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- abc.ABC(builtins.object)
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- AnimationBase
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- ClassificationAnimation
- ForcastingAnimation
- RegressionAnimation
class AnimationBase(abc.ABC) |
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AnimationBase(model, train_series, test_series, dynamic_parameter=None, static_parameters=None, keep_previous=None, **kwargs)
Base class for creating animations of machine learning models.
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- Method resolution order:
- AnimationBase
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, model, train_series, test_series, dynamic_parameter=None, static_parameters=None, keep_previous=None, **kwargs)
- Initialize the animation base class.
Args:
model: The forecasting model or any machine learning model.
train_series: Training time series data.
test_series: Testing time series data.
dynamic_parameter: The parameter to update dynamically (e.g., 'window', 'alpha', 'beta').
static_parameters: Static parameters for the model.
Should be a dictionary with parameter names as keys and their values.
keep_previous: Whether to keep all previous lines with reduced opacity.
**kwargs: Additional customization options (e.g., colors, line styles).
- animate(self, frames, interval=150, blit=True, repeat=False)
- Create the animation.
Args:
frames: Range of frames (e.g., window sizes).
interval: Delay between frames in milliseconds.
blit: Whether to use blitting for faster rendering.
repeat: Whether to repeat the animation.
- save(self, filename, writer='pillow', fps=5, dpi=100)
- Save the animation to a file.
Args:
filename: Path to save the animation.
writer: Writer to use (e.g., 'pillow' for GIF).
fps: Frames per second.
dpi: Dots per inch for the saved figure.
- setup_plot(self, title, xlabel, ylabel, legend_loc='upper left', grid=True, figsize=(12, 6))
- Set up the plot for the animation.
Args:
title: Title of the plot.
xlabel: Label for the x-axis.
ylabel: Label for the y-axis.
legend_loc: Location of the legend.
grid: Whether to show grid lines.
figsize: Size of the figure.
- show(self)
- Display the animation.
- update_model(self, frame)
- Abstract method to update the model for a given frame. Must be implemented by subclasses.
- update_plot(self, frame)
- Abstract method to update the plot for a given frame.Must be implemented by subclasses.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
Data and other attributes defined here:
- __abstractmethods__ = frozenset({'update_model', 'update_plot'})
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class ClassificationAnimation(AnimationBase) |
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ClassificationAnimation(model, X, y, test_size=0.3, dynamic_parameter=None, static_parameters=None, keep_previous=False, scaler=None, pca_components=2, plot_step=0.02, **kwargs)
Class for creating animations of classification models.
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- Method resolution order:
- ClassificationAnimation
- AnimationBase
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, model, X, y, test_size=0.3, dynamic_parameter=None, static_parameters=None, keep_previous=False, scaler=None, pca_components=2, plot_step=0.02, **kwargs)
- Initialize the classification animation class.
Args:
model: The classification model.
X: Feature matrix (input data).
y: Target vector (output data).
test_size: Proportion of the dataset to include in the test split.
dynamic_parameter: The parameter to update dynamically (e.g., 'alpha', 'beta').
static_parameters: Additional static parameters for the model.
Should be a dictionary with parameter names as keys and their values.
keep_previous: Whether to keep all previous lines with reduced opacity.
scaler: Optional scaler for preprocessing the data.
pca_components: Number of components to use for PCA.
plot_step: Resolution of the decision boundary mesh.
**kwargs: Additional customization options (e.g., colors, line styles).
- setup_plot(self, title, xlabel, ylabel, legend_loc='upper left', grid=True, figsize=(12, 6))
- Set up the plot for classification animation.
- update_model(self, frame)
- Update the classification model for the current frame.
Args:
frame: The current frame (e.g., parameter value).
- update_plot(self, frame)
- Update the plot for the current frame.
Args:
frame: The current frame (e.g., parameter value).
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
Methods inherited from AnimationBase:
- animate(self, frames, interval=150, blit=True, repeat=False)
- Create the animation.
Args:
frames: Range of frames (e.g., window sizes).
interval: Delay between frames in milliseconds.
blit: Whether to use blitting for faster rendering.
repeat: Whether to repeat the animation.
- save(self, filename, writer='pillow', fps=5, dpi=100)
- Save the animation to a file.
Args:
filename: Path to save the animation.
writer: Writer to use (e.g., 'pillow' for GIF).
fps: Frames per second.
dpi: Dots per inch for the saved figure.
- show(self)
- Display the animation.
Data descriptors inherited from AnimationBase:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class ForcastingAnimation(AnimationBase) |
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ForcastingAnimation(model, train_series, test_series, forecast_steps, dynamic_parameter=None, static_parameters=None, keep_previous=False, max_previous=None, **kwargs)
Class for creating animations of forecasting models.
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- Method resolution order:
- ForcastingAnimation
- AnimationBase
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, model, train_series, test_series, forecast_steps, dynamic_parameter=None, static_parameters=None, keep_previous=False, max_previous=None, **kwargs)
- Initialize the forecasting animation class.
Args:
model: The forecasting model.
train_series: Training time series data.
test_series: Testing time series data.
forecast_steps: Number of steps to forecast.
dynamic_parameter: The parameter to update dynamically (e.g., 'window', 'alpha', 'beta').
static_parameters: Static parameters for the model.
Should be a dictionary with parameter names as keys and their values.
keep_previous: Whether to keep all previous lines with reduced opacity.
max_previous: Maximum number of previous lines to keep.
**kwargs: Additional customization options (e.g., colors, line styles).
- setup_plot(self, title, xlabel, ylabel, legend_loc='upper left', grid=True, figsize=(12, 6))
- Set up the plot for forecasting animation.
- update_model(self, frame)
- Update the model for the current frame.
Args:
frame: The current frame (e.g., parameter value).
- update_plot(self, frame)
- Update the plot for the current frame.
Args:
frame: The current frame (e.g., parameter value).
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
Methods inherited from AnimationBase:
- animate(self, frames, interval=150, blit=True, repeat=False)
- Create the animation.
Args:
frames: Range of frames (e.g., window sizes).
interval: Delay between frames in milliseconds.
blit: Whether to use blitting for faster rendering.
repeat: Whether to repeat the animation.
- save(self, filename, writer='pillow', fps=5, dpi=100)
- Save the animation to a file.
Args:
filename: Path to save the animation.
writer: Writer to use (e.g., 'pillow' for GIF).
fps: Frames per second.
dpi: Dots per inch for the saved figure.
- show(self)
- Display the animation.
Data descriptors inherited from AnimationBase:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class RegressionAnimation(AnimationBase) |
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RegressionAnimation(model, X, y, test_size=0.3, dynamic_parameter=None, static_parameters=None, keep_previous=False, max_previous=None, pca_components=1, **kwargs)
Class for creating animations of regression models.
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- Method resolution order:
- RegressionAnimation
- AnimationBase
- abc.ABC
- builtins.object
Methods defined here:
- __init__(self, model, X, y, test_size=0.3, dynamic_parameter=None, static_parameters=None, keep_previous=False, max_previous=None, pca_components=1, **kwargs)
- Initialize the regression animation class.
Args:
model: The regression model.
X: Feature matrix (input data).
y: Target vector (output data).
test_size: Proportion of the dataset to include in the test split.
dynamic_parameter: The parameter to update dynamically (e.g., 'alpha', 'beta').
static_parameters: Additional static parameters for the model.
Should be a dictionary with parameter names as keys and their values.
keep_previous: Whether to keep all previous lines with reduced opacity.
max_previous: Maximum number of previous lines to keep.
pca_components: Number of components to use for PCA.
**kwargs: Additional customization options (e.g., colors, line styles).
- setup_plot(self, title, xlabel, ylabel, legend_loc='upper left', grid=True, figsize=(12, 6))
- Set up the plot for regression animation.
- update_model(self, frame)
- Update the regression model for the current frame.
Args:
frame: The current frame (e.g., parameter value).
- update_plot(self, frame)
- Update the plot for the current frame.
Args:
frame: The current frame (e.g., parameter value).
Data and other attributes defined here:
- __abstractmethods__ = frozenset()
Methods inherited from AnimationBase:
- animate(self, frames, interval=150, blit=True, repeat=False)
- Create the animation.
Args:
frames: Range of frames (e.g., window sizes).
interval: Delay between frames in milliseconds.
blit: Whether to use blitting for faster rendering.
repeat: Whether to repeat the animation.
- save(self, filename, writer='pillow', fps=5, dpi=100)
- Save the animation to a file.
Args:
filename: Path to save the animation.
writer: Writer to use (e.g., 'pillow' for GIF).
fps: Frames per second.
dpi: Dots per inch for the saved figure.
- show(self)
- Display the animation.
Data descriptors inherited from AnimationBase:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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