Merge pull request #33 from EFS-OpenSource/develop

Dtype fixes in netcal.scaling package
EFS-OpenSource · Nov 11, 2022 · 4e8ab3b · 4e8ab3b
2 parents 3b135a8 + ea3db0e
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diff --git a/docs/build/doctrees/_autosummary/_autosummary_binning/netcal.binning.BBQ.doctree b/docs/build/doctrees/_autosummary/_autosummary_binning/netcal.binning.BBQ.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_binning/netcal.binning.ENIR.doctree b/docs/build/doctrees/_autosummary/_autosummary_binning/netcal.binning.ENIR.doctree
diff --git a/.../build/doctrees/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.doctree b/.../build/doctrees/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.doctree
diff --git a/...uild/doctrees/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.doctree b/...uild/doctrees/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.ACE.doctree b/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.ACE.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.ENCE.doctree b/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.ENCE.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.PinballLoss.doctree b/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.PinballLoss.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.QCE.doctree b/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.QCE.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.UCE.doctree b/docs/build/doctrees/_autosummary/_autosummary_metric/netcal.metrics.UCE.doctree
diff --git a/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.confidence.ACE.doctree b/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.confidence.ACE.doctree
diff --git a/..._autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.ENCE.doctree b/..._autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.ENCE.doctree
diff --git a/...mmary_metric_submodules/_autosummary_metric/netcal.metrics.regression.PinballLoss.doctree b/...mmary_metric_submodules/_autosummary_metric/netcal.metrics.regression.PinballLoss.doctree
diff --git a/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.QCE.doctree b/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.QCE.doctree
diff --git a/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.UCE.doctree b/.../_autosummary_metric_submodules/_autosummary_metric/netcal.metrics.regression.UCE.doctree
diff --git a/...ld/doctrees/_autosummary/_autosummary_metric_submodules/netcal.metrics.confidence.doctree b/...ld/doctrees/_autosummary/_autosummary_metric_submodules/netcal.metrics.confidence.doctree
diff --git a/...ld/doctrees/_autosummary/_autosummary_metric_submodules/netcal.metrics.regression.doctree b/...ld/doctrees/_autosummary/_autosummary_metric_submodules/netcal.metrics.regression.doctree
diff --git a/...octrees/_autosummary/_autosummary_presentation/netcal.presentation.ReliabilityQCE.doctree b/...octrees/_autosummary/_autosummary_presentation/netcal.presentation.ReliabilityQCE.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPBeta.doctree b/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPBeta.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPCauchy.doctree b/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPCauchy.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPNormal.doctree b/docs/build/doctrees/_autosummary/_autosummary_regression/netcal.regression.GPNormal.doctree
diff --git a/...octrees/_autosummary/_autosummary_regression/netcal.regression.IsotonicRegression.doctree b/...octrees/_autosummary/_autosummary_regression/netcal.regression.IsotonicRegression.doctree
diff --git a/...d/doctrees/_autosummary/_autosummary_regression/netcal.regression.VarianceScaling.doctree b/...d/doctrees/_autosummary/_autosummary_regression/netcal.regression.VarianceScaling.doctree
diff --git a/...osummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.AbstractGP.doctree b/...osummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.AbstractGP.doctree
diff --git a/..._autosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPBeta.doctree b/..._autosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPBeta.doctree
diff --git a/...utosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPCauchy.doctree b/...utosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPCauchy.doctree
diff --git a/...utosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPNormal.doctree b/...utosummary_regression_gp/_autosummary_regression_gp/netcal.regression.gp.GPNormal.doctree
diff --git a/...s/_autosummary_regression_gp_kernel/netcal.regression.gp.kernel.GaussianRBFKernel.doctree b/...s/_autosummary_regression_gp_kernel/netcal.regression.gp.kernel.GaussianRBFKernel.doctree
diff --git a/...tosummary_regression_gp_likelihood/netcal.regression.gp.likelihood.BetaLikelihood.doctree b/...tosummary_regression_gp_likelihood/netcal.regression.gp.likelihood.BetaLikelihood.doctree
diff --git a/...summary_regression_gp_likelihood/netcal.regression.gp.likelihood.CauchyLikelihood.doctree b/...summary_regression_gp_likelihood/netcal.regression.gp.likelihood.CauchyLikelihood.doctree
diff --git a/..._gp_likelihood/netcal.regression.gp.likelihood.ScaledMultivariateNormalLikelihood.doctree b/..._gp_likelihood/netcal.regression.gp.likelihood.ScaledMultivariateNormalLikelihood.doctree
diff --git a/...y_regression_gp_likelihood/netcal.regression.gp.likelihood.ScaledNormalLikelihood.doctree b/...y_regression_gp_likelihood/netcal.regression.gp.likelihood.ScaledNormalLikelihood.doctree
diff --git a/...y_regression_gp/_autosummary_regression_gp_submodules/netcal.regression.gp.kernel.doctree b/...y_regression_gp/_autosummary_regression_gp_submodules/netcal.regression.gp.kernel.doctree
diff --git a/...gression_gp/_autosummary_regression_gp_submodules/netcal.regression.gp.likelihood.doctree b/...gression_gp/_autosummary_regression_gp_submodules/netcal.regression.gp.likelihood.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_regression_gp/netcal.regression.gp.doctree b/docs/build/doctrees/_autosummary/_autosummary_regression_gp/netcal.regression.gp.doctree
diff --git a/...osummary/_autosummary_regularization_func/netcal.regularization.ConfidencePenalty.doctree b/...osummary/_autosummary_regularization_func/netcal.regularization.ConfidencePenalty.doctree
diff --git a/...es/_autosummary/_autosummary_regularization_func/netcal.regularization.DCAPenalty.doctree b/...es/_autosummary/_autosummary_regularization_func/netcal.regularization.DCAPenalty.doctree
diff --git a/...s/_autosummary/_autosummary_regularization_func/netcal.regularization.MMCEPenalty.doctree b/...s/_autosummary/_autosummary_regularization_func/netcal.regularization.MMCEPenalty.doctree
diff --git a/...trees/_autosummary/_autosummary_scaling/netcal.scaling.AbstractLogisticRegression.doctree b/...trees/_autosummary/_autosummary_scaling/netcal.scaling.AbstractLogisticRegression.doctree
diff --git a/docs/build/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.BetaCalibration.doctree b/docs/build/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.BetaCalibration.doctree
diff --git a/...octrees/_autosummary/_autosummary_scaling/netcal.scaling.BetaCalibrationDependent.doctree b/...octrees/_autosummary/_autosummary_scaling/netcal.scaling.BetaCalibrationDependent.doctree
diff --git a/...ild/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.LogisticCalibration.doctree b/...ild/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.LogisticCalibration.doctree
diff --git a/...ees/_autosummary/_autosummary_scaling/netcal.scaling.LogisticCalibrationDependent.doctree b/...ees/_autosummary/_autosummary_scaling/netcal.scaling.LogisticCalibrationDependent.doctree
diff --git a/...uild/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.TemperatureScaling.doctree b/...uild/doctrees/_autosummary/_autosummary_scaling/netcal.scaling.TemperatureScaling.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.binning.doctree b/docs/build/doctrees/_autosummary/netcal.binning.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.metrics.doctree b/docs/build/doctrees/_autosummary/netcal.metrics.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.presentation.doctree b/docs/build/doctrees/_autosummary/netcal.presentation.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.regression.doctree b/docs/build/doctrees/_autosummary/netcal.regression.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.regularization.doctree b/docs/build/doctrees/_autosummary/netcal.regularization.doctree
diff --git a/docs/build/doctrees/_autosummary/netcal.scaling.doctree b/docs/build/doctrees/_autosummary/netcal.scaling.doctree
diff --git a/docs/build/doctrees/_autosummary_abstract_calibration/netcal.AbstractCalibration.doctree b/docs/build/doctrees/_autosummary_abstract_calibration/netcal.AbstractCalibration.doctree
diff --git a/docs/build/doctrees/environment.pickle b/docs/build/doctrees/environment.pickle
diff --git a/docs/build/doctrees/index.doctree b/docs/build/doctrees/index.doctree
diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.BBQ.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.BBQ.html
@@ -35,7 +35,7 @@
 <script type="text/javascript">
   var DOCUMENTATION_OPTIONS = {
             URL_ROOT:    '../../',
-            VERSION:     '1.3.1',
+            VERSION:     '1.3.2',
             COLLAPSE_INDEX: false,
             FILE_SUFFIX: '.html',
             HAS_SOURCE:  true
@@ -247,25 +247,28 @@ <h1>netcal.binning.BBQ<a class="headerlink" href="#netcal-binning-bbq" title="Pe
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
 <td><p>Clear model parameters.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">epsilon</span></code>(dtype)</p></td>
+<td><p>Get the smallest digit that is representable depending on the passed dtype (NumPy or PyTorch).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
 <td><p>Build BBQ calibration model.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
 <td><p>Fit to data, then transform it.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
 <td><p>Get parameters for this estimator.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
 <td><p>Load model from saved torch dump.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
 <td><p>Save model instance as with torch's save function as this is safer for torch tensors.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
 <td><p>Set the parameters of this estimator.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
 <td><p>After model calibration, this function is used to get calibrated outputs of uncalibrated confidence estimates.</p></td>
 </tr>
 </tbody>

diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.ENIR.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.ENIR.html
@@ -35,7 +35,7 @@
 <script type="text/javascript">
   var DOCUMENTATION_OPTIONS = {
             URL_ROOT:    '../../',
-            VERSION:     '1.3.1',
+            VERSION:     '1.3.2',
             COLLAPSE_INDEX: false,
             FILE_SUFFIX: '.html',
             HAS_SOURCE:  true
@@ -246,25 +246,28 @@ <h1>netcal.binning.ENIR<a class="headerlink" href="#netcal-binning-enir" title="
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
 <td><p>Clear model parameters.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">epsilon</span></code>(dtype)</p></td>
+<td><p>Get the smallest digit that is representable depending on the passed dtype (NumPy or PyTorch).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
 <td><p>Build ENIR calibration model.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
 <td><p>Fit to data, then transform it.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
 <td><p>Get parameters for this estimator.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
 <td><p>Load model from saved torch dump.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
 <td><p>Save model instance as with torch's save function as this is safer for torch tensors.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
 <td><p>Set the parameters of this estimator.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
 <td><p>After model calibration, this function is used to get calibrated outputs of uncalibrated confidence estimates.</p></td>
 </tr>
 </tbody>

diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.HistogramBinning.html
@@ -35,7 +35,7 @@
 <script type="text/javascript">
   var DOCUMENTATION_OPTIONS = {
             URL_ROOT:    '../../',
-            VERSION:     '1.3.1',
+            VERSION:     '1.3.2',
             COLLAPSE_INDEX: false,
             FILE_SUFFIX: '.html',
             HAS_SOURCE:  true
@@ -240,28 +240,31 @@ <h1>netcal.binning.HistogramBinning<a class="headerlink" href="#netcal-binning-h
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
 <td><p>Clear model parameters.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y[, random_state, tensorboard, log_dir])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">epsilon</span></code>(dtype)</p></td>
+<td><p>Get the smallest digit that is representable depending on the passed dtype (NumPy or PyTorch).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y[, random_state, tensorboard, log_dir])</p></td>
 <td><p>Function call to build the calibration model.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
 <td><p>Fit to data, then transform it.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_degrees_of_freedom</span></code>()</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_degrees_of_freedom</span></code>()</p></td>
 <td><p>Needed for BIC.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
 <td><p>Get parameters for this estimator.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
 <td><p>Load model from saved torch dump.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
 <td><p>Save model instance as with torch's save function as this is safer for torch tensors.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
 <td><p>Set the parameters of this estimator.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X[, num_samples, random_state])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X[, num_samples, random_state])</p></td>
 <td><p>After model calibration, this function is used to get calibrated outputs of uncalibrated confidence estimates.</p></td>
 </tr>
 </tbody>

diff --git a/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.html b/docs/build/html/_autosummary/_autosummary_binning/netcal.binning.IsotonicRegression.html
@@ -35,7 +35,7 @@
 <script type="text/javascript">
   var DOCUMENTATION_OPTIONS = {
             URL_ROOT:    '../../',
-            VERSION:     '1.3.1',
+            VERSION:     '1.3.2',
             COLLAPSE_INDEX: false,
             FILE_SUFFIX: '.html',
             HAS_SOURCE:  true
@@ -219,25 +219,28 @@ <h1>netcal.binning.IsotonicRegression<a class="headerlink" href="#netcal-binning
 <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
 <td><p>Clear model parameters.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">epsilon</span></code>(dtype)</p></td>
+<td><p>Get the smallest digit that is representable depending on the passed dtype (NumPy or PyTorch).</p></td>
+</tr>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit</span></code>(X, y)</p></td>
 <td><p>Build Isotonic Regression model.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fit_transform</span></code>(X[, y])</p></td>
 <td><p>Fit to data, then transform it.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_params</span></code>([deep])</p></td>
 <td><p>Get parameters for this estimator.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">load_model</span></code>(filename)</p></td>
 <td><p>Load model from saved torch dump.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_model</span></code>(filename)</p></td>
 <td><p>Save model instance as with torch's save function as this is safer for torch tensors.</p></td>
 </tr>
-<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
+<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_params</span></code>(**params)</p></td>
 <td><p>Set the parameters of this estimator.</p></td>
 </tr>
-<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
+<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">transform</span></code>(X)</p></td>
 <td><p>After model calibration, this function is used to get calibrated outputs of uncalibrated confidence estimates.</p></td>
 </tr>
 </tbody>

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