diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index a5b8c8b..1c32c20 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -30,8 +30,8 @@ jobs: - name: Run examples run: pip install .&&find examples -maxdepth 2 -name "*.py" -exec python3 {} \; - #- name: Publish to PyPI - # uses: pypa/gh-action-pypi-publish@release/v1 - # with: - # password: ${{ secrets.PYPI_GLOBAL_MLSAUCE }} - # repository-url: https://upload.pypi.org/legacy/ + - name: Publish to PyPI + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_GLOBAL_MLSAUCE }} + repository-url: https://upload.pypi.org/legacy/ diff --git a/CHANGES.md b/CHANGES.md index cdc9acb..fa01fbf 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,4 +1,4 @@ -# version 0.18.0 +# version 0.18.2 - Gaussian weights in `LSBoostRegressor` and `LSBoostClassifier` randomized hidden layer diff --git a/examples/lsboost_classifier.py b/examples/lsboost_classifier.py index e871b8f..048385b 100644 --- a/examples/lsboost_classifier.py +++ b/examples/lsboost_classifier.py @@ -270,3 +270,16 @@ print(time()-start) +obj = ms.LSBoostClassifier(solver="lasso", + n_clusters=3, degree=2, + clustering_method="gmm", + weights_distr="gaussian") +print(obj.get_params()) +start = time() +obj.fit(X_train, y_train) +print(time()-start) +start = time() +print(obj.score(X_test, y_test)) +print(time()-start) + + diff --git a/mlsauce-docs/mlsauce.html b/mlsauce-docs/mlsauce.html index 160baa7..7b23a9d 100644 --- a/mlsauce-docs/mlsauce.html +++ b/mlsauce-docs/mlsauce.html @@ -137,6 +137,8 @@

API Documentation

    • +
  • +
    • @@ -269,6 +271,8 @@

    API Documentation

    • +
  • +
    • @@ -1339,235 +1343,241 @@

    80 degree: int 81 degree of features interactions to include in the model 82 - 83 """ - 84 - 85 def __init__( - 86 self, - 87 n_estimators=100, - 88 learning_rate=0.1, - 89 n_hidden_features=5, - 90 reg_lambda=0.1, - 91 alpha=0.5, - 92 row_sample=1, - 93 col_sample=1, - 94 dropout=0, - 95 tolerance=1e-4, - 96 direct_link=1, - 97 verbose=1, - 98 seed=123, - 99 backend="cpu", -100 solver="ridge", -101 activation="relu", -102 n_clusters=0, -103 clustering_method="kmeans", -104 cluster_scaling="standard", -105 degree=0, -106 ): -107 if n_clusters > 0: -108 assert clustering_method in ( -109 "kmeans", -110 "gmm", -111 ), "`clustering_method` must be in ('kmeans', 'gmm')" -112 assert cluster_scaling in ( -113 "standard", -114 "robust", -115 "minmax", -116 ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')" -117 -118 assert backend in ( -119 "cpu", -120 "gpu", -121 "tpu", -122 ), "`backend` must be in ('cpu', 'gpu', 'tpu')" -123 -124 assert solver in ( -125 "ridge", -126 "lasso", -127 "enet", -128 ), "`solver` must be in ('ridge', 'lasso', 'enet')" -129 -130 sys_platform = platform.system() -131 -132 if (sys_platform == "Windows") and (backend in ("gpu", "tpu")): -133 warnings.warn( -134 "No GPU/TPU computing on Windows yet, backend set to 'cpu'" -135 ) -136 backend = "cpu" -137 -138 self.n_estimators = n_estimators -139 self.learning_rate = learning_rate -140 self.n_hidden_features = n_hidden_features -141 self.reg_lambda = reg_lambda -142 assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]" -143 self.alpha = alpha -144 self.row_sample = row_sample -145 self.col_sample = col_sample -146 self.dropout = dropout -147 self.tolerance = tolerance -148 self.direct_link = direct_link -149 self.verbose = verbose -150 self.seed = seed -151 self.backend = backend -152 self.obj = None -153 self.solver = solver -154 self.activation = activation -155 self.n_clusters = n_clusters -156 self.clustering_method = clustering_method -157 self.cluster_scaling = cluster_scaling -158 self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None -159 self.degree = degree -160 self.poly_ = None -161 -162 def fit(self, X, y, **kwargs): -163 """Fit Booster (classifier) to training data (X, y) -164 -165 Args: -166 -167 X: {array-like}, shape = [n_samples, n_features] -168 Training vectors, where n_samples is the number -169 of samples and n_features is the number of features. + 83 weights_distr: str + 84 distribution of weights for constructing the model's hidden layer; + 85 currently 'uniform', 'gaussian' + 86 + 87 """ + 88 + 89 def __init__( + 90 self, + 91 n_estimators=100, + 92 learning_rate=0.1, + 93 n_hidden_features=5, + 94 reg_lambda=0.1, + 95 alpha=0.5, + 96 row_sample=1, + 97 col_sample=1, + 98 dropout=0, + 99 tolerance=1e-4, +100 direct_link=1, +101 verbose=1, +102 seed=123, +103 backend="cpu", +104 solver="ridge", +105 activation="relu", +106 n_clusters=0, +107 clustering_method="kmeans", +108 cluster_scaling="standard", +109 degree=0, +110 weights_distr="uniform", +111 ): +112 if n_clusters > 0: +113 assert clustering_method in ( +114 "kmeans", +115 "gmm", +116 ), "`clustering_method` must be in ('kmeans', 'gmm')" +117 assert cluster_scaling in ( +118 "standard", +119 "robust", +120 "minmax", +121 ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')" +122 +123 assert backend in ( +124 "cpu", +125 "gpu", +126 "tpu", +127 ), "`backend` must be in ('cpu', 'gpu', 'tpu')" +128 +129 assert solver in ( +130 "ridge", +131 "lasso", +132 "enet", +133 ), "`solver` must be in ('ridge', 'lasso', 'enet')" +134 +135 sys_platform = platform.system() +136 +137 if (sys_platform == "Windows") and (backend in ("gpu", "tpu")): +138 warnings.warn( +139 "No GPU/TPU computing on Windows yet, backend set to 'cpu'" +140 ) +141 backend = "cpu" +142 +143 self.n_estimators = n_estimators +144 self.learning_rate = learning_rate +145 self.n_hidden_features = n_hidden_features +146 self.reg_lambda = reg_lambda +147 assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]" +148 self.alpha = alpha +149 self.row_sample = row_sample +150 self.col_sample = col_sample +151 self.dropout = dropout +152 self.tolerance = tolerance +153 self.direct_link = direct_link +154 self.verbose = verbose +155 self.seed = seed +156 self.backend = backend +157 self.obj = None +158 self.solver = solver +159 self.activation = activation +160 self.n_clusters = n_clusters +161 self.clustering_method = clustering_method +162 self.cluster_scaling = cluster_scaling +163 self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None +164 self.degree = degree +165 self.poly_ = None +166 self.weights_distr = weights_distr +167 +168 def fit(self, X, y, **kwargs): +169 """Fit Booster (classifier) to training data (X, y) 170 -171 y: array-like, shape = [n_samples] -172 Target values. -173 -174 **kwargs: additional parameters to be passed to self.cook_training_set. -175 -176 Returns: -177 -178 self: object. -179 """ -180 -181 if isinstance(X, pd.DataFrame): -182 X = X.values +171 Args: +172 +173 X: {array-like}, shape = [n_samples, n_features] +174 Training vectors, where n_samples is the number +175 of samples and n_features is the number of features. +176 +177 y: array-like, shape = [n_samples] +178 Target values. +179 +180 **kwargs: additional parameters to be passed to self.cook_training_set. +181 +182 Returns: 183 -184 if self.degree > 1: -185 self.poly_ = PolynomialFeatures( -186 degree=self.degree, interaction_only=True, include_bias=False -187 ) -188 X = self.poly_.fit_transform(X) +184 self: object. +185 """ +186 +187 if isinstance(X, pd.DataFrame): +188 X = X.values 189 -190 if self.n_clusters > 0: -191 clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = ( -192 cluster( -193 X, -194 n_clusters=self.n_clusters, -195 method=self.clustering_method, -196 type_scaling=self.cluster_scaling, -197 training=True, -198 seed=self.seed, -199 ) -200 ) -201 X = np.column_stack((X, clustered_X)) -202 -203 try: -204 self.obj = boosterc.fit_booster_classifier( -205 np.asarray(X, order="C"), -206 np.asarray(y, order="C"), -207 n_estimators=self.n_estimators, -208 learning_rate=self.learning_rate, -209 n_hidden_features=self.n_hidden_features, -210 reg_lambda=self.reg_lambda, -211 alpha=self.alpha, -212 row_sample=self.row_sample, -213 col_sample=self.col_sample, -214 dropout=self.dropout, -215 tolerance=self.tolerance, -216 direct_link=self.direct_link, -217 verbose=self.verbose, -218 seed=self.seed, -219 backend=self.backend, -220 solver=self.solver, -221 activation=self.activation, -222 ) -223 except ValueError: -224 self.obj = _boosterc.fit_booster_classifier( -225 np.asarray(X, order="C"), -226 np.asarray(y, order="C"), -227 n_estimators=self.n_estimators, -228 learning_rate=self.learning_rate, -229 n_hidden_features=self.n_hidden_features, -230 reg_lambda=self.reg_lambda, -231 alpha=self.alpha, -232 row_sample=self.row_sample, -233 col_sample=self.col_sample, -234 dropout=self.dropout, -235 tolerance=self.tolerance, -236 direct_link=self.direct_link, -237 verbose=self.verbose, -238 seed=self.seed, -239 backend=self.backend, -240 solver=self.solver, -241 activation=self.activation, -242 ) -243 -244 self.n_classes_ = len(np.unique(y)) # for compatibility with sklearn -245 self.n_estimators = self.obj["n_estimators"] -246 return self -247 -248 def predict(self, X, **kwargs): -249 """Predict test data X. -250 -251 Args: -252 -253 X: {array-like}, shape = [n_samples, n_features] -254 Training vectors, where n_samples is the number -255 of samples and n_features is the number of features. +190 if self.degree > 1: +191 self.poly_ = PolynomialFeatures( +192 degree=self.degree, interaction_only=True, include_bias=False +193 ) +194 X = self.poly_.fit_transform(X) +195 +196 if self.n_clusters > 0: +197 clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = ( +198 cluster( +199 X, +200 n_clusters=self.n_clusters, +201 method=self.clustering_method, +202 type_scaling=self.cluster_scaling, +203 training=True, +204 seed=self.seed, +205 ) +206 ) +207 X = np.column_stack((X, clustered_X)) +208 +209 try: +210 self.obj = boosterc.fit_booster_classifier( +211 np.asarray(X, order="C"), +212 np.asarray(y, order="C"), +213 n_estimators=self.n_estimators, +214 learning_rate=self.learning_rate, +215 n_hidden_features=self.n_hidden_features, +216 reg_lambda=self.reg_lambda, +217 alpha=self.alpha, +218 row_sample=self.row_sample, +219 col_sample=self.col_sample, +220 dropout=self.dropout, +221 tolerance=self.tolerance, +222 direct_link=self.direct_link, +223 verbose=self.verbose, +224 seed=self.seed, +225 backend=self.backend, +226 solver=self.solver, +227 activation=self.activation, +228 ) +229 except ValueError: +230 self.obj = _boosterc.fit_booster_classifier( +231 np.asarray(X, order="C"), +232 np.asarray(y, order="C"), +233 n_estimators=self.n_estimators, +234 learning_rate=self.learning_rate, +235 n_hidden_features=self.n_hidden_features, +236 reg_lambda=self.reg_lambda, +237 alpha=self.alpha, +238 row_sample=self.row_sample, +239 col_sample=self.col_sample, +240 dropout=self.dropout, +241 tolerance=self.tolerance, +242 direct_link=self.direct_link, +243 verbose=self.verbose, +244 seed=self.seed, +245 backend=self.backend, +246 solver=self.solver, +247 activation=self.activation, +248 ) +249 +250 self.n_classes_ = len(np.unique(y)) # for compatibility with sklearn +251 self.n_estimators = self.obj["n_estimators"] +252 return self +253 +254 def predict(self, X, **kwargs): +255 """Predict test data X. 256 -257 **kwargs: additional parameters to be passed to `predict_proba` +257 Args: 258 -259 -260 Returns: -261 -262 model predictions: {array-like} -263 """ +259 X: {array-like}, shape = [n_samples, n_features] +260 Training vectors, where n_samples is the number +261 of samples and n_features is the number of features. +262 +263 **kwargs: additional parameters to be passed to `predict_proba` 264 -265 return np.argmax(self.predict_proba(X, **kwargs), axis=1) -266 -267 def predict_proba(self, X, **kwargs): -268 """Predict probabilities for test data X. -269 -270 Args: -271 -272 X: {array-like}, shape = [n_samples, n_features] -273 Training vectors, where n_samples is the number -274 of samples and n_features is the number of features. +265 +266 Returns: +267 +268 model predictions: {array-like} +269 """ +270 +271 return np.argmax(self.predict_proba(X, **kwargs), axis=1) +272 +273 def predict_proba(self, X, **kwargs): +274 """Predict probabilities for test data X. 275 -276 **kwargs: additional parameters to be passed to -277 self.cook_test_set -278 -279 Returns: -280 -281 probability estimates for test data: {array-like} -282 """ -283 -284 if isinstance(X, pd.DataFrame): -285 X = X.values +276 Args: +277 +278 X: {array-like}, shape = [n_samples, n_features] +279 Training vectors, where n_samples is the number +280 of samples and n_features is the number of features. +281 +282 **kwargs: additional parameters to be passed to +283 self.cook_test_set +284 +285 Returns: 286 -287 if self.degree > 0: -288 X = self.poly_.transform(X) +287 probability estimates for test data: {array-like} +288 """ 289 -290 if self.n_clusters > 0: -291 X = np.column_stack( -292 ( -293 X, -294 cluster( -295 X, -296 training=False, -297 scaler=self.scaler_, -298 label_encoder=self.label_encoder_, -299 clusterer=self.clusterer_, -300 seed=self.seed, -301 ), -302 ) -303 ) -304 try: -305 return boosterc.predict_proba_booster_classifier( -306 self.obj, np.asarray(X, order="C") -307 ) -308 except ValueError: -309 return _boosterc.predict_proba_booster_classifier( -310 self.obj, np.asarray(X, order="C") -311 ) +290 if isinstance(X, pd.DataFrame): +291 X = X.values +292 +293 if self.degree > 0: +294 X = self.poly_.transform(X) +295 +296 if self.n_clusters > 0: +297 X = np.column_stack( +298 ( +299 X, +300 cluster( +301 X, +302 training=False, +303 scaler=self.scaler_, +304 label_encoder=self.label_encoder_, +305 clusterer=self.clusterer_, +306 seed=self.seed, +307 ), +308 ) +309 ) +310 try: +311 return boosterc.predict_proba_booster_classifier( +312 self.obj, np.asarray(X, order="C") +313 ) +314 except ValueError: +315 return _boosterc.predict_proba_booster_classifier( +316 self.obj, np.asarray(X, order="C") +317 ) @@ -1635,6 +1645,10 @@

    degree: int degree of features interactions to include in the model + +weights_distr: str + distribution of weights for constructing the model's hidden layer; + currently 'uniform', 'gaussian' @@ -1650,91 +1664,91 @@

    - 
    162    def fit(self, X, y, **kwargs):
-163        """Fit Booster (classifier) to training data (X, y)
-164
-165        Args:
-166
-167            X: {array-like}, shape = [n_samples, n_features]
-168                Training vectors, where n_samples is the number
-169                of samples and n_features is the number of features.
+            
    168    def fit(self, X, y, **kwargs):
+169        """Fit Booster (classifier) to training data (X, y)
 170
-171            y: array-like, shape = [n_samples]
-172                Target values.
-173
-174            **kwargs: additional parameters to be passed to self.cook_training_set.
-175
-176        Returns:
-177
-178            self: object.
-179        """
-180
-181        if isinstance(X, pd.DataFrame):
-182            X = X.values
+171        Args:
+172
+173            X: {array-like}, shape = [n_samples, n_features]
+174                Training vectors, where n_samples is the number
+175                of samples and n_features is the number of features.
+176
+177            y: array-like, shape = [n_samples]
+178                Target values.
+179
+180            **kwargs: additional parameters to be passed to self.cook_training_set.
+181
+182        Returns:
 183
-184        if self.degree > 1:
-185            self.poly_ = PolynomialFeatures(
-186                degree=self.degree, interaction_only=True, include_bias=False
-187            )
-188            X = self.poly_.fit_transform(X)
+184            self: object.
+185        """
+186
+187        if isinstance(X, pd.DataFrame):
+188            X = X.values
 189
-190        if self.n_clusters > 0:
-191            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-192                cluster(
-193                    X,
-194                    n_clusters=self.n_clusters,
-195                    method=self.clustering_method,
-196                    type_scaling=self.cluster_scaling,
-197                    training=True,
-198                    seed=self.seed,
-199                )
-200            )
-201            X = np.column_stack((X, clustered_X))
-202
-203        try:
-204            self.obj = boosterc.fit_booster_classifier(
-205                np.asarray(X, order="C"),
-206                np.asarray(y, order="C"),
-207                n_estimators=self.n_estimators,
-208                learning_rate=self.learning_rate,
-209                n_hidden_features=self.n_hidden_features,
-210                reg_lambda=self.reg_lambda,
-211                alpha=self.alpha,
-212                row_sample=self.row_sample,
-213                col_sample=self.col_sample,
-214                dropout=self.dropout,
-215                tolerance=self.tolerance,
-216                direct_link=self.direct_link,
-217                verbose=self.verbose,
-218                seed=self.seed,
-219                backend=self.backend,
-220                solver=self.solver,
-221                activation=self.activation,
-222            )
-223        except ValueError:
-224            self.obj = _boosterc.fit_booster_classifier(
-225                np.asarray(X, order="C"),
-226                np.asarray(y, order="C"),
-227                n_estimators=self.n_estimators,
-228                learning_rate=self.learning_rate,
-229                n_hidden_features=self.n_hidden_features,
-230                reg_lambda=self.reg_lambda,
-231                alpha=self.alpha,
-232                row_sample=self.row_sample,
-233                col_sample=self.col_sample,
-234                dropout=self.dropout,
-235                tolerance=self.tolerance,
-236                direct_link=self.direct_link,
-237                verbose=self.verbose,
-238                seed=self.seed,
-239                backend=self.backend,
-240                solver=self.solver,
-241                activation=self.activation,
-242            )
-243
-244        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
-245        self.n_estimators = self.obj["n_estimators"]
-246        return self
+190        if self.degree > 1:
+191            self.poly_ = PolynomialFeatures(
+192                degree=self.degree, interaction_only=True, include_bias=False
+193            )
+194            X = self.poly_.fit_transform(X)
+195
+196        if self.n_clusters > 0:
+197            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+198                cluster(
+199                    X,
+200                    n_clusters=self.n_clusters,
+201                    method=self.clustering_method,
+202                    type_scaling=self.cluster_scaling,
+203                    training=True,
+204                    seed=self.seed,
+205                )
+206            )
+207            X = np.column_stack((X, clustered_X))
+208
+209        try:
+210            self.obj = boosterc.fit_booster_classifier(
+211                np.asarray(X, order="C"),
+212                np.asarray(y, order="C"),
+213                n_estimators=self.n_estimators,
+214                learning_rate=self.learning_rate,
+215                n_hidden_features=self.n_hidden_features,
+216                reg_lambda=self.reg_lambda,
+217                alpha=self.alpha,
+218                row_sample=self.row_sample,
+219                col_sample=self.col_sample,
+220                dropout=self.dropout,
+221                tolerance=self.tolerance,
+222                direct_link=self.direct_link,
+223                verbose=self.verbose,
+224                seed=self.seed,
+225                backend=self.backend,
+226                solver=self.solver,
+227                activation=self.activation,
+228            )
+229        except ValueError:
+230            self.obj = _boosterc.fit_booster_classifier(
+231                np.asarray(X, order="C"),
+232                np.asarray(y, order="C"),
+233                n_estimators=self.n_estimators,
+234                learning_rate=self.learning_rate,
+235                n_hidden_features=self.n_hidden_features,
+236                reg_lambda=self.reg_lambda,
+237                alpha=self.alpha,
+238                row_sample=self.row_sample,
+239                col_sample=self.col_sample,
+240                dropout=self.dropout,
+241                tolerance=self.tolerance,
+242                direct_link=self.direct_link,
+243                verbose=self.verbose,
+244                seed=self.seed,
+245                backend=self.backend,
+246                solver=self.solver,
+247                activation=self.activation,
+248            )
+249
+250        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
+251        self.n_estimators = self.obj["n_estimators"]
+252        return self
 
    
 
 
@@ -1771,24 +1785,24 @@
    - 
    248    def predict(self, X, **kwargs):
-249        """Predict test data X.
-250
-251        Args:
-252
-253            X: {array-like}, shape = [n_samples, n_features]
-254                Training vectors, where n_samples is the number
-255                of samples and n_features is the number of features.
+            
    254    def predict(self, X, **kwargs):
+255        """Predict test data X.
 256
-257            **kwargs: additional parameters to be passed to `predict_proba`
+257        Args:
 258
-259
-260        Returns:
-261
-262            model predictions: {array-like}
-263        """
+259            X: {array-like}, shape = [n_samples, n_features]
+260                Training vectors, where n_samples is the number
+261                of samples and n_features is the number of features.
+262
+263            **kwargs: additional parameters to be passed to `predict_proba`
 264
-265        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
+265
+266        Returns:
+267
+268            model predictions: {array-like}
+269        """
+270
+271        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
 
    
 
 
@@ -1822,51 +1836,51 @@
    - 
    267    def predict_proba(self, X, **kwargs):
-268        """Predict probabilities for test data X.
-269
-270        Args:
-271
-272            X: {array-like}, shape = [n_samples, n_features]
-273                Training vectors, where n_samples is the number
-274                of samples and n_features is the number of features.
+            
    273    def predict_proba(self, X, **kwargs):
+274        """Predict probabilities for test data X.
 275
-276            **kwargs: additional parameters to be passed to
-277                self.cook_test_set
-278
-279        Returns:
-280
-281            probability estimates for test data: {array-like}
-282        """
-283
-284        if isinstance(X, pd.DataFrame):
-285            X = X.values
+276        Args:
+277
+278            X: {array-like}, shape = [n_samples, n_features]
+279                Training vectors, where n_samples is the number
+280                of samples and n_features is the number of features.
+281
+282            **kwargs: additional parameters to be passed to
+283                self.cook_test_set
+284
+285        Returns:
 286
-287        if self.degree > 0:
-288            X = self.poly_.transform(X)
+287            probability estimates for test data: {array-like}
+288        """
 289
-290        if self.n_clusters > 0:
-291            X = np.column_stack(
-292                (
-293                    X,
-294                    cluster(
-295                        X,
-296                        training=False,
-297                        scaler=self.scaler_,
-298                        label_encoder=self.label_encoder_,
-299                        clusterer=self.clusterer_,
-300                        seed=self.seed,
-301                    ),
-302                )
-303            )
-304        try:
-305            return boosterc.predict_proba_booster_classifier(
-306                self.obj, np.asarray(X, order="C")
-307            )
-308        except ValueError:
-309            return _boosterc.predict_proba_booster_classifier(
-310                self.obj, np.asarray(X, order="C")
-311            )
+290        if isinstance(X, pd.DataFrame):
+291            X = X.values
+292
+293        if self.degree > 0:
+294            X = self.poly_.transform(X)
+295
+296        if self.n_clusters > 0:
+297            X = np.column_stack(
+298                (
+299                    X,
+300                    cluster(
+301                        X,
+302                        training=False,
+303                        scaler=self.scaler_,
+304                        label_encoder=self.label_encoder_,
+305                        clusterer=self.clusterer_,
+306                        seed=self.seed,
+307                    ),
+308                )
+309            )
+310        try:
+311            return boosterc.predict_proba_booster_classifier(
+312                self.obj, np.asarray(X, order="C")
+313            )
+314        except ValueError:
+315            return _boosterc.predict_proba_booster_classifier(
+316                self.obj, np.asarray(X, order="C")
+317            )
 
    
 
 
@@ -2789,327 +2803,333 @@
    - 
     14class LSBoostRegressor(BaseEstimator, RegressorMixin):
- 15    """LSBoost regressor.
- 16
- 17    Attributes:
- 18
- 19        n_estimators: int
- 20            number of boosting iterations.
- 21
- 22        learning_rate: float
- 23            controls the learning speed at training time.
- 24
- 25        n_hidden_features: int
- 26            number of nodes in successive hidden layers.
- 27
- 28        reg_lambda: float
- 29            L2 regularization parameter for successive errors in the optimizer
- 30            (at training time).
+            
     18class LSBoostRegressor(BaseEstimator, RegressorMixin):
+ 19    """LSBoost regressor.
+ 20
+ 21    Attributes:
+ 22
+ 23        n_estimators: int
+ 24            number of boosting iterations.
+ 25
+ 26        learning_rate: float
+ 27            controls the learning speed at training time.
+ 28
+ 29        n_hidden_features: int
+ 30            number of nodes in successive hidden layers.
  31
- 32        alpha: float
- 33            compromise between L1 and L2 regularization (must be in [0, 1]),
- 34            for `solver` == 'enet'
+ 32        reg_lambda: float
+ 33            L2 regularization parameter for successive errors in the optimizer
+ 34            (at training time).
  35
- 36        row_sample: float
- 37            percentage of rows chosen from the training set.
- 38
- 39        col_sample: float
- 40            percentage of columns chosen from the training set.
- 41
- 42        dropout: float
- 43            percentage of nodes dropped from the training set.
- 44
- 45        tolerance: float
- 46            controls early stopping in gradient descent (at training time).
- 47
- 48        direct_link: bool
- 49            indicates whether the original features are included (True) in model's
- 50            fitting or not (False).
+ 36        alpha: float
+ 37            compromise between L1 and L2 regularization (must be in [0, 1]),
+ 38            for `solver` == 'enet'
+ 39
+ 40        row_sample: float
+ 41            percentage of rows chosen from the training set.
+ 42
+ 43        col_sample: float
+ 44            percentage of columns chosen from the training set.
+ 45
+ 46        dropout: float
+ 47            percentage of nodes dropped from the training set.
+ 48
+ 49        tolerance: float
+ 50            controls early stopping in gradient descent (at training time).
  51
- 52        verbose: int
- 53            progress bar (yes = 1) or not (no = 0) (currently).
- 54
- 55        seed: int
- 56            reproducibility seed for nodes_sim=='uniform', clustering and dropout.
- 57
- 58        backend: str
- 59            type of backend; must be in ('cpu', 'gpu', 'tpu')
- 60
- 61        solver: str
- 62            type of 'weak' learner; currently in ('ridge', 'lasso')
- 63
- 64        activation: str
- 65            activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'
- 66
- 67        type_pi: str.
- 68            type of prediction interval; currently "kde" (default) or "bootstrap".
- 69            Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`
- 70            in ('gaussian', 'tophat'). Default is `None`.
- 71
- 72        replications: int.
- 73            number of replications (if needed) for predictive simulation.
- 74            Used only in `self.predict`, for `self.kernel` in ('gaussian',
- 75            'tophat') and `self.type_pi = 'kde'`. Default is `None`.
- 76
- 77        n_clusters: int
- 78            number of clusters for clustering the features
- 79
- 80        clustering_method: str
- 81            clustering method: currently 'kmeans', 'gmm'
- 82
- 83        cluster_scaling: str
- 84            scaling method for clustering: currently 'standard', 'robust', 'minmax'
- 85
- 86        degree: int
- 87            degree of features interactions to include in the model
- 88
- 89    """
- 90
- 91    def __init__(
- 92        self,
- 93        n_estimators=100,
- 94        learning_rate=0.1,
- 95        n_hidden_features=5,
- 96        reg_lambda=0.1,
- 97        alpha=0.5,
- 98        row_sample=1,
- 99        col_sample=1,
-100        dropout=0,
-101        tolerance=1e-4,
-102        direct_link=1,
-103        verbose=1,
-104        seed=123,
-105        backend="cpu",
-106        solver="ridge",
-107        activation="relu",
-108        type_pi=None,
-109        replications=None,
-110        kernel=None,
-111        n_clusters=0,
-112        clustering_method="kmeans",
-113        cluster_scaling="standard",
-114        degree=0,
-115    ):
-116        if n_clusters > 0:
-117            assert clustering_method in (
-118                "kmeans",
-119                "gmm",
-120            ), "`clustering_method` must be in ('kmeans', 'gmm')"
-121            assert cluster_scaling in (
-122                "standard",
-123                "robust",
-124                "minmax",
-125            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
-126
-127        assert backend in (
-128            "cpu",
-129            "gpu",
-130            "tpu",
-131        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
-132
-133        assert solver in (
-134            "ridge",
-135            "lasso",
-136            "enet",
-137        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
-138
-139        sys_platform = platform.system()
-140
-141        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
-142            warnings.warn(
-143                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
-144            )
-145            backend = "cpu"
-146
-147        self.n_estimators = n_estimators
-148        self.learning_rate = learning_rate
-149        self.n_hidden_features = n_hidden_features
-150        self.reg_lambda = reg_lambda
-151        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
-152        self.alpha = alpha
-153        self.row_sample = row_sample
-154        self.col_sample = col_sample
-155        self.dropout = dropout
-156        self.tolerance = tolerance
-157        self.direct_link = direct_link
-158        self.verbose = verbose
-159        self.seed = seed
-160        self.backend = backend
-161        self.obj = None
-162        self.solver = solver
-163        self.activation = activation
-164        self.type_pi = type_pi
-165        self.replications = replications
-166        self.kernel = kernel
-167        self.n_clusters = n_clusters
-168        self.clustering_method = clustering_method
-169        self.cluster_scaling = cluster_scaling
-170        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
-171        self.degree = degree
-172        self.poly_ = None
-173
-174    def fit(self, X, y, **kwargs):
-175        """Fit Booster (regressor) to training data (X, y)
-176
-177        Args:
-178
-179            X: {array-like}, shape = [n_samples, n_features]
-180                Training vectors, where n_samples is the number
-181                of samples and n_features is the number of features.
-182
-183            y: array-like, shape = [n_samples]
-184               Target values.
-185
-186            **kwargs: additional parameters to be passed to self.cook_training_set.
-187
-188        Returns:
-189
-190            self: object.
-191        """
+ 52        direct_link: bool
+ 53            indicates whether the original features are included (True) in model's
+ 54            fitting or not (False).
+ 55
+ 56        verbose: int
+ 57            progress bar (yes = 1) or not (no = 0) (currently).
+ 58
+ 59        seed: int
+ 60            reproducibility seed for nodes_sim=='uniform', clustering and dropout.
+ 61
+ 62        backend: str
+ 63            type of backend; must be in ('cpu', 'gpu', 'tpu')
+ 64
+ 65        solver: str
+ 66            type of 'weak' learner; currently in ('ridge', 'lasso')
+ 67
+ 68        activation: str
+ 69            activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'
+ 70
+ 71        type_pi: str.
+ 72            type of prediction interval; currently "kde" (default) or "bootstrap".
+ 73            Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`
+ 74            in ('gaussian', 'tophat'). Default is `None`.
+ 75
+ 76        replications: int.
+ 77            number of replications (if needed) for predictive simulation.
+ 78            Used only in `self.predict`, for `self.kernel` in ('gaussian',
+ 79            'tophat') and `self.type_pi = 'kde'`. Default is `None`.
+ 80
+ 81        n_clusters: int
+ 82            number of clusters for clustering the features
+ 83
+ 84        clustering_method: str
+ 85            clustering method: currently 'kmeans', 'gmm'
+ 86
+ 87        cluster_scaling: str
+ 88            scaling method for clustering: currently 'standard', 'robust', 'minmax'
+ 89
+ 90        degree: int
+ 91            degree of features interactions to include in the model
+ 92
+ 93        weights_distr: str
+ 94            distribution of weights for constructing the model's hidden layer;
+ 95            either 'uniform' or 'gaussian'
+ 96
+ 97    """
+ 98
+ 99    def __init__(
+100        self,
+101        n_estimators=100,
+102        learning_rate=0.1,
+103        n_hidden_features=5,
+104        reg_lambda=0.1,
+105        alpha=0.5,
+106        row_sample=1,
+107        col_sample=1,
+108        dropout=0,
+109        tolerance=1e-4,
+110        direct_link=1,
+111        verbose=1,
+112        seed=123,
+113        backend="cpu",
+114        solver="ridge",
+115        activation="relu",
+116        type_pi=None,
+117        replications=None,
+118        kernel=None,
+119        n_clusters=0,
+120        clustering_method="kmeans",
+121        cluster_scaling="standard",
+122        degree=0,
+123        weights_distr="uniform",
+124    ):
+125        if n_clusters > 0:
+126            assert clustering_method in (
+127                "kmeans",
+128                "gmm",
+129            ), "`clustering_method` must be in ('kmeans', 'gmm')"
+130            assert cluster_scaling in (
+131                "standard",
+132                "robust",
+133                "minmax",
+134            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
+135
+136        assert backend in (
+137            "cpu",
+138            "gpu",
+139            "tpu",
+140        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
+141
+142        assert solver in (
+143            "ridge",
+144            "lasso",
+145            "enet",
+146        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
+147
+148        sys_platform = platform.system()
+149
+150        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
+151            warnings.warn(
+152                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
+153            )
+154            backend = "cpu"
+155
+156        self.n_estimators = n_estimators
+157        self.learning_rate = learning_rate
+158        self.n_hidden_features = n_hidden_features
+159        self.reg_lambda = reg_lambda
+160        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
+161        self.alpha = alpha
+162        self.row_sample = row_sample
+163        self.col_sample = col_sample
+164        self.dropout = dropout
+165        self.tolerance = tolerance
+166        self.direct_link = direct_link
+167        self.verbose = verbose
+168        self.seed = seed
+169        self.backend = backend
+170        self.obj = None
+171        self.solver = solver
+172        self.activation = activation
+173        self.type_pi = type_pi
+174        self.replications = replications
+175        self.kernel = kernel
+176        self.n_clusters = n_clusters
+177        self.clustering_method = clustering_method
+178        self.cluster_scaling = cluster_scaling
+179        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
+180        self.degree = degree
+181        self.poly_ = None
+182        self.weights_distr = weights_distr
+183
+184    def fit(self, X, y, **kwargs):
+185        """Fit Booster (regressor) to training data (X, y)
+186
+187        Args:
+188
+189            X: {array-like}, shape = [n_samples, n_features]
+190                Training vectors, where n_samples is the number
+191                of samples and n_features is the number of features.
 192
-193        if isinstance(X, pd.DataFrame):
-194            X = X.values
+193            y: array-like, shape = [n_samples]
+194               Target values.
 195
-196        if self.degree > 1:
-197            self.poly_ = PolynomialFeatures(
-198                degree=self.degree, interaction_only=True, include_bias=False
-199            )
-200            X = self.poly_.fit_transform(X)
-201
-202        if self.n_clusters > 0:
-203            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-204                cluster(
-205                    X,
-206                    n_clusters=self.n_clusters,
-207                    method=self.clustering_method,
-208                    type_scaling=self.cluster_scaling,
-209                    training=True,
-210                    seed=self.seed,
-211                )
-212            )
-213            X = np.column_stack((X, clustered_X))
-214
-215        try:
-216            self.obj = boosterc.fit_booster_regressor(
-217                X=np.asarray(X, order="C"),
-218                y=np.asarray(y, order="C"),
-219                n_estimators=self.n_estimators,
-220                learning_rate=self.learning_rate,
-221                n_hidden_features=self.n_hidden_features,
-222                reg_lambda=self.reg_lambda,
-223                alpha=self.alpha,
-224                row_sample=self.row_sample,
-225                col_sample=self.col_sample,
-226                dropout=self.dropout,
-227                tolerance=self.tolerance,
-228                direct_link=self.direct_link,
-229                verbose=self.verbose,
-230                seed=self.seed,
-231                backend=self.backend,
-232                solver=self.solver,
-233                activation=self.activation,
-234            )
-235        except ValueError:
-236            self.obj = _boosterc.fit_booster_regressor(
-237                X=np.asarray(X, order="C"),
-238                y=np.asarray(y, order="C"),
-239                n_estimators=self.n_estimators,
-240                learning_rate=self.learning_rate,
-241                n_hidden_features=self.n_hidden_features,
-242                reg_lambda=self.reg_lambda,
-243                alpha=self.alpha,
-244                row_sample=self.row_sample,
-245                col_sample=self.col_sample,
-246                dropout=self.dropout,
-247                tolerance=self.tolerance,
-248                direct_link=self.direct_link,
-249                verbose=self.verbose,
-250                seed=self.seed,
-251                backend=self.backend,
-252                solver=self.solver,
-253                activation=self.activation,
-254            )
-255
-256        self.n_estimators = self.obj["n_estimators"]
-257
-258        self.X_ = X
-259
-260        self.y_ = y
-261
-262        return self
-263
-264    def predict(self, X, level=95, method=None, **kwargs):
-265        """Predict probabilities for test data X.
-266
-267        Args:
-268
-269            X: {array-like}, shape = [n_samples, n_features]
-270                Training vectors, where n_samples is the number
-271                of samples and n_features is the number of features.
-272
-273            level: int
-274                Level of confidence (default = 95)
-275
-276            method: str
-277                `None`, or 'splitconformal', 'localconformal'
-278                prediction (if you specify `return_pi = True`)
-279
-280            **kwargs: additional parameters to be passed to
-281                self.cook_test_set
+196            **kwargs: additional parameters to be passed to self.cook_training_set.
+197
+198        Returns:
+199
+200            self: object.
+201        """
+202
+203        if isinstance(X, pd.DataFrame):
+204            X = X.values
+205
+206        if self.degree > 1:
+207            self.poly_ = PolynomialFeatures(
+208                degree=self.degree, interaction_only=True, include_bias=False
+209            )
+210            X = self.poly_.fit_transform(X)
+211
+212        if self.n_clusters > 0:
+213            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+214                cluster(
+215                    X,
+216                    n_clusters=self.n_clusters,
+217                    method=self.clustering_method,
+218                    type_scaling=self.cluster_scaling,
+219                    training=True,
+220                    seed=self.seed,
+221                )
+222            )
+223            X = np.column_stack((X, clustered_X))
+224
+225        try:
+226            self.obj = boosterc.fit_booster_regressor(
+227                X=np.asarray(X, order="C"),
+228                y=np.asarray(y, order="C"),
+229                n_estimators=self.n_estimators,
+230                learning_rate=self.learning_rate,
+231                n_hidden_features=self.n_hidden_features,
+232                reg_lambda=self.reg_lambda,
+233                alpha=self.alpha,
+234                row_sample=self.row_sample,
+235                col_sample=self.col_sample,
+236                dropout=self.dropout,
+237                tolerance=self.tolerance,
+238                direct_link=self.direct_link,
+239                verbose=self.verbose,
+240                seed=self.seed,
+241                backend=self.backend,
+242                solver=self.solver,
+243                activation=self.activation,
+244            )
+245        except ValueError:
+246            self.obj = _boosterc.fit_booster_regressor(
+247                X=np.asarray(X, order="C"),
+248                y=np.asarray(y, order="C"),
+249                n_estimators=self.n_estimators,
+250                learning_rate=self.learning_rate,
+251                n_hidden_features=self.n_hidden_features,
+252                reg_lambda=self.reg_lambda,
+253                alpha=self.alpha,
+254                row_sample=self.row_sample,
+255                col_sample=self.col_sample,
+256                dropout=self.dropout,
+257                tolerance=self.tolerance,
+258                direct_link=self.direct_link,
+259                verbose=self.verbose,
+260                seed=self.seed,
+261                backend=self.backend,
+262                solver=self.solver,
+263                activation=self.activation,
+264            )
+265
+266        self.n_estimators = self.obj["n_estimators"]
+267
+268        self.X_ = X
+269
+270        self.y_ = y
+271
+272        return self
+273
+274    def predict(self, X, level=95, method=None, **kwargs):
+275        """Predict probabilities for test data X.
+276
+277        Args:
+278
+279            X: {array-like}, shape = [n_samples, n_features]
+280                Training vectors, where n_samples is the number
+281                of samples and n_features is the number of features.
 282
-283        Returns:
-284
-285            probability estimates for test data: {array-like}
-286        """
-287
-288        if isinstance(X, pd.DataFrame):
-289            X = X.values
-290
-291        if self.degree > 0:
-292            X = self.poly_.transform(X)
-293
-294        if self.n_clusters > 0:
-295            X = np.column_stack(
-296                (
-297                    X,
-298                    cluster(
-299                        X,
-300                        training=False,
-301                        scaler=self.scaler_,
-302                        label_encoder=self.label_encoder_,
-303                        clusterer=self.clusterer_,
-304                        seed=self.seed,
-305                    ),
-306                )
-307            )
-308        if "return_pi" in kwargs:
-309            assert method in (
-310                "splitconformal",
-311                "localconformal",
-312            ), "method must be in ('splitconformal', 'localconformal')"
-313            self.pi = PredictionInterval(
-314                obj=self,
-315                method=method,
-316                level=level,
-317                type_pi=self.type_pi,
-318                replications=self.replications,
-319                kernel=self.kernel,
-320            )
-321            self.pi.fit(self.X_, self.y_)
-322            self.X_ = None
-323            self.y_ = None
-324            preds = self.pi.predict(X, return_pi=True)
-325            return preds
-326
-327        try:
-328            return boosterc.predict_booster_regressor(
-329                self.obj, np.asarray(X, order="C")
+283            level: int
+284                Level of confidence (default = 95)
+285
+286            method: str
+287                `None`, or 'splitconformal', 'localconformal'
+288                prediction (if you specify `return_pi = True`)
+289
+290            **kwargs: additional parameters to be passed to
+291                self.cook_test_set
+292
+293        Returns:
+294
+295            probability estimates for test data: {array-like}
+296        """
+297
+298        if isinstance(X, pd.DataFrame):
+299            X = X.values
+300
+301        if self.degree > 0:
+302            X = self.poly_.transform(X)
+303
+304        if self.n_clusters > 0:
+305            X = np.column_stack(
+306                (
+307                    X,
+308                    cluster(
+309                        X,
+310                        training=False,
+311                        scaler=self.scaler_,
+312                        label_encoder=self.label_encoder_,
+313                        clusterer=self.clusterer_,
+314                        seed=self.seed,
+315                    ),
+316                )
+317            )
+318        if "return_pi" in kwargs:
+319            assert method in (
+320                "splitconformal",
+321                "localconformal",
+322            ), "method must be in ('splitconformal', 'localconformal')"
+323            self.pi = PredictionInterval(
+324                obj=self,
+325                method=method,
+326                level=level,
+327                type_pi=self.type_pi,
+328                replications=self.replications,
+329                kernel=self.kernel,
 330            )
-331        except ValueError:
-332            return _boosterc.predict_booster_regressor(
-333                self.obj, np.asarray(X, order="C")
-334            )
+331            self.pi.fit(self.X_, self.y_)
+332            self.X_ = None
+333            self.y_ = None
+334            preds = self.pi.predict(X, return_pi=True)
+335            return preds
+336
+337        try:
+338            return boosterc.predict_booster_regressor(
+339                self.obj, np.asarray(X, order="C")
+340            )
+341        except ValueError:
+342            return _boosterc.predict_booster_regressor(
+343                self.obj, np.asarray(X, order="C")
+344            )
 
    
 
 
@@ -3186,6 +3206,10 @@ 
    
 
 degree: int
     degree of features interactions to include in the model
+
+weights_distr: str
+    distribution of weights for constructing the model's hidden layer;
+    either 'uniform' or 'gaussian'
    @@ -3201,95 +3225,95 @@

    - 
    174    def fit(self, X, y, **kwargs):
-175        """Fit Booster (regressor) to training data (X, y)
-176
-177        Args:
-178
-179            X: {array-like}, shape = [n_samples, n_features]
-180                Training vectors, where n_samples is the number
-181                of samples and n_features is the number of features.
-182
-183            y: array-like, shape = [n_samples]
-184               Target values.
-185
-186            **kwargs: additional parameters to be passed to self.cook_training_set.
-187
-188        Returns:
-189
-190            self: object.
-191        """
+            
    184    def fit(self, X, y, **kwargs):
+185        """Fit Booster (regressor) to training data (X, y)
+186
+187        Args:
+188
+189            X: {array-like}, shape = [n_samples, n_features]
+190                Training vectors, where n_samples is the number
+191                of samples and n_features is the number of features.
 192
-193        if isinstance(X, pd.DataFrame):
-194            X = X.values
+193            y: array-like, shape = [n_samples]
+194               Target values.
 195
-196        if self.degree > 1:
-197            self.poly_ = PolynomialFeatures(
-198                degree=self.degree, interaction_only=True, include_bias=False
-199            )
-200            X = self.poly_.fit_transform(X)
-201
-202        if self.n_clusters > 0:
-203            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-204                cluster(
-205                    X,
-206                    n_clusters=self.n_clusters,
-207                    method=self.clustering_method,
-208                    type_scaling=self.cluster_scaling,
-209                    training=True,
-210                    seed=self.seed,
-211                )
-212            )
-213            X = np.column_stack((X, clustered_X))
-214
-215        try:
-216            self.obj = boosterc.fit_booster_regressor(
-217                X=np.asarray(X, order="C"),
-218                y=np.asarray(y, order="C"),
-219                n_estimators=self.n_estimators,
-220                learning_rate=self.learning_rate,
-221                n_hidden_features=self.n_hidden_features,
-222                reg_lambda=self.reg_lambda,
-223                alpha=self.alpha,
-224                row_sample=self.row_sample,
-225                col_sample=self.col_sample,
-226                dropout=self.dropout,
-227                tolerance=self.tolerance,
-228                direct_link=self.direct_link,
-229                verbose=self.verbose,
-230                seed=self.seed,
-231                backend=self.backend,
-232                solver=self.solver,
-233                activation=self.activation,
-234            )
-235        except ValueError:
-236            self.obj = _boosterc.fit_booster_regressor(
-237                X=np.asarray(X, order="C"),
-238                y=np.asarray(y, order="C"),
-239                n_estimators=self.n_estimators,
-240                learning_rate=self.learning_rate,
-241                n_hidden_features=self.n_hidden_features,
-242                reg_lambda=self.reg_lambda,
-243                alpha=self.alpha,
-244                row_sample=self.row_sample,
-245                col_sample=self.col_sample,
-246                dropout=self.dropout,
-247                tolerance=self.tolerance,
-248                direct_link=self.direct_link,
-249                verbose=self.verbose,
-250                seed=self.seed,
-251                backend=self.backend,
-252                solver=self.solver,
-253                activation=self.activation,
-254            )
-255
-256        self.n_estimators = self.obj["n_estimators"]
-257
-258        self.X_ = X
-259
-260        self.y_ = y
-261
-262        return self
+196            **kwargs: additional parameters to be passed to self.cook_training_set.
+197
+198        Returns:
+199
+200            self: object.
+201        """
+202
+203        if isinstance(X, pd.DataFrame):
+204            X = X.values
+205
+206        if self.degree > 1:
+207            self.poly_ = PolynomialFeatures(
+208                degree=self.degree, interaction_only=True, include_bias=False
+209            )
+210            X = self.poly_.fit_transform(X)
+211
+212        if self.n_clusters > 0:
+213            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+214                cluster(
+215                    X,
+216                    n_clusters=self.n_clusters,
+217                    method=self.clustering_method,
+218                    type_scaling=self.cluster_scaling,
+219                    training=True,
+220                    seed=self.seed,
+221                )
+222            )
+223            X = np.column_stack((X, clustered_X))
+224
+225        try:
+226            self.obj = boosterc.fit_booster_regressor(
+227                X=np.asarray(X, order="C"),
+228                y=np.asarray(y, order="C"),
+229                n_estimators=self.n_estimators,
+230                learning_rate=self.learning_rate,
+231                n_hidden_features=self.n_hidden_features,
+232                reg_lambda=self.reg_lambda,
+233                alpha=self.alpha,
+234                row_sample=self.row_sample,
+235                col_sample=self.col_sample,
+236                dropout=self.dropout,
+237                tolerance=self.tolerance,
+238                direct_link=self.direct_link,
+239                verbose=self.verbose,
+240                seed=self.seed,
+241                backend=self.backend,
+242                solver=self.solver,
+243                activation=self.activation,
+244            )
+245        except ValueError:
+246            self.obj = _boosterc.fit_booster_regressor(
+247                X=np.asarray(X, order="C"),
+248                y=np.asarray(y, order="C"),
+249                n_estimators=self.n_estimators,
+250                learning_rate=self.learning_rate,
+251                n_hidden_features=self.n_hidden_features,
+252                reg_lambda=self.reg_lambda,
+253                alpha=self.alpha,
+254                row_sample=self.row_sample,
+255                col_sample=self.col_sample,
+256                dropout=self.dropout,
+257                tolerance=self.tolerance,
+258                direct_link=self.direct_link,
+259                verbose=self.verbose,
+260                seed=self.seed,
+261                backend=self.backend,
+262                solver=self.solver,
+263                activation=self.activation,
+264            )
+265
+266        self.n_estimators = self.obj["n_estimators"]
+267
+268        self.X_ = X
+269
+270        self.y_ = y
+271
+272        return self
 
    
 
 
@@ -3326,77 +3350,77 @@
    - 
    264    def predict(self, X, level=95, method=None, **kwargs):
-265        """Predict probabilities for test data X.
-266
-267        Args:
-268
-269            X: {array-like}, shape = [n_samples, n_features]
-270                Training vectors, where n_samples is the number
-271                of samples and n_features is the number of features.
-272
-273            level: int
-274                Level of confidence (default = 95)
-275
-276            method: str
-277                `None`, or 'splitconformal', 'localconformal'
-278                prediction (if you specify `return_pi = True`)
-279
-280            **kwargs: additional parameters to be passed to
-281                self.cook_test_set
+            
    274    def predict(self, X, level=95, method=None, **kwargs):
+275        """Predict probabilities for test data X.
+276
+277        Args:
+278
+279            X: {array-like}, shape = [n_samples, n_features]
+280                Training vectors, where n_samples is the number
+281                of samples and n_features is the number of features.
 282
-283        Returns:
-284
-285            probability estimates for test data: {array-like}
-286        """
-287
-288        if isinstance(X, pd.DataFrame):
-289            X = X.values
-290
-291        if self.degree > 0:
-292            X = self.poly_.transform(X)
-293
-294        if self.n_clusters > 0:
-295            X = np.column_stack(
-296                (
-297                    X,
-298                    cluster(
-299                        X,
-300                        training=False,
-301                        scaler=self.scaler_,
-302                        label_encoder=self.label_encoder_,
-303                        clusterer=self.clusterer_,
-304                        seed=self.seed,
-305                    ),
-306                )
-307            )
-308        if "return_pi" in kwargs:
-309            assert method in (
-310                "splitconformal",
-311                "localconformal",
-312            ), "method must be in ('splitconformal', 'localconformal')"
-313            self.pi = PredictionInterval(
-314                obj=self,
-315                method=method,
-316                level=level,
-317                type_pi=self.type_pi,
-318                replications=self.replications,
-319                kernel=self.kernel,
-320            )
-321            self.pi.fit(self.X_, self.y_)
-322            self.X_ = None
-323            self.y_ = None
-324            preds = self.pi.predict(X, return_pi=True)
-325            return preds
-326
-327        try:
-328            return boosterc.predict_booster_regressor(
-329                self.obj, np.asarray(X, order="C")
+283            level: int
+284                Level of confidence (default = 95)
+285
+286            method: str
+287                `None`, or 'splitconformal', 'localconformal'
+288                prediction (if you specify `return_pi = True`)
+289
+290            **kwargs: additional parameters to be passed to
+291                self.cook_test_set
+292
+293        Returns:
+294
+295            probability estimates for test data: {array-like}
+296        """
+297
+298        if isinstance(X, pd.DataFrame):
+299            X = X.values
+300
+301        if self.degree > 0:
+302            X = self.poly_.transform(X)
+303
+304        if self.n_clusters > 0:
+305            X = np.column_stack(
+306                (
+307                    X,
+308                    cluster(
+309                        X,
+310                        training=False,
+311                        scaler=self.scaler_,
+312                        label_encoder=self.label_encoder_,
+313                        clusterer=self.clusterer_,
+314                        seed=self.seed,
+315                    ),
+316                )
+317            )
+318        if "return_pi" in kwargs:
+319            assert method in (
+320                "splitconformal",
+321                "localconformal",
+322            ), "method must be in ('splitconformal', 'localconformal')"
+323            self.pi = PredictionInterval(
+324                obj=self,
+325                method=method,
+326                level=level,
+327                type_pi=self.type_pi,
+328                replications=self.replications,
+329                kernel=self.kernel,
 330            )
-331        except ValueError:
-332            return _boosterc.predict_booster_regressor(
-333                self.obj, np.asarray(X, order="C")
-334            )
+331            self.pi.fit(self.X_, self.y_)
+332            self.X_ = None
+333            self.y_ = None
+334            preds = self.pi.predict(X, return_pi=True)
+335            return preds
+336
+337        try:
+338            return boosterc.predict_booster_regressor(
+339                self.obj, np.asarray(X, order="C")
+340            )
+341        except ValueError:
+342            return _boosterc.predict_booster_regressor(
+343                self.obj, np.asarray(X, order="C")
+344            )
 
    
 
 
diff --git a/mlsauce-docs/mlsauce/booster.html b/mlsauce-docs/mlsauce/booster.html
index 9355fc0..ba2ef69 100644
--- a/mlsauce-docs/mlsauce/booster.html
+++ b/mlsauce-docs/mlsauce/booster.html
@@ -87,6 +87,8 @@ 
    API Documentation
    
                         
                         
  • 
                         
    • 
+                        
  • 
+                        
    • 
                 
 
                 
@@ -152,6 +154,8 @@ 
    API Documentation
    
                         
                         
  • 
                         
    • 
+                        
  • 
+                        
    • 
                 
 
                 
@@ -262,235 +266,241 @@ 
    
  80        degree: int
  81            degree of features interactions to include in the model
  82
- 83    """
- 84
- 85    def __init__(
- 86        self,
- 87        n_estimators=100,
- 88        learning_rate=0.1,
- 89        n_hidden_features=5,
- 90        reg_lambda=0.1,
- 91        alpha=0.5,
- 92        row_sample=1,
- 93        col_sample=1,
- 94        dropout=0,
- 95        tolerance=1e-4,
- 96        direct_link=1,
- 97        verbose=1,
- 98        seed=123,
- 99        backend="cpu",
-100        solver="ridge",
-101        activation="relu",
-102        n_clusters=0,
-103        clustering_method="kmeans",
-104        cluster_scaling="standard",
-105        degree=0,
-106    ):
-107        if n_clusters > 0:
-108            assert clustering_method in (
-109                "kmeans",
-110                "gmm",
-111            ), "`clustering_method` must be in ('kmeans', 'gmm')"
-112            assert cluster_scaling in (
-113                "standard",
-114                "robust",
-115                "minmax",
-116            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
-117
-118        assert backend in (
-119            "cpu",
-120            "gpu",
-121            "tpu",
-122        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
-123
-124        assert solver in (
-125            "ridge",
-126            "lasso",
-127            "enet",
-128        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
-129
-130        sys_platform = platform.system()
-131
-132        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
-133            warnings.warn(
-134                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
-135            )
-136            backend = "cpu"
-137
-138        self.n_estimators = n_estimators
-139        self.learning_rate = learning_rate
-140        self.n_hidden_features = n_hidden_features
-141        self.reg_lambda = reg_lambda
-142        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
-143        self.alpha = alpha
-144        self.row_sample = row_sample
-145        self.col_sample = col_sample
-146        self.dropout = dropout
-147        self.tolerance = tolerance
-148        self.direct_link = direct_link
-149        self.verbose = verbose
-150        self.seed = seed
-151        self.backend = backend
-152        self.obj = None
-153        self.solver = solver
-154        self.activation = activation
-155        self.n_clusters = n_clusters
-156        self.clustering_method = clustering_method
-157        self.cluster_scaling = cluster_scaling
-158        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
-159        self.degree = degree
-160        self.poly_ = None
-161
-162    def fit(self, X, y, **kwargs):
-163        """Fit Booster (classifier) to training data (X, y)
-164
-165        Args:
-166
-167            X: {array-like}, shape = [n_samples, n_features]
-168                Training vectors, where n_samples is the number
-169                of samples and n_features is the number of features.
+ 83        weights_distr: str
+ 84            distribution of weights for constructing the model's hidden layer;
+ 85            currently 'uniform', 'gaussian'
+ 86
+ 87    """
+ 88
+ 89    def __init__(
+ 90        self,
+ 91        n_estimators=100,
+ 92        learning_rate=0.1,
+ 93        n_hidden_features=5,
+ 94        reg_lambda=0.1,
+ 95        alpha=0.5,
+ 96        row_sample=1,
+ 97        col_sample=1,
+ 98        dropout=0,
+ 99        tolerance=1e-4,
+100        direct_link=1,
+101        verbose=1,
+102        seed=123,
+103        backend="cpu",
+104        solver="ridge",
+105        activation="relu",
+106        n_clusters=0,
+107        clustering_method="kmeans",
+108        cluster_scaling="standard",
+109        degree=0,
+110        weights_distr="uniform",
+111    ):
+112        if n_clusters > 0:
+113            assert clustering_method in (
+114                "kmeans",
+115                "gmm",
+116            ), "`clustering_method` must be in ('kmeans', 'gmm')"
+117            assert cluster_scaling in (
+118                "standard",
+119                "robust",
+120                "minmax",
+121            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
+122
+123        assert backend in (
+124            "cpu",
+125            "gpu",
+126            "tpu",
+127        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
+128
+129        assert solver in (
+130            "ridge",
+131            "lasso",
+132            "enet",
+133        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
+134
+135        sys_platform = platform.system()
+136
+137        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
+138            warnings.warn(
+139                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
+140            )
+141            backend = "cpu"
+142
+143        self.n_estimators = n_estimators
+144        self.learning_rate = learning_rate
+145        self.n_hidden_features = n_hidden_features
+146        self.reg_lambda = reg_lambda
+147        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
+148        self.alpha = alpha
+149        self.row_sample = row_sample
+150        self.col_sample = col_sample
+151        self.dropout = dropout
+152        self.tolerance = tolerance
+153        self.direct_link = direct_link
+154        self.verbose = verbose
+155        self.seed = seed
+156        self.backend = backend
+157        self.obj = None
+158        self.solver = solver
+159        self.activation = activation
+160        self.n_clusters = n_clusters
+161        self.clustering_method = clustering_method
+162        self.cluster_scaling = cluster_scaling
+163        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
+164        self.degree = degree
+165        self.poly_ = None
+166        self.weights_distr = weights_distr
+167
+168    def fit(self, X, y, **kwargs):
+169        """Fit Booster (classifier) to training data (X, y)
 170
-171            y: array-like, shape = [n_samples]
-172                Target values.
-173
-174            **kwargs: additional parameters to be passed to self.cook_training_set.
-175
-176        Returns:
-177
-178            self: object.
-179        """
-180
-181        if isinstance(X, pd.DataFrame):
-182            X = X.values
+171        Args:
+172
+173            X: {array-like}, shape = [n_samples, n_features]
+174                Training vectors, where n_samples is the number
+175                of samples and n_features is the number of features.
+176
+177            y: array-like, shape = [n_samples]
+178                Target values.
+179
+180            **kwargs: additional parameters to be passed to self.cook_training_set.
+181
+182        Returns:
 183
-184        if self.degree > 1:
-185            self.poly_ = PolynomialFeatures(
-186                degree=self.degree, interaction_only=True, include_bias=False
-187            )
-188            X = self.poly_.fit_transform(X)
+184            self: object.
+185        """
+186
+187        if isinstance(X, pd.DataFrame):
+188            X = X.values
 189
-190        if self.n_clusters > 0:
-191            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-192                cluster(
-193                    X,
-194                    n_clusters=self.n_clusters,
-195                    method=self.clustering_method,
-196                    type_scaling=self.cluster_scaling,
-197                    training=True,
-198                    seed=self.seed,
-199                )
-200            )
-201            X = np.column_stack((X, clustered_X))
-202
-203        try:
-204            self.obj = boosterc.fit_booster_classifier(
-205                np.asarray(X, order="C"),
-206                np.asarray(y, order="C"),
-207                n_estimators=self.n_estimators,
-208                learning_rate=self.learning_rate,
-209                n_hidden_features=self.n_hidden_features,
-210                reg_lambda=self.reg_lambda,
-211                alpha=self.alpha,
-212                row_sample=self.row_sample,
-213                col_sample=self.col_sample,
-214                dropout=self.dropout,
-215                tolerance=self.tolerance,
-216                direct_link=self.direct_link,
-217                verbose=self.verbose,
-218                seed=self.seed,
-219                backend=self.backend,
-220                solver=self.solver,
-221                activation=self.activation,
-222            )
-223        except ValueError:
-224            self.obj = _boosterc.fit_booster_classifier(
-225                np.asarray(X, order="C"),
-226                np.asarray(y, order="C"),
-227                n_estimators=self.n_estimators,
-228                learning_rate=self.learning_rate,
-229                n_hidden_features=self.n_hidden_features,
-230                reg_lambda=self.reg_lambda,
-231                alpha=self.alpha,
-232                row_sample=self.row_sample,
-233                col_sample=self.col_sample,
-234                dropout=self.dropout,
-235                tolerance=self.tolerance,
-236                direct_link=self.direct_link,
-237                verbose=self.verbose,
-238                seed=self.seed,
-239                backend=self.backend,
-240                solver=self.solver,
-241                activation=self.activation,
-242            )
-243
-244        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
-245        self.n_estimators = self.obj["n_estimators"]
-246        return self
-247
-248    def predict(self, X, **kwargs):
-249        """Predict test data X.
-250
-251        Args:
-252
-253            X: {array-like}, shape = [n_samples, n_features]
-254                Training vectors, where n_samples is the number
-255                of samples and n_features is the number of features.
+190        if self.degree > 1:
+191            self.poly_ = PolynomialFeatures(
+192                degree=self.degree, interaction_only=True, include_bias=False
+193            )
+194            X = self.poly_.fit_transform(X)
+195
+196        if self.n_clusters > 0:
+197            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+198                cluster(
+199                    X,
+200                    n_clusters=self.n_clusters,
+201                    method=self.clustering_method,
+202                    type_scaling=self.cluster_scaling,
+203                    training=True,
+204                    seed=self.seed,
+205                )
+206            )
+207            X = np.column_stack((X, clustered_X))
+208
+209        try:
+210            self.obj = boosterc.fit_booster_classifier(
+211                np.asarray(X, order="C"),
+212                np.asarray(y, order="C"),
+213                n_estimators=self.n_estimators,
+214                learning_rate=self.learning_rate,
+215                n_hidden_features=self.n_hidden_features,
+216                reg_lambda=self.reg_lambda,
+217                alpha=self.alpha,
+218                row_sample=self.row_sample,
+219                col_sample=self.col_sample,
+220                dropout=self.dropout,
+221                tolerance=self.tolerance,
+222                direct_link=self.direct_link,
+223                verbose=self.verbose,
+224                seed=self.seed,
+225                backend=self.backend,
+226                solver=self.solver,
+227                activation=self.activation,
+228            )
+229        except ValueError:
+230            self.obj = _boosterc.fit_booster_classifier(
+231                np.asarray(X, order="C"),
+232                np.asarray(y, order="C"),
+233                n_estimators=self.n_estimators,
+234                learning_rate=self.learning_rate,
+235                n_hidden_features=self.n_hidden_features,
+236                reg_lambda=self.reg_lambda,
+237                alpha=self.alpha,
+238                row_sample=self.row_sample,
+239                col_sample=self.col_sample,
+240                dropout=self.dropout,
+241                tolerance=self.tolerance,
+242                direct_link=self.direct_link,
+243                verbose=self.verbose,
+244                seed=self.seed,
+245                backend=self.backend,
+246                solver=self.solver,
+247                activation=self.activation,
+248            )
+249
+250        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
+251        self.n_estimators = self.obj["n_estimators"]
+252        return self
+253
+254    def predict(self, X, **kwargs):
+255        """Predict test data X.
 256
-257            **kwargs: additional parameters to be passed to `predict_proba`
+257        Args:
 258
-259
-260        Returns:
-261
-262            model predictions: {array-like}
-263        """
+259            X: {array-like}, shape = [n_samples, n_features]
+260                Training vectors, where n_samples is the number
+261                of samples and n_features is the number of features.
+262
+263            **kwargs: additional parameters to be passed to `predict_proba`
 264
-265        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
-266
-267    def predict_proba(self, X, **kwargs):
-268        """Predict probabilities for test data X.
-269
-270        Args:
-271
-272            X: {array-like}, shape = [n_samples, n_features]
-273                Training vectors, where n_samples is the number
-274                of samples and n_features is the number of features.
+265
+266        Returns:
+267
+268            model predictions: {array-like}
+269        """
+270
+271        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
+272
+273    def predict_proba(self, X, **kwargs):
+274        """Predict probabilities for test data X.
 275
-276            **kwargs: additional parameters to be passed to
-277                self.cook_test_set
-278
-279        Returns:
-280
-281            probability estimates for test data: {array-like}
-282        """
-283
-284        if isinstance(X, pd.DataFrame):
-285            X = X.values
+276        Args:
+277
+278            X: {array-like}, shape = [n_samples, n_features]
+279                Training vectors, where n_samples is the number
+280                of samples and n_features is the number of features.
+281
+282            **kwargs: additional parameters to be passed to
+283                self.cook_test_set
+284
+285        Returns:
 286
-287        if self.degree > 0:
-288            X = self.poly_.transform(X)
+287            probability estimates for test data: {array-like}
+288        """
 289
-290        if self.n_clusters > 0:
-291            X = np.column_stack(
-292                (
-293                    X,
-294                    cluster(
-295                        X,
-296                        training=False,
-297                        scaler=self.scaler_,
-298                        label_encoder=self.label_encoder_,
-299                        clusterer=self.clusterer_,
-300                        seed=self.seed,
-301                    ),
-302                )
-303            )
-304        try:
-305            return boosterc.predict_proba_booster_classifier(
-306                self.obj, np.asarray(X, order="C")
-307            )
-308        except ValueError:
-309            return _boosterc.predict_proba_booster_classifier(
-310                self.obj, np.asarray(X, order="C")
-311            )
+290        if isinstance(X, pd.DataFrame):
+291            X = X.values
+292
+293        if self.degree > 0:
+294            X = self.poly_.transform(X)
+295
+296        if self.n_clusters > 0:
+297            X = np.column_stack(
+298                (
+299                    X,
+300                    cluster(
+301                        X,
+302                        training=False,
+303                        scaler=self.scaler_,
+304                        label_encoder=self.label_encoder_,
+305                        clusterer=self.clusterer_,
+306                        seed=self.seed,
+307                    ),
+308                )
+309            )
+310        try:
+311            return boosterc.predict_proba_booster_classifier(
+312                self.obj, np.asarray(X, order="C")
+313            )
+314        except ValueError:
+315            return _boosterc.predict_proba_booster_classifier(
+316                self.obj, np.asarray(X, order="C")
+317            )
    @@ -558,6 +568,10 @@

    degree: int degree of features interactions to include in the model + +weights_distr: str + distribution of weights for constructing the model's hidden layer; + currently 'uniform', 'gaussian' @@ -573,91 +587,91 @@

    - 
    162    def fit(self, X, y, **kwargs):
-163        """Fit Booster (classifier) to training data (X, y)
-164
-165        Args:
-166
-167            X: {array-like}, shape = [n_samples, n_features]
-168                Training vectors, where n_samples is the number
-169                of samples and n_features is the number of features.
+            
    168    def fit(self, X, y, **kwargs):
+169        """Fit Booster (classifier) to training data (X, y)
 170
-171            y: array-like, shape = [n_samples]
-172                Target values.
-173
-174            **kwargs: additional parameters to be passed to self.cook_training_set.
-175
-176        Returns:
-177
-178            self: object.
-179        """
-180
-181        if isinstance(X, pd.DataFrame):
-182            X = X.values
+171        Args:
+172
+173            X: {array-like}, shape = [n_samples, n_features]
+174                Training vectors, where n_samples is the number
+175                of samples and n_features is the number of features.
+176
+177            y: array-like, shape = [n_samples]
+178                Target values.
+179
+180            **kwargs: additional parameters to be passed to self.cook_training_set.
+181
+182        Returns:
 183
-184        if self.degree > 1:
-185            self.poly_ = PolynomialFeatures(
-186                degree=self.degree, interaction_only=True, include_bias=False
-187            )
-188            X = self.poly_.fit_transform(X)
+184            self: object.
+185        """
+186
+187        if isinstance(X, pd.DataFrame):
+188            X = X.values
 189
-190        if self.n_clusters > 0:
-191            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-192                cluster(
-193                    X,
-194                    n_clusters=self.n_clusters,
-195                    method=self.clustering_method,
-196                    type_scaling=self.cluster_scaling,
-197                    training=True,
-198                    seed=self.seed,
-199                )
-200            )
-201            X = np.column_stack((X, clustered_X))
-202
-203        try:
-204            self.obj = boosterc.fit_booster_classifier(
-205                np.asarray(X, order="C"),
-206                np.asarray(y, order="C"),
-207                n_estimators=self.n_estimators,
-208                learning_rate=self.learning_rate,
-209                n_hidden_features=self.n_hidden_features,
-210                reg_lambda=self.reg_lambda,
-211                alpha=self.alpha,
-212                row_sample=self.row_sample,
-213                col_sample=self.col_sample,
-214                dropout=self.dropout,
-215                tolerance=self.tolerance,
-216                direct_link=self.direct_link,
-217                verbose=self.verbose,
-218                seed=self.seed,
-219                backend=self.backend,
-220                solver=self.solver,
-221                activation=self.activation,
-222            )
-223        except ValueError:
-224            self.obj = _boosterc.fit_booster_classifier(
-225                np.asarray(X, order="C"),
-226                np.asarray(y, order="C"),
-227                n_estimators=self.n_estimators,
-228                learning_rate=self.learning_rate,
-229                n_hidden_features=self.n_hidden_features,
-230                reg_lambda=self.reg_lambda,
-231                alpha=self.alpha,
-232                row_sample=self.row_sample,
-233                col_sample=self.col_sample,
-234                dropout=self.dropout,
-235                tolerance=self.tolerance,
-236                direct_link=self.direct_link,
-237                verbose=self.verbose,
-238                seed=self.seed,
-239                backend=self.backend,
-240                solver=self.solver,
-241                activation=self.activation,
-242            )
-243
-244        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
-245        self.n_estimators = self.obj["n_estimators"]
-246        return self
+190        if self.degree > 1:
+191            self.poly_ = PolynomialFeatures(
+192                degree=self.degree, interaction_only=True, include_bias=False
+193            )
+194            X = self.poly_.fit_transform(X)
+195
+196        if self.n_clusters > 0:
+197            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+198                cluster(
+199                    X,
+200                    n_clusters=self.n_clusters,
+201                    method=self.clustering_method,
+202                    type_scaling=self.cluster_scaling,
+203                    training=True,
+204                    seed=self.seed,
+205                )
+206            )
+207            X = np.column_stack((X, clustered_X))
+208
+209        try:
+210            self.obj = boosterc.fit_booster_classifier(
+211                np.asarray(X, order="C"),
+212                np.asarray(y, order="C"),
+213                n_estimators=self.n_estimators,
+214                learning_rate=self.learning_rate,
+215                n_hidden_features=self.n_hidden_features,
+216                reg_lambda=self.reg_lambda,
+217                alpha=self.alpha,
+218                row_sample=self.row_sample,
+219                col_sample=self.col_sample,
+220                dropout=self.dropout,
+221                tolerance=self.tolerance,
+222                direct_link=self.direct_link,
+223                verbose=self.verbose,
+224                seed=self.seed,
+225                backend=self.backend,
+226                solver=self.solver,
+227                activation=self.activation,
+228            )
+229        except ValueError:
+230            self.obj = _boosterc.fit_booster_classifier(
+231                np.asarray(X, order="C"),
+232                np.asarray(y, order="C"),
+233                n_estimators=self.n_estimators,
+234                learning_rate=self.learning_rate,
+235                n_hidden_features=self.n_hidden_features,
+236                reg_lambda=self.reg_lambda,
+237                alpha=self.alpha,
+238                row_sample=self.row_sample,
+239                col_sample=self.col_sample,
+240                dropout=self.dropout,
+241                tolerance=self.tolerance,
+242                direct_link=self.direct_link,
+243                verbose=self.verbose,
+244                seed=self.seed,
+245                backend=self.backend,
+246                solver=self.solver,
+247                activation=self.activation,
+248            )
+249
+250        self.n_classes_ = len(np.unique(y))  # for compatibility with sklearn
+251        self.n_estimators = self.obj["n_estimators"]
+252        return self
 
    
 
 
@@ -694,24 +708,24 @@
    - 
    248    def predict(self, X, **kwargs):
-249        """Predict test data X.
-250
-251        Args:
-252
-253            X: {array-like}, shape = [n_samples, n_features]
-254                Training vectors, where n_samples is the number
-255                of samples and n_features is the number of features.
+            
    254    def predict(self, X, **kwargs):
+255        """Predict test data X.
 256
-257            **kwargs: additional parameters to be passed to `predict_proba`
+257        Args:
 258
-259
-260        Returns:
-261
-262            model predictions: {array-like}
-263        """
+259            X: {array-like}, shape = [n_samples, n_features]
+260                Training vectors, where n_samples is the number
+261                of samples and n_features is the number of features.
+262
+263            **kwargs: additional parameters to be passed to `predict_proba`
 264
-265        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
+265
+266        Returns:
+267
+268            model predictions: {array-like}
+269        """
+270
+271        return np.argmax(self.predict_proba(X, **kwargs), axis=1)
 
    
 
 
@@ -745,51 +759,51 @@
    - 
    267    def predict_proba(self, X, **kwargs):
-268        """Predict probabilities for test data X.
-269
-270        Args:
-271
-272            X: {array-like}, shape = [n_samples, n_features]
-273                Training vectors, where n_samples is the number
-274                of samples and n_features is the number of features.
+            
    273    def predict_proba(self, X, **kwargs):
+274        """Predict probabilities for test data X.
 275
-276            **kwargs: additional parameters to be passed to
-277                self.cook_test_set
-278
-279        Returns:
-280
-281            probability estimates for test data: {array-like}
-282        """
-283
-284        if isinstance(X, pd.DataFrame):
-285            X = X.values
+276        Args:
+277
+278            X: {array-like}, shape = [n_samples, n_features]
+279                Training vectors, where n_samples is the number
+280                of samples and n_features is the number of features.
+281
+282            **kwargs: additional parameters to be passed to
+283                self.cook_test_set
+284
+285        Returns:
 286
-287        if self.degree > 0:
-288            X = self.poly_.transform(X)
+287            probability estimates for test data: {array-like}
+288        """
 289
-290        if self.n_clusters > 0:
-291            X = np.column_stack(
-292                (
-293                    X,
-294                    cluster(
-295                        X,
-296                        training=False,
-297                        scaler=self.scaler_,
-298                        label_encoder=self.label_encoder_,
-299                        clusterer=self.clusterer_,
-300                        seed=self.seed,
-301                    ),
-302                )
-303            )
-304        try:
-305            return boosterc.predict_proba_booster_classifier(
-306                self.obj, np.asarray(X, order="C")
-307            )
-308        except ValueError:
-309            return _boosterc.predict_proba_booster_classifier(
-310                self.obj, np.asarray(X, order="C")
-311            )
+290        if isinstance(X, pd.DataFrame):
+291            X = X.values
+292
+293        if self.degree > 0:
+294            X = self.poly_.transform(X)
+295
+296        if self.n_clusters > 0:
+297            X = np.column_stack(
+298                (
+299                    X,
+300                    cluster(
+301                        X,
+302                        training=False,
+303                        scaler=self.scaler_,
+304                        label_encoder=self.label_encoder_,
+305                        clusterer=self.clusterer_,
+306                        seed=self.seed,
+307                    ),
+308                )
+309            )
+310        try:
+311            return boosterc.predict_proba_booster_classifier(
+312                self.obj, np.asarray(X, order="C")
+313            )
+314        except ValueError:
+315            return _boosterc.predict_proba_booster_classifier(
+316                self.obj, np.asarray(X, order="C")
+317            )
 
    
 
 
@@ -826,327 +840,333 @@
    - 
     14class LSBoostRegressor(BaseEstimator, RegressorMixin):
- 15    """LSBoost regressor.
- 16
- 17    Attributes:
- 18
- 19        n_estimators: int
- 20            number of boosting iterations.
- 21
- 22        learning_rate: float
- 23            controls the learning speed at training time.
- 24
- 25        n_hidden_features: int
- 26            number of nodes in successive hidden layers.
- 27
- 28        reg_lambda: float
- 29            L2 regularization parameter for successive errors in the optimizer
- 30            (at training time).
+            
     18class LSBoostRegressor(BaseEstimator, RegressorMixin):
+ 19    """LSBoost regressor.
+ 20
+ 21    Attributes:
+ 22
+ 23        n_estimators: int
+ 24            number of boosting iterations.
+ 25
+ 26        learning_rate: float
+ 27            controls the learning speed at training time.
+ 28
+ 29        n_hidden_features: int
+ 30            number of nodes in successive hidden layers.
  31
- 32        alpha: float
- 33            compromise between L1 and L2 regularization (must be in [0, 1]),
- 34            for `solver` == 'enet'
+ 32        reg_lambda: float
+ 33            L2 regularization parameter for successive errors in the optimizer
+ 34            (at training time).
  35
- 36        row_sample: float
- 37            percentage of rows chosen from the training set.
- 38
- 39        col_sample: float
- 40            percentage of columns chosen from the training set.
- 41
- 42        dropout: float
- 43            percentage of nodes dropped from the training set.
- 44
- 45        tolerance: float
- 46            controls early stopping in gradient descent (at training time).
- 47
- 48        direct_link: bool
- 49            indicates whether the original features are included (True) in model's
- 50            fitting or not (False).
+ 36        alpha: float
+ 37            compromise between L1 and L2 regularization (must be in [0, 1]),
+ 38            for `solver` == 'enet'
+ 39
+ 40        row_sample: float
+ 41            percentage of rows chosen from the training set.
+ 42
+ 43        col_sample: float
+ 44            percentage of columns chosen from the training set.
+ 45
+ 46        dropout: float
+ 47            percentage of nodes dropped from the training set.
+ 48
+ 49        tolerance: float
+ 50            controls early stopping in gradient descent (at training time).
  51
- 52        verbose: int
- 53            progress bar (yes = 1) or not (no = 0) (currently).
- 54
- 55        seed: int
- 56            reproducibility seed for nodes_sim=='uniform', clustering and dropout.
- 57
- 58        backend: str
- 59            type of backend; must be in ('cpu', 'gpu', 'tpu')
- 60
- 61        solver: str
- 62            type of 'weak' learner; currently in ('ridge', 'lasso')
- 63
- 64        activation: str
- 65            activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'
- 66
- 67        type_pi: str.
- 68            type of prediction interval; currently "kde" (default) or "bootstrap".
- 69            Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`
- 70            in ('gaussian', 'tophat'). Default is `None`.
- 71
- 72        replications: int.
- 73            number of replications (if needed) for predictive simulation.
- 74            Used only in `self.predict`, for `self.kernel` in ('gaussian',
- 75            'tophat') and `self.type_pi = 'kde'`. Default is `None`.
- 76
- 77        n_clusters: int
- 78            number of clusters for clustering the features
- 79
- 80        clustering_method: str
- 81            clustering method: currently 'kmeans', 'gmm'
- 82
- 83        cluster_scaling: str
- 84            scaling method for clustering: currently 'standard', 'robust', 'minmax'
- 85
- 86        degree: int
- 87            degree of features interactions to include in the model
- 88
- 89    """
- 90
- 91    def __init__(
- 92        self,
- 93        n_estimators=100,
- 94        learning_rate=0.1,
- 95        n_hidden_features=5,
- 96        reg_lambda=0.1,
- 97        alpha=0.5,
- 98        row_sample=1,
- 99        col_sample=1,
-100        dropout=0,
-101        tolerance=1e-4,
-102        direct_link=1,
-103        verbose=1,
-104        seed=123,
-105        backend="cpu",
-106        solver="ridge",
-107        activation="relu",
-108        type_pi=None,
-109        replications=None,
-110        kernel=None,
-111        n_clusters=0,
-112        clustering_method="kmeans",
-113        cluster_scaling="standard",
-114        degree=0,
-115    ):
-116        if n_clusters > 0:
-117            assert clustering_method in (
-118                "kmeans",
-119                "gmm",
-120            ), "`clustering_method` must be in ('kmeans', 'gmm')"
-121            assert cluster_scaling in (
-122                "standard",
-123                "robust",
-124                "minmax",
-125            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
-126
-127        assert backend in (
-128            "cpu",
-129            "gpu",
-130            "tpu",
-131        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
-132
-133        assert solver in (
-134            "ridge",
-135            "lasso",
-136            "enet",
-137        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
-138
-139        sys_platform = platform.system()
-140
-141        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
-142            warnings.warn(
-143                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
-144            )
-145            backend = "cpu"
-146
-147        self.n_estimators = n_estimators
-148        self.learning_rate = learning_rate
-149        self.n_hidden_features = n_hidden_features
-150        self.reg_lambda = reg_lambda
-151        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
-152        self.alpha = alpha
-153        self.row_sample = row_sample
-154        self.col_sample = col_sample
-155        self.dropout = dropout
-156        self.tolerance = tolerance
-157        self.direct_link = direct_link
-158        self.verbose = verbose
-159        self.seed = seed
-160        self.backend = backend
-161        self.obj = None
-162        self.solver = solver
-163        self.activation = activation
-164        self.type_pi = type_pi
-165        self.replications = replications
-166        self.kernel = kernel
-167        self.n_clusters = n_clusters
-168        self.clustering_method = clustering_method
-169        self.cluster_scaling = cluster_scaling
-170        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
-171        self.degree = degree
-172        self.poly_ = None
-173
-174    def fit(self, X, y, **kwargs):
-175        """Fit Booster (regressor) to training data (X, y)
-176
-177        Args:
-178
-179            X: {array-like}, shape = [n_samples, n_features]
-180                Training vectors, where n_samples is the number
-181                of samples and n_features is the number of features.
-182
-183            y: array-like, shape = [n_samples]
-184               Target values.
-185
-186            **kwargs: additional parameters to be passed to self.cook_training_set.
-187
-188        Returns:
-189
-190            self: object.
-191        """
+ 52        direct_link: bool
+ 53            indicates whether the original features are included (True) in model's
+ 54            fitting or not (False).
+ 55
+ 56        verbose: int
+ 57            progress bar (yes = 1) or not (no = 0) (currently).
+ 58
+ 59        seed: int
+ 60            reproducibility seed for nodes_sim=='uniform', clustering and dropout.
+ 61
+ 62        backend: str
+ 63            type of backend; must be in ('cpu', 'gpu', 'tpu')
+ 64
+ 65        solver: str
+ 66            type of 'weak' learner; currently in ('ridge', 'lasso')
+ 67
+ 68        activation: str
+ 69            activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'
+ 70
+ 71        type_pi: str.
+ 72            type of prediction interval; currently "kde" (default) or "bootstrap".
+ 73            Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`
+ 74            in ('gaussian', 'tophat'). Default is `None`.
+ 75
+ 76        replications: int.
+ 77            number of replications (if needed) for predictive simulation.
+ 78            Used only in `self.predict`, for `self.kernel` in ('gaussian',
+ 79            'tophat') and `self.type_pi = 'kde'`. Default is `None`.
+ 80
+ 81        n_clusters: int
+ 82            number of clusters for clustering the features
+ 83
+ 84        clustering_method: str
+ 85            clustering method: currently 'kmeans', 'gmm'
+ 86
+ 87        cluster_scaling: str
+ 88            scaling method for clustering: currently 'standard', 'robust', 'minmax'
+ 89
+ 90        degree: int
+ 91            degree of features interactions to include in the model
+ 92
+ 93        weights_distr: str
+ 94            distribution of weights for constructing the model's hidden layer;
+ 95            either 'uniform' or 'gaussian'
+ 96
+ 97    """
+ 98
+ 99    def __init__(
+100        self,
+101        n_estimators=100,
+102        learning_rate=0.1,
+103        n_hidden_features=5,
+104        reg_lambda=0.1,
+105        alpha=0.5,
+106        row_sample=1,
+107        col_sample=1,
+108        dropout=0,
+109        tolerance=1e-4,
+110        direct_link=1,
+111        verbose=1,
+112        seed=123,
+113        backend="cpu",
+114        solver="ridge",
+115        activation="relu",
+116        type_pi=None,
+117        replications=None,
+118        kernel=None,
+119        n_clusters=0,
+120        clustering_method="kmeans",
+121        cluster_scaling="standard",
+122        degree=0,
+123        weights_distr="uniform",
+124    ):
+125        if n_clusters > 0:
+126            assert clustering_method in (
+127                "kmeans",
+128                "gmm",
+129            ), "`clustering_method` must be in ('kmeans', 'gmm')"
+130            assert cluster_scaling in (
+131                "standard",
+132                "robust",
+133                "minmax",
+134            ), "`cluster_scaling` must be in ('standard', 'robust', 'minmax')"
+135
+136        assert backend in (
+137            "cpu",
+138            "gpu",
+139            "tpu",
+140        ), "`backend` must be in ('cpu', 'gpu', 'tpu')"
+141
+142        assert solver in (
+143            "ridge",
+144            "lasso",
+145            "enet",
+146        ), "`solver` must be in ('ridge', 'lasso', 'enet')"
+147
+148        sys_platform = platform.system()
+149
+150        if (sys_platform == "Windows") and (backend in ("gpu", "tpu")):
+151            warnings.warn(
+152                "No GPU/TPU computing on Windows yet, backend set to 'cpu'"
+153            )
+154            backend = "cpu"
+155
+156        self.n_estimators = n_estimators
+157        self.learning_rate = learning_rate
+158        self.n_hidden_features = n_hidden_features
+159        self.reg_lambda = reg_lambda
+160        assert alpha >= 0 and alpha <= 1, "`alpha` must be in [0, 1]"
+161        self.alpha = alpha
+162        self.row_sample = row_sample
+163        self.col_sample = col_sample
+164        self.dropout = dropout
+165        self.tolerance = tolerance
+166        self.direct_link = direct_link
+167        self.verbose = verbose
+168        self.seed = seed
+169        self.backend = backend
+170        self.obj = None
+171        self.solver = solver
+172        self.activation = activation
+173        self.type_pi = type_pi
+174        self.replications = replications
+175        self.kernel = kernel
+176        self.n_clusters = n_clusters
+177        self.clustering_method = clustering_method
+178        self.cluster_scaling = cluster_scaling
+179        self.scaler_, self.label_encoder_, self.clusterer_ = None, None, None
+180        self.degree = degree
+181        self.poly_ = None
+182        self.weights_distr = weights_distr
+183
+184    def fit(self, X, y, **kwargs):
+185        """Fit Booster (regressor) to training data (X, y)
+186
+187        Args:
+188
+189            X: {array-like}, shape = [n_samples, n_features]
+190                Training vectors, where n_samples is the number
+191                of samples and n_features is the number of features.
 192
-193        if isinstance(X, pd.DataFrame):
-194            X = X.values
+193            y: array-like, shape = [n_samples]
+194               Target values.
 195
-196        if self.degree > 1:
-197            self.poly_ = PolynomialFeatures(
-198                degree=self.degree, interaction_only=True, include_bias=False
-199            )
-200            X = self.poly_.fit_transform(X)
-201
-202        if self.n_clusters > 0:
-203            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-204                cluster(
-205                    X,
-206                    n_clusters=self.n_clusters,
-207                    method=self.clustering_method,
-208                    type_scaling=self.cluster_scaling,
-209                    training=True,
-210                    seed=self.seed,
-211                )
-212            )
-213            X = np.column_stack((X, clustered_X))
-214
-215        try:
-216            self.obj = boosterc.fit_booster_regressor(
-217                X=np.asarray(X, order="C"),
-218                y=np.asarray(y, order="C"),
-219                n_estimators=self.n_estimators,
-220                learning_rate=self.learning_rate,
-221                n_hidden_features=self.n_hidden_features,
-222                reg_lambda=self.reg_lambda,
-223                alpha=self.alpha,
-224                row_sample=self.row_sample,
-225                col_sample=self.col_sample,
-226                dropout=self.dropout,
-227                tolerance=self.tolerance,
-228                direct_link=self.direct_link,
-229                verbose=self.verbose,
-230                seed=self.seed,
-231                backend=self.backend,
-232                solver=self.solver,
-233                activation=self.activation,
-234            )
-235        except ValueError:
-236            self.obj = _boosterc.fit_booster_regressor(
-237                X=np.asarray(X, order="C"),
-238                y=np.asarray(y, order="C"),
-239                n_estimators=self.n_estimators,
-240                learning_rate=self.learning_rate,
-241                n_hidden_features=self.n_hidden_features,
-242                reg_lambda=self.reg_lambda,
-243                alpha=self.alpha,
-244                row_sample=self.row_sample,
-245                col_sample=self.col_sample,
-246                dropout=self.dropout,
-247                tolerance=self.tolerance,
-248                direct_link=self.direct_link,
-249                verbose=self.verbose,
-250                seed=self.seed,
-251                backend=self.backend,
-252                solver=self.solver,
-253                activation=self.activation,
-254            )
-255
-256        self.n_estimators = self.obj["n_estimators"]
-257
-258        self.X_ = X
-259
-260        self.y_ = y
-261
-262        return self
-263
-264    def predict(self, X, level=95, method=None, **kwargs):
-265        """Predict probabilities for test data X.
-266
-267        Args:
-268
-269            X: {array-like}, shape = [n_samples, n_features]
-270                Training vectors, where n_samples is the number
-271                of samples and n_features is the number of features.
-272
-273            level: int
-274                Level of confidence (default = 95)
-275
-276            method: str
-277                `None`, or 'splitconformal', 'localconformal'
-278                prediction (if you specify `return_pi = True`)
-279
-280            **kwargs: additional parameters to be passed to
-281                self.cook_test_set
+196            **kwargs: additional parameters to be passed to self.cook_training_set.
+197
+198        Returns:
+199
+200            self: object.
+201        """
+202
+203        if isinstance(X, pd.DataFrame):
+204            X = X.values
+205
+206        if self.degree > 1:
+207            self.poly_ = PolynomialFeatures(
+208                degree=self.degree, interaction_only=True, include_bias=False
+209            )
+210            X = self.poly_.fit_transform(X)
+211
+212        if self.n_clusters > 0:
+213            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+214                cluster(
+215                    X,
+216                    n_clusters=self.n_clusters,
+217                    method=self.clustering_method,
+218                    type_scaling=self.cluster_scaling,
+219                    training=True,
+220                    seed=self.seed,
+221                )
+222            )
+223            X = np.column_stack((X, clustered_X))
+224
+225        try:
+226            self.obj = boosterc.fit_booster_regressor(
+227                X=np.asarray(X, order="C"),
+228                y=np.asarray(y, order="C"),
+229                n_estimators=self.n_estimators,
+230                learning_rate=self.learning_rate,
+231                n_hidden_features=self.n_hidden_features,
+232                reg_lambda=self.reg_lambda,
+233                alpha=self.alpha,
+234                row_sample=self.row_sample,
+235                col_sample=self.col_sample,
+236                dropout=self.dropout,
+237                tolerance=self.tolerance,
+238                direct_link=self.direct_link,
+239                verbose=self.verbose,
+240                seed=self.seed,
+241                backend=self.backend,
+242                solver=self.solver,
+243                activation=self.activation,
+244            )
+245        except ValueError:
+246            self.obj = _boosterc.fit_booster_regressor(
+247                X=np.asarray(X, order="C"),
+248                y=np.asarray(y, order="C"),
+249                n_estimators=self.n_estimators,
+250                learning_rate=self.learning_rate,
+251                n_hidden_features=self.n_hidden_features,
+252                reg_lambda=self.reg_lambda,
+253                alpha=self.alpha,
+254                row_sample=self.row_sample,
+255                col_sample=self.col_sample,
+256                dropout=self.dropout,
+257                tolerance=self.tolerance,
+258                direct_link=self.direct_link,
+259                verbose=self.verbose,
+260                seed=self.seed,
+261                backend=self.backend,
+262                solver=self.solver,
+263                activation=self.activation,
+264            )
+265
+266        self.n_estimators = self.obj["n_estimators"]
+267
+268        self.X_ = X
+269
+270        self.y_ = y
+271
+272        return self
+273
+274    def predict(self, X, level=95, method=None, **kwargs):
+275        """Predict probabilities for test data X.
+276
+277        Args:
+278
+279            X: {array-like}, shape = [n_samples, n_features]
+280                Training vectors, where n_samples is the number
+281                of samples and n_features is the number of features.
 282
-283        Returns:
-284
-285            probability estimates for test data: {array-like}
-286        """
-287
-288        if isinstance(X, pd.DataFrame):
-289            X = X.values
-290
-291        if self.degree > 0:
-292            X = self.poly_.transform(X)
-293
-294        if self.n_clusters > 0:
-295            X = np.column_stack(
-296                (
-297                    X,
-298                    cluster(
-299                        X,
-300                        training=False,
-301                        scaler=self.scaler_,
-302                        label_encoder=self.label_encoder_,
-303                        clusterer=self.clusterer_,
-304                        seed=self.seed,
-305                    ),
-306                )
-307            )
-308        if "return_pi" in kwargs:
-309            assert method in (
-310                "splitconformal",
-311                "localconformal",
-312            ), "method must be in ('splitconformal', 'localconformal')"
-313            self.pi = PredictionInterval(
-314                obj=self,
-315                method=method,
-316                level=level,
-317                type_pi=self.type_pi,
-318                replications=self.replications,
-319                kernel=self.kernel,
-320            )
-321            self.pi.fit(self.X_, self.y_)
-322            self.X_ = None
-323            self.y_ = None
-324            preds = self.pi.predict(X, return_pi=True)
-325            return preds
-326
-327        try:
-328            return boosterc.predict_booster_regressor(
-329                self.obj, np.asarray(X, order="C")
+283            level: int
+284                Level of confidence (default = 95)
+285
+286            method: str
+287                `None`, or 'splitconformal', 'localconformal'
+288                prediction (if you specify `return_pi = True`)
+289
+290            **kwargs: additional parameters to be passed to
+291                self.cook_test_set
+292
+293        Returns:
+294
+295            probability estimates for test data: {array-like}
+296        """
+297
+298        if isinstance(X, pd.DataFrame):
+299            X = X.values
+300
+301        if self.degree > 0:
+302            X = self.poly_.transform(X)
+303
+304        if self.n_clusters > 0:
+305            X = np.column_stack(
+306                (
+307                    X,
+308                    cluster(
+309                        X,
+310                        training=False,
+311                        scaler=self.scaler_,
+312                        label_encoder=self.label_encoder_,
+313                        clusterer=self.clusterer_,
+314                        seed=self.seed,
+315                    ),
+316                )
+317            )
+318        if "return_pi" in kwargs:
+319            assert method in (
+320                "splitconformal",
+321                "localconformal",
+322            ), "method must be in ('splitconformal', 'localconformal')"
+323            self.pi = PredictionInterval(
+324                obj=self,
+325                method=method,
+326                level=level,
+327                type_pi=self.type_pi,
+328                replications=self.replications,
+329                kernel=self.kernel,
 330            )
-331        except ValueError:
-332            return _boosterc.predict_booster_regressor(
-333                self.obj, np.asarray(X, order="C")
-334            )
+331            self.pi.fit(self.X_, self.y_)
+332            self.X_ = None
+333            self.y_ = None
+334            preds = self.pi.predict(X, return_pi=True)
+335            return preds
+336
+337        try:
+338            return boosterc.predict_booster_regressor(
+339                self.obj, np.asarray(X, order="C")
+340            )
+341        except ValueError:
+342            return _boosterc.predict_booster_regressor(
+343                self.obj, np.asarray(X, order="C")
+344            )
 
    
 
 
@@ -1223,6 +1243,10 @@ 
    
 
 degree: int
     degree of features interactions to include in the model
+
+weights_distr: str
+    distribution of weights for constructing the model's hidden layer;
+    either 'uniform' or 'gaussian'
    @@ -1238,95 +1262,95 @@

    - 
    174    def fit(self, X, y, **kwargs):
-175        """Fit Booster (regressor) to training data (X, y)
-176
-177        Args:
-178
-179            X: {array-like}, shape = [n_samples, n_features]
-180                Training vectors, where n_samples is the number
-181                of samples and n_features is the number of features.
-182
-183            y: array-like, shape = [n_samples]
-184               Target values.
-185
-186            **kwargs: additional parameters to be passed to self.cook_training_set.
-187
-188        Returns:
-189
-190            self: object.
-191        """
+            
    184    def fit(self, X, y, **kwargs):
+185        """Fit Booster (regressor) to training data (X, y)
+186
+187        Args:
+188
+189            X: {array-like}, shape = [n_samples, n_features]
+190                Training vectors, where n_samples is the number
+191                of samples and n_features is the number of features.
 192
-193        if isinstance(X, pd.DataFrame):
-194            X = X.values
+193            y: array-like, shape = [n_samples]
+194               Target values.
 195
-196        if self.degree > 1:
-197            self.poly_ = PolynomialFeatures(
-198                degree=self.degree, interaction_only=True, include_bias=False
-199            )
-200            X = self.poly_.fit_transform(X)
-201
-202        if self.n_clusters > 0:
-203            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
-204                cluster(
-205                    X,
-206                    n_clusters=self.n_clusters,
-207                    method=self.clustering_method,
-208                    type_scaling=self.cluster_scaling,
-209                    training=True,
-210                    seed=self.seed,
-211                )
-212            )
-213            X = np.column_stack((X, clustered_X))
-214
-215        try:
-216            self.obj = boosterc.fit_booster_regressor(
-217                X=np.asarray(X, order="C"),
-218                y=np.asarray(y, order="C"),
-219                n_estimators=self.n_estimators,
-220                learning_rate=self.learning_rate,
-221                n_hidden_features=self.n_hidden_features,
-222                reg_lambda=self.reg_lambda,
-223                alpha=self.alpha,
-224                row_sample=self.row_sample,
-225                col_sample=self.col_sample,
-226                dropout=self.dropout,
-227                tolerance=self.tolerance,
-228                direct_link=self.direct_link,
-229                verbose=self.verbose,
-230                seed=self.seed,
-231                backend=self.backend,
-232                solver=self.solver,
-233                activation=self.activation,
-234            )
-235        except ValueError:
-236            self.obj = _boosterc.fit_booster_regressor(
-237                X=np.asarray(X, order="C"),
-238                y=np.asarray(y, order="C"),
-239                n_estimators=self.n_estimators,
-240                learning_rate=self.learning_rate,
-241                n_hidden_features=self.n_hidden_features,
-242                reg_lambda=self.reg_lambda,
-243                alpha=self.alpha,
-244                row_sample=self.row_sample,
-245                col_sample=self.col_sample,
-246                dropout=self.dropout,
-247                tolerance=self.tolerance,
-248                direct_link=self.direct_link,
-249                verbose=self.verbose,
-250                seed=self.seed,
-251                backend=self.backend,
-252                solver=self.solver,
-253                activation=self.activation,
-254            )
-255
-256        self.n_estimators = self.obj["n_estimators"]
-257
-258        self.X_ = X
-259
-260        self.y_ = y
-261
-262        return self
+196            **kwargs: additional parameters to be passed to self.cook_training_set.
+197
+198        Returns:
+199
+200            self: object.
+201        """
+202
+203        if isinstance(X, pd.DataFrame):
+204            X = X.values
+205
+206        if self.degree > 1:
+207            self.poly_ = PolynomialFeatures(
+208                degree=self.degree, interaction_only=True, include_bias=False
+209            )
+210            X = self.poly_.fit_transform(X)
+211
+212        if self.n_clusters > 0:
+213            clustered_X, self.scaler_, self.label_encoder_, self.clusterer_ = (
+214                cluster(
+215                    X,
+216                    n_clusters=self.n_clusters,
+217                    method=self.clustering_method,
+218                    type_scaling=self.cluster_scaling,
+219                    training=True,
+220                    seed=self.seed,
+221                )
+222            )
+223            X = np.column_stack((X, clustered_X))
+224
+225        try:
+226            self.obj = boosterc.fit_booster_regressor(
+227                X=np.asarray(X, order="C"),
+228                y=np.asarray(y, order="C"),
+229                n_estimators=self.n_estimators,
+230                learning_rate=self.learning_rate,
+231                n_hidden_features=self.n_hidden_features,
+232                reg_lambda=self.reg_lambda,
+233                alpha=self.alpha,
+234                row_sample=self.row_sample,
+235                col_sample=self.col_sample,
+236                dropout=self.dropout,
+237                tolerance=self.tolerance,
+238                direct_link=self.direct_link,
+239                verbose=self.verbose,
+240                seed=self.seed,
+241                backend=self.backend,
+242                solver=self.solver,
+243                activation=self.activation,
+244            )
+245        except ValueError:
+246            self.obj = _boosterc.fit_booster_regressor(
+247                X=np.asarray(X, order="C"),
+248                y=np.asarray(y, order="C"),
+249                n_estimators=self.n_estimators,
+250                learning_rate=self.learning_rate,
+251                n_hidden_features=self.n_hidden_features,
+252                reg_lambda=self.reg_lambda,
+253                alpha=self.alpha,
+254                row_sample=self.row_sample,
+255                col_sample=self.col_sample,
+256                dropout=self.dropout,
+257                tolerance=self.tolerance,
+258                direct_link=self.direct_link,
+259                verbose=self.verbose,
+260                seed=self.seed,
+261                backend=self.backend,
+262                solver=self.solver,
+263                activation=self.activation,
+264            )
+265
+266        self.n_estimators = self.obj["n_estimators"]
+267
+268        self.X_ = X
+269
+270        self.y_ = y
+271
+272        return self
 
    
 
 
@@ -1363,77 +1387,77 @@
    - 
    264    def predict(self, X, level=95, method=None, **kwargs):
-265        """Predict probabilities for test data X.
-266
-267        Args:
-268
-269            X: {array-like}, shape = [n_samples, n_features]
-270                Training vectors, where n_samples is the number
-271                of samples and n_features is the number of features.
-272
-273            level: int
-274                Level of confidence (default = 95)
-275
-276            method: str
-277                `None`, or 'splitconformal', 'localconformal'
-278                prediction (if you specify `return_pi = True`)
-279
-280            **kwargs: additional parameters to be passed to
-281                self.cook_test_set
+            
    274    def predict(self, X, level=95, method=None, **kwargs):
+275        """Predict probabilities for test data X.
+276
+277        Args:
+278
+279            X: {array-like}, shape = [n_samples, n_features]
+280                Training vectors, where n_samples is the number
+281                of samples and n_features is the number of features.
 282
-283        Returns:
-284
-285            probability estimates for test data: {array-like}
-286        """
-287
-288        if isinstance(X, pd.DataFrame):
-289            X = X.values
-290
-291        if self.degree > 0:
-292            X = self.poly_.transform(X)
-293
-294        if self.n_clusters > 0:
-295            X = np.column_stack(
-296                (
-297                    X,
-298                    cluster(
-299                        X,
-300                        training=False,
-301                        scaler=self.scaler_,
-302                        label_encoder=self.label_encoder_,
-303                        clusterer=self.clusterer_,
-304                        seed=self.seed,
-305                    ),
-306                )
-307            )
-308        if "return_pi" in kwargs:
-309            assert method in (
-310                "splitconformal",
-311                "localconformal",
-312            ), "method must be in ('splitconformal', 'localconformal')"
-313            self.pi = PredictionInterval(
-314                obj=self,
-315                method=method,
-316                level=level,
-317                type_pi=self.type_pi,
-318                replications=self.replications,
-319                kernel=self.kernel,
-320            )
-321            self.pi.fit(self.X_, self.y_)
-322            self.X_ = None
-323            self.y_ = None
-324            preds = self.pi.predict(X, return_pi=True)
-325            return preds
-326
-327        try:
-328            return boosterc.predict_booster_regressor(
-329                self.obj, np.asarray(X, order="C")
+283            level: int
+284                Level of confidence (default = 95)
+285
+286            method: str
+287                `None`, or 'splitconformal', 'localconformal'
+288                prediction (if you specify `return_pi = True`)
+289
+290            **kwargs: additional parameters to be passed to
+291                self.cook_test_set
+292
+293        Returns:
+294
+295            probability estimates for test data: {array-like}
+296        """
+297
+298        if isinstance(X, pd.DataFrame):
+299            X = X.values
+300
+301        if self.degree > 0:
+302            X = self.poly_.transform(X)
+303
+304        if self.n_clusters > 0:
+305            X = np.column_stack(
+306                (
+307                    X,
+308                    cluster(
+309                        X,
+310                        training=False,
+311                        scaler=self.scaler_,
+312                        label_encoder=self.label_encoder_,
+313                        clusterer=self.clusterer_,
+314                        seed=self.seed,
+315                    ),
+316                )
+317            )
+318        if "return_pi" in kwargs:
+319            assert method in (
+320                "splitconformal",
+321                "localconformal",
+322            ), "method must be in ('splitconformal', 'localconformal')"
+323            self.pi = PredictionInterval(
+324                obj=self,
+325                method=method,
+326                level=level,
+327                type_pi=self.type_pi,
+328                replications=self.replications,
+329                kernel=self.kernel,
 330            )
-331        except ValueError:
-332            return _boosterc.predict_booster_regressor(
-333                self.obj, np.asarray(X, order="C")
-334            )
+331            self.pi.fit(self.X_, self.y_)
+332            self.X_ = None
+333            self.y_ = None
+334            preds = self.pi.predict(X, return_pi=True)
+335            return preds
+336
+337        try:
+338            return boosterc.predict_booster_regressor(
+339                self.obj, np.asarray(X, order="C")
+340            )
+341        except ValueError:
+342            return _boosterc.predict_booster_regressor(
+343                self.obj, np.asarray(X, order="C")
+344            )
 
    
 
 
diff --git a/mlsauce-docs/search.js b/mlsauce-docs/search.js
index 1ce57c4..ab3cd2b 100644
--- a/mlsauce-docs/search.js
+++ b/mlsauce-docs/search.js
@@ -1,6 +1,6 @@
 window.pdocSearch = (function(){
 /** elasticlunr - http://weixsong.github.io * Copyright (C) 2017 Oliver Nightingale * Copyright (C) 2017 Wei Song * MIT Licensed */!function(){function e(e){if(null===e||"object"!=typeof e)return e;var t=e.constructor();for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}var t=function(e){var n=new t.Index;return n.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),e&&e.call(n,n),n};t.version="0.9.5",lunr=t,t.utils={},t.utils.warn=function(e){return function(t){e.console&&console.warn&&console.warn(t)}}(this),t.utils.toString=function(e){return void 0===e||null===e?"":e.toString()},t.EventEmitter=function(){this.events={}},t.EventEmitter.prototype.addListener=function(){var e=Array.prototype.slice.call(arguments),t=e.pop(),n=e;if("function"!=typeof t)throw new TypeError("last argument must be a function");n.forEach(function(e){this.hasHandler(e)||(this.events[e]=[]),this.events[e].push(t)},this)},t.EventEmitter.prototype.removeListener=function(e,t){if(this.hasHandler(e)){var n=this.events[e].indexOf(t);-1!==n&&(this.events[e].splice(n,1),0==this.events[e].length&&delete this.events[e])}},t.EventEmitter.prototype.emit=function(e){if(this.hasHandler(e)){var t=Array.prototype.slice.call(arguments,1);this.events[e].forEach(function(e){e.apply(void 0,t)},this)}},t.EventEmitter.prototype.hasHandler=function(e){return e in this.events},t.tokenizer=function(e){if(!arguments.length||null===e||void 0===e)return[];if(Array.isArray(e)){var n=e.filter(function(e){return null===e||void 0===e?!1:!0});n=n.map(function(e){return t.utils.toString(e).toLowerCase()});var i=[];return n.forEach(function(e){var n=e.split(t.tokenizer.seperator);i=i.concat(n)},this),i}return e.toString().trim().toLowerCase().split(t.tokenizer.seperator)},t.tokenizer.defaultSeperator=/[\s\-]+/,t.tokenizer.seperator=t.tokenizer.defaultSeperator,t.tokenizer.setSeperator=function(e){null!==e&&void 0!==e&&"object"==typeof e&&(t.tokenizer.seperator=e)},t.tokenizer.resetSeperator=function(){t.tokenizer.seperator=t.tokenizer.defaultSeperator},t.tokenizer.getSeperator=function(){return t.tokenizer.seperator},t.Pipeline=function(){this._queue=[]},t.Pipeline.registeredFunctions={},t.Pipeline.registerFunction=function(e,n){n in t.Pipeline.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+n),e.label=n,t.Pipeline.registeredFunctions[n]=e},t.Pipeline.getRegisteredFunction=function(e){return e in t.Pipeline.registeredFunctions!=!0?null:t.Pipeline.registeredFunctions[e]},t.Pipeline.warnIfFunctionNotRegistered=function(e){var n=e.label&&e.label in this.registeredFunctions;n||t.utils.warn("Function is not registered with pipeline. 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configuration"),this.buildDefaultConfig(n)}},t.Configuration.prototype.buildDefaultConfig=function(e){this.reset(),e.forEach(function(e){this.config[e]={boost:1,bool:"OR",expand:!1}},this)},t.Configuration.prototype.buildUserConfig=function(e,n){var i="OR",o=!1;if(this.reset(),"bool"in e&&(i=e.bool||i),"expand"in e&&(o=e.expand||o),"fields"in e)for(var r in e.fields)if(n.indexOf(r)>-1){var s=e.fields[r],u=o;void 0!=s.expand&&(u=s.expand),this.config[r]={boost:s.boost||0===s.boost?s.boost:1,bool:s.bool||i,expand:u}}else t.utils.warn("field name in user configuration not found in index instance fields");else this.addAllFields2UserConfig(i,o,n)},t.Configuration.prototype.addAllFields2UserConfig=function(e,t,n){n.forEach(function(n){this.config[n]={boost:1,bool:e,expand:t}},this)},t.Configuration.prototype.get=function(){return this.config},t.Configuration.prototype.reset=function(){this.config={}},lunr.SortedSet=function(){this.length=0,this.elements=[]},lunr.SortedSet.load=function(e){var t=new this;return t.elements=e,t.length=e.length,t},lunr.SortedSet.prototype.add=function(){var e,t;for(e=0;e1;){if(r===e)return o;e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o]}return r===e?o:-1},lunr.SortedSet.prototype.locationFor=function(e){for(var t=0,n=this.elements.length,i=n-t,o=t+Math.floor(i/2),r=this.elements[o];i>1;)e>r&&(t=o),r>e&&(n=o),i=n-t,o=t+Math.floor(i/2),r=this.elements[o];return r>e?o:e>r?o+1:void 0},lunr.SortedSet.prototype.intersect=function(e){for(var t=new lunr.SortedSet,n=0,i=0,o=this.length,r=e.length,s=this.elements,u=e.elements;;){if(n>o-1||i>r-1)break;s[n]!==u[i]?s[n]u[i]&&i++:(t.add(s[n]),n++,i++)}return t},lunr.SortedSet.prototype.clone=function(){var e=new lunr.SortedSet;return e.elements=this.toArray(),e.length=e.elements.length,e},lunr.SortedSet.prototype.union=function(e){var t,n,i;this.length>=e.length?(t=this,n=e):(t=e,n=this),i=t.clone();for(var o=0,r=n.toArray();o
    \n"}, "mlsauce.AdaOpt": {"fullname": "mlsauce.AdaOpt", "modulename": "mlsauce", "qualname": "AdaOpt", "kind": "class", "doc": "
    AdaOpt classifier.
    \n\n
    Attributes:
    \n\n
    n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.AdaOpt.__init__": {"fullname": "mlsauce.AdaOpt.__init__", "modulename": "mlsauce", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.AdaOpt.n_iterations": {"fullname": "mlsauce.AdaOpt.n_iterations", "modulename": "mlsauce", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.learning_rate": {"fullname": "mlsauce.AdaOpt.learning_rate", "modulename": "mlsauce", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.reg_lambda": {"fullname": "mlsauce.AdaOpt.reg_lambda", "modulename": "mlsauce", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.reg_alpha": {"fullname": "mlsauce.AdaOpt.reg_alpha", "modulename": "mlsauce", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.eta": {"fullname": "mlsauce.AdaOpt.eta", "modulename": "mlsauce", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.gamma": {"fullname": "mlsauce.AdaOpt.gamma", "modulename": "mlsauce", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.k": {"fullname": "mlsauce.AdaOpt.k", "modulename": "mlsauce", "qualname": "AdaOpt.k", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.tolerance": {"fullname": "mlsauce.AdaOpt.tolerance", "modulename": "mlsauce", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.n_clusters": {"fullname": "mlsauce.AdaOpt.n_clusters", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.batch_size": {"fullname": "mlsauce.AdaOpt.batch_size", "modulename": "mlsauce", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.row_sample": {"fullname": "mlsauce.AdaOpt.row_sample", "modulename": "mlsauce", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.type_dist": {"fullname": "mlsauce.AdaOpt.type_dist", "modulename": "mlsauce", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.n_jobs": {"fullname": "mlsauce.AdaOpt.n_jobs", "modulename": "mlsauce", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.cache": {"fullname": "mlsauce.AdaOpt.cache", "modulename": "mlsauce", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.verbose": {"fullname": "mlsauce.AdaOpt.verbose", "modulename": "mlsauce", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.n_clusters_input": {"fullname": "mlsauce.AdaOpt.n_clusters_input", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.clustering_method": {"fullname": "mlsauce.AdaOpt.clustering_method", "modulename": "mlsauce", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.cluster_scaling": {"fullname": "mlsauce.AdaOpt.cluster_scaling", "modulename": "mlsauce", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.seed": {"fullname": "mlsauce.AdaOpt.seed", "modulename": "mlsauce", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.fit": {"fullname": "mlsauce.AdaOpt.fit", "modulename": "mlsauce", "qualname": "AdaOpt.fit", "kind": "function", "doc": "
    Fit AdaOpt to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict": {"fullname": "mlsauce.AdaOpt.predict", "modulename": "mlsauce", "qualname": "AdaOpt.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict_proba": {"fullname": "mlsauce.AdaOpt.predict_proba", "modulename": "mlsauce", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.set_score_request": {"fullname": "mlsauce.AdaOpt.set_score_request", "modulename": "mlsauce", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostClassifier": {"fullname": "mlsauce.LSBoostClassifier", "modulename": "mlsauce", "qualname": "LSBoostClassifier", "kind": "class", "doc": "
    LSBoost classifier.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.LSBoostClassifier.__init__": {"fullname": "mlsauce.LSBoostClassifier.__init__", "modulename": "mlsauce", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0)"}, "mlsauce.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.LSBoostClassifier.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.LSBoostClassifier.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.LSBoostClassifier.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.alpha": {"fullname": "mlsauce.LSBoostClassifier.alpha", "modulename": "mlsauce", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.row_sample": {"fullname": "mlsauce.LSBoostClassifier.row_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.col_sample": {"fullname": "mlsauce.LSBoostClassifier.col_sample", "modulename": "mlsauce", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.dropout": {"fullname": "mlsauce.LSBoostClassifier.dropout", "modulename": "mlsauce", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.tolerance": {"fullname": "mlsauce.LSBoostClassifier.tolerance", "modulename": "mlsauce", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.direct_link": {"fullname": "mlsauce.LSBoostClassifier.direct_link", "modulename": "mlsauce", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.verbose": {"fullname": "mlsauce.LSBoostClassifier.verbose", "modulename": "mlsauce", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.seed": {"fullname": "mlsauce.LSBoostClassifier.seed", "modulename": "mlsauce", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.backend": {"fullname": "mlsauce.LSBoostClassifier.backend", "modulename": "mlsauce", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.obj": {"fullname": "mlsauce.LSBoostClassifier.obj", "modulename": "mlsauce", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.solver": {"fullname": "mlsauce.LSBoostClassifier.solver", "modulename": "mlsauce", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.activation": {"fullname": "mlsauce.LSBoostClassifier.activation", "modulename": "mlsauce", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.LSBoostClassifier.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.LSBoostClassifier.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.degree": {"fullname": "mlsauce.LSBoostClassifier.degree", "modulename": "mlsauce", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.poly_": {"fullname": "mlsauce.LSBoostClassifier.poly_", "modulename": "mlsauce", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.fit": {"fullname": "mlsauce.LSBoostClassifier.fit", "modulename": "mlsauce", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "
    Fit Booster (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict": {"fullname": "mlsauce.LSBoostClassifier.predict", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.LSBoostClassifier.predict_proba", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.LSBoostClassifier.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier": {"fullname": "mlsauce.StumpClassifier", "modulename": "mlsauce", "qualname": "StumpClassifier", "kind": "class", "doc": "
    Stump classifier.
    \n\n
    Attributes:
    \n\n
    bins: int\n    Number of histogram bins; as in numpy.histogram.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.StumpClassifier.__init__": {"fullname": "mlsauce.StumpClassifier.__init__", "modulename": "mlsauce", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(bins='auto')"}, "mlsauce.StumpClassifier.bins": {"fullname": "mlsauce.StumpClassifier.bins", "modulename": "mlsauce", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "
    \n"}, "mlsauce.StumpClassifier.obj": {"fullname": "mlsauce.StumpClassifier.obj", "modulename": "mlsauce", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.StumpClassifier.fit": {"fullname": "mlsauce.StumpClassifier.fit", "modulename": "mlsauce", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "
    Fit Stump to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict": {"fullname": "mlsauce.StumpClassifier.predict", "modulename": "mlsauce", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict_proba": {"fullname": "mlsauce.StumpClassifier.predict_proba", "modulename": "mlsauce", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_fit_request": {"fullname": "mlsauce.StumpClassifier.set_fit_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_score_request": {"fullname": "mlsauce.StumpClassifier.set_score_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor": {"fullname": "mlsauce.ElasticNetRegressor", "modulename": "mlsauce", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "
    Elasticnet.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ElasticNetRegressor.__init__": {"fullname": "mlsauce.ElasticNetRegressor.__init__", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.alpha": {"fullname": "mlsauce.ElasticNetRegressor.alpha", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.backend": {"fullname": "mlsauce.ElasticNetRegressor.backend", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.fit": {"fullname": "mlsauce.ElasticNetRegressor.fit", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.predict": {"fullname": "mlsauce.ElasticNetRegressor.predict", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.ElasticNetRegressor.set_score_request", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LassoRegressor": {"fullname": "mlsauce.LassoRegressor", "modulename": "mlsauce", "qualname": "LassoRegressor", "kind": "class", "doc": "
    Lasso.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LassoRegressor.__init__": {"fullname": "mlsauce.LassoRegressor.__init__", "modulename": "mlsauce", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.LassoRegressor.reg_lambda": {"fullname": "mlsauce.LassoRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.max_iter": {"fullname": "mlsauce.LassoRegressor.max_iter", "modulename": "mlsauce", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.tol": {"fullname": "mlsauce.LassoRegressor.tol", "modulename": "mlsauce", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.backend": {"fullname": "mlsauce.LassoRegressor.backend", "modulename": "mlsauce", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.fit": {"fullname": "mlsauce.LassoRegressor.fit", "modulename": "mlsauce", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.predict": {"fullname": "mlsauce.LassoRegressor.predict", "modulename": "mlsauce", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.set_score_request": {"fullname": "mlsauce.LassoRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor": {"fullname": "mlsauce.LSBoostRegressor", "modulename": "mlsauce", "qualname": "LSBoostRegressor", "kind": "class", "doc": "
    LSBoost regressor.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LSBoostRegressor.__init__": {"fullname": "mlsauce.LSBoostRegressor.__init__", "modulename": "mlsauce", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0)"}, "mlsauce.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.LSBoostRegressor.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.LSBoostRegressor.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.LSBoostRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.alpha": {"fullname": "mlsauce.LSBoostRegressor.alpha", "modulename": "mlsauce", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.row_sample": {"fullname": "mlsauce.LSBoostRegressor.row_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.col_sample": {"fullname": "mlsauce.LSBoostRegressor.col_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.dropout": {"fullname": "mlsauce.LSBoostRegressor.dropout", "modulename": "mlsauce", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.tolerance": {"fullname": "mlsauce.LSBoostRegressor.tolerance", "modulename": "mlsauce", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.direct_link": {"fullname": "mlsauce.LSBoostRegressor.direct_link", "modulename": "mlsauce", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.verbose": {"fullname": "mlsauce.LSBoostRegressor.verbose", "modulename": "mlsauce", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.seed": {"fullname": "mlsauce.LSBoostRegressor.seed", "modulename": "mlsauce", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.backend": {"fullname": "mlsauce.LSBoostRegressor.backend", "modulename": "mlsauce", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.obj": {"fullname": "mlsauce.LSBoostRegressor.obj", "modulename": "mlsauce", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.solver": {"fullname": "mlsauce.LSBoostRegressor.solver", "modulename": "mlsauce", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.activation": {"fullname": "mlsauce.LSBoostRegressor.activation", "modulename": "mlsauce", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.type_pi": {"fullname": "mlsauce.LSBoostRegressor.type_pi", "modulename": "mlsauce", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.replications": {"fullname": "mlsauce.LSBoostRegressor.replications", "modulename": "mlsauce", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.kernel": {"fullname": "mlsauce.LSBoostRegressor.kernel", "modulename": "mlsauce", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.LSBoostRegressor.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.LSBoostRegressor.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.degree": {"fullname": "mlsauce.LSBoostRegressor.degree", "modulename": "mlsauce", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.poly_": {"fullname": "mlsauce.LSBoostRegressor.poly_", "modulename": "mlsauce", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.fit": {"fullname": "mlsauce.LSBoostRegressor.fit", "modulename": "mlsauce", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "
    Fit Booster (regressor) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.predict": {"fullname": "mlsauce.LSBoostRegressor.predict", "modulename": "mlsauce", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.LSBoostRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.LSBoostRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.RidgeRegressor": {"fullname": "mlsauce.RidgeRegressor", "modulename": "mlsauce", "qualname": "RidgeRegressor", "kind": "class", "doc": "
    Ridge.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.RidgeRegressor.__init__": {"fullname": "mlsauce.RidgeRegressor.__init__", "modulename": "mlsauce", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.RidgeRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.RidgeRegressor.backend": {"fullname": "mlsauce.RidgeRegressor.backend", "modulename": "mlsauce", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.RidgeRegressor.fit": {"fullname": "mlsauce.RidgeRegressor.fit", "modulename": "mlsauce", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.predict": {"fullname": "mlsauce.RidgeRegressor.predict", "modulename": "mlsauce", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.set_score_request": {"fullname": "mlsauce.RidgeRegressor.set_score_request", "modulename": "mlsauce", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.download": {"fullname": "mlsauce.download", "modulename": "mlsauce", "qualname": "download", "kind": "function", "doc": "
    \n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.get_config": {"fullname": "mlsauce.get_config", "modulename": "mlsauce", "qualname": "get_config", "kind": "function", "doc": "
    Retrieve current values for configuration set by set_config()
    \n\n
    Returns
    \n\n
    config : dict\n    Keys are parameter names that can be passed to set_config().
    \n\n
    See Also
    \n\n
    config_context: Context manager for global mlsauce configuration\nset_config: Set global mlsauce configuration
    \n", "signature": "():", "funcdef": "def"}, "mlsauce.set_config": {"fullname": "mlsauce.set_config", "modulename": "mlsauce", "qualname": "set_config", "kind": "function", "doc": "
    Set global mlsauce configuration
    \n\n
    New in version 0.3.0.
    \n\n
    Parameters
    \n\n
    assume_finite : bool, optional\n    If True, validation for finiteness will be skipped,\n    saving time, but leading to potential crashes. If\n    False, validation for finiteness will be performed,\n    avoiding error.  Global default: False.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    working_memory : int, optional\n    If set, mlsauce will attempt to limit the size of temporary arrays\n    to this number of MiB (per job when parallelised), often saving both\n    computation time and memory on expensive operations that can be\n    performed in chunks. Global default: 1024.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    print_changed_only : bool, optional\n    If True, only the parameters that were set to non-default\n    values will be printed when printing an estimator. For example,\n    print(SVC()) while True will only print 'SVC()' while the default\n    behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with\n    all the non-changed parameters.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    display : {'text', 'diagram'}, optional\n    If 'diagram', estimators will be displayed as text in a jupyter lab\n    of notebook context. If 'text', estimators will be displayed as\n    text. Default is 'text'.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    See Also
    \n\n
    config_context: Context manager for global mlsauce configuration\nget_config: Retrieve current values of the global configuration
    \n", "signature": "(\tassume_finite=None,\tworking_memory=None,\tprint_changed_only=None,\tdisplay=None):", "funcdef": "def"}, "mlsauce.config_context": {"fullname": "mlsauce.config_context", "modulename": "mlsauce", "qualname": "config_context", "kind": "function", "doc": "
    Context manager for global mlsauce configuration
    \n\n
    Parameters
    \n\n
    assume_finite : bool, optional\n    If True, validation for finiteness will be skipped,\n    saving time, but leading to potential crashes. If\n    False, validation for finiteness will be performed,\n    avoiding error.  Global default: False.
    \n\n
    working_memory : int, optional\n    If set, mlsauce will attempt to limit the size of temporary arrays\n    to this number of MiB (per job when parallelised), often saving both\n    computation time and memory on expensive operations that can be\n    performed in chunks. Global default: 1024.
    \n\n
    print_changed_only : bool, optional\n    If True, only the parameters that were set to non-default\n    values will be printed when printing an estimator. For example,\n    print(SVC()) while True will only print 'SVC()', but would print\n    'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters\n    when False. Default is True.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    display : {'text', 'diagram'}, optional\n    If 'diagram', estimators will be displayed as text in a jupyter lab\n    of notebook context. If 'text', estimators will be displayed as\n    text. Default is 'text'.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    Notes
    \n\n
    All settings, not just those presently modified, will be returned to\ntheir previous values when the context manager is exited. This is not\nthread-safe.
    \n\n
    Examples
    \n\n
    \n
    >>> import mlsauce\n>>> from mlsauce.utils.validation import assert_all_finite\n>>> with mlsauce.config_context(assume_finite=True):\n...     assert_all_finite([float('nan')])\n>>> with mlsauce.config_context(assume_finite=True):\n...     with mlsauce.config_context(assume_finite=False):\n...         assert_all_finite([float('nan')])\nTraceback (most recent call last):\n...\nValueError: Input contains NaN, ...\n
    \n
    \n\n
    See Also
    \n\n
    set_config: Set global mlsauce configuration\nget_config: Retrieve current values of the global configuration
    \n", "signature": "(**new_config):", "funcdef": "def"}, "mlsauce.adaopt": {"fullname": "mlsauce.adaopt", "modulename": "mlsauce.adaopt", "kind": "module", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt": {"fullname": "mlsauce.adaopt.AdaOpt", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt", "kind": "class", "doc": "
    AdaOpt classifier.
    \n\n
    Attributes:
    \n\n
    n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.adaopt.AdaOpt.__init__": {"fullname": "mlsauce.adaopt.AdaOpt.__init__", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.adaopt.AdaOpt.n_iterations": {"fullname": "mlsauce.adaopt.AdaOpt.n_iterations", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.learning_rate": {"fullname": "mlsauce.adaopt.AdaOpt.learning_rate", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.reg_lambda": {"fullname": "mlsauce.adaopt.AdaOpt.reg_lambda", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.reg_alpha": {"fullname": "mlsauce.adaopt.AdaOpt.reg_alpha", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.eta": {"fullname": "mlsauce.adaopt.AdaOpt.eta", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.gamma": {"fullname": "mlsauce.adaopt.AdaOpt.gamma", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.k": {"fullname": "mlsauce.adaopt.AdaOpt.k", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.k", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.tolerance": {"fullname": "mlsauce.adaopt.AdaOpt.tolerance", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_clusters": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.batch_size": {"fullname": "mlsauce.adaopt.AdaOpt.batch_size", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.row_sample": {"fullname": "mlsauce.adaopt.AdaOpt.row_sample", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.type_dist": {"fullname": "mlsauce.adaopt.AdaOpt.type_dist", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_jobs": {"fullname": "mlsauce.adaopt.AdaOpt.n_jobs", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.cache": {"fullname": "mlsauce.adaopt.AdaOpt.cache", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.verbose": {"fullname": "mlsauce.adaopt.AdaOpt.verbose", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_clusters_input": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters_input", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.clustering_method": {"fullname": "mlsauce.adaopt.AdaOpt.clustering_method", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.cluster_scaling": {"fullname": "mlsauce.adaopt.AdaOpt.cluster_scaling", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.seed": {"fullname": "mlsauce.adaopt.AdaOpt.seed", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.fit": {"fullname": "mlsauce.adaopt.AdaOpt.fit", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.fit", "kind": "function", "doc": "
    Fit AdaOpt to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict": {"fullname": "mlsauce.adaopt.AdaOpt.predict", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict_proba": {"fullname": "mlsauce.adaopt.AdaOpt.predict_proba", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.set_score_request": {"fullname": "mlsauce.adaopt.AdaOpt.set_score_request", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster": {"fullname": "mlsauce.booster", "modulename": "mlsauce.booster", "kind": "module", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier": {"fullname": "mlsauce.booster.LSBoostClassifier", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier", "kind": "class", "doc": "
    LSBoost classifier.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.booster.LSBoostClassifier.__init__": {"fullname": "mlsauce.booster.LSBoostClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0)"}, "mlsauce.booster.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.booster.LSBoostClassifier.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.booster.LSBoostClassifier.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.booster.LSBoostClassifier.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.alpha": {"fullname": "mlsauce.booster.LSBoostClassifier.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.row_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.col_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.dropout": {"fullname": "mlsauce.booster.LSBoostClassifier.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.tolerance": {"fullname": "mlsauce.booster.LSBoostClassifier.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.direct_link": {"fullname": "mlsauce.booster.LSBoostClassifier.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.verbose": {"fullname": "mlsauce.booster.LSBoostClassifier.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.seed": {"fullname": "mlsauce.booster.LSBoostClassifier.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.backend": {"fullname": "mlsauce.booster.LSBoostClassifier.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.obj": {"fullname": "mlsauce.booster.LSBoostClassifier.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.solver": {"fullname": "mlsauce.booster.LSBoostClassifier.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.activation": {"fullname": "mlsauce.booster.LSBoostClassifier.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.booster.LSBoostClassifier.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.booster.LSBoostClassifier.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.degree": {"fullname": "mlsauce.booster.LSBoostClassifier.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.poly_": {"fullname": "mlsauce.booster.LSBoostClassifier.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.fit": {"fullname": "mlsauce.booster.LSBoostClassifier.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "
    Fit Booster (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict": {"fullname": "mlsauce.booster.LSBoostClassifier.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.booster.LSBoostClassifier.predict_proba", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.booster.LSBoostClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor": {"fullname": "mlsauce.booster.LSBoostRegressor", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor", "kind": "class", "doc": "
    LSBoost regressor.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.booster.LSBoostRegressor.__init__": {"fullname": "mlsauce.booster.LSBoostRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0)"}, "mlsauce.booster.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.booster.LSBoostRegressor.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.booster.LSBoostRegressor.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.booster.LSBoostRegressor.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.alpha": {"fullname": "mlsauce.booster.LSBoostRegressor.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.row_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.col_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.dropout": {"fullname": "mlsauce.booster.LSBoostRegressor.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.tolerance": {"fullname": "mlsauce.booster.LSBoostRegressor.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.direct_link": {"fullname": "mlsauce.booster.LSBoostRegressor.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.verbose": {"fullname": "mlsauce.booster.LSBoostRegressor.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.seed": {"fullname": "mlsauce.booster.LSBoostRegressor.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.backend": {"fullname": "mlsauce.booster.LSBoostRegressor.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.obj": {"fullname": "mlsauce.booster.LSBoostRegressor.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.solver": {"fullname": "mlsauce.booster.LSBoostRegressor.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.activation": {"fullname": "mlsauce.booster.LSBoostRegressor.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.type_pi": {"fullname": "mlsauce.booster.LSBoostRegressor.type_pi", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.replications": {"fullname": "mlsauce.booster.LSBoostRegressor.replications", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.kernel": {"fullname": "mlsauce.booster.LSBoostRegressor.kernel", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.booster.LSBoostRegressor.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.booster.LSBoostRegressor.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.degree": {"fullname": "mlsauce.booster.LSBoostRegressor.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.poly_": {"fullname": "mlsauce.booster.LSBoostRegressor.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.fit": {"fullname": "mlsauce.booster.LSBoostRegressor.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "
    Fit Booster (regressor) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.predict": {"fullname": "mlsauce.booster.LSBoostRegressor.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.datasets": {"fullname": "mlsauce.datasets", "modulename": "mlsauce.datasets", "kind": "module", "doc": "
    \n"}, "mlsauce.datasets.dowload": {"fullname": "mlsauce.datasets.dowload", "modulename": "mlsauce.datasets.dowload", "kind": "module", "doc": "
    \n"}, "mlsauce.datasets.dowload.download": {"fullname": "mlsauce.datasets.dowload.download", "modulename": "mlsauce.datasets.dowload", "qualname": "download", "kind": "function", "doc": "
    \n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.demo": {"fullname": "mlsauce.demo", "modulename": "mlsauce.demo", "kind": "module", "doc": "
    \n"}, "mlsauce.elasticnet": {"fullname": "mlsauce.elasticnet", "modulename": "mlsauce.elasticnet", "kind": "module", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "
    Elasticnet.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.elasticnet.ElasticNetRegressor.__init__": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.__init__", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.alpha": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.alpha", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.backend": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.backend", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.fit": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.fit", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.predict": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.predict", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.set_score_request", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lasso": {"fullname": "mlsauce.lasso", "modulename": "mlsauce.lasso", "kind": "module", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor": {"fullname": "mlsauce.lasso.LassoRegressor", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor", "kind": "class", "doc": "
    Lasso.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.lasso.LassoRegressor.__init__": {"fullname": "mlsauce.lasso.LassoRegressor.__init__", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.lasso.LassoRegressor.reg_lambda": {"fullname": "mlsauce.lasso.LassoRegressor.reg_lambda", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.max_iter": {"fullname": "mlsauce.lasso.LassoRegressor.max_iter", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.tol": {"fullname": "mlsauce.lasso.LassoRegressor.tol", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.backend": {"fullname": "mlsauce.lasso.LassoRegressor.backend", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.fit": {"fullname": "mlsauce.lasso.LassoRegressor.fit", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.predict": {"fullname": "mlsauce.lasso.LassoRegressor.predict", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.set_score_request": {"fullname": "mlsauce.lasso.LassoRegressor.set_score_request", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist": {"fullname": "mlsauce.nonconformist", "modulename": "mlsauce.nonconformist", "kind": "module", "doc": "
    docstring
    \n"}, "mlsauce.nonconformist.AbsErrorErrFunc": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc", "kind": "class", "doc": "
    Calculates absolute error nonconformity for regression problems.
    \n\n
    For each correct output in y, nonconformity is defined as
    \n\n
    $$| y_i - \\hat{y}_i |$$
    \n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply", "kind": "function", "doc": "
    Apply the nonconformity function.
    \n\n
    Parameters
    \n\n
    prediction : numpy array of shape [n_samples, n_classes]\n    Class probability estimates for each sample.
    \n\n
    y : numpy array of shape [n_samples]\n    True output labels of each sample.
    \n\n
    Returns
    \n\n
    nc : numpy array of shape [n_samples]\n    Nonconformity scores of the samples.
    \n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply_inverse", "kind": "function", "doc": "
    Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).
    \n\n
    Parameters
    \n\n
    nc : numpy array of shape [n_calibration_samples]\n    Nonconformity scores obtained for conformal predictor.
    \n\n
    significance : float\n    Significance level (0, 1).
    \n\n
    Returns
    \n\n
    interval : numpy array of shape [n_samples, 2]\n    Minimum and maximum interval boundaries for each prediction.
    \n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc", "kind": "class", "doc": "
    Calculates conformalized quantile regression error.
    \n\n
    For each correct output in y, nonconformity is defined as
    \n\n
    $$max{\\hat{q}_low - y, y - \\hat{q}_high}$$
    \n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply", "kind": "function", "doc": "
    Apply the nonconformity function.
    \n\n
    Parameters
    \n\n
    prediction : numpy array of shape [n_samples, n_classes]\n    Class probability estimates for each sample.
    \n\n
    y : numpy array of shape [n_samples]\n    True output labels of each sample.
    \n\n
    Returns
    \n\n
    nc : numpy array of shape [n_samples]\n    Nonconformity scores of the samples.
    \n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply_inverse", "kind": "function", "doc": "
    Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).
    \n\n
    Parameters
    \n\n
    nc : numpy array of shape [n_calibration_samples]\n    Nonconformity scores obtained for conformal predictor.
    \n\n
    significance : float\n    Significance level (0, 1).
    \n\n
    Returns
    \n\n
    interval : numpy array of shape [n_samples, 2]\n    Minimum and maximum interval boundaries for each prediction.
    \n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter": {"fullname": "mlsauce.nonconformist.RegressorAdapter", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Inheriting from this class provides default implementations of:
    \n\n
      \n
    • setting and getting parameters used by GridSearchCV and friends;
      • \n
    • textual and HTML representation displayed in terminals and IDEs;
      • \n
    • estimator serialization;
      • \n
    • parameters validation;
      • \n
    • data validation;
      • \n
    • feature names validation.
      • \n
    \n\n
    Read more in the :ref:User Guide <rolling_your_own_estimator>.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
    \n
    \n", "bases": "mlsauce.nonconformist.base.BaseModelAdapter"}, "mlsauce.nonconformist.RegressorAdapter.__init__": {"fullname": "mlsauce.nonconformist.RegressorAdapter.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.__init__", "kind": "function", "doc": "
    \n", "signature": "(model, fit_params=None)"}, "mlsauce.nonconformist.RegressorAdapter.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc": {"fullname": "mlsauce.nonconformist.RegressorNc", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc", "kind": "class", "doc": "
    Nonconformity scorer using an underlying regression model.
    \n\n
    Parameters
    \n\n
    model : RegressorAdapter\n    Underlying regression model used for calculating nonconformity scores.
    \n\n
    err_func : RegressionErrFunc\n    Error function object.
    \n\n
    normalizer : BaseScorer\n    Normalization model.
    \n\n
    beta : float\n    Normalization smoothing parameter. As the beta-value increases,\n    the normalized nonconformity function approaches a non-normalized\n    equivalent.
    \n\n
    Attributes
    \n\n
    model : RegressorAdapter\n    Underlying model object.
    \n\n
    err_func : RegressionErrFunc\n    Scorer function used to calculate nonconformity scores.
    \n\n
    See also
    \n\n
    ProbEstClassifierNc, NormalizedRegressorNc
    \n", "bases": "mlsauce.nonconformist.nc.BaseModelNc"}, "mlsauce.nonconformist.RegressorNc.__init__": {"fullname": "mlsauce.nonconformist.RegressorNc.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tmodel,\terr_func=<mlsauce.nonconformist.nc.AbsErrorErrFunc object>,\tnormalizer=None,\tbeta=1e-06)"}, "mlsauce.nonconformist.RegressorNc.predict": {"fullname": "mlsauce.nonconformist.RegressorNc.predict", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.predict", "kind": "function", "doc": "
    Constructs prediction intervals for a set of test examples.
    \n\n
    Predicts the output of each test pattern using the underlying model,\nand applies the (partial) inverse nonconformity function to each\nprediction, resulting in a prediction interval for each test pattern.
    \n\n
    Parameters
    \n\n
    x : numpy array of shape [n_samples, n_features]\n    Inputs of patters for which to predict output values.
    \n\n
    significance : float\n    Significance level (maximum allowed error rate) of predictions.\n    Should be a float between 0 and 1. If None, then intervals for\n    all significance levels (0.01, 0.02, ..., 0.99) are output in a\n    3d-matrix.
    \n\n
    Returns
    \n\n
    p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99]\n    If significance is None, then p contains the interval (minimum\n    and maximum boundaries) for each test pattern, and each significance\n    level (0.01, 0.02, ..., 0.99). If significance is a float between\n    0 and 1, then p contains the prediction intervals (minimum and\n    maximum     boundaries) for the set of test patterns at the chosen\n    significance level.
    \n", "signature": "(self, x, nc, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer": {"fullname": "mlsauce.nonconformist.RegressorNormalizer", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Inheriting from this class provides default implementations of:
    \n\n
      \n
    • setting and getting parameters used by GridSearchCV and friends;
      • \n
    • textual and HTML representation displayed in terminals and IDEs;
      • \n
    • estimator serialization;
      • \n
    • parameters validation;
      • \n
    • data validation;
      • \n
    • feature names validation.
      • \n
    \n\n
    Read more in the :ref:User Guide <rolling_your_own_estimator>.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
    \n
    \n", "bases": "mlsauce.nonconformist.nc.BaseScorer"}, "mlsauce.nonconformist.RegressorNormalizer.__init__": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.__init__", "kind": "function", "doc": "
    \n", "signature": "(base_model, normalizer_model, err_func)"}, "mlsauce.nonconformist.RegressorNormalizer.base_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.base_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.base_model", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.normalizer_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.normalizer_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.normalizer_model", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.err_func": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.err_func", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.err_func", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.fit": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.fit", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.fit", "kind": "function", "doc": "
    \n", "signature": "(self, x, y):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.score": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.score", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.score", "kind": "function", "doc": "
    \n", "signature": "(self, x, y=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor": {"fullname": "mlsauce.nonconformist.IcpRegressor", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor", "kind": "class", "doc": "
    Inductive conformal regressor.
    \n\n
    Parameters
    \n\n
    nc_function : BaseScorer\n        Nonconformity scorer object used to calculate nonconformity of\n        calibration examples and test patterns. Should implement fit(x, y),\n        calc_nc(x, y) and predict(x, nc_scores, significance).
    \n\n
    Attributes
    \n\n
    cal_x : numpy array of shape [n_cal_examples, n_features]\n        Inputs of calibration set.
    \n\n
    cal_y : numpy array of shape [n_cal_examples]\n        Outputs of calibration set.
    \n\n
    nc_function : BaseScorer\n        Nonconformity scorer object used to calculate nonconformity scores.
    \n\n
    See also
    \n\n
    IcpClassifier
    \n\n
    References
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.datasets import load_boston\n>>> from sklearn.tree import DecisionTreeRegressor\n>>> from nonconformist.base import RegressorAdapter\n>>> from nonconformist.icp import IcpRegressor\n>>> from nonconformist.nc import RegressorNc, AbsErrorErrFunc\n>>> boston = load_boston()\n>>> idx = np.random.permutation(boston.target.size)\n>>> train = idx[:int(idx.size / 3)]\n>>> cal = idx[int(idx.size / 3):int(2 * idx.size / 3)]\n>>> test = idx[int(2 * idx.size / 3):]\n>>> model = RegressorAdapter(DecisionTreeRegressor())\n>>> nc = RegressorNc(model, AbsErrorErrFunc())\n>>> icp = IcpRegressor(nc)\n>>> icp.fit(boston.data[train, :], boston.target[train])\n>>> icp.calibrate(boston.data[cal, :], boston.target[cal])\n>>> icp.predict(boston.data[test, :], significance=0.10)\n...     # doctest: +SKIP\narray([[  5. ,  20.6],\n        [ 15.5,  31.1],\n        ...,\n        [ 14.2,  29.8],\n        [ 11.6,  27.2]])\n
    \n
    \n\n
    \n
    \n
      \n
    \n
    \n", "bases": "mlsauce.nonconformist.icp.BaseIcp, mlsauce.nonconformist.base.RegressorMixin"}, "mlsauce.nonconformist.IcpRegressor.__init__": {"fullname": "mlsauce.nonconformist.IcpRegressor.__init__", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(nc_function, condition=None)"}, "mlsauce.nonconformist.IcpRegressor.predict": {"fullname": "mlsauce.nonconformist.IcpRegressor.predict", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.predict", "kind": "function", "doc": "
    Predict the output values for a set of input patterns.
    \n\n
    Parameters
    \n\n
    x : numpy array of shape [n_samples, n_features]\n        Inputs of patters for which to predict output values.
    \n\n
    significance : float\n        Significance level (maximum allowed error rate) of predictions.\n        Should be a float between 0 and 1. If None, then intervals for\n        all significance levels (0.01, 0.02, ..., 0.99) are output in a\n        3d-matrix.
    \n\n
    Returns
    \n\n
    p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99}\n        If significance is None, then p contains the interval (minimum\n        and maximum boundaries) for each test pattern, and each significance\n        level (0.01, 0.02, ..., 0.99). If significance is a float between\n        0 and 1, then p contains the prediction intervals (minimum and\n        maximum boundaries) for the set of test patterns at the chosen\n        significance level.
    \n", "signature": "(self, x, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_fit_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_predict_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval": {"fullname": "mlsauce.predictioninterval", "modulename": "mlsauce.predictioninterval", "kind": "module", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval": {"fullname": "mlsauce.predictioninterval.PredictionInterval", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval", "kind": "class", "doc": "
    Class PredictionInterval: Obtain prediction intervals.
    \n\n
    Attributes:
    \n\n
    obj: an object;\n    fitted object containing methods `fit` and `predict`\n\nmethod: a string;\n    method for constructing the prediction intervals.\n    Currently \"splitconformal\" (default) and \"localconformal\"\n\nlevel: a float;\n    Confidence level for prediction intervals. Default is 95,\n    equivalent to a miscoverage error of 5 (%)\n\nreplications: an integer;\n    Number of replications for simulated conformal (default is `None`)\n\ntype_pi: a string;\n    type of prediction interval: currently \"kde\" (default) or \"bootstrap\"\n\nseed: an integer;\n    Reproducibility of fit (there's a random split between fitting and calibration data)\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.predictioninterval.PredictionInterval.__init__": {"fullname": "mlsauce.predictioninterval.PredictionInterval.__init__", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tobj,\tmethod='splitconformal',\tlevel=95,\ttype_pi='bootstrap',\treplications=None,\tkernel=None,\tagg='mean',\tseed=123)"}, "mlsauce.predictioninterval.PredictionInterval.obj": {"fullname": "mlsauce.predictioninterval.PredictionInterval.obj", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.method": {"fullname": "mlsauce.predictioninterval.PredictionInterval.method", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.method", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.level": {"fullname": "mlsauce.predictioninterval.PredictionInterval.level", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.level", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.type_pi": {"fullname": "mlsauce.predictioninterval.PredictionInterval.type_pi", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.replications": {"fullname": "mlsauce.predictioninterval.PredictionInterval.replications", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.kernel": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kernel", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.agg": {"fullname": "mlsauce.predictioninterval.PredictionInterval.agg", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.agg", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.seed": {"fullname": "mlsauce.predictioninterval.PredictionInterval.seed", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.alpha_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.alpha_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.alpha_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.quantile_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.quantile_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.quantile_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.icp_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.icp_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.icp_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.scaled_calibrated_residuals_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_scaler_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.kde_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kde_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kde_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.fit": {"fullname": "mlsauce.predictioninterval.PredictionInterval.fit", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.fit", "kind": "function", "doc": "
    Fit the method to training data (X, y).
    \n\n
    Args:
    \n\n
    X: array-like, shape = [n_samples, n_features];\n    Training set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples, ]; Target values.\n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.predict": {"fullname": "mlsauce.predictioninterval.PredictionInterval.predict", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.predict", "kind": "function", "doc": "
    Obtain predictions and prediction intervals
    \n\n
    Args:
    \n\n
    X: array-like, shape = [n_samples, n_features];\n    Testing set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nreturn_pi: boolean\n    Whether the prediction interval is returned or not.\n    Default is False, for compatibility with other _estimators_.\n    If True, a tuple containing the predictions + lower and upper\n    bounds is returned.\n
    \n", "signature": "(self, X, return_pi=False):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_predict_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_predict_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_score_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_score_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ridge": {"fullname": "mlsauce.ridge", "modulename": "mlsauce.ridge", "kind": "module", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor": {"fullname": "mlsauce.ridge.RidgeRegressor", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor", "kind": "class", "doc": "
    Ridge.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ridge.RidgeRegressor.__init__": {"fullname": "mlsauce.ridge.RidgeRegressor.__init__", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.ridge.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.ridge.RidgeRegressor.reg_lambda", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor.backend": {"fullname": "mlsauce.ridge.RidgeRegressor.backend", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor.fit": {"fullname": "mlsauce.ridge.RidgeRegressor.fit", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.predict": {"fullname": "mlsauce.ridge.RidgeRegressor.predict", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.set_score_request": {"fullname": "mlsauce.ridge.RidgeRegressor.set_score_request", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.setup": {"fullname": "mlsauce.setup", "modulename": "mlsauce.setup", "kind": "module", "doc": "
    \n"}, "mlsauce.stump": {"fullname": "mlsauce.stump", "modulename": "mlsauce.stump", "kind": "module", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier": {"fullname": "mlsauce.stump.StumpClassifier", "modulename": "mlsauce.stump", "qualname": "StumpClassifier", "kind": "class", "doc": "
    Stump classifier.
    \n\n
    Attributes:
    \n\n
    bins: int\n    Number of histogram bins; as in numpy.histogram.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.stump.StumpClassifier.__init__": {"fullname": "mlsauce.stump.StumpClassifier.__init__", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(bins='auto')"}, "mlsauce.stump.StumpClassifier.bins": {"fullname": "mlsauce.stump.StumpClassifier.bins", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier.obj": {"fullname": "mlsauce.stump.StumpClassifier.obj", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier.fit": {"fullname": "mlsauce.stump.StumpClassifier.fit", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "
    Fit Stump to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict": {"fullname": "mlsauce.stump.StumpClassifier.predict", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict_proba": {"fullname": "mlsauce.stump.StumpClassifier.predict_proba", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_fit_request": {"fullname": "mlsauce.stump.StumpClassifier.set_fit_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_score_request": {"fullname": "mlsauce.stump.StumpClassifier.set_score_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.utils": {"fullname": "mlsauce.utils", "modulename": "mlsauce.utils", "kind": "module", "doc": "
    \n"}, "mlsauce.utils.cluster": {"fullname": "mlsauce.utils.cluster", "modulename": "mlsauce.utils", "qualname": "cluster", "kind": "function", "doc": "
    \n", "signature": "(\tX,\tn_clusters=None,\tmethod='kmeans',\ttype_scaling='standard',\ttraining=True,\tscaler=None,\tlabel_encoder=None,\tclusterer=None,\tseed=123):", "funcdef": "def"}, "mlsauce.utils.subsample": {"fullname": "mlsauce.utils.subsample", "modulename": "mlsauce.utils", "qualname": "subsample", "kind": "function", "doc": "
    \n", "signature": "(y, row_sample=0.8, seed=123):", "funcdef": "def"}, "mlsauce.utils.merge_two_dicts": {"fullname": "mlsauce.utils.merge_two_dicts", "modulename": "mlsauce.utils", "qualname": "merge_two_dicts", "kind": "function", "doc": "
    \n", "signature": "(x, y):", "funcdef": "def"}, "mlsauce.utils.flatten": {"fullname": "mlsauce.utils.flatten", "modulename": "mlsauce.utils", "qualname": "flatten", "kind": "function", "doc": "
    \n", "signature": "(l):", "funcdef": "def"}, "mlsauce.utils.is_float": {"fullname": "mlsauce.utils.is_float", "modulename": "mlsauce.utils", "qualname": "is_float", "kind": "function", "doc": "
    \n", "signature": "(x):", "funcdef": "def"}, "mlsauce.utils.is_factor": {"fullname": "mlsauce.utils.is_factor", "modulename": "mlsauce.utils", "qualname": "is_factor", "kind": "function", "doc": "
    \n", "signature": "(y):", "funcdef": "def"}, "mlsauce.utils.Progbar": {"fullname": "mlsauce.utils.Progbar", "modulename": "mlsauce.utils", "qualname": "Progbar", "kind": "class", "doc": "
    Displays a progress bar.
    \n\n
    Arguments
    \n\n
    target: Total number of steps expected, None if unknown.\nwidth: Progress bar width on screen.\nverbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)\nstateful_metrics: Iterable of string names of metrics that\n    should *not* be averaged over time. Metrics in this list\n    will be displayed as-is. All others will be averaged\n    by the progbar before display.\ninterval: Minimum visual progress update interval (in seconds).\n
    \n"}, "mlsauce.utils.Progbar.__init__": {"fullname": "mlsauce.utils.Progbar.__init__", "modulename": "mlsauce.utils", "qualname": "Progbar.__init__", "kind": "function", "doc": "
    \n", "signature": "(target, width=30, verbose=1, interval=0.05, stateful_metrics=None)"}, "mlsauce.utils.Progbar.target": {"fullname": "mlsauce.utils.Progbar.target", "modulename": "mlsauce.utils", "qualname": "Progbar.target", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.width": {"fullname": "mlsauce.utils.Progbar.width", "modulename": "mlsauce.utils", "qualname": "Progbar.width", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.verbose": {"fullname": "mlsauce.utils.Progbar.verbose", "modulename": "mlsauce.utils", "qualname": "Progbar.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.interval": {"fullname": "mlsauce.utils.Progbar.interval", "modulename": "mlsauce.utils", "qualname": "Progbar.interval", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.update": {"fullname": "mlsauce.utils.Progbar.update", "modulename": "mlsauce.utils", "qualname": "Progbar.update", "kind": "function", "doc": "
    Updates the progress bar.
    \n\n
    Arguments
    \n\n
    current: Index of current step.\nvalues: List of tuples:\n    `(name, value_for_last_step)`.\n    If `name` is in `stateful_metrics`,\n    `value_for_last_step` will be displayed as-is.\n    Else, an average of the metric over time will be displayed.\n
    \n", "signature": "(self, current, values=None):", "funcdef": "def"}, "mlsauce.utils.Progbar.add": {"fullname": "mlsauce.utils.Progbar.add", "modulename": "mlsauce.utils", "qualname": "Progbar.add", "kind": "function", "doc": "
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+    /** pdoc search index */const docs = {"version": "0.9.5", "fields": ["qualname", "fullname", "annotation", "default_value", "signature", "bases", "doc"], "ref": "fullname", "documentStore": {"docs": {"mlsauce": {"fullname": "mlsauce", "modulename": "mlsauce", "kind": "module", "doc": "
    \n"}, "mlsauce.AdaOpt": {"fullname": "mlsauce.AdaOpt", "modulename": "mlsauce", "qualname": "AdaOpt", "kind": "class", "doc": "
    AdaOpt classifier.
    \n\n
    Attributes:
    \n\n
    n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.AdaOpt.__init__": {"fullname": "mlsauce.AdaOpt.__init__", "modulename": "mlsauce", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.AdaOpt.n_iterations": {"fullname": "mlsauce.AdaOpt.n_iterations", "modulename": "mlsauce", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.learning_rate": {"fullname": "mlsauce.AdaOpt.learning_rate", "modulename": "mlsauce", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.reg_lambda": {"fullname": "mlsauce.AdaOpt.reg_lambda", "modulename": "mlsauce", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.reg_alpha": {"fullname": "mlsauce.AdaOpt.reg_alpha", "modulename": "mlsauce", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.eta": {"fullname": "mlsauce.AdaOpt.eta", "modulename": "mlsauce", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.gamma": {"fullname": "mlsauce.AdaOpt.gamma", "modulename": "mlsauce", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "
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    \n"}, "mlsauce.AdaOpt.n_clusters": {"fullname": "mlsauce.AdaOpt.n_clusters", "modulename": "mlsauce", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "
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    \n"}, "mlsauce.AdaOpt.row_sample": {"fullname": "mlsauce.AdaOpt.row_sample", "modulename": "mlsauce", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "
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    \n"}, "mlsauce.AdaOpt.cluster_scaling": {"fullname": "mlsauce.AdaOpt.cluster_scaling", "modulename": "mlsauce", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.seed": {"fullname": "mlsauce.AdaOpt.seed", "modulename": "mlsauce", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.AdaOpt.fit": {"fullname": "mlsauce.AdaOpt.fit", "modulename": "mlsauce", "qualname": "AdaOpt.fit", "kind": "function", "doc": "
    Fit AdaOpt to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict": {"fullname": "mlsauce.AdaOpt.predict", "modulename": "mlsauce", "qualname": "AdaOpt.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.predict_proba": {"fullname": "mlsauce.AdaOpt.predict_proba", "modulename": "mlsauce", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.AdaOpt.set_score_request": {"fullname": "mlsauce.AdaOpt.set_score_request", "modulename": "mlsauce", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostClassifier": {"fullname": "mlsauce.LSBoostClassifier", "modulename": "mlsauce", "qualname": "LSBoostClassifier", "kind": "class", "doc": "
    LSBoost classifier.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
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    \n"}, "mlsauce.LSBoostClassifier.tolerance": {"fullname": "mlsauce.LSBoostClassifier.tolerance", "modulename": "mlsauce", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.direct_link": {"fullname": "mlsauce.LSBoostClassifier.direct_link", "modulename": "mlsauce", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.verbose": {"fullname": "mlsauce.LSBoostClassifier.verbose", "modulename": "mlsauce", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.seed": {"fullname": "mlsauce.LSBoostClassifier.seed", "modulename": "mlsauce", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.backend": {"fullname": "mlsauce.LSBoostClassifier.backend", "modulename": "mlsauce", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.obj": {"fullname": "mlsauce.LSBoostClassifier.obj", "modulename": "mlsauce", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.solver": {"fullname": "mlsauce.LSBoostClassifier.solver", "modulename": "mlsauce", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.activation": {"fullname": "mlsauce.LSBoostClassifier.activation", "modulename": "mlsauce", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.LSBoostClassifier.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.LSBoostClassifier.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.degree": {"fullname": "mlsauce.LSBoostClassifier.degree", "modulename": "mlsauce", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.poly_": {"fullname": "mlsauce.LSBoostClassifier.poly_", "modulename": "mlsauce", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.LSBoostClassifier.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostClassifier.fit": {"fullname": "mlsauce.LSBoostClassifier.fit", "modulename": "mlsauce", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "
    Fit Booster (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict": {"fullname": "mlsauce.LSBoostClassifier.predict", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.LSBoostClassifier.predict_proba", "modulename": "mlsauce", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.LSBoostClassifier.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier": {"fullname": "mlsauce.StumpClassifier", "modulename": "mlsauce", "qualname": "StumpClassifier", "kind": "class", "doc": "
    Stump classifier.
    \n\n
    Attributes:
    \n\n
    bins: int\n    Number of histogram bins; as in numpy.histogram.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.StumpClassifier.__init__": {"fullname": "mlsauce.StumpClassifier.__init__", "modulename": "mlsauce", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(bins='auto')"}, "mlsauce.StumpClassifier.bins": {"fullname": "mlsauce.StumpClassifier.bins", "modulename": "mlsauce", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "
    \n"}, "mlsauce.StumpClassifier.obj": {"fullname": "mlsauce.StumpClassifier.obj", "modulename": "mlsauce", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.StumpClassifier.fit": {"fullname": "mlsauce.StumpClassifier.fit", "modulename": "mlsauce", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "
    Fit Stump to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict": {"fullname": "mlsauce.StumpClassifier.predict", "modulename": "mlsauce", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.predict_proba": {"fullname": "mlsauce.StumpClassifier.predict_proba", "modulename": "mlsauce", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_fit_request": {"fullname": "mlsauce.StumpClassifier.set_fit_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.StumpClassifier.set_score_request": {"fullname": "mlsauce.StumpClassifier.set_score_request", "modulename": "mlsauce", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor": {"fullname": "mlsauce.ElasticNetRegressor", "modulename": "mlsauce", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "
    Elasticnet.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ElasticNetRegressor.__init__": {"fullname": "mlsauce.ElasticNetRegressor.__init__", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.alpha": {"fullname": "mlsauce.ElasticNetRegressor.alpha", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.backend": {"fullname": "mlsauce.ElasticNetRegressor.backend", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.ElasticNetRegressor.fit": {"fullname": "mlsauce.ElasticNetRegressor.fit", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.predict": {"fullname": "mlsauce.ElasticNetRegressor.predict", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.ElasticNetRegressor.set_score_request", "modulename": "mlsauce", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LassoRegressor": {"fullname": "mlsauce.LassoRegressor", "modulename": "mlsauce", "qualname": "LassoRegressor", "kind": "class", "doc": "
    Lasso.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LassoRegressor.__init__": {"fullname": "mlsauce.LassoRegressor.__init__", "modulename": "mlsauce", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.LassoRegressor.reg_lambda": {"fullname": "mlsauce.LassoRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.max_iter": {"fullname": "mlsauce.LassoRegressor.max_iter", "modulename": "mlsauce", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.tol": {"fullname": "mlsauce.LassoRegressor.tol", "modulename": "mlsauce", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.backend": {"fullname": "mlsauce.LassoRegressor.backend", "modulename": "mlsauce", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LassoRegressor.fit": {"fullname": "mlsauce.LassoRegressor.fit", "modulename": "mlsauce", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.predict": {"fullname": "mlsauce.LassoRegressor.predict", "modulename": "mlsauce", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.LassoRegressor.set_score_request": {"fullname": "mlsauce.LassoRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor": {"fullname": "mlsauce.LSBoostRegressor", "modulename": "mlsauce", "qualname": "LSBoostRegressor", "kind": "class", "doc": "
    LSBoost regressor.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.LSBoostRegressor.__init__": {"fullname": "mlsauce.LSBoostRegressor.__init__", "modulename": "mlsauce", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0,\tweights_distr='uniform')"}, "mlsauce.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.LSBoostRegressor.n_estimators", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.LSBoostRegressor.learning_rate", "modulename": "mlsauce", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.LSBoostRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.alpha": {"fullname": "mlsauce.LSBoostRegressor.alpha", "modulename": "mlsauce", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.row_sample": {"fullname": "mlsauce.LSBoostRegressor.row_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.col_sample": {"fullname": "mlsauce.LSBoostRegressor.col_sample", "modulename": "mlsauce", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.dropout": {"fullname": "mlsauce.LSBoostRegressor.dropout", "modulename": "mlsauce", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.tolerance": {"fullname": "mlsauce.LSBoostRegressor.tolerance", "modulename": "mlsauce", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.direct_link": {"fullname": "mlsauce.LSBoostRegressor.direct_link", "modulename": "mlsauce", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.verbose": {"fullname": "mlsauce.LSBoostRegressor.verbose", "modulename": "mlsauce", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.seed": {"fullname": "mlsauce.LSBoostRegressor.seed", "modulename": "mlsauce", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.backend": {"fullname": "mlsauce.LSBoostRegressor.backend", "modulename": "mlsauce", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.obj": {"fullname": "mlsauce.LSBoostRegressor.obj", "modulename": "mlsauce", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.solver": {"fullname": "mlsauce.LSBoostRegressor.solver", "modulename": "mlsauce", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.activation": {"fullname": "mlsauce.LSBoostRegressor.activation", "modulename": "mlsauce", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.type_pi": {"fullname": "mlsauce.LSBoostRegressor.type_pi", "modulename": "mlsauce", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.replications": {"fullname": "mlsauce.LSBoostRegressor.replications", "modulename": "mlsauce", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.kernel": {"fullname": "mlsauce.LSBoostRegressor.kernel", "modulename": "mlsauce", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.LSBoostRegressor.n_clusters", "modulename": "mlsauce", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.LSBoostRegressor.clustering_method", "modulename": "mlsauce", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.degree": {"fullname": "mlsauce.LSBoostRegressor.degree", "modulename": "mlsauce", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.poly_": {"fullname": "mlsauce.LSBoostRegressor.poly_", "modulename": "mlsauce", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.LSBoostRegressor.weights_distr", "modulename": "mlsauce", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "
    \n"}, "mlsauce.LSBoostRegressor.fit": {"fullname": "mlsauce.LSBoostRegressor.fit", "modulename": "mlsauce", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "
    Fit Booster (regressor) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.predict": {"fullname": "mlsauce.LSBoostRegressor.predict", "modulename": "mlsauce", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.LSBoostRegressor.set_predict_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.LSBoostRegressor.set_score_request", "modulename": "mlsauce", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.RidgeRegressor": {"fullname": "mlsauce.RidgeRegressor", "modulename": "mlsauce", "qualname": "RidgeRegressor", "kind": "class", "doc": "
    Ridge.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.RidgeRegressor.__init__": {"fullname": "mlsauce.RidgeRegressor.__init__", "modulename": "mlsauce", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.RidgeRegressor.reg_lambda", "modulename": "mlsauce", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.RidgeRegressor.backend": {"fullname": "mlsauce.RidgeRegressor.backend", "modulename": "mlsauce", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.RidgeRegressor.fit": {"fullname": "mlsauce.RidgeRegressor.fit", "modulename": "mlsauce", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.predict": {"fullname": "mlsauce.RidgeRegressor.predict", "modulename": "mlsauce", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.RidgeRegressor.set_score_request": {"fullname": "mlsauce.RidgeRegressor.set_score_request", "modulename": "mlsauce", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.download": {"fullname": "mlsauce.download", "modulename": "mlsauce", "qualname": "download", "kind": "function", "doc": "
    \n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.get_config": {"fullname": "mlsauce.get_config", "modulename": "mlsauce", "qualname": "get_config", "kind": "function", "doc": "
    Retrieve current values for configuration set by set_config()
    \n\n
    Returns
    \n\n
    config : dict\n    Keys are parameter names that can be passed to set_config().
    \n\n
    See Also
    \n\n
    config_context: Context manager for global mlsauce configuration\nset_config: Set global mlsauce configuration
    \n", "signature": "():", "funcdef": "def"}, "mlsauce.set_config": {"fullname": "mlsauce.set_config", "modulename": "mlsauce", "qualname": "set_config", "kind": "function", "doc": "
    Set global mlsauce configuration
    \n\n
    New in version 0.3.0.
    \n\n
    Parameters
    \n\n
    assume_finite : bool, optional\n    If True, validation for finiteness will be skipped,\n    saving time, but leading to potential crashes. If\n    False, validation for finiteness will be performed,\n    avoiding error.  Global default: False.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    working_memory : int, optional\n    If set, mlsauce will attempt to limit the size of temporary arrays\n    to this number of MiB (per job when parallelised), often saving both\n    computation time and memory on expensive operations that can be\n    performed in chunks. Global default: 1024.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    print_changed_only : bool, optional\n    If True, only the parameters that were set to non-default\n    values will be printed when printing an estimator. For example,\n    print(SVC()) while True will only print 'SVC()' while the default\n    behaviour would be to print 'SVC(C=1.0, cache_size=200, ...)' with\n    all the non-changed parameters.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    display : {'text', 'diagram'}, optional\n    If 'diagram', estimators will be displayed as text in a jupyter lab\n    of notebook context. If 'text', estimators will be displayed as\n    text. Default is 'text'.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    See Also
    \n\n
    config_context: Context manager for global mlsauce configuration\nget_config: Retrieve current values of the global configuration
    \n", "signature": "(\tassume_finite=None,\tworking_memory=None,\tprint_changed_only=None,\tdisplay=None):", "funcdef": "def"}, "mlsauce.config_context": {"fullname": "mlsauce.config_context", "modulename": "mlsauce", "qualname": "config_context", "kind": "function", "doc": "
    Context manager for global mlsauce configuration
    \n\n
    Parameters
    \n\n
    assume_finite : bool, optional\n    If True, validation for finiteness will be skipped,\n    saving time, but leading to potential crashes. If\n    False, validation for finiteness will be performed,\n    avoiding error.  Global default: False.
    \n\n
    working_memory : int, optional\n    If set, mlsauce will attempt to limit the size of temporary arrays\n    to this number of MiB (per job when parallelised), often saving both\n    computation time and memory on expensive operations that can be\n    performed in chunks. Global default: 1024.
    \n\n
    print_changed_only : bool, optional\n    If True, only the parameters that were set to non-default\n    values will be printed when printing an estimator. For example,\n    print(SVC()) while True will only print 'SVC()', but would print\n    'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters\n    when False. Default is True.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    display : {'text', 'diagram'}, optional\n    If 'diagram', estimators will be displayed as text in a jupyter lab\n    of notebook context. If 'text', estimators will be displayed as\n    text. Default is 'text'.
    \n\n
    *New in version 0.3.0.*\n
    \n\n
    Notes
    \n\n
    All settings, not just those presently modified, will be returned to\ntheir previous values when the context manager is exited. This is not\nthread-safe.
    \n\n
    Examples
    \n\n
    \n
    >>> import mlsauce\n>>> from mlsauce.utils.validation import assert_all_finite\n>>> with mlsauce.config_context(assume_finite=True):\n...     assert_all_finite([float('nan')])\n>>> with mlsauce.config_context(assume_finite=True):\n...     with mlsauce.config_context(assume_finite=False):\n...         assert_all_finite([float('nan')])\nTraceback (most recent call last):\n...\nValueError: Input contains NaN, ...\n
    \n
    \n\n
    See Also
    \n\n
    set_config: Set global mlsauce configuration\nget_config: Retrieve current values of the global configuration
    \n", "signature": "(**new_config):", "funcdef": "def"}, "mlsauce.adaopt": {"fullname": "mlsauce.adaopt", "modulename": "mlsauce.adaopt", "kind": "module", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt": {"fullname": "mlsauce.adaopt.AdaOpt", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt", "kind": "class", "doc": "
    AdaOpt classifier.
    \n\n
    Attributes:
    \n\n
    n_iterations: int\n    number of iterations of the optimizer at training time.\n\nlearning_rate: float\n    controls the speed of the optimizer at training time.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nreg_alpha: float\n    L1 regularization parameter for successive errors in the optimizer\n    (at training time).\n\neta: float\n    controls the slope in gradient descent (at training time).\n\ngamma: float\n    controls the step size in gradient descent (at training time).\n\nk: int\n    number of nearest neighbors selected at test time for classification.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\nn_clusters: int\n    number of clusters, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nbatch_size: int\n    size of the batch, if MiniBatch k-means is used at test time\n    (for faster prediction).\n\nrow_sample: float\n    percentage of rows chosen from training set (by stratified subsampling,\n    for faster prediction).\n\ntype_dist: str\n    distance used for finding the nearest neighbors; currently `euclidean-f`\n    (euclidean distances calculated as whole), `euclidean` (euclidean distances\n    calculated row by row), `cosine` (cosine distance).\n\nn_jobs: int\n    number of cpus for parallel processing (default: None)\n\nverbose: int\n    progress bar for parallel processing (yes = 1) or not (no = 0)\n\ncache: boolean\n    if the nearest neighbors are cached or not, for faster retrieval in\n    subsequent calls.\n\nn_clusters_input: int\n    number of clusters (a priori) for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.adaopt.AdaOpt.__init__": {"fullname": "mlsauce.adaopt.AdaOpt.__init__", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_iterations=50,\tlearning_rate=0.3,\treg_lambda=0.1,\treg_alpha=0.5,\teta=0.01,\tgamma=0.01,\tk=3,\ttolerance=0,\tn_clusters=0,\tbatch_size=100,\trow_sample=0.8,\ttype_dist='euclidean-f',\tn_jobs=None,\tverbose=0,\tcache=True,\tn_clusters_input=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tseed=123)"}, "mlsauce.adaopt.AdaOpt.n_iterations": {"fullname": "mlsauce.adaopt.AdaOpt.n_iterations", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_iterations", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.learning_rate": {"fullname": "mlsauce.adaopt.AdaOpt.learning_rate", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.reg_lambda": {"fullname": "mlsauce.adaopt.AdaOpt.reg_lambda", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.reg_alpha": {"fullname": "mlsauce.adaopt.AdaOpt.reg_alpha", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.reg_alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.eta": {"fullname": "mlsauce.adaopt.AdaOpt.eta", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.eta", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.gamma": {"fullname": "mlsauce.adaopt.AdaOpt.gamma", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.gamma", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.k": {"fullname": "mlsauce.adaopt.AdaOpt.k", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.k", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.tolerance": {"fullname": "mlsauce.adaopt.AdaOpt.tolerance", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_clusters": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.batch_size": {"fullname": "mlsauce.adaopt.AdaOpt.batch_size", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.batch_size", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.row_sample": {"fullname": "mlsauce.adaopt.AdaOpt.row_sample", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.type_dist": {"fullname": "mlsauce.adaopt.AdaOpt.type_dist", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.type_dist", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_jobs": {"fullname": "mlsauce.adaopt.AdaOpt.n_jobs", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_jobs", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.cache": {"fullname": "mlsauce.adaopt.AdaOpt.cache", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cache", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.verbose": {"fullname": "mlsauce.adaopt.AdaOpt.verbose", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.n_clusters_input": {"fullname": "mlsauce.adaopt.AdaOpt.n_clusters_input", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.n_clusters_input", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.clustering_method": {"fullname": "mlsauce.adaopt.AdaOpt.clustering_method", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.cluster_scaling": {"fullname": "mlsauce.adaopt.AdaOpt.cluster_scaling", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.seed": {"fullname": "mlsauce.adaopt.AdaOpt.seed", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.adaopt.AdaOpt.fit": {"fullname": "mlsauce.adaopt.AdaOpt.fit", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.fit", "kind": "function", "doc": "
    Fit AdaOpt to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict": {"fullname": "mlsauce.adaopt.AdaOpt.predict", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.predict_proba": {"fullname": "mlsauce.adaopt.AdaOpt.predict_proba", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.adaopt.AdaOpt.set_score_request": {"fullname": "mlsauce.adaopt.AdaOpt.set_score_request", "modulename": "mlsauce.adaopt", "qualname": "AdaOpt.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster": {"fullname": "mlsauce.booster", "modulename": "mlsauce.booster", "kind": "module", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier": {"fullname": "mlsauce.booster.LSBoostClassifier", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier", "kind": "class", "doc": "
    LSBoost classifier.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso', 'enet').\n    'enet' is a combination of 'ridge' and 'lasso' called Elastic Net.\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    currently 'uniform', 'gaussian'\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.booster.LSBoostClassifier.__init__": {"fullname": "mlsauce.booster.LSBoostClassifier.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0,\tweights_distr='uniform')"}, "mlsauce.booster.LSBoostClassifier.n_estimators": {"fullname": "mlsauce.booster.LSBoostClassifier.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.learning_rate": {"fullname": "mlsauce.booster.LSBoostClassifier.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostClassifier.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.reg_lambda": {"fullname": "mlsauce.booster.LSBoostClassifier.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.alpha": {"fullname": "mlsauce.booster.LSBoostClassifier.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.row_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.col_sample": {"fullname": "mlsauce.booster.LSBoostClassifier.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.dropout": {"fullname": "mlsauce.booster.LSBoostClassifier.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.tolerance": {"fullname": "mlsauce.booster.LSBoostClassifier.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.direct_link": {"fullname": "mlsauce.booster.LSBoostClassifier.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.verbose": {"fullname": "mlsauce.booster.LSBoostClassifier.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.seed": {"fullname": "mlsauce.booster.LSBoostClassifier.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.backend": {"fullname": "mlsauce.booster.LSBoostClassifier.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.obj": {"fullname": "mlsauce.booster.LSBoostClassifier.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.solver": {"fullname": "mlsauce.booster.LSBoostClassifier.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.activation": {"fullname": "mlsauce.booster.LSBoostClassifier.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.n_clusters": {"fullname": "mlsauce.booster.LSBoostClassifier.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.clustering_method": {"fullname": "mlsauce.booster.LSBoostClassifier.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostClassifier.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.degree": {"fullname": "mlsauce.booster.LSBoostClassifier.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.poly_": {"fullname": "mlsauce.booster.LSBoostClassifier.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.weights_distr": {"fullname": "mlsauce.booster.LSBoostClassifier.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.weights_distr", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostClassifier.fit": {"fullname": "mlsauce.booster.LSBoostClassifier.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.fit", "kind": "function", "doc": "
    Fit Booster (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict": {"fullname": "mlsauce.booster.LSBoostClassifier.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.predict_proba": {"fullname": "mlsauce.booster.LSBoostClassifier.predict_proba", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostClassifier.set_score_request": {"fullname": "mlsauce.booster.LSBoostClassifier.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor": {"fullname": "mlsauce.booster.LSBoostRegressor", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor", "kind": "class", "doc": "
    LSBoost regressor.
    \n\n
    Attributes:
    \n\n
    n_estimators: int\n    number of boosting iterations.\n\nlearning_rate: float\n    controls the learning speed at training time.\n\nn_hidden_features: int\n    number of nodes in successive hidden layers.\n\nreg_lambda: float\n    L2 regularization parameter for successive errors in the optimizer\n    (at training time).\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'\n\nrow_sample: float\n    percentage of rows chosen from the training set.\n\ncol_sample: float\n    percentage of columns chosen from the training set.\n\ndropout: float\n    percentage of nodes dropped from the training set.\n\ntolerance: float\n    controls early stopping in gradient descent (at training time).\n\ndirect_link: bool\n    indicates whether the original features are included (True) in model's\n    fitting or not (False).\n\nverbose: int\n    progress bar (yes = 1) or not (no = 0) (currently).\n\nseed: int\n    reproducibility seed for nodes_sim=='uniform', clustering and dropout.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n\nsolver: str\n    type of 'weak' learner; currently in ('ridge', 'lasso')\n\nactivation: str\n    activation function: currently 'relu', 'relu6', 'sigmoid', 'tanh'\n\ntype_pi: str.\n    type of prediction interval; currently \"kde\" (default) or \"bootstrap\".\n    Used only in `self.predict`, for `self.replications` > 0 and `self.kernel`\n    in ('gaussian', 'tophat'). Default is `None`.\n\nreplications: int.\n    number of replications (if needed) for predictive simulation.\n    Used only in `self.predict`, for `self.kernel` in ('gaussian',\n    'tophat') and `self.type_pi = 'kde'`. Default is `None`.\n\nn_clusters: int\n    number of clusters for clustering the features\n\nclustering_method: str\n    clustering method: currently 'kmeans', 'gmm'\n\ncluster_scaling: str\n    scaling method for clustering: currently 'standard', 'robust', 'minmax'\n\ndegree: int\n    degree of features interactions to include in the model\n\nweights_distr: str\n    distribution of weights for constructing the model's hidden layer;\n    either 'uniform' or 'gaussian'\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.booster.LSBoostRegressor.__init__": {"fullname": "mlsauce.booster.LSBoostRegressor.__init__", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tn_estimators=100,\tlearning_rate=0.1,\tn_hidden_features=5,\treg_lambda=0.1,\talpha=0.5,\trow_sample=1,\tcol_sample=1,\tdropout=0,\ttolerance=0.0001,\tdirect_link=1,\tverbose=1,\tseed=123,\tbackend='cpu',\tsolver='ridge',\tactivation='relu',\ttype_pi=None,\treplications=None,\tkernel=None,\tn_clusters=0,\tclustering_method='kmeans',\tcluster_scaling='standard',\tdegree=0,\tweights_distr='uniform')"}, "mlsauce.booster.LSBoostRegressor.n_estimators": {"fullname": "mlsauce.booster.LSBoostRegressor.n_estimators", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_estimators", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.learning_rate": {"fullname": "mlsauce.booster.LSBoostRegressor.learning_rate", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.learning_rate", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.n_hidden_features": {"fullname": "mlsauce.booster.LSBoostRegressor.n_hidden_features", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_hidden_features", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.reg_lambda": {"fullname": "mlsauce.booster.LSBoostRegressor.reg_lambda", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.alpha": {"fullname": "mlsauce.booster.LSBoostRegressor.alpha", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.row_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.row_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.row_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.col_sample": {"fullname": "mlsauce.booster.LSBoostRegressor.col_sample", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.col_sample", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.dropout": {"fullname": "mlsauce.booster.LSBoostRegressor.dropout", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.dropout", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.tolerance": {"fullname": "mlsauce.booster.LSBoostRegressor.tolerance", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.tolerance", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.direct_link": {"fullname": "mlsauce.booster.LSBoostRegressor.direct_link", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.direct_link", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.verbose": {"fullname": "mlsauce.booster.LSBoostRegressor.verbose", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.seed": {"fullname": "mlsauce.booster.LSBoostRegressor.seed", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.backend": {"fullname": "mlsauce.booster.LSBoostRegressor.backend", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.obj": {"fullname": "mlsauce.booster.LSBoostRegressor.obj", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.solver": {"fullname": "mlsauce.booster.LSBoostRegressor.solver", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.solver", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.activation": {"fullname": "mlsauce.booster.LSBoostRegressor.activation", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.activation", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.type_pi": {"fullname": "mlsauce.booster.LSBoostRegressor.type_pi", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.replications": {"fullname": "mlsauce.booster.LSBoostRegressor.replications", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.kernel": {"fullname": "mlsauce.booster.LSBoostRegressor.kernel", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.n_clusters": {"fullname": "mlsauce.booster.LSBoostRegressor.n_clusters", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.n_clusters", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.clustering_method": {"fullname": "mlsauce.booster.LSBoostRegressor.clustering_method", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.clustering_method", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.cluster_scaling": {"fullname": "mlsauce.booster.LSBoostRegressor.cluster_scaling", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.cluster_scaling", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.degree": {"fullname": "mlsauce.booster.LSBoostRegressor.degree", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.degree", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.poly_": {"fullname": "mlsauce.booster.LSBoostRegressor.poly_", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.poly_", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.weights_distr": {"fullname": "mlsauce.booster.LSBoostRegressor.weights_distr", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.weights_distr", "kind": "variable", "doc": "
    \n"}, "mlsauce.booster.LSBoostRegressor.fit": {"fullname": "mlsauce.booster.LSBoostRegressor.fit", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.fit", "kind": "function", "doc": "
    Fit Booster (regressor) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n   Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.predict": {"fullname": "mlsauce.booster.LSBoostRegressor.predict", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.predict", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nlevel: int\n    Level of confidence (default = 95)\n\nmethod: str\n    `None`, or 'splitconformal', 'localconformal'\n    prediction (if you specify `return_pi = True`)\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, level=95, method=None, **kwargs):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_predict_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_predict_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.booster.LSBoostRegressor.set_score_request": {"fullname": "mlsauce.booster.LSBoostRegressor.set_score_request", "modulename": "mlsauce.booster", "qualname": "LSBoostRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.datasets": {"fullname": "mlsauce.datasets", "modulename": "mlsauce.datasets", "kind": "module", "doc": "
    \n"}, "mlsauce.datasets.dowload": {"fullname": "mlsauce.datasets.dowload", "modulename": "mlsauce.datasets.dowload", "kind": "module", "doc": "
    \n"}, "mlsauce.datasets.dowload.download": {"fullname": "mlsauce.datasets.dowload.download", "modulename": "mlsauce.datasets.dowload", "qualname": "download", "kind": "function", "doc": "
    \n", "signature": "(\tpkgname='MASS',\tdataset='Boston',\tsource='https://cran.r-universe.dev/',\t**kwargs):", "funcdef": "def"}, "mlsauce.demo": {"fullname": "mlsauce.demo", "modulename": "mlsauce.demo", "kind": "module", "doc": "
    \n"}, "mlsauce.elasticnet": {"fullname": "mlsauce.elasticnet", "modulename": "mlsauce.elasticnet", "kind": "module", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor", "kind": "class", "doc": "
    Elasticnet.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nalpha: float\n    compromise between L1 and L2 regularization (must be in [0, 1]),\n    for `solver` == 'enet'.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.elasticnet.ElasticNetRegressor.__init__": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.__init__", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, alpha=0.5, backend='cpu')"}, "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.reg_lambda", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.alpha": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.alpha", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.alpha", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.backend": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.backend", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.elasticnet.ElasticNetRegressor.fit": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.fit", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.predict": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.predict", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.elasticnet.ElasticNetRegressor.set_score_request": {"fullname": "mlsauce.elasticnet.ElasticNetRegressor.set_score_request", "modulename": "mlsauce.elasticnet", "qualname": "ElasticNetRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.lasso": {"fullname": "mlsauce.lasso", "modulename": "mlsauce.lasso", "kind": "module", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor": {"fullname": "mlsauce.lasso.LassoRegressor", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor", "kind": "class", "doc": "
    Lasso.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    L1 regularization parameter.\n\nmax_iter: int\n    number of iterations of lasso shooting algorithm.\n\ntol: float\n    tolerance for convergence of lasso shooting algorithm.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu').\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.lasso.LassoRegressor.__init__": {"fullname": "mlsauce.lasso.LassoRegressor.__init__", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, max_iter=10, tol=0.001, backend='cpu')"}, "mlsauce.lasso.LassoRegressor.reg_lambda": {"fullname": "mlsauce.lasso.LassoRegressor.reg_lambda", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.max_iter": {"fullname": "mlsauce.lasso.LassoRegressor.max_iter", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.max_iter", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.tol": {"fullname": "mlsauce.lasso.LassoRegressor.tol", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.tol", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.backend": {"fullname": "mlsauce.lasso.LassoRegressor.backend", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.lasso.LassoRegressor.fit": {"fullname": "mlsauce.lasso.LassoRegressor.fit", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.predict": {"fullname": "mlsauce.lasso.LassoRegressor.predict", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.lasso.LassoRegressor.set_score_request": {"fullname": "mlsauce.lasso.LassoRegressor.set_score_request", "modulename": "mlsauce.lasso", "qualname": "LassoRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist": {"fullname": "mlsauce.nonconformist", "modulename": "mlsauce.nonconformist", "kind": "module", "doc": "
    docstring
    \n"}, "mlsauce.nonconformist.AbsErrorErrFunc": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc", "kind": "class", "doc": "
    Calculates absolute error nonconformity for regression problems.
    \n\n
    For each correct output in y, nonconformity is defined as
    \n\n
    $$| y_i - \\hat{y}_i |$$
    \n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply", "kind": "function", "doc": "
    Apply the nonconformity function.
    \n\n
    Parameters
    \n\n
    prediction : numpy array of shape [n_samples, n_classes]\n    Class probability estimates for each sample.
    \n\n
    y : numpy array of shape [n_samples]\n    True output labels of each sample.
    \n\n
    Returns
    \n\n
    nc : numpy array of shape [n_samples]\n    Nonconformity scores of the samples.
    \n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.AbsErrorErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "AbsErrorErrFunc.apply_inverse", "kind": "function", "doc": "
    Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).
    \n\n
    Parameters
    \n\n
    nc : numpy array of shape [n_calibration_samples]\n    Nonconformity scores obtained for conformal predictor.
    \n\n
    significance : float\n    Significance level (0, 1).
    \n\n
    Returns
    \n\n
    interval : numpy array of shape [n_samples, 2]\n    Minimum and maximum interval boundaries for each prediction.
    \n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc", "kind": "class", "doc": "
    Calculates conformalized quantile regression error.
    \n\n
    For each correct output in y, nonconformity is defined as
    \n\n
    $$max{\\hat{q}_low - y, y - \\hat{q}_high}$$
    \n", "bases": "mlsauce.nonconformist.nc.RegressionErrFunc"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply", "kind": "function", "doc": "
    Apply the nonconformity function.
    \n\n
    Parameters
    \n\n
    prediction : numpy array of shape [n_samples, n_classes]\n    Class probability estimates for each sample.
    \n\n
    y : numpy array of shape [n_samples]\n    True output labels of each sample.
    \n\n
    Returns
    \n\n
    nc : numpy array of shape [n_samples]\n    Nonconformity scores of the samples.
    \n", "signature": "(self, prediction, y):", "funcdef": "def"}, "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse": {"fullname": "mlsauce.nonconformist.QuantileRegErrFunc.apply_inverse", "modulename": "mlsauce.nonconformist", "qualname": "QuantileRegErrFunc.apply_inverse", "kind": "function", "doc": "
    Apply the inverse of the nonconformity function (i.e.,\ncalculate prediction interval).
    \n\n
    Parameters
    \n\n
    nc : numpy array of shape [n_calibration_samples]\n    Nonconformity scores obtained for conformal predictor.
    \n\n
    significance : float\n    Significance level (0, 1).
    \n\n
    Returns
    \n\n
    interval : numpy array of shape [n_samples, 2]\n    Minimum and maximum interval boundaries for each prediction.
    \n", "signature": "(self, nc, significance):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter": {"fullname": "mlsauce.nonconformist.RegressorAdapter", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Inheriting from this class provides default implementations of:
    \n\n
      \n
    • setting and getting parameters used by GridSearchCV and friends;
      • \n
    • textual and HTML representation displayed in terminals and IDEs;
      • \n
    • estimator serialization;
      • \n
    • parameters validation;
      • \n
    • data validation;
      • \n
    • feature names validation.
      • \n
    \n\n
    Read more in the :ref:User Guide <rolling_your_own_estimator>.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
    \n
    \n", "bases": "mlsauce.nonconformist.base.BaseModelAdapter"}, "mlsauce.nonconformist.RegressorAdapter.__init__": {"fullname": "mlsauce.nonconformist.RegressorAdapter.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.__init__", "kind": "function", "doc": "
    \n", "signature": "(model, fit_params=None)"}, "mlsauce.nonconformist.RegressorAdapter.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorAdapter.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorAdapter.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorAdapter.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc": {"fullname": "mlsauce.nonconformist.RegressorNc", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc", "kind": "class", "doc": "
    Nonconformity scorer using an underlying regression model.
    \n\n
    Parameters
    \n\n
    model : RegressorAdapter\n    Underlying regression model used for calculating nonconformity scores.
    \n\n
    err_func : RegressionErrFunc\n    Error function object.
    \n\n
    normalizer : BaseScorer\n    Normalization model.
    \n\n
    beta : float\n    Normalization smoothing parameter. As the beta-value increases,\n    the normalized nonconformity function approaches a non-normalized\n    equivalent.
    \n\n
    Attributes
    \n\n
    model : RegressorAdapter\n    Underlying model object.
    \n\n
    err_func : RegressionErrFunc\n    Scorer function used to calculate nonconformity scores.
    \n\n
    See also
    \n\n
    ProbEstClassifierNc, NormalizedRegressorNc
    \n", "bases": "mlsauce.nonconformist.nc.BaseModelNc"}, "mlsauce.nonconformist.RegressorNc.__init__": {"fullname": "mlsauce.nonconformist.RegressorNc.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tmodel,\terr_func=<mlsauce.nonconformist.nc.AbsErrorErrFunc object>,\tnormalizer=None,\tbeta=1e-06)"}, "mlsauce.nonconformist.RegressorNc.predict": {"fullname": "mlsauce.nonconformist.RegressorNc.predict", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.predict", "kind": "function", "doc": "
    Constructs prediction intervals for a set of test examples.
    \n\n
    Predicts the output of each test pattern using the underlying model,\nand applies the (partial) inverse nonconformity function to each\nprediction, resulting in a prediction interval for each test pattern.
    \n\n
    Parameters
    \n\n
    x : numpy array of shape [n_samples, n_features]\n    Inputs of patters for which to predict output values.
    \n\n
    significance : float\n    Significance level (maximum allowed error rate) of predictions.\n    Should be a float between 0 and 1. If None, then intervals for\n    all significance levels (0.01, 0.02, ..., 0.99) are output in a\n    3d-matrix.
    \n\n
    Returns
    \n\n
    p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99]\n    If significance is None, then p contains the interval (minimum\n    and maximum boundaries) for each test pattern, and each significance\n    level (0.01, 0.02, ..., 0.99). If significance is a float between\n    0 and 1, then p contains the prediction intervals (minimum and\n    maximum     boundaries) for the set of test patterns at the chosen\n    significance level.
    \n", "signature": "(self, x, nc, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_predict_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNc.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNc.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNc.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer": {"fullname": "mlsauce.nonconformist.RegressorNormalizer", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer", "kind": "class", "doc": "
    Base class for all estimators in scikit-learn.
    \n\n
    Inheriting from this class provides default implementations of:
    \n\n
      \n
    • setting and getting parameters used by GridSearchCV and friends;
      • \n
    • textual and HTML representation displayed in terminals and IDEs;
      • \n
    • estimator serialization;
      • \n
    • parameters validation;
      • \n
    • data validation;
      • \n
    • feature names validation.
      • \n
    \n\n
    Read more in the :ref:User Guide <rolling_your_own_estimator>.
    \n\n
    Notes
    \n\n
    All estimators should specify all the parameters that can be set\nat the class level in their __init__ as explicit keyword\narguments (no *args or **kwargs).
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.base import BaseEstimator\n>>> class MyEstimator(BaseEstimator):\n...     def __init__(self, *, param=1):\n...         self.param = param\n...     def fit(self, X, y=None):\n...         self.is_fitted_ = True\n...         return self\n...     def predict(self, X):\n...         return np.full(shape=X.shape[0], fill_value=self.param)\n>>> estimator = MyEstimator(param=2)\n>>> estimator.get_params()\n{'param': 2}\n>>> X = np.array([[1, 2], [2, 3], [3, 4]])\n>>> y = np.array([1, 0, 1])\n>>> estimator.fit(X, y).predict(X)\narray([2, 2, 2])\n>>> estimator.set_params(param=3).fit(X, y).predict(X)\narray([3, 3, 3])\n
    \n
    \n", "bases": "mlsauce.nonconformist.nc.BaseScorer"}, "mlsauce.nonconformist.RegressorNormalizer.__init__": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.__init__", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.__init__", "kind": "function", "doc": "
    \n", "signature": "(base_model, normalizer_model, err_func)"}, "mlsauce.nonconformist.RegressorNormalizer.base_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.base_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.base_model", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.normalizer_model": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.normalizer_model", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.normalizer_model", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.err_func": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.err_func", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.err_func", "kind": "variable", "doc": "
    \n"}, "mlsauce.nonconformist.RegressorNormalizer.fit": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.fit", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.fit", "kind": "function", "doc": "
    \n", "signature": "(self, x, y):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.score": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.score", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.score", "kind": "function", "doc": "
    \n", "signature": "(self, x, y=None):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_fit_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.RegressorNormalizer.set_score_request": {"fullname": "mlsauce.nonconformist.RegressorNormalizer.set_score_request", "modulename": "mlsauce.nonconformist", "qualname": "RegressorNormalizer.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor": {"fullname": "mlsauce.nonconformist.IcpRegressor", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor", "kind": "class", "doc": "
    Inductive conformal regressor.
    \n\n
    Parameters
    \n\n
    nc_function : BaseScorer\n        Nonconformity scorer object used to calculate nonconformity of\n        calibration examples and test patterns. Should implement fit(x, y),\n        calc_nc(x, y) and predict(x, nc_scores, significance).
    \n\n
    Attributes
    \n\n
    cal_x : numpy array of shape [n_cal_examples, n_features]\n        Inputs of calibration set.
    \n\n
    cal_y : numpy array of shape [n_cal_examples]\n        Outputs of calibration set.
    \n\n
    nc_function : BaseScorer\n        Nonconformity scorer object used to calculate nonconformity scores.
    \n\n
    See also
    \n\n
    IcpClassifier
    \n\n
    References
    \n\n
    Examples
    \n\n
    \n
    >>> import numpy as np\n>>> from sklearn.datasets import load_boston\n>>> from sklearn.tree import DecisionTreeRegressor\n>>> from nonconformist.base import RegressorAdapter\n>>> from nonconformist.icp import IcpRegressor\n>>> from nonconformist.nc import RegressorNc, AbsErrorErrFunc\n>>> boston = load_boston()\n>>> idx = np.random.permutation(boston.target.size)\n>>> train = idx[:int(idx.size / 3)]\n>>> cal = idx[int(idx.size / 3):int(2 * idx.size / 3)]\n>>> test = idx[int(2 * idx.size / 3):]\n>>> model = RegressorAdapter(DecisionTreeRegressor())\n>>> nc = RegressorNc(model, AbsErrorErrFunc())\n>>> icp = IcpRegressor(nc)\n>>> icp.fit(boston.data[train, :], boston.target[train])\n>>> icp.calibrate(boston.data[cal, :], boston.target[cal])\n>>> icp.predict(boston.data[test, :], significance=0.10)\n...     # doctest: +SKIP\narray([[  5. ,  20.6],\n        [ 15.5,  31.1],\n        ...,\n        [ 14.2,  29.8],\n        [ 11.6,  27.2]])\n
    \n
    \n\n
    \n
    \n
      \n
    \n
    \n", "bases": "mlsauce.nonconformist.icp.BaseIcp, mlsauce.nonconformist.base.RegressorMixin"}, "mlsauce.nonconformist.IcpRegressor.__init__": {"fullname": "mlsauce.nonconformist.IcpRegressor.__init__", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(nc_function, condition=None)"}, "mlsauce.nonconformist.IcpRegressor.predict": {"fullname": "mlsauce.nonconformist.IcpRegressor.predict", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.predict", "kind": "function", "doc": "
    Predict the output values for a set of input patterns.
    \n\n
    Parameters
    \n\n
    x : numpy array of shape [n_samples, n_features]\n        Inputs of patters for which to predict output values.
    \n\n
    significance : float\n        Significance level (maximum allowed error rate) of predictions.\n        Should be a float between 0 and 1. If None, then intervals for\n        all significance levels (0.01, 0.02, ..., 0.99) are output in a\n        3d-matrix.
    \n\n
    Returns
    \n\n
    p : numpy array of shape [n_samples, 2] or [n_samples, 2, 99}\n        If significance is None, then p contains the interval (minimum\n        and maximum boundaries) for each test pattern, and each significance\n        level (0.01, 0.02, ..., 0.99). If significance is a float between\n        0 and 1, then p contains the prediction intervals (minimum and\n        maximum boundaries) for the set of test patterns at the chosen\n        significance level.
    \n", "signature": "(self, x, significance=None):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_fit_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_fit_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.nonconformist.IcpRegressor.set_predict_request": {"fullname": "mlsauce.nonconformist.IcpRegressor.set_predict_request", "modulename": "mlsauce.nonconformist", "qualname": "IcpRegressor.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval": {"fullname": "mlsauce.predictioninterval", "modulename": "mlsauce.predictioninterval", "kind": "module", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval": {"fullname": "mlsauce.predictioninterval.PredictionInterval", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval", "kind": "class", "doc": "
    Class PredictionInterval: Obtain prediction intervals.
    \n\n
    Attributes:
    \n\n
    obj: an object;\n    fitted object containing methods `fit` and `predict`\n\nmethod: a string;\n    method for constructing the prediction intervals.\n    Currently \"splitconformal\" (default) and \"localconformal\"\n\nlevel: a float;\n    Confidence level for prediction intervals. Default is 95,\n    equivalent to a miscoverage error of 5 (%)\n\nreplications: an integer;\n    Number of replications for simulated conformal (default is `None`)\n\ntype_pi: a string;\n    type of prediction interval: currently \"kde\" (default) or \"bootstrap\"\n\nseed: an integer;\n    Reproducibility of fit (there's a random split between fitting and calibration data)\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.predictioninterval.PredictionInterval.__init__": {"fullname": "mlsauce.predictioninterval.PredictionInterval.__init__", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.__init__", "kind": "function", "doc": "
    \n", "signature": "(\tobj,\tmethod='splitconformal',\tlevel=95,\ttype_pi='bootstrap',\treplications=None,\tkernel=None,\tagg='mean',\tseed=123)"}, "mlsauce.predictioninterval.PredictionInterval.obj": {"fullname": "mlsauce.predictioninterval.PredictionInterval.obj", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.method": {"fullname": "mlsauce.predictioninterval.PredictionInterval.method", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.method", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.level": {"fullname": "mlsauce.predictioninterval.PredictionInterval.level", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.level", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.type_pi": {"fullname": "mlsauce.predictioninterval.PredictionInterval.type_pi", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.type_pi", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.replications": {"fullname": "mlsauce.predictioninterval.PredictionInterval.replications", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.replications", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.kernel": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kernel", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kernel", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.agg": {"fullname": "mlsauce.predictioninterval.PredictionInterval.agg", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.agg", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.seed": {"fullname": "mlsauce.predictioninterval.PredictionInterval.seed", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.seed", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.alpha_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.alpha_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.alpha_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.quantile_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.quantile_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.quantile_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.icp_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.icp_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.icp_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.scaled_calibrated_residuals_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.scaled_calibrated_residuals_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.calibrated_residuals_scaler_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.calibrated_residuals_scaler_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.kde_": {"fullname": "mlsauce.predictioninterval.PredictionInterval.kde_", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.kde_", "kind": "variable", "doc": "
    \n"}, "mlsauce.predictioninterval.PredictionInterval.fit": {"fullname": "mlsauce.predictioninterval.PredictionInterval.fit", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.fit", "kind": "function", "doc": "
    Fit the method to training data (X, y).
    \n\n
    Args:
    \n\n
    X: array-like, shape = [n_samples, n_features];\n    Training set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples, ]; Target values.\n
    \n", "signature": "(self, X, y):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.predict": {"fullname": "mlsauce.predictioninterval.PredictionInterval.predict", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.predict", "kind": "function", "doc": "
    Obtain predictions and prediction intervals
    \n\n
    Args:
    \n\n
    X: array-like, shape = [n_samples, n_features];\n    Testing set vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\nreturn_pi: boolean\n    Whether the prediction interval is returned or not.\n    Default is False, for compatibility with other _estimators_.\n    If True, a tuple containing the predictions + lower and upper\n    bounds is returned.\n
    \n", "signature": "(self, X, return_pi=False):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_predict_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_predict_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_predict_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.predictioninterval.PredictionInterval.set_score_request": {"fullname": "mlsauce.predictioninterval.PredictionInterval.set_score_request", "modulename": "mlsauce.predictioninterval", "qualname": "PredictionInterval.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.ridge": {"fullname": "mlsauce.ridge", "modulename": "mlsauce.ridge", "kind": "module", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor": {"fullname": "mlsauce.ridge.RidgeRegressor", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor", "kind": "class", "doc": "
    Ridge.
    \n\n
    Attributes:
    \n\n
    reg_lambda: float\n    regularization parameter.\n\nbackend: str\n    type of backend; must be in ('cpu', 'gpu', 'tpu')\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.RegressorMixin"}, "mlsauce.ridge.RidgeRegressor.__init__": {"fullname": "mlsauce.ridge.RidgeRegressor.__init__", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.__init__", "kind": "function", "doc": "
    \n", "signature": "(reg_lambda=0.1, backend='cpu')"}, "mlsauce.ridge.RidgeRegressor.reg_lambda": {"fullname": "mlsauce.ridge.RidgeRegressor.reg_lambda", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.reg_lambda", "kind": "variable", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor.backend": {"fullname": "mlsauce.ridge.RidgeRegressor.backend", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.backend", "kind": "variable", "doc": "
    \n"}, "mlsauce.ridge.RidgeRegressor.fit": {"fullname": "mlsauce.ridge.RidgeRegressor.fit", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.fit", "kind": "function", "doc": "
    Fit matrixops (classifier) to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\n**kwargs: additional parameters to be passed to self.cook_training_set.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.predict": {"fullname": "mlsauce.ridge.RidgeRegressor.predict", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.ridge.RidgeRegressor.set_score_request": {"fullname": "mlsauce.ridge.RidgeRegressor.set_score_request", "modulename": "mlsauce.ridge", "qualname": "RidgeRegressor.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.setup": {"fullname": "mlsauce.setup", "modulename": "mlsauce.setup", "kind": "module", "doc": "
    \n"}, "mlsauce.stump": {"fullname": "mlsauce.stump", "modulename": "mlsauce.stump", "kind": "module", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier": {"fullname": "mlsauce.stump.StumpClassifier", "modulename": "mlsauce.stump", "qualname": "StumpClassifier", "kind": "class", "doc": "
    Stump classifier.
    \n\n
    Attributes:
    \n\n
    bins: int\n    Number of histogram bins; as in numpy.histogram.\n
    \n", "bases": "sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin"}, "mlsauce.stump.StumpClassifier.__init__": {"fullname": "mlsauce.stump.StumpClassifier.__init__", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.__init__", "kind": "function", "doc": "
    \n", "signature": "(bins='auto')"}, "mlsauce.stump.StumpClassifier.bins": {"fullname": "mlsauce.stump.StumpClassifier.bins", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.bins", "kind": "variable", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier.obj": {"fullname": "mlsauce.stump.StumpClassifier.obj", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.obj", "kind": "variable", "doc": "
    \n"}, "mlsauce.stump.StumpClassifier.fit": {"fullname": "mlsauce.stump.StumpClassifier.fit", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.fit", "kind": "function", "doc": "
    Fit Stump to training data (X, y)
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\ny: array-like, shape = [n_samples]\n    Target values.\n\nsample_weight: array_like, shape = [n_samples]\n    Observations weights.\n
    \n\n
    Returns:
    \n\n
    self: object.\n
    \n", "signature": "(self, X, y, sample_weight=None, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict": {"fullname": "mlsauce.stump.StumpClassifier.predict", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict", "kind": "function", "doc": "
    Predict test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to `predict_proba`\n
    \n\n
    Returns:
    \n\n
    model predictions: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.predict_proba": {"fullname": "mlsauce.stump.StumpClassifier.predict_proba", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.predict_proba", "kind": "function", "doc": "
    Predict probabilities for test data X.
    \n\n
    Args:
    \n\n
    X: {array-like}, shape = [n_samples, n_features]\n    Training vectors, where n_samples is the number\n    of samples and n_features is the number of features.\n\n**kwargs: additional parameters to be passed to\n    self.cook_test_set\n
    \n\n
    Returns:
    \n\n
    probability estimates for test data: {array-like}\n
    \n", "signature": "(self, X, **kwargs):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_fit_request": {"fullname": "mlsauce.stump.StumpClassifier.set_fit_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_fit_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.stump.StumpClassifier.set_score_request": {"fullname": "mlsauce.stump.StumpClassifier.set_score_request", "modulename": "mlsauce.stump", "qualname": "StumpClassifier.set_score_request", "kind": "function", "doc": "
    A descriptor for request methods.
    \n\n
    New in version 1.3.
    \n\n
    Parameters
    \n\n
    name : str\n    The name of the method for which the request function should be\n    created, e.g. \"fit\" would create a set_fit_request function.
    \n\n
    keys : list of str\n    A list of strings which are accepted parameters by the created\n    function, e.g. [\"sample_weight\"] if the corresponding method\n    accepts it as a metadata.
    \n\n
    validate_keys : bool, default=True\n    Whether to check if the requested parameters fit the actual parameters\n    of the method.
    \n\n
    Notes
    \n\n
    This class is a descriptor 1 and uses PEP-362 to set the signature of\nthe returned function 2.
    \n\n
    References
    \n\n
    \n
    \n
      \n
    1. \n
      https://docs.python.org/3/howto/descriptor.html ↩
      \n
      2. \n\n
    2. \n
      https://www.python.org/dev/peps/pep-0362/ ↩
      \n
      3. \n
    \n
    \n", "signature": "(unknown):", "funcdef": "def"}, "mlsauce.utils": {"fullname": "mlsauce.utils", "modulename": "mlsauce.utils", "kind": "module", "doc": "
    \n"}, "mlsauce.utils.cluster": {"fullname": "mlsauce.utils.cluster", "modulename": "mlsauce.utils", "qualname": "cluster", "kind": "function", "doc": "
    \n", "signature": "(\tX,\tn_clusters=None,\tmethod='kmeans',\ttype_scaling='standard',\ttraining=True,\tscaler=None,\tlabel_encoder=None,\tclusterer=None,\tseed=123):", "funcdef": "def"}, "mlsauce.utils.subsample": {"fullname": "mlsauce.utils.subsample", "modulename": "mlsauce.utils", "qualname": "subsample", "kind": "function", "doc": "
    \n", "signature": "(y, row_sample=0.8, seed=123):", "funcdef": "def"}, "mlsauce.utils.merge_two_dicts": {"fullname": "mlsauce.utils.merge_two_dicts", "modulename": "mlsauce.utils", "qualname": "merge_two_dicts", "kind": "function", "doc": "
    \n", "signature": "(x, y):", "funcdef": "def"}, "mlsauce.utils.flatten": {"fullname": "mlsauce.utils.flatten", "modulename": "mlsauce.utils", "qualname": "flatten", "kind": "function", "doc": "
    \n", "signature": "(l):", "funcdef": "def"}, "mlsauce.utils.is_float": {"fullname": "mlsauce.utils.is_float", "modulename": "mlsauce.utils", "qualname": "is_float", "kind": "function", "doc": "
    \n", "signature": "(x):", "funcdef": "def"}, "mlsauce.utils.is_factor": {"fullname": "mlsauce.utils.is_factor", "modulename": "mlsauce.utils", "qualname": "is_factor", "kind": "function", "doc": "
    \n", "signature": "(y):", "funcdef": "def"}, "mlsauce.utils.Progbar": {"fullname": "mlsauce.utils.Progbar", "modulename": "mlsauce.utils", "qualname": "Progbar", "kind": "class", "doc": "
    Displays a progress bar.
    \n\n
    Arguments
    \n\n
    target: Total number of steps expected, None if unknown.\nwidth: Progress bar width on screen.\nverbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)\nstateful_metrics: Iterable of string names of metrics that\n    should *not* be averaged over time. Metrics in this list\n    will be displayed as-is. All others will be averaged\n    by the progbar before display.\ninterval: Minimum visual progress update interval (in seconds).\n
    \n"}, "mlsauce.utils.Progbar.__init__": {"fullname": "mlsauce.utils.Progbar.__init__", "modulename": "mlsauce.utils", "qualname": "Progbar.__init__", "kind": "function", "doc": "
    \n", "signature": "(target, width=30, verbose=1, interval=0.05, stateful_metrics=None)"}, "mlsauce.utils.Progbar.target": {"fullname": "mlsauce.utils.Progbar.target", "modulename": "mlsauce.utils", "qualname": "Progbar.target", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.width": {"fullname": "mlsauce.utils.Progbar.width", "modulename": "mlsauce.utils", "qualname": "Progbar.width", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.verbose": {"fullname": "mlsauce.utils.Progbar.verbose", "modulename": "mlsauce.utils", "qualname": "Progbar.verbose", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.interval": {"fullname": "mlsauce.utils.Progbar.interval", "modulename": "mlsauce.utils", "qualname": "Progbar.interval", "kind": "variable", "doc": "
    \n"}, "mlsauce.utils.Progbar.update": {"fullname": "mlsauce.utils.Progbar.update", "modulename": "mlsauce.utils", "qualname": "Progbar.update", "kind": "function", "doc": "
    Updates the progress bar.
    \n\n
    Arguments
    \n\n
    current: Index of current step.\nvalues: List of tuples:\n    `(name, value_for_last_step)`.\n    If `name` is in `stateful_metrics`,\n    `value_for_last_step` will be displayed as-is.\n    Else, an average of the metric over time will be displayed.\n
    \n", "signature": "(self, current, values=None):", "funcdef": "def"}, "mlsauce.utils.Progbar.add": {"fullname": "mlsauce.utils.Progbar.add", "modulename": "mlsauce.utils", "qualname": "Progbar.add", "kind": "function", "doc": "
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1}}, "df": 1}}}}}}}}}}}, "pipeline": ["trimmer"], "_isPrebuiltIndex": true};
 
     // mirrored in build-search-index.js (part 1)
     // Also split on html tags. this is a cheap heuristic, but good enough.
diff --git a/mlsauce.egg-info/PKG-INFO b/mlsauce.egg-info/PKG-INFO
index 376e206..a3d8367 100644
--- a/mlsauce.egg-info/PKG-INFO
+++ b/mlsauce.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: mlsauce
-Version: 0.18.1
+Version: 0.18.2
 Summary: Miscellaneous Statistical/Machine Learning tools
 Maintainer: T. Moudiki
 Maintainer-email: thierry.moudiki@gmail.com
diff --git a/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so b/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so
index 63246a6..71739dc 100755
Binary files a/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so and b/mlsauce/adaopt/_adaoptc.cpython-311-darwin.so differ
diff --git a/mlsauce/adaopt/setup2.py b/mlsauce/adaopt/setup2.py
index 7c8d0cd..c373a95 100644
--- a/mlsauce/adaopt/setup2.py
+++ b/mlsauce/adaopt/setup2.py
@@ -1,5 +1,5 @@
 import os
-from distutils.core import setup
+from setuptools import setup
 from Cython.Build import cythonize
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/mlsauce/booster/_booster_classifier.py b/mlsauce/booster/_booster_classifier.py
index fff451a..a060ad4 100644
--- a/mlsauce/booster/_booster_classifier.py
+++ b/mlsauce/booster/_booster_classifier.py
@@ -5,6 +5,7 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import ClassifierMixin
 from sklearn.preprocessing import PolynomialFeatures
+
 try:
     from . import _boosterc as boosterc
 except ImportError:
@@ -77,7 +78,7 @@ class LSBoostClassifier(BaseEstimator, ClassifierMixin):
 
         degree: int
             degree of features interactions to include in the model
-        
+
         weights_distr: str
             distribution of weights for constructing the model's hidden layer;
             currently 'uniform', 'gaussian'
@@ -105,7 +106,7 @@ def __init__(
         clustering_method="kmeans",
         cluster_scaling="standard",
         degree=0,
-        weights_distr="uniform"
+        weights_distr="uniform",
     ):
         if n_clusters > 0:
             assert clustering_method in (
diff --git a/mlsauce/booster/_booster_regressor.py b/mlsauce/booster/_booster_regressor.py
index 6eef63a..8fe7744 100644
--- a/mlsauce/booster/_booster_regressor.py
+++ b/mlsauce/booster/_booster_regressor.py
@@ -5,6 +5,7 @@
 from sklearn.base import BaseEstimator
 from sklearn.base import RegressorMixin
 from sklearn.preprocessing import PolynomialFeatures
+
 try:
     from . import _boosterc as boosterc
 except ImportError:
@@ -87,9 +88,9 @@ class LSBoostRegressor(BaseEstimator, RegressorMixin):
 
         degree: int
             degree of features interactions to include in the model
-        
+
         weights_distr: str
-            distribution of weights for constructing the model's hidden layer; 
+            distribution of weights for constructing the model's hidden layer;
             either 'uniform' or 'gaussian'
 
     """
@@ -118,7 +119,7 @@ def __init__(
         clustering_method="kmeans",
         cluster_scaling="standard",
         degree=0,
-        weights_distr="uniform"
+        weights_distr="uniform",
     ):
         if n_clusters > 0:
             assert clustering_method in (
diff --git a/mlsauce/booster/_boosterc.cpython-311-darwin.so b/mlsauce/booster/_boosterc.cpython-311-darwin.so
index 704da88..5910406 100755
Binary files a/mlsauce/booster/_boosterc.cpython-311-darwin.so and b/mlsauce/booster/_boosterc.cpython-311-darwin.so differ
diff --git a/mlsauce/lasso/_lassoc.cpython-311-darwin.so b/mlsauce/lasso/_lassoc.cpython-311-darwin.so
index 2fbc0d5..59f6236 100755
Binary files a/mlsauce/lasso/_lassoc.cpython-311-darwin.so and b/mlsauce/lasso/_lassoc.cpython-311-darwin.so differ
diff --git a/mlsauce/lasso/setup2.py b/mlsauce/lasso/setup2.py
index 39625ba..5cf1434 100644
--- a/mlsauce/lasso/setup2.py
+++ b/mlsauce/lasso/setup2.py
@@ -1,5 +1,5 @@
 import os
-from distutils.core import setup
+from setuptools import setup
 from Cython.Build import cythonize
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/mlsauce/ridge/_ridgec.cpython-311-darwin.so b/mlsauce/ridge/_ridgec.cpython-311-darwin.so
index a0fdc57..3807bcb 100755
Binary files a/mlsauce/ridge/_ridgec.cpython-311-darwin.so and b/mlsauce/ridge/_ridgec.cpython-311-darwin.so differ
diff --git a/mlsauce/ridge/setup2.py b/mlsauce/ridge/setup2.py
index 1228d19..d8531f3 100644
--- a/mlsauce/ridge/setup2.py
+++ b/mlsauce/ridge/setup2.py
@@ -1,5 +1,5 @@
 import os
-from distutils.core import setup
+from setuptools import setup
 from Cython.Build import cythonize
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/mlsauce/stump/_stumpc.cpython-311-darwin.so b/mlsauce/stump/_stumpc.cpython-311-darwin.so
index d250126..9fa67a7 100755
Binary files a/mlsauce/stump/_stumpc.cpython-311-darwin.so and b/mlsauce/stump/_stumpc.cpython-311-darwin.so differ
diff --git a/mlsauce/stump/setup2.py b/mlsauce/stump/setup2.py
index 1caa2d8..f86e81f 100644
--- a/mlsauce/stump/setup2.py
+++ b/mlsauce/stump/setup2.py
@@ -1,5 +1,5 @@
 import os
-from distutils.core import setup
+from setuptools import setup
 from Cython.Build import cythonize
 
 dir_path = os.path.dirname(os.path.realpath(__file__))
diff --git a/setup.py b/setup.py
index 74b6b2a..b1f9668 100644
--- a/setup.py
+++ b/setup.py
@@ -37,7 +37,7 @@
 MAINTAINER_EMAIL = 'thierry.moudiki@gmail.com'
 LICENSE = 'BSD3 Clause Clear'
 
-__version__ = '0.18.1'
+__version__ = '0.18.2'
 
 VERSION = __version__