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Api extras

Models

Classes:

Name Description
ConditionalInteraction
ConditionalInteraction4Regions
DoubleConditionalInteraction
GeneralInteraction

ConditionalInteraction()

Bases: Base

Define a simple model.

\(f(x_1, x_2, x_3) = -x_1^2\mathbb{1}_{x_2 < 0} + x_1^2\mathbb{1}_{x_2 \geq 0} + e^{x_3}\)

Methods:

Name Description
jacobian

Calculate the Jacobian of the model.
predict

Predict.
Source code in effector/models.py 
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def __init__(self):
    """Define a simple model.

    $f(x_1, x_2, x_3) = -x_1^2\mathbb{1}_{x_2 < 0} + x_1^2\mathbb{1}_{x_2 \geq 0} + e^{x_3}$

    """
    super().__init__(name=self.__class__.__name__)

jacobian(x)

Calculate the Jacobian of the model.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Jacobian of the model, shape (N, 3)
Source code in effector/models.py 
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def jacobian(self, x: np.ndarray) -> np.ndarray:
    """Calculate the Jacobian of the model.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Jacobian of the model, shape (N, 3)
    """
    y = np.zeros_like(x)
    y[:, 2] = np.exp(x[:, 2])
    ind = x[:, 1] < 0
    y[ind, 0] = -2*x[ind, 0]
    y[~ind, 0] = 2*x[~ind, 0]
    return y

predict(x)

Predict.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Output of the model, shape (N,)
Source code in effector/models.py 
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def predict(self, x: np.ndarray) -> np.ndarray:
    """Predict.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Output of the model, shape (N,)
    """
    y = np.exp(x[:, 2])
    ind = x[:, 1] < 0
    y[ind] += -x[ind, 0]**2
    y[~ind] += x[~ind, 0]**2
    return y

ConditionalInteraction4Regions()

Bases: Base

$f(x_1, x_2, x_3) = egin{cases} -x_1^2 + e^{x_3}, & ext{if } x_2 < 0 ext{ and } x_3 < 0 \ x_1^2 + e^{x_3}, & ext{if } x_2 < 0 ext{ and } x_3 \geq 0 \ -x_1^4 + e^{x_3}, & ext{if } x_2 \geq 0 ext{ and } x_3 < 0 \ x_1^4 + e^{x_3}, & ext{if } x_2 \geq 0 ext{ and } x_3 \geq 0 \end{cases}

Methods:

Name Description
jacobian

Calculate the Jacobian of the model.
predict

Predict.
Source code in effector/models.py 
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def __init__(self):
    """
    $f(x_1, x_2, x_3) = 
    \begin{cases} 
    -x_1^2 + e^{x_3}, & \text{if } x_2 < 0 \text{ and } x_3 < 0 \\
    x_1^2 + e^{x_3}, & \text{if } x_2 < 0 \text{ and } x_3 \geq 0 \\
    -x_1^4 + e^{x_3}, & \text{if } x_2 \geq 0 \text{ and } x_3 < 0 \\
    x_1^4 + e^{x_3}, & \text{if } x_2 \geq 0 \text{ and } x_3 \geq 0
    \end{cases}
    """
    super().__init__(name=self.__class__.__name__)

jacobian(x)

Calculate the Jacobian of the model.

Parameters:

Name Type Description Default
x

Input data, shape (N, 4)

 required

Returns:

Type Description
ndarray

Jacobian of the model, shape (N, 4)
Source code in effector/models.py 
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def jacobian(self, x: np.ndarray) -> np.ndarray:
    """Calculate the Jacobian of the model.

    Args:
        x : Input data, shape (N, 4)

    Returns:
        Jacobian of the model, shape (N, 4)
    """
    y = np.zeros(x.shape)

    y[:, 2] = np.exp(x[:, 2])

    mask1 = (x[:, 1] < 0) & (x[:, 2] < 0)
    mask2 = (x[:, 1] < 0) & (x[:, 2] >= 0)
    mask3 = (x[:, 1] >= 0) & (x[:, 2] < 0)
    mask4 = (x[:, 1] >= 0) & (x[:, 2] >= 0)

    y[mask1, 0] = -2 * x[mask1, 0]
    y[mask2, 0] = 2 * x[mask2, 0]
    y[mask3, 0] = -4 * x[mask3, 0] ** 3
    y[mask4, 0] = 4 * x[mask4, 0] ** 3

    return y

predict(x)

Predict.

Parameters:

Name Type Description Default
x

Input data, shape (N, 4)

 required

Returns:

Type Description
ndarray

Output of the model, shape (N,)
Source code in effector/models.py 
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def predict(self, x: np.ndarray) -> np.ndarray:
    """Predict.

    Args:
        x : Input data, shape (N, 4)

    Returns:
        Output of the model, shape (N,)
    """
    y = np.exp(x[:, 2])

    mask1 = (x[:, 1] < 0) & (x[:, 2] < 0)
    mask2 = (x[:, 1] < 0) & (x[:, 2] >= 0)
    mask3 = (x[:, 1] >= 0) & (x[:, 2] < 0)
    mask4 = (x[:, 1] >= 0) & (x[:, 2] >= 0)

    y[mask1] += -x[mask1, 0] ** 2
    y[mask2] += x[mask2, 0] ** 2
    y[mask3] += -x[mask3, 0] ** 4
    y[mask4] += x[mask4, 0] ** 4

    return y

DoubleConditionalInteraction()

Bases: Base

Define a simple model.

\(f(x_1, x_2, x_3) = -3x_1^2\mathbb{1}_{x_2 < 0}\mathbb{1}_{x_3 < 0} + +x_1^2\mathbb{1}_{x_2 < 0}\mathbb{1}_{x_3 \geq 0} -e^{x_1}\mathbb{1}_{x_2 \geq 0}\mathbb{1}_{x_3 < 0} +e^{3x_1}\mathbb{1}_{x_2 \geq 0}\mathbb{1}_{x_3 \geq 0}\) $

Methods:

Name Description
jacobian

Calculate the Jacobian of the model.
predict

Predict.
Source code in effector/models.py 
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def __init__(self):
    """Define a simple model.

    $f(x_1, x_2, x_3) = -3x_1^2\mathbb{1}_{x_2 < 0}\mathbb{1}_{x_3 < 0} +
                        +x_1^2\mathbb{1}_{x_2 < 0}\mathbb{1}_{x_3 \geq 0}
                        -e^{x_1}\mathbb{1}_{x_2 \geq 0}\mathbb{1}_{x_3 < 0}
                        +e^{3x_1}\mathbb{1}_{x_2 \geq 0}\mathbb{1}_{x_3 \geq 0}$
                        $

    """
    super().__init__(name=self.__class__.__name__)

jacobian(x)

Calculate the Jacobian of the model.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Jacobian of the model, shape (N, 3)
Source code in effector/models.py 
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def jacobian(self, x: np.ndarray) -> np.ndarray:
    """Calculate the Jacobian of the model.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Jacobian of the model, shape (N, 3)
    """
    y = np.zeros_like(x)
    ind1 = x[:, 1] < 0
    ind2 = x[:, 2] < 0
    y[ind1 & ind2, 0] = -2*3*x[ind1 & ind2, 0]
    y[ind1 & ~ind2, 0] = 2*x[ind1 & ~ind2, 0]
    y[~ind1 & ind2, 0] = -np.exp(x[~ind1 & ind2, 0])
    y[~ind1 & ~ind2, 0] = 3*np.exp(3*x[~ind1 & ~ind2, 0])
    return y

predict(x)

Predict.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Output of the model, shape (N,)
Source code in effector/models.py 
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def predict(self, x: np.ndarray) -> np.ndarray:
    """Predict.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Output of the model, shape (N,)
    """
    y = np.zeros(x.shape[0])
    ind1 = x[:, 1] < 0
    ind2 = x[:, 2] < 0
    y[ind1 & ind2] = -3*x[ind1 & ind2, 0]**2
    y[ind1 & ~ind2] = x[ind1 & ~ind2, 0]**2
    y[~ind1 & ind2] = -np.exp(x[~ind1 & ind2, 0])
    y[~ind1 & ~ind2] = np.exp(3*x[~ind1 & ~ind2, 0])
    return y

GeneralInteraction()

Bases: Base

Define a simple model.

\(f(x_1, x_2, x_3) = x_1 x_2^2 + e^{x_3}\)

Methods:

Name Description
jacobian

Calculate the Jacobian of the model.
predict

Predict.
Source code in effector/models.py 
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def __init__(self):
    """Define a simple model.

    $f(x_1, x_2, x_3) = x_1 x_2^2 + e^{x_3}$

    """
    super().__init__(name=self.__class__.__name__)

jacobian(x)

Calculate the Jacobian of the model.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Jacobian of the model, shape (N, 3)
Source code in effector/models.py 
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def jacobian(self, x: np.ndarray) -> np.ndarray:
    """Calculate the Jacobian of the model.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Jacobian of the model, shape (N, 3)
    """
    y = np.zeros_like(x)
    y[:, 0] = x[:, 1] ** 2
    y[:, 1] = 2 * x[:, 0] * x[:, 1]
    y[:, 2] = np.exp(x[:, 2])
    return y

predict(x)

Predict.

Parameters:

Name Type Description Default
x

Input data, shape (N, 3)

 required

Returns:

Type Description
ndarray

Output of the model, shape (N,)
Source code in effector/models.py 
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def predict(self, x: np.ndarray) -> np.ndarray:
    """Predict.

    Args:
        x : Input data, shape (N, 3)

    Returns:
        Output of the model, shape (N,)
    """
    y = x[:, 0] * x[:, 1] ** 2 + np.exp(x[:, 2])
    return y

Datasets

Classes:

Name Description
Base
IndependentUniform

Base(name, dim, axis_limits)

Methods:

Name Description
generate_data

Generate N samples
Source code in effector/datasets.py 
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def __init__(self, name: str, dim: int, axis_limits: np.array):
    self.name = helpers.camel_to_snake(name)
    self.dim = dim
    self.axis_limits = axis_limits

generate_data(n, seed=21)

Generate N samples Args: n : int Number of samples seed : int Seed for generating samples

Returns:

Type Description
array

ndarray, shape: [n,2] The samples
Source code in effector/datasets.py 
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def generate_data(self, n: int, seed: int = 21) -> np.array:
    """Generate N samples
    Args:
        n : int
            Number of samples
        seed : int
            Seed for generating samples

    Returns:
        ndarray, shape: [n,2]
            The samples
    """
    raise NotImplementedError

IndependentUniform(dim=2, low=0, high=1)

Bases: Base

Methods:

Name Description
generate_data

Generate N samples
Source code in effector/datasets.py 
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def __init__(self, dim: int =2, low: float = 0, high: float = 1):
    axis_limits = np.array([[low, high] for _ in range(dim)]).T
    super().__init__(name=self.__class__.__name__, dim=dim, axis_limits=axis_limits)

generate_data(n, seed=21)

Generate N samples

Parameters:

Name Type Description Default
n

int Number of samples

 required
seed

int Seed for generating samples

 21

Returns:

Type Description
array

ndarray, shape: [n,2] The samples
Source code in effector/datasets.py 
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def generate_data(self, n: int, seed: int = 21) -> np.array:
    """Generate N samples

    Args:
        n : int
            Number of samples
        seed : int
            Seed for generating samples

    Returns:
        ndarray, shape: [n,2]
            The samples

    """
    np.random.seed(seed)
    x = np.random.uniform(self.axis_limits[0, :], self.axis_limits[1, :], (n, self.dim))
    np.random.shuffle(x)
    return x