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moderngl/moderngl | examples/window/base.py | BaseWindow.set_default_viewport | def set_default_viewport(self):
"""
Calculates the viewport based on the configured aspect ratio.
Will add black borders and center the viewport if the window
do not match the configured viewport.
If aspect ratio is None the viewport will be scaled
to the entire w... | python | def set_default_viewport(self):
"""
Calculates the viewport based on the configured aspect ratio.
Will add black borders and center the viewport if the window
do not match the configured viewport.
If aspect ratio is None the viewport will be scaled
to the entire w... | [
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Will add black borders and center the viewport if the window
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moderngl/moderngl | examples/window/base.py | BaseWindow.print_context_info | def print_context_info(self):
"""
Prints moderngl context info.
"""
print("Context Version:")
print('ModernGL:', moderngl.__version__)
print('vendor:', self.ctx.info['GL_VENDOR'])
print('renderer:', self.ctx.info['GL_RENDERER'])
print('version:', s... | python | def print_context_info(self):
"""
Prints moderngl context info.
"""
print("Context Version:")
print('ModernGL:', moderngl.__version__)
print('vendor:', self.ctx.info['GL_VENDOR'])
print('renderer:', self.ctx.info['GL_RENDERER'])
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moderngl/moderngl | moderngl/texture.py | Texture.read | def read(self, *, level=0, alignment=1) -> bytes:
'''
Read the content of the texture into a buffer.
Keyword Args:
level (int): The mipmap level.
alignment (int): The byte alignment of the pixels.
Returns:
bytes
'''
... | python | def read(self, *, level=0, alignment=1) -> bytes:
'''
Read the content of the texture into a buffer.
Keyword Args:
level (int): The mipmap level.
alignment (int): The byte alignment of the pixels.
Returns:
bytes
'''
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alignment (int): The byte alignment of the pixels.
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moderngl/moderngl | examples/window/__init__.py | parse_args | def parse_args(args=None):
"""Parse arguments from sys.argv"""
parser = argparse.ArgumentParser()
parser.add_argument(
'-w', '--window',
default="pyqt5",
choices=find_window_classes(),
help='Name for the window type to use',
)
parser.add_argument(
... | python | def parse_args(args=None):
"""Parse arguments from sys.argv"""
parser = argparse.ArgumentParser()
parser.add_argument(
'-w', '--window',
default="pyqt5",
choices=find_window_classes(),
help='Name for the window type to use',
)
parser.add_argument(
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moderngl/moderngl | moderngl/compute_shader.py | ComputeShader.run | def run(self, group_x=1, group_y=1, group_z=1) -> None:
'''
Run the compute shader.
Args:
group_x (int): The number of work groups to be launched in the X dimension.
group_y (int): The number of work groups to be launched in the Y dimension.
... | python | def run(self, group_x=1, group_y=1, group_z=1) -> None:
'''
Run the compute shader.
Args:
group_x (int): The number of work groups to be launched in the X dimension.
group_y (int): The number of work groups to be launched in the Y dimension.
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group_x (int): The number of work groups to be launched in the X dimension.
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group_z (int): The number of work groups to be launched in the Z dimension. | [
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moderngl/moderngl | moderngl/compute_shader.py | ComputeShader.get | def get(self, key, default) -> Union[Uniform, UniformBlock, Subroutine, Attribute, Varying]:
'''
Returns a Uniform, UniformBlock, Subroutine, Attribute or Varying.
Args:
default: This is the value to be returned in case key does not exist.
Returns:
... | python | def get(self, key, default) -> Union[Uniform, UniformBlock, Subroutine, Attribute, Varying]:
'''
Returns a Uniform, UniformBlock, Subroutine, Attribute or Varying.
Args:
default: This is the value to be returned in case key does not exist.
Returns:
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moderngl/moderngl | examples/window/sdl2/window.py | Window.resize | def resize(self, width, height):
"""
Sets the new size and buffer size internally
"""
self.width = width
self.height = height
self.buffer_width, self.buffer_height = self.width, self.height
self.set_default_viewport()
super().resize(self.buffer_w... | python | def resize(self, width, height):
"""
Sets the new size and buffer size internally
"""
self.width = width
self.height = height
self.buffer_width, self.buffer_height = self.width, self.height
self.set_default_viewport()
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moderngl/moderngl | examples/window/sdl2/window.py | Window.process_events | def process_events(self):
"""
Loop through and handle all the queued events.
"""
for event in sdl2.ext.get_events():
if event.type == sdl2.SDL_MOUSEMOTION:
self.example.mouse_position_event(event.motion.x, event.motion.y)
elif event.type =... | python | def process_events(self):
"""
Loop through and handle all the queued events.
"""
for event in sdl2.ext.get_events():
if event.type == sdl2.SDL_MOUSEMOTION:
self.example.mouse_position_event(event.motion.x, event.motion.y)
elif event.type =... | [
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moderngl/moderngl | examples/window/sdl2/window.py | Window.destroy | def destroy(self):
"""
Gracefully close the window
"""
sdl2.SDL_GL_DeleteContext(self.context)
sdl2.SDL_DestroyWindow(self.window)
sdl2.SDL_Quit() | python | def destroy(self):
"""
Gracefully close the window
"""
sdl2.SDL_GL_DeleteContext(self.context)
sdl2.SDL_DestroyWindow(self.window)
sdl2.SDL_Quit() | [
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moderngl/moderngl | examples/window/pyglet/window.py | Window.on_key_press | def on_key_press(self, symbol, modifiers):
"""
Pyglet specific key press callback.
Forwards and translates the events to the example
"""
self.example.key_event(symbol, self.keys.ACTION_PRESS) | python | def on_key_press(self, symbol, modifiers):
"""
Pyglet specific key press callback.
Forwards and translates the events to the example
"""
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moderngl/moderngl | examples/window/pyglet/window.py | Window.on_key_release | def on_key_release(self, symbol, modifiers):
"""
Pyglet specific key release callback.
Forwards and translates the events to the example
"""
self.example.key_event(symbol, self.keys.ACTION_RELEASE) | python | def on_key_release(self, symbol, modifiers):
"""
Pyglet specific key release callback.
Forwards and translates the events to the example
"""
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moderngl/moderngl | examples/window/pyglet/window.py | Window.on_mouse_motion | def on_mouse_motion(self, x, y, dx, dy):
"""
Pyglet specific mouse motion callback.
Forwards and traslates the event to the example
"""
# Screen coordinates relative to the lower-left corner
# so we have to flip the y axis to make this consistent with
# oth... | python | def on_mouse_motion(self, x, y, dx, dy):
"""
Pyglet specific mouse motion callback.
Forwards and traslates the event to the example
"""
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# so we have to flip the y axis to make this consistent with
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moderngl/moderngl | examples/window/pyglet/window.py | Window.on_mouse_press | def on_mouse_press(self, x: int, y: int, button, mods):
"""
Handle mouse press events and forward to example window
"""
if button in [1, 4]:
self.example.mouse_press_event(
x, self.buffer_height - y,
1 if button == 1 else 2,
... | python | def on_mouse_press(self, x: int, y: int, button, mods):
"""
Handle mouse press events and forward to example window
"""
if button in [1, 4]:
self.example.mouse_press_event(
x, self.buffer_height - y,
1 if button == 1 else 2,
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moderngl/moderngl | examples/window/pyglet/window.py | Window.on_mouse_release | def on_mouse_release(self, x: int, y: int, button, mods):
"""
Handle mouse release events and forward to example window
"""
if button in [1, 4]:
self.example.mouse_release_event(
x, self.buffer_height - y,
1 if button == 1 else 2,
... | python | def on_mouse_release(self, x: int, y: int, button, mods):
"""
Handle mouse release events and forward to example window
"""
if button in [1, 4]:
self.example.mouse_release_event(
x, self.buffer_height - y,
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moderngl/moderngl | moderngl/texture_cube.py | TextureCube.write | def write(self, face, data, viewport=None, *, alignment=1) -> None:
'''
Update the content of the texture.
Args:
face (int): The face to update.
data (bytes): The pixel data.
viewport (tuple): The viewport.
Keyword Args:
... | python | def write(self, face, data, viewport=None, *, alignment=1) -> None:
'''
Update the content of the texture.
Args:
face (int): The face to update.
data (bytes): The pixel data.
viewport (tuple): The viewport.
Keyword Args:
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moderngl/moderngl | examples/00_empty_window.py | EmptyWindow.key_event | def key_event(self, key, action):
"""
Handle key events in a generic way
supporting all window types.
"""
if action == self.wnd.keys.ACTION_PRESS:
if key == self.wnd.keys.SPACE:
print("Space was pressed")
if action == self.wnd.keys.ACTION_RELE... | python | def key_event(self, key, action):
"""
Handle key events in a generic way
supporting all window types.
"""
if action == self.wnd.keys.ACTION_PRESS:
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moderngl/moderngl | examples/00_empty_window.py | EmptyWindow.mouse_press_event | def mouse_press_event(self, x, y, button):
"""Reports left and right mouse button presses + position"""
if button == 1:
print("Left mouse button pressed @", x, y)
if button == 2:
print("Right mouse button pressed @", x, y) | python | def mouse_press_event(self, x, y, button):
"""Reports left and right mouse button presses + position"""
if button == 1:
print("Left mouse button pressed @", x, y)
if button == 2:
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moderngl/moderngl | examples/00_empty_window.py | EmptyWindow.mouse_release_event | def mouse_release_event(self, x, y, button):
"""Reports left and right mouse button releases + position"""
if button == 1:
print("Left mouse button released @", x, y)
if button == 2:
print("Right mouse button released @", x, y) | python | def mouse_release_event(self, x, y, button):
"""Reports left and right mouse button releases + position"""
if button == 1:
print("Left mouse button released @", x, y)
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moderngl/moderngl | moderngl/program.py | detect_format | def detect_format(program, attributes) -> str:
'''
Detect format for vertex attributes.
The format returned does not contain padding.
Args:
program (Program): The program.
attributes (list): A list of attribute names.
Returns:
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'''
de... | python | def detect_format(program, attributes) -> str:
'''
Detect format for vertex attributes.
The format returned does not contain padding.
Args:
program (Program): The program.
attributes (list): A list of attribute names.
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dswah/pyGAM | pygam/callbacks.py | validate_callback_data | def validate_callback_data(method):
"""
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Parameters
----------
method : callable
Returns
-------
validated callable
"""
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"""
wraps a callback's method to pull the desired arguments from the vars dict
also checks to ensure the method's arguments are in the vars dict
Parameters
----------
method : callable
Returns
-------
validated callable
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dswah/pyGAM | pygam/callbacks.py | validate_callback | def validate_callback(callback):
"""
validates a callback's on_loop_start and on_loop_end methods
Parameters
----------
callback : Callback object
Returns
-------
validated callback
"""
if not(hasattr(callback, '_validated')) or callback._validated == False:
assert hasa... | python | def validate_callback(callback):
"""
validates a callback's on_loop_start and on_loop_end methods
Parameters
----------
callback : Callback object
Returns
-------
validated callback
"""
if not(hasattr(callback, '_validated')) or callback._validated == False:
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dswah/pyGAM | pygam/distributions.py | Distribution.phi | def phi(self, y, mu, edof, weights):
"""
GLM scale parameter.
for Binomial and Poisson families this is unity
for Normal family this is variance
Parameters
----------
y : array-like of length n
target values
mu : array-like of length n
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"""
GLM scale parameter.
for Binomial and Poisson families this is unity
for Normal family this is variance
Parameters
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y : array-like of length n
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dswah/pyGAM | pygam/distributions.py | GammaDist.sample | def sample(self, mu):
"""
Return random samples from this Gamma distribution.
Parameters
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mu : array-like of shape n_samples or shape (n_simulations, n_samples)
expected values
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-------
random_samples : np.array of same shape... | python | def sample(self, mu):
"""
Return random samples from this Gamma distribution.
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mu : array-like of shape n_samples or shape (n_simulations, n_samples)
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dswah/pyGAM | pygam/datasets/load_datasets.py | _clean_X_y | def _clean_X_y(X, y):
"""ensure that X and y data are float and correct shapes
"""
return make_2d(X, verbose=False).astype('float'), y.astype('float') | python | def _clean_X_y(X, y):
"""ensure that X and y data are float and correct shapes
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dswah/pyGAM | pygam/datasets/load_datasets.py | mcycle | def mcycle(return_X_y=True):
"""motorcyle acceleration dataset
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return_X_y : bool,
if True, returns a model-ready tuple of data (X, y)
otherwise, returns a Pandas DataFrame
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-------
model-ready tuple of data (X, y)
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"""motorcyle acceleration dataset
Parameters
----------
return_X_y : bool,
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dswah/pyGAM | pygam/datasets/load_datasets.py | coal | def coal(return_X_y=True):
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"""coal-mining accidents dataset
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return_X_y : bool,
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dswah/pyGAM | pygam/datasets/load_datasets.py | faithful | def faithful(return_X_y=True):
"""old-faithful dataset
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return_X_y : bool,
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model-ready tuple of data (X, y)
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"""old-faithful dataset
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dswah/pyGAM | pygam/datasets/load_datasets.py | trees | def trees(return_X_y=True):
"""cherry trees dataset
Parameters
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return_X_y : bool,
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model-ready tuple of data (X, y)
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Pandas DataFrame
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"""cherry trees dataset
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model-ready tuple of data (X, y)
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dswah/pyGAM | pygam/datasets/load_datasets.py | default | def default(return_X_y=True):
"""credit default dataset
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model-ready tuple of data (X, y)
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"""credit default dataset
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return_X_y : bool,
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model-ready tuple of data (X, y)
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dswah/pyGAM | pygam/datasets/load_datasets.py | hepatitis | def hepatitis(return_X_y=True):
"""hepatitis in Bulgaria dataset
Parameters
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return_X_y : bool,
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Returns
-------
model-ready tuple of data (X, y)
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"""hepatitis in Bulgaria dataset
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return_X_y : bool,
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otherwise, returns a Pandas DataFrame
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dswah/pyGAM | pygam/datasets/load_datasets.py | toy_classification | def toy_classification(return_X_y=True, n=5000):
"""toy classification dataset with irrelevant features
fitting a logistic model on this data and performing a model summary
should reveal that features 2,3,4 are not significant.
Parameters
----------
return_X_y : bool,
if True, returns ... | python | def toy_classification(return_X_y=True, n=5000):
"""toy classification dataset with irrelevant features
fitting a logistic model on this data and performing a model summary
should reveal that features 2,3,4 are not significant.
Parameters
----------
return_X_y : bool,
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dswah/pyGAM | pygam/datasets/load_datasets.py | head_circumference | def head_circumference(return_X_y=True):
"""head circumference for dutch boys
Parameters
----------
return_X_y : bool,
if True, returns a model-ready tuple of data (X, y)
otherwise, returns a Pandas DataFrame
Returns
-------
model-ready tuple of data (X, y)
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P... | python | def head_circumference(return_X_y=True):
"""head circumference for dutch boys
Parameters
----------
return_X_y : bool,
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dswah/pyGAM | pygam/datasets/load_datasets.py | chicago | def chicago(return_X_y=True):
"""Chicago air pollution and death rate data
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----------
return_X_y : bool,
if True, returns a model-ready tuple of data (X, y)
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model-ready tuple of data (X, y)
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"""Chicago air pollution and death rate data
Parameters
----------
return_X_y : bool,
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dswah/pyGAM | pygam/datasets/load_datasets.py | toy_interaction | def toy_interaction(return_X_y=True, n=50000, stddev=0.1):
"""a sinusoid modulated by a linear function
this is a simple dataset to test a model's capacity to fit interactions
between features.
a GAM with no interaction terms will have an R-squared close to 0,
while a GAM with a tensor product wil... | python | def toy_interaction(return_X_y=True, n=50000, stddev=0.1):
"""a sinusoid modulated by a linear function
this is a simple dataset to test a model's capacity to fit interactions
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a GAM with no interaction terms will have an R-squared close to 0,
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dswah/pyGAM | gen_imgs.py | gen_multi_data | def gen_multi_data(n=5000):
"""
multivariate Logistic problem
"""
X, y = toy_classification(return_X_y=True, n=10000)
lgam = LogisticGAM(s(0) + s(1) + s(2) + s(3) + s(4) + f(5))
lgam.fit(X, y)
plt.figure()
for i, term in enumerate(lgam.terms):
if term.isintercept:
c... | python | def gen_multi_data(n=5000):
"""
multivariate Logistic problem
"""
X, y = toy_classification(return_X_y=True, n=10000)
lgam = LogisticGAM(s(0) + s(1) + s(2) + s(3) + s(4) + f(5))
lgam.fit(X, y)
plt.figure()
for i, term in enumerate(lgam.terms):
if term.isintercept:
c... | [
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dswah/pyGAM | gen_imgs.py | gen_tensor_data | def gen_tensor_data():
"""
toy interaction data
"""
X, y = toy_interaction(return_X_y=True, n=10000)
gam = LinearGAM(te(0, 1,lam=0.1)).fit(X, y)
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ax = p... | python | def gen_tensor_data():
"""
toy interaction data
"""
X, y = toy_interaction(return_X_y=True, n=10000)
gam = LinearGAM(te(0, 1,lam=0.1)).fit(X, y)
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dswah/pyGAM | gen_imgs.py | expectiles | def expectiles():
"""
a bunch of expectiles
"""
X, y = mcycle(return_X_y=True)
# lets fit the mean model first by CV
gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y)
# and copy the smoothing to the other models
lam = gam50.lam
# now fit a few more models
gam95 = Expectile... | python | def expectiles():
"""
a bunch of expectiles
"""
X, y = mcycle(return_X_y=True)
# lets fit the mean model first by CV
gam50 = ExpectileGAM(expectile=0.5).gridsearch(X, y)
# and copy the smoothing to the other models
lam = gam50.lam
# now fit a few more models
gam95 = Expectile... | [
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dswah/pyGAM | pygam/utils.py | cholesky | def cholesky(A, sparse=True, verbose=True):
"""
Choose the best possible cholesky factorizor.
if possible, import the Scikit-Sparse sparse Cholesky method.
Permutes the output L to ensure A = L.H . L
otherwise defaults to numpy's non-sparse version
Parameters
----------
A : array-like... | python | def cholesky(A, sparse=True, verbose=True):
"""
Choose the best possible cholesky factorizor.
if possible, import the Scikit-Sparse sparse Cholesky method.
Permutes the output L to ensure A = L.H . L
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dswah/pyGAM | pygam/utils.py | make_2d | def make_2d(array, verbose=True):
"""
tiny tool to expand 1D arrays the way i want
Parameters
----------
array : array-like
verbose : bool, default: True
whether to print warnings
Returns
-------
np.array of with ndim = 2
"""
array = np.asarray(array)
if array.... | python | def make_2d(array, verbose=True):
"""
tiny tool to expand 1D arrays the way i want
Parameters
----------
array : array-like
verbose : bool, default: True
whether to print warnings
Returns
-------
np.array of with ndim = 2
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array = np.asarray(array)
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dswah/pyGAM | pygam/utils.py | check_array | def check_array(array, force_2d=False, n_feats=None, ndim=None,
min_samples=1, name='Input data', verbose=True):
"""
tool to perform basic data validation.
called by check_X and check_y.
ensures that data:
- is ndim dimensional
- contains float-compatible data-types
- has at... | python | def check_array(array, force_2d=False, n_feats=None, ndim=None,
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tool to perform basic data validation.
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dswah/pyGAM | pygam/utils.py | check_param | def check_param(param, param_name, dtype, constraint=None, iterable=True,
max_depth=2):
"""
checks the dtype of a parameter,
and whether it satisfies a numerical contraint
Parameters
---------
param : object
param_name : str, name of the parameter
dtype : str, desired dt... | python | def check_param(param, param_name, dtype, constraint=None, iterable=True,
max_depth=2):
"""
checks the dtype of a parameter,
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---------
param : object
param_name : str, name of the parameter
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dswah/pyGAM | pygam/utils.py | get_link_domain | def get_link_domain(link, dist):
"""
tool to identify the domain of a given monotonic link function
Parameters
----------
link : Link object
dist : Distribution object
Returns
-------
domain : list of length 2, representing the interval of the domain.
"""
domain = np.array(... | python | def get_link_domain(link, dist):
"""
tool to identify the domain of a given monotonic link function
Parameters
----------
link : Link object
dist : Distribution object
Returns
-------
domain : list of length 2, representing the interval of the domain.
"""
domain = np.array(... | [
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dswah/pyGAM | pygam/utils.py | load_diagonal | def load_diagonal(cov, load=None):
"""Return the given square matrix with a small amount added to the diagonal
to make it positive semi-definite.
"""
n, m = cov.shape
assert n == m, "matrix must be square, but found shape {}".format((n, m))
if load is None:
l... | python | def load_diagonal(cov, load=None):
"""Return the given square matrix with a small amount added to the diagonal
to make it positive semi-definite.
"""
n, m = cov.shape
assert n == m, "matrix must be square, but found shape {}".format((n, m))
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dswah/pyGAM | pygam/utils.py | round_to_n_decimal_places | def round_to_n_decimal_places(array, n=3):
"""
tool to keep round a float to n decimal places.
n=3 by default
Parameters
----------
array : np.array
n : int. number of decimal places to keep
Returns
-------
array : rounded np.array
"""
# check if in scientific notation... | python | def round_to_n_decimal_places(array, n=3):
"""
tool to keep round a float to n decimal places.
n=3 by default
Parameters
----------
array : np.array
n : int. number of decimal places to keep
Returns
-------
array : rounded np.array
"""
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dswah/pyGAM | pygam/utils.py | space_row | def space_row(left, right, filler=' ', total_width=-1):
"""space the data in a row with optional filling
Arguments
---------
left : str, to be aligned left
right : str, to be aligned right
filler : str, default ' '.
must be of length 1
total_width : int, width of line.
if ne... | python | def space_row(left, right, filler=' ', total_width=-1):
"""space the data in a row with optional filling
Arguments
---------
left : str, to be aligned left
right : str, to be aligned right
filler : str, default ' '.
must be of length 1
total_width : int, width of line.
if ne... | [
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dswah/pyGAM | pygam/utils.py | gen_edge_knots | def gen_edge_knots(data, dtype, verbose=True):
"""
generate uniform knots from data including the edges of the data
for discrete data, assumes k categories in [0, k-1] interval
Parameters
----------
data : array-like with one dimension
dtype : str in {'categorical', 'numerical'}
verbos... | python | def gen_edge_knots(data, dtype, verbose=True):
"""
generate uniform knots from data including the edges of the data
for discrete data, assumes k categories in [0, k-1] interval
Parameters
----------
data : array-like with one dimension
dtype : str in {'categorical', 'numerical'}
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dswah/pyGAM | pygam/utils.py | ylogydu | def ylogydu(y, u):
"""
tool to give desired output for the limit as y -> 0, which is 0
Parameters
----------
y : array-like of len(n)
u : array-like of len(n)
Returns
-------
np.array len(n)
"""
mask = (np.atleast_1d(y)!=0.)
out = np.zeros_like(u)
out[mask] = y[mask... | python | def ylogydu(y, u):
"""
tool to give desired output for the limit as y -> 0, which is 0
Parameters
----------
y : array-like of len(n)
u : array-like of len(n)
Returns
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np.array len(n)
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dswah/pyGAM | pygam/utils.py | combine | def combine(*args):
"""
tool to perform tree search via recursion
useful for developing the grid in a grid search
Parameters
----------
args : list of lists
Returns
-------
list of all the combinations of the elements in the input lists
"""
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"""
tool to perform tree search via recursion
useful for developing the grid in a grid search
Parameters
----------
args : list of lists
Returns
-------
list of all the combinations of the elements in the input lists
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dswah/pyGAM | pygam/utils.py | isiterable | def isiterable(obj, reject_string=True):
"""convenience tool to detect if something is iterable.
in python3, strings count as iterables to we have the option to exclude them
Parameters:
-----------
obj : object to analyse
reject_string : bool, whether to ignore strings
Returns:
-------... | python | def isiterable(obj, reject_string=True):
"""convenience tool to detect if something is iterable.
in python3, strings count as iterables to we have the option to exclude them
Parameters:
-----------
obj : object to analyse
reject_string : bool, whether to ignore strings
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dswah/pyGAM | pygam/utils.py | check_iterable_depth | def check_iterable_depth(obj, max_depth=100):
"""find the maximum depth of nesting of the iterable
Parameters
----------
obj : iterable
max_depth : int, default: 100
maximum depth beyond which we stop counting
Returns
-------
int
"""
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"""find the maximum depth of nesting of the iterable
Parameters
----------
obj : iterable
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maximum depth beyond which we stop counting
Returns
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int
"""
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dswah/pyGAM | pygam/utils.py | flatten | def flatten(iterable):
"""convenience tool to flatten any nested iterable
example:
flatten([[[],[4]],[[[5,[6,7, []]]]]])
>>> [4, 5, 6, 7]
flatten('hello')
>>> 'hello'
Parameters
----------
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Returns
-------
flattened object
"""
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"""convenience tool to flatten any nested iterable
example:
flatten([[[],[4]],[[[5,[6,7, []]]]]])
>>> [4, 5, 6, 7]
flatten('hello')
>>> 'hello'
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Returns
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flattened object
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dswah/pyGAM | pygam/utils.py | tensor_product | def tensor_product(a, b, reshape=True):
"""
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if a is (n, m_a), b is (n, m_b),
then the result is
(n, m_a * m_b) if reshape = True.
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(n, m_a, m_b) otherwise
Parameters
---------
a : array-like of shape (n, m_a)
b :... | python | def tensor_product(a, b, reshape=True):
"""
compute the tensor protuct of two matrices a and b
if a is (n, m_a), b is (n, m_b),
then the result is
(n, m_a * m_b) if reshape = True.
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(n, m_a, m_b) otherwise
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a : array-like of shape (n, m_a)
b :... | [
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dswah/pyGAM | pygam/links.py | LogitLink.link | def link(self, mu, dist):
"""
glm link function
this is useful for going from mu to the linear prediction
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
lp : np.array of length n
"""
... | python | def link(self, mu, dist):
"""
glm link function
this is useful for going from mu to the linear prediction
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
lp : np.array of length n
"""
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dswah/pyGAM | pygam/links.py | LogitLink.mu | def mu(self, lp, dist):
"""
glm mean function, ie inverse of link function
this is useful for going from the linear prediction to mu
Parameters
----------
lp : array-like of legth n
dist : Distribution instance
Returns
-------
mu : np.arr... | python | def mu(self, lp, dist):
"""
glm mean function, ie inverse of link function
this is useful for going from the linear prediction to mu
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----------
lp : array-like of legth n
dist : Distribution instance
Returns
-------
mu : np.arr... | [
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dswah/pyGAM | pygam/links.py | LogitLink.gradient | def gradient(self, mu, dist):
"""
derivative of the link function wrt mu
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
grad : np.array of length n
"""
return dist.levels/(mu*(dist.le... | python | def gradient(self, mu, dist):
"""
derivative of the link function wrt mu
Parameters
----------
mu : array-like of legth n
dist : Distribution instance
Returns
-------
grad : np.array of length n
"""
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dswah/pyGAM | pygam/penalties.py | derivative | def derivative(n, coef, derivative=2, periodic=False):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes the squared differences between basis coefficients.
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with cons... | python | def derivative(n, coef, derivative=2, periodic=False):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes the squared differences between basis coefficients.
Parameters
----------
n : int
number of splines
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dswah/pyGAM | pygam/penalties.py | monotonicity_ | def monotonicity_(n, coef, increasing=True):
"""
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Penalizes violation of monotonicity in the feature function.
Parameters
----------
n : int
number of splines
coef : array-like
coefficients of the feature functio... | python | def monotonicity_(n, coef, increasing=True):
"""
Builds a penalty matrix for P-Splines with continuous features.
Penalizes violation of monotonicity in the feature function.
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----------
n : int
number of splines
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dswah/pyGAM | pygam/penalties.py | none | def none(n, coef):
"""
Build a matrix of zeros for features that should go unpenalized
Parameters
----------
n : int
number of splines
coef : unused
for compatibility with constraints
Returns
-------
penalty matrix : sparse csc matrix of shape (n,n)
"""
retu... | python | def none(n, coef):
"""
Build a matrix of zeros for features that should go unpenalized
Parameters
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n : int
number of splines
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-------
penalty matrix : sparse csc matrix of shape (n,n)
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coef : unused
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penalty matrix : sparse csc matrix of shape (n,n) | [
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dswah/pyGAM | pygam/penalties.py | wrap_penalty | def wrap_penalty(p, fit_linear, linear_penalty=0.):
"""
tool to account for unity penalty on the linear term of any feature.
example:
p = wrap_penalty(derivative, fit_linear=True)(n, coef)
Parameters
----------
p : callable.
penalty-matrix-generating function.
fit_linear : ... | python | def wrap_penalty(p, fit_linear, linear_penalty=0.):
"""
tool to account for unity penalty on the linear term of any feature.
example:
p = wrap_penalty(derivative, fit_linear=True)(n, coef)
Parameters
----------
p : callable.
penalty-matrix-generating function.
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dswah/pyGAM | pygam/penalties.py | sparse_diff | def sparse_diff(array, n=1, axis=-1):
"""
A ported sparse version of np.diff.
Uses recursion to compute higher order differences
Parameters
----------
array : sparse array
n : int, default: 1
differencing order
axis : int, default: -1
axis along which differences are com... | python | def sparse_diff(array, n=1, axis=-1):
"""
A ported sparse version of np.diff.
Uses recursion to compute higher order differences
Parameters
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array : sparse array
n : int, default: 1
differencing order
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dswah/pyGAM | pygam/pygam.py | GAM._linear_predictor | def _linear_predictor(self, X=None, modelmat=None, b=None, term=-1):
"""linear predictor
compute the linear predictor portion of the model
ie multiply the model matrix by the spline basis coefficients
Parameters
---------
at least 1 of (X, modelmat)
and
... | python | def _linear_predictor(self, X=None, modelmat=None, b=None, term=-1):
"""linear predictor
compute the linear predictor portion of the model
ie multiply the model matrix by the spline basis coefficients
Parameters
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at least 1 of (X, modelmat)
and
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dswah/pyGAM | pygam/pygam.py | GAM.predict_mu | def predict_mu(self, X):
"""
preduct expected value of target given model and input X
Parameters
---------
X : array-like of shape (n_samples, m_features),
containing the input dataset
Returns
-------
y : np.array of shape (n_samples,)
... | python | def predict_mu(self, X):
"""
preduct expected value of target given model and input X
Parameters
---------
X : array-like of shape (n_samples, m_features),
containing the input dataset
Returns
-------
y : np.array of shape (n_samples,)
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dswah/pyGAM | pygam/pygam.py | GAM._modelmat | def _modelmat(self, X, term=-1):
"""
Builds a model matrix, B, out of the spline basis for each feature
B = [B_0, B_1, ..., B_p]
Parameters
---------
X : array-like of shape (n_samples, m_features)
containing the input dataset
term : int, optional
... | python | def _modelmat(self, X, term=-1):
"""
Builds a model matrix, B, out of the spline basis for each feature
B = [B_0, B_1, ..., B_p]
Parameters
---------
X : array-like of shape (n_samples, m_features)
containing the input dataset
term : int, optional
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dswah/pyGAM | pygam/pygam.py | GAM._cholesky | def _cholesky(self, A, **kwargs):
"""
method to handle potential problems with the cholesky decomposition.
will try to increase L2 regularization of the penalty matrix to
do away with non-positive-definite errors
Parameters
----------
A : np.array
Retur... | python | def _cholesky(self, A, **kwargs):
"""
method to handle potential problems with the cholesky decomposition.
will try to increase L2 regularization of the penalty matrix to
do away with non-positive-definite errors
Parameters
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A : np.array
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dswah/pyGAM | pygam/pygam.py | GAM._pseudo_data | def _pseudo_data(self, y, lp, mu):
"""
compute the pseudo data for a PIRLS iterations
Parameters
---------
y : array-like of shape (n,)
containing target data
lp : array-like of shape (n,)
containing linear predictions by the model
mu : ar... | python | def _pseudo_data(self, y, lp, mu):
"""
compute the pseudo data for a PIRLS iterations
Parameters
---------
y : array-like of shape (n,)
containing target data
lp : array-like of shape (n,)
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dswah/pyGAM | pygam/pygam.py | GAM._initial_estimate | def _initial_estimate(self, y, modelmat):
"""
Makes an inital estimate for the model coefficients.
For a LinearGAM we simply initialize to small coefficients.
For other GAMs we transform the problem to the linear space
and solve an unpenalized version.
Parameters
... | python | def _initial_estimate(self, y, modelmat):
"""
Makes an inital estimate for the model coefficients.
For a LinearGAM we simply initialize to small coefficients.
For other GAMs we transform the problem to the linear space
and solve an unpenalized version.
Parameters
... | [
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dswah/pyGAM | pygam/pygam.py | GAM._on_loop_start | def _on_loop_start(self, variables):
"""
performs on-loop-start actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
for callba... | python | def _on_loop_start(self, variables):
"""
performs on-loop-start actions like callbacks
variables contains local namespace variables.
Parameters
---------
variables : dict of available variables
Returns
-------
None
"""
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dswah/pyGAM | pygam/pygam.py | GAM._on_loop_end | def _on_loop_end(self, variables):
"""
performs on-loop-end actions like callbacks
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Parameters
---------
variables : dict of available variables
Returns
-------
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"""
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performs on-loop-end actions like callbacks
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variables : dict of available variables
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dswah/pyGAM | pygam/pygam.py | GAM.deviance_residuals | def deviance_residuals(self, X, y, weights=None, scaled=False):
"""
method to compute the deviance residuals of the model
these are analogous to the residuals of an OLS.
Parameters
----------
X : array-like
Input data array of shape (n_saples, m_features)
... | python | def deviance_residuals(self, X, y, weights=None, scaled=False):
"""
method to compute the deviance residuals of the model
these are analogous to the residuals of an OLS.
Parameters
----------
X : array-like
Input data array of shape (n_saples, m_features)
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_model_statistics | def _estimate_model_statistics(self, y, modelmat, inner=None, BW=None,
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"""
method to compute all of the model statistics
results are stored in the 'statistics_' attribute of the model, as a
dictionary keyed by:
... | python | def _estimate_model_statistics(self, y, modelmat, inner=None, BW=None,
B=None, weights=None, U1=None):
"""
method to compute all of the model statistics
results are stored in the 'statistics_' attribute of the model, as a
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_AIC | def _estimate_AIC(self, y, mu, weights=None):
"""
estimate the Akaike Information Criterion
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu : array-like of shape (n_samples,),
expected value of the targets given th... | python | def _estimate_AIC(self, y, mu, weights=None):
"""
estimate the Akaike Information Criterion
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu : array-like of shape (n_samples,),
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_AICc | def _estimate_AICc(self, y, mu, weights=None):
"""
estimate the corrected Akaike Information Criterion
relies on the estimated degrees of freedom, which must be computed
before.
Parameters
----------
y : array-like of shape (n_samples,)
output data v... | python | def _estimate_AICc(self, y, mu, weights=None):
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y : array-like of shape (n_samples,)
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_r2 | def _estimate_r2(self, X=None, y=None, mu=None, weights=None):
"""
estimate some pseudo R^2 values
currently only computes explained deviance.
results are stored
Parameters
----------
y : array-like of shape (n_samples,)
output data vector
mu... | python | def _estimate_r2(self, X=None, y=None, mu=None, weights=None):
"""
estimate some pseudo R^2 values
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results are stored
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y : array-like of shape (n_samples,)
output data vector
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_GCV_UBRE | def _estimate_GCV_UBRE(self, X=None, y=None, modelmat=None, gamma=1.4,
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"""
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dswah/pyGAM | pygam/pygam.py | GAM._estimate_p_values | def _estimate_p_values(self):
"""estimate the p-values for all features
"""
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raise AttributeError('GAM has not been fitted. Call fit first.')
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for term_i in range(len(self.terms)):
p_values.append(self._compute_p_value(... | python | def _estimate_p_values(self):
"""estimate the p-values for all features
"""
if not self._is_fitted:
raise AttributeError('GAM has not been fitted. Call fit first.')
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dswah/pyGAM | pygam/pygam.py | GAM._compute_p_value | def _compute_p_value(self, term_i):
"""compute the p-value of the desired feature
Arguments
---------
term_i : int
term to select from the data
Returns
-------
p_value : float
Notes
-----
Wood 2006, section 4.8.5:
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"""compute the p-value of the desired feature
Arguments
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term_i : int
term to select from the data
Returns
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p_value : float
Notes
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dswah/pyGAM | pygam/pygam.py | GAM.confidence_intervals | def confidence_intervals(self, X, width=.95, quantiles=None):
"""estimate confidence intervals for the model.
Parameters
----------
X : array-like of shape (n_samples, m_features)
Input data matrix
width : float on [0,1], optional
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"""estimate confidence intervals for the model.
Parameters
----------
X : array-like of shape (n_samples, m_features)
Input data matrix
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dswah/pyGAM | pygam/pygam.py | GAM._get_quantiles | def _get_quantiles(self, X, width, quantiles, modelmat=None, lp=None,
prediction=False, xform=True, term=-1):
"""
estimate prediction intervals for LinearGAM
Parameters
----------
X : array
input data of shape (n_samples, m_features)
wi... | python | def _get_quantiles(self, X, width, quantiles, modelmat=None, lp=None,
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estimate prediction intervals for LinearGAM
Parameters
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X : array
input data of shape (n_samples, m_features)
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dswah/pyGAM | pygam/pygam.py | GAM._flatten_mesh | def _flatten_mesh(self, Xs, term):
"""flatten the mesh and distribute into a feature matrix"""
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if self.terms[term].istensor:
terms = self.terms[term]
else:
terms = [self.terms[term]]
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... | python | def _flatten_mesh(self, Xs, term):
"""flatten the mesh and distribute into a feature matrix"""
n = Xs[0].size
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terms = self.terms[term]
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terms = [self.terms[term]]
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dswah/pyGAM | pygam/pygam.py | GAM.generate_X_grid | def generate_X_grid(self, term, n=100, meshgrid=False):
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dswah/pyGAM | pygam/pygam.py | GAM.partial_dependence | def partial_dependence(self, term, X=None, width=None, quantiles=None,
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Computes the term functions for the GAM
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dswah/pyGAM | pygam/pygam.py | GAM.sample | def sample(self, X, y, quantity='y', sample_at_X=None,
weights=None, n_draws=100, n_bootstraps=5, objective='auto'):
"""Simulate from the posterior of the coefficients and smoothing params.
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dswah/pyGAM | pygam/pygam.py | GAM._sample_coef | def _sample_coef(self, X, y, weights=None, n_draws=100, n_bootstraps=1,
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"""Simulate from the posterior of the coefficients.
NOTE: A `gridsearch` is done `n_bootstraps` many times, so keep
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dswah/pyGAM | pygam/pygam.py | GAM._bootstrap_samples_of_smoothing | def _bootstrap_samples_of_smoothing(self, X, y, weights=None,
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"""Sample the smoothing parameters using simulated response data.
For now, the grid of `lam` values is 11 random points in M-dimensional
space, where M = the... | python | def _bootstrap_samples_of_smoothing(self, X, y, weights=None,
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# Sample indices uniformly from {0, ..., n_bootstraps - 1}
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... | python | def _simulate_coef_from_bootstraps(
self, n_draws, coef_bootstraps, cov_bootstraps):
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# Sample indices uniformly from {0, ..., n_bootstraps - 1}
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dswah/pyGAM | pygam/pygam.py | LogisticGAM.accuracy | def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
containing input data
... | python | def accuracy(self, X=None, y=None, mu=None):
"""
computes the accuracy of the LogisticGAM
Parameters
----------
note: X or mu must be defined. defaults to mu
X : array-like of shape (n_samples, m_features), optional (default=None)
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dswah/pyGAM | pygam/pygam.py | PoissonGAM._exposure_to_weights | def _exposure_to_weights(self, y, exposure=None, weights=None):
"""simple tool to create a common API
Parameters
----------
y : array-like, shape (n_samples,)
Target values (integers in classification, real numbers in
regression)
For classification, l... | python | def _exposure_to_weights(self, y, exposure=None, weights=None):
"""simple tool to create a common API
Parameters
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dswah/pyGAM | pygam/pygam.py | PoissonGAM.predict | def predict(self, X, exposure=None):
"""
preduct expected value of target given model and input X
often this is done via expected value of GAM given input X
Parameters
---------
X : array-like of shape (n_samples, m_features), default: None
containing the inp... | python | def predict(self, X, exposure=None):
"""
preduct expected value of target given model and input X
often this is done via expected value of GAM given input X
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X : array-like of shape (n_samples, m_features), default: None
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dswah/pyGAM | pygam/pygam.py | PoissonGAM.gridsearch | def gridsearch(self, X, y, exposure=None, weights=None,
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**param_grids):
"""
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dswah/pyGAM | pygam/pygam.py | ExpectileGAM._get_quantile_ratio | def _get_quantile_ratio(self, X, y):
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Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
and m_features is the number of features.
y : a... | python | def _get_quantile_ratio(self, X, y):
"""find the expirical quantile of the model
Parameters
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X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
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dswah/pyGAM | pygam/pygam.py | ExpectileGAM.fit_quantile | def fit_quantile(self, X, y, quantile, max_iter=20, tol=0.01, weights=None):
"""fit ExpectileGAM to a desired quantile via binary search
Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
... | python | def fit_quantile(self, X, y, quantile, max_iter=20, tol=0.01, weights=None):
"""fit ExpectileGAM to a desired quantile via binary search
Parameters
----------
X : array-like, shape (n_samples, m_features)
Training vectors, where n_samples is the number of samples
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dswah/pyGAM | pygam/core.py | nice_repr | def nice_repr(name, param_kvs, line_width=30, line_offset=5, decimals=3, args=None, flatten_attrs=True):
"""
tool to do a nice repr of a class.
Parameters
----------
name : str
class name
param_kvs : dict
dict containing class parameters names as keys,
and the correspond... | python | def nice_repr(name, param_kvs, line_width=30, line_offset=5, decimals=3, args=None, flatten_attrs=True):
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tool to do a nice repr of a class.
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----------
name : str
class name
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dswah/pyGAM | pygam/core.py | Core.get_params | def get_params(self, deep=False):
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Parameters
----------
deep : boolean, default: False
when True, also gets non-user-facing paramters
Returns
-------
dict
"""
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"""
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Parameters
----------
deep : boolean, default: False
when True, also gets non-user-facing paramters
Returns
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dswah/pyGAM | pygam/core.py | Core.set_params | def set_params(self, deep=False, force=False, **parameters):
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sets an object's paramters
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deep : boolean, default: False
when True, also sets non-user-facing paramters
force : boolean, default: False
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"""
sets an object's paramters
Parameters
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deep : boolean, default: False
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force : boolean, default: False
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dswah/pyGAM | pygam/terms.py | Term.build_from_info | def build_from_info(cls, info):
"""build a Term instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
Term instance
"""
info = deepcopy(info)
... | python | def build_from_info(cls, info):
"""build a Term instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
Term instance
"""
info = deepcopy(info)
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dswah/pyGAM | pygam/terms.py | MetaTermMixin._has_terms | def _has_terms(self):
"""bool, whether the instance has any sub-terms
"""
loc = self._super_get('_term_location')
return self._super_has(loc) \
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"""
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dswah/pyGAM | pygam/terms.py | TensorTerm.build_from_info | def build_from_info(cls, info):
"""build a TensorTerm instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
TensorTerm instance
"""
terms = []
... | python | def build_from_info(cls, info):
"""build a TensorTerm instance from a dict
Parameters
----------
cls : class
info : dict
contains all information needed to build the term
Return
------
TensorTerm instance
"""
terms = []
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dswah/pyGAM | pygam/terms.py | TensorTerm.hasconstraint | def hasconstraint(self):
"""bool, whether the term has any constraints
"""
constrained = False
for term in self._terms:
constrained = constrained or term.hasconstraint
return constrained | python | def hasconstraint(self):
"""bool, whether the term has any constraints
"""
constrained = False
for term in self._terms:
constrained = constrained or term.hasconstraint
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dswah/pyGAM | pygam/terms.py | TensorTerm._build_marginal_constraints | def _build_marginal_constraints(self, i, coef, constraint_lam, constraint_l2):
"""builds a constraint matrix for a marginal term in the tensor term
takes a tensor's coef vector, and slices it into pieces corresponding
to term i, then builds a constraint matrix for each piece of the coef vector,... | python | def _build_marginal_constraints(self, i, coef, constraint_lam, constraint_l2):
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dswah/pyGAM | pygam/terms.py | TensorTerm._iterate_marginal_coef_slices | def _iterate_marginal_coef_slices(self, i):
"""iterator of indices into tensor's coef vector for marginal term i's coefs
takes a tensor_term and returns an iterator of indices
that chop up the tensor's coef vector into slices belonging to term i
Parameters
----------
i ... | python | def _iterate_marginal_coef_slices(self, i):
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