leaf_id] + + # Get the path to this leaf + path = tree.decision_path(samples_in_leaf[:1]).toarray()[0] + + # Extract the rules + raw_rules = [] + + # Get the path from root to leaf + feature = tree.tree_.feature + threshold = tree.tree_.threshold + + for node_id in range(len(path)): + if path[node_id] == 1: # This node is in the path + if feature[node_id] != -2: # Not a leaf node + # Determine if we went left or right + sample_feature_value = samples_in_leaf[0, feature[node_id]] + feat_name = feature_names[feature[node_id]] + if sample_feature_value <= threshold[node_id]: + raw_rules.append((feat_name, "<=", threshold[node_id])) + else: + raw_rules.append((feat_name, ">", threshold[node_id])) + + # Combine rules for the same feature + feature_bounds = {} + for feat_name, operator, value in raw_rules: + if feat_name not in feature_bounds: + feature_bounds[feat_name] = {"min": None, "max": None} + + if operator == "<=": + if ( + feature_bounds[feat_name]["max"] is None + or value < feature_bounds[feat_name]["max"] + ): + feature_bounds[feat_name]["max"] = value + else: # operator == ">" + if ( + feature_bounds[feat_name]["min"] is None + or value > feature_bounds[feat_name]["min"] + ): + feature_bounds[feat_name]["min"] = value + + # Convert bounds to readable rules + combined_rules = [] + for feat_name, bounds in feature_bounds.items(): + if bounds["min"] is not None and bounds["max"] is not None: + combined_rules.append( + f"{bounds['min']:.1f} < {feat_name} <= {bounds['max']:.1f}" + ) + elif bounds["min"] is not None: + combined_rules.append(f"{feat_name} > {bounds['min']:.1f}") + elif bounds["max"] is not None: + combined_rules.append(f"{feat_name} <= {bounds['max']:.1f}") + + group_definitions[leaf_id] = { + "rules": combined_rules, + "n_samples": len(samples_in_leaf), + "sample_indices": np.where(leaf_ids == leaf_id)[0], + "heterogeneity": self.r_j[leaf_id], + } + self.group_definitions = group_definitions + return group_definitions + + def groups(self): + """ + Convert group definitions to a human-readable string format. - # Store partition id - if partitioner is not None and n_samples_bin > 0: - predict_method = getattr(partitioner, self.predict_method) - labels[bin_idx, 1] = predict_method(X_bin) + Parameters + ---------- + group_definitions : dict + Dictionary with leaf IDs as keys and group information as values + Returns + ------- + str + A formatted string with group definitions + """ + group_definitions = self.group_definitions + lines = [] + lines.append("=" * 80) + lines.append("GROUP DEFINITIONS") + lines.append("=" * 80) + + for leaf_id in sorted(group_definitions.keys()): + info = group_definitions[leaf_id] + lines.append(f"\nGroup {leaf_id}:") + lines.append(f" Heterogeneity detected: {info['heterogeneity']:.4f}") + lines.append(f" Number of samples: {info['n_samples']}") + lines.append(" Rules:") + if info["rules"]: + for rule in info["rules"]: + lines.append(f" • {rule}") else: - # no partitioner was fit in this bin because not enough training samples - # hence gather all test samples in same group - labels[bin_idx, 1] = np.zeros(n_samples_bin) + # lines.append(f" • No splitting rules (root/single leaf)") + lines.append("-" * 80) - # Store bin id - labels[bin_idx, 0] = i + result = "\n".join(lines) + print(result) + return self.group_definitions - return labels +class ResidualEstimator: + """Estimate residuals for a fitted probabilistic classifier. -class GLEstimator: - """Estimate the grouping loss of a fitted probabilistic classifier. + This class provides methods to estimate the residuals of a probabilistic + classifier by partitioning the feature space and analyzing calibration + residuals within each partition. Parameters ---------- - classifier : object - The classifier for which to estimate the grouping loss. The - classifier must implement a `predict_proba` method. The classifier - must already be fit since GLEstimator only evaluates the classifier. - partitioner : {"decision_tree", "decision_stump", "kmean", None} - | Partitioner, optional - The partitioning strategy to use for estimating the grouping loss. - If string given, use corresponding predefined strategy. If - `Partitioner` instance given, use this as partitioner. - By default "decision_tree". - train_size : float, optional - The size of the training set size. To avoid overfitting, the - estimation of the grouping loss is evaluated on a test set and the - partition is created on the training set. By default 0.5. - random_state : int, optional - Controls the randomness of the estimator used in the partitioner. - By default None. - verbose : int, optional - Whether to print progress. By default 0. - """ + partitioning_estimate : str or PartitioningEstimate, default="decision_tree" + The partitioning strategy to use for estimating the residuals. + If string, must be one of {"decision_tree", "decision_stump", "kmeans", None}. + If PartitioningEstimate instance, uses the provided partitioner. + train_size : float, default=0.5 + The proportion of the dataset to use for training the partitioner. + The remaining data is used for evaluation to avoid overfitting. + random_state : int, default=None + Controls the randomness of the partitioner and data splitting. + verbose : int, default=0 + Controls the verbosity of output during fitting and estimation. + Higher values produce more detailed output. - default_n_bins: int = 15 - default_strategy: str = "uniform" - default_binwise_fit: bool = True + Attributes + ---------- + partitioner : PartitioningEstimate + The fitted partitioning estimator. + + Examples + -------- + >>> from glestimation import ResidualEstimator + >>> estimator = ResidualEstimator(partitioning_estimate="decision_tree") + >>> estimator.fit(X, y_scores, y_true) + >>> residuals = estimator.predict(X_new) + """ def __init__( self, - classifier, - partitioner: str | Partitioner = "decision_tree", - train_size: float = 0.5, + estimator: str = "hgb", random_state: int = None, verbose: int = 0, ) -> None: - self.classifier = classifier - self.partitioner = partitioner - self.train_size = train_size + self.estimator = HistGradientBoostingRegressor( + random_state=random_state, + ) self.random_state = random_state self.verbose = verbose - @staticmethod - def _validate_scores(y_scores): - """Uniformize probability array shape to (n,) from either (n,), (n, 1) - or (n, 2).""" - if y_scores.ndim == 2 and y_scores.shape[1] == 2: - y_scores = y_scores[ - :, 1 - ] # since y_type is binary take only the positive class - elif y_scores.ndim != 1: - raise ValueError( - f"Invalid proba array shape: {y_scores.shape}. Expecting (n,)" - ) - - y_scores = np.array(y_scores).squeeze() - return y_scores - - @staticmethod - def _is_valid_classifier(est): - """Check what is considered a valid classifier.""" - return hasattr(est, "predict_proba") - - @staticmethod - def _probas_from_estimator(est, X): - """Get the probability array by checking if estimator is either a - classifier or an array.""" - if GLEstimator._is_valid_classifier(est): - y_scores = est.predict_proba(X) - else: - try: - y_scores = np.array(est) - y_scores.shape[0] - except Exception: - raise ValueError( - "classifier must either implement a predict_proba method, " - "or be an array of probability." - ) - if X.shape[0] != y_scores.shape[0]: - raise ValueError( - f"Shape mismatch between proba array given as classifier " - f"and the data given in fit: X.shape[0]={X.shape[0]} " - f"y_scores.shape[0]={y_scores.shape[0]}" - ) - y_scores = np.array(y_scores) - y_scores = GLEstimator._validate_scores(y_scores) - return y_scores - - def fit(self, X, y, test_data=None, partition=None): - """Create the partitions and evaluate the grouping loss. After fit, - the metrics are accessible at GL_, GL_ind_, GL_bias_. + def from_name( + cls, + name: str, + verbose: int = 0, + random_state: int = None, + ): + """Load a predefined partitioning estimator from a name. Parameters ---------- - X : array-like of shape (n, d) - The features. - y : array-like of shape (n,) - The binary labels. - test_data : tuple of array-likes, optional - The test data on which to evaluate the grouping loss. - If None, the data (X, y) is split into a training and test data - based on the `train_size` argument. The partitions are created - on the training data and the grouping loss is evaluated on the test - data. If `(X2, y2)` given, (X, y) is taken as training set and - (X2, y2) as test set. If `classifier` is not an estimator but an - array of probabilities, then `test_data` must either be None or - a tuple (X2, y2, y_scores2). By default None. - partition : array-like of shape (n,), optional - The predefined partition along which to evaluate the grouping loss. - If set, (X, y) is taken as the test data on which is evaluated the - grouping loss. `partition` and `test_data` are thus incompatible - and only one of them can be set at the same time. If None, - the partition is created using the `partitioner`. By default None. + name : {"decision_tree", "decision_stump", "kmeans", None} + The predefined estimator to use for partitioning. + verbose : int, default=0 + Controls verbosity of output. + random_state : int, default=None + Controls the randomness of the estimator. Returns ------- - GLEstimator - The fitted instance. + estimator : object or None + The configured estimator instance. """ - X, y = check_X_y(X, y) - check_classification_targets(y) - y_type = type_of_target(y, input_name="y") - if y_type != "binary": - raise ValueError(f"y must be binary. Got {y_type}.") - y = column_or_1d(y) - - if partition is not None and test_data is not None: + available_names = [ + "hgb", + "rf", + "mlp", + None, + ] + + if name not in available_names: raise ValueError( - f"partition and test_data cannot be both not None. " - f"Got partition={type(partition)} and test_data={type(test_data)}." + f'Unknown name "{name}". Available names are: {available_names}.' ) - # Get the scores - y_scores = GLEstimator._probas_from_estimator(self.classifier, X) - - if self.partitioner is None or isinstance(self.partitioner, str): - self.partitioner_ = Partitioner.from_name( - name=self.partitioner, - n_bins=GLEstimator.default_n_bins, - strategy=GLEstimator.default_strategy, - binwise_fit=GLEstimator.default_binwise_fit, - random_state=self.random_state, - verbose=self.verbose - 1, + if name == "hgb": + estimator = HistGradientBoostingRegressor( + random_state=random_state, ) - else: - self.partitioner_: Partitioner = self.partitioner - - if partition is not None: - if ( - hasattr(self.partitioner_, "estimator") - and self.partitioner_.estimator is not None - ): - raise ValueError( - "Specifying a custom partition is only available when " - "partitioner=None or " - "partitioner=Partitioner.from_name(None, ...)" - ) - self.partitioner_.fit_bins(y_scores) - return self._evaluate(X, y, y_scores, partition=partition) - - if test_data is None: - self.partitioner_.fit_bins(y_scores) - y_bins, _ = scores_to_bin_ids( - y_scores, self.partitioner_.bins_, self.partitioner_.strategy + + elif name == "rf": + estimator = RandomForestRegressor( + n_estimators=100, + random_state=random_state, ) - # We use a stratified shuffle split to keep the split balance in each bin - sss = StratifiedShuffleSplit( - n_splits=1, train_size=self.train_size, random_state=self.random_state + + elif name == "mlp": + estimator = MLPRegressor( + hidden_layer_sizes=(100,), + max_iter=500, + random_state=random_state, ) - train_index, test_index = next(sss.split(X, y_bins)) - X_train = X[train_index] - y_train = y[train_index] - X_test = X[test_index] - y_test = y[test_index] - y_scores_train = y_scores[train_index] - y_scores_test = y_scores[test_index] - else: - X_train, y_train = X, y - y_scores_train = y_scores - if GLEstimator._is_valid_classifier(self.classifier): - X_test, y_test = test_data - y_scores_test = GLEstimator._probas_from_estimator( - self.classifier, X_test - ) - else: - try: - X_test, y_test, y_scores_test = test_data - except Exception as e: - raise ValueError( - f"When manually passing the probabilities as classifier," - f"the test_data must also pass the probabilities " - f"(X, y, y_probas). {e}" - ) - y_scores_test = GLEstimator._probas_from_estimator( - y_scores_test, X_test - ) + elif name is None: + estimator = None + + return estimator + + def fit(self, X, y_scores, y_true, seed: int = 42): + """ + Fit the ResidualEstimator with data. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Fitted ResidualEstimator. + """ + if self.verbose > 0: + print("Starting fit process...") + + y_scores = y_scores.reshape(-1, 1) + + X_train, X_test, y_scores_train, y_scores_test, y_true_train, y_true_test = ( + train_test_split(X, y_scores, y_true, test_size=0.5, random_state=seed) + ) + + X_train, X_cal, y_scores_train, y_scores_cal, y_true_train, y_true_cal = ( + train_test_split( + X_train, y_scores_train, y_true_train, test_size=0.2, random_state=seed + ) + ) if self.verbose > 0: - print("Fitting.") - self._fit(X_train, y_train, y_scores_train) - self._evaluate(X_test, y_test, y_scores_test) + print(f"Calibration set size: {len(X_cal)}") + print(f"Train set size: {len(X_train)}") + print(f"Test set size: {len(X_test)}") + + self.calibrate(y_scores_cal, y_true_cal) + self.train(X_train, y_scores_train, y_true_train) + self.evaluate(X_test, y_scores_test, y_true_test) + + if self.verbose > 0: + print("Fit process completed.") + return self - def _fit(self, X, y, y_scores): - self.partitioner_.fit(X, y_scores, y) - self.n_features_in_ = X.shape[1] + def calibrate(self, y_scores, y_true): + """ + Calibrate the predicted scores using logistic regression. + Parameters + ---------- + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Fitted calibrator. + """ + if self.verbose > 1: + print("Calibrating scores...") + + calibrator = LogisticRegression() + calibrator.fit(y_scores, y_true) + self.calibrator = calibrator + + if self.verbose > 1: + print("Calibration completed.") + return self - def _evaluate(self, X, y, y_scores, partition=None): - if partition is None: - check_is_fitted(self) + def train(self, X, y_scores, y_true): + """ + Train the partitioning estimator on residuals. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Fitted partitioning estimator. + """ + if self.verbose > 1: + print("Training partitioning estimator...") - y_bins = self.partitioner_.transform_bins(y_scores) + if isinstance(self.estimator, str): + self.estimator = ResidualEstimator.from_name(self.estimator) - if partition is not None: - if partition.shape != y_bins.shape: - raise ValueError( - f"Given partition must have the same shape as y_probas. " - f"Got partition.shape={partition.shape} and " - f"y_probas.shape={y_scores.shape}" - ) - labels = np.stack([y_bins, partition], axis=1) - else: - labels = self.partitioner_.predict(X, y_scores) + residuals_train = y_true - self.calibrator.predict(y_scores) + self.estimator.fit(X, residuals_train) - frac_pos = [] - counts = [] - mean_scores = [] + if self.verbose > 1: + print("Training completed.") - for i in range(len(self.partitioner_.bins_) - 1): - if self.verbose: - print(f"Bin {i+1}/{len(self.partitioner_.bins_) - 1}: evaluating.") - bin_idx = y_bins == i - y_bin = y[bin_idx] - y_scores_bin = y_scores[bin_idx] + return self - unique_labels, unique_counts = np.unique( - labels[bin_idx, 1], return_counts=True - ) + def evaluate(self, X, y_scores, y_true): + c_hat = self.calibrator.predict_proba(y_scores)[:, 1] + self.cal_err = np.mean(np.square(y_scores.flatten() - c_hat)) - frac_pos.append([]) - counts.append([]) - mean_scores.append([]) + self.r_j = self.estimator.predict(X) - for label in unique_labels: - if len((labels == label)[bin_idx, 1]) > 0: - frac_pos[i].append(np.mean(y_bin[(labels == label)[bin_idx, 1]])) - mean_scores[i].append( - np.mean(y_scores_bin[(labels == label)[bin_idx, 1]]) - ) + return self + + def predict(self, X): + return self.estimator.predict(X) + + +class GLEstimator: + """Estimate the grouping loss of a fitted probabilistic classifier. + + This class provides methods to estimate the grouping loss (GL) of a probabilistic + classifier by partitioning the feature space and analyzing calibration residuals + within each partition. + + Parameters + ---------- + partitioning_estimate : str or PartitioningEstimate, default="decision_tree" + The partitioning strategy to use for estimating the grouping loss. + If string, must be one of {"decision_tree", "decision_stump", "kmeans", None}. + If PartitioningEstimate instance, uses the provided partitioner. + train_size : float, default=0.5 + The proportion of the dataset to use for training the partitioner. + The remaining data is used for evaluation to avoid overfitting. + random_state : int, default=None + Controls the randomness of the partitioner and data splitting. + verbose : int, default=0 + Controls the verbosity of output during fitting and estimation. + Higher values produce more detailed output. + + Attributes + ---------- + partitioner : PartitioningEstimate + The fitted partitioning estimator. + gl_estimate : float + The bias-corrected grouping loss estimate. + gl_uncorrected : float + The uncorrected grouping loss (without bias correction). + gl_bias : float + The estimated bias in the grouping loss. + gl_j : ndarray + Per-partition grouping loss values. + + Examples + -------- + >>> from glestimation import GLEstimator + >>> estimator = GLEstimator(partitioning_estimate="decision_tree") + >>> estimator.fit(X, y_scores, y_true) + >>> estimator.estimate() + >>> print(estimator.GL()) + """ - counts[i].extend(unique_counts) + def __init__( + self, + partitioning_estimate: str | PartitioningEstimate = "decision_tree", + random_state: int = None, + verbose: int = 0, + residual_estimator: ResidualEstimator = None, + ) -> None: + self.partitioner = PartitioningEstimate(partitioning_estimate) + self.random_state = random_state + self.verbose = verbose + self.residual_estimator = ( + ResidualEstimator(residual_estimator) + if residual_estimator is not None + else None + ) - frac_pos = list_list_to_array(frac_pos, fill_value=0) - counts = list_list_to_array(counts, fill_value=0, dtype=int) - mean_scores = list_list_to_array(mean_scores, fill_value=0) + def fit(self, X, y_scores, y_true, seed: int = 42): + """ + Fit the GLEstimator with data. + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The input features. + y_scores : array-like of shape (n_samples,) + The predicted probability scores from a classifier. + y_true : array-like of shape (n_samples,) + The true binary labels. + Returns + ------- + self : object + Fitted GLEstimator. + """ + if self.residual_estimator is not None: + self.residual_estimator.fit(X, y_scores, y_true, seed=seed) - self.frac_pos_ = frac_pos - self.counts_ = counts - self.mean_scores_ = mean_scores + else: + self.partitioner.fit(X, y_scores, y_true, seed=seed) + self.brier = brier_score_loss(y_true, y_scores) + return self - self.c_hat_ = CEstimator(y_scores, y).c_hat() - self.y_bins_, _ = scores_to_bin_ids(y_scores, self.partitioner_.bins_, None) + def estimate(self): + """ + Estimate the grouping loss (GL) using the fitted partitioner. + Returns + ------- + self : object + GLEstimator with computed GL estimates. + """ + + if self.residual_estimator is not None: + r_j = self.residual_estimator.r_j + gl_uncorrected = np.mean(r_j**2) + gl_bias = "Non existant due to using a residual estimator" + gl_estimate = gl_uncorrected + self.gl_estimate = 2 * gl_estimate + self.gl_bias = gl_bias + self.gl_uncorrected = 2 * gl_uncorrected + self.cal_err = 2 * self.residual_estimator.cal_err + else: + r_j = self.partitioner.r_j + n_j = self.partitioner.n_j + v_j = self.partitioner.v_j + N = np.sum(n_j) + + gl_j_uncorrected = r_j**2 + gl_uncorrected = np.sum(n_j * gl_j_uncorrected) / N + + gl_j_bias = np.divide(v_j, n_j, out=np.zeros_like(v_j), where=n_j != 0) + gl_bias = np.sum(n_j * gl_j_bias) / N + + gl_j = gl_j_uncorrected - gl_j_bias + gl_estimate = np.sum(n_j * gl_j) / N + + self.gl_j = 2 * gl_j + self.gl_uncorrected = 2 * gl_uncorrected + self.gl_bias = 2 * gl_bias + self.gl_estimate = 2 * gl_estimate + self.cal_err = 2 * self.partitioner.cal_err return self def GL(self, psr: str = "brier"): - return self.GL_uncorrected(psr) - self.GL_bias(psr) - self.GL_induced(psr) + return self.gl_estimate def GL_uncorrected(self, psr: str = "brier"): if not self.is_fitted(): raise ValueError("GLEstimator is not fitted.") - return compute_GL_uncorrected(self.frac_pos_, self.counts_, psr) + return self.gl_uncorrected def GL_bias(self, psr: str = "brier"): if not self.is_fitted(): raise ValueError("GLEstimator is not fitted.") - return compute_GL_bias(self.frac_pos_, self.counts_, psr) - - def GL_induced(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError("GLEstimator is not fitted.") - - return compute_GL_induced(self.c_hat_, self.y_bins_, psr) + return self.gl_bias def metrics(self, psr: str = "brier"): if not self.is_fitted(): raise ValueError('GLEstimator must be fitted to call "metrics".') - GL_ind = self.GL_induced(psr) - GL_uncorrected = self.GL_uncorrected(psr) - GL_bias = self.GL_bias(psr) - GL = GL_uncorrected - GL_bias - GL_ind - return { "psr": psr, - "GL": GL, - "GL_induced": GL_ind, - "GL_uncorrected": GL_uncorrected, - "GL_bias": GL_bias, + "GL": self.gl_estimate, + "GL_uncorrected": self.gl_uncorrected, + "GL_bias": self.gl_bias, + "CL": self.cal_err, + "EL": self.gl_estimate + self.cal_err, } - def plot( - self, - ax: plt.Axes = None, - plot_bins: bool = True, - plot_calibration: bool = True, - plot_hist: bool = True, - plot_legend: bool = True, - plot_cbar: bool = True, - fig_kw: dict = None, - scatter_kw: dict = None, - calibration_kw: dict = None, - hist_kw: dict = None, - bin_kw: dict = None, - legend_kw: dict = None, - ) -> plt.Figure: - """Plot the grouping diagram. - - Parameters - ---------- - ax : plt.Axes, optional - The axis on which to plot. If None, a new figure is created. - By default None. - plot_bins : bool, optional - Whether to plot the vertical lines for bins. - By default True. - plot_calibration : bool, optional - Whether to plot the calibration curve. - By default True. - plot_hist : bool, optional - Whether to plot the x-axis histogram. - By default True. - plot_legend : bool, optional - Whether to plot the legend. - By default True. - plot_cbar : bool, optional - Whether to plot the colorbar. - By default True. - fig_kw : dict, optional - Keyword arguments to pass to plt.subplots. - By default None. - scatter_kw : dict, optional - Keyword arguments to pass to ax.scatter. - By default None. - calibration_kw : dict, optional - Keyword arguments to pass to ax.plot for the calibration curve. - By default None. - hist_kw : dict, optional - Keyword arguments to pass to ax.hist for the x-axis histogram. - By default None. - bin_kw : dict, optional - Keyword arguments to pass to ax.axvline for the bin edges. - By default None. - legend_kw : dict, optional - Keyword arguments to pass to ax.legend. - By default None. - - Returns - ------- - plt.Figure - The grouping diagram figure. - """ - check_is_fitted(self) - - counts = filter_valid_counts(self.counts_) - - fig = grouping_diagram( - frac_pos=self.frac_pos_, - counts=counts, - mean_scores=self.mean_scores_, - bins=self.partitioner_.bins_, - ax=ax, - plot_bins=plot_bins, - plot_calibration=plot_calibration, - plot_hist=plot_hist, - plot_legend=plot_legend, - plot_cbar=plot_cbar, - fig_kw=fig_kw, - scatter_kw=scatter_kw, - calibration_kw=calibration_kw, - hist_kw=hist_kw, - bin_kw=bin_kw, - legend_kw=legend_kw, - ) - - return fig + def plot(self): + self.partitioner.plot() + return self def is_fitted(self): return ( - hasattr(self, "frac_pos_") - and hasattr(self, "counts_") - and hasattr(self, "mean_scores_") - and hasattr(self, "c_hat_") - and hasattr(self, "y_bins_") + hasattr(self, "gl_estimate") + and hasattr(self, "gl_bias") + and hasattr(self, "gl_uncorrected") ) def __format__(self, psr: str) -> str: @@ -736,16 +846,15 @@ def __format__(self, psr: str) -> str: if self.is_fitted(): if not psr: - psr = "brier" + psr = "Brier" metrics = self.metrics(psr) extra = ( - f" Scoring Rule : {psr}\n" + f" Scoring Rule : {psr}: {self.brier:.4f}\n" f" Grouping loss : {metrics['GL']:.4f}\n" - f" ↳ Uncorrected GL : {metrics['GL_uncorrected']:.4f}\n" - f" ↳ Bias : {metrics['GL_bias']:.4f}\n" - f" ↳ Binning induced: {metrics['GL_induced']:.4f}\n" + f" Calibration Loss : {metrics['CL']:.4f}\n" + f" Epistemic Loss : {metrics['EL']:.4f}\n" ) s = f"{s}\n{extra}" @@ -758,157 +867,102 @@ def __repr__(self) -> str: return f"{self}" -class GLEstimatorCV: - """Estimate the grouping loss of a probabilistic classifier. - +class RiskEstimator: + """ + Estimate the 0-1 risk of a fitted probabilistic classifier. + This class provides methods to estimate the risk of a probabilistic + classifier by partitioning the feature space and analyzing calibration + residuals within each partition. Parameters ---------- - classifier : object - The classifier for which to estimate the grouping loss. The - classifier must implement a `predict_proba` method. - partitioner : {"decision_tree", "decision_stump", "kmean", None} - | Partitioner, optional - The partitioning strategy to use for estimating the grouping loss. - If string given, use corresponding predefined strategy. If - `Partitioner` instance given, use this as partitioner. - By default "decision_tree". - cv : int, cross-validation generator or an iterable - Determines the cross-validation splitting strategy using - `sklearn.model_selection._split.check_cv`. See scikit-learn doc - for more details (e.g. `sklearn.model_selection.cross_validate`). - random_state : int, optional - Controls the randomness of the estimator used in the partitioner. - By default None. - verbose : int, optional - Whether to print progress. By default 0. - + partitioning_estimate : str or PartitioningEstimate, default="decision_tree" + The partitioning strategy to use for estimating the risk. + If string, must be one of {"decision_tree", "decision_stump", "kmeans", None}. + If PartitioningEstimate instance, uses the provided partitioner. + train_size : float, default=0.5 + The proportion of the dataset to use for training the partitioner. + The remaining data is used for evaluation to avoid overfitting. + random_state : int, default=None + Controls the randomness of the partitioner and data splitting. + verbose : int, default=0 + Controls the verbosity of output during fitting and estimation. + Higher values produce more detailed output. + Attributes + ---------- + partitioner : PartitioningEstimate + The fitted partitioning estimator. """ def __init__( self, - classifier, - partitioner="decision_tree", - cv=None, - random_state: int = None, # not the rs of the cv + partitioning_estimate: str | PartitioningEstimate = "decision_tree", + train_size: float = 0.5, + random_state: int = None, verbose: int = 0, - ): - self.classifier = classifier - self.partitioner = partitioner - self.cv = cv + residual_estimator: ResidualEstimator = None, + # t: float = 0.5, + ) -> None: + self.partitioner = PartitioningEstimate(partitioning_estimate) + self.train_size = train_size self.random_state = random_state self.verbose = verbose + self.residual_estimator = ( + ResidualEstimator(residual_estimator) + if residual_estimator is not None + else None + ) + # self.t = t - def GL(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError("GLEstimatorCV is not fitted.") - return np.array([glest.GL(psr) for glest in self.glests_]) - - def GL_uncorrected(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError("GLEstimatorCV is not fitted.") - return np.array([glest.GL_uncorrected(psr) for glest in self.glests_]) - - def GL_bias(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError("GLEstimatorCV is not fitted.") - return np.array([glest.GL_bias(psr) for glest in self.glests_]) - - def GL_induced(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError("GLEstimatorCV is not fitted.") - return np.array([glest.GL_induced(psr) for glest in self.glests_]) + def fit(self, X, y_scores, y_true, seed: int = 42): + if self.residual_estimator is not None: + self.residual_estimator.fit(X, y_scores, y_true, seed=seed) + else: + self.partitioner.fit(X, y_scores, y_true, seed=seed) + return self - def fit(self, X, y, groups=None): - """Fit a GLEstimator instance on each of the train/test split yield - by `cv`. Each instance is stored in the `glests_` attribute. + def compute_regret(self, C: np.ndarray, t: np.ndarray, a: np.ndarray) -> np.ndarray: + """Compute Regret estimations. Parameters ---------- - X : array-like of shape (n, d) - The features. - y : array-like of shape (n,) - The binary labels. - groups : array-like of shape (n,), optional - Group labels for the samples used while splitting the dataset into - train/test set. Only used in conjunction with a “Group” cv - instance. See `sklearn.model_selection.cross_validate` for - details. By default None. + C : np.ndarray of shape (n,) + The calibrated scores of each samples. + t : np.ndarray of shape (k,) + The thresholds t* derived from the utilities. + a : np.ndarray of shape (n, k) + The action taken on each sample. Returns ------- - GLEstimatorCV - The fitted instance. - """ - X, y, groups = indexable(X, y, groups) - cv = check_cv(self.cv, y=y, classifier=True) - indices = cv.split(X, y, groups) - glests: List[GLEstimator] = [] - for i, (train, test) in enumerate(indices): - if self.verbose > 0: - print(f"Split {i+1}/{cv.get_n_splits()}") - glest = GLEstimator( - classifier=self.classifier, - partitioner=self.partitioner, - random_state=self.random_state, - verbose=self.verbose - 1, - ) - glest.fit(X[train], y[train], test_data=(X[test], y[test])) - glests.append(glest) - - self.glests_ = glests - self.cv_ = cv - - return self - - def is_fitted(self): - return hasattr(self, "glests_") - - def metrics(self, psr: str = "brier"): - if not self.is_fitted(): - raise ValueError('GLEstimator must be fitted to call "metrics".') - - GL_ind = self.GL_induced(psr) - GL_uncorrected = self.GL_uncorrected(psr) - GL_bias = self.GL_bias(psr) - GL = GL_uncorrected - GL_bias - GL_ind - - return { - "psr": psr, - "GL": GL, - "GL_induced": GL_ind, - "GL_uncorrected": GL_uncorrected, - "GL_bias": GL_bias, - } + RCL : np.ndarray of shape (n, k) + The regret of the estimated probabilities to the calibrated scores. - def __format__(self, psr: str) -> str: - """Print the computed average metrics with variance.""" - s = "GLEstimatorCV()" - - def format_trials(values): - mean = np.mean(values) - std = np.std(values) - return f"{mean:.4f} ({std:.4f})" + """ + a_star = (C >= t).astype(int) # (n,) + R = np.zeros(C.shape[0]) # (n,) + idx_disagreement = a.flatten() != a_star # (n,) + R[idx_disagreement] = np.abs(C - t)[idx_disagreement] - if self.is_fitted(): - if not psr: - psr = "brier" + return R # (n,) - metrics = self.metrics(psr) + def predict(self, X, y_scores, t): + if self.residual_estimator is not None: + r_hat = self.residual_estimator.predict(X) + c_hat = self.residual_estimator.calibrator.predict(y_scores) + else: + r_hat = self.partitioner.predict(X) + c_hat = self.partitioner.calibrator.predict(y_scores) + a = (y_scores >= t).astype(int) + RCL = self.compute_regret(c_hat, t, a) - extra = ( - # f" Splits : {self.cv_}\n" - f" Scoring rule : {psr}\n" - f" Grouping loss : {format_trials(metrics['GL'])}\n" - f" ↳ Uncorrected GL : {format_trials(metrics['GL_uncorrected'])}\n" - f" ↳ Bias : {format_trials(metrics['GL_bias'])}\n" - f" ↳ Binning induced: {format_trials(metrics['GL_induced'])}\n" - ) - s = f"{s}\n{extra}" + REL = self.compute_regret(c_hat + r_hat, t, a) + return RCL, REL - return s + def predict_total(self, X, y_scores, t): + RCL, REL = self.predict(X, y_scores, t) - def __str__(self) -> str: - return f"{self}" + return RCL.mean(), REL.mean() - def __repr__(self) -> str: - return f"{self}" + def plot(self): + self.partitioner.plot() + return self diff --git a/glest/helpers.py b/glest/helpers.py deleted file mode 100644 index 05913c9..0000000 --- a/glest/helpers.py +++ /dev/null @@ -1,629 +0,0 @@ -import numbers - -import matplotlib as mpl -import numpy as np -from matplotlib import colors -from sklearn.neighbors import KNeighborsRegressor - - -def bins_from_strategy(n_bins, strategy, y_prob=None): - """Define the bin edges based on the strategy. - If `n_bins` is already an `array-like`, it is used as-is by - converting it to a `ndarray`. - Parameters - ---------- - n_bins : int or array-like - Define the discretization applied to `y_prob`, ranging in [0, 1]. - - if an integer is provided, the discretization depends on the - `strategy` parameter with n_bins as the number of bins. - - if an array-like is provided, the `strategy` parameter is overlooked - and the array is used as bin edges directly. - strategy : {'uniform', 'quantile'} - Strategy used to define the widths of the bins. - uniform - The bins have identical widths. - quantile - The bins have the same number of samples and depend on `y_prob`. - Ignored if `n_bins` is an array-like containing the bin edges. - y_prob : array-like of shape (n_samples,), default=None - Probabilities of the positive class. Used when `strategy='quantile'`. - Returns - ------- - bins : ndarray - The bin edges. If `n_bins` is an integer, `bins` is of shape (n_bins+1,). - If `n_bins` is an array-like, `bins` has same shape as `n_bins`. - """ - if isinstance(n_bins, numbers.Real): - if strategy == "quantile": - # Determine bin edges by distribution of data - quantiles = np.linspace(0, 1, n_bins + 1) - bins = np.percentile(y_prob, quantiles * 100) - bins[0] = 0 - bins[-1] = 1 - elif strategy == "uniform": - bins = np.linspace(0.0, 1.0, n_bins + 1) - else: - raise ValueError( - "Invalid entry to 'strategy' input. Strategy " - "must be either 'quantile' or 'uniform'." - ) - - else: # array-like - bins = np.asarray(n_bins) - - return bins - - -def scores_to_bin_ids(y_scores, n_bins, strategy): - """Get bin id from continuous scores. - - Parameters - ---------- - y_scores : array-like of shape (n_samples,) - Probabilities of the positive class in [0, 1]. Probabilities - outside of [0, 1] while be clipped to the nearest bins. - n_bins : int or array-like - Define the discretization applied to `y_scores`, ranging in [0, 1]. - - if an integer is provided, the discretization depends on the - `strategy` parameter with n_bins as the number of bins. - - if an array-like is provided, the `strategy` parameter is overlooked - and the array is used as bin edges directly. - strategy : {'uniform', 'quantile'} - Strategy used to define the widths of the bins. - uniform - The bins have identical widths. - quantile - The bins have the same number of samples and depend on `y_prob`. - Ignored if `n_bins` is an array-like containing the bin edges. - - Returns - ------- - y_bins : ndarray of shape (n_samples,) and type int64 - Integers ranging between 0 and n_bins - 1 - bins : ndarray of shape (n_bins + 1,) - The bin edges. - """ - bins = bins_from_strategy(n_bins, strategy, y_scores) - n_bins = len(bins) - 1 # bins are the bin edges - - # Get bin assignment for each sample - y_bins = np.digitize(y_scores, bins=bins) - 1 - y_bins = np.clip(y_bins, a_min=None, a_max=n_bins - 1) - - return y_bins, bins - - -def list_list_to_array(L, fill_value=None, dtype=None): - """Convert a list of list of varying size into a numpy array with - smaller shape possible. - - Parameters - ---------- - L : list of lists. - - fill_value : any - Value to fill the blank with. - - Returns - ------- - a : array - - """ - max_length = max(map(len, L)) - L = [Li + [fill_value] * (max_length - len(Li)) for Li in L] - return np.array(L, dtype=dtype) - - -def _validate_clustering(*args): - if len(args) == 2: - frac_pos, counts = args - elif len(args) == 3: - frac_pos, counts, mean_scores = args - else: - raise ValueError(f"2 or 3 args must be given. Got {len(args)}.") - - if frac_pos.shape != counts.shape: - raise ValueError( - f"Shape mismatch between frac_pos {frac_pos.shape} " - f"and counts {counts.shape}." - ) - - if len(args) == 3 and frac_pos.shape != mean_scores.shape: - raise ValueError( - f"Shape mismatch between frac_pos {frac_pos.shape} and " - f"mean_scores {mean_scores.shape}." - ) - - if frac_pos.ndim < 2: - raise ValueError( - f"frac_pos, counts and mean_scores must bet at least " - f"2D. Got {frac_pos.ndim}D." - ) - - -def check_2D_array(x): - if x.ndim == 1: - x = x.reshape(-1, 1) - - elif x.ndim == 2 and x.shape[1] != 1: - raise ValueError(f"x must have one feature. Got shape " f"{x.shape}") - - elif x.ndim > 2: - raise ValueError(f"x must be at most 2 dimensional. " f"Got shape {x.shape}") - - return x - - -class CEstimator: - def __init__(self, y_scores, y_labels): - y_scores = np.array(y_scores) - y_labels = np.array(y_labels) - y_scores = check_2D_array(y_scores) - self.y_scores = y_scores - self.y_labels = y_labels - - def _c_hat(self, test_scores): - test_scores = check_2D_array(test_scores) - n_neighbors = min(2000, int(0.1 * len(test_scores))) - est = KNeighborsRegressor(n_neighbors=n_neighbors) - est.fit(self.y_scores.reshape(-1, 1), self.y_labels) - c_hat = est.predict(test_scores) - return c_hat - - def c_hat(self): - return self._c_hat(self.y_scores.reshape(-1, 1)) - - -def psr_name_to_entropy(psr: str): - """Get the entropy of a scoring rule. - - Parameters - ---------- - psr : str | Callable - The name of the scoring rule in {"brier", "log"}. Or its entropy - given as a callable `lambda p: entropy(p)`. - - Returns - ------- - Callable - The entropy of the scoring rule. - - Raises - ------ - ValueError - If psr is neither a valid string nor a callable. - - """ - available_metrics = ["brier", "log"] - - if callable(psr): - return psr - - elif psr == "brier": - return lambda x: 2 * x * (1 - x) - - elif psr == "log": - return lambda x: -(x * np.log(x) + (1 - x) * np.log(1 - x)) - - else: - raise ValueError(f'Unknown metric "{psr}". Choices: {available_metrics}.') - - -def compute_GL_induced(c_hat, y_bins, psr: str = "brier"): - """Estimate GL induced for the Brier score.""" - - uniques, counts = np.unique(y_bins, return_counts=True) - diff = [] - - entropy = psr_name_to_entropy(psr) - - for i in uniques: - c_bin = c_hat[y_bins == i] - d = entropy(np.mean(c_bin)) - np.mean(entropy(c_bin)) - diff.append(d) - - GL_ind = np.vdot(diff, counts) / np.sum(counts) - - return GL_ind - - -def filter_valid_counts(counts): - """Discard regions with only one sample since the debiasing is not valid - for this situation.""" - counts = counts.copy() - counts[counts == 1] = 0 - return counts - - -def compute_GL_uncorrected(frac_pos, counts, psr: str = "brier"): - counts = filter_valid_counts(counts) - - prob_bins = calibration_curve( - frac_pos, counts, remove_empty=False, return_mean_bins=False - ) - entropy = psr_name_to_entropy(psr) - diff = entropy(prob_bins[:, None]) - entropy(frac_pos) - - n_samples = np.sum(counts) - if n_samples > 0: - return np.nansum(counts * diff) / n_samples - else: - return 0 - - -def compute_GL_bias(frac_pos, counts, psr: str = "brier"): - if psr != "brier": - print('Warning: GL bias computation is only available for "brier" psr.') - return np.nan - - counts = filter_valid_counts(counts) - - prob_bins = calibration_curve( - frac_pos, counts, remove_empty=False, return_mean_bins=False - ) - n_bins = np.sum(counts, axis=1) # number of samples in bin - n = np.sum(n_bins) - var = np.divide( - frac_pos * (1 - frac_pos), - counts - 1, - np.full_like(frac_pos, 0, dtype=float), - where=counts > 1, - ) - var = var * np.divide( - counts, - n_bins[:, None], - np.full_like(frac_pos, 0, dtype=float), - where=n_bins[:, None] > 0, - ) - var2 = np.divide( - prob_bins * (1 - prob_bins), - n_bins - 1, - np.full_like(prob_bins, 0, dtype=float), - where=n_bins > 1, - ) - bias = np.sum(var, axis=1) - var2 - bias *= np.divide( - n_bins, - n, - np.full_like(n_bins, 0, dtype=float), - where=n > 0, - ) - bias *= 2 # for the Brier score - bias = np.sum(bias) - return bias - - -def calibration_curve( - frac_pos, counts, mean_scores=None, remove_empty=True, return_mean_bins=True -): - """Compute calibration curve from output of clustering. - Result is the same as sklearn's calibration_curve. - - Parameters - ---------- - frac_pos : (bins, n_clusters) array - The fraction of positives in each cluster for each bin. - - counts : (bins, n_clusters) array - The number of samples in each cluster for each bin. - - mean_scores : (bins, n_clusters) array - The mean score of samples in each cluster for each bin. - - remove_empty : bool - Whether to remove empty bins. - - return_mean_bins : bool - Whether to return mean_bins. - - Returns - ------- - prob_bins : (bins,) arrays - Fraction of positives in each bin. - - mean_bins : (bins,) arrays - Mean score in each bin. Returned only if return_mean_bins=True. - - """ - if return_mean_bins and mean_scores is None: - raise ValueError("mean_scores cannot be None when " "return_mean_bins=True.") - - if not return_mean_bins: - _validate_clustering(frac_pos, counts) - - else: - _validate_clustering(frac_pos, counts, mean_scores) - - count_sums = np.sum(counts, axis=1, dtype=float) - non_empty = count_sums > 0 - prob_bins = np.divide( - np.sum(frac_pos * counts, axis=1), - count_sums, - where=non_empty, - out=np.full_like(count_sums, np.nan), - ) - - if return_mean_bins: - mean_bins = np.divide( - np.sum(mean_scores * counts, axis=1), - count_sums, - where=non_empty, - out=np.full_like(count_sums, np.nan), - ) - - # The calibration_curve of sklearn removes empty bins. - # Should do the same to give same result. - if frac_pos.ndim == 2 and remove_empty: - prob_bins = prob_bins[non_empty] - if return_mean_bins: - mean_bins = mean_bins[non_empty] - - if return_mean_bins: - return prob_bins, mean_bins - - return prob_bins - - -# Register flare colormap from Seaborn -_flare_lut = [ - [0.92907237, 0.68878959, 0.50411509], - [0.92891402, 0.68494686, 0.50173994], - [0.92864754, 0.68116207, 0.4993754], - [0.92836112, 0.67738527, 0.49701572], - [0.9280599, 0.67361354, 0.49466044], - [0.92775569, 0.66983999, 0.49230866], - [0.9274375, 0.66607098, 0.48996097], - [0.927111, 0.66230315, 0.48761688], - [0.92677996, 0.6585342, 0.485276], - [0.92644317, 0.65476476, 0.48293832], - [0.92609759, 0.65099658, 0.48060392], - [0.925747, 0.64722729, 0.47827244], - [0.92539502, 0.64345456, 0.47594352], - [0.92503106, 0.6396848, 0.47361782], - [0.92466877, 0.6359095, 0.47129427], - [0.92429828, 0.63213463, 0.46897349], - [0.92392172, 0.62835879, 0.46665526], - [0.92354597, 0.62457749, 0.46433898], - [0.9231622, 0.6207962, 0.46202524], - [0.92277222, 0.61701365, 0.45971384], - [0.92237978, 0.61322733, 0.45740444], - [0.92198615, 0.60943622, 0.45509686], - [0.92158735, 0.60564276, 0.45279137], - [0.92118373, 0.60184659, 0.45048789], - [0.92077582, 0.59804722, 0.44818634], - [0.92036413, 0.59424414, 0.44588663], - [0.91994924, 0.5904368, 0.44358868], - [0.91952943, 0.58662619, 0.4412926], - [0.91910675, 0.58281075, 0.43899817], - [0.91868096, 0.57899046, 0.4367054], - [0.91825103, 0.57516584, 0.43441436], - [0.91781857, 0.57133556, 0.43212486], - [0.9173814, 0.56750099, 0.4298371], - [0.91694139, 0.56366058, 0.42755089], - [0.91649756, 0.55981483, 0.42526631], - [0.91604942, 0.55596387, 0.42298339], - [0.9155979, 0.55210684, 0.42070204], - [0.9151409, 0.54824485, 0.4184247], - [0.91466138, 0.54438817, 0.41617858], - [0.91416896, 0.54052962, 0.41396347], - [0.91366559, 0.53666778, 0.41177769], - [0.91315173, 0.53280208, 0.40962196], - [0.91262605, 0.52893336, 0.40749715], - [0.91208866, 0.52506133, 0.40540404], - [0.91153952, 0.52118582, 0.40334346], - [0.91097732, 0.51730767, 0.4013163], - [0.910403, 0.51342591, 0.39932342], - [0.90981494, 0.50954168, 0.39736571], - [0.90921368, 0.5056543, 0.39544411], - [0.90859797, 0.50176463, 0.39355952], - [0.90796841, 0.49787195, 0.39171297], - [0.90732341, 0.4939774, 0.38990532], - [0.90666382, 0.49008006, 0.38813773], - [0.90598815, 0.486181, 0.38641107], - [0.90529624, 0.48228017, 0.38472641], - [0.90458808, 0.47837738, 0.38308489], - [0.90386248, 0.47447348, 0.38148746], - [0.90311921, 0.4705685, 0.37993524], - [0.90235809, 0.46666239, 0.37842943], - [0.90157824, 0.46275577, 0.37697105], - [0.90077904, 0.45884905, 0.37556121], - [0.89995995, 0.45494253, 0.37420106], - [0.89912041, 0.4510366, 0.37289175], - [0.8982602, 0.44713126, 0.37163458], - [0.89737819, 0.44322747, 0.37043052], - [0.89647387, 0.43932557, 0.36928078], - [0.89554477, 0.43542759, 0.36818855], - [0.89458871, 0.4315354, 0.36715654], - [0.89360794, 0.42764714, 0.36618273], - [0.89260152, 0.42376366, 0.36526813], - [0.8915687, 0.41988565, 0.36441384], - [0.89050882, 0.41601371, 0.36362102], - [0.8894159, 0.41215334, 0.36289639], - [0.888292, 0.40830288, 0.36223756], - [0.88713784, 0.40446193, 0.36164328], - [0.88595253, 0.40063149, 0.36111438], - [0.88473115, 0.39681635, 0.3606566], - [0.88347246, 0.39301805, 0.36027074], - [0.88217931, 0.38923439, 0.35995244], - [0.880851, 0.38546632, 0.35970244], - [0.87947728, 0.38172422, 0.35953127], - [0.87806542, 0.37800172, 0.35942941], - [0.87661509, 0.37429964, 0.35939659], - [0.87511668, 0.37062819, 0.35944178], - [0.87357554, 0.36698279, 0.35955811], - [0.87199254, 0.3633634, 0.35974223], - [0.87035691, 0.35978174, 0.36000516], - [0.86867647, 0.35623087, 0.36033559], - [0.86694949, 0.35271349, 0.36073358], - [0.86516775, 0.34923921, 0.36120624], - [0.86333996, 0.34580008, 0.36174113], - [0.86145909, 0.3424046, 0.36234402], - [0.85952586, 0.33905327, 0.36301129], - [0.85754536, 0.33574168, 0.36373567], - [0.855514, 0.33247568, 0.36451271], - [0.85344392, 0.32924217, 0.36533344], - [0.8513284, 0.32604977, 0.36620106], - [0.84916723, 0.32289973, 0.36711424], - [0.84696243, 0.31979068, 0.36806976], - [0.84470627, 0.31673295, 0.36907066], - [0.84240761, 0.31371695, 0.37010969], - [0.84005337, 0.31075974, 0.37119284], - [0.83765537, 0.30784814, 0.3723105], - [0.83520234, 0.30499724, 0.37346726], - [0.83270291, 0.30219766, 0.37465552], - [0.83014895, 0.29946081, 0.37587769], - [0.82754694, 0.29677989, 0.37712733], - [0.82489111, 0.29416352, 0.37840532], - [0.82218644, 0.29160665, 0.37970606], - [0.81942908, 0.28911553, 0.38102921], - [0.81662276, 0.28668665, 0.38236999], - [0.81376555, 0.28432371, 0.383727], - [0.81085964, 0.28202508, 0.38509649], - [0.8079055, 0.27979128, 0.38647583], - [0.80490309, 0.27762348, 0.3878626], - [0.80185613, 0.2755178, 0.38925253], - [0.79876118, 0.27347974, 0.39064559], - [0.79562644, 0.27149928, 0.39203532], - [0.79244362, 0.2695883, 0.39342447], - [0.78922456, 0.26773176, 0.3948046], - [0.78596161, 0.26594053, 0.39617873], - [0.7826624, 0.26420493, 0.39754146], - [0.77932717, 0.26252522, 0.39889102], - [0.77595363, 0.2609049, 0.4002279], - [0.77254999, 0.25933319, 0.40154704], - [0.76911107, 0.25781758, 0.40284959], - [0.76564158, 0.25635173, 0.40413341], - [0.76214598, 0.25492998, 0.40539471], - [0.75861834, 0.25356035, 0.40663694], - [0.75506533, 0.25223402, 0.40785559], - [0.75148963, 0.2509473, 0.40904966], - [0.74788835, 0.24970413, 0.41022028], - [0.74426345, 0.24850191, 0.41136599], - [0.74061927, 0.24733457, 0.41248516], - [0.73695678, 0.24620072, 0.41357737], - [0.73327278, 0.24510469, 0.41464364], - [0.72957096, 0.24404127, 0.4156828], - [0.72585394, 0.24300672, 0.41669383], - [0.7221226, 0.24199971, 0.41767651], - [0.71837612, 0.24102046, 0.41863486], - [0.71463236, 0.24004289, 0.41956983], - [0.7108932, 0.23906316, 0.42048681], - [0.70715842, 0.23808142, 0.42138647], - [0.70342811, 0.2370976, 0.42226844], - [0.69970218, 0.23611179, 0.42313282], - [0.69598055, 0.2351247, 0.42397678], - [0.69226314, 0.23413578, 0.42480327], - [0.68854988, 0.23314511, 0.42561234], - [0.68484064, 0.23215279, 0.42640419], - [0.68113541, 0.23115942, 0.42717615], - [0.67743412, 0.23016472, 0.42792989], - [0.67373662, 0.22916861, 0.42866642], - [0.67004287, 0.22817117, 0.42938576], - [0.66635279, 0.22717328, 0.43008427], - [0.66266621, 0.22617435, 0.43076552], - [0.65898313, 0.22517434, 0.43142956], - [0.65530349, 0.22417381, 0.43207427], - [0.65162696, 0.22317307, 0.4327001], - [0.64795375, 0.22217149, 0.43330852], - [0.64428351, 0.22116972, 0.43389854], - [0.64061624, 0.22016818, 0.43446845], - [0.63695183, 0.21916625, 0.43502123], - [0.63329016, 0.21816454, 0.43555493], - [0.62963102, 0.2171635, 0.43606881], - [0.62597451, 0.21616235, 0.43656529], - [0.62232019, 0.21516239, 0.43704153], - [0.61866821, 0.21416307, 0.43749868], - [0.61501835, 0.21316435, 0.43793808], - [0.61137029, 0.21216761, 0.4383556], - [0.60772426, 0.2111715, 0.43875552], - [0.60407977, 0.21017746, 0.43913439], - [0.60043678, 0.20918503, 0.43949412], - [0.59679524, 0.20819447, 0.43983393], - [0.59315487, 0.20720639, 0.44015254], - [0.58951566, 0.20622027, 0.44045213], - [0.58587715, 0.20523751, 0.44072926], - [0.5822395, 0.20425693, 0.44098758], - [0.57860222, 0.20328034, 0.44122241], - [0.57496549, 0.20230637, 0.44143805], - [0.57132875, 0.20133689, 0.4416298], - [0.56769215, 0.20037071, 0.44180142], - [0.5640552, 0.19940936, 0.44194923], - [0.56041794, 0.19845221, 0.44207535], - [0.55678004, 0.1975, 0.44217824], - [0.55314129, 0.19655316, 0.44225723], - [0.54950166, 0.19561118, 0.44231412], - [0.54585987, 0.19467771, 0.44234111], - [0.54221157, 0.19375869, 0.44233698], - [0.5385549, 0.19285696, 0.44229959], - [0.5348913, 0.19197036, 0.44222958], - [0.53122177, 0.1910974, 0.44212735], - [0.52754464, 0.19024042, 0.44199159], - [0.52386353, 0.18939409, 0.44182449], - [0.52017476, 0.18856368, 0.44162345], - [0.51648277, 0.18774266, 0.44139128], - [0.51278481, 0.18693492, 0.44112605], - [0.50908361, 0.18613639, 0.4408295], - [0.50537784, 0.18534893, 0.44050064], - [0.50166912, 0.18457008, 0.44014054], - [0.49795686, 0.18380056, 0.43974881], - [0.49424218, 0.18303865, 0.43932623], - [0.49052472, 0.18228477, 0.43887255], - [0.48680565, 0.1815371, 0.43838867], - [0.48308419, 0.18079663, 0.43787408], - [0.47936222, 0.18006056, 0.43733022], - [0.47563799, 0.17933127, 0.43675585], - [0.47191466, 0.17860416, 0.43615337], - [0.46818879, 0.17788392, 0.43552047], - [0.46446454, 0.17716458, 0.43486036], - [0.46073893, 0.17645017, 0.43417097], - [0.45701462, 0.17573691, 0.43345429], - [0.45329097, 0.17502549, 0.43271025], - [0.44956744, 0.17431649, 0.4319386], - [0.44584668, 0.17360625, 0.43114133], - [0.44212538, 0.17289906, 0.43031642], - [0.43840678, 0.17219041, 0.42946642], - [0.43469046, 0.17148074, 0.42859124], - [0.4309749, 0.17077192, 0.42769008], - [0.42726297, 0.17006003, 0.42676519], - [0.42355299, 0.16934709, 0.42581586], - [0.41984535, 0.16863258, 0.42484219], - [0.41614149, 0.16791429, 0.42384614], - [0.41244029, 0.16719372, 0.42282661], - [0.40874177, 0.16647061, 0.42178429], - [0.40504765, 0.16574261, 0.42072062], - [0.401357, 0.16501079, 0.41963528], - [0.397669, 0.16427607, 0.418528], - [0.39398585, 0.16353554, 0.41740053], - [0.39030735, 0.16278924, 0.41625344], - [0.3866314, 0.16203977, 0.41508517], - [0.38295904, 0.16128519, 0.41389849], - [0.37928736, 0.16052483, 0.41270599], - [0.37562649, 0.15974704, 0.41151182], - [0.37197803, 0.15895049, 0.41031532], - [0.36833779, 0.15813871, 0.40911916], - [0.36470944, 0.15730861, 0.40792149], - [0.36109117, 0.15646169, 0.40672362], - [0.35748213, 0.15559861, 0.40552633], - [0.353885, 0.15471714, 0.40432831], - [0.35029682, 0.15381967, 0.4031316], - [0.34671861, 0.1529053, 0.40193587], - [0.34315191, 0.15197275, 0.40074049], - [0.33959331, 0.15102466, 0.3995478], - [0.33604378, 0.15006017, 0.39835754], - [0.33250529, 0.14907766, 0.39716879], - [0.32897621, 0.14807831, 0.39598285], - [0.3254559, 0.14706248, 0.39480044], - [0.32194567, 0.14602909, 0.39362106], - [0.31844477, 0.14497857, 0.39244549], - [0.31494974, 0.14391333, 0.39127626], - [0.31146605, 0.14282918, 0.39011024], - [0.30798857, 0.1417297, 0.38895105], - [0.30451661, 0.14061515, 0.38779953], - [0.30105136, 0.13948445, 0.38665531], - [0.2975886, 0.1383403, 0.38552159], - [0.29408557, 0.13721193, 0.38442775], -] - -_cmap = colors.ListedColormap(_flare_lut, "flare") -if "flare" not in mpl.colormaps: - mpl.colormaps.register(_cmap, name="flare") diff --git a/glest/plot.py b/glest/plot.py index 8bec167..5552006 100644 --- a/glest/plot.py +++ b/glest/plot.py @@ -5,65 +5,66 @@ from matplotlib.colors import Normalize from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable -from glest.helpers import calibration_curve as calibration_curve - def grouping_diagram( - frac_pos, - counts, - mean_scores, - bins, + c_hat, + r_hat, + n_in_leaf, + f, + leaf_ids, + groups="all", ax: plt.Axes = None, - plot_calibration: bool = True, - plot_bins: bool = True, + plot_calibration=True, plot_cbar: bool = True, plot_hist: bool = True, plot_legend: bool = True, - fig_kw: dict = None, - scatter_kw: dict = None, - calibration_kw: dict = None, - hist_kw: dict = None, - bin_kw: dict = None, - legend_kw: dict = None, ): - frac_pos = np.array(frac_pos) - counts = np.array(counts) - mean_scores = np.array(mean_scores) - bins = np.array(bins) - - assert frac_pos.shape == counts.shape == mean_scores.shape - assert bins.shape[0] == frac_pos.shape[0] + 1 - + """ + Plot a grouping diagram for residuals. + Parameters + ---------- + c_hat : array-like + Predicted probabilities. + r_hat : array-like + Predicted residuals. + n_in_leaf : array-like + Number of samples in each leaf. + f : callable + Function to compute the grouping diagram. + Returns + ------- + fig : matplotlib.figure.Figure + The figure containing the grouping diagram. + """ # Scatter color norm = Normalize(vmin=1, vmax=None) - sm = ScalarMappable(norm=norm, cmap='flare') - color = sm.to_rgba(counts.flat) + sm = ScalarMappable(norm=norm, cmap="viridis") + color = sm.to_rgba(leaf_ids.flat) # Scatter sizes norm = Normalize(vmin=1, vmax=100, clip=True) - sizes = 15+ 20*norm(counts.flat) # Default parameters _fig_kw = dict( - figsize=(3, 3), + figsize=(4, 4), ) _scatter_kw = dict( - edgecolor='white', + edgecolor="white", linewidth=0.3, color=color, - s=sizes, - label='Subgroups', + label="Subgroups", + alpha=0.5, ) _calibration_kw = dict( - marker='.', - color='black', - markersize=5, - label='Calibration curve', + color="black", + linewidth=5, + label="Calibration curve", + zorder=3, ) _hist_kw = dict( - edgecolor='black', + edgecolor="black", linewidth=0.2, - color='#dfa0b3', + color="#dfa0b3", ) _bin_kw = dict( lw=0.2, @@ -73,85 +74,111 @@ def grouping_diagram( ) _legend_kw = dict( framealpha=0, - loc='lower center', + loc="lower center", bbox_to_anchor=(0.5, 1.1) if plot_hist else (0.5, 1), ncols=2, ) - # Update default parameters with input - if calibration_kw is not None: - _calibration_kw.update(calibration_kw) - if hist_kw is not None: - _hist_kw.update(hist_kw) - if scatter_kw is not None: - _scatter_kw.update(scatter_kw) - if fig_kw is not None: - _fig_kw.update(fig_kw) - if bin_kw is not None: - _bin_kw.update(bin_kw) - if legend_kw is not None: - _legend_kw.update(legend_kw) - - # Create or retrieve existing figure + # Update default parameters with input# Create or retrieve existing figure if ax is None: fig, ax = plt.subplots(1, 1, **_fig_kw) else: fig = ax.figure - # Main axis - p1 = ax.scatter(mean_scores.flat, frac_pos.flat, **_scatter_kw) + f_star_hat = r_hat + c_hat - ax.set_aspect('equal') + ax.set_aspect("equal") ticks = [0, 0.25, 0.5, 0.75, 1] ax.set( xticks=ticks, yticks=ticks, - xlabel='Predicted probability', - ylabel='Fraction of positives', - xlim=(-0.03, 1.03), - ylim=(-0.03, 1.03), + xlabel="Predicted probability", + ylabel="Fraction of positives", + xlim=(0, 1.0), + ylim=(0, 1.0), ) + ax.xaxis.label.set_fontsize(16) + ax.yaxis.label.set_fontsize(16) - if plot_bins: - for x in bins: - p_bin = ax.axvline(x, **_bin_kw) - - if plot_calibration: - ax.plot([0, 1], [0, 1], ls="--", lw=1, color="black", zorder=0) - prob_bins, mean_bins = calibration_curve(frac_pos, counts, mean_scores) - p2, = ax.plot(mean_bins, prob_bins, **_calibration_kw) - - # Histogram on upper axis divider = make_axes_locatable(ax) - if plot_hist: - ax_hist = divider.append_axes("top", size="10%", pad=0.0) - ax_hist.set_xlim(ax.get_xlim()) - ax_hist.get_xaxis().set_visible(False) - ax_hist.get_yaxis().set_visible(False) - ax_hist.spines["right"].set_visible(False) - ax_hist.spines["top"].set_visible(False) - ax_hist.spines["left"].set_visible(False) - ax_hist.hist(mean_scores.flat, bins=bins, weights=counts.flat, **_hist_kw) - # Colorbar on right axis if plot_cbar: ax_cb = divider.append_axes("right", size="4%", pad=0.05) - ax_cb.set_title('Count', loc='left') - Colorbar(ax_cb, mappable=sm, spacing='proportional') - - # Legend on top of the figure - if plot_legend: - handles_labels = { - p1: p1.get_label(), - } - if plot_bins: - handles_labels[p_bin]= 'Bin edges' - if plot_calibration: - handles_labels[p2] = p2.get_label() + ax_cb.set_title("Group", loc="left") + Colorbar(ax_cb, mappable=sm, spacing="proportional") + legend_handles = [] + legend_labels = [] + if plot_calibration: + ax.plot([0, 1], [0, 1], ls="--", lw=1, color="black", zorder=0) + # prob_bins, mean_bins = calibration_curve(y, f, n_bins=100) + sort_idx = np.argsort(f) + (line,) = ax.plot(f[sort_idx], c_hat[sort_idx], **_calibration_kw) + legend_handles.append(line) + legend_labels.append("Calibration curve") + + if groups == "all" or groups is None: + for i, leaf in enumerate(np.unique(leaf_ids)): + mask = leaf_ids == leaf + n_leaf = n_in_leaf[i] + if np.sum(mask) > 0: + # Sort by f values to create a proper curve + f_leaf = f[mask] + f_star_hat_leaf = f_star_hat[mask] + sort_idx = np.argsort(f_leaf) + # Get the color for this leaf from the colormap + leaf_color = sm.to_rgba(leaf) + # Make line width proportional to number of samples + line_width = +3 * (n_leaf / np.max(n_in_leaf)) + ax.plot( + f_leaf[sort_idx], + f_star_hat_leaf[sort_idx], + color=leaf_color, + alpha=0.7, + linewidth=line_width, + ) + else: + for i, leaf in enumerate(np.unique(leaf_ids)): + mask = leaf_ids == leaf + n_leaf = n_in_leaf[i] + if np.sum(mask) > 0: + f_leaf = f[mask] + f_star_hat_leaf = f_star_hat[mask] + sort_idx = np.argsort(f_leaf) + + if leaf in groups: + # Plot selected groups with color and add to legend + leaf_color = sm.to_rgba(leaf) + line_width = 1 + 5 * (n_leaf / np.max(n_in_leaf)) + (line,) = ax.plot( + f_leaf[sort_idx], + f_star_hat_leaf[sort_idx], + color=leaf_color, + alpha=0.7, + linewidth=line_width, + zorder=2, + ) + legend_handles.append(line) + legend_labels.append(f"Group {leaf}") + else: + # Plot non-selected groups in grey (background) + line_width = +3 * (n_leaf / np.max(n_in_leaf)) + ax.plot( + f_leaf[sort_idx], + f_star_hat_leaf[sort_idx], + color="grey", + alpha=0.3, + linewidth=line_width, + zorder=1, + ) + + if legend_handles and plot_legend: ax.legend( - handles=handles_labels.keys(), - labels=handles_labels.values(), - **_legend_kw + legend_handles, + legend_labels, + loc="upper center", + bbox_to_anchor=(0.5, -0.2), + ncols=2, + framealpha=0, ) return fig