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  • wavy.panel Module
  • wavy.models Module
  • wavy.plot Module
    • -
  • wavy.utils Module
    • @@ -88,7 +87,6 @@

    All modules for which code is available

    diff --git a/_modules/wavy/models.html b/_modules/wavy/models.html index bf7df4f..5d91009 100644 --- a/_modules/wavy/models.html +++ b/_modules/wavy/models.html @@ -22,7 +22,7 @@ @@ -58,7 +58,6 @@
  • wavy.panel Module
  • wavy.models Module
  • wavy.plot Module
    • -
  • wavy.utils Module
    • @@ -86,16 +85,17 @@

    Source code for wavy.models

    -import warnings
-from typing import List
+from __future__ import annotations
+
+import warnings
 
 import numpy as np
 import pandas as pd
 import tensorflow as tf
+from keras import Sequential
+from keras.layers import Conv1D, Dense, Flatten, Reshape
 from sklearn.base import is_classifier
 from sklearn.metrics import auc, roc_curve
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Conv1D, Dense, Flatten, Reshape
 
 from .panel import Panel, set_training_split
 
@@ -110,7 +110,7 @@ 
    Source code for wavy.models
             return inputs
 
 
-class _BaseModel:
+
    [docs]class BaseModel:
     """Base class for panel models."""
 
     def __init__(
@@ -120,9 +120,21 @@ 
    Source code for wavy.models
             model_type: str = None,
         loss: str = None,
         optimizer: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
         last_activation: str = None,
     ):
+        """
+        Base model class.
+
+        Args:
+            x (``Panel``): Panel of input data.
+            y (``Panel``): Panel of output data.
+            model_type (``str``): Type of model.
+            loss (``str``): Loss function.
+            optimizer (``str``): Optimizer.
+            metrics (``list[str]``): Metrics.
+            last_activation (``str``): Last activation.
+        """
 
         PARAMS = {
             "regression": {
@@ -196,7 +208,7 @@ 
    Source code for wavy.models
             self.compile()
         self.model._name = self.__class__.__name__
 
-    def set_arrays(self):
+
    [docs]    def set_arrays(self) -> None:
         """Set the arrays."""
         self.x_train = self.x.train.values_panel
         self.x_val = self.x.val.values_panel
@@ -204,17 +216,17 @@ 
    Source code for wavy.models
     
         self.y_train = self.y.train.values_panel.squeeze(axis=2)
         self.y_val = self.y.val.values_panel.squeeze(axis=2)
-        self.y_test = self.y.test.values_panel.squeeze(axis=2)
+        self.y_test = self.y.test.values_panel.squeeze(axis=2)
    
 
-    def get_auc(self):
+
    [docs]    def get_auc(self) -> float:
         """Get the AUC score."""
         y = self.y_test.squeeze()
         prediction = self.model.predict(self.x_test).squeeze()
         fpr, tpr, _ = roc_curve(y, prediction)
         fpr, tpr, _ = roc_curve(y, prediction)
-        return auc(fpr, tpr)
+        return auc(fpr, tpr)
    
 
-    def fit(self, **kwargs):
+
    [docs]    def fit(self, **kwargs) -> None:
         """
         Fit the model.
 
@@ -226,9 +238,9 @@ 
    Source code for wavy.models
                 self.y_train,
             validation_data=(self.x_val, self.y_val),
             **kwargs,
-        )
+        )
    
 
-    def compile(self, **kwargs):
+
    [docs]    def compile(self, **kwargs) -> None:
         """Compile the model.
 
         Args:
@@ -236,17 +248,17 @@ 
    Source code for wavy.models
             """
         self.model.compile(
             loss=self.loss, optimizer=self.optimizer, metrics=self.metrics, **kwargs
-        )
+        )
    
 
-    def build(self):
+
    [docs]    def build(self) -> None:
         """Build the model."""
-        pass
+        pass
    
 
-    def predict_proba(self, data: Panel = None, **kwargs):
+
    [docs]    def predict_proba(self, data: Panel = None, **kwargs) -> Panel:
         """Predict probabilities.
 
         Args:
-            data: Panel of data to predict.
+            data (``Panel``): Panel of data to predict.
             **kwargs: Additional arguments to pass to the predict method.
 
         Returns:
@@ -280,17 +292,17 @@ 
    Source code for wavy.models
                 self.model.predict(x),
             columns=self.y.columns,
             index=index,
-        )
+        )
    
 
-    def predict(self, data: Panel = None, **kwargs):
+
    [docs]    def predict(self, data: Panel = None, **kwargs) -> Panel:
         """Predict.
 
         Args:
-            data: Panel of data to predict.
+            data (``Panel``): Panel of data to predict.
             **kwargs: Additional arguments to pass to the predict method.
 
         Returns:
-            Panel of predicted values.
+            ``Panel`` of predicted values.
         """
 
         threshold = self.get_auc() if self.model_type == "classification" else None
@@ -299,13 +311,13 @@ 
    Source code for wavy.models
     
         return (
             panel if threshold is None else panel.apply(lambda x: (x > threshold) + 0)
-        )
+        )
    
 
-    def score(self, on=None, **kwargs):
+
    [docs]    def score(self, on: list[str] | str = None, **kwargs) -> pd.DataFrame:
         """Score the model.
 
         Args:
-            on: Columns to score on.
+            on (``list[str]`` or ``str``): Columns to score on.
             **kwargs: Additional arguments to pass to the score method.
 
         Returns:
@@ -334,35 +346,37 @@ 
    Source code for wavy.models
             return pd.DataFrame(
             {key: [value[index] for index in indexes] for key, value in dic.items()},
             index=self.metrics,
-        )
+        )
    
 
-    def residuals(self):
+
    [docs]    def residuals(self) -> Panel:
         """Residuals.
 
         Returns:
-            Panel of residuals.
+            ``Panel`` of residuals.
         """
-        return self.predict() - self.y
+        return self.predict() - self.y
    
 
 
-class _Baseline(_BaseModel):
+class _Baseline(BaseModel):
     def __init__(
         self,
         x,
         y,
         model_type: str = None,
         loss: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
     ):
 
         super().__init__(x=x, y=y, model_type=model_type, loss=loss, metrics=metrics)
 
-    def build(self):
+    def build(self) -> None:
         """Build the model."""
         self.model = _ConstantKerasModel()
 
 
 
    [docs]class BaselineShift(_Baseline):
+    """Baseline shift model."""
+
     # ! Maybe shift should be y.horizon by default, to avoid leakage
     # TODO test with different gap and horizon values
 
@@ -372,7 +386,7 @@ 
    Source code for wavy.models
             y,
         model_type: str = None,
         loss: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
         fillna=0,
         shift=1,
     ):
@@ -403,12 +417,14 @@ 
    Source code for wavy.models
             self.y_val = self.y.val.values
         self.y_test = self.y.test.values
    
 
-
    [docs]    def build(self):
+
    [docs]    def build(self) -> None:
         """Build the model."""
         self.model = _ConstantKerasModel()
    
 
 
 
    [docs]class BaselineConstant(_Baseline):
+    """Baseline constant model."""
+
     # TODO BUG: Not working when model_type="classification"
     def __init__(
         self,
@@ -416,14 +432,14 @@ 
    Source code for wavy.models
             y,
         model_type: str = None,
         loss: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
         constant: float = 0,
     ):
 
         self.constant = constant if model_type == "regression" else int(constant)
         super().__init__(x=x, y=y, model_type=model_type, loss=loss, metrics=metrics)
 
-
    [docs]    def set_arrays(self):
+
    [docs]    def set_arrays(self) -> None:
         """Set the arrays."""
         self.x_train = np.full(self.y.train.shape, self.constant)
         self.x_val = np.full(self.y.val.shape, self.constant)
@@ -434,7 +450,9 @@ 
    Source code for wavy.models
             self.y_test = self.y.test.values
    
 
 
-
    [docs]class DenseModel(_BaseModel):
+
    [docs]class DenseModel(BaseModel):
+    """Dense model."""
+
     def __init__(
         self,
         x,
@@ -445,22 +463,22 @@ 
    Source code for wavy.models
             activation: str = "relu",
         loss: str = None,
         optimizer: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
         last_activation: str = None,
     ):
         """
         Dense Model.
 
         Args:
-            panel (Panel): Panel with data
-            model_type (str): Model type (regression, classification, multi_classification)
-            dense_layers (int): Number of dense layers
-            dense_units (int): Number of neurons in each dense layer
-            activation (str): Activation type of each dense layer
-            loss (str): Loss name
-            optimizer (str): Optimizer name
-            metrics (List[str]): Metrics list
-            last_activation (str): Activation type of the last layer
+            panel (``Panel``): Panel with data
+            model_type (``str``): Model type (regression, classification, multi_classification)
+            dense_layers (``int``): Number of dense layers
+            dense_units (``int``)t``)t``)t``)t``): Number of neurons in each dense layer
+            activation (``str``): Activation type of each dense layer
+            loss (``str``): Loss name
+            optimizer (``str``): Optimizer name
+            metrics (``list[str]``): Metrics list
+            last_activation (``str``): Activation type of the last layer
 
         Returns:
             ``DenseModel``: Constructed DenseModel
@@ -480,7 +498,7 @@ 
    Source code for wavy.models
                 last_activation=last_activation,
         )
 
-
    [docs]    def build(self):
+
    [docs]    def build(self) -> None:
         """Build the model."""
         dense = Dense(units=self.dense_units, activation=self.activation)
         layers = [Flatten()]  # (time, features) => (time*features)
@@ -497,11 +515,13 @@ 
    Source code for wavy.models
             self.model = Sequential(layers)
    
 
 
-
    [docs]class ConvModel(_BaseModel):
+
    [docs]class ConvModel(BaseModel):
+    """Convolutional model."""
+
     def __init__(
         self,
-        x,
-        y,
+        x: Panel,
+        y: Panel,
         model_type: str = None,
         conv_layers: int = 1,
         conv_filters: int = 32,
@@ -511,25 +531,26 @@ 
    Source code for wavy.models
             activation: str = "relu",
         loss: str = None,
         optimizer: str = None,
-        metrics: List[str] = None,
+        metrics: list[str] = None,
         last_activation: str = None,
     ):
         """
         Convolution Model.
 
         Args:
-            panel (Panel): Panel with data
-            model_type (str): Model type (regression, classification, multi_classification)
-            conv_layers (int): Number of convolution layers
-            conv_filters (int): Number of convolution filters
-            kernel_size (int): Kernel size of convolution layer
-            dense_layers (int): Number of dense layers
-            dense_units (int): Number of neurons in each dense layer
-            activation (str): Activation type of each dense layer
-            loss (str): Loss name
-            optimizer (str): Optimizer name
-            metrics (List[str]): Metrics list
-            last_activation (str): Activation type of the last layer
+            x (``Panel``): Panel with x data
+            y (``Panel``): Panel with y data
+            model_type (``str``): Model type (regression, classification, multi_classification)
+            conv_layers (``int``): Number of convolution layers
+            conv_filters (``int``): Number of convolution filters
+            kernel_size (``int``): Kernel size of convolution layer
+            dense_layers (``int``): Number of dense layers
+            dense_units (``int``): Number of neurons in each dense layer
+            activation (``str``): Activation type of each dense layer
+            loss (``str``): Loss name
+            optimizer (``str``): Optimizer name
+            metrics (``list[str]``): Metrics list
+            last_activation (``str``): Activation type of the last layer
 
         Returns:
             ``DenseModel``: Constructed DenseModel
@@ -557,7 +578,7 @@ 
    Source code for wavy.models
                 last_activation=last_activation,
         )
 
-
    [docs]    def build(self):
+
    [docs]    def build(self) -> None:
         """Build the model."""
 
         if self.x.num_timesteps % self.kernel_size != 0:
@@ -587,11 +608,15 @@ 
    Source code for wavy.models
     
 
 
    [docs]class LinearRegression(DenseModel):
+    """Linear regression model."""
+
     def __init__(self, x, y, **kwargs):
         super().__init__(x=x, y=y, model_type="regression", dense_layers=0, **kwargs)
    
 
 
 
    [docs]class LogisticRegression(DenseModel):
+    """Logistic regression model."""
+
     def __init__(self, x, y, **kwargs):
         super().__init__(
             x=x, y=y, model_type="classification", dense_layers=0, **kwargs
@@ -599,14 +624,16 @@ 
    Source code for wavy.models
     
 
 
    [docs]class ShallowModel:
-    def __init__(self, x, y, model, metrics, **kwargs):
+    """Shallow model."""
+
+    def __init__(self, x: Panel, y: Panel, model: str, metrics: list[str], **kwargs):
         """Shallow Model.
 
         Args:
-            x (Panel): Panel with data
-            y (Panel): Panel with data
-            model (str): Model (regression, classification, multi_classification)
-            metrics (List[str]): Metrics list
+            x (``Panel``): Panel with x data
+            y (``Panel``): Panel with y data
+            model (``str``): Model (regression, classification, multi_classification)
+            metrics (``list[str]``): Metrics list
             **kwargs: Additional arguments
 
         Returns:
@@ -623,7 +650,7 @@ 
    Source code for wavy.models
             self.metrics = metrics
         self.set_arrays()
 
-
    [docs]    def set_arrays(self):
+
    [docs]    def set_arrays(self) -> None:
         """
         Sets arrays for training, testing, and validation.
         """
@@ -642,13 +669,10 @@ 
    Source code for wavy.models
     
         Args:
             **kwargs: Keyword arguments for the fit method of the model.
-
-        Returns:
-            ``ShallowModel``: The fitted model.
         """
-        return self.model.fit(X=self.x_train, y=self.y_train, **kwargs)
    
+        self.model.fit(X=self.x_train, y=self.y_train, **kwargs)
    
 
-
    [docs]    def get_auc(self):
+
    [docs]    def get_auc(self) -> float:
         """Get the AUC score."""
 
         y = self.y_test.squeeze()
@@ -656,11 +680,11 @@ 
    Source code for wavy.models
             fpr, tpr, _ = roc_curve(y, prediction)
         return auc(fpr, tpr)
    
 
-
    [docs]    def predict_proba(self, data: Panel = None):
+
    [docs]    def predict_proba(self, data: Panel = None) -> Panel:
         """Predict probabilities.
 
         Args:
-            data (Panel): Panel with data
+            data (``Panel``): Panel with data
 
         Returns:
             ``ShallowModel``: The predicted probabilities.
@@ -706,14 +730,14 @@ 
    Source code for wavy.models
                 index=index,
         )
    
 
-
    [docs]    def predict(self, data: Panel = None):
+
    [docs]    def predict(self, data: Panel = None) -> Panel:
         """Predict on data.
 
         Args:
-            data (Panel, optional): Data to predict on. Defaults to None.
+            data (``Panel``, optional): Data to predict on. Defaults to None.
 
         Returns:
-            Panel: Predicted data
+            ``Panel``: Predicted data
         """
         if is_classifier(self.model):
             threshold = self.get_auc()
@@ -739,14 +763,14 @@ 
    Source code for wavy.models
                     index=index,
             )
    
 
-
    [docs]    def score(self, on=None):
+
    [docs]    def score(self, on: list[str] | str = None) -> pd.DataFrame:
         """Score the model.
 
         Args:
-            on (str): Data to use for scoring
+            on (``list[str]`` or ``str``): Data to use for scoring
 
         Returns:
-            pd.Series: Score
+            ``pd.Series``: Score
         """
         on = [on] if on else ["train", "val", "test"]
 
@@ -782,13 +806,14 @@ 
    Source code for wavy.models
                 }
 
             dic["val"] = metrics_dict
+
         return pd.DataFrame(dic, index=[a.__name__ for a in self.metrics])
    
 
-
    [docs]    def residuals(self):
+
    [docs]    def residuals(self) -> Panel:
         """Residuals.
 
         Returns:
-            Panel: Residuals
+            ``Panel``: Residuals
         """
         return self.predict() - self.y
    
 
@@ -800,7 +825,7 @@ 
    Source code for wavy.models
     
     Args:
         *models: Models to score
-        on (str, optional): Data to use for scoring. Defaults to "val".
+        on (``str``, optional): Data to use for scoring. Defaults to "val".
 
     Returns:
         pd.DataFrame: Scores
@@ -818,11 +843,11 @@ 
    Source code for wavy.models
         Compute default scores for a model.
 
     Args:
-        x (Panel): X data
-        y (Panel): Y data
-        model_type (str): Model type
-        epochs (int, optional): Number of epochs. Defaults to 10.
-        verbose (int, optional): Verbosity. Defaults to 0.
+        x (``Panel``): X data.
+        y (``Panel``): Y data.
+        model_type (``str``): Model type.
+        epochs (``int``, optional): Number of epochs. Defaults to 10.
+        verbose (``int``, optional): Verbosity. Defaults to 0.
         **kwargs: Keyword arguments for the model.
 
     Returns:
diff --git a/_modules/wavy/panel.html b/_modules/wavy/panel.html
index 89e5087..dd2fe0d 100644
--- a/_modules/wavy/panel.html
+++ b/_modules/wavy/panel.html
@@ -22,7 +22,7 @@
     
 
 
@@ -58,7 +58,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -88,11 +87,10 @@
   
    Source code for wavy.panel
     from __future__ import annotations
 
-import contextlib
+# import contextlib
 import random
 import warnings
 from itertools import chain
-from typing import Optional, Tuple, Union
 
 import numpy as np
 import pandas as pd
@@ -108,18 +106,18 @@ 
    Source code for wavy.panel
     # Define column to y
 
    [docs]def create_panels(
     df: pd.DataFrame, lookback: int, horizon: int, gap: int = 0
-) -> Tuple[Panel, Panel]:
+) -> tuple[Panel, Panel]:
     """
     Create panels from a dataframe.
 
     Args:
-        df (pd.DataFrame): Dataframe
-        lookback (int): Lookback size
-        horizon (int): Horizon size
-        gap (int): Gap size
+        df (``pd.DataFrame``): Dataframe
+        lookback (``int``): Lookback size
+        horizon (``int``): Horizon size
+        gap (``int``): Gap size
 
     Returns:
-        ``Tuple``: Tuple of panels
+        ``tuple[Panel, Panel]``: Tuple of panels
     """
 
     indices = df.index
@@ -194,8 +192,8 @@ 
    Source code for wavy.panel
         Reset ids of a panel.
 
     Args:
-        panels (list): List of panels
-        inplace (bool): Whether to reset ids inplace or not.
+        panels (``list``): List of panels
+        inplace (``bool``): Whether to reset ids inplace or not.
 
     Returns:
         ``Panel``: Reset id of panel
@@ -212,11 +210,11 @@ 
    Source code for wavy.panel
     
 
    [docs]def dropna_match(x, y):
     """
-    Drop frames with NaN in both x and y and match ids.
+    Drop frames with NaN in panels and match ids.
 
     Args:
-        x (Panel): Panel with x data
-        y (Panel): Panel with y data
+        x (``Panel``): Panel with x data
+        y (``Panel``): Panel with y data
 
     Returns:
         ``Panel``: Panel with dropped frames and matched ids
@@ -238,9 +236,9 @@ 
    Source code for wavy.panel
         Concatenate panels.
 
     Args:
-        panels (list): List of panels
-        reset_ids (bool): Whether to reset ids
-        sort (bool): Whether to sort by id
+        panels (``list``): List of panels
+        reset_ids (``bool``): Whether to reset ids
+        sort (``bool``): Whether to sort by id
 
     Returns:
         ``Panel``: Concatenated panels
@@ -267,21 +265,21 @@ 
    Source code for wavy.panel
     
    [docs]def set_training_split(
     x: Panel,
     y: Panel,
-    train_size: Union[float, int] = 0.7,
-    val_size: Union[float, int] = 0.2,
-    test_size: Union[float, int] = 0.1,
+    train_size: float | int = 0.7,
+    val_size: float | int = 0.2,
+    test_size: float | int = 0.1,
 ) -> None:
     """
-    Splits the panel in training, validation, and test, accessed with the
-    properties .train, .val and .test.
+    Splits panel in training, validation, and test.
 
     Args:
-        train_size (float, int): Fraction of data to use for training.
-        test_size (float, int): Fraction of data to use for testing.
-        val_size (float, int): Fraction of data to use for validation.
+        train_size (``float`` or ``int``): Fraction of data to use for training.
+        test_size (``float`` or ``int``): Fraction of data to use for testing.
+        val_size (``float`` or ``int``): Fraction of data to use for validation.
 
     Example:
-    >>> x,y = set_training_split(x, y, val_size=0.2, test_size=0.1)
+
+    >>> x, y = set_training_split(x, y, train_size=0.8, val_size=0.2, test_size=0.1)
     """
 
     x.set_training_split(train_size=train_size, val_size=val_size, test_size=test_size)
@@ -320,7 +318,7 @@ 
    Source code for wavy.panel
     
 
    [docs]class Panel(pd.DataFrame):
     """
-    Panel data structure.
+    Panel class.
     """
 
     def __init__(self, *args, **kw):
@@ -381,30 +379,32 @@ 
    Source code for wavy.panel
     
     @property
     def frames(self) -> DataFrameGroupBy:
-        """Returns the frames in the panel."""
+        """
+        Returns panel's frames.
+        """
         return self.groupby(level=0, as_index=True)
 
     @property
     def timesteps(self) -> pd.Int64Index:
         """
-        Returns the ids of the panel.
+        Returns panel's timesteps.
         """
         return self.index.get_level_values(1)
 
     @property
     def ids(self) -> pd.Int64Index:
         """
-        Returns the ids of the panel without duplicates.
+        Returns panel's ids without duplicates.
         """
         return self.index.get_level_values(0).drop_duplicates()
 
     @ids.setter
     def ids(self, ids: list[int]) -> None:
         """
-        Set the ids of the panel.
+        Set panel's ids.
 
         Args:
-            ids (list): List of ids.
+            ids (``list``): List of ids.
         """
 
         ids = np.repeat(ids, self.shape_panel[1])
@@ -414,12 +414,12 @@ 
    Source code for wavy.panel
     
         self.index = index
 
-
    [docs]    def reset_ids(self, inplace: bool = False) -> Optional[Panel]:
+
    [docs]    def reset_ids(self, inplace: bool = False) -> Panel | None:
         """
-        Reset the ids of the panel.
+        Reset panel's ids.
 
         Args:
-            inplace (bool): Whether to reset ids inplace.
+            inplace (``bool``): Whether to reset ids inplace.
         """
         new_ids = np.repeat(np.arange(self.num_frames), self.num_timesteps)
         new_index = pd.MultiIndex.from_arrays(
@@ -430,15 +430,18 @@ 
    Source code for wavy.panel
             return self.set_index(new_index, inplace=inplace)
    
 
     @property
-    def shape_panel(self) -> Tuple[int, int, int]:
+    def shape_panel(self) -> tuple[int, int, int]:
         """
-        Returns the shape of the panel.
+        Return a tuple representing the dimensionality of the Panel.
         """
         return (len(self.ids), int(self.shape[0] / len(self.ids)), self.shape[1])
 
-
    [docs]    def row_panel(self, n: Union[list, int] = 0) -> Panel:
+
    [docs]    def row_panel(self, n: list[int] | int = 0) -> Panel:
         """
         Returns the nth row of each frame.
+
+        Args:
+            n (``list[int]`` or ``int``): Row index.
         """
         if isinstance(n, int):
             n = [n]
@@ -450,12 +453,12 @@ 
    Source code for wavy.panel
             self._copy_attrs(new_panel)
         return new_panel
    
 
-
    [docs]    def get_timesteps(self, n: Union[list, int] = 0) -> Panel:
+
    [docs]    def get_timesteps(self, n: list[int] | int = 0) -> Panel:
         """
         Returns the first timestep of each frame in the panel.
 
         Args:
-            n (int): Timestep to return.
+            n (``list[int]`` or ``int``): Timestep to return.
         """
 
         if isinstance(n, int):
@@ -510,13 +513,13 @@ 
    Source code for wavy.panel
     
         return panel
    
 
-
    [docs]    def drop_ids(self, ids: Union[list, int], inplace: bool = False) -> Optional[Panel]:
+
    [docs]    def drop_ids(self, ids: list[int] | int, inplace: bool = False) -> Panel | None:
         """
         Drop frames by id.
 
         Args:
-            ids (list, int): List of ids to drop.
-            inplace (bool): Whether to drop ids inplace.
+            ids (``list[int]`` or ``int``): List of ids to drop.
+            inplace (``bool``): Whether to drop ids inplace.
 
         Returns:
             ``Panel``: Panel with frames dropped.
@@ -541,26 +544,27 @@ 
    Source code for wavy.panel
                 else pd.Int64Index([], name="id")
         )
    
 
-
    [docs]    def dropna_frames(self, inplace: bool = False) -> Optional[Panel]:
+
    [docs]    def dropna_frames(self, inplace: bool = False) -> Panel | None:
         """
         Drop frames with missing values from the panel.
 
         Args:
-            inplace (bool): Whether to drop frames inplace.
+            inplace (``bool``): Whether to drop frames inplace.
 
         Returns:
             ``Panel``: Panel with frames dropped.
         """
         return self.drop_ids(self.findna_frames(), inplace=inplace)
    
 
-
    [docs]    def match_frames(self, other: Panel, inplace: bool = False) -> Optional[Panel]:
+
    [docs]    def match_frames(self, other: Panel, inplace: bool = False) -> Panel | None:
         """
-        Match panel with other panel. This function will match the ids and id
-        order of self based on the ids of other.
+        Match panel with other panel.
+
+        This function will match the ids and id order of self based on the ids of other.
 
         Args:
             other (``Panel``): Panel to match with.
-            inplace (bool): Whether to match inplace.
+            inplace (``bool``): Whether to match inplace.
 
         Returns:
             ``Panel``: Result of match function.
@@ -579,21 +583,21 @@ 
    Source code for wavy.panel
     
 
    [docs]    def set_training_split(
         self,
-        train_size: Union[float, int] = 0.7,
-        val_size: Union[float, int] = 0.2,
-        test_size: Union[float, int] = 0.1,
+        train_size: float | int = 0.7,
+        val_size: float | int = 0.2,
+        test_size: float | int = 0.1,
     ) -> None:
         """
-        Splits the panel in training, validation, and test, accessed with the
-        properties .train, .val and .test.
+        Splits Panel into training, validation, and test.
 
         Args:
-            train_size (float, int): Fraction of data to use for training.
-            test_size (float, int): Fraction of data to use for testing.
-            val_size (float, int): Fraction of data to use for validation.
+            train_size (``float`` or ``int``): Fraction of data to use for training.
+            test_size (``float`` or ``int``): Fraction of data to use for testing.
+            val_size (``float`` or ``int``): Fraction of data to use for validation.
 
         Example:
-        >>> panel.set_training_split(val_size=0.2, test_size=0.1)
+
+        >>> panel.set_training_split(train_size=0.8, val_size=0.2, test_size=0.1)
         """
 
         n_train, n_val, n_test = _validate_training_split(
@@ -619,8 +623,7 @@ 
    Source code for wavy.panel
         @property
     def train(self) -> Panel:
         """
-        Returns the Panel with the training set, according to
-        the parameters given in the 'set_training_split' function.
+        Returns the Panel with the training set.
 
         Returns:
             ``Panel``: Panel with the training set.
@@ -644,8 +647,7 @@ 
    Source code for wavy.panel
         @property
     def val(self) -> Panel:
         """
-        Returns the Panel with the validation set, according to
-        the parameters given in the 'set_training_split' function.
+        Returns the Panel with the validation set.
 
         Returns:
             ``Panel``: Panel with the validation set.
@@ -685,8 +687,7 @@ 
    Source code for wavy.panel
         @property
     def test(self) -> Panel:
         """
-        Returns the Panel with the testing set, according to
-        the parameters given in the 'set_training_split' function.
+        Returns the Panel with the testing set.
 
         Returns:
             ``Panel``: Panel with the testing set.
@@ -712,7 +713,7 @@ 
    Source code for wavy.panel
             Return the first n frames of the panel.
 
         Args:
-            n (int): Number of frames to return.
+            n (``int``): Number of frames to return.
 
         Returns:
             ``Panel``: Result of head function.
@@ -724,7 +725,7 @@ 
    Source code for wavy.panel
             Return the last n frames of the panel.
 
         Args:
-            n (int): Number of frames to return.
+            n (``int``): Number of frames to return.
 
         Returns:
             ``Panel``: Result of tail function.
@@ -737,18 +738,18 @@ 
    Source code for wavy.panel
             inplace: bool = False,
         kind: str = "quicksort",
         key: callable = None,
-    ) -> Optional[Panel]:
+    ) -> Panel | None:
         """
         Sort panel by ids.
 
         Args:
-            ascending (bool or list-like of bools, default True): Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
-            inplace (bool, default False): If True, perform operation in-place.
+            ascending (``bool`` or list-like of ``bools``, default True): Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
+            inplace (``bool``, default False): If True, perform operation in-place.
             kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort'): Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
             key (callable, optional): If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.
 
         Returns:
-            ``Panel or None``: The original DataFrame sorted by the labels or None if `inplace=True`.
+            ``Panel`` or ``None``: The original DataFrame sorted by the labels or None if `inplace=True`.
         """
 
         return self.sort_index(
@@ -762,19 +763,19 @@ 
    Source code for wavy.panel
     
 
    [docs]    def sample_panel(
         self,
-        samples: Union[int, float] = 5,
+        samples: int | float = 5,
         how: str = "spaced",
         reset_ids: bool = False,
         seed: int = 42,
-    ) -> Optional[Panel]:
+    ) -> Panel | None:
         """
         Sample panel returning a subset of frames.
 
         Args:
-            samples (int or float): Number or percentage of samples to return.
-            how (str): Sampling method, 'spaced' or 'random'
-            reset_ids (bool): If True, reset the index of the sampled panel.
-            seed (int): Random seed.
+            samples (``int`` or ``float``): Number or percentage of samples to return.
+            how (``str``): Sampling method, 'spaced' or 'random'
+            reset_ids (``bool``): If True, reset the index of the sampled panel.
+            seed (``int``): Random seed.
 
         Returns:
             ``Panel``: Result of sample function.
@@ -861,15 +862,13 @@ 
    Source code for wavy.panel
     
         return new_panel
    
 
-
    [docs]    def shuffle_panel(
-        self, seed: int = None, reset_ids: bool = False
-    ) -> Optional[Panel]:
+
    [docs]    def shuffle_panel(self, seed: int = None, reset_ids: bool = False) -> Panel | None:
         """
         Shuffle the panel.
 
         Args:
-            seed (int): Random seed.
-            reset_ids (bool): If True, reset the index of the shuffled panel.
+            seed (``int``): Random seed.
+            reset_ids (``bool``): If True, reset the index of the shuffled panel.
 
         Returns:
             ``Panel``: Result of shuffle function.
@@ -915,9 +914,9 @@ 
    Source code for wavy.panel
             Plot the panel.
 
         Args:
-            add_annotation (bool): If True, plot the training, validation, and test annotation.
-            max (int): Maximum number of samples to plot.
-            use_timestep (bool): If True, plot the timestep instead of the sample index.
+            add_annotation (``bool``): If True, plot the training, validation, and test annotation.
+            max (``int``): Maximum number of samples to plot.
+            use_timestep (``bool``): If True, plot the timestep instead of the sample index.
             **kwargs: Additional arguments to pass to the plot function.
 
         Returns:
diff --git a/_modules/wavy/plot.html b/_modules/wavy/plot.html
index 8a50a34..fdc6a18 100644
--- a/_modules/wavy/plot.html
+++ b/_modules/wavy/plot.html
@@ -22,7 +22,7 @@
     
 
 
@@ -58,7 +58,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -88,14 +87,22 @@
   
    Source code for wavy.plot
     from __future__ import annotations
 
+from functools import wraps
+
 import plotly.express as px
 from plotlab import Figure
 
+# from .panel import Panel
+
 # TODO: Set plotting configs and add kwargs to functions
 # TODO: Check if kwargs would overwrite fig.add_trace if same params are used
 
 
 
    [docs]class PanelFigure(Figure):
+    """
+    PanelFigure class.
+    """
+
     def __init__(self):
         # TODO: Add dynamic color changing once new traces are added
         # TODO: Add candlestick plot
@@ -104,14 +111,14 @@ 
    Source code for wavy.plot
             self.colors = px.colors.qualitative.Plotly
         self.color_index = 0
 
-
    [docs]    def add_annotation(self, panel, color="gray", opacity=1):
+
    [docs]    def add_annotation(self, panel, color: str = "gray", opacity: float = 1.0) -> None:
         """
-        Split panel into sets.
+        Plot vertical lines showing train, val, and test periods.
 
         Args:
-            panel (wavy.Panel): Panel to split
-            color (str): Color of the sets
-            opacity (float): Opacity of the sets
+            panel (``Panel``): Panel to split.
+            color (``str``): Color of the sets.
+            opacity (``float``): Opacity of the sets.
         """
         # BUG: Seems to break if using "ggplot2"
         # ! Won't take effect until next trace is added (no axis was added)
@@ -146,7 +153,8 @@ 
    Source code for wavy.plot
                 )
    
 
     # Add decorator for instance check and for loop
-
    [docs]    def iterator(func):
+    def _iterator(func):
+        @wraps(func)
         def inner(self, *args, **kwargs):
 
             args = list(args)
@@ -158,55 +166,55 @@ 
    Source code for wavy.plot
                     if col != "frame":
                     func(self, df[col], *tuple(args), **kwargs)
 
-        return inner
    
+        return inner
 
-
    [docs]    @iterator
-    def add_line(self, col: str, *args, **kwargs):
+
    [docs]    @_iterator
+    def add_line(self, col: str, *args, **kwargs) -> None:
         """
         Add a line to the figure.
 
         Args:
-            col (str): Column to plot
+            col (``str``): Column to plot
         """
         self.line(col, *args, **kwargs)
    
 
-
    [docs]    @iterator
-    def add_area(self, col: str, *args, **kwargs):
+
    [docs]    @_iterator
+    def add_area(self, col: str, *args, **kwargs) -> None:
         """
         Add an area to the figure.
 
         Args:
-            col (str): Column to plot
+            col (``str``): Column to plot
         """
         self.area(col, *args, **kwargs)
    
 
-
    [docs]    @iterator
-    def add_bar(self, col: str, *args, **kwargs):
+
    [docs]    @_iterator
+    def add_bar(self, col: str, *args, **kwargs) -> None:
         """
         Add a bar to the figure.
 
         Args:
-            col (str): Column to plot
+            col (``str``): Column to plot
         """
         self.bar(col, *args, **kwargs)
    
 
-
    [docs]    @iterator
-    def add_scatter(self, col: str, *args, **kwargs):
+
    [docs]    @_iterator
+    def add_scatter(self, col: str, *args, **kwargs) -> None:
         """
         Add a scatter to the figure.
 
         Args:
-            col (str): Column to plot
+            col (``str``): Column to plot.
         """
         self.scatter(col, *args, **kwargs)
    
 
-
    [docs]    @iterator
-    def add_dotline(self, col: str, *args, **kwargs):
+
    [docs]    @_iterator
+    def add_dotline(self, col: str, *args, **kwargs) -> None:
         """
         Add a dotline to the figure.
 
         Args:
-            col (str): Column to plot
+            col (``str``): Column to plot.
         """
         self.dotline(col, *args, **kwargs)
    
 
@@ -215,12 +223,12 @@ 
    Source code for wavy.plot
         panel, use_timestep: bool = False, add_annotation: bool = False, **kwargs
 ) -> PanelFigure:
     """
-    Plot a panel.
+    Plot panel.
 
     Args:
-        panel (Panel): Panel object
-        use_timestep (bool): Use timestep instead of id
-        add_annotation (bool): If True, plot vertical lines showing train, val, and test periods
+        panel (``Panel``): Panel object.
+        use_timestep (``bool``): Use timestep instead of id.
+        add_annotation (``bool``): If True, plot vertical lines showing train, val, and test periods.
 
     Returns:
         ``Plot``: Plotted data
diff --git a/_modules/wavy/utils.html b/_modules/wavy/utils.html
deleted file mode 100644
index 1994521..0000000
--- a/_modules/wavy/utils.html
+++ /dev/null
@@ -1,153 +0,0 @@
-
-
-
-  
-  
-  wavy.utils — Wavy 0.1.9 documentation
-      
-      
-      
-  
-  
-        
-        
-        
-        
-        
-        
-    
-    
-    
-
-
-
-
- 
-  
    
-    
-
-    
    
-
-      
    
-        
    
-          
    
-  
-  
    
-
    
-          
    
-           
    
-             
-  
    Source code for wavy.utils
    -from collections.abc import Iterable
-
-import pandas as pd
-
-
-
    [docs]def reverse_pct_change(change_df, original_df, periods=1):
-    """
-    Reverse the pct_change function.
-
-    Args:
-        change_df (pd.DataFrame): Dataframe to reverse
-        original_df (pd.DataFrame): Reference Dataframe
-        periods (int): Number of periods used on pct_change operation
-
-    Returns:
-        pd.DataFrame: Reversed dataframe
-    """
-
-    return original_df.shift(periods) * (change_df + 1)
    
-
-
-
    [docs]def reverse_diff(diff_df, original_df, periods=1):
-    """
-    Reverse the pct_diff function.
-
-    Args:
-        diff_df (pd.DataFrame): Dataframe to reverse
-        original_df (pd.DataFrame): Reference Dataframe
-        periods (int): Number of periods used on diff operation
-
-    Returns:
-        pd.DataFrame: Reversed dataframe
-    """
-
-    return original_df.shift(periods) + diff_df
    
-
    
-
-           
    
-          
    
-          
-        
    
-      
    
-    
    
-  
    
-   
-
-
-
\ No newline at end of file
diff --git a/_sources/api/models.rst.txt b/_sources/api/models.rst.txt
index 2d74b67..5fc913d 100644
--- a/_sources/api/models.rst.txt
+++ b/_sources/api/models.rst.txt
@@ -1,3 +1,4 @@
 
 .. automodapi:: wavy.models
-   :no-inheritance-diagram:
\ No newline at end of file
+   :no-inheritance-diagram:
+   :inherited-members:
diff --git a/_sources/api/utils.rst.txt b/_sources/api/utils.rst.txt
deleted file mode 100644
index c012f1c..0000000
--- a/_sources/api/utils.rst.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-
-.. automodapi:: wavy.utils
-   :no-inheritance-diagram:
\ No newline at end of file
diff --git a/_sources/api/wavy.models.BaseModel.rst.txt b/_sources/api/wavy.models.BaseModel.rst.txt
new file mode 100644
index 0000000..c48f1ad
--- /dev/null
+++ b/_sources/api/wavy.models.BaseModel.rst.txt
@@ -0,0 +1,33 @@
+BaseModel
+=========
+
+.. currentmodule:: wavy.models
+
+.. autoclass:: BaseModel
+   :show-inheritance:
+
+   .. rubric:: Methods Summary
+
+   .. autosummary::
+
+      ~BaseModel.build
+      ~BaseModel.compile
+      ~BaseModel.fit
+      ~BaseModel.get_auc
+      ~BaseModel.predict
+      ~BaseModel.predict_proba
+      ~BaseModel.residuals
+      ~BaseModel.score
+      ~BaseModel.set_arrays
+
+   .. rubric:: Methods Documentation
+
+   .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
+   .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.BaselineConstant.rst.txt b/_sources/api/wavy.models.BaselineConstant.rst.txt
index 28a7a3c..471f5ba 100644
--- a/_sources/api/wavy.models.BaselineConstant.rst.txt
+++ b/_sources/api/wavy.models.BaselineConstant.rst.txt
@@ -10,8 +10,24 @@ BaselineConstant
 
    .. autosummary::
 
+      ~BaselineConstant.build
+      ~BaselineConstant.compile
+      ~BaselineConstant.fit
+      ~BaselineConstant.get_auc
+      ~BaselineConstant.predict
+      ~BaselineConstant.predict_proba
+      ~BaselineConstant.residuals
+      ~BaselineConstant.score
       ~BaselineConstant.set_arrays
 
    .. rubric:: Methods Documentation
 
+   .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
    .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.BaselineShift.rst.txt b/_sources/api/wavy.models.BaselineShift.rst.txt
index 8efd2fc..5e6cab2 100644
--- a/_sources/api/wavy.models.BaselineShift.rst.txt
+++ b/_sources/api/wavy.models.BaselineShift.rst.txt
@@ -11,9 +11,23 @@ BaselineShift
    .. autosummary::
 
       ~BaselineShift.build
+      ~BaselineShift.compile
+      ~BaselineShift.fit
+      ~BaselineShift.get_auc
+      ~BaselineShift.predict
+      ~BaselineShift.predict_proba
+      ~BaselineShift.residuals
+      ~BaselineShift.score
       ~BaselineShift.set_arrays
 
    .. rubric:: Methods Documentation
 
    .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
    .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.ConvModel.rst.txt b/_sources/api/wavy.models.ConvModel.rst.txt
index 5ae65eb..e89f5a7 100644
--- a/_sources/api/wavy.models.ConvModel.rst.txt
+++ b/_sources/api/wavy.models.ConvModel.rst.txt
@@ -11,7 +11,23 @@ ConvModel
    .. autosummary::
 
       ~ConvModel.build
+      ~ConvModel.compile
+      ~ConvModel.fit
+      ~ConvModel.get_auc
+      ~ConvModel.predict
+      ~ConvModel.predict_proba
+      ~ConvModel.residuals
+      ~ConvModel.score
+      ~ConvModel.set_arrays
 
    .. rubric:: Methods Documentation
 
    .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
+   .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.DenseModel.rst.txt b/_sources/api/wavy.models.DenseModel.rst.txt
index 372bf54..f46d93a 100644
--- a/_sources/api/wavy.models.DenseModel.rst.txt
+++ b/_sources/api/wavy.models.DenseModel.rst.txt
@@ -11,7 +11,23 @@ DenseModel
    .. autosummary::
 
       ~DenseModel.build
+      ~DenseModel.compile
+      ~DenseModel.fit
+      ~DenseModel.get_auc
+      ~DenseModel.predict
+      ~DenseModel.predict_proba
+      ~DenseModel.residuals
+      ~DenseModel.score
+      ~DenseModel.set_arrays
 
    .. rubric:: Methods Documentation
 
    .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
+   .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.LinearRegression.rst.txt b/_sources/api/wavy.models.LinearRegression.rst.txt
index 8cf4d43..ad20d4b 100644
--- a/_sources/api/wavy.models.LinearRegression.rst.txt
+++ b/_sources/api/wavy.models.LinearRegression.rst.txt
@@ -5,3 +5,29 @@ LinearRegression
 
 .. autoclass:: LinearRegression
    :show-inheritance:
+
+   .. rubric:: Methods Summary
+
+   .. autosummary::
+
+      ~LinearRegression.build
+      ~LinearRegression.compile
+      ~LinearRegression.fit
+      ~LinearRegression.get_auc
+      ~LinearRegression.predict
+      ~LinearRegression.predict_proba
+      ~LinearRegression.residuals
+      ~LinearRegression.score
+      ~LinearRegression.set_arrays
+
+   .. rubric:: Methods Documentation
+
+   .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
+   .. automethod:: set_arrays
diff --git a/_sources/api/wavy.models.LogisticRegression.rst.txt b/_sources/api/wavy.models.LogisticRegression.rst.txt
index 81fceaf..d6e06e0 100644
--- a/_sources/api/wavy.models.LogisticRegression.rst.txt
+++ b/_sources/api/wavy.models.LogisticRegression.rst.txt
@@ -5,3 +5,29 @@ LogisticRegression
 
 .. autoclass:: LogisticRegression
    :show-inheritance:
+
+   .. rubric:: Methods Summary
+
+   .. autosummary::
+
+      ~LogisticRegression.build
+      ~LogisticRegression.compile
+      ~LogisticRegression.fit
+      ~LogisticRegression.get_auc
+      ~LogisticRegression.predict
+      ~LogisticRegression.predict_proba
+      ~LogisticRegression.residuals
+      ~LogisticRegression.score
+      ~LogisticRegression.set_arrays
+
+   .. rubric:: Methods Documentation
+
+   .. automethod:: build
+   .. automethod:: compile
+   .. automethod:: fit
+   .. automethod:: get_auc
+   .. automethod:: predict
+   .. automethod:: predict_proba
+   .. automethod:: residuals
+   .. automethod:: score
+   .. automethod:: set_arrays
diff --git a/_sources/api/wavy.plot.PanelFigure.rst.txt b/_sources/api/wavy.plot.PanelFigure.rst.txt
index 8a5cc2b..ca18496 100644
--- a/_sources/api/wavy.plot.PanelFigure.rst.txt
+++ b/_sources/api/wavy.plot.PanelFigure.rst.txt
@@ -16,7 +16,6 @@ PanelFigure
       ~PanelFigure.add_dotline
       ~PanelFigure.add_line
       ~PanelFigure.add_scatter
-      ~PanelFigure.iterator
 
    .. rubric:: Methods Documentation
 
@@ -26,4 +25,3 @@ PanelFigure
    .. automethod:: add_dotline
    .. automethod:: add_line
    .. automethod:: add_scatter
-   .. automethod:: iterator
diff --git a/_sources/api/wavy.utils.reverse_diff.rst.txt b/_sources/api/wavy.utils.reverse_diff.rst.txt
deleted file mode 100644
index 00593e9..0000000
--- a/_sources/api/wavy.utils.reverse_diff.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-reverse_diff
-============
-
-.. currentmodule:: wavy.utils
-
-.. autofunction:: reverse_diff
diff --git a/_sources/api/wavy.utils.reverse_pct_change.rst.txt b/_sources/api/wavy.utils.reverse_pct_change.rst.txt
deleted file mode 100644
index 8029f2f..0000000
--- a/_sources/api/wavy.utils.reverse_pct_change.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-reverse_pct_change
-==================
-
-.. currentmodule:: wavy.utils
-
-.. autofunction:: reverse_pct_change
diff --git a/_sources/manual/models.rst.txt b/_sources/manual/models.rst.txt
deleted file mode 100644
index b569c2a..0000000
--- a/_sources/manual/models.rst.txt
+++ /dev/null
@@ -1,23 +0,0 @@
-Models
-======
-
-BaselineShift
--------------
-
-BaselineConstant
-----------------
-
-DenseModel
-----------
-
-ConvModel
----------
-
-LinearRegression
-----------------
-
-LogisticRegression
-------------------
-
-ShallowModel
-------------
\ No newline at end of file
diff --git a/_sources/notebooks/quickstart2.ipynb.txt b/_sources/notebooks/quickstart2.ipynb.txt
index 2bd06ce..5ebee98 100644
--- a/_sources/notebooks/quickstart2.ipynb.txt
+++ b/_sources/notebooks/quickstart2.ipynb.txt
@@ -16,19 +16,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Import libraries\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import wavy"
+    "import wavy\n",
+    "from wavy import models\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = 'pdf'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,6 +49,16 @@
     "x, y = wavy.create_panels(df, lookback=10, horizon=1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training split\n",
+    "wavy.set_training_split(x, y, train_size=0.4, val_size=0.3, test_size=0.3)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -62,7 +75,7 @@
    "source": [
     "# x and y are contain the past and corresponding future data.\n",
     "# lookback and horizon are the number of timesteps.\n",
-    "print(\"Lookback:\", len(x[0]), \"Horizon:\", len(y[0]))"
+    "print(\"Lookback:\", x.num_timesteps, \"Horizon:\", y.num_timesteps)"
    ]
   },
   {
@@ -72,2891 +85,7 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "line": {
-          "color": "#636EFA",
-          "width": 1.5
-         },
-         "mode": "lines",
-         "name": "price",
-         "type": "scatter",
-         "x": [
-          10,
-          11,
-          12,
-          13,
-          14,
-          15,
-          16,
-          17,
-          18,
-          19,
-          20,
-          21,
-          22,
-          23,
-          24,
-          25,
-          26,
-          27,
-          28,
-          29,
-          30,
-          31,
-          32,
-          33,
-          34,
-          35,
-          36,
-          37,
-          38,
-          39,
-          40,
-          41,
-          42,
-          43,
-          44,
-          45,
-          46,
-          47,
-          48,
-          49,
-          50,
-          51,
-          52,
-          53,
-          54,
-          55,
-          56,
-          57,
-          58,
-          59,
-          60,
-          61,
-          62,
-          63,
-          64,
-          65,
-          66,
-          67,
-          68,
-          69,
-          70,
-          71,
-          72,
-          73,
-          74,
-          75,
-          76,
-          77,
-          78,
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-          80,
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-          82,
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-          84,
-          85,
-          86,
-          87,
-          88,
-          89,
-          90,
-          91,
-          92,
-          93,
-          94,
-          95,
-          96,
-          97,
-          98,
-          99,
-          100,
-          101,
-          102,
-          103,
-          104,
-          105,
-          106,
-          107,
-          108,
-          109,
-          110,
-          111,
-          112,
-          113,
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-          115,
-          116,
-          117,
-          118,
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-          120,
-          121,
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-          124,
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-          141,
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-          159,
-          160,
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"
      },
      "metadata": {},
      "output_type": "display_data"
@@ -2976,12 +105,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(712, 10, 1) (712, 1, 1)\n"
+      "(3960, 1) (396, 1)\n"
      ]
     }
    ],
    "source": [
-    "# Convert to numpy arrays. Panels contain a train-val-test split by default.\n",
+    "# Convert to numpy arrays\n",
     "x_train, y_train = x.train.values, y.train.values\n",
     "x_test, y_test = x.test.values, y.test.values\n",
     "print(x_train.shape, y_train.shape)"
@@ -2989,59 +118,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
    \n",
-       "\n",
-       "\n",
-       "  \n",
-       "    \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "  \n",
-       "    \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "
    traintestval
    MAE1.3989961.4842631.412011
    \n",
-       "
    "
-      ],
-      "text/plain": [
-       "        train      test       val\n",
-       "MAE  1.398996  1.484263  1.412011"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Or just instantiate a model.\n",
-    "model = wavy.LinearRegression(x, y)\n",
+    "model = models.LinearRegression(x, y)\n",
     "model.score()"
    ]
   }
diff --git a/_static/basic.css b/_static/basic.css
index 9039e02..eeb0519 100644
--- a/_static/basic.css
+++ b/_static/basic.css
@@ -236,7 +236,6 @@ div.body p, div.body dd, div.body li, div.body blockquote {
 a.headerlink {
     visibility: hidden;
 }
-
 a.brackets:before,
 span.brackets > a:before{
     content: "[";
@@ -247,6 +246,7 @@ span.brackets > a:after {
     content: "]";
 }
 
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -334,14 +334,12 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
-
-div.admonition, div.topic, aside.topic, blockquote {
+div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
-
-div.topic, aside.topic {
+div.topic {
     border: 1px solid #ccc;
     padding: 7px;
     margin: 10px 0 10px 0;
@@ -380,7 +378,6 @@ div.body p.centered {
 div.sidebar > :last-child,
 aside.sidebar > :last-child,
 div.topic > :last-child,
-aside.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
 }
@@ -388,7 +385,6 @@ div.admonition > :last-child {
 div.sidebar::after,
 aside.sidebar::after,
 div.topic::after,
-aside.topic::after,
 div.admonition::after,
 blockquote::after {
     display: block;
@@ -612,8 +608,6 @@ ol.simple p,
 ul.simple p {
     margin-bottom: 0;
 }
-
-/* Docutils 0.17 and older (footnotes & citations) */
 dl.footnote > dt,
 dl.citation > dt {
     float: left;
@@ -631,33 +625,6 @@ dl.citation > dd:after {
     clear: both;
 }
 
-/* Docutils 0.18+ (footnotes & citations) */
-aside.footnote > span,
-div.citation > span {
-    float: left;
-}
-aside.footnote > span:last-of-type,
-div.citation > span:last-of-type {
-  padding-right: 0.5em;
-}
-aside.footnote > p {
-  margin-left: 2em;
-}
-div.citation > p {
-  margin-left: 4em;
-}
-aside.footnote > p:last-of-type,
-div.citation > p:last-of-type {
-    margin-bottom: 0em;
-}
-aside.footnote > p:last-of-type:after,
-div.citation > p:last-of-type:after {
-    content: "";
-    clear: both;
-}
-
-/* Footnotes & citations ends */
-
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;
@@ -669,11 +636,11 @@ dl.field-list > dt {
     padding-left: 0.5em;
     padding-right: 5px;
 }
-
 dl.field-list > dt:after {
     content: ":";
 }
 
+
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
index ac8fb22..17fd07e 100644
--- a/_static/documentation_options.js
+++ b/_static/documentation_options.js
@@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = {
     SOURCELINK_SUFFIX: '.txt',
     NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
index ac4d586..f2fb7d5 100644
--- a/_static/searchtools.js
+++ b/_static/searchtools.js
@@ -88,7 +88,7 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
   linkEl.href = linkUrl + "?" + params.toString() + anchor;
   linkEl.innerHTML = title;
   if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
+    listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
   else if (showSearchSummary)
     fetch(requestUrl)
@@ -155,10 +155,8 @@ const Search = {
   _pulse_status: -1,
 
   htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
     const docContent = htmlElement.querySelector('[role="main"]');
     if (docContent !== undefined) return docContent.textContent;
     console.warn(
@@ -504,11 +502,12 @@ const Search = {
    * latter for highlighting it.
    */
   makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
+    const text = Search.htmlToText(htmlText);
     if (text === "") return null;
 
+    const textLower = text.toLowerCase();
     const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
+      .map((k) => textLower.indexOf(k.toLowerCase()))
       .filter((i) => i > -1)
       .slice(-1)[0];
     const startWithContext = Math.max(actualStartPosition - 120, 0);
@@ -516,9 +515,9 @@ const Search = {
     const top = startWithContext === 0 ? "" : "...";
     const tail = startWithContext + 240 < text.length ? "..." : "";
 
-    let summary = document.createElement("div");
+    let summary = document.createElement("p");
     summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
 
     highlightWords.forEach((highlightWord) =>
       _highlightText(summary, highlightWord, "highlighted")
diff --git a/api/models.html b/api/models.html
index c43e76b..34f9036 100644
--- a/api/models.html
+++ b/api/models.html
@@ -25,7 +25,7 @@
     
 
 
@@ -66,6 +66,7 @@
 
 
 
  • Classes
 
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -156,26 +156,29 @@ 
    Classes
 
 
-
    BaselineConstant(x, y[, model_type, loss, ...])
    
-
    
+
    BaseModel(x, y[, model_type, loss, ...])
    
+
    Base class for panel models.
    
 
-
    BaselineShift(x, y[, model_type, loss, ...])
    
-
    
+
    BaselineConstant(x, y[, model_type, loss, ...])
    
+
    Baseline constant model.
    
 
-
    ConvModel(x, y[, model_type, conv_layers, ...])
    
-
    
+
    BaselineShift(x, y[, model_type, loss, ...])
    
+
    Baseline shift model.
    
 
-
    DenseModel(x, y[, model_type, dense_layers, ...])
    
-
    
+
    ConvModel(x, y[, model_type, conv_layers, ...])
    
+
    Convolutional model.
    
 
-
    LinearRegression(x, y, **kwargs)
    
-
    
+
    DenseModel(x, y[, model_type, dense_layers, ...])
    
+
    Dense model.
    
 
-
    LogisticRegression(x, y, **kwargs)
    
-
    
+
    LinearRegression(x, y, **kwargs)
    
+
    Linear regression model.
    
 
-
    ShallowModel(x, y, model, metrics, **kwargs)
    
-
    
+
    LogisticRegression(x, y, **kwargs)
    
+
    Logistic regression model.
    
+
+
    ShallowModel(x, y, model, metrics, **kwargs)
    
+
    Shallow model.
    
 
 
 
diff --git a/api/panel.html b/api/panel.html
index 7f2b5d0..4dfc759 100644
--- a/api/panel.html
+++ b/api/panel.html
@@ -25,7 +25,7 @@
     
 
 
@@ -75,7 +75,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -143,13 +142,13 @@ 
    Functions
    dropna_match(x, y)
    
-
    Drop frames with NaN in both x and y and match ids.
    
+
    Drop frames with NaN in panels and match ids.
    
 
 
    reset_ids(panels[, inplace])
    
 
    Reset ids of a panel.
    
 
 
    set_training_split(x, y[, train_size, ...])
    
-
    Splits the panel in training, validation, and test, accessed with the properties .train, .val and .test.
    
+
    Splits panel in training, validation, and test.
    
 
 
 
@@ -163,7 +162,7 @@ 
    Classes
    Panel(*args, **kw)
    
-
    Panel data structure.
    
+
    Panel class.
    
 
 
 
diff --git a/api/plot.html b/api/plot.html
index c535c8f..d7f3f1b 100644
--- a/api/plot.html
+++ b/api/plot.html
@@ -25,7 +25,7 @@
     
 
 
@@ -71,7 +71,6 @@
 
 
 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -133,7 +132,7 @@ 
    Functions
    plot(panel[, use_timestep, add_annotation])
    
-
    Plot a panel.
    
+
    Plot panel.
    
 
 
 
@@ -147,7 +146,7 @@ 
    Classes
    PanelFigure()
    
-
    
+
    PanelFigure class.
    
 
 
 
diff --git a/api/utils.html b/api/utils.html
deleted file mode 100644
index 9e0d156..0000000
--- a/api/utils.html
+++ /dev/null
@@ -1,174 +0,0 @@
-
-
-
-  
-
-  
-  wavy.utils Module — Wavy 0.1.9 documentation
-      
-      
-      
-  
-  
-        
-        
-        
-        
-        
-        
-    
-    
-    
-    
-    
-
-
-
-
- 
-  
    
-    
-
-    
    
-
-      
    
-        
    
-          
    
-  
-  
    
-
    
-          
    
-           
    
-             
-  
-
-
    
-
    wavy.utils Module
    
-
    
-
    Functions
    
-
----
-
-
-
-
-
-
-
-
-
    



    reverse_diff(diff_df, original_df[, periods])
    Reverse the pct_diff function.
    reverse_pct_change(change_df, original_df[, ...])
    Reverse the pct_change function.
    
-
    
-
    
-
-
-           
    
-          
    
-          
-        
    
-      
    
-    
    
-  
    
-   
-
-
-
\ No newline at end of file
diff --git a/api/wavy.models.BaseModel.html b/api/wavy.models.BaseModel.html
new file mode 100644
index 0000000..a409c40
--- /dev/null
+++ b/api/wavy.models.BaseModel.html
@@ -0,0 +1,310 @@
+
+
+
+  
+
+  
+  BaseModel — Wavy 0.1.9 documentation
+      
+      
+      
+  
+  
+        
+        
+        
+        
+        
+        
+    
+    
+    
+    
+    
+
+
+
+
+ 
+  
    
+    
+
+    
    
+
+      
    
+        
    
+          
    
+  
+  
    
+
    
+          
    
+           
    
+             
+  
+
+
    
+
    BaseModel
    
+
    
+
    
+class BaseModel(x: Panel, y: Panel, model_type: str = None, loss: str = None, optimizer: str = None, metrics: list[str] = None, last_activation: str = None)[source]
    
+
    Bases: object
    
+
    Base class for panel models.
    
+
    Methods Summary
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
+
+
+
+
    



    build()
    Build the model.
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    Set the arrays.
    
+
    Methods Documentation
    
+
    
+
    
+build() → None[source]
    
+
    Build the model.
    
+
    
+
+
    
+
    
+compile(**kwargs) → None[source]
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None[source]
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float[source]
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel[source]
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel[source]
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel[source]
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame[source]
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
+
    
+
    
+set_arrays() → None[source]
    
+
    Set the arrays.
    
+
    
+
+
    
+
+
    
+
+
+           
    
+          
    
+          
+        
    
+      
    
+    
    
+  
    
+   
+
+
+
\ No newline at end of file
diff --git a/api/wavy.models.BaselineConstant.html b/api/wavy.models.BaselineConstant.html
index 37bcdfe..8909674 100644
--- a/api/wavy.models.BaselineConstant.html
+++ b/api/wavy.models.BaselineConstant.html
@@ -22,10 +22,10 @@
     
     
     
-    
+    
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -130,8 +130,9 @@
 
    BaselineConstant
    
 
    
 
    
-class BaselineConstant(x, y, model_type: Optional[str] = None, loss: Optional[str] = None, metrics: Optional[List[str]] = None, constant: float = 0)[source]
    
+class BaselineConstant(x, y, model_type: str = None, loss: str = None, metrics: list[str] = None, constant: float = 0)[source]
 
    Bases: _Baseline
    
+
    Baseline constant model.
    
 
    Methods Summary
    
 @@ -139,15 +140,135 @@ 
    BaselineConstant
 
 
    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
 

    build()
    Build the model.
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    
 
    set_arrays()
    		
 
    Set the arrays.
    		
 
    
     
 
    
 
    Methods Documentation
    
+
    
+
    
+build() → None
    
+
    Build the model.
    
+
    
+
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
 
    
 
    
-set_arrays()[source]
    
+set_arrays() → None[source]
 
    Set the arrays.
    
 
    
 
@@ -159,7 +280,7 @@ 
    BaselineConstant
-        
+        
         
     
    
 
diff --git a/api/wavy.models.BaselineShift.html b/api/wavy.models.BaselineShift.html
index aed841d..767735c 100644
--- a/api/wavy.models.BaselineShift.html
+++ b/api/wavy.models.BaselineShift.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -130,8 +130,9 @@
 
    BaselineShift
    
 
    
 
    
-class BaselineShift(x, y, model_type: Optional[str] = None, loss: Optional[str] = None, metrics: Optional[List[str]] = None, fillna=0, shift=1)[source]
    
+class BaselineShift(x, y, model_type: str = None, loss: str = None, metrics: list[str] = None, fillna=0, shift=1)[source]
 
    Bases: _Baseline
    
+
    Baseline shift model.
    
 
    Methods Summary
    
 @@ -142,7 +143,28 @@ 
    BaselineShift
    
-
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
@@ -150,10 +172,100 @@ 
    BaselineShiftMethods Documentation
    
 
    
 
    
-build()[source]
    
+build() → None[source]
 
    Build the model.
    
 
    
 
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
 
    
 
    
 set_arrays()[source]
    
diff --git a/api/wavy.models.ConvModel.html b/api/wavy.models.ConvModel.html
index 4e0bf28..9c1a68b 100644
--- a/api/wavy.models.ConvModel.html
+++ b/api/wavy.models.ConvModel.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
@@ -130,8 +130,9 @@
 
    ConvModel
    
 
    
 
    
-class ConvModel(x, y, model_type: Optional[str] = None, conv_layers: int = 1, conv_filters: int = 32, kernel_size: int = 3, dense_layers: int = 1, dense_units: int = 32, activation: str = 'relu', loss: Optional[str] = None, optimizer: Optional[str] = None, metrics: Optional[List[str]] = None, last_activation: Optional[str] = None)[source]
    
-
    Bases: _BaseModel
    
+class ConvModel(x: Panel, y: Panel, model_type: str = None, conv_layers: int = 1, conv_filters: int = 32, kernel_size: int = 3, dense_layers: int = 1, dense_units: int = 32, activation: str = 'relu', loss: str = None, optimizer: str = None, metrics: list[str] = None, last_activation: str = None)[source]
+
    Bases: BaseModel
    
+
    Convolutional model.
    
 
    Methods Summary
    
 
    • 
 

    build()
    		
 
    Build the model.
    		
 
    set_arrays()
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    		
 
    Set the arrays.
    		
 
    
 
    @@ -142,15 +143,135 @@ 
    ConvModel
    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
 

    build()
    		
 
    Build the model.
    		
 
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    Set the arrays.
    
     
 
    
 
    Methods Documentation
    
 
    
 
    
-build()[source]
    
+build() → None[source]
 
    Build the model.
    
 
    
 
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
+
    
+
    
+set_arrays() → None
    
+
    Set the arrays.
    
+
    
+
 
    
 
 
diff --git a/api/wavy.models.DenseModel.html b/api/wavy.models.DenseModel.html
index 6ed94ee..f406392 100644
--- a/api/wavy.models.DenseModel.html
+++ b/api/wavy.models.DenseModel.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -130,8 +130,9 @@
 
    DenseModel
    
 
    
 
    
-class DenseModel(x, y, model_type: Optional[str] = None, dense_layers: int = 1, dense_units: int = 32, activation: str = 'relu', loss: Optional[str] = None, optimizer: Optional[str] = None, metrics: Optional[List[str]] = None, last_activation: Optional[str] = None)[source]
    
-
    Bases: _BaseModel
    
+class DenseModel(x, y, model_type: str = None, dense_layers: int = 1, dense_units: int = 32, activation: str = 'relu', loss: str = None, optimizer: str = None, metrics: list[str] = None, last_activation: str = None)[source]
+
    Bases: BaseModel
    
+
    Dense model.
    
 
    Methods Summary
    
 @@ -142,15 +143,135 @@ 
    DenseModel
    
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    
 

    build()
    		
 
    Build the model.
    		
 
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    Set the arrays.
    
     
 
    
 
    Methods Documentation
    
 
    
 
    
-build()[source]
    
+build() → None[source]
 
    Build the model.
    
 
    
 
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
+
    
+
    
+set_arrays() → None
    
+
    Set the arrays.
    
+
    
+
 
    
 
 
diff --git a/api/wavy.models.LinearRegression.html b/api/wavy.models.LinearRegression.html
index fe89d77..ded346a 100644
--- a/api/wavy.models.LinearRegression.html
+++ b/api/wavy.models.LinearRegression.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -132,6 +132,146 @@ 
    LinearRegression
 class LinearRegression(x, y, **kwargs)[source]
 
    Bases: DenseModel
    
+
    Linear regression model.
    
+
    Methods Summary
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
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+
    



    build()
    Build the model.
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    Set the arrays.
    
+
    Methods Documentation
    
+
    
+
    
+build() → None
    
+
    Build the model.
    
+
    
+
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
+
    
+
    
+set_arrays() → None
    
+
    Set the arrays.
    
+
    
+
 
    
 
 
diff --git a/api/wavy.models.LogisticRegression.html b/api/wavy.models.LogisticRegression.html
index 5db45cc..77507d0 100644
--- a/api/wavy.models.LogisticRegression.html
+++ b/api/wavy.models.LogisticRegression.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -132,6 +132,146 @@ 
    LogisticRegression
 class LogisticRegression(x, y, **kwargs)[source]
 
    Bases: DenseModel
    
+
    Logistic regression model.
    
+
    Methods Summary
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
+
    



    build()
    Build the model.
    compile(**kwargs)
    Compile the model.
    fit(**kwargs)
    Fit the model.
    get_auc()
    Get the AUC score.
    predict([data])
    Predict.
    predict_proba([data])
    Predict probabilities.
    residuals()
    Residuals.
    score([on])
    Score the model.
    set_arrays()
    Set the arrays.
    
+
    Methods Documentation
    
+
    
+
    
+build() → None
    
+
    Build the model.
    
+
    
+
+
    
+
    
+compile(**kwargs) → None
    
+
    Compile the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the compile method.
    
+
    
+
    
+
    
+
+
    
+
    
+fit(**kwargs) → None
    
+
    Fit the model.
    
+
    
+
    Parameters
    
+
    **kwargs – Additional arguments to pass to the fit method.
    
+
    
+
    
+
    
+
+
    
+
    
+get_auc() → float
    
+
    Get the AUC score.
    
+
    
+
+
    
+
    
+predict(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted values.
    
+
    
+
    
+
    
+
+
    
+
    
+predict_proba(data: Optional[Panel] = None, **kwargs) → Panel
    
+
    Predict probabilities.
    
+
    
+
    Parameters
    
+
      
+
    • data (Panel) – Panel of data to predict.
      • 
+
    • **kwargs – Additional arguments to pass to the predict method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of predicted probabilities.
    
+
    
+
    
+
    
+
+
    
+
    
+residuals() → Panel
    
+
    Residuals.
    
+
    
+
    Returns
    
+
    Panel of residuals.
    
+
    
+
    
+
    
+
+
    
+
    
+score(on: list[str] | str = None, **kwargs) → pd.DataFrame
    
+
    Score the model.
    
+
    
+
    Parameters
    
+
      
+
    • on (list[str] or str) – Columns to score on.
      • 
+
    • **kwargs – Additional arguments to pass to the score method.
      • 
+
    
+
    
+
    Returns
    
+
    Panel of scores.
    
+
    
+
    
+
    
+
+
    
+
    
+set_arrays() → None
    
+
    Set the arrays.
    
+
    
+
 
    
 
 
diff --git a/api/wavy.models.ShallowModel.html b/api/wavy.models.ShallowModel.html
index 10ef169..be57d2d 100644
--- a/api/wavy.models.ShallowModel.html
+++ b/api/wavy.models.ShallowModel.html
@@ -25,7 +25,7 @@
     
 
 
@@ -62,6 +62,7 @@
 
  • wavy.models Module
 
         
    • 
@@ -130,8 +130,9 @@
 
    ShallowModel
    
 
    
 
    
-class ShallowModel(x, y, model, metrics, **kwargs)[source]
    
+class ShallowModel(x: Panel, y: Panel, model: str, metrics: list[str], **kwargs)[source]
 
    Bases: object
    
+
    Shallow model.
    
 
    Methods Summary
    
 @@ -171,45 +172,39 @@ 
    ShallowModelParameters
 
    **kwargs – Keyword arguments for the fit method of the model.
    
 
    
-
    Returns
    
-
    The fitted model.
    
-
    
-
    Return type
    
-
    ShallowModel
    
-
    
 
 
 
 
    
 
    
-get_auc()[source]
    
+get_auc() → float[source]
 
    Get the AUC score.
    
 
    
 
 
    
 
    
-predict(data: Optional[Panel] = None)[source]
    
+predict(data: Optional[Panel] = None) → Panel[source]
 
    Predict on data.
    
 
    
 
    Parameters
    
-
    data (Panel, optional) – Data to predict on. Defaults to None.
    
+
    data (Panel, optional) – Data to predict on. Defaults to None.
    
 
    
 
    Returns
    
 
    Predicted data
    
 
    
 
    Return type
    
-
    Panel
    
+
    Panel
    
 
    
 
    
 
    
 
 
    
 
    
-predict_proba(data: Optional[Panel] = None)[source]
    
+predict_proba(data: Optional[Panel] = None) → Panel[source]
 
    Predict probabilities.
    
 
    
 
    Parameters
    
-
    data (Panel) – Panel with data
    
+
    data (Panel) – Panel with data
    
 
    
 
    Returns
    
 
    The predicted probabilities.
    
@@ -222,38 +217,38 @@ 
    ShallowModel
 
    
-residuals()[source]
    
+residuals() → Panel[source]
 
    Residuals.
    
 
    
 
    Returns
    
 
    Residuals
    
 
    
 
    Return type
    
-
    Panel
    
+
    Panel
    
 
    
 
    
 
    
 
 
    
 
    
-score(on=None)[source]
    
+score(on: list[str] | str = None) → pd.DataFrame[source]
 
    Score the model.
    
 
    
 
    Parameters
    
-
    on (str) – Data to use for scoring
    
+
    on (list[str] or str) – Data to use for scoring
    
 
    
 
    Returns
    
 
    Score
    
 
    
 
    Return type
    
-
    pd.Series
    
+
    pd.Series
    
 
    
 
    
 
    
 
 
    
 
    
-set_arrays()[source]
    
+set_arrays() → None[source]
 
    Sets arrays for training, testing, and validation.
    
 
    
 
diff --git a/api/wavy.models.compute_default_scores.html b/api/wavy.models.compute_default_scores.html
index 3ffd466..1bbc273 100644
--- a/api/wavy.models.compute_default_scores.html
+++ b/api/wavy.models.compute_default_scores.html
@@ -25,7 +25,7 @@
     
 
 
@@ -69,7 +69,6 @@
 
 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -130,11 +129,11 @@ 
    compute_default_scores
 
    Parameters
    
 
      
-
    • x (Panel) – X data
      • 
-
    • y (Panel) – Y data
      • 
-
    • model_type (str) – Model type
      • 
-
    • epochs (int, optional) – Number of epochs. Defaults to 10.
      • 
-
    • verbose (int, optional) – Verbosity. Defaults to 0.
      • 
+
    • x (Panel) – X data.
      • 
+
    • y (Panel) – Y data.
      • 
+
    • model_type (str) – Model type.
      • 
+
    • epochs (int, optional) – Number of epochs. Defaults to 10.
      • 
+
    • verbose (int, optional) – Verbosity. Defaults to 0.
      • 
 
    • **kwargs – Keyword arguments for the model.
      • 
 
    
 
    
diff --git a/api/wavy.models.compute_score_per_model.html b/api/wavy.models.compute_score_per_model.html
index 821b0b4..cc15200 100644
--- a/api/wavy.models.compute_score_per_model.html
+++ b/api/wavy.models.compute_score_per_model.html
@@ -21,11 +21,11 @@
     
     
     
-    
+    
     
 
 
@@ -69,7 +69,6 @@
 
 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -131,7 +130,7 @@ 
    compute_score_per_modelParameters
 
      
 
    • *models – Models to score
      • 
-
    • on (str, optional) – Data to use for scoring. Defaults to “val”.
      • 
+
    • on (str, optional) – Data to use for scoring. Defaults to “val”.
      • 
 
    
 
    
 
    Returns
    
@@ -150,7 +149,7 @@ 
    compute_score_per_model
           
    
 
   
    
diff --git a/api/wavy.panel.Panel.html b/api/wavy.panel.Panel.html
index accf546..258deab 100644
--- a/api/wavy.panel.Panel.html
+++ b/api/wavy.panel.Panel.html
@@ -25,7 +25,7 @@
     
 
 
@@ -68,7 +68,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -126,7 +125,7 @@ 
    Panel
 
    
 class Panel(*args, **kw)[source]
    
 
    Bases: DataFrame
    
-
    Panel data structure.
    
+
    Panel class.
    
 
    Attributes Summary
    
 
    
 

    @@ -135,10 +134,10 @@ 
    Panel
 
 
    
-
+
-
+
@@ -150,19 +149,19 @@ 
    Panel
 
    
-
+
-
+
-
+
-
+
-
+
@@ -201,7 +200,7 @@ 
    Panel
 
    
-
+
@@ -210,7 +209,7 @@ 
    Panel
 
    
-
+
@@ -230,13 +229,13 @@ 
    Panel
 
    
 
    
 frames
    
-
    Returns the frames in the panel.
    
+
    Returns panel’s frames.
    
 
    
 
 
    
 
    
 ids
    
-
    Returns the ids of the panel without duplicates.
    
+
    Returns panel’s ids without duplicates.
    
 
    
 
 
    
@@ -260,14 +259,13 @@ 
    Panel
 
    
 
    
 shape_panel
    
-
    Returns the shape of the panel.
    
+
    Return a tuple representing the dimensionality of the Panel.
    
 
    
 
 
    
 
    
 test
    
-
    Returns the Panel with the testing set, according to
-the parameters given in the ‘set_training_split’ function.
    
+
    Returns the Panel with the testing set.
    
 
    
 
    Returns
    
 
    Panel with the testing set.
    
@@ -281,14 +279,13 @@ 
    Panel
 
    
 
    
 timesteps
    
-
    Returns the ids of the panel.
    
+
    Returns panel’s timesteps.
    
 
    
 
 
    
 
    
 train
    
-
    Returns the Panel with the training set, according to
-the parameters given in the ‘set_training_split’ function.
    
+
    Returns the Panel with the training set.
    
 
    
 
    Returns
    
 
    Panel with the training set.
    
@@ -302,8 +299,7 @@ 
    Panel
 
    
 
    
 val
    
-
    Returns the Panel with the validation set, according to
-the parameters given in the ‘set_training_split’ function.
    
+
    Returns the Panel with the validation set.
    
 
    
 
    Returns
    
 
    Panel with the validation set.
    
@@ -335,13 +331,13 @@ 
    Panel
 
    Methods Documentation
    
 
    
 
    
-drop_ids(ids: Union[list, int], inplace: bool = False) → Optional[Panel][source]
    
+drop_ids(ids: list[int] | int, inplace: bool = False) → Panel | None[source]
 
    Drop frames by id.
    
 
    
 
    Parameters
    
 
      
-
    • ids (list, int) – List of ids to drop.
      • 
-
    • inplace (bool) – Whether to drop ids inplace.
      • 
+
    • ids (list[int] or int) – List of ids to drop.
      • 
+
    • inplace (bool) – Whether to drop ids inplace.
      • 
 
    
 
    
 
    Returns
    
@@ -355,11 +351,11 @@ 
    Panel
 
 
    
 
    
-dropna_frames(inplace: bool = False) → Optional[Panel][source]
    
+dropna_frames(inplace: bool = False) → Panel | None[source]
 
    Drop frames with missing values from the panel.
    
 
    
 
    Parameters
    
-
    inplace (bool) – Whether to drop frames inplace.
    
+
    inplace (bool) – Whether to drop frames inplace.
    
 
    
 
    Returns
    
 
    Panel with frames dropped.
    
@@ -392,11 +388,11 @@ 
    Panel
 
 
    
 
    
-get_timesteps(n: Union[list, int] = 0) → Panel[source]
    
+get_timesteps(n: list[int] | int = 0) → Panel[source]
 
    Returns the first timestep of each frame in the panel.
    
 
    
 
    Parameters
    
-
    n (int) – Timestep to return.
    
+
    n (list[int] or int) – Timestep to return.
    
 
    
 
    
 
    
@@ -407,7 +403,7 @@ 
    Panel
 
    Return the first n frames of the panel.
    
 
    
 
    Parameters
    
-
    n (int) – Number of frames to return.
    
+
    n (int) – Number of frames to return.
    
 
    
 
    Returns
    
 
    Result of head function.
    
@@ -420,14 +416,14 @@ 
    Panel
 
 
    
 
    
-match_frames(other: Panel, inplace: bool = False) → Optional[Panel][source]
    
-
    Match panel with other panel. This function will match the ids and id
-order of self based on the ids of other.
    
+match_frames(other: Panel, inplace: bool = False) → Panel | None[source]
+
    Match panel with other panel.
    
+
    This function will match the ids and id order of self based on the ids of other.
    
 
    
 
    Parameters
    
 
      
 
    • other (Panel) – Panel to match with.
      • 
-
    • inplace (bool) – Whether to match inplace.
      • 
+
    • inplace (bool) – Whether to match inplace.
      • 
 
    
 
    
 
    Returns
    
@@ -446,9 +442,9 @@ 
    Panel
 
    
 
    Parameters
    
 
      
-
    • add_annotation (bool) – If True, plot the training, validation, and test annotation.
      • 
-
    • max (int) – Maximum number of samples to plot.
      • 
-
    • use_timestep (bool) – If True, plot the timestep instead of the sample index.
      • 
+
    • add_annotation (bool) – If True, plot the training, validation, and test annotation.
      • 
+
    • max (int) – Maximum number of samples to plot.
      • 
+
    • use_timestep (bool) – If True, plot the timestep instead of the sample index.
      • 
 
    • **kwargs – Additional arguments to pass to the plot function.
      • 
 
    
 
    
@@ -463,32 +459,37 @@ 
    Panel
 
 
    
 
    
-reset_ids(inplace: bool = False) → Optional[Panel][source]
    
-
    Reset the ids of the panel.
    
+reset_ids(inplace: bool = False) → Panel | None[source]
+
    Reset panel’s ids.
    
 
    
 
    Parameters
    
-
    inplace (bool) – Whether to reset ids inplace.
    
+
    inplace (bool) – Whether to reset ids inplace.
    
 
    
 
    
 
    
 
 
    
 
    
-row_panel(n: Union[list, int] = 0) → Panel[source]
    
+row_panel(n: list[int] | int = 0) → Panel[source]
 
    Returns the nth row of each frame.
    
+
    
+
    Parameters
    
+
    n (list[int] or int) – Row index.
    
+
    
+
    
 
    
 
 
    
 
    
-sample_panel(samples: Union[int, float] = 5, how: str = 'spaced', reset_ids: bool = False, seed: int = 42) → Optional[Panel][source]
    
+sample_panel(samples: int | float = 5, how: str = 'spaced', reset_ids: bool = False, seed: int = 42) → Panel | None[source]
 
    Sample panel returning a subset of frames.
    
 
    
 
    Parameters
    
 
      
-
    • samples (int or float) – Number or percentage of samples to return.
      • 
-
    • how (str) – Sampling method, ‘spaced’ or ‘random’
      • 
-
    • reset_ids (bool) – If True, reset the index of the sampled panel.
      • 
-
    • seed (int) – Random seed.
      • 
+
    • samples (int or float) – Number or percentage of samples to return.
      • 
+
    • how (str) – Sampling method, ‘spaced’ or ‘random’
      • 
+
    • reset_ids (bool) – If True, reset the index of the sampled panel.
      • 
+
    • seed (int) – Random seed.
      • 
 
    
 
    
 
    Returns
    
@@ -502,31 +503,32 @@ 
    Panel
 
 
    
 
    
-set_training_split(train_size: Union[float, int] = 0.7, val_size: Union[float, int] = 0.2, test_size: Union[float, int] = 0.1) → None[source]
    
-
    Splits the panel in training, validation, and test, accessed with the
-properties .train, .val and .test.
    
+set_training_split(train_size: float | int = 0.7, val_size: float | int = 0.2, test_size: float | int = 0.1) → None[source]
+
    Splits Panel into training, validation, and test.
    
 
    
 
    Parameters
    
 
      
-
    • train_size (float, int) – Fraction of data to use for training.
      • 
-
    • test_size (float, int) – Fraction of data to use for testing.
      • 
-
    • val_size (float, int) – Fraction of data to use for validation.
      • 
+
    • train_size (float or int) – Fraction of data to use for training.
      • 
+
    • test_size (float or int) – Fraction of data to use for testing.
      • 
+
    • val_size (float or int) – Fraction of data to use for validation.
      • 
 
    
 
    
 
    
-
    Example:
->>> panel.set_training_split(val_size=0.2, test_size=0.1)
    
+
    Example:
    
+
    >>> panel.set_training_split(train_size=0.8, val_size=0.2, test_size=0.1)
+
    
+
    
 
    
 
 
    
 
    
-shuffle_panel(seed: Optional[int] = None, reset_ids: bool = False) → Optional[Panel][source]
    
+shuffle_panel(seed: int = None, reset_ids: bool = False) → Panel | None[source]
 
    Shuffle the panel.
    
 
    
 
    Parameters
    
 
      
-
    • seed (int) – Random seed.
      • 
-
    • reset_ids (bool) – If True, reset the index of the shuffled panel.
      • 
+
    • seed (int) – Random seed.
      • 
+
    • reset_ids (bool) – If True, reset the index of the shuffled panel.
      • 
 
    
 
    
 
    Returns
    
@@ -540,13 +542,13 @@ 
    Panel
 
 
    
 
    
-sort_panel(ascending: bool = True, inplace: bool = False, kind: str = 'quicksort', key: Optional[callable] = None) → Optional[Panel][source]
    
+sort_panel(ascending: bool = True, inplace: bool = False, kind: str = 'quicksort', key: callable = None) → Panel | None[source]
 
    Sort panel by ids.
    
 
    
 
    Parameters
    
 
      
-
    • ascending (bool or list-like of bools, default True) – Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
      • 
-
    • inplace (bool, default False) – If True, perform operation in-place.
      • 
+
    • ascending (bool or list-like of bools, default True) – Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
      • 
+
    • inplace (bool, default False) – If True, perform operation in-place.
      • 
 
    • kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, default 'quicksort') – Choice of sorting algorithm. See also numpy.sort() for more information. mergesort and stable are the only stable algorithms. For DataFrames, this option is only applied when sorting on a single column or label.
      • 
 
    • key (callable, optional) – If not None, apply the key function to the index values before sorting. This is similar to the key argument in the builtin sorted() function, with the notable difference that this key function should be vectorized. It should expect an Index and return an Index of the same shape. For MultiIndex inputs, the key is applied per level.
      • 
 
    
@@ -555,7 +557,7 @@ 
    Panel
 
    The original DataFrame sorted by the labels or None if inplace=True.
    
 
    
 
    Return type
    
-
    Panel or None
    
+
    Panel or None
    
 
    
 
    
 
    
@@ -566,7 +568,7 @@ 
    Panel
 
    Return the last n frames of the panel.
    
 
    
 
    Parameters
    
-
    n (int) – Number of frames to return.
    
+
    n (int) – Number of frames to return.
    
 
    
 
    Returns
    
 
    Result of tail function.
    
diff --git a/api/wavy.panel.concat_panels.html b/api/wavy.panel.concat_panels.html
index 03cc718..e643c22 100644
--- a/api/wavy.panel.concat_panels.html
+++ b/api/wavy.panel.concat_panels.html
@@ -25,7 +25,7 @@
     
 
 
@@ -72,7 +72,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -133,9 +132,9 @@ 
    concat_panels
 
    Parameters
    
 
      
-
    • panels (list) – List of panels
      • 
-
    • reset_ids (bool) – Whether to reset ids
      • 
-
    • sort (bool) – Whether to sort by id
      • 
+
    • panels (list) – List of panels
      • 
+
    • reset_ids (bool) – Whether to reset ids
      • 
+
    • sort (bool) – Whether to sort by id
      • 
 
    
 
    
 
    Returns
    
diff --git a/api/wavy.panel.create_panels.html b/api/wavy.panel.create_panels.html
index b8e9777..35c75ae 100644
--- a/api/wavy.panel.create_panels.html
+++ b/api/wavy.panel.create_panels.html
@@ -25,7 +25,7 @@
     
 
 
@@ -72,7 +72,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -128,22 +127,22 @@
 
    create_panels
    
 
    
 
    
-create_panels(df: DataFrame, lookback: int, horizon: int, gap: int = 0) → Tuple[Panel, Panel][source]
    
+create_panels(df: pd.DataFrame, lookback: int, horizon: int, gap: int = 0) → tuple[Panel, Panel][source]
 
    Create panels from a dataframe.
    
 
    
 
    Parameters
    
 
      
-
    • df (pd.DataFrame) – Dataframe
      • 
-
    • lookback (int) – Lookback size
      • 
-
    • horizon (int) – Horizon size
      • 
-
    • gap (int) – Gap size
      • 
+
    • df (pd.DataFrame) – Dataframe
      • 
+
    • lookback (int) – Lookback size
      • 
+
    • horizon (int) – Horizon size
      • 
+
    • gap (int) – Gap size
      • 
 
    
 
    
 
    Returns
    
 
    Tuple of panels
    
 
    
 
    Return type
    
-
    Tuple
    
+
    tuple[Panel, Panel]
    
 
    
 
    
 
    
diff --git a/api/wavy.panel.dropna_match.html b/api/wavy.panel.dropna_match.html
index 0302b77..1f2cda8 100644
--- a/api/wavy.panel.dropna_match.html
+++ b/api/wavy.panel.dropna_match.html
@@ -25,7 +25,7 @@
     
 
 
@@ -72,7 +72,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -129,12 +128,12 @@ 
    dropna_match
 
    
 dropna_match(x, y)[source]
    
-
    Drop frames with NaN in both x and y and match ids.
    
+
    Drop frames with NaN in panels and match ids.
    
 
    
 
    Parameters
    
 
      
-
    • x (Panel) – Panel with x data
      • 
-
    • y (Panel) – Panel with y data
      • 
+
    • x (Panel) – Panel with x data
      • 
+
    • y (Panel) – Panel with y data
      • 
 
    
 
    
 
    Returns
    
diff --git a/api/wavy.panel.reset_ids.html b/api/wavy.panel.reset_ids.html
index 54aba83..caf5c63 100644
--- a/api/wavy.panel.reset_ids.html
+++ b/api/wavy.panel.reset_ids.html
@@ -25,7 +25,7 @@
     
 
 
@@ -72,7 +72,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -133,8 +132,8 @@ 
    reset_ids
 
    Parameters
    
 
      
-
    • panels (list) – List of panels
      • 
-
    • inplace (bool) – Whether to reset ids inplace or not.
      • 
+
    • panels (list) – List of panels
      • 
+
    • inplace (bool) – Whether to reset ids inplace or not.
      • 
 
    
 
    
 
    Returns
    
diff --git a/api/wavy.panel.set_training_split.html b/api/wavy.panel.set_training_split.html
index 6ea22bc..236633c 100644
--- a/api/wavy.panel.set_training_split.html
+++ b/api/wavy.panel.set_training_split.html
@@ -25,7 +25,7 @@
     
 
 
@@ -72,7 +72,6 @@
 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -128,20 +127,21 @@
 
    set_training_split
    
 
    
 
    
-set_training_split(x: Panel, y: Panel, train_size: Union[float, int] = 0.7, val_size: Union[float, int] = 0.2, test_size: Union[float, int] = 0.1) → None[source]
    
-
    Splits the panel in training, validation, and test, accessed with the
-properties .train, .val and .test.
    
+set_training_split(x: Panel, y: Panel, train_size: float | int = 0.7, val_size: float | int = 0.2, test_size: float | int = 0.1) → None[source]
+
    Splits panel in training, validation, and test.
    
 
    
 
    Parameters
    
 
      
-
    • train_size (float, int) – Fraction of data to use for training.
      • 
-
    • test_size (float, int) – Fraction of data to use for testing.
      • 
-
    • val_size (float, int) – Fraction of data to use for validation.
      • 
+
    • train_size (float or int) – Fraction of data to use for training.
      • 
+
    • test_size (float or int) – Fraction of data to use for testing.
      • 
+
    • val_size (float or int) – Fraction of data to use for validation.
      • 
 
    
 
    
 
    
-
    Example:
->>> x,y = set_training_split(x, y, val_size=0.2, test_size=0.1)
    
+
    Example:
    
+
    >>> x, y = set_training_split(x, y, train_size=0.8, val_size=0.2, test_size=0.1)
+
    
+
    
 
    
 
 
diff --git a/api/wavy.plot.PanelFigure.html b/api/wavy.plot.PanelFigure.html
index 9fafb37..8cc9eab 100644
--- a/api/wavy.plot.PanelFigure.html
+++ b/api/wavy.plot.PanelFigure.html
@@ -21,11 +21,10 @@
     
     
     
-    
     
 
 
@@ -68,7 +67,6 @@
 
 
 
-
  • wavy.utils Module
    • 
 
 
         
@@ -126,6 +124,7 @@ 
    PanelFigure
 class PanelFigure[source]
 
    Bases: Figure
    
+
    PanelFigure class.
    
 
    Methods Summary
    
 
    
 

 
    frames
    Returns the frames in the panel.
    Returns panel's frames.
    		
 
    
 
    ids
    Returns the ids of the panel without duplicates.
    Returns panel's ids without duplicates.
    		
 
    
 
    num_columns
    		
 
    Returns the number of columns in the panel.
    Returns the number of timesteps in the panel.
    		
 
    
 
    shape_panel
    Returns the shape of the panel.
    Return a tuple representing the dimensionality of the Panel.
    		
 
    
 
    test
    Returns the Panel with the testing set, according to the parameters given in the 'set_training_split' function.
    Returns the Panel with the testing set.
    		
 
    
 
    timesteps
    Returns the ids of the panel.
    Returns panel's timesteps.
    		
 
    
 
    train
    Returns the Panel with the training set, according to the parameters given in the 'set_training_split' function.
    Returns the Panel with the training set.
    		
 
    
 
    val
    Returns the Panel with the validation set, according to the parameters given in the 'set_training_split' function.
    Returns the Panel with the validation set.
    		
 
    
 
    values_panel
    		
 
    3D matrix with Panel value.
    Plot the panel.
    		
 
    
 
    reset_ids([inplace])
    Reset the ids of the panel.
    Reset panel's ids.
    		
 
    
 
    row_panel([n])
    		
 
    Returns the nth row of each frame.
    Sample panel returning a subset of frames.
    		
 
    
 
    set_training_split([train_size, val_size, ...])
    Splits the panel in training, validation, and test, accessed with the properties .train, .val and .test.
    Splits Panel into training, validation, and test.
    		
 
    
 
    shuffle_panel([seed, reset_ids])
    		
 
    Shuffle the panel.
    @@ -134,39 +133,36 @@ 
    PanelFigure
    
-
+
-
-
+
+
-
-
+
+
-
-
+
+
-
-
+
+
-
-
-
-
-
+
+
    
 

    add_annotation(panel[, color, opacity])
    Split panel into sets.
    Plot vertical lines showing train, val, and test periods.
    		
 
    add_area(*args, **kwargs)
    add_area(col, *args, **kwargs)
    Add an area to the figure.
    		
 
    add_bar(*args, **kwargs)
    add_bar(col, *args, **kwargs)
    Add a bar to the figure.
    		
 
    add_dotline(*args, **kwargs)
    add_dotline(col, *args, **kwargs)
    Add a dotline to the figure.
    		
 
    add_line(*args, **kwargs)
    add_line(col, *args, **kwargs)
    Add a line to the figure.
    		
 
    add_scatter(*args, **kwargs)
    iterator()
    add_scatter(col, *args, **kwargs)
    Add a scatter to the figure.
    		
 
    
     
 
    
 
    Methods Documentation
    
 
    
 
    
-add_annotation(panel, color='gray', opacity=1)[source]
    
-
    Split panel into sets.
    
+add_annotation(panel, color: str = 'gray', opacity: float = 1.0) → None[source]
+
    Plot vertical lines showing train, val, and test periods.
    
 
    
 
    Parameters
    
 
      
-
    • panel (wavy.Panel) – Panel to split
      • 
-
    • color (str) – Color of the sets
      • 
-
    • opacity (float) – Opacity of the sets
      • 
+
    • panel (Panel) – Panel to split.
      • 
+
    • color (str) – Color of the sets.
      • 
+
    • opacity (float) – Opacity of the sets.
      • 
 
    
 
    
 
    
@@ -174,33 +170,58 @@ 
    PanelFigure
 
    
-add_area(*args, **kwargs)[source]
    
-
    
+add_area(col: str, *args, **kwargs) → None[source]
+
    Add an area to the figure.
    
+
    
+
    Parameters
    
+
    col (str) – Column to plot
    
+
    
+
    
+
    
 
 
    
 
    
-add_bar(*args, **kwargs)[source]
    
-
    
+add_bar(col: str, *args, **kwargs) → None[source]
+
    Add a bar to the figure.
    
+
    
+
    Parameters
    
+
    col (str) – Column to plot
    
+
    
+
    
+
    
 
 
    
 
    
-add_dotline(*args, **kwargs)[source]
    
-
    
+add_dotline(col: str, *args, **kwargs) → None[source]
+
    Add a dotline to the figure.
    
+
    
+
    Parameters
    
+
    col (str) – Column to plot.
    
+
    
+
    
+
    
 
 
    
 
    
-add_line(*args, **kwargs)[source]
    
-
    
+add_line(col: str, *args, **kwargs) → None[source]
+
    Add a line to the figure.
    
+
    
+
    Parameters
    
+
    col (str) – Column to plot
    
+
    
+
    
+
    
 
 
    
 
    
-add_scatter(*args, **kwargs)[source]
    
-
    
-
-
    
-
    
-iterator()[source]
    
-
    
+add_scatter(col: str, *args, **kwargs) → None[source]
+
    Add a scatter to the figure.
    
+
    
+
    Parameters
    
+
    col (str) – Column to plot.
    
+
    
+
    
+
    
 
 
 
@@ -211,7 +232,6 @@ 
    PanelFigure
         
-        
     
    
 
   
    
diff --git a/api/wavy.plot.plot.html b/api/wavy.plot.plot.html
index 10ab6bc..8b7c310 100644
--- a/api/wavy.plot.plot.html
+++ b/api/wavy.plot.plot.html
@@ -25,7 +25,7 @@
     
 
 
@@ -68,7 +68,6 @@
 
  • Classes
    • 
 
 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -125,13 +124,13 @@ 
    plot
    
 
    
 plot(panel, use_timestep: bool = False, add_annotation: bool = False, **kwargs) → PanelFigure[source]
    
-
    Plot a panel.
    
+
    Plot panel.
    
 
    
 
    Parameters
    
 
      
-
    • panel (Panel) – Panel object
      • 
-
    • use_timestep (bool) – Use timestep instead of id
      • 
-
    • add_annotation (bool) – If True, plot vertical lines showing train, val, and test periods
      • 
+
    • panel (Panel) – Panel object.
      • 
+
    • use_timestep (bool) – Use timestep instead of id.
      • 
+
    • add_annotation (bool) – If True, plot vertical lines showing train, val, and test periods.
      • 
 
    
 
    
 
    Returns
    
diff --git a/api/wavy.utils.reverse_diff.html b/api/wavy.utils.reverse_diff.html
deleted file mode 100644
index ca154ff..0000000
--- a/api/wavy.utils.reverse_diff.html
+++ /dev/null
@@ -1,179 +0,0 @@
-
-
-
-  
-
-  
-  reverse_diff — Wavy 0.1.9 documentation
-      
-      
-      
-  
-  
-        
-        
-        
-        
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-        
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-    
-    
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-    
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-    
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-      
    
-        
    
-          
    
-  
-  
    
-
    
-          
    
-           
    
-             
-  
-
-
    
-
    reverse_diff
    
-
    
-
    
-reverse_diff(diff_df, original_df, periods=1)[source]
    
-
    Reverse the pct_diff function.
    
-
    
-
    Parameters
    
-
      
-
    • diff_df (pd.DataFrame) – Dataframe to reverse
      • 
-
    • original_df (pd.DataFrame) – Reference Dataframe
      • 
-
    • periods (int) – Number of periods used on diff operation
      • 
-
    
-
    
-
    Returns
    
-
    Reversed dataframe
    
-
    
-
    Return type
    
-
    pd.DataFrame
    
-
    
-
    
-
    
-
-
    
-
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-           
    
-          
    
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-  
    
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\ No newline at end of file
diff --git a/api/wavy.utils.reverse_pct_change.html b/api/wavy.utils.reverse_pct_change.html
deleted file mode 100644
index a5c5755..0000000
--- a/api/wavy.utils.reverse_pct_change.html
+++ /dev/null
@@ -1,177 +0,0 @@
-
-
-
-  
-
-  
-  reverse_pct_change — Wavy 0.1.9 documentation
-      
-      
-      
-  
-  
-        
-        
-        
-        
-        
-        
-    
-    
-    
-    
-
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- 
-  
    
-    
-
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-      
    
-        
    
-          
    
-  
-  
    
-
    
-          
    
-           
    
-             
-  
-
-
    
-
    reverse_pct_change
    
-
    
-
    
-reverse_pct_change(change_df, original_df, periods=1)[source]
    
-
    Reverse the pct_change function.
    
-
    
-
    Parameters
    
-
      
-
    • change_df (pd.DataFrame) – Dataframe to reverse
      • 
-
    • original_df (pd.DataFrame) – Reference Dataframe
      • 
-
    • periods (int) – Number of periods used on pct_change operation
      • 
-
    
-
    
-
    Returns
    
-
    Reversed dataframe
    
-
    
-
    Return type
    
-
    pd.DataFrame
    
-
    
-
    
-
    
-
-
    
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\ No newline at end of file
diff --git a/genindex.html b/genindex.html
index cf11bdc..7d19d40 100644
--- a/genindex.html
+++ b/genindex.html
@@ -22,7 +22,7 @@
     
 
 
@@ -58,7 +58,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -134,14 +133,22 @@ 
    B
    
 
       
  • BaselineShift (class in wavy.models)
 
    • 
-  
-  
@@ -150,12 +157,28 @@ 
    B
    
 
    C
    
 
-  
+  
@@ -325,12 +418,38 @@ 
    S
    
diff --git a/index.html b/index.html
index c3fdda8..ff90227 100644
--- a/index.html
+++ b/index.html
@@ -24,7 +24,7 @@
     
 
@@ -60,7 +60,6 @@
 
  • wavy.panel Module
  • wavy.models Module
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
@@ -129,7 +128,6 @@ 
    Wavy
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
 
diff --git a/manual/installation.html b/manual/installation.html
index a17a4de..3695f02 100644
--- a/manual/installation.html
+++ b/manual/installation.html
@@ -25,7 +25,7 @@
     
 
 
@@ -61,7 +61,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
diff --git a/manual/introduction.html b/manual/introduction.html
index dae378e..8ab2c12 100644
--- a/manual/introduction.html
+++ b/manual/introduction.html
@@ -25,7 +25,7 @@
     
 
 
@@ -61,7 +61,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
diff --git a/manual/link.html b/manual/link.html
index eef84a2..5e6c126 100644
--- a/manual/link.html
+++ b/manual/link.html
@@ -23,7 +23,7 @@
     
 
 
@@ -59,7 +59,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -139,7 +138,7 @@ 
    Link
 {
    
 
    
 
    
 
    ]
    
 
@@ -155,7 +157,7 @@ 
    Link
 {
    
 
    
 
    “cell_type”: “code”,
-“execution_count”: 3,
+“execution_count”: 2,
 “metadata”: {},
 “outputs”: [],
 “source”: [
    
@@ -169,7 +171,7 @@ 
    Link
 {
    
 
    
 
    “cell_type”: “code”,
-“execution_count”: 4,
+“execution_count”: 3,
 “metadata”: {},
 “outputs”: [],
 “source”: [
    
@@ -183,6 +185,20 @@ 
    Link
 {
    
 
    
 
    “cell_type”: “code”,
+“execution_count”: 4,
+“metadata”: {},
+“outputs”: [],
+“source”: [
    
+
    
+
    “# Set training splitn”,
+“wavy.set_training_split(x, y, train_size=0.4, val_size=0.3, test_size=0.3)”
    
+
    
+
    ]
    
+
    
+
    },
+{
    
+
    
+
    
@@ -217,3501 +233,66 @@ 
    Link
 “outputs”: [
    
 
    
 
    
-
    {
    
-
    “data”: {
    
-
    “application/vnd.plotly.v1+json”: {
    
-
    “config”: {
    “plotlyServerURL”: “https://plot.ly”
    
-
    
-
    
-
    },
-“data”: [
    
-
    
-
    
 
    {
    
-
    “line”: {
    “color”: “#636EFA”,
-“width”: 1.5
    
+
    “data”: {
    “application/pdf”: 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-“polar”: {
    
-
    
-
    
-
    “angularaxis”: {
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-“linecolor”: “white”,
-“ticks”: “”
    
-
    
-
    
-
    },
-“bgcolor”: “#E5ECF6”,
-“radialaxis”: {
    
-
    
-
    “gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”
    
-
    
-
    }
    
-
    
-
    },
-“scene”: {
    
-
    
-
    
-
    “xaxis”: {
    “backgroundcolor”: “#E5ECF6”,
-“gridcolor”: “white”,
-“gridwidth”: 2,
-“linecolor”: “white”,
-“showbackground”: true,
-“ticks”: “”,
-“zerolinecolor”: “white”
    
-
    
-
    
-
    },
-“yaxis”: {
    
-
    
-
    “backgroundcolor”: “#E5ECF6”,
-“gridcolor”: “white”,
-“gridwidth”: 2,
-“linecolor”: “white”,
-“showbackground”: true,
-“ticks”: “”,
-“zerolinecolor”: “white”
    
-
    
-
    },
-“zaxis”: {
    
-
    
-
    “backgroundcolor”: “#E5ECF6”,
-“gridcolor”: “white”,
-“gridwidth”: 2,
-“linecolor”: “white”,
-“showbackground”: true,
-“ticks”: “”,
-“zerolinecolor”: “white”
    
-
    
-
    }
    
-
    
-
    },
-“shapedefaults”: {
    
-
    
-
    
-
    “line”: {
    “color”: “#2a3f5f”
    
-
    
-
    
-
    }
    
-
    
-
    },
-“ternary”: {
    
-
    
-
    
-
    “aaxis”: {
    “gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”
    
-
    
-
    
-
    },
-“baxis”: {
    
-
    
-
    “gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”
    
-
    
-
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-“bgcolor”: “#E5ECF6”,
-“caxis”: {
    
-
    
-
    “gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”
    
-
    
-
    }
    
-
    
-
    },
-“title”: {
    
-
    
-
    “x”: 0.05
    
-
    
-
    },
-“xaxis”: {
    
-
    
-
    “automargin”: true,
-“gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”,
-“title”: {
    
-
    
-
    “standoff”: 15
    
-
    
-
    },
-“zerolinecolor”: “white”,
-“zerolinewidth”: 2
    
-
    
-
    },
-“yaxis”: {
    
-
    
-
    “automargin”: true,
-“gridcolor”: “white”,
-“linecolor”: “white”,
-“ticks”: “”,
-“title”: {
    
-
    
-
    “standoff”: 15
    
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-
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-“zerolinecolor”: “white”,
-“zerolinewidth”: 2
    
-
    
-
    }
    
-
    
-
    }
    
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-
    }
    
-
    
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    }
    
-
    
-
    
-
    }
    
-
    
-
    
-
    },
-“metadata”: {},
-“output_type”: “display_data”
    
-
    
-
    
-
    }
    
-
    
-
    ],
-“source”: [
    
-
    
-
    “# Plot the target.n”,
-“y.plot()”
    
-
    
-
    ]
    
-
    
-
    },
-{
    
-
    
-
    “cell_type”: “code”,
-“execution_count”: 7,
-“metadata”: {},
-“outputs”: [
    
-
    
-
    
-
    {
    “name”: “stdout”,
-“output_type”: “stream”,
-“text”: [
    
-
    
-
    “(712, 10, 1) (712, 1, 1)n”
    
-
    
-
    ]
    
-
    
-
    
-
    }
    
-
    
-
    ],
-“source”: [
    
-
    
-
    “# Convert to numpy arrays. Panels contain a train-val-test split by default.n”,
-“x_train, y_train = x.train.values, y.train.valuesn”,
-“x_test, y_test = x.test.values, y.test.valuesn”,
-“print(x_train.shape, y_train.shape)”
    
-
    
-
    ]
    
-
    
-
    },
-{
    
+{
    
 
    
 
    “cell_type”: “code”,
-“execution_count”: 9,
-“metadata”: {},
-“outputs”: [
    
-
    
-
    
-
    {
    
-
    “data”: {
    
-
    “text/html”: [
    “<div>n”,
-“<style scoped>n”,
-”    .dataframe tbody tr th:only-of-type {n”,
-”        vertical-align: middle;n”,
-”    }n”,
-“n”,
-”    .dataframe tbody tr th {n”,
-”        vertical-align: top;n”,
-”    }n”,
-“n”,
-”    .dataframe thead th {n”,
-”        text-align: right;n”,
-”    }n”,
-“</style>n”,
-“<table border="1" class="dataframe">n”,
-”  <thead>n”,
-”    <tr style="text-align: right;">n”,
-”      <th></th>n”,
-”      <th>train</th>n”,
-”      <th>test</th>n”,
-”      <th>val</th>n”,
-”    </tr>n”,
-”  </thead>n”,
-”  <tbody>n”,
-”    <tr>n”,
-”      <th>MAE</th>n”,
-”      <td>1.398996</td>n”,
-”      <td>1.484263</td>n”,
-”      <td>1.412011</td>n”,
-”    </tr>n”,
-”  </tbody>n”,
-“</table>n”,
-“</div>”
    
-
    
-
    
-
    ],
-“text/plain”: [
    
-
    
-
    ”        train      test       valn”,
-“MAE  1.398996  1.484263  1.412011”
    
-
    
-
    ]
    
-
    
-
    
-
    },
-“execution_count”: 9,
+“execution_count”: null,
 “metadata”: {},
-“output_type”: “execute_result”
    
-
    
-
    
-
    }
    
-
    
-
    ],
+“outputs”: [],
 “source”: [
    
 
    
 
    “# Or just instantiate a model.n”,
-“model = wavy.LinearRegression(x, y)n”,
+“model = models.LinearRegression(x, y)n”,
 “model.score()”
    
 
    
 
    ]
    
diff --git a/manual/models.html b/manual/models.html
deleted file mode 100644
index 5032844..0000000
--- a/manual/models.html
+++ /dev/null
@@ -1,166 +0,0 @@
-
-
-
-  
-
-  
-  Models — Wavy 0.1.9 documentation
-      
-      
-      
-  
-  
-        
-        
-        
-        
-        
-        
-    
-    
-    
-
-
-
-
- 
-  
    
-    
-
-    
    
-
-      
    
-        
    
-          
    
-  
-  
    
-
    
-          
    
-           
    
-             
-  
-
-
    
-
    Models
    
-
    
-
    BaselineShift
    
-
    
-
    
-
    BaselineConstant
    
-
    
-
    
-
    DenseModel
    
-
    
-
    
-
    ConvModel
    
-
    
-
    
-
    LinearRegression
    
-
    
-
    
-
    LogisticRegression
    
-
    
-
    
-
    ShallowModel
    
-
    
-
    
-
-
-           
    
-          
    
-          
-        
    
-      
    
-    
    
-  
    
-   
-
-
-
\ No newline at end of file
diff --git a/manual/quickstart.html b/manual/quickstart.html
index 04c5109..e4bfa4f 100644
--- a/manual/quickstart.html
+++ b/manual/quickstart.html
@@ -23,7 +23,7 @@
     
 
 
@@ -59,7 +59,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
diff --git a/manual/tutorials.html b/manual/tutorials.html
index f0c54d9..ddb16df 100644
--- a/manual/tutorials.html
+++ b/manual/tutorials.html
@@ -25,7 +25,7 @@
     
 
 
@@ -64,7 +64,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
diff --git a/notebooks/quickstart2.html b/notebooks/quickstart2.html
index f849acd..ee801ee 100644
--- a/notebooks/quickstart2.html
+++ b/notebooks/quickstart2.html
@@ -27,7 +27,7 @@
     
 
 
@@ -63,7 +63,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
@@ -372,18 +371,21 @@
 
    Quickstart
    
 
    This is a short introduction to wavy, geared mainly for new users.
    
 
    
-
    [2]:
+
    [1]:
 
    
 
    
 
    # Import libraries
 import numpy as np
 import pandas as pd
 import wavy
+from wavy import models
+import plotly.io as pio
+pio.renderers.default = 'pdf'
 
    
 
    
 
    
 
    
-
    [3]:
+
    [2]:
 
    
 
    
 
    # Start with any time series dataframe
@@ -392,7 +394,7 @@ 
    Quickstart
-
    [4]:
+
    [3]:
 
    
 
    
 
    # Create panels. Each panel is composed of a list of frames.
@@ -400,13 +402,22 @@ 
    Quickstart
+
    [4]:
+
    
+
    
+
    # Set training split
+wavy.set_training_split(x, y, train_size=0.4, val_size=0.3, test_size=0.3)
+
    
+
    
+
    
 
    
 
    [5]:
 
    
 
    
 
    # x and y are contain the past and corresponding future data.
 # lookback and horizon are the number of timesteps.
-print("Lookback:", len(x[0]), "Horizon:", len(y[0]))
+print("Lookback:", x.num_timesteps, "Horizon:", y.num_timesteps)
 
    
 
    
 
    
@@ -431,16 +442,14 @@ 
    Quickstart
 
    
 
    
-
    
-
    Data type cannot be displayed: application/vnd.plotly.v1+json
    
-
    
+../_images/notebooks_quickstart2_7_0.pdf
 
    
 
    
 
    
 
    [7]:
 
    
 
    
-
    # Convert to numpy arrays. Panels contain a train-val-test split by default.
+
    # Convert to numpy arrays
 x_train, y_train = x.train.values, y.train.values
 x_test, y_test = x.test.values, y.test.values
 print(x_train.shape, y_train.shape)
@@ -452,58 +461,19 @@ 
    Quickstart
 
    -(712, 10, 1) (712, 1, 1)
+(3960, 1) (396, 1)
 
    
 
    
-
    
-
    [9]:
+
    
+
    [ ]:
 
    
 
    
 
    # Or just instantiate a model.
-model = wavy.LinearRegression(x, y)
+model = models.LinearRegression(x, y)
 model.score()
 
    
 
    
 
    
-
    
-
    [9]:
-
    
-
    
-
    
-
    
-
-
    
   
   
   
-      
  • residuals() (ShallowModel method)
+      
  • residuals() (BaselineConstant method)
+
+      
    		• 
+  
   
 
 
 
 
 
         
    
-  
-    
-      
-      
-      
-      
-    
-  
-  
-    
-      
-      
-      
-      
-    
-  
-
    traintestval
    MAE1.3989961.4842631.412011
    
-
    
-
    
 
 
 
diff --git a/notebooks/quickstart2.ipynb b/notebooks/quickstart2.ipynb
index 2df12dd..08f9401 100644
--- a/notebooks/quickstart2.ipynb
+++ b/notebooks/quickstart2.ipynb
@@ -16,19 +16,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Import libraries\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import wavy"
+    "import wavy\n",
+    "from wavy import models\n",
+    "import plotly.io as pio\n",
+    "pio.renderers.default = 'pdf'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,6 +49,16 @@
     "x, y = wavy.create_panels(df, lookback=10, horizon=1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set training split\n",
+    "wavy.set_training_split(x, y, train_size=0.4, val_size=0.3, test_size=0.3)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -62,7 +75,7 @@
    "source": [
     "# x and y are contain the past and corresponding future data.\n",
     "# lookback and horizon are the number of timesteps.\n",
-    "print(\"Lookback:\", len(x[0]), \"Horizon:\", len(y[0]))"
+    "print(\"Lookback:\", x.num_timesteps, \"Horizon:\", y.num_timesteps)"
    ]
   },
   {
@@ -72,2891 +85,7 @@
    "outputs": [
     {
      "data": {
-      "application/vnd.plotly.v1+json": {
-       "config": {
-        "plotlyServerURL": "https://plot.ly"
-       },
-       "data": [
-        {
-         "line": {
-          "color": "#636EFA",
-          "width": 1.5
-         },
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-         "name": "price",
-         "type": "scatter",
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-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
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-           "zerolinecolor": "white",
-           "zerolinewidth": 2
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-       }
-      }
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      },
      "metadata": {},
      "output_type": "display_data"
@@ -2976,12 +105,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "(712, 10, 1) (712, 1, 1)\n"
+      "(3960, 1) (396, 1)\n"
      ]
     }
    ],
    "source": [
-    "# Convert to numpy arrays. Panels contain a train-val-test split by default.\n",
+    "# Convert to numpy arrays\n",
     "x_train, y_train = x.train.values, y.train.values\n",
     "x_test, y_test = x.test.values, y.test.values\n",
     "print(x_train.shape, y_train.shape)"
@@ -2989,59 +118,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
    \n",
-       "\n",
-       "\n",
-       "  \n",
-       "    \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "  \n",
-       "    \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "      \n",
-       "    \n",
-       "  \n",
-       "
    traintestval
    MAE1.3989961.4842631.412011
    \n",
-       "
    "
-      ],
-      "text/plain": [
-       "        train      test       val\n",
-       "MAE  1.398996  1.484263  1.412011"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Or just instantiate a model.\n",
-    "model = wavy.LinearRegression(x, y)\n",
+    "model = models.LinearRegression(x, y)\n",
     "model.score()"
    ]
   }
diff --git a/objects.inv b/objects.inv
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diff --git a/py-modindex.html b/py-modindex.html
index b47744c..a64d960 100644
--- a/py-modindex.html
+++ b/py-modindex.html
@@ -23,7 +23,7 @@
 
 
 
@@ -61,7 +61,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
@@ -119,11 +118,6 @@ 
    Python Module Index
    
           
        wavy.plot
        
-     
-       
-          
-       wavy.utils
-       
    
 
 
diff --git a/search.html b/search.html
index 9540977..53766b0 100644
--- a/search.html
+++ b/search.html
@@ -25,7 +25,7 @@
     
 
 
@@ -61,7 +61,6 @@
 
  • wavy.panel Module
    • 
 
  • wavy.models Module
    • 
 
  • wavy.plot Module
    • 
-
  • wavy.utils Module
    • 
 
 
         
    
diff --git a/searchindex.js b/searchindex.js
index 548d03e..223bbd5 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
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