From 2eb8e5493f74d60ea123c8746b010c072af0449e Mon Sep 17 00:00:00 2001 From: Amit Sharma Date: Wed, 1 Jun 2022 21:46:38 +0530 Subject: [PATCH] Version Bump [0.8]: Updated version and docs (#301) * updated version and docs * updated models using _save template models to be compatible with sklearn 1.1 * added environment yml files to manifest * removed test for 3.6 Signed-off-by: Amit Sharma --- .github/workflows/pythonpackage.yml | 5 +- MANIFEST.in | 3 + .../utils/sample_trained_models/custom.sav | Bin 53460 -> 43789 bytes .../sample_trained_models/custom_binary.sav | Bin 68791 -> 63051 bytes .../custom_multiclass.sav | Bin 80017 -> 75842 bytes .../custom_regression.sav | Bin 63809 -> 67664 bytes .../sample_trained_models/custom_vars.sav | Bin 41902 -> 46290 bytes docs/_modules/dice_ml/constants.html | 159 +- .../dice_ml/counterfactual_explanations.html | 161 +- docs/_modules/dice_ml/data.html | 152 +- .../private_data_interface.html | 162 +- .../public_data_interface.html | 204 +- docs/_modules/dice_ml/dice.html | 162 +- .../dice_ml/diverse_counterfactuals.html | 161 +- .../dice_ml/explainer_interfaces/dice_KD.html | 171 +- .../explainer_interfaces/dice_genetic.html | 168 +- .../explainer_interfaces/dice_pytorch.html | 162 +- .../explainer_interfaces/dice_random.html | 161 +- .../dice_tensorflow1.html | 172 +- .../dice_tensorflow2.html | 164 +- .../explainer_interfaces/explainer_base.html | 331 +-- .../feasible_base_vae.html | 165 +- .../feasible_model_approx.html | 160 +- docs/_modules/dice_ml/model.html | 156 +- .../dice_ml/model_interfaces/base_model.html | 160 +- .../keras_tensorflow_model.html | 157 +- .../model_interfaces/pytorch_model.html | 157 +- docs/_modules/dice_ml/utils/exception.html | 152 +- docs/_modules/dice_ml/utils/helpers.html | 175 +- .../utils/sample_architecture/vae_model.html | 361 +++ docs/_modules/dice_ml/utils/serialize.html | 152 +- docs/_modules/index.html | 152 +- docs/_static/basic.css | 114 +- docs/_static/css/theme.css | 4 +- docs/_static/doctools.js | 14 +- docs/_static/documentation_options.js | 2 +- docs/_static/js/theme.js | 2 +- docs/_static/language_data.js | 6 +- docs/_static/pygments.css | 8 +- docs/_static/searchtools.js | 43 +- docs/_static/underscore-1.13.1.js | 2042 +++++++++++++++++ docs/_static/underscore.js | 37 +- docs/dice_ml.data_interfaces.html | 385 ++-- docs/dice_ml.explainer_interfaces.html | 734 +++--- docs/dice_ml.html | 417 ++-- docs/dice_ml.model_interfaces.html | 270 +-- docs/dice_ml.schema.html | 181 +- docs/dice_ml.utils.html | 312 +-- docs/dice_ml.utils.sample_architecture.html | 258 +-- docs/genindex.html | 213 +- docs/index.html | 246 +- docs/modules.html | 159 +- ...king_different_CF_explanation_methods.html | 268 +-- docs/notebooks/DiCE_feature_importances.html | 1176 +++++----- docs/notebooks/DiCE_getting_started.html | 494 ++-- .../DiCE_getting_started_feasible.html | 304 +-- docs/notebooks/DiCE_model_agnostic_CFs.html | 736 +++--- ...ticlass_classification_and_regression.html | 1141 +++------ .../notebooks/DiCE_with_advanced_options.html | 291 +-- docs/notebooks/DiCE_with_private_data.html | 259 +-- docs/notebooks/nb_index.html | 169 +- docs/objects.inv | Bin 9190 -> 9593 bytes docs/py-modindex.html | 151 +- docs/readme.html | 238 +- docs/search.html | 158 +- docs/searchindex.js | 2 +- docs/source/conf.py | 2 +- ...ing_different_CF_explanation_methods.ipynb | 2 +- setup.py | 2 +- tests/conftest.py | 83 +- 70 files changed, 6449 insertions(+), 8919 deletions(-) create mode 100644 docs/_modules/dice_ml/utils/sample_architecture/vae_model.html create mode 100644 docs/_static/underscore-1.13.1.js diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 7df0cdf9..4888547b 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -15,11 +15,8 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - python-version: [3.6, 3.7, 3.8, 3.9, "3.10"] + python-version: [3.7, 3.8, 3.9, "3.10"] os: [ubuntu-latest, macos-latest] - exclude: - - os: macos-latest - python-version: 3.6 steps: - uses: actions/checkout@v3 - name: Set up Python ${{ matrix.python-version }} ${{ matrix.os }} diff --git a/MANIFEST.in b/MANIFEST.in index 6dbce984..c4c5ed93 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,6 +1,9 @@ include requirements.txt include requirements-deeplearning.txt include requirements-test.txt +include requirements-linting.txt +include environment.yml +include environment-deeplearning.yml include LICENSE recursive-include docs * recursive-include tests *.py diff --git a/dice_ml/utils/sample_trained_models/custom.sav b/dice_ml/utils/sample_trained_models/custom.sav index b56b67e6abe771efddf0f7e35afa42215ad5c921..f5254e8fac1eeebe9dbe4566c59946bf78820c19 100644 GIT binary patch literal 43789 zcmeI5d5{#<9mi*8XLe`r1r*kBED$1i4Irm-tgzgJgR+a!2y}X8-tOz!neL&xhhID@7c~vN`)~v3$zPNAw3L-C>PokMs>)Y 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- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/explainer_interfaces/explainer_base.html b/docs/_modules/dice_ml/explainer_interfaces/explainer_base.html index f74305fc..7659a780 100644 --- a/docs/_modules/dice_ml/explainer_interfaces/explainer_base.html +++ b/docs/_modules/dice_ml/explainer_interfaces/explainer_base.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.explainer_interfaces.explainer_base — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.explainer_interfaces.explainer_base — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/explainer_interfaces/feasible_base_vae.html b/docs/_modules/dice_ml/explainer_interfaces/feasible_base_vae.html index 60fe57ce..9d0202b8 100644 --- a/docs/_modules/dice_ml/explainer_interfaces/feasible_base_vae.html +++ b/docs/_modules/dice_ml/explainer_interfaces/feasible_base_vae.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.explainer_interfaces.feasible_base_vae — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.explainer_interfaces.feasible_base_vae — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/explainer_interfaces/feasible_model_approx.html b/docs/_modules/dice_ml/explainer_interfaces/feasible_model_approx.html index f3f9b408..76a2bf64 100644 --- a/docs/_modules/dice_ml/explainer_interfaces/feasible_model_approx.html +++ b/docs/_modules/dice_ml/explainer_interfaces/feasible_model_approx.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.explainer_interfaces.feasible_model_approx — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.explainer_interfaces.feasible_model_approx — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/model.html b/docs/_modules/dice_ml/model.html index 9f0fa5a8..0e616719 100644 --- a/docs/_modules/dice_ml/model.html +++ b/docs/_modules/dice_ml/model.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.model — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.model — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/model_interfaces/base_model.html b/docs/_modules/dice_ml/model_interfaces/base_model.html index 3b5b3fd0..091d017f 100644 --- a/docs/_modules/dice_ml/model_interfaces/base_model.html +++ b/docs/_modules/dice_ml/model_interfaces/base_model.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.model_interfaces.base_model — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.model_interfaces.base_model — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/model_interfaces/keras_tensorflow_model.html b/docs/_modules/dice_ml/model_interfaces/keras_tensorflow_model.html index 00fdb07e..2947e256 100644 --- a/docs/_modules/dice_ml/model_interfaces/keras_tensorflow_model.html +++ b/docs/_modules/dice_ml/model_interfaces/keras_tensorflow_model.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.model_interfaces.keras_tensorflow_model — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.model_interfaces.keras_tensorflow_model — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/model_interfaces/pytorch_model.html b/docs/_modules/dice_ml/model_interfaces/pytorch_model.html index ce98408b..d5669ed7 100644 --- a/docs/_modules/dice_ml/model_interfaces/pytorch_model.html +++ b/docs/_modules/dice_ml/model_interfaces/pytorch_model.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.model_interfaces.pytorch_model — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.model_interfaces.pytorch_model — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/utils/exception.html b/docs/_modules/dice_ml/utils/exception.html index c811fb28..a73c0fbf 100644 --- a/docs/_modules/dice_ml/utils/exception.html +++ b/docs/_modules/dice_ml/utils/exception.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.utils.exception — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.utils.exception — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
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- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/utils/helpers.html b/docs/_modules/dice_ml/utils/helpers.html index 5e0dc9fa..b8eb5a7f 100644 --- a/docs/_modules/dice_ml/utils/helpers.html +++ b/docs/_modules/dice_ml/utils/helpers.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.utils.helpers — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.utils.helpers — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/dice_ml/utils/sample_architecture/vae_model.html b/docs/_modules/dice_ml/utils/sample_architecture/vae_model.html new file mode 100644 index 00000000..ecb8f0c0 --- /dev/null +++ b/docs/_modules/dice_ml/utils/sample_architecture/vae_model.html @@ -0,0 +1,361 @@ + + + + + + dice_ml.utils.sample_architecture.vae_model — DiCE 0.8 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • »
    • +
  • Module code »
    • +
  • dice_ml.utils.sample_architecture.vae_model
    • +
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    • +
+
+
+
+
+ +

Source code for dice_ml.utils.sample_architecture.vae_model

+import torch
+import torch.utils.data
+from torch import nn
+
+
+
[docs]class CF_VAE(nn.Module):
+
+    def __init__(self, d, encoded_size):
+
+        super(CF_VAE, self).__init__()
+
+        self.encoded_size = encoded_size
+        self.data_size = len(d.ohe_encoded_feature_names)
+        self.minx, self.maxx, self.encoded_categorical_feature_indexes, self.encoded_continuous_feature_indexes, \
+            self.cont_minx, self.cont_maxx, self.cont_precisions = d.get_data_params_for_gradient_dice()
+
+        flattened_indexes = [item for sublist in self.encoded_categorical_feature_indexes for item in sublist]
+        self.encoded_continuous_feature_indexes = [ix for ix in range(len(self.minx[0])) if ix not in flattened_indexes]
+        self.encoded_start_cat = len(self.encoded_continuous_feature_indexes)
+
+        # Plus 1 to the input encoding size and data size to incorporate the target class label
+        self.encoder_mean = nn.Sequential(
+            nn.Linear(self.data_size+1, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, self.encoded_size)
+            )
+
+        self.encoder_var = nn.Sequential(
+            nn.Linear(self.data_size+1, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, self.encoded_size),
+            nn.Sigmoid()
+            )
+
+        # Plus 1 to the input encoding size and data size to incorporate the target class label
+        self.decoder_mean = nn.Sequential(
+            nn.Linear(self.encoded_size+1, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, self.data_size),
+            nn.Sigmoid()
+            )
+
+
[docs]    def encoder(self, x):
+        mean = self.encoder_mean(x)
+        logvar = 0.5 + self.encoder_var(x)
+        return mean, logvar

+
+
[docs]    def decoder(self, z):
+        mean = self.decoder_mean(z)
+        return mean

+
+
[docs]    def sample_latent_code(self, mean, logvar):
+        eps = torch.randn_like(logvar)
+        return mean + torch.sqrt(logvar)*eps

+
+
[docs]    def normal_likelihood(self, x, mean, logvar, raxis=1):
+        return torch.sum(-0.5 * ((x - mean)*(1./logvar)*(x-mean) + torch.log(logvar)), axis=1)

+
+
[docs]    def forward(self, x, c):
+        c = c.view(c.shape[0], 1)
+        c = torch.tensor(c).float()
+        res = {}
+        mc_samples = 50
+        em, ev = self.encoder(torch.cat((x, c), 1))
+        res['em'] = em
+        res['ev'] = ev
+        res['z'] = []
+        res['x_pred'] = []
+        res['mc_samples'] = mc_samples
+        for _ in range(mc_samples):
+            z = self.sample_latent_code(em, ev)
+            x_pred = self.decoder(torch.cat((z, c), 1))
+            res['z'].append(z)
+            res['x_pred'].append(x_pred)
+
+        return res

+
+
[docs]    def compute_elbo(self, x, c, pred_model):
+        c = torch.tensor(c).float()
+        c = c.view(c.shape[0], 1)
+        em, ev = self.encoder(torch.cat((x, c), 1))
+        kl_divergence = 0.5*torch.mean(em**2 + ev - torch.log(ev) - 1, axis=1)
+
+        z = self.sample_latent_code(em, ev)
+        dm = self.decoder(torch.cat((z, c), 1))
+        log_px_z = torch.tensor(0.0)
+
+        x_pred = dm
+        return torch.mean(log_px_z), torch.mean(kl_divergence), x, x_pred, torch.argmax(pred_model(x_pred), dim=1)

+
+
+
[docs]class AutoEncoder(nn.Module):
+
+    def __init__(self, d, encoded_size):
+
+        super(AutoEncoder, self).__init__()
+
+        self.encoded_size = encoded_size
+        self.data_size = len(d.encoded_feature_names)
+        self.encoded_categorical_feature_indexes = d.get_data_params()[2]
+
+        self.encoded_continuous_feature_indexes = []
+        for i in range(self.data_size):
+            valid = 1
+            for v in self.encoded_categorical_feature_indexes:
+                if i in v:
+                    valid = 0
+            if valid:
+                self.encoded_continuous_feature_indexes.append(i)
+
+        self.encoded_start_cat = len(self.encoded_continuous_feature_indexes)
+
+        self.encoder_mean = nn.Sequential(
+            nn.Linear(self.data_size, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, self.encoded_size)
+        )
+
+        self.encoder_var = nn.Sequential(
+            nn.Linear(self.data_size, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, self.encoded_size),
+            nn.Sigmoid()
+         )
+
+        self.decoder_mean = nn.Sequential(
+            nn.Linear(self.encoded_size, 12),
+            nn.BatchNorm1d(12),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(12, 14),
+            nn.BatchNorm1d(14),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(14, 16),
+            nn.BatchNorm1d(16),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(16, 20),
+            nn.BatchNorm1d(20),
+            nn.Dropout(0.1),
+            nn.ReLU(),
+            nn.Linear(20, self.data_size),
+            nn.Sigmoid()
+            )
+
+
[docs]    def encoder(self, x):
+        mean = self.encoder_mean(x)
+        logvar = 0.05 + self.encoder_var(x)
+        return mean, logvar

+
+
[docs]    def decoder(self, z):
+        mean = self.decoder_mean(z)
+        return mean

+
+
[docs]    def sample_latent_code(self, mean, logvar):
+        eps = torch.randn_like(logvar)
+        return mean + torch.sqrt(logvar)*eps

+
+
[docs]    def normal_likelihood(self, x, mean, logvar, raxis=1):
+        return torch.sum(-0.5 * ((x - mean)*(1./logvar)*(x-mean) + torch.log(logvar)), axis=1)

+
+
[docs]    def forward(self, x):
+        res = {}
+        mc_samples = 50
+        em, ev = self.encoder(x)
+        res['em'] = em
+        res['ev'] = ev
+        res['z'] = []
+        res['x_pred'] = []
+        res['mc_samples'] = mc_samples
+        for _ in range(mc_samples):
+            z = self.sample_latent_code(em, ev)
+            x_pred = self.decoder(z)
+            res['z'].append(z)
+            res['x_pred'].append(x_pred)
+
+        return res

+
+ +
+
+
+ +
+ +
+

© Copyright 2020, Ramaravind, Amit, Chenhao.

+
+ + Built with Sphinx using a + theme + provided by Read the Docs. + + +
+
+
+
+
+ + + + \ No newline at end of file diff --git a/docs/_modules/dice_ml/utils/serialize.html b/docs/_modules/dice_ml/utils/serialize.html index 418363ba..94acd05d 100644 --- a/docs/_modules/dice_ml/utils/serialize.html +++ b/docs/_modules/dice_ml/utils/serialize.html @@ -1,69 +1,32 @@ - - - - - - - dice_ml.utils.serialize — DiCE 0.7 documentation - - - - - - - - - - - - + + + dice_ml.utils.serialize — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_modules/index.html b/docs/_modules/index.html index 168aa07c..6ab59f55 100644 --- a/docs/_modules/index.html +++ b/docs/_modules/index.html @@ -1,69 +1,32 @@ - - - - - - - Overview: module code — DiCE 0.7 documentation - - - - - - - - - - - - + + + Overview: module code — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
- -
- - -
- - - - - - - - + \ No newline at end of file diff --git a/docs/_static/basic.css b/docs/_static/basic.css index 24a49f09..bf18350b 100644 --- a/docs/_static/basic.css +++ b/docs/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -130,7 +130,7 @@ ul.search li a { font-weight: bold; } -ul.search li div.context { +ul.search li p.context { color: #888; margin: 2px 0 0 30px; text-align: left; @@ -277,25 +277,25 @@ p.rubric { font-weight: bold; } -img.align-left, .figure.align-left, object.align-left { +img.align-left, figure.align-left, .figure.align-left, object.align-left { clear: left; float: left; margin-right: 1em; } -img.align-right, .figure.align-right, object.align-right { +img.align-right, figure.align-right, .figure.align-right, object.align-right { clear: right; float: right; margin-left: 1em; } -img.align-center, .figure.align-center, object.align-center { +img.align-center, figure.align-center, .figure.align-center, object.align-center { display: block; margin-left: auto; margin-right: auto; } -img.align-default, .figure.align-default { +img.align-default, figure.align-default, .figure.align-default { display: block; margin-left: auto; margin-right: auto; @@ -319,7 +319,8 @@ img.align-default, .figure.align-default { /* -- sidebars -------------------------------------------------------------- */ -div.sidebar { +div.sidebar, +aside.sidebar { margin: 0 0 0.5em 1em; border: 1px solid #ddb; padding: 7px; @@ -377,12 +378,14 @@ div.body p.centered { /* -- content of sidebars/topics/admonitions -------------------------------- */ div.sidebar > :last-child, +aside.sidebar > :last-child, div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; } div.sidebar::after, +aside.sidebar::after, div.topic::after, div.admonition::after, blockquote::after { @@ -455,20 +458,22 @@ td > :last-child { /* -- figures --------------------------------------------------------------- */ -div.figure { +div.figure, figure { margin: 0.5em; padding: 0.5em; } -div.figure p.caption { +div.figure p.caption, figcaption { padding: 0.3em; } -div.figure p.caption span.caption-number { +div.figure p.caption span.caption-number, +figcaption span.caption-number { font-style: italic; } -div.figure p.caption span.caption-text { +div.figure p.caption span.caption-text, +figcaption span.caption-text { } /* -- field list styles ----------------------------------------------------- */ @@ -503,6 +508,63 @@ table.hlist td { vertical-align: top; } +/* -- object description styles --------------------------------------------- */ + +.sig { + font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; +} + +.sig-name, code.descname { + background-color: transparent; + font-weight: bold; +} + +.sig-name { + font-size: 1.1em; +} + +code.descname { + font-size: 1.2em; +} + +.sig-prename, code.descclassname { + background-color: transparent; +} + +.optional { + font-size: 1.3em; +} + +.sig-paren { + font-size: larger; +} + +.sig-param.n { + font-style: italic; +} + +/* C++ specific styling */ + +.sig-inline.c-texpr, +.sig-inline.cpp-texpr { + font-family: unset; +} + +.sig.c .k, .sig.c .kt, +.sig.cpp .k, .sig.cpp .kt { + color: #0033B3; +} + +.sig.c .m, +.sig.cpp .m { + color: #1750EB; +} + +.sig.c .s, .sig.c .sc, +.sig.cpp .s, .sig.cpp .sc { + color: #067D17; +} + /* -- other body styles ----------------------------------------------------- */ @@ -629,14 +691,6 @@ dl.glossary dt { font-size: 1.1em; } -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - .versionmodified { font-style: italic; } @@ -677,8 +731,9 @@ dl.glossary dt { .classifier:before { font-style: normal; - margin: 0.5em; + margin: 0 0.5em; content: ":"; + display: inline-block; } abbr, acronym { @@ -702,6 +757,7 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'', url); }, /** @@ -296,12 +304,14 @@ var Documentation = { window.location.href = prevHref; return false; } + break; case 39: // right var nextHref = $('link[rel="next"]').prop('href'); if (nextHref) { window.location.href = nextHref; return false; } + break; } } }); diff --git a/docs/_static/documentation_options.js b/docs/_static/documentation_options.js index 6b431870..f788b8b0 100644 --- a/docs/_static/documentation_options.js +++ b/docs/_static/documentation_options.js @@ -1,6 +1,6 @@ var DOCUMENTATION_OPTIONS = { URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '0.7', + VERSION: '0.8', LANGUAGE: 'en', COLLAPSE_INDEX: false, BUILDER: 'html', diff --git a/docs/_static/js/theme.js b/docs/_static/js/theme.js index 839d07e2..1fddb6ee 100644 --- a/docs/_static/js/theme.js +++ b/docs/_static/js/theme.js @@ -1 +1 @@ -!function(n){var e={};function t(i){if(e[i])return e[i].exports;var o=e[i]={i:i,l:!1,exports:{}};return 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  • '); var requestUrl = ""; var linkUrl = ""; if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') { @@ -273,28 +273,31 @@ var Search = { if (item[3]) { listItem.append($(' (' + item[3] + ')')); Search.output.append(listItem); - listItem.slideDown(5, function() { + setTimeout(function() { displayNextItem(); - }); + }, 5); } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) { $.ajax({url: requestUrl, dataType: "text", complete: function(jqxhr, textstatus) { var data = jqxhr.responseText; if (data !== '' && data !== undefined) { - listItem.append(Search.makeSearchSummary(data, searchterms, hlterms)); + var summary = Search.makeSearchSummary(data, searchterms, hlterms); + if (summary) { + listItem.append(summary); + } } Search.output.append(listItem); - listItem.slideDown(5, function() { + setTimeout(function() { displayNextItem(); - }); + }, 5); }}); } else { // no source available, just display title Search.output.append(listItem); - listItem.slideDown(5, function() { + setTimeout(function() { displayNextItem(); - }); + }, 5); } } // search finished, update title and status message @@ -325,7 +328,9 @@ var Search = { var results = []; for (var prefix in objects) { - for (var name in objects[prefix]) { + for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) { + var match = objects[prefix][iMatch]; + var name = match[4]; var fullname = (prefix ? prefix + '.' : '') + name; var fullnameLower = fullname.toLowerCase() if (fullnameLower.indexOf(object) > -1) { @@ -339,7 +344,6 @@ var Search = { } else if (parts[parts.length - 1].indexOf(object) > -1) { score += Scorer.objPartialMatch; } - var match = objects[prefix][name]; var objname = objnames[match[1]][2]; var title = titles[match[0]]; // If more than one term searched for, we require other words to be @@ -379,6 +383,13 @@ var Search = { return results; }, + /** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions + */ + escapeRegExp : function(string) { + return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string + }, + /** * search for full-text terms in the index */ @@ -402,13 +413,14 @@ var Search = { ]; // add support for partial matches if (word.length > 2) { + var word_regex = this.escapeRegExp(word); for (var w in terms) { - if (w.match(word) && !terms[word]) { + if (w.match(word_regex) && !terms[word]) { _o.push({files: terms[w], score: Scorer.partialTerm}) } } for (var w in titleterms) { - if (w.match(word) && !titleterms[word]) { + if (w.match(word_regex) && !titleterms[word]) { _o.push({files: titleterms[w], score: Scorer.partialTitle}) } } @@ -490,6 +502,9 @@ var Search = { */ makeSearchSummary : function(htmlText, keywords, hlwords) { var text = Search.htmlToText(htmlText); + if (text == "") { + return null; + } var textLower = text.toLowerCase(); var start = 0; $.each(keywords, function() { @@ -501,7 +516,7 @@ var Search = { var excerpt = ((start > 0) ? '...' : '') + $.trim(text.substr(start, 240)) + ((start + 240 - text.length) ? '...' : ''); - var rv = $('
    ').text(excerpt); + var rv = $('

    ').text(excerpt); $.each(hlwords, function() { rv = rv.highlightText(this, 'highlighted'); }); diff --git a/docs/_static/underscore-1.13.1.js b/docs/_static/underscore-1.13.1.js new file mode 100644 index 00000000..ffd77af9 --- /dev/null +++ b/docs/_static/underscore-1.13.1.js @@ -0,0 +1,2042 @@ +(function (global, factory) { + typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() : + typeof define === 'function' && define.amd ? define('underscore', factory) : + (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () { + var current = global._; + var exports = global._ = factory(); + exports.noConflict = function () { global._ = current; return exports; }; + }())); +}(this, (function () { + // Underscore.js 1.13.1 + // https://underscorejs.org + // (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors + // Underscore may be freely distributed under the MIT license. + + // Current version. + var VERSION = '1.13.1'; + + // Establish the root object, `window` (`self`) in the browser, `global` + // on the server, or `this` in some virtual machines. We use `self` + // instead of `window` for `WebWorker` support. + var root = typeof self == 'object' && self.self === self && self || + typeof global == 'object' && global.global === global && global || + Function('return this')() || + {}; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype; + var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null; + + // Create quick reference variables for speed access to core prototypes. + var push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // Modern feature detection. + var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined', + supportsDataView = typeof DataView !== 'undefined'; + + // All **ECMAScript 5+** native function implementations that we hope to use + // are declared here. + var nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeCreate = Object.create, + nativeIsView = supportsArrayBuffer && ArrayBuffer.isView; + + // Create references to these builtin functions because we override them. + var _isNaN = isNaN, + _isFinite = isFinite; + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + // The largest integer that can be represented exactly. + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + + // Some functions take a variable number of arguments, or a few expected + // arguments at the beginning and then a variable number of values to operate + // on. This helper accumulates all remaining arguments past the function’s + // argument length (or an explicit `startIndex`), into an array that becomes + // the last argument. Similar to ES6’s "rest parameter". + function restArguments(func, startIndex) { + startIndex = startIndex == null ? func.length - 1 : +startIndex; + return function() { + var length = Math.max(arguments.length - startIndex, 0), + rest = Array(length), + index = 0; + for (; index < length; index++) { + rest[index] = arguments[index + startIndex]; + } + switch (startIndex) { + case 0: return func.call(this, rest); + case 1: return func.call(this, arguments[0], rest); + case 2: return func.call(this, arguments[0], arguments[1], rest); + } + var args = Array(startIndex + 1); + for (index = 0; index < startIndex; index++) { + args[index] = arguments[index]; + } + args[startIndex] = rest; + return func.apply(this, args); + }; + } + + // Is a given variable an object? + function isObject(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + } + + // Is a given value equal to null? + function isNull(obj) { + return obj === null; + } + + // Is a given variable undefined? + function isUndefined(obj) { + return obj === void 0; + } + + // Is a given value a boolean? + function isBoolean(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + } + + // Is a given value a DOM element? + function isElement(obj) { + return !!(obj && obj.nodeType === 1); + } + + // Internal function for creating a `toString`-based type tester. + function tagTester(name) { + var tag = '[object ' + name + ']'; + return function(obj) { + return toString.call(obj) === tag; + }; + } + + var isString = tagTester('String'); + + var isNumber = tagTester('Number'); + + var isDate = tagTester('Date'); + + var isRegExp = tagTester('RegExp'); + + var isError = tagTester('Error'); + + var isSymbol = tagTester('Symbol'); + + var isArrayBuffer = tagTester('ArrayBuffer'); + + var isFunction = tagTester('Function'); + + // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old + // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236). + var nodelist = root.document && root.document.childNodes; + if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') { + isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + var isFunction$1 = isFunction; + + var hasObjectTag = tagTester('Object'); + + // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`. + // In IE 11, the most common among them, this problem also applies to + // `Map`, `WeakMap` and `Set`. + var hasStringTagBug = ( + supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8))) + ), + isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map)); + + var isDataView = tagTester('DataView'); + + // In IE 10 - Edge 13, we need a different heuristic + // to determine whether an object is a `DataView`. + function ie10IsDataView(obj) { + return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer); + } + + var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView); + + // Is a given value an array? + // Delegates to ECMA5's native `Array.isArray`. + var isArray = nativeIsArray || tagTester('Array'); + + // Internal function to check whether `key` is an own property name of `obj`. + function has$1(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + } + + var isArguments = tagTester('Arguments'); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + (function() { + if (!isArguments(arguments)) { + isArguments = function(obj) { + return has$1(obj, 'callee'); + }; + } + }()); + + var isArguments$1 = isArguments; + + // Is a given object a finite number? + function isFinite$1(obj) { + return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj)); + } + + // Is the given value `NaN`? + function isNaN$1(obj) { + return isNumber(obj) && _isNaN(obj); + } + + // Predicate-generating function. Often useful outside of Underscore. + function constant(value) { + return function() { + return value; + }; + } + + // Common internal logic for `isArrayLike` and `isBufferLike`. + function createSizePropertyCheck(getSizeProperty) { + return function(collection) { + var sizeProperty = getSizeProperty(collection); + return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX; + } + } + + // Internal helper to generate a function to obtain property `key` from `obj`. + function shallowProperty(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + } + + // Internal helper to obtain the `byteLength` property of an object. + var getByteLength = shallowProperty('byteLength'); + + // Internal helper to determine whether we should spend extensive checks against + // `ArrayBuffer` et al. + var isBufferLike = createSizePropertyCheck(getByteLength); + + // Is a given value a typed array? + var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/; + function isTypedArray(obj) { + // `ArrayBuffer.isView` is the most future-proof, so use it when available. + // Otherwise, fall back on the above regular expression. + return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) : + isBufferLike(obj) && typedArrayPattern.test(toString.call(obj)); + } + + var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false); + + // Internal helper to obtain the `length` property of an object. + var getLength = shallowProperty('length'); + + // Internal helper to create a simple lookup structure. + // `collectNonEnumProps` used to depend on `_.contains`, but this led to + // circular imports. `emulatedSet` is a one-off solution that only works for + // arrays of strings. + function emulatedSet(keys) { + var hash = {}; + for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true; + return { + contains: function(key) { return hash[key]; }, + push: function(key) { + hash[key] = true; + return keys.push(key); + } + }; + } + + // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't + // be iterated by `for key in ...` and thus missed. Extends `keys` in place if + // needed. + function collectNonEnumProps(obj, keys) { + keys = emulatedSet(keys); + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = isFunction$1(constructor) && constructor.prototype || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys`. + function keys(obj) { + if (!isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (has$1(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + } + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + function isEmpty(obj) { + if (obj == null) return true; + // Skip the more expensive `toString`-based type checks if `obj` has no + // `.length`. + var length = getLength(obj); + if (typeof length == 'number' && ( + isArray(obj) || isString(obj) || isArguments$1(obj) + )) return length === 0; + return getLength(keys(obj)) === 0; + } + + // Returns whether an object has a given set of `key:value` pairs. + function isMatch(object, attrs) { + var _keys = keys(attrs), length = _keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = _keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + } + + // If Underscore is called as a function, it returns a wrapped object that can + // be used OO-style. This wrapper holds altered versions of all functions added + // through `_.mixin`. Wrapped objects may be chained. + function _$1(obj) { + if (obj instanceof _$1) return obj; + if (!(this instanceof _$1)) return new _$1(obj); + this._wrapped = obj; + } + + _$1.VERSION = VERSION; + + // Extracts the result from a wrapped and chained object. + _$1.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxies for some methods used in engine operations + // such as arithmetic and JSON stringification. + _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value; + + _$1.prototype.toString = function() { + return String(this._wrapped); + }; + + // Internal function to wrap or shallow-copy an ArrayBuffer, + // typed array or DataView to a new view, reusing the buffer. + function toBufferView(bufferSource) { + return new Uint8Array( + bufferSource.buffer || bufferSource, + bufferSource.byteOffset || 0, + getByteLength(bufferSource) + ); + } + + // We use this string twice, so give it a name for minification. + var tagDataView = '[object DataView]'; + + // Internal recursive comparison function for `_.isEqual`. + function eq(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // `null` or `undefined` only equal to itself (strict comparison). + if (a == null || b == null) return false; + // `NaN`s are equivalent, but non-reflexive. + if (a !== a) return b !== b; + // Exhaust primitive checks + var type = typeof a; + if (type !== 'function' && type !== 'object' && typeof b != 'object') return false; + return deepEq(a, b, aStack, bStack); + } + + // Internal recursive comparison function for `_.isEqual`. + function deepEq(a, b, aStack, bStack) { + // Unwrap any wrapped objects. + if (a instanceof _$1) a = a._wrapped; + if (b instanceof _$1) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + // Work around a bug in IE 10 - Edge 13. + if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) { + if (!isDataView$1(b)) return false; + className = tagDataView; + } + switch (className) { + // These types are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN. + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + case '[object Symbol]': + return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b); + case '[object ArrayBuffer]': + case tagDataView: + // Coerce to typed array so we can fall through. + return deepEq(toBufferView(a), toBufferView(b), aStack, bStack); + } + + var areArrays = className === '[object Array]'; + if (!areArrays && isTypedArray$1(a)) { + var byteLength = getByteLength(a); + if (byteLength !== getByteLength(b)) return false; + if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true; + areArrays = true; + } + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor && + isFunction$1(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var _keys = keys(a), key; + length = _keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = _keys[length]; + if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + } + + // Perform a deep comparison to check if two objects are equal. + function isEqual(a, b) { + return eq(a, b); + } + + // Retrieve all the enumerable property names of an object. + function allKeys(obj) { + if (!isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + } + + // Since the regular `Object.prototype.toString` type tests don't work for + // some types in IE 11, we use a fingerprinting heuristic instead, based + // on the methods. It's not great, but it's the best we got. + // The fingerprint method lists are defined below. + function ie11fingerprint(methods) { + var length = getLength(methods); + return function(obj) { + if (obj == null) return false; + // `Map`, `WeakMap` and `Set` have no enumerable keys. + var keys = allKeys(obj); + if (getLength(keys)) return false; + for (var i = 0; i < length; i++) { + if (!isFunction$1(obj[methods[i]])) return false; + } + // If we are testing against `WeakMap`, we need to ensure that + // `obj` doesn't have a `forEach` method in order to distinguish + // it from a regular `Map`. + return methods !== weakMapMethods || !isFunction$1(obj[forEachName]); + }; + } + + // In the interest of compact minification, we write + // each string in the fingerprints only once. + var forEachName = 'forEach', + hasName = 'has', + commonInit = ['clear', 'delete'], + mapTail = ['get', hasName, 'set']; + + // `Map`, `WeakMap` and `Set` each have slightly different + // combinations of the above sublists. + var mapMethods = commonInit.concat(forEachName, mapTail), + weakMapMethods = commonInit.concat(mapTail), + setMethods = ['add'].concat(commonInit, forEachName, hasName); + + var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map'); + + var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap'); + + var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set'); + + var isWeakSet = tagTester('WeakSet'); + + // Retrieve the values of an object's properties. + function values(obj) { + var _keys = keys(obj); + var length = _keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[_keys[i]]; + } + return values; + } + + // Convert an object into a list of `[key, value]` pairs. + // The opposite of `_.object` with one argument. + function pairs(obj) { + var _keys = keys(obj); + var length = _keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [_keys[i], obj[_keys[i]]]; + } + return pairs; + } + + // Invert the keys and values of an object. The values must be serializable. + function invert(obj) { + var result = {}; + var _keys = keys(obj); + for (var i = 0, length = _keys.length; i < length; i++) { + result[obj[_keys[i]]] = _keys[i]; + } + return result; + } + + // Return a sorted list of the function names available on the object. + function functions(obj) { + var names = []; + for (var key in obj) { + if (isFunction$1(obj[key])) names.push(key); + } + return names.sort(); + } + + // An internal function for creating assigner functions. + function createAssigner(keysFunc, defaults) { + return function(obj) { + var length = arguments.length; + if (defaults) obj = Object(obj); + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!defaults || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + } + + // Extend a given object with all the properties in passed-in object(s). + var extend = createAssigner(allKeys); + + // Assigns a given object with all the own properties in the passed-in + // object(s). + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + var extendOwn = createAssigner(keys); + + // Fill in a given object with default properties. + var defaults = createAssigner(allKeys, true); + + // Create a naked function reference for surrogate-prototype-swapping. + function ctor() { + return function(){}; + } + + // An internal function for creating a new object that inherits from another. + function baseCreate(prototype) { + if (!isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + var Ctor = ctor(); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + } + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + function create(prototype, props) { + var result = baseCreate(prototype); + if (props) extendOwn(result, props); + return result; + } + + // Create a (shallow-cloned) duplicate of an object. + function clone(obj) { + if (!isObject(obj)) return obj; + return isArray(obj) ? obj.slice() : extend({}, obj); + } + + // Invokes `interceptor` with the `obj` and then returns `obj`. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + function tap(obj, interceptor) { + interceptor(obj); + return obj; + } + + // Normalize a (deep) property `path` to array. + // Like `_.iteratee`, this function can be customized. + function toPath$1(path) { + return isArray(path) ? path : [path]; + } + _$1.toPath = toPath$1; + + // Internal wrapper for `_.toPath` to enable minification. + // Similar to `cb` for `_.iteratee`. + function toPath(path) { + return _$1.toPath(path); + } + + // Internal function to obtain a nested property in `obj` along `path`. + function deepGet(obj, path) { + var length = path.length; + for (var i = 0; i < length; i++) { + if (obj == null) return void 0; + obj = obj[path[i]]; + } + return length ? obj : void 0; + } + + // Get the value of the (deep) property on `path` from `object`. + // If any property in `path` does not exist or if the value is + // `undefined`, return `defaultValue` instead. + // The `path` is normalized through `_.toPath`. + function get(object, path, defaultValue) { + var value = deepGet(object, toPath(path)); + return isUndefined(value) ? defaultValue : value; + } + + // Shortcut function for checking if an object has a given property directly on + // itself (in other words, not on a prototype). Unlike the internal `has` + // function, this public version can also traverse nested properties. + function has(obj, path) { + path = toPath(path); + var length = path.length; + for (var i = 0; i < length; i++) { + var key = path[i]; + if (!has$1(obj, key)) return false; + obj = obj[key]; + } + return !!length; + } + + // Keep the identity function around for default iteratees. + function identity(value) { + return value; + } + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + function matcher(attrs) { + attrs = extendOwn({}, attrs); + return function(obj) { + return isMatch(obj, attrs); + }; + } + + // Creates a function that, when passed an object, will traverse that object’s + // properties down the given `path`, specified as an array of keys or indices. + function property(path) { + path = toPath(path); + return function(obj) { + return deepGet(obj, path); + }; + } + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + function optimizeCb(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + // The 2-argument case is omitted because we’re not using it. + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + } + + // An internal function to generate callbacks that can be applied to each + // element in a collection, returning the desired result — either `_.identity`, + // an arbitrary callback, a property matcher, or a property accessor. + function baseIteratee(value, context, argCount) { + if (value == null) return identity; + if (isFunction$1(value)) return optimizeCb(value, context, argCount); + if (isObject(value) && !isArray(value)) return matcher(value); + return property(value); + } + + // External wrapper for our callback generator. Users may customize + // `_.iteratee` if they want additional predicate/iteratee shorthand styles. + // This abstraction hides the internal-only `argCount` argument. + function iteratee(value, context) { + return baseIteratee(value, context, Infinity); + } + _$1.iteratee = iteratee; + + // The function we call internally to generate a callback. It invokes + // `_.iteratee` if overridden, otherwise `baseIteratee`. + function cb(value, context, argCount) { + if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context); + return baseIteratee(value, context, argCount); + } + + // Returns the results of applying the `iteratee` to each element of `obj`. + // In contrast to `_.map` it returns an object. + function mapObject(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var _keys = keys(obj), + length = _keys.length, + results = {}; + for (var index = 0; index < length; index++) { + var currentKey = _keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + } + + // Predicate-generating function. Often useful outside of Underscore. + function noop(){} + + // Generates a function for a given object that returns a given property. + function propertyOf(obj) { + if (obj == null) return noop; + return function(path) { + return get(obj, path); + }; + } + + // Run a function **n** times. + function times(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + } + + // Return a random integer between `min` and `max` (inclusive). + function random(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + } + + // A (possibly faster) way to get the current timestamp as an integer. + var now = Date.now || function() { + return new Date().getTime(); + }; + + // Internal helper to generate functions for escaping and unescaping strings + // to/from HTML interpolation. + function createEscaper(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped. + var source = '(?:' + keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + } + + // Internal list of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + + // Function for escaping strings to HTML interpolation. + var _escape = createEscaper(escapeMap); + + // Internal list of HTML entities for unescaping. + var unescapeMap = invert(escapeMap); + + // Function for unescaping strings from HTML interpolation. + var _unescape = createEscaper(unescapeMap); + + // By default, Underscore uses ERB-style template delimiters. Change the + // following template settings to use alternative delimiters. + var templateSettings = _$1.templateSettings = { + evaluate: /<%([\s\S]+?)%>/g, + interpolate: /<%=([\s\S]+?)%>/g, + escape: /<%-([\s\S]+?)%>/g + }; + + // When customizing `_.templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g; + + function escapeChar(match) { + return '\\' + escapes[match]; + } + + // In order to prevent third-party code injection through + // `_.templateSettings.variable`, we test it against the following regular + // expression. It is intentionally a bit more liberal than just matching valid + // identifiers, but still prevents possible loopholes through defaults or + // destructuring assignment. + var bareIdentifier = /^\s*(\w|\$)+\s*$/; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + function template(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = defaults({}, settings, _$1.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escapeRegExp, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offset. + return match; + }); + source += "';\n"; + + var argument = settings.variable; + if (argument) { + // Insure against third-party code injection. (CVE-2021-23358) + if (!bareIdentifier.test(argument)) throw new Error( + 'variable is not a bare identifier: ' + argument + ); + } else { + // If a variable is not specified, place data values in local scope. + source = 'with(obj||{}){\n' + source + '}\n'; + argument = 'obj'; + } + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + var render; + try { + render = new Function(argument, '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _$1); + }; + + // Provide the compiled source as a convenience for precompilation. + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + } + + // Traverses the children of `obj` along `path`. If a child is a function, it + // is invoked with its parent as context. Returns the value of the final + // child, or `fallback` if any child is undefined. + function result(obj, path, fallback) { + path = toPath(path); + var length = path.length; + if (!length) { + return isFunction$1(fallback) ? fallback.call(obj) : fallback; + } + for (var i = 0; i < length; i++) { + var prop = obj == null ? void 0 : obj[path[i]]; + if (prop === void 0) { + prop = fallback; + i = length; // Ensure we don't continue iterating. + } + obj = isFunction$1(prop) ? prop.call(obj) : prop; + } + return obj; + } + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + function uniqueId(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + } + + // Start chaining a wrapped Underscore object. + function chain(obj) { + var instance = _$1(obj); + instance._chain = true; + return instance; + } + + // Internal function to execute `sourceFunc` bound to `context` with optional + // `args`. Determines whether to execute a function as a constructor or as a + // normal function. + function executeBound(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (isObject(result)) return result; + return self; + } + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. `_` acts + // as a placeholder by default, allowing any combination of arguments to be + // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument. + var partial = restArguments(function(func, boundArgs) { + var placeholder = partial.placeholder; + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }); + + partial.placeholder = _$1; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). + var bind = restArguments(function(func, context, args) { + if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function'); + var bound = restArguments(function(callArgs) { + return executeBound(func, bound, context, this, args.concat(callArgs)); + }); + return bound; + }); + + // Internal helper for collection methods to determine whether a collection + // should be iterated as an array or as an object. + // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var isArrayLike = createSizePropertyCheck(getLength); + + // Internal implementation of a recursive `flatten` function. + function flatten$1(input, depth, strict, output) { + output = output || []; + if (!depth && depth !== 0) { + depth = Infinity; + } else if (depth <= 0) { + return output.concat(input); + } + var idx = output.length; + for (var i = 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) { + // Flatten current level of array or arguments object. + if (depth > 1) { + flatten$1(value, depth - 1, strict, output); + idx = output.length; + } else { + var j = 0, len = value.length; + while (j < len) output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + } + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + var bindAll = restArguments(function(obj, keys) { + keys = flatten$1(keys, false, false); + var index = keys.length; + if (index < 1) throw new Error('bindAll must be passed function names'); + while (index--) { + var key = keys[index]; + obj[key] = bind(obj[key], obj); + } + return obj; + }); + + // Memoize an expensive function by storing its results. + function memoize(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!has$1(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + } + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + var delay = restArguments(function(func, wait, args) { + return setTimeout(function() { + return func.apply(null, args); + }, wait); + }); + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + var defer = partial(delay, _$1, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + function throttle(func, wait, options) { + var timeout, context, args, result; + var previous = 0; + if (!options) options = {}; + + var later = function() { + previous = options.leading === false ? 0 : now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + + var throttled = function() { + var _now = now(); + if (!previous && options.leading === false) previous = _now; + var remaining = wait - (_now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = _now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + + throttled.cancel = function() { + clearTimeout(timeout); + previous = 0; + timeout = context = args = null; + }; + + return throttled; + } + + // When a sequence of calls of the returned function ends, the argument + // function is triggered. The end of a sequence is defined by the `wait` + // parameter. If `immediate` is passed, the argument function will be + // triggered at the beginning of the sequence instead of at the end. + function debounce(func, wait, immediate) { + var timeout, previous, args, result, context; + + var later = function() { + var passed = now() - previous; + if (wait > passed) { + timeout = setTimeout(later, wait - passed); + } else { + timeout = null; + if (!immediate) result = func.apply(context, args); + // This check is needed because `func` can recursively invoke `debounced`. + if (!timeout) args = context = null; + } + }; + + var debounced = restArguments(function(_args) { + context = this; + args = _args; + previous = now(); + if (!timeout) { + timeout = setTimeout(later, wait); + if (immediate) result = func.apply(context, args); + } + return result; + }); + + debounced.cancel = function() { + clearTimeout(timeout); + timeout = args = context = null; + }; + + return debounced; + } + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + function wrap(func, wrapper) { + return partial(wrapper, func); + } + + // Returns a negated version of the passed-in predicate. + function negate(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + } + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + function compose() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + } + + // Returns a function that will only be executed on and after the Nth call. + function after(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + } + + // Returns a function that will only be executed up to (but not including) the + // Nth call. + function before(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + } + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + var once = partial(before, 2); + + // Returns the first key on an object that passes a truth test. + function findKey(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = keys(obj), key; + for (var i = 0, length = _keys.length; i < length; i++) { + key = _keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + } + + // Internal function to generate `_.findIndex` and `_.findLastIndex`. + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a truth test. + var findIndex = createPredicateIndexFinder(1); + + // Returns the last index on an array-like that passes a truth test. + var findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + function sortedIndex(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + } + + // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions. + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), isNaN$1); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + var indexOf = createIndexFinder(1, findIndex, sortedIndex); + + // Return the position of the last occurrence of an item in an array, + // or -1 if the item is not included in the array. + var lastIndexOf = createIndexFinder(-1, findLastIndex); + + // Return the first value which passes a truth test. + function find(obj, predicate, context) { + var keyFinder = isArrayLike(obj) ? findIndex : findKey; + var key = keyFinder(obj, predicate, context); + if (key !== void 0 && key !== -1) return obj[key]; + } + + // Convenience version of a common use case of `_.find`: getting the first + // object containing specific `key:value` pairs. + function findWhere(obj, attrs) { + return find(obj, matcher(attrs)); + } + + // The cornerstone for collection functions, an `each` + // implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + function each(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var _keys = keys(obj); + for (i = 0, length = _keys.length; i < length; i++) { + iteratee(obj[_keys[i]], _keys[i], obj); + } + } + return obj; + } + + // Return the results of applying the iteratee to each element. + function map(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + } + + // Internal helper to create a reducing function, iterating left or right. + function createReduce(dir) { + // Wrap code that reassigns argument variables in a separate function than + // the one that accesses `arguments.length` to avoid a perf hit. (#1991) + var reducer = function(obj, iteratee, memo, initial) { + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length, + index = dir > 0 ? 0 : length - 1; + if (!initial) { + memo = obj[_keys ? _keys[index] : index]; + index += dir; + } + for (; index >= 0 && index < length; index += dir) { + var currentKey = _keys ? _keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + }; + + return function(obj, iteratee, memo, context) { + var initial = arguments.length >= 3; + return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + var reduce = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + var reduceRight = createReduce(-1); + + // Return all the elements that pass a truth test. + function filter(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + } + + // Return all the elements for which a truth test fails. + function reject(obj, predicate, context) { + return filter(obj, negate(cb(predicate)), context); + } + + // Determine whether all of the elements pass a truth test. + function every(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + } + + // Determine if at least one element in the object passes a truth test. + function some(obj, predicate, context) { + predicate = cb(predicate, context); + var _keys = !isArrayLike(obj) && keys(obj), + length = (_keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = _keys ? _keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + } + + // Determine if the array or object contains a given item (using `===`). + function contains(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return indexOf(obj, item, fromIndex) >= 0; + } + + // Invoke a method (with arguments) on every item in a collection. + var invoke = restArguments(function(obj, path, args) { + var contextPath, func; + if (isFunction$1(path)) { + func = path; + } else { + path = toPath(path); + contextPath = path.slice(0, -1); + path = path[path.length - 1]; + } + return map(obj, function(context) { + var method = func; + if (!method) { + if (contextPath && contextPath.length) { + context = deepGet(context, contextPath); + } + if (context == null) return void 0; + method = context[path]; + } + return method == null ? method : method.apply(context, args); + }); + }); + + // Convenience version of a common use case of `_.map`: fetching a property. + function pluck(obj, key) { + return map(obj, property(key)); + } + + // Convenience version of a common use case of `_.filter`: selecting only + // objects containing specific `key:value` pairs. + function where(obj, attrs) { + return filter(obj, matcher(attrs)); + } + + // Return the maximum element (or element-based computation). + function max(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) { + obj = isArrayLike(obj) ? obj : values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value != null && value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + each(obj, function(v, index, list) { + computed = iteratee(v, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = v; + lastComputed = computed; + } + }); + } + return result; + } + + // Return the minimum element (or element-based computation). + function min(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) { + obj = isArrayLike(obj) ? obj : values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value != null && value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + each(obj, function(v, index, list) { + computed = iteratee(v, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = v; + lastComputed = computed; + } + }); + } + return result; + } + + // Sample **n** random values from a collection using the modern version of the + // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle). + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `_.map`. + function sample(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = values(obj); + return obj[random(obj.length - 1)]; + } + var sample = isArrayLike(obj) ? clone(obj) : values(obj); + var length = getLength(sample); + n = Math.max(Math.min(n, length), 0); + var last = length - 1; + for (var index = 0; index < n; index++) { + var rand = random(index, last); + var temp = sample[index]; + sample[index] = sample[rand]; + sample[rand] = temp; + } + return sample.slice(0, n); + } + + // Shuffle a collection. + function shuffle(obj) { + return sample(obj, Infinity); + } + + // Sort the object's values by a criterion produced by an iteratee. + function sortBy(obj, iteratee, context) { + var index = 0; + iteratee = cb(iteratee, context); + return pluck(map(obj, function(value, key, list) { + return { + value: value, + index: index++, + criteria: iteratee(value, key, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + } + + // An internal function used for aggregate "group by" operations. + function group(behavior, partition) { + return function(obj, iteratee, context) { + var result = partition ? [[], []] : {}; + iteratee = cb(iteratee, context); + each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + } + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + var groupBy = group(function(result, value, key) { + if (has$1(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `_.groupBy`, but for + // when you know that your index values will be unique. + var indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + var countBy = group(function(result, value, key) { + if (has$1(result, key)) result[key]++; else result[key] = 1; + }); + + // Split a collection into two arrays: one whose elements all pass the given + // truth test, and one whose elements all do not pass the truth test. + var partition = group(function(result, value, pass) { + result[pass ? 0 : 1].push(value); + }, true); + + // Safely create a real, live array from anything iterable. + var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g; + function toArray(obj) { + if (!obj) return []; + if (isArray(obj)) return slice.call(obj); + if (isString(obj)) { + // Keep surrogate pair characters together. + return obj.match(reStrSymbol); + } + if (isArrayLike(obj)) return map(obj, identity); + return values(obj); + } + + // Return the number of elements in a collection. + function size(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : keys(obj).length; + } + + // Internal `_.pick` helper function to determine whether `key` is an enumerable + // property name of `obj`. + function keyInObj(value, key, obj) { + return key in obj; + } + + // Return a copy of the object only containing the allowed properties. + var pick = restArguments(function(obj, keys) { + var result = {}, iteratee = keys[0]; + if (obj == null) return result; + if (isFunction$1(iteratee)) { + if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]); + keys = allKeys(obj); + } else { + iteratee = keyInObj; + keys = flatten$1(keys, false, false); + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }); + + // Return a copy of the object without the disallowed properties. + var omit = restArguments(function(obj, keys) { + var iteratee = keys[0], context; + if (isFunction$1(iteratee)) { + iteratee = negate(iteratee); + if (keys.length > 1) context = keys[1]; + } else { + keys = map(flatten$1(keys, false, false), String); + iteratee = function(value, key) { + return !contains(keys, key); + }; + } + return pick(obj, iteratee, context); + }); + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + function initial(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + } + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. The **guard** check allows it to work with `_.map`. + function first(array, n, guard) { + if (array == null || array.length < 1) return n == null || guard ? void 0 : []; + if (n == null || guard) return array[0]; + return initial(array, array.length - n); + } + + // Returns everything but the first entry of the `array`. Especially useful on + // the `arguments` object. Passing an **n** will return the rest N values in the + // `array`. + function rest(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + } + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + function last(array, n, guard) { + if (array == null || array.length < 1) return n == null || guard ? void 0 : []; + if (n == null || guard) return array[array.length - 1]; + return rest(array, Math.max(0, array.length - n)); + } + + // Trim out all falsy values from an array. + function compact(array) { + return filter(array, Boolean); + } + + // Flatten out an array, either recursively (by default), or up to `depth`. + // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively. + function flatten(array, depth) { + return flatten$1(array, depth, false); + } + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + var difference = restArguments(function(array, rest) { + rest = flatten$1(rest, true, true); + return filter(array, function(value){ + return !contains(rest, value); + }); + }); + + // Return a version of the array that does not contain the specified value(s). + var without = restArguments(function(array, otherArrays) { + return difference(array, otherArrays); + }); + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // The faster algorithm will not work with an iteratee if the iteratee + // is not a one-to-one function, so providing an iteratee will disable + // the faster algorithm. + function uniq(array, isSorted, iteratee, context) { + if (!isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted && !iteratee) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!contains(result, value)) { + result.push(value); + } + } + return result; + } + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + var union = restArguments(function(arrays) { + return uniq(flatten$1(arrays, true, true)); + }); + + // Produce an array that contains every item shared between all the + // passed-in arrays. + function intersection(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (contains(result, item)) continue; + var j; + for (j = 1; j < argsLength; j++) { + if (!contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + } + + // Complement of zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices. + function unzip(array) { + var length = array && max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = pluck(array, index); + } + return result; + } + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + var zip = restArguments(unzip); + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. Passing by pairs is the reverse of `_.pairs`. + function object(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + } + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](https://docs.python.org/library/functions.html#range). + function range(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + if (!step) { + step = stop < start ? -1 : 1; + } + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + } + + // Chunk a single array into multiple arrays, each containing `count` or fewer + // items. + function chunk(array, count) { + if (count == null || count < 1) return []; + var result = []; + var i = 0, length = array.length; + while (i < length) { + result.push(slice.call(array, i, i += count)); + } + return result; + } + + // Helper function to continue chaining intermediate results. + function chainResult(instance, obj) { + return instance._chain ? _$1(obj).chain() : obj; + } + + // Add your own custom functions to the Underscore object. + function mixin(obj) { + each(functions(obj), function(name) { + var func = _$1[name] = obj[name]; + _$1.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return chainResult(this, func.apply(_$1, args)); + }; + }); + return _$1; + } + + // Add all mutator `Array` functions to the wrapper. + each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _$1.prototype[name] = function() { + var obj = this._wrapped; + if (obj != null) { + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) { + delete obj[0]; + } + } + return chainResult(this, obj); + }; + }); + + // Add all accessor `Array` functions to the wrapper. + each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _$1.prototype[name] = function() { + var obj = this._wrapped; + if (obj != null) obj = method.apply(obj, arguments); + return chainResult(this, obj); + }; + }); + + // Named Exports + + var allExports = { + __proto__: null, + VERSION: VERSION, + restArguments: restArguments, + isObject: isObject, + isNull: isNull, + isUndefined: isUndefined, + isBoolean: isBoolean, + isElement: isElement, + isString: isString, + isNumber: isNumber, + isDate: isDate, + isRegExp: isRegExp, + isError: isError, + isSymbol: isSymbol, + isArrayBuffer: isArrayBuffer, + isDataView: isDataView$1, + isArray: isArray, + isFunction: isFunction$1, + isArguments: isArguments$1, + isFinite: isFinite$1, + isNaN: isNaN$1, + isTypedArray: isTypedArray$1, + isEmpty: isEmpty, + isMatch: isMatch, + isEqual: isEqual, + isMap: isMap, + isWeakMap: isWeakMap, + isSet: isSet, + isWeakSet: isWeakSet, + keys: keys, + allKeys: allKeys, + values: values, + pairs: pairs, + invert: invert, + functions: functions, + methods: functions, + extend: extend, + extendOwn: extendOwn, + assign: extendOwn, + defaults: defaults, + create: create, + clone: clone, + tap: tap, + get: get, + has: has, + mapObject: mapObject, + identity: identity, + constant: constant, + noop: noop, + toPath: toPath$1, + property: property, + propertyOf: propertyOf, + matcher: matcher, + matches: matcher, + times: times, + random: random, + now: now, + escape: _escape, + unescape: _unescape, + templateSettings: templateSettings, + template: template, + result: result, + uniqueId: uniqueId, + chain: chain, + iteratee: iteratee, + partial: partial, + bind: bind, + bindAll: bindAll, + memoize: memoize, + delay: delay, + defer: defer, + throttle: throttle, + debounce: debounce, + wrap: wrap, + negate: negate, + compose: compose, + after: after, + before: before, + once: once, + findKey: findKey, + findIndex: findIndex, + findLastIndex: findLastIndex, + sortedIndex: sortedIndex, + indexOf: indexOf, + lastIndexOf: lastIndexOf, + find: find, + detect: find, + findWhere: findWhere, + each: each, + forEach: each, + map: map, + collect: map, + reduce: reduce, + foldl: reduce, + inject: reduce, + reduceRight: reduceRight, + foldr: reduceRight, + filter: filter, + select: filter, + reject: reject, + every: every, + all: every, + some: some, + any: some, + contains: contains, + includes: contains, + include: contains, + invoke: invoke, + pluck: pluck, + where: where, + max: max, + min: min, + shuffle: shuffle, + sample: sample, + sortBy: sortBy, + groupBy: groupBy, + indexBy: indexBy, + countBy: countBy, + partition: partition, + toArray: toArray, + size: size, + pick: pick, + omit: omit, + first: first, + head: first, + take: first, + initial: initial, + last: last, + rest: rest, + tail: rest, + drop: rest, + compact: compact, + flatten: flatten, + without: without, + uniq: uniq, + unique: uniq, + union: union, + intersection: intersection, + difference: difference, + unzip: unzip, + transpose: unzip, + zip: zip, + object: object, + range: range, + chunk: chunk, + mixin: mixin, + 'default': _$1 + }; + + // Default Export + + // Add all of the Underscore functions to the wrapper object. + var _ = mixin(allExports); + // Legacy Node.js API. + _._ = _; + + return _; + +}))); +//# sourceMappingURL=underscore-umd.js.map diff --git a/docs/_static/underscore.js b/docs/_static/underscore.js index 5b55f32b..cf177d42 100644 --- a/docs/_static/underscore.js +++ b/docs/_static/underscore.js @@ -1,31 +1,6 @@ -// Underscore.js 1.3.1 -// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc. -// Underscore is freely distributable under the MIT license. -// Portions of Underscore are inspired or borrowed from Prototype, -// Oliver Steele's Functional, and John Resig's Micro-Templating. -// For all details and documentation: -// http://documentcloud.github.com/underscore -(function(){function q(a,c,d){if(a===c)return a!==0||1/a==1/c;if(a==null||c==null)return a===c;if(a._chain)a=a._wrapped;if(c._chain)c=c._wrapped;if(a.isEqual&&b.isFunction(a.isEqual))return a.isEqual(c);if(c.isEqual&&b.isFunction(c.isEqual))return c.isEqual(a);var e=l.call(a);if(e!=l.call(c))return 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mr(n,Dn(r))},each:jr,forEach:jr,map:_r,collect:_r,reduce:Ar,foldl:Ar,inject:Ar,reduceRight:xr,foldr:xr,filter:Sr,select:Sr,reject:function(n,r,t){return Sr(n,fr(qn(r)),t)},every:Or,all:Or,some:Mr,any:Mr,contains:Er,includes:Er,include:Er,invoke:Br,pluck:Nr,where:function(n,r){return Sr(n,Dn(r))},max:Ir,min:function(n,r,t){var e,u,o=1/0,i=1/0;if(null==r||"number"==typeof r&&"object"!=typeof n[0]&&null!=n)for(var a=0,f=(n=er(n)?n:jn(n)).length;ae||void 0===t)return 1;if(t - - - - - dice_ml.data_interfaces package — DiCE 0.7 documentation - - - - - - - - - - + - + + dice_ml.data_interfaces package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    -
    -

    dice_ml.data_interfaces.public_data_interface module

    + +
    +

    dice_ml.data_interfaces.public_data_interface module

    Module containing all required information about the interface between raw (or transformed) public data and DiCE explainers.

    -
    -class dice_ml.data_interfaces.public_data_interface.PublicData(params)[source]
    +
    +class dice_ml.data_interfaces.public_data_interface.PublicData(params)[source]

    Bases: dice_ml.data_interfaces.base_data_interface._BaseData

    A data interface for public data. This class is an interface to DiCE explainers and contains methods to transform user-fed raw data into the format a DiCE explainer requires, and vice versa.

    -
    -check_features_to_vary(features_to_vary)[source]
    +
    +check_features_to_vary(features_to_vary)[source]
    -
    -check_mad_validity(feature_weights)[source]
    +
    +check_mad_validity(feature_weights)[source]

    checks feature MAD validity and throw warnings. TODO: add comments as to where this is used if this function is necessary, else remove.

    -
    -check_permitted_range(permitted_range)[source]
    +
    +check_permitted_range(permitted_range)[source]
    -
    -create_ohe_params()[source]
    +
    +create_ohe_params()[source]
    -
    -de_normalize_data(df)[source]
    +
    +de_normalize_data(df)[source]

    De-normalizes continuous features from [0,1] range to original range.

    -
    -from_dummies(data, prefix_sep='_')[source]
    +
    +from_dummies(data, prefix_sep='_')[source]

    Gets the original data from dummy encoded data with k levels.

    -
    -from_label(data)[source]
    +
    +from_label(data)[source]

    Transforms label encoded data back to categorical values

    -
    -get_data_params_for_gradient_dice()[source]
    +
    +get_data_params_for_gradient_dice()[source]

    Gets all data related params for DiCE.

    -
    -get_data_type(col)[source]
    +
    +get_data_type(col)[source]

    Infers data type of a continuous feature from the training data.

    -
    -get_decimal_precisions(output_type='list')[source]
    +
    +get_decimal_precisions(output_type='list')[source]

    “Gets the precision of continuous features in the data.

    -
    -get_decoded_data(data, encoding='one-hot')[source]
    +
    +get_decoded_data(data, encoding='one-hot')[source]

    Gets the original data from encoded data.

    -
    -get_encoded_categorical_feature_indexes()[source]
    +
    +get_encoded_categorical_feature_indexes()[source]

    Gets the column indexes categorical features after one-hot-encoding.

    -
    -get_features_range(permitted_range_input=None)[source]
    +
    +get_features_range(permitted_range_input=None)[source]
    -
    -get_indexes_of_features_to_vary(features_to_vary='all')[source]
    +
    +get_indexes_of_features_to_vary(features_to_vary='all')[source]

    Gets indexes from feature names of one-hot-encoded data.

    -
    -get_inverse_ohe_min_max_normalized_data(transformed_data)[source]
    +
    +get_inverse_ohe_min_max_normalized_data(transformed_data)[source]

    Transforms one-hot-encoded and min-max normalized data into raw user-fed data format. transformed_data should be a dataframe or an array

    -
    -get_mads(normalized=False)[source]
    +
    +get_mads(normalized=False)[source]

    Computes Median Absolute Deviation of features.

    -
    -get_minx_maxx(normalized=True)[source]
    +
    +get_minx_maxx(normalized=True)[source]

    Gets the min/max value of features in normalized or de-normalized form.

    -
    -get_ohe_min_max_normalized_data(query_instance)[source]
    +
    +get_ohe_min_max_normalized_data(query_instance)[source]

    Transforms query_instance into one-hot-encoded and min-max normalized data. query_instance should be a dict, a dataframe, a list, or a list of dicts

    -
    -get_quantiles_from_training_data(quantile=0.05, normalized=False)[source]
    +
    +get_quantiles_from_training_data(quantile=0.05, normalized=False)[source]

    Computes required quantile of Absolute Deviations of features.

    -
    -get_valid_feature_range(feature_range_input, normalized=True)[source]
    +
    +get_valid_feature_range(feature_range_input, normalized=True)[source]

    Gets the min/max value of features in normalized or de-normalized form. Assumes that all features are already encoded to numerical form such that the number of features remains the same.

    @@ -487,97 +397,72 @@

    Submodules -
    -get_valid_mads(normalized=False, display_warnings=False, return_mads=True)[source]
    +
    +get_valid_mads(normalized=False, display_warnings=False, return_mads=True)[source]

    Computes Median Absolute Deviation of features. If they are <=0, returns a practical value instead

    -
    -normalize_data(df)[source]
    +
    +normalize_data(df)[source]

    Normalizes continuous features to make them fall in the range [0,1].

    -
    -one_hot_encode_data(data)[source]
    +
    +one_hot_encode_data(data)[source]

    One-hot-encodes the data.

    -
    -prepare_df_for_ohe_encoding()[source]
    +
    +prepare_df_for_ohe_encoding()[source]

    Create base dataframe to do OHE for a single instance or a set of instances

    -
    -prepare_query_instance(query_instance)[source]
    +
    +prepare_query_instance(query_instance)[source]

    Prepares user defined test input(s) for DiCE.

    -
    -
    -

    Module contents

    -
    - + +
    +

    Module contents

    +
    + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.explainer_interfaces.html b/docs/dice_ml.explainer_interfaces.html index 0205b3a7..7dfb634e 100644 --- a/docs/dice_ml.explainer_interfaces.html +++ b/docs/dice_ml.explainer_interfaces.html @@ -1,71 +1,35 @@ - - - - - - - dice_ml.explainer_interfaces package — DiCE 0.7 documentation - - - - - - - - - - + - + + dice_ml.explainer_interfaces package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    -
    -

    dice_ml.explainer_interfaces.dice_pytorch module

    + +
    +

    dice_ml.explainer_interfaces.dice_pytorch module

    Module to generate diverse counterfactual explanations based on PyTorch framework

    -
    -class dice_ml.explainer_interfaces.dice_pytorch.DicePyTorch(data_interface, model_interface)[source]
    +
    +class dice_ml.explainer_interfaces.dice_pytorch.DicePyTorch(data_interface, model_interface)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -compute_dist(x_hat, x1)[source]
    +
    +compute_dist(x_hat, x1)[source]

    Compute weighted distance between two vectors.

    -
    -compute_diversity_loss()[source]
    +
    +compute_diversity_loss()[source]

    Computes the third part (diversity) of the loss function.

    -
    -compute_loss()[source]
    +
    +compute_loss()[source]

    Computes the overall loss

    -
    -compute_proximity_loss()[source]
    +
    +compute_proximity_loss()[source]

    Compute the second part (distance from x1) of the loss function.

    -
    -compute_regularization_loss()[source]
    +
    +compute_regularization_loss()[source]

    Adds a linear equality constraints to the loss functions - to ensure all levels of a categorical variable sums to one

    -
    -compute_yloss()[source]
    +
    +compute_yloss()[source]

    Computes the first part (y-loss) of the loss function.

    -
    -do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]
    +
    +do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]

    Intializes CFs and other related variables.

    -
    -do_loss_initializations(yloss_type, diversity_loss_type, feature_weights)[source]
    +
    +do_loss_initializations(yloss_type, diversity_loss_type, feature_weights)[source]

    Intializes variables related to main loss function

    -
    -do_optimizer_initializations(optimizer, learning_rate)[source]
    +
    +do_optimizer_initializations(optimizer, learning_rate)[source]

    Initializes gradient-based PyTorch optimizers.

    -
    -dpp_style(submethod)[source]
    +
    +dpp_style(submethod)[source]

    Computes the DPP of a matrix.

    -
    -find_counterfactuals(query_instance, desired_class, optimizer, learning_rate, min_iter, max_iter, project_iter, loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]
    +
    +find_counterfactuals(query_instance, desired_class, optimizer, learning_rate, min_iter, max_iter, project_iter, loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]

    Finds counterfactuals by gradient-descent.

    -
    -generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='pytorch:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]
    +
    +generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='pytorch:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]

    Generates diverse counterfactual explanations.

    Parameters
    @@ -511,142 +424,142 @@

    Submodules -
    -get_model_output(input_instance)[source]
    +
    +get_model_output(input_instance)[source]

    get output probability of ML model

    -
    -initialize_CFs(query_instance, init_near_query_instance=False)[source]
    +
    +initialize_CFs(query_instance, init_near_query_instance=False)[source]

    Initialize counterfactuals.

    -
    -predict_fn(input_instance)[source]
    +
    +predict_fn(input_instance)[source]

    prediction function

    -
    -predict_fn_for_sparsity(input_instance)[source]
    +
    +predict_fn_for_sparsity(input_instance)[source]

    prediction function for sparsity correction

    -
    -round_off_cfs(assign=False)[source]
    +
    +round_off_cfs(assign=False)[source]

    function for intermediate projection of CFs.

    -
    -stop_loop(itr, loss_diff)[source]
    +
    +stop_loop(itr, loss_diff)[source]

    Determines the stopping condition for gradient descent.

    -
    -update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty)[source]
    +
    +update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty)[source]

    Update hyperparameters of the loss function

    -
    -
    -

    dice_ml.explainer_interfaces.dice_random module

    + +
    +

    dice_ml.explainer_interfaces.dice_random module

    Module to generate diverse counterfactual explanations based on random sampling. A simple implementation.

    -
    -class dice_ml.explainer_interfaces.dice_random.DiceRandom(data_interface, model_interface)[source]
    +
    +class dice_ml.explainer_interfaces.dice_random.DiceRandom(data_interface, model_interface)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -get_continuous_samples(low, high, precision, size=1000, seed=None)[source]
    +
    +get_continuous_samples(low, high, precision, size=1000, seed=None)[source]
    -
    -get_samples(fixed_features_values, feature_range, sampling_random_seed, sampling_size)[source]
    +
    +get_samples(fixed_features_values, feature_range, sampling_random_seed, sampling_size)[source]
    -
    -
    -

    dice_ml.explainer_interfaces.dice_tensorflow1 module

    + +
    +

    dice_ml.explainer_interfaces.dice_tensorflow1 module

    Module to generate diverse counterfactual explanations based on tensorflow 1.x

    -
    -class dice_ml.explainer_interfaces.dice_tensorflow1.DiceTensorFlow1(data_interface, model_interface)[source]
    +
    +class dice_ml.explainer_interfaces.dice_tensorflow1.DiceTensorFlow1(data_interface, model_interface)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -compute_dist(x_hat, x1)[source]
    +
    +compute_dist(x_hat, x1)[source]

    Compute weighted distance between two vectors.

    -
    -compute_diversity_loss(method)[source]
    +
    +compute_diversity_loss(method)[source]

    Computes the third part (diversity) of the loss function.

    -
    -compute_proximity_loss()[source]
    +
    +compute_proximity_loss()[source]

    Compute the second part (distance from x1) of the loss function.

    -
    -compute_regularization_loss()[source]
    +
    +compute_regularization_loss()[source]

    Adds a linear equality constraints to the loss functions - to ensure all levels of a categorical variable sums to one

    -
    -compute_yloss(method)[source]
    +
    +compute_yloss(method)[source]

    Computes the first part (y-loss) of the loss function.

    -
    -do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]
    +
    +do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]

    Intializes TF variables required for CF generation.

    -
    -do_loss_initializations(yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad')[source]
    +
    +do_loss_initializations(yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad')[source]

    Defines the optimization loss

    -
    -do_optimizer_initializations(optimizer)[source]
    +
    +do_optimizer_initializations(optimizer)[source]

    Initializes gradient-based TF optimizers.

    -
    -dpp_style(submethod)[source]
    +
    +dpp_style(submethod)[source]

    Computes the DPP of a matrix.

    -
    -find_counterfactuals(query_instance, desired_class='opposite', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=False, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]
    +
    +find_counterfactuals(query_instance, desired_class='opposite', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=False, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]

    Finds counterfactuals by gradient-descent.

    -
    -generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='tensorflow:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]
    +
    +generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='tensorflow:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]

    Generates diverse counterfactual explanations

    Parameters
    @@ -699,132 +612,132 @@

    Submodules -
    -initialize_CFs(query_instance, init_near_query_instance=False)[source]
    +
    +initialize_CFs(query_instance, init_near_query_instance=False)[source]

    Initialize counterfactuals.

    -
    -predict_fn(input_instance)[source]
    +
    +predict_fn(input_instance)[source]

    prediction function

    -
    -predict_fn_for_sparsity(input_instance)[source]
    +
    +predict_fn_for_sparsity(input_instance)[source]

    prediction function for sparsity correction

    -
    -round_off_cfs(assign=False)[source]
    +
    +round_off_cfs(assign=False)[source]

    function for intermediate projection of CFs.

    -
    -scipy_optimizers(method='Nelder-Mead')[source]
    +
    +scipy_optimizers(method='Nelder-Mead')[source]
    -
    -stop_loop(itr, loss_diff)[source]
    +
    +stop_loop(itr, loss_diff)[source]

    Determines the stopping condition for gradient descent.

    -
    -tensorflow_optimizers(method='adam')[source]
    +
    +tensorflow_optimizers(method='adam')[source]

    Initializes tensorflow optimizers.

    -
    -update_hyperparameters(proximity_weight=0.5, diversity_weight=0.5, categorical_penalty=0.1)[source]
    +
    +update_hyperparameters(proximity_weight=0.5, diversity_weight=0.5, categorical_penalty=0.1)[source]

    Updates hyperparameters.

    -
    -
    -

    dice_ml.explainer_interfaces.dice_tensorflow2 module

    + +
    +

    dice_ml.explainer_interfaces.dice_tensorflow2 module

    Module to generate diverse counterfactual explanations based on tensorflow 2.x

    -
    -class dice_ml.explainer_interfaces.dice_tensorflow2.DiceTensorFlow2(data_interface, model_interface)[source]
    +
    +class dice_ml.explainer_interfaces.dice_tensorflow2.DiceTensorFlow2(data_interface, model_interface)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -compute_dist(x_hat, x1)[source]
    +
    +compute_dist(x_hat, x1)[source]

    Compute weighted distance between two vectors.

    -
    -compute_diversity_loss()[source]
    +
    +compute_diversity_loss()[source]

    Computes the third part (diversity) of the loss function.

    -
    -compute_loss()[source]
    +
    +compute_loss()[source]

    Computes the overall loss

    -
    -compute_proximity_loss()[source]
    +
    +compute_proximity_loss()[source]

    Compute the second part (distance from x1) of the loss function.

    -
    -compute_regularization_loss()[source]
    +
    +compute_regularization_loss()[source]

    Adds a linear equality constraints to the loss functions - to ensure all levels of a categorical variable sums to one

    -
    -compute_yloss()[source]
    +
    +compute_yloss()[source]

    Computes the first part (y-loss) of the loss function.

    -
    -do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]
    +
    +do_cf_initializations(total_CFs, algorithm, features_to_vary)[source]

    Intializes CFs and other related variables.

    -
    -do_loss_initializations(yloss_type, diversity_loss_type, feature_weights)[source]
    +
    +do_loss_initializations(yloss_type, diversity_loss_type, feature_weights)[source]

    Intializes variables related to main loss function

    -
    -do_optimizer_initializations(optimizer, learning_rate)[source]
    +
    +do_optimizer_initializations(optimizer, learning_rate)[source]

    Initializes gradient-based TensorFLow optimizers.

    -
    -dpp_style(submethod)[source]
    +
    +dpp_style(submethod)[source]

    Computes the DPP of a matrix.

    -
    -find_counterfactuals(query_instance, desired_class, optimizer, learning_rate, min_iter, max_iter, project_iter, loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]
    +
    +find_counterfactuals(query_instance, desired_class, optimizer, learning_rate, min_iter, max_iter, project_iter, loss_diff_thres, loss_converge_maxiter, verbose, init_near_query_instance, tie_random, stopping_threshold, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]

    Finds counterfactuals by gradient-descent.

    -
    -generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='tensorflow:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]
    +
    +generate_counterfactuals(query_instance, total_CFs, desired_class='opposite', proximity_weight=0.5, diversity_weight=1.0, categorical_penalty=0.1, algorithm='DiverseCF', features_to_vary='all', permitted_range=None, yloss_type='hinge_loss', diversity_loss_type='dpp_style:inverse_dist', feature_weights='inverse_mad', optimizer='tensorflow:adam', learning_rate=0.05, min_iter=500, max_iter=5000, project_iter=0, loss_diff_thres=1e-05, loss_converge_maxiter=1, verbose=False, init_near_query_instance=True, tie_random=False, stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear')[source]

    Generates diverse counterfactual explanations

    Parameters
    @@ -877,92 +790,98 @@

    Submodules -
    -initialize_CFs(query_instance, init_near_query_instance=False)[source]
    +
    +initialize_CFs(query_instance, init_near_query_instance=False)[source]

    Initialize counterfactuals.

    -
    -predict_fn(input_instance)[source]
    +
    +predict_fn(input_instance)[source]

    prediction function

    -
    -predict_fn_for_sparsity(input_instance)[source]
    +
    +predict_fn_for_sparsity(input_instance)[source]

    prediction function for sparsity correction

    -
    -round_off_cfs(assign=False)[source]
    +
    +round_off_cfs(assign=False)[source]

    function for intermediate projection of CFs.

    -
    -stop_loop(itr, loss_diff)[source]
    +
    +stop_loop(itr, loss_diff)[source]

    Determines the stopping condition for gradient descent.

    -
    -update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty)[source]
    +
    +update_hyperparameters(proximity_weight, diversity_weight, categorical_penalty)[source]

    Update hyperparameters of the loss function

    -
    -
    -

    dice_ml.explainer_interfaces.explainer_base module

    + +
    +

    dice_ml.explainer_interfaces.explainer_base module

    Module containing a template class to generate counterfactual explanations. Subclasses implement interfaces for different ML frameworks such as TensorFlow or PyTorch. All methods are in dice_ml.explainer_interfaces

    -
    -class dice_ml.explainer_interfaces.explainer_base.ExplainerBase(data_interface, model_interface=None)[source]
    +
    +class dice_ml.explainer_interfaces.explainer_base.ExplainerBase(data_interface, model_interface=None)[source]

    Bases: abc.ABC

    -
    -build_KD_tree(data_df_copy, desired_range, desired_class, predicted_outcome_name)[source]
    +
    +build_KD_tree(data_df_copy, desired_range, desired_class, predicted_outcome_name)[source]
    -
    -check_permitted_range(permitted_range)[source]
    +
    +check_permitted_range(permitted_range)[source]

    checks permitted range for continuous features TODO: add comments as to where this is used if this function is necessary, else remove.

    -
    -check_query_instance_validity(features_to_vary, permitted_range, query_instance, feature_ranges_orig)[source]
    +
    +check_query_instance_validity(features_to_vary, permitted_range, query_instance, feature_ranges_orig)[source]
    -
    -decide_cf_validity(model_outputs)[source]
    +
    +decide_cf_validity(model_outputs)[source]
    - +
    +static deserialize_explainer(path)[source]
    +

    Reload the explainer into the memory by reading the file specified by path.

    +
    + +
    +

    Performs a binary search between continuous features of a CF and corresponding values in query_instance until the prediction class changes.

    - +

    Performs a greedy linear search - moves the continuous features in CFs towards original values in query_instance greedily until the prediction class changes.

    -
    -do_posthoc_sparsity_enhancement(final_cfs_sparse, query_instance, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]
    +
    +do_posthoc_sparsity_enhancement(final_cfs_sparse, query_instance, posthoc_sparsity_param, posthoc_sparsity_algorithm)[source]

    Post-hoc method to encourage sparsity in a generated counterfactuals.

    Parameters
    @@ -981,8 +900,8 @@

    Submodules -
    -feature_importance(query_instances, cf_examples_list=None, total_CFs=10, local_importance=True, global_importance=True, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]
    +
    +feature_importance(query_instances, cf_examples_list=None, total_CFs=10, local_importance=True, global_importance=True, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]

    Estimate feature importance scores for the given inputs.

    Parameters
    @@ -1006,8 +925,8 @@

    Submodules -
    -generate_counterfactuals(query_instances, total_CFs, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', verbose=False, **kwargs)[source]
    +
    +generate_counterfactuals(query_instances, total_CFs, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, proximity_weight=0.2, sparsity_weight=0.2, diversity_weight=5.0, categorical_penalty=0.1, posthoc_sparsity_algorithm='linear', verbose=False, **kwargs)[source]

    General method for generating counterfactuals.

    Parameters
    @@ -1018,12 +937,22 @@

    Submodules -
    -get_model_output_from_scores(model_scores)[source]
    +
    +get_model_output_from_scores(model_scores)[source]

    -
    -global_feature_importance(query_instances, cf_examples_list=None, total_CFs=10, local_importance=True, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]
    +
    +global_feature_importance(query_instances, cf_examples_list=None, total_CFs=10, local_importance=True, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]

    Estimate global feature importance scores for the given inputs.

    Parameters
    @@ -1075,8 +1004,8 @@

    Submodules -
    -infer_target_cfs_class(desired_class_input, original_pred, num_output_nodes)[source]
    +
    +infer_target_cfs_class(desired_class_input, original_pred, num_output_nodes)[source]

    Infer the target class for generating CFs. Only called when model_type==”classifier”. TODO: Add support for opposite desired class in multiclass. @@ -1084,19 +1013,19 @@

    Submodules -
    -infer_target_cfs_range(desired_range_input)[source]
    +
    +infer_target_cfs_range(desired_range_input)[source]

    -
    -is_cf_valid(model_score)[source]
    +
    +is_cf_valid(model_score)[source]

    Check if a cf belongs to the target class or target range.

    -
    -local_feature_importance(query_instances, cf_examples_list=None, total_CFs=10, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]
    +
    +local_feature_importance(query_instances, cf_examples_list=None, total_CFs=10, desired_class='opposite', desired_range=None, permitted_range=None, features_to_vary='all', stopping_threshold=0.5, posthoc_sparsity_param=0.1, posthoc_sparsity_algorithm='linear', **kwargs)[source]

    Estimate local feature importance scores for the given inputs.

    Parameters
    @@ -1121,55 +1050,61 @@

    Submodules -
    -misc_init(stopping_threshold, desired_class, desired_range, test_pred)[source]
    +
    +misc_init(stopping_threshold, desired_class, desired_range, test_pred)[source]

    -
    -predict_fn(input_instance)[source]
    +
    +predict_fn(input_instance)[source]

    prediction function

    -
    -predict_fn_for_sparsity(input_instance)[source]
    +
    +predict_fn_for_sparsity(input_instance)[source]

    prediction function for sparsity correction

    -
    -round_to_precision()[source]
    +
    +round_to_precision()[source]
    -
    -setup(features_to_vary, permitted_range, query_instance, feature_weights)[source]
    +
    +serialize_explainer(path)[source]
    +

    Serialize the explainer to the file specified by path.

    +
    + +
    +
    +setup(features_to_vary, permitted_range, query_instance, feature_weights)[source]
    -
    -sigmoid(z)[source]
    +
    +sigmoid(z)[source]

    This is used in VAE-based CF explainers.

    -

    -
    -

    dice_ml.explainer_interfaces.feasible_base_vae module

    + +
    +

    dice_ml.explainer_interfaces.feasible_base_vae module

    -
    -class dice_ml.explainer_interfaces.feasible_base_vae.FeasibleBaseVAE(data_interface, model_interface, **kwargs)[source]
    +
    +class dice_ml.explainer_interfaces.feasible_base_vae.FeasibleBaseVAE(data_interface, model_interface, **kwargs)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -compute_loss(model_out, x, target_label)[source]
    +
    +compute_loss(model_out, x, target_label)[source]
    -
    -generate_counterfactuals(query_instance, total_CFs, desired_class='opposite')[source]
    +
    +generate_counterfactuals(query_instance, total_CFs, desired_class='opposite')[source]

    General method for generating counterfactuals.

    Parameters
    @@ -1180,12 +1115,22 @@

    Submodules -
    -train(pre_trained=False)[source]
    +
    +train(pre_trained=False)[source]

    pre_trained: Bool Variable to check whether pre trained model exists to avoid training again

    -
    -
    -

    dice_ml.explainer_interfaces.feasible_model_approx module

    + +
    +

    dice_ml.explainer_interfaces.feasible_model_approx module

    -
    -class dice_ml.explainer_interfaces.feasible_model_approx.FeasibleModelApprox(data_interface, model_interface, **kwargs)[source]
    +
    +class dice_ml.explainer_interfaces.feasible_model_approx.FeasibleModelApprox(data_interface, model_interface, **kwargs)[source]

    Bases: dice_ml.explainer_interfaces.feasible_base_vae.FeasibleBaseVAE, dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    -
    -train(constraint_type, constraint_variables, constraint_direction, constraint_reg, pre_trained=False)[source]
    +
    +train(constraint_type, constraint_variables, constraint_direction, constraint_reg, pre_trained=False)[source]
    Parameters
    - + +
    +

    Module contents

    +
    + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.html b/docs/dice_ml.html index ef34d2d9..19d8e332 100644 --- a/docs/dice_ml.html +++ b/docs/dice_ml.html @@ -1,71 +1,35 @@ - - - - - - - dice_ml package — DiCE 0.7 documentation - - - - - - - - - - + - + + dice_ml package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    -
    -

    dice_ml.data module

    + +
    +

    dice_ml.data module

    Module pointing to different implementations of Data class

    DiCE requires only few parameters about the data such as the range of continuous features and the levels of categorical features. Hence, DiCE can be used for a private data whose meta data are only available (such as the feature names and range/levels of different features) by specifying appropriate parameters.

    -
    -class dice_ml.data.Data(**params)[source]
    +
    +class dice_ml.data.Data(**params)[source]

    Bases: dice_ml.data_interfaces.base_data_interface._BaseData

    Class containing all required information about the data for DiCE.

    -
    -decide_implementation_type(params)[source]
    +
    +decide_implementation_type(params)[source]

    Decides if the Data class is for public or private data.

    -
    -dice_ml.data.decide(params)[source]
    +
    +dice_ml.data.decide(params)[source]

    Decides if the Data class is for public or private data.

    To add new implementations of Data, add the class in data_interfaces subpackage and import-and-return the class in an elif loop as shown in the below method.

    -
    -
    -

    dice_ml.dice module

    + +
    +

    dice_ml.dice module

    Module pointing to different implementations of DiCE based on different frameworks such as Tensorflow or PyTorch or sklearn, and different methods such as RandomSampling, DiCEKD or DiCEGenetic

    -
    -class dice_ml.dice.Dice(data_interface, model_interface, method='random', **kwargs)[source]
    +
    +class dice_ml.dice.Dice(data_interface, model_interface, method='random', **kwargs)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    An interface class to different DiCE implementations.

    -
    -decide_implementation_type(data_interface, model_interface, method, **kwargs)[source]
    +
    +decide_implementation_type(data_interface, model_interface, method, **kwargs)[source]

    Decides DiCE implementation type.

    -
    -dice_ml.dice.decide(model_interface, method)[source]
    +
    +dice_ml.dice.decide(model_interface, method)[source]

    Decides DiCE implementation type.

    To add new implementations of DiCE, add the class in explainer_interfaces subpackage and import-and-return the class in an elif loop as shown in the below method.

    -
    -
    -

    dice_ml.diverse_counterfactuals module

    + +
    +

    dice_ml.diverse_counterfactuals module

    -
    -class dice_ml.diverse_counterfactuals.CounterfactualExamples(data_interface=None, final_cfs_df=None, test_instance_df=None, final_cfs_df_sparse=None, posthoc_sparsity_param=0, desired_range=None, desired_class='opposite', model_type='classifier')[source]
    +
    +class dice_ml.diverse_counterfactuals.CounterfactualExamples(data_interface=None, final_cfs_df=None, test_instance_df=None, final_cfs_df_sparse=None, posthoc_sparsity_param=0, desired_range=None, desired_class='opposite', model_type='classifier')[source]

    Bases: object

    A class to store and visualize the resulting counterfactual explanations.

    -
    -display_df(df, show_only_changes)[source]
    +
    +display_df(df, show_only_changes)[source]
    -
    -static from_json(cf_example_json_str)[source]
    +
    +static from_json(cf_example_json_str)[source]
    -
    -print_list(li, show_only_changes)[source]
    +
    +print_list(li, show_only_changes)[source]
    -
    -to_json(serialization_version)[source]
    +
    +to_json(serialization_version)[source]
    -
    -visualize_as_dataframe(display_sparse_df=True, show_only_changes=False)[source]
    +
    +visualize_as_dataframe(display_sparse_df=True, show_only_changes=False)[source]
    -
    -visualize_as_list(display_sparse_df=True, show_only_changes=False)[source]
    +
    +visualize_as_list(display_sparse_df=True, show_only_changes=False)[source]
    -
    -dice_ml.diverse_counterfactuals.json_converter(obj)[source]
    +
    +dice_ml.diverse_counterfactuals.json_converter(obj)[source]

    Helper function to convert object to json.

    -
    -
    -

    dice_ml.model module

    + +
    +

    dice_ml.model module

    Module pointing to different implementations of Model class

    The implementations contain methods to access the output or gradients of ML models trained based on different frameworks such as Tensorflow or PyTorch.

    -
    -class dice_ml.model.Model(model=None, model_path='', backend='TF1', model_type='classifier', func=None, kw_args=None)[source]
    +
    +class dice_ml.model.Model(model=None, model_path='', backend='TF1', model_type='classifier', func=None, kw_args=None)[source]

    Bases: object

    An interface class to different ML Model implementations.

    -
    -decide_implementation_type(model, model_path, backend, func, kw_args)[source]
    +
    +decide_implementation_type(model, model_path, backend, func, kw_args)[source]

    Decides the Model implementation type.

    -
    -dice_ml.model.decide(backend)[source]
    +
    +dice_ml.model.decide(backend)[source]

    Decides the Model implementation type.

    To add new implementations of Model, add the class in model_interfaces subpackage and import-and-return the class in an elif loop as shown in the below method.

    -
    -
    -

    Module contents

    + +
    +

    Module contents

    -
    -class dice_ml.Data(**params)[source]
    +
    +class dice_ml.Data(**params)[source]

    Bases: dice_ml.data_interfaces.base_data_interface._BaseData

    Class containing all required information about the data for DiCE.

    -
    -decide_implementation_type(params)[source]
    +
    +decide_implementation_type(params)[source]

    Decides if the Data class is for public or private data.

    -
    -class dice_ml.Dice(data_interface, model_interface, method='random', **kwargs)[source]
    +
    +class dice_ml.Dice(data_interface, model_interface, method='random', **kwargs)[source]

    Bases: dice_ml.explainer_interfaces.explainer_base.ExplainerBase

    An interface class to different DiCE implementations.

    -
    -decide_implementation_type(data_interface, model_interface, method, **kwargs)[source]
    +
    +decide_implementation_type(data_interface, model_interface, method, **kwargs)[source]

    Decides DiCE implementation type.

    -
    -class dice_ml.Model(model=None, model_path='', backend='TF1', model_type='classifier', func=None, kw_args=None)[source]
    +
    +class dice_ml.Model(model=None, model_path='', backend='TF1', model_type='classifier', func=None, kw_args=None)[source]

    Bases: object

    An interface class to different ML Model implementations.

    -
    -decide_implementation_type(model, model_path, backend, func, kw_args)[source]
    +
    +decide_implementation_type(model, model_path, backend, func, kw_args)[source]

    Decides the Model implementation type.

    -
    - + + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.model_interfaces.html b/docs/dice_ml.model_interfaces.html index e5d23059..7e436f06 100644 --- a/docs/dice_ml.model_interfaces.html +++ b/docs/dice_ml.model_interfaces.html @@ -1,71 +1,35 @@ - - - - - - - dice_ml.model_interfaces package — DiCE 0.7 documentation - - - - - - - - - - + - + + dice_ml.model_interfaces package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    -
    -

    dice_ml.model_interfaces.pytorch_model module

    + +
    +

    dice_ml.model_interfaces.pytorch_model module

    Module containing an interface to trained PyTorch model.

    -
    -class dice_ml.model_interfaces.pytorch_model.PyTorchModel(model=None, model_path='', backend='PYT', func=None, kw_args=None)[source]
    +
    +class dice_ml.model_interfaces.pytorch_model.PyTorchModel(model=None, model_path='', backend='PYT', func=None, kw_args=None)[source]

    Bases: dice_ml.model_interfaces.base_model.BaseModel

    -
    -get_gradient(input)[source]
    +
    +get_gradient(input_instance)[source]
    -
    -get_num_output_nodes(inp_size)[source]
    +
    +get_num_output_nodes(inp_size)[source]
    -
    -get_output(input_tensor, transform_data=False)[source]
    +
    +get_output(input_tensor, transform_data=False)[source]

    returns prediction probabilities

    Parameters
    @@ -341,77 +254,52 @@

    Submodules -
    -load_model()[source]
    +
    +load_model()[source]

    -
    -set_eval_mode()[source]
    +
    +set_eval_mode()[source]
    -
    -
    -

    Module contents

    -
    - + +
    +

    Module contents

    +
    + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.schema.html b/docs/dice_ml.schema.html index 482ca92d..58907b77 100644 --- a/docs/dice_ml.schema.html +++ b/docs/dice_ml.schema.html @@ -1,71 +1,35 @@ - - - - - - - dice_ml.schema package — DiCE 0.7 documentation - - - - - + - - - - - - + + dice_ml.schema package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.utils.html b/docs/dice_ml.utils.html index 8c73d6c6..87dceb57 100644 --- a/docs/dice_ml.utils.html +++ b/docs/dice_ml.utils.html @@ -1,71 +1,35 @@ - - - - - - - dice_ml.utils package — DiCE 0.7 documentation - + - - - - - - - - - - + + dice_ml.utils package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    -
    -

    Module contents

    -
    - + +
    +

    Module contents

    +
    + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/dice_ml.utils.sample_architecture.html b/docs/dice_ml.utils.sample_architecture.html index 89d51238..8ff72da0 100644 --- a/docs/dice_ml.utils.sample_architecture.html +++ b/docs/dice_ml.utils.sample_architecture.html @@ -1,70 +1,34 @@ - - - - - - - dice_ml.utils.sample_architecture package — DiCE 0.7 documentation - - - - - - - - - - + - + + dice_ml.utils.sample_architecture package — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    - - - - - - - - + \ No newline at end of file diff --git a/docs/genindex.html b/docs/genindex.html index 38a0784a..7f38acbc 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -1,69 +1,32 @@ - - - - - - - Index — DiCE 0.7 documentation - - - - - - - - - - - - + + + Index — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    @@ -2400,123 +2301,123 @@

    Convert the json output to a counterfactual object 0 + 80.0 + Government + Bachelors + Married - - - - Widowed - - - Other - - - - 0 + 1 1 + 35.0 + Self-Employed + Prof-school + Married - - - - Widowed - - - - - - 56.0 - 0 + 1 2 + 46.0 - + Assoc + Married - - + Male - - Other/Unknown - - - - - 35.0 - 0 + 1 3 + 47.0 - + Prof-school + Married + Sales - - School - - - - - Other - - - 0 + 1 4 - 17.0 - - - - - - - - + 80.0 + Government + Bachelors + Married - - + Male - - 0 + 1 5 + 47.0 + Other/Unknown + Prof-school + Married + Sales - - - - - - - - - - - - 91.0 - 0 + 1 6 - 18.0 - - - - + 53.0 - + Masters + Married + White-Collar - - - - 55.0 - 0 + 1 - 7 - - - - - Assoc - Divorced - - - - + 7 + 71.0 - + Some-college + Married + White-Collar - - 0 + Male + 59.0 + 1 8 - + Self-Employed + Doctorate + Married + Professional + Other - - - - - - - - - - - 72.0 - 0 + 1 9 + 50.0 - - Government - - - - - Blue-Collar - - + Masters + Married + White-Collar - - - 0 + 46.0 + 1 @@ -2566,14 +2467,14 @@

    Convert the json output to a counterfactual object 0 - 36 - Private + 19 + Other/Unknown HS-grad - Married - Blue-Collar - White - Male - 45 + Single + Other/Unknown + Other + Female + 40 0 @@ -2625,108 +2526,108 @@

    Convert the json output to a counterfactual object 0 - 58.0 - - - - - - - - + Masters + Married - - + Male + 90.0 1 1 + 85.0 + Government + Some-college + Married - - - Assoc - - - - - - - Female - + 53.0 1 2 - - - - School - - - - + Government + Prof-school + Married + White-Collar - - - 46.0 + 84.0 1 3 - - - Prof-school - - - - + Masters + Married + Service - - Female + Male + 90.0 - - 1 4 + 54.0 - - Government - Doctorate - - - - - - + Some-college + Married - - + Male + 96.0 1 5 - 54.0 + 86.0 - - - - - - Service + School + Married + Professional + White - - - - 1 6 - 66.0 - - - Doctorate - - - - + 75.0 - + Some-college + Married - - + Male + 92.0 1 7 - - + 88.0 - Doctorate - - - - - + Professional + White - - 1 8 - 45.0 - - - - - - + 78.0 - + Bachelors + Married - + White - - 1 @@ -2735,10 +2636,10 @@

    Convert the json output to a counterfactual object9 - - - Doctorate - - - White-Collar - - + Masters + Married + Professional + White - - 1 @@ -2791,14 +2692,14 @@

    Convert the json output to a counterfactual object 0 - 47 - Private + 28 + Other/Unknown Some-college Married - Service + Other/Unknown White - Male - 47 + Female + 40 1 @@ -2850,34 +2751,34 @@

    Convert the json output to a counterfactual object 0 - 82.0 - Government - - - + Separated - - - + 79.0 0 1 - 17.0 - - Prof-school - + Assoc - - - + Male - 0 2 + 65.0 - - - - - Single + Widowed - - - @@ -2889,11 +2790,11 @@

    Convert the json output to a counterfactual object- - - + Separated - - - Other - - 66.0 + 8.0 0 @@ -2901,22 +2802,22 @@

    Convert the json output to a counterfactual object- - - - Separated - + Blue-Collar - - Female - + 91.0 0 5 - 67.0 - - - Widowed + School - - - + Male - 0 @@ -2924,22 +2825,22 @@

    Convert the json output to a counterfactual object6 - - - Assoc - - Other/Unknown + Divorced - - - + 79.0 0 7 - - Other/Unknown - - - Other/Unknown - + Blue-Collar + Other - - 0 @@ -2948,9 +2849,9 @@

    Convert the json output to a counterfactual object8 - - - School - - - + Single + White-Collar - - - @@ -2959,13 +2860,13 @@

    Convert the json output to a counterfactual object 9 - - Government - - - + Blue-Collar - - - 78.0 + 1.0 0 @@ -2977,7 +2878,7 @@

    Convert the json output to a counterfactual object 
    -Query instance (original outcome : 0)
+Query instance (original outcome : 1)

    @@ -3016,15 +2917,15 @@

    Convert the json output to a counterfactual object 0 - 30 - Other/Unknown - HS-grad + 34 + Private + Bachelors Married - Other/Unknown - Other - Female + Professional + White + Male 40 - 0 + 1 @@ -3036,7 +2937,7 @@

    Convert the json output to a counterfactual object 
     
-Counterfactual set (new outcome: 1.0)
+Counterfactual set (new outcome: 0.0)

    @@ -3075,15 +2976,15 @@

    Convert the json output to a counterfactual object 0 - 78.0 - - + Prof-school - - Professional - - - - 1 + 7.0 + 0 1 @@ -3091,107 +2992,107 @@

    Convert the json output to a counterfactual object- - - - Professional + Sales - + Female - - 71.0 - 1 + 0 2 - + Self-Employed + School - - - - Professional - White - - - 1 + 0 3 - + Self-Employed - + Widowed - - - Professional - - - 10.0 - 1 + 0 4 + 77.0 - - - Masters - - Professional + Service - - - - 1 + 0 5 + 68.0 - - Self-Employed - Masters - + Single - - - - - 1 + 0 6 + 38.0 + Government - - - Masters - - - - - 17.0 - 1 + 0 7 - - - Masters + Some-college + Single - - - - - 94.0 - 1 + 0 8 - 76.0 + 89.0 - - - - Professional - - - - 1 + 21.0 + 0 9 - - - + Divorced - - Professional - - + Other - - - 1 + 0 @@ -3203,67 +3104,42 @@

    Convert the json output to a counterfactual object 
     [None, None, None, None, None, None, None, None, None, None]
-[{'marital_status': 0.5, 'age': 0.4, 'occupation': 0.4, 'hours_per_week': 0.3, 'workclass': 0.1, 'education': 0.1, 'race': 0.1, 'gender': 0.0}, {'marital_status': 0.8, 'age': 0.6, 'education': 0.3, 'gender': 0.2, 'occupation': 0.1, 'workclass': 0.0, 'race': 0.0, 'hours_per_week': 0.0}, {'age': 1.0, 'education': 1.0, 'marital_status': 1.0, 'hours_per_week': 0.7, 'workclass': 0.3, 'gender': 0.3, 'occupation': 0.1, 'race': 0.0}, {'marital_status': 0.5, 'hours_per_week': 0.4, 'age': 0.3, 'workclass': 0.2, 'education': 0.2, 'occupation': 0.2, 'race': 0.1, 'gender': 0.0}, {'education': 0.8, 'hours_per_week': 0.8, 'marital_status': 0.4, 'age': 0.0, 'workclass': 0.0, 'occupation': 0.0, 'race': 0.0, 'gender': 0.0}, {'age': 0.4, 'hours_per_week': 0.4, 'workclass': 0.2, 'education': 0.2, 'marital_status': 0.1, 'occupation': 0.1, 'race': 0.1, 'gender': 0.1}, {'hours_per_week': 0.5, 'marital_status': 0.3, 'age': 0.2, 'education': 0.2, 'occupation': 0.2, 'race': 0.2, 'workclass': 0.1, 'gender': 0.0}, {'education': 0.7, 'age': 0.4, 'occupation': 0.2, 'gender': 0.2, 'workclass': 0.1, 'hours_per_week': 0.1, 'marital_status': 0.0, 'race': 0.0}, {'age': 0.3, 'workclass': 0.3, 'education': 0.3, 'marital_status': 0.3, 'occupation': 0.2, 'hours_per_week': 0.2, 'race': 0.1, 'gender': 0.1}, {'occupation': 0.7, 'education': 0.4, 'hours_per_week': 0.4, 'age': 0.2, 'workclass': 0.1, 'race': 0.1, 'marital_status': 0.0, 'gender': 0.0}]
-{'education': 0.42, 'marital_status': 0.39, 'age': 0.38, 'hours_per_week': 0.38, 'occupation': 0.22, 'workclass': 0.14, 'gender': 0.09, 'race': 0.07}
+[{'hours_per_week': 0.6, 'education': 0.4, 'occupation': 0.4, 'age': 0.2, 'race': 0.2, 'workclass': 0.1, 'marital_status': 0.0, 'gender': 0.0}, {'hours_per_week': 0.8, 'marital_status': 0.5, 'age': 0.2, 'education': 0.2, 'race': 0.1, 'workclass': 0.0, 'occupation': 0.0, 'gender': 0.0}, {'age': 1.0, 'marital_status': 0.8, 'education': 0.6, 'hours_per_week': 0.4, 'workclass': 0.3, 'occupation': 0.3, 'race': 0.1, 'gender': 0.0}, {'age': 1.0, 'education': 0.7, 'marital_status': 0.6, 'gender': 0.3, 'occupation': 0.2, 'hours_per_week': 0.2, 'workclass': 0.1, 'race': 0.0}, {'marital_status': 0.5, 'occupation': 0.4, 'age': 0.3, 'workclass': 0.2, 'education': 0.1, 'gender': 0.1, 'hours_per_week': 0.1, 'race': 0.0}, {'hours_per_week': 0.6, 'marital_status': 0.5, 'education': 0.4, 'occupation': 0.3, 'age': 0.1, 'race': 0.1, 'workclass': 0.0, 'gender': 0.0}, {'education': 1.0, 'marital_status': 1.0, 'age': 0.9, 'occupation': 0.6, 'workclass': 0.5, 'gender': 0.3, 'hours_per_week': 0.2, 'race': 0.1}, {'education': 1.0, 'marital_status': 0.9, 'age': 0.6, 'hours_per_week': 0.6, 'occupation': 0.5, 'race': 0.4, 'gender': 0.4, 'workclass': 0.2}, {'marital_status': 0.5, 'hours_per_week': 0.5, 'occupation': 0.4, 'education': 0.2, 'gender': 0.2, 'age': 0.1, 'race': 0.1, 'workclass': 0.0}, {'age': 0.4, 'marital_status': 0.4, 'workclass': 0.3, 'education': 0.3, 'occupation': 0.2, 'hours_per_week': 0.2, 'race': 0.1, 'gender': 0.1}]
+{'marital_status': 0.57, 'education': 0.49, 'age': 0.48, 'hours_per_week': 0.42, 'occupation': 0.33, 'workclass': 0.17, 'gender': 0.14, 'race': 0.12}

    - - + + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_getting_started.html b/docs/notebooks/DiCE_getting_started.html index c0b58b7b..12bbf737 100644 --- a/docs/notebooks/DiCE_getting_started.html +++ b/docs/notebooks/DiCE_getting_started.html @@ -1,71 +1,37 @@ - - - - - - - Quick introduction to generating counterfactual explanations using DiCE — DiCE 0.7 documentation - - - - - - - - - - + - + + Quick introduction to generating counterfactual explanations using DiCE — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_getting_started_feasible.html b/docs/notebooks/DiCE_getting_started_feasible.html index c75bd075..01f3614b 100644 --- a/docs/notebooks/DiCE_getting_started_feasible.html +++ b/docs/notebooks/DiCE_getting_started_feasible.html @@ -1,71 +1,37 @@ - - - - - - - Generate feasible counterfactual explanations using a VAE — DiCE 0.7 documentation - + - - - - - - - - - - + + Generate feasible counterfactual explanations using a VAE — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_model_agnostic_CFs.html b/docs/notebooks/DiCE_model_agnostic_CFs.html index 9018f75c..f565e63b 100644 --- a/docs/notebooks/DiCE_model_agnostic_CFs.html +++ b/docs/notebooks/DiCE_model_agnostic_CFs.html @@ -1,71 +1,37 @@ - - - - - - - Generating counterfactual explanations with any ML model — DiCE 0.7 documentation - - - - - - - - - - + - + + Generating counterfactual explanations with any ML model — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    +
    +

    3. Querying a KD Tree

    Here, we show how to use DiCE can be used to generate CFs for any ML model by finding the closest points in the dataset that give the output as the desired class. We do this efficiently by building KD trees for each class, and querying the KD tree of the desired class to find the k closest counterfactuals from the dataset. The idea behind finding the closest points from the training data itself is to ensure that the counterfactuals displayed are feasible.

    -
    -

    Training a custom ML model

    +
    +

    Training a custom ML model

    Currently, the KD tree algorithm method works with scikit-learn models. Again, we will use the same model as shown previously in the notebook. Support for Tensorflow 1&2 and Pytorch will be implemented soon.

    -
    -
    -

    Generate diverse counterfactuals

    +
    +
    +

    Generate diverse counterfactuals

    [23]:
    -
    -# initiate DiceKD
+
    # initiate DiceKD
 exp_KD = dice_ml.Dice(d, m, method='kdtree')
 
    
 
    
@@ -3016,8 +2907,7 @@ 
    Generate diverse counterfactuals
    [24]:
    -
    -# generate counterfactuals
+
    # generate counterfactuals
 dice_exp_KD = exp_KD.generate_counterfactuals(query_instances, total_CFs=4, desired_class="opposite")
 
    
 
    
@@ -3027,15 +2917,14 @@ 
    Generate diverse counterfactuals
 
    -100%|██████████| 2/2 [00:01<00:00,  1.59it/s]
+100%|███████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.31it/s]
 
    [25]:
    -
    -dice_exp_KD.visualize_as_dataframe(show_only_changes=True)
+
    dice_exp_KD.visualize_as_dataframe(show_only_changes=True)
    @@ -3103,7 +2992,7 @@

    Generate diverse counterfactuals 
     
-Diverse Counterfactual set (new outcome: 1.0)
+Diverse Counterfactual set (new outcome: 1)

    @@ -3141,50 +3030,50 @@

    Generate diverse counterfactuals 
     
-Diverse Counterfactual set (new outcome: 1.0)
+Diverse Counterfactual set (new outcome: 1)

    @@ -3294,7 +3183,7 @@

    Generate diverse counterfactuals -

    Selecting the features to vary

    +
    +

    Selecting the features to vary

    Here, again, you can vary only features that you wish to vary. Please note that the output counterfactuals are only from the training data. If you want other counterfactuals, please use the random or genetic method.

    [26]:
    -
    -# generate counterfactuals
+
    # generate counterfactuals
 dice_exp_KD = exp_KD.generate_counterfactuals(
     query_instances, total_CFs=4, desired_class="opposite",
     features_to_vary=['age', 'workclass', 'education', 'occupation', 'hours_per_week'])
@@ -3365,15 +3253,14 @@ 
    Selecting the features to vary
 
    -100%|██████████| 2/2 [00:01<00:00,  1.80it/s]
+100%|███████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.50it/s]
 
    
 
    
 
    
 
    [27]:
 
    
 
    
-
    -dice_exp_KD.visualize_as_dataframe(show_only_changes=True)
+
    dice_exp_KD.visualize_as_dataframe(show_only_changes=True)
 
    
 
    
 
    
@@ -3441,64 +3328,7 @@ 
    Selecting the features to vary
 
     
-Diverse Counterfactual set (new outcome: 1.0)
-
    
-
    
-
    
-
    
-
    
-
    
-
    
-
-
-  
-    
-      
-      
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-  
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-    
-      
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-    
-  
-
    ageworkclasseducationmarital_statusoccupationracegenderhours_per_weekincome
    025.0-Assoc-Sales---1
    
-
    
-
    
-
    
-
    
-
    
-
    
-
    +No counterfactuals found!
 Query instance (original outcome : 1)
 
    
 
    
@@ -3558,7 +3388,7 @@ 
    Selecting the features to vary
 
     
-Diverse Counterfactual set (new outcome: 1.0)
+Diverse Counterfactual set (new outcome: 1)
    @@ -3596,7 +3426,7 @@

    Selecting the features to vary -

    Selecting the feature ranges

    +

    +
    +

    Selecting the feature ranges

    Here, you can control the ranges of continuous features.

    [28]:
    -
    -# generate counterfactuals
+
    # generate counterfactuals
 dice_exp_KD = exp_KD.generate_counterfactuals(
     query_instances, total_CFs=5, desired_class="opposite",
     permitted_range={'age': [30, 50], 'hours_per_week': [40, 60]})
@@ -3645,7 +3474,7 @@ 
    Selecting the feature ranges
 
    -100%|██████████| 2/2 [00:01<00:00,  1.74it/s]
+100%|███████████████████████████████████████████████████████████████████| 2/2 [00:01<00:00,  1.41it/s]
 
    
 
    
 
    
@@ -3780,7 +3609,7 @@ 
    Selecting the feature ranges
 
     
-Diverse Counterfactual set (new outcome: 1.0)
+Diverse Counterfactual set (new outcome: 1)
    @@ -3818,7 +3647,7 @@

    Selecting the feature ranges - - - - - - +
    -
    -

    - - © Copyright 2020, Ramaravind, Amit, Chenhao - -

    +

    © Copyright 2020, Ramaravind, Amit, Chenhao.

    - - - - Built with Sphinx using a - - theme - - provided by Read the Docs. -
    + Built with Sphinx using a + theme + provided by Read the Docs. + +

    -
    -

    - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_multiclass_classification_and_regression.html b/docs/notebooks/DiCE_multiclass_classification_and_regression.html index 2ee5e6cd..0d85d215 100644 --- a/docs/notebooks/DiCE_multiclass_classification_and_regression.html +++ b/docs/notebooks/DiCE_multiclass_classification_and_regression.html @@ -1,71 +1,37 @@ - - - - - - - Generating counterfactuals for multi-class classification and regression models — DiCE 0.7 documentation - - - - - + - - - - - - + + Generating counterfactuals for multi-class classification and regression models — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    +
    - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_with_advanced_options.html b/docs/notebooks/DiCE_with_advanced_options.html index a82737e4..6ec02779 100644 --- a/docs/notebooks/DiCE_with_advanced_options.html +++ b/docs/notebooks/DiCE_with_advanced_options.html @@ -1,71 +1,37 @@ - - - - - - - Advanced options to customize Counterfactual Explanations — DiCE 0.7 documentation - - - - - - - - - - + - + + Advanced options to customize Counterfactual Explanations — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/DiCE_with_private_data.html b/docs/notebooks/DiCE_with_private_data.html index 735477f2..76732160 100644 --- a/docs/notebooks/DiCE_with_private_data.html +++ b/docs/notebooks/DiCE_with_private_data.html @@ -1,71 +1,37 @@ - - - - - - - Generating Counterfactual Explanations without access to training data — DiCE 0.7 documentation - - - - - - - - - - + - + + Generating Counterfactual Explanations without access to training data — DiCE 0.8 documentation + + - - + - - - - - - - + + + - - - +
    - -
    - - -
    - - - - - - - - + \ No newline at end of file diff --git a/docs/notebooks/nb_index.html b/docs/notebooks/nb_index.html index 23177ec1..8d1d6522 100644 --- a/docs/notebooks/nb_index.html +++ b/docs/notebooks/nb_index.html @@ -1,71 +1,35 @@ - - - - - - - <no title> — DiCE 0.7 documentation - - - - - + - - - - - - + + <no title> — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
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    - -
    - - -
    - - - - - - - - + \ No newline at end of file diff --git a/docs/readme.html b/docs/readme.html index 58e664de..2d786adb 100644 --- a/docs/readme.html +++ b/docs/readme.html @@ -1,71 +1,35 @@ - - - - - - - Diverse Counterfactual Explanations (DiCE) for ML — DiCE 0.7 documentation - + - - - - - - - - - - + + Diverse Counterfactual Explanations (DiCE) for ML — DiCE 0.8 documentation + + - - + - - - - - - - + - - - +
    - -
    - - -
    - -
    -

    Contributing

    + +
    +

    Contributing

    This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

    @@ -437,63 +358,38 @@

    ContributingMicrosoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

    -

    - + + - - - - - - - - - - - - + \ No newline at end of file diff --git a/docs/search.html b/docs/search.html index 99e153ea..37883ffb 100644 --- a/docs/search.html +++ b/docs/search.html @@ -1,71 +1,35 @@ - - - - - - - Search — DiCE 0.7 documentation - - - - - - - - - - - - + + + Search — DiCE 0.8 documentation + + - - + - - - - - - - - + + + - - - +
    - -
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package","dice_ml.data_interfaces package","dice_ml.explainer_interfaces package","dice_ml.model_interfaces package","dice_ml.schema package","dice_ml.utils package","dice_ml.utils.sample_architecture package","Diverse Counterfactual Explanations (DiCE) for ML","dice_ml","Benchmarking different CF explanation methods","Estimating local and global feature importance scores using DiCE","Quick introduction to generating counterfactual explanations using DiCE","Generate feasible counterfactual explanations using a VAE","Generating counterfactual explanations with any ML model","Generating counterfactuals for multi-class classification and regression models","Advanced options to customize Counterfactual Explanations","Generating Counterfactual Explanations without access to training data","<no title>","<no title>","Diverse Counterfactual Explanations (DiCE) for 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\ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index d140e827..487d84d3 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -23,7 +23,7 @@ author = 'Ramaravind, Amit, Chenhao' # The full version, including alpha/beta/rc tags -release = '0.7.2' +release = '0.8' # -- General configuration --------------------------------------------------- diff --git a/docs/source/notebooks/Benchmarking_different_CF_explanation_methods.ipynb b/docs/source/notebooks/Benchmarking_different_CF_explanation_methods.ipynb index 9786d701..628da24a 100644 --- a/docs/source/notebooks/Benchmarking_different_CF_explanation_methods.ipynb +++ b/docs/source/notebooks/Benchmarking_different_CF_explanation_methods.ipynb @@ -274,7 +274,7 @@ " for q in query_instances:\n", " if q in d.categorical_feature_names:\n", " query_instances.loc[:, q] = \\\n", - " [random.choice(dataset[q].values.unique()) for _ in query_instances.index]\n", + " [np.unique(random.choice(dataset[q].values)) for _ in query_instances.index]\n", " else:\n", " query_instances.loc[:, q] = \\\n", " [np.random.uniform(dataset[q].min(), dataset[q].max()) for _ in query_instances.index]\n", diff --git a/setup.py b/setup.py index 077c1b67..03ad7aeb 100644 --- a/setup.py +++ b/setup.py @@ -1,6 +1,6 @@ import setuptools -VERSION_STR = "0.7.2" +VERSION_STR = "0.8" with open("README.rst", "r") as fh: long_description = fh.read() diff --git a/tests/conftest.py b/tests/conftest.py index 398dc307..1050b0fe 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,7 +5,7 @@ import pytest from sklearn.compose import ColumnTransformer from sklearn.datasets import fetch_california_housing, load_iris -from sklearn.ensemble import RandomForestClassifier +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor from sklearn.impute import SimpleImputer from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline @@ -116,13 +116,92 @@ def private_data_object(): return dice_ml.Data(features=features_dict, outcome_name='income') +def _save_custom_testing_model(): + numeric_trans = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler())]) + cat_trans = Pipeline(steps=[('imputer', + SimpleImputer(fill_value='missing', + strategy='constant')), + ('onehot', OneHotEncoder(handle_unknown='ignore'))]) + transformations = ColumnTransformer(transformers=[('num', numeric_trans, + ['Numerical']), + ('cat', cat_trans, + pd.Index(['Categorical'], dtype='object'))]) + clf = Pipeline(steps=[('preprocessor', transformations), + ('classifier', RandomForestClassifier())]) + dataset = helpers.load_custom_testing_dataset() + model = clf.fit(dataset[["Categorical", "Numerical"]], + dataset["Outcome"]) + modelpath = helpers.get_custom_dataset_modelpath_pipeline() + pickle.dump(model, open(modelpath, 'wb')) + + +def _save_custom_testing_binary_model(): + numeric_trans = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler())]) + cat_trans = Pipeline(steps=[('imputer', + SimpleImputer(fill_value='missing', + strategy='constant')), + ('onehot', OneHotEncoder(handle_unknown='ignore'))]) + transformations = ColumnTransformer(transformers=[('num', numeric_trans, + ['Numerical']), + ('cat', cat_trans, + pd.Index(['Categorical'], dtype='object'))]) + clf = Pipeline(steps=[('preprocessor', transformations), + ('classifier', RandomForestClassifier())]) + dataset = helpers.load_custom_testing_dataset_binary() + model = clf.fit(dataset[["Categorical", "Numerical"]], + dataset["Outcome"]) + modelpath = helpers.get_custom_dataset_modelpath_pipeline_binary() + pickle.dump(model, open(modelpath, 'wb')) + + +def _save_custom_testing_multiclass_model(): + numeric_trans = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler())]) + cat_trans = Pipeline(steps=[('imputer', + SimpleImputer(fill_value='missing', + strategy='constant')), + ('onehot', OneHotEncoder(handle_unknown='ignore'))]) + transformations = ColumnTransformer(transformers=[('num', numeric_trans, + ['Numerical']), + ('cat', cat_trans, + pd.Index(['Categorical'], dtype='object'))]) + clf = Pipeline(steps=[('preprocessor', transformations), + ('regressor', RandomForestClassifier())]) + dataset = helpers.load_custom_testing_dataset_multiclass() + model = clf.fit(dataset[["Categorical", "Numerical"]], + dataset["Outcome"]) + modelpath = helpers.get_custom_dataset_modelpath_pipeline_multiclass() + pickle.dump(model, open(modelpath, 'wb')) + + +def _save_custom_testing_regression_model(): + numeric_trans = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), + ('scaler', StandardScaler())]) + cat_trans = Pipeline(steps=[('imputer', + SimpleImputer(fill_value='missing', + strategy='constant')), + ('onehot', OneHotEncoder(handle_unknown='ignore'))]) + transformations = ColumnTransformer(transformers=[('num', numeric_trans, + ['Numerical']), + ('cat', cat_trans, + pd.Index(['Categorical'], dtype='object'))]) + clf = Pipeline(steps=[('preprocessor', transformations), + ('regressor', RandomForestRegressor())]) + dataset = helpers.load_custom_testing_dataset_regression() + model = clf.fit(dataset[["Categorical", "Numerical"]], + dataset["Outcome"]) + modelpath = helpers.get_custom_dataset_modelpath_pipeline_regression() + pickle.dump(model, open(modelpath, 'wb')) + + @pytest.fixture() def load_custom_vars_testing_dataset(): data = [['a', 0, 10, 0], ['b', 1, 10000, 0], ['c', 0, 14, 0], ['a', 2, 88, 0], ['c', 1, 14, 0]] return pd.DataFrame(data, columns=['Categorical', 'CategoricalNum', 'Numerical', 'Outcome']) -@pytest.fixture() def _save_custom_vars_dataset_model(): numeric_trans = Pipeline(steps=[('imputer', SimpleImputer(strategy='median')), ('scaler', StandardScaler())])