diff --git a/.github/workflows/run-tests.yml b/.github/workflows/run-tests.yml
index 4c6977b..1f39526 100644
--- a/.github/workflows/run-tests.yml
+++ b/.github/workflows/run-tests.yml
@@ -14,7 +14,7 @@ jobs:
strategy:
fail-fast: false
matrix:
- python-version: ["3.9", "3.10"]
+ python-version: ["3.10"]
steps:
- uses: actions/checkout@v4
diff --git a/mlpp_lib/__init__.py b/mlpp_lib/__init__.py
index ef2d419..3213f00 100644
--- a/mlpp_lib/__init__.py
+++ b/mlpp_lib/__init__.py
@@ -2,4 +2,4 @@
import os
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
+os.environ["KERAS_BACKEND"] = "torch"
diff --git a/mlpp_lib/callbacks.py b/mlpp_lib/callbacks.py
index 7c5ed33..07c8e9e 100644
--- a/mlpp_lib/callbacks.py
+++ b/mlpp_lib/callbacks.py
@@ -2,7 +2,7 @@
import numpy as np
import properscoring as ps
-from tensorflow.keras import callbacks
+from keras import callbacks
class EnsembleMetrics(callbacks.Callback):
@@ -16,7 +16,7 @@ def add_validation_data(self, validation_data) -> None:
def on_epoch_end(self, epoch, logs):
"""Compute a range of probabilistic scores at the end of each epoch."""
- y_pred = self.model(self.X_val).sample(self.n_samples)
+ y_pred = self.model(self.X_val).sample((self.n_samples,))
y_pred = y_pred.numpy()[:, :, 0].T
y_val = np.squeeze(self.y_val)
diff --git a/mlpp_lib/custom_distributions.py b/mlpp_lib/custom_distributions.py
new file mode 100644
index 0000000..30d3d30
--- /dev/null
+++ b/mlpp_lib/custom_distributions.py
@@ -0,0 +1,178 @@
+import torch
+from torch.distributions import Distribution, constraints, Normal
+
+class TruncatedNormalDistribution(Distribution):
+ """
+ Implementation of a truncated normal distribution in [a, b] with
+ differentiable sampling.
+
+ Source: The Truncated Normal Distribution, John Burkardt 2023
+ """
+
+ def __init__(self, mu_bar: torch.Tensor, sigma_bar: torch.Tensor, a: torch.Tensor,b: torch.Tensor):
+ """_summary_
+
+ Args:
+ mu_bar (torch.Tensor): The mean of the underlying Normal. It is not the true mean.
+ sigma_bar (torch.Tensor): The std of the underlying Normal. It is not the true std.
+ a (torch.Tensor): The left boundary
+ b (torch.Tensor): The right boundary
+ """
+ self._n = Normal(mu_bar, sigma_bar)
+ self.mu_bar = mu_bar
+ self.sigma_bar = sigma_bar
+ super().__init__()
+
+ self.a = a
+ self.b = b
+
+
+ def icdf(self, p):
+ # inverse cdf
+ p_ = self._n.cdf(self.a) + p * (self._n.cdf(self.b) - self._n.cdf(self.a))
+ return self._n.icdf(p_)
+
+ def mean(self) -> torch.Tensor:
+ """
+ Returns:
+ torch.Tensor: Returns the true mean of the distribution.
+ """
+ alpha = (self.a - self.mu_bar) / self.sigma_bar
+ beta = (self.b - self.mu_bar) / self.sigma_bar
+
+ sn = torch.distributions.Normal(torch.zeros_like(self.mu_bar), torch.ones_like(self.mu_bar))
+
+ scale = (torch.exp(sn.log_prob(beta)) - torch.exp(sn.log_prob(alpha)))/(sn.cdf(beta) - sn.cdf(alpha))
+
+ return self.mu_bar - self.sigma_bar * scale
+
+ def variance(self) -> torch.Tensor:
+ """
+ Returns:
+ torch.Tensor: Returns the true variance of the distribution.
+ """
+ alpha = (self.a - self.mu_bar) / self.sigma_bar
+ beta = (self.b - self.mu_bar) / self.sigma_bar
+
+ sn = torch.distributions.Normal(torch.zeros_like(self.mu_bar), torch.ones_like(self.mu_bar))
+
+ pdf_a = torch.exp(sn.log_prob(alpha))
+ pdf_b = torch.exp(sn.log_prob(beta))
+ CDF_a = sn.cdf(alpha)
+ CDF_b = sn.cdf(beta)
+
+ return self.sigma_bar**2 * (1.0 - (beta*pdf_b - alpha*pdf_a)/(CDF_b - CDF_a) - ((pdf_b - pdf_a)/(CDF_b - CDF_a))**2)
+
+
+ def moment(self, k):
+ # Source: A Recursive Formula for the Moments of a Truncated Univariate Normal Distribution (Eric Orjebin)
+ if k == -1:
+ return torch.zeros_like(self.mu_bar)
+ if k == 0:
+ return torch.ones_like(self.mu_bar)
+
+ alpha = (self.a - self.mu_bar) / self.sigma_bar
+ beta = (self.b - self.mu_bar) / self.sigma_bar
+ sn = torch.distributions.Normal(torch.zeros_like(self.mu_bar), torch.ones_like(self.mu_bar))
+
+ scale = ((self.b**(k-1) * torch.exp(sn.log_prob(beta)) - self.a**(k-1) * torch.exp(sn.log_prob(alpha))) / (sn.cdf(beta) - sn.cdf(alpha)))
+
+ return (k-1)* self.sigma_bar ** 2 * self.moment(k-2) + self.mu_bar * self.moment(k-1) - self.sigma_bar * scale
+
+ def sample(self, shape):
+ return self.rsample(shape)
+
+ def rsample(self, shape):
+ # get some random probability [0,1]
+ p = torch.distributions.Uniform(0,1).sample(shape)
+ # apply the inverse cdf on p
+ return self.icdf(p)
+
+ @property
+ def arg_constraints(self):
+ return {
+ 'mu_bar': constraints.real,
+ 'sigma_bar': constraints.positive,
+ }
+
+ @property
+ def has_rsample(self):
+ return True
+
+class CensoredNormalDistribution(Distribution):
+ r"""Implements a censored Normal distribution.
+ Values of the underlying normal that lie outside the range [a,b]
+ are assigned to a and b respectively.
+
+ .. math::
+ f_Y(y) =
+ \begin{cases}
+ a, & \text{if } y \leq a \\
+ \sim N(\bar{\mu}, \bar{\sigma}) & \text{if } a < y < b \\
+ b, & \text{if } y \geq b \\
+ \end{cases}
+
+
+ """
+
+ def __init__(self, mu_bar: torch.Tensor, sigma_bar: torch.Tensor, a: torch.Tensor,b: torch.Tensor):
+ """
+ Args:
+ mu_bar (torch.Tensor): The mean of the latent normal distribution
+ sigma_bar (torch.Tensor): The std of the latend normal distribution
+ a (torch.Tensor): The lower bound of the distribution.
+ b (torch.Tensor): The upper bound of the distribution.
+ """
+
+
+ self._n = Normal(mu_bar, sigma_bar)
+ self.mu_bar = mu_bar
+ self.sigma_bar = sigma_bar
+ super().__init__()
+
+ self.a = a
+ self.b = b
+
+
+ def mean(self):
+ alpha = (self.a - self.mu_bar) / self.sigma_bar
+ beta = (self.b - self.mu_bar) / self.sigma_bar
+
+ sn = torch.distributions.Normal(torch.zeros_like(self.mu_bar), torch.ones_like(self.mu_bar))
+ E_z = TruncatedNormalDistribution(self.mu_bar, self.sigma_bar, self.a, self.b).mean()
+ return (
+ self.b * (1-sn.cdf(beta))
+ + self.a * sn.cdf(alpha)
+ + E_z * (sn.cdf(beta) - sn.cdf(alpha))
+ )
+
+
+ def variance(self):
+ # Variance := Var(Y) = E(Y^2) - E(Y)^2
+ alpha = (self.a - self.mu_bar) / self.sigma_bar
+ beta = (self.b - self.mu_bar) / self.sigma_bar
+ sn = torch.distributions.Normal(torch.zeros_like(self.mu_bar), torch.ones_like(self.sigma_bar))
+ tn = TruncatedNormalDistribution(mu_bar=self.mu_bar, sigma_bar=self.sigma_bar, a=self.a, b=self.b)
+
+ # Law of total expectation:
+ # E(Y^2) = E(Y^2|X>b)*P(X>b) + E(Y^2|Xb) + a^2 * P(X str:
return out
-class DataLoader(tf.keras.utils.Sequence):
+class DataLoader(keras.utils.Sequence):
"""A dataloader for mlpp.
Parameters
@@ -598,16 +599,16 @@ def __init__(
self.shuffle = shuffle
self.block_size = block_size
self.num_samples = len(self.dataset.x)
- self.num_batches = int(np.ceil(self.num_samples / batch_size))
- self._indices = tf.range(self.num_samples)
+ self.num_batches_ = int(np.ceil(self.num_samples / batch_size))
+ self._indices = keras.ops.arange(self.num_samples)
self._seed = 0
self._reset()
def __len__(self) -> int:
- return self.num_batches
+ return self.num_batches_
- def __getitem__(self, index) -> tuple[tf.Tensor, ...]:
- if index >= self.num_batches:
+ def __getitem__(self, index) -> tuple[KerasTensor, ...]:
+ if index >= self.num_batches_:
self._reset()
raise IndexError
start = index * self.batch_size
@@ -626,38 +627,37 @@ def _shuffle_indices(self) -> None:
each block stay in their original order, but the blocks themselves are shuffled.
"""
if self.block_size == 1:
- self._indices = tf.random.shuffle(self._indices, seed=self._seed)
+ self._indices = keras.random.shuffle(self._indices, seed=self._seed)
return
num_blocks = self._indices.shape[0] // self.block_size
- reshaped_indices = tf.reshape(
+ reshaped_indices = keras.ops.reshape(
self._indices[: num_blocks * self.block_size], (num_blocks, self.block_size)
)
- shuffled_blocks = tf.random.shuffle(reshaped_indices, seed=self._seed)
- shuffled_indices = tf.reshape(shuffled_blocks, [-1])
+ shuffled_blocks = keras.random.shuffle(reshaped_indices, seed=self._seed)
+ shuffled_indices = keras.reshape(shuffled_blocks, [-1])
# Append any remaining elements if the number of indices isn't a multiple of the block size
if shuffled_indices.shape[0] % self.block_size:
remainder = self._indices[num_blocks * self.block_size :]
- shuffled_indices = tf.concat([shuffled_indices, remainder], axis=0)
+ shuffled_indices = keras.ops.concatenate([shuffled_indices, remainder], axis=0)
self._indices = shuffled_indices
-
def _reset(self) -> None:
"""Reset iterator and shuffles data if needed"""
self.index = 0
if self.shuffle:
self._shuffle_indices()
- self.dataset.x = tf.gather(self.dataset.x, self._indices)
- self.dataset.y = tf.gather(self.dataset.y, self._indices)
+ self.dataset.x = keras.ops.take(self.dataset.x, self._indices, axis=0)
+ self.dataset.y = keras.ops.take(self.dataset.y, self._indices, axis=0)
if self.dataset.w is not None:
- self.dataset.w = tf.gather(self.dataset.w, self._indices)
+ self.dataset.w = keras.ops.take(self.dataset.w, self._indices, axis=0)
self._seed += 1
def _to_device(self, device) -> None:
"""Transfer data to a device"""
- with tf.device(device):
- self.dataset.x = tf.constant(self.dataset.x)
- self.dataset.y = tf.constant(self.dataset.y)
+ with keras.device(device):
+ self.dataset.x = keras.ops.array(self.dataset.x)
+ self.dataset.y = keras.ops.array(self.dataset.y)
if self.dataset.w is not None:
- self.dataset.w = tf.constant(self.dataset.w)
+ self.dataset.w = keras.ops.array(self.dataset.w)
class DataFilter:
diff --git a/mlpp_lib/exceptions.py b/mlpp_lib/exceptions.py
new file mode 100644
index 0000000..e90be88
--- /dev/null
+++ b/mlpp_lib/exceptions.py
@@ -0,0 +1,3 @@
+class MissingReparameterizationError(Exception):
+ """Raised when a sampling function without 'rsample' is used in a context requiring reparameterization."""
+ pass
\ No newline at end of file
diff --git a/mlpp_lib/layers.py b/mlpp_lib/layers.py
new file mode 100644
index 0000000..56db108
--- /dev/null
+++ b/mlpp_lib/layers.py
@@ -0,0 +1,200 @@
+from keras.src.layers import Layer
+from typing import Optional, Union, Literal
+import keras
+import keras.ops as ops
+import torch
+from keras.src.layers import (
+ Add,
+ Dense,
+ Dropout,
+ BatchNormalization,
+ Activation,
+)
+
+@keras.saving.register_keras_serializable()
+class MonteCarloDropout(Dropout):
+ def call(self, inputs):
+ return super().call(inputs, training=True)
+
+@keras.saving.register_keras_serializable()
+class FullyConnectedLayer(Layer):
+ """ A fully connected layer composed of a sequence
+ of linear layers interleaved by optional
+ batch norms, and dropouts/MC dropouts.
+ """
+ def __init__(self,
+ hidden_layers: list,
+ batchnorm: bool = False,
+ activations: Optional[Union[str, list[str]]] = "relu",
+ dropout: Optional[Union[float, list[float]]] = None,
+ mc_dropout: bool = False,
+ skip_connection: bool = False,
+ skip_connection_act: str = 'linear',
+ indx=0):
+ super().__init__()
+
+ if isinstance(activations, list):
+ assert len(activations) == len(hidden_layers)
+ elif isinstance(activations, str):
+ activations = [activations] * len(hidden_layers)
+
+ if isinstance(dropout, list):
+ assert len(dropout) == len(hidden_layers)
+ elif isinstance(dropout, float):
+ dropout = [dropout] * (len(hidden_layers))
+ else:
+ dropout = []
+
+
+ self.skip_conn = skip_connection
+ self.layers = []
+ self.hidden_layers = hidden_layers
+
+ for i,units in enumerate(hidden_layers):
+ self.layers.append(Dense(units, name=f"dense_{indx}:{i}"))
+ if batchnorm:
+ self.layers.append(BatchNormalization())
+ self.layers.append(Activation(activations[i]))
+ if i < len(dropout) and 0.0 < dropout[i] < 1.0:
+ if mc_dropout:
+ self.layers.append(MonteCarloDropout(dropout[i], name=f"mc_dropout_{indx}:{i}"))
+ else:
+ self.layers.append(Dropout(dropout[i], name=f"dropout_{indx}:{i}"))
+
+ if skip_connection:
+ self.skip_enc = Dense(hidden_layers[-1], name=f"skip_dense")
+ self.skip_add = Add(name=f"skip_add")
+ self.skip_act = Activation(activation=skip_connection_act, name=f"skip_activation")
+
+
+ def compute_output_shape(self, input_shape):
+ return (input_shape[0], self.hidden_layers[-1])
+
+
+ def call(self, inputs):
+ # iterate layers
+ out = inputs
+ for l in self.layers:
+ out = l(out)
+ # optional skip connection
+ if self.skip_conn:
+ inputs = self.skip_enc(inputs)
+ out = self.skip_add([out, inputs])
+ out = self.skip_act(out)
+ return out
+
+
+
+class MultibranchLayer(Layer):
+ def __init__(self, branches: list[Layer], aggregation: Literal['sum', 'concat']='concat'):
+ super().__init__()
+
+ self.branches = branches
+ self.aggr = keras.layers.Concatenate(axis=1) if aggregation == 'concat' else keras.layers.Add()
+
+
+ def call(self, inputs):
+ branch_outputs = [branch(inputs) for branch in self.branches]
+ return self.aggr(branch_outputs)
+
+class CrossNetLayer(keras.layers.Layer):
+ def __init__(self, hidden_size, depth=1):
+ super().__init__()
+
+ self.ws = [
+ self.add_weight(
+ shape=(hidden_size, 1),
+ initializer="random_normal",
+ trainable=True)
+ for _ in range(depth)
+ ]
+ self.bs = [
+ self.add_weight(
+ shape=(hidden_size, 1),
+ initializer="random_normal",
+ trainable=True)
+ for _ in range(depth)
+ ]
+
+ self.hidden_size = hidden_size
+ self.encoder = Dense(self.hidden_size)
+
+ def build(self, input_shape):
+ super().build(input_shape)
+
+ def call(self, x):
+ x = self.encoder(x)
+ # x_{l+1} = x_0*x_l^T*w_l + b_l + x_l = f(x_l, w_l, b_l) + x_l
+ # where f learns the residual of x_{l+1} - x+l
+ x0 = x
+ x_l = x
+ for l in range(len(self.ws)):
+ outer_prod = x0.unsqueeze(2) * x_l.unsqueeze(1)
+ residual = ops.matmul(outer_prod, self.ws[l]) + self.bs[l]
+ residual = residual.squeeze()
+
+ x_l = residual + x_l
+
+ return x_l
+
+ def compute_output_shape(self, input_shape, *args, **kwargs):
+ return (input_shape[0], self.hidden_size)
+
+
+class ParallelConcatenateLayer(Layer):
+ """Feeds the same input to all given layers
+ and concatenates their outputs along the last dimension.
+ """
+ def __init__(self, layers: list[Layer]):
+ super().__init__()
+
+ self.layers = layers
+
+ def call(self, inputs):
+
+ return keras.layers.Concatenate(axis=-1)([l(inputs) for l in self.layers])
+
+
+class MeanAndTriLCovLayer(Layer):
+ """ Layer that learns to output the mean of a distribution and
+ a lower triangular matrix which could be interpreted
+ as L such that LL^T=Cov(x) by a downstream distribution.
+ The layer does not apply any constraints on the outputs.
+ """
+
+ def __init__(self, d1, bias_init='zeros'):
+ super().__init__()
+ d2 = d1 * (d1 + 1) // 2
+
+ self.mean_layer = Dense(d1, bias_initializer=bias_init, name='mean_layer')
+ self.tril_cov_layer = Dense(d2, name='tril_cov_layer')
+ self.d1 = d1
+
+ def call(self, inputs):
+ mean = self.mean_layer(inputs)
+ flat_cov = self.tril_cov_layer(inputs)
+
+ tril_cov = self._build_lower_triangular(flat=flat_cov)
+
+ return mean, tril_cov
+
+
+ def _build_lower_triangular(self, flat):
+ """
+ Convert the flat tensor into a lower triangular matrix.
+ Does not apply any constraints.
+ Args:
+ - flat: Tensor of shape (batch_size, dim * (dim + 1) // 2)
+
+ Returns:
+ - L: Lower triangular matrix of shape (batch_size, dim, dim)
+ """
+ batch_size = flat.size(0)
+
+ L = torch.zeros(batch_size, self.d1, self.d1, device=flat.device, dtype=flat.dtype)
+
+ tril_indices = torch.tril_indices(row=self.d1, col=self.d1, offset=0)
+
+ L[:, tril_indices[0], tril_indices[1]] = flat
+
+ return L
\ No newline at end of file
diff --git a/mlpp_lib/losses.py b/mlpp_lib/losses.py
index c61d833..c8c5e34 100644
--- a/mlpp_lib/losses.py
+++ b/mlpp_lib/losses.py
@@ -1,680 +1,878 @@
-from typing import Literal, Optional, Union
+import os
+
+from mlpp_lib.exceptions import MissingReparameterizationError
+from mlpp_lib.probabilistic_layers import WrappingTorchDist
+os.environ["KERAS_BACKEND"] = "torch"
+import torch
+import keras
+import scoringrules as sr
+import typing as tp
+from keras.src import ops
+from keras import tree
+from keras.src.losses.loss import reduce_weighted_values
+from keras.src.losses.losses import LossFunctionWrapper
+from keras.src import backend
+import warnings
+
+class DistributionLoss(keras.Loss):
+ """Loss base class allowing for non-tensor (distributions) inputs.
+
+ We simply override the `__call__` method in order to avoid calling `conver_to_tensor`
+ on `Distribution` objects, which would raise an error.
-import numpy as np
-import tensorflow as tf
-import tensorflow_probability as tfp
-from tensorflow_probability import distributions as tfd
-
-from mlpp_lib.decorators import with_attrs
-
-
-@with_attrs(loss_type="probabilistic")
-def crps_energy_ensemble(
- obs: Union[tf.Tensor, np.ndarray],
- fct_ens: Union[tf.Tensor, np.ndarray],
-) -> tf.Tensor:
- """
- Energy form of the Continuous Ranked Probability Score from Gneiting and Raftery (2007),
- where the expectations terms are approximated from an ensemble.
-
- .. math::
- CRPS(F, y) = E_F|X - y| - 1/2 * E_F|X - X'|
-
- Parameters
- ----------
- fct_ens: array-like
- Ensemble forecasts, with ensemble members along the first dimension.
- obs: array-like
- Observations.
-
- Return
- ------
- crps: tf.Tensor
- The CRPS for each sample.
- """
-
- # first term
- E_1 = tf.abs(fct_ens - obs[None, :])
- E_1 = tf.reduce_mean(E_1, axis=0)
-
- # second term
- E_2 = tf.abs(fct_ens[None, :] - fct_ens[:, None])
- E_2 = tf.reduce_mean(E_2, axis=(0, 1))
- crps = E_1 - E_2 / 2
-
- return crps
-
-
-@with_attrs(loss_type="probabilistic")
-def crps_energy(
- obs: Union[tf.Tensor, np.ndarray],
- fct_dist: tfp.distributions.Distribution,
-) -> tf.Tensor:
- """
- Energy form of the Continuous Ranked Probability Score from Gneiting and Raftery (2007),
- where the expectation terms are approximated from the distribution using monte-carlo methods.
-
- .. math::
- CRPS(F, y) = E_F|X - y| - 1/2 * E_F|X - X'|
-
- Parameters
- ----------
- obs: array-like
- Array of observations.
- fct_dist: tensorflow-probability Distribution
- The predicted distribution.
-
- Return
- ------
- crps: tf.Tensor
- The CRPS for each sample.
- """
-
- n_samples = 1000
-
- obs = tf.debugging.check_numerics(obs, "Target values")
-
- use_reparameterization = (
- fct_dist.reparameterization_type == tfd.FULLY_REPARAMETERIZED
- )
-
- samples_1 = fct_dist.sample(n_samples)
- samples_2 = fct_dist.sample(n_samples)
-
- # first term
- E_1 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - obs[None, :], ord=1, axis=-1),
- samples=samples_1,
- log_prob=fct_dist.log_prob,
- use_reparameterization=use_reparameterization,
- )
-
- # second term
- E_2 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
- samples=samples_1,
- log_prob=fct_dist.log_prob,
- use_reparameterization=use_reparameterization,
- )
- crps = E_1 - E_2 / 2
- # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
- crps = tf.abs(crps)
-
- return crps[..., None]
-
-
-class WeightedCRPSEnergy(tf.keras.losses.Loss):
- """
- Compute threshold-weighted CRPS using its kernel score representation.
-
- Parameters
- ----------
- threshold : float
- The threshold for the weight function within the threshold-weighted CRPS.
- Specifically, the weight function w(x) is 1{x > threshold}.
- n_samples : int, optional
- The number of Monte Carlo samples to be used for estimating expectations. Must be greater than 1.
- Used only if `y_pred` is of type `tfp.Distribution`.
- correct_crps : bool, optional
- If True, applies a bias correction to the CRPS, as detailed in Eq. 4 of Fricker et al. (2013).
- **kwargs : dict, optional
- Additional keyword arguments to pass to the parent `Loss` class.
-
- Methods
- -------
- call(y_true, y_pred, scale=None):
- Compute the CRPS for predictions and ground truth data. Optionally, compute the
- CRPS over maximum value for blocks of size `scale`.
-
- Notes
- -----
- - The implemented weight function is w(x) = 1{x > threshold}.
- - For computational stability, a small offset is added to the final CRPS value.
"""
-
- def __init__(
- self,
- threshold: float,
- n_samples: int = 1000,
- correct_crps: bool = True,
- **kwargs,
- ) -> None:
- super(WeightedCRPSEnergy, self).__init__(**kwargs)
-
- self.threshold = float(threshold)
- self.n_samples = int(n_samples)
- if self.n_samples < 2:
- raise ValueError("n_samples must be > 1")
- self.correct_crps = bool(correct_crps)
- self.bias_correction = n_samples / (n_samples - 1) if correct_crps else 1.0
-
- def get_config(self) -> dict:
- custom_config = {
- "threshold": self.threshold,
- "n_samples": self.n_samples,
- "correct_crps": self.correct_crps,
- }
- config = super().get_config()
- config.update(custom_config)
- return config
-
- def call(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
- scale: Optional[int] = None,
- ) -> tf.Tensor:
- """
- Compute the loss.
-
- Parameters
- ----------
- y_true: array-like
- Values representing the ground truth.
- y_pred: array_like or tfp.Distribution
- Predicted values or distributions.
- scale : int, optional
- If provided, the CRPS is computed over the maximum values within blocks of
- size `scale` in both `y_true` and `y_pred`. The input tensors' first dimension
- (typically batch size) should be divisible by this scale.
- """
-
- threshold = tf.constant(self.threshold, dtype=y_true.dtype)
- n_samples = self.n_samples
- y_true = tf.debugging.check_numerics(y_true, "Target values")
- v_obs = tf.math.maximum(y_true, threshold)
-
- if tf.is_tensor(y_pred) or isinstance(y_pred, np.ndarray):
-
- n_samples = y_pred.shape[0]
-
- v_ens = tf.math.maximum(y_pred, threshold)
-
- if scale is not None and scale > 1:
- # Reshape the tensors and compute the block maxima
- reshaped_true = tf.reshape(v_obs, (-1, scale, 1))
- v_obs = tf.reduce_max(reshaped_true, axis=1)
-
- reshaped_pred = tf.reshape(v_ens, (n_samples, -1, scale, 1))
- v_ens = tf.reduce_max(reshaped_pred, axis=2)
-
- # first term
- E_1 = tf.abs(v_ens - v_obs[None, :])
- E_1 = tf.reduce_mean(E_1, axis=0)
-
- # second term
- E_2 = tf.abs(v_ens[None, :] - v_ens[:, None])
- E_2 = tf.reduce_mean(E_2, axis=(0, 1))
-
- else:
-
- use_reparameterization = (
- y_pred.reparameterization_type == tfd.FULLY_REPARAMETERIZED
+ def __call__(self, y_true, y_pred, sample_weight=None):
+ in_mask = backend.get_keras_mask(y_pred)
+
+ with ops.name_scope(self.name):
+ # added to avoid convert_to_tensor when y_pred is a distribution
+ def _maybe_convert_to_tensor(x):
+ if isinstance(x, torch.distributions.Distribution) or isinstance(x, WrappingTorchDist):
+ return x
+ return ops.convert_to_tensor(x, dtype=self.dtype)
+
+ y_pred = tree.map_structure(_maybe_convert_to_tensor, y_pred)
+ # ----
+ y_true = tree.map_structure(
+ lambda x: ops.convert_to_tensor(x, dtype=self.dtype), y_true
)
- samples_1 = tf.math.maximum(y_pred.sample(n_samples), threshold)
- samples_2 = tf.math.maximum(y_pred.sample(n_samples), threshold)
-
- if scale is not None and scale > 1:
- # Reshape the tensors and compute the block maxima
- reshaped_true = tf.reshape(v_obs, (-1, scale, 1))
- v_obs = tf.reduce_max(reshaped_true, axis=1)
-
- reshaped_pred = tf.reshape(samples_1, (n_samples, -1, scale, 1))
- samples_1 = tf.reduce_max(reshaped_pred, axis=2)
-
- reshaped_pred = tf.reshape(samples_2, (n_samples, -1, scale, 1))
- samples_2 = tf.reduce_max(reshaped_pred, axis=2)
-
- # first term
- E_1 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - v_obs[None, :], ord=1, axis=-1),
- samples=samples_1,
- log_prob=y_pred.log_prob,
- use_reparameterization=use_reparameterization,
- )[..., None]
-
- # second term
- E_2 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
- samples=samples_1,
- log_prob=y_pred.log_prob,
- use_reparameterization=use_reparameterization,
- )[..., None]
-
- twcrps = E_1 - self.bias_correction * E_2 / 2
-
- # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
- twcrps = tf.abs(twcrps)
- # Add a small offset to ensure stability
- twcrps += 1e-7
-
- return twcrps
-
-
-class MultiScaleCRPSEnergy(WeightedCRPSEnergy):
- """
- Compute threshold-weighted CRPS over multiple scales of data.
-
- Parameters
- ----------
- scales: list[int]
- List of scales (block sizes) over which CRPS is computed. The batch size used for
- training must be divisible by all scales.
- threshold: float
- The threshold to be used within the weight function of the threshold-weighted CRPS.
- n_samples: int, optional (default=1000)
- Number of samples used to compute the Monte Carlo expectations.
- correct_crps: bool, optional (default=True)
- Whether to bias correct the CRPS following Eq. 4 in Fricker et al. (2013).
- **kwargs:
- (Optional) Additional keyword arguments to be passed to the parent `WeightedCRPSEnergy` class.
-
- Notes
- -----
- The CRPS is computed over the maximum value in each block (scale) of `y_true`
- and the samples sampled from `y_pred`.
- """
-
- def __init__(self, scales: list[int], **kwargs) -> None:
- """Initialize the MultiScaleCRPSEnergy with scales and other parameters."""
- super(MultiScaleCRPSEnergy, self).__init__(**kwargs)
- self.scales = scales
+ losses = self.call(y_true, y_pred)
+ out_mask = backend.get_keras_mask(losses)
- def call(
- self,
- y_true: tf.Tensor,
- y_pred: Union[tf.Tensor, tfp.distributions.Distribution],
- ) -> tf.Tensor:
- """
- Compute the threshold-weighted CRPS over multiple scales of data.
-
- Parameters
- ----------
- y_true: tf.Tensor
- Ground truth tensor.
- y_pred: Union[tf.Tensor, tfp.distributions.Distribution]
- Predicted tensor or distribution.
-
- Returns
- -------
- tf.Tensor
- Loss tensor, computed as the average CRPS over the provided scales.
- """
-
- all_losses = []
-
- for scale in self.scales:
-
- if scale > 1:
- tf.debugging.assert_equal(
- tf.shape(y_true)[0] % scale,
- 0,
- message=f"Input tensor length ({tf.shape(y_true)[0]}) is not divisible by scale {scale}.",
- )
-
- scale_loss = super(MultiScaleCRPSEnergy, self).call(
- y_true, y_pred, scale=scale
+ if in_mask is not None and out_mask is not None:
+ mask = in_mask & out_mask
+ elif in_mask is not None:
+ mask = in_mask
+ elif out_mask is not None:
+ mask = out_mask
+ else:
+ mask = None
+
+ return reduce_weighted_values(
+ losses,
+ sample_weight=sample_weight,
+ mask=mask,
+ reduction=self.reduction,
+ dtype=self.dtype,
)
- # Repeat loss to match the original shape for CRPS computation
- if scale > 1:
- scale_loss = tf.repeat(scale_loss, scale, axis=0)
-
- all_losses.append(scale_loss)
-
- # Average the losses over all scales
- total_loss = tf.reduce_mean(tf.stack(all_losses, axis=0), axis=0)
-
- return total_loss
-
-
-class EnergyScore(tf.keras.losses.Loss):
- """
- Compute Energy Score.
-
- Parameters
- ----------
- threshold: float
- The threshold to be used within the weight function of the Energy Score.
- n_samples: int
- Number of samples used to compute the Monte Carlo expectations.
- **kwargs:
- (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
- """
-
- def __init__(
- self,
- n_samples: int = 1000,
- **kwargs,
- ) -> None:
- super(EnergyScore, self).__init__(**kwargs)
-
- self.n_samples = int(n_samples)
-
- def get_config(self) -> None:
- custom_config = {
- "n_samples": self.n_samples,
- }
- config = super().get_config()
- config.update(custom_config)
- return config
-
- def call(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: tfp.distributions.Distribution,
- ) -> tf.Tensor:
+# class CRPSNormal(DistributionLossWrapper):
+# def __init__(self):
+# super(scoringrule...)
+
+class DistributionLossWrapper(DistributionLoss, LossFunctionWrapper):
+ '''
+ Wraps a scoringrules score function with analytical fomulation into a keras loss function,
+ such that it can be used with y_true being a tensor and y_pred
+ being a torch.distributions.Distribution. This means that the loss value is computed
+ directly from the parameters of the distribution rather than samples.
+ '''
+ def __init__(self, fn: tp.Callable[[torch.Tensor], torch.Tensor],**kwargs):
+ """_summary_
+
+ Args:
+ fn (tp.Callable[[torch.Tensor], torch.Tensor]): the scoringrules function
"""
- Compute the loss.
-
- Parameters
- ----------
- y_true: array-like
- Values representing the ground truth.
- y_pred: tfp.Distribution
- Predicted distributions.
+ kwargs = {'backend': backend.backend(), **kwargs}
+
+ def _extract_wrapper(y_true, y_pred: torch.distributions.Distribution | WrappingTorchDist, **kwargs):
+ if isinstance(y_pred, torch.distributions.Distribution):
+ params = [getattr(y_pred, p) for p in self._sr_param_order(y_pred)]
+ else:
+ params = [getattr(y_pred._distribution, p) for p in self._sr_param_order(y_pred)]
+ params = self._sr_reparametrization(y_pred)(*params)
+
+ return fn(y_true, *params, **kwargs)
+
+ super().__init__(_extract_wrapper, **kwargs)
+
+ def call(self, y_true, y_pred):
+ losses = self.fn(y_true, y_pred, **self._fn_kwargs)
+ if losses.numel() != y_true.shape[0]:
+ warnings.warn(
+ f"The number of elements in the losses tensor (shape {losses.shape}) is not as expected. There probably is an error.",
+ UserWarning,)
+ return losses
+
+
+
+ @staticmethod
+ def _sr_param_order(dist: WrappingTorchDist) -> list[str]:
+ """Given a distribution, returns a list
+ of strings representing the attribute names of the
+ distribution in the order expected by scoringrules.
+
+ Args:
+ dist (torch.distributions.Distribution): the distribution
+
+ Raises:
+ ValueError: when the order is not specified in the SR_PARAM_ORDER
+ mapping.
+
+ Returns:
+ list[str]: the attributes in the expected order.
"""
-
- y_true = tf.debugging.check_numerics(y_true, "Target values")
-
- use_reparameterization = (
- y_pred.reparameterization_type == tfd.FULLY_REPARAMETERIZED
- )
-
- samples_1 = y_pred.sample(self.n_samples)
- samples_2 = y_pred.sample(self.n_samples)
-
- # first term
- E_1 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - y_true[None, ...], ord=1, axis=-1),
- samples=samples_1,
- log_prob=y_pred.log_prob,
- use_reparameterization=use_reparameterization,
- )
-
- E_2 = tfp.monte_carlo.expectation(
- f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
- samples=samples_1,
- log_prob=y_pred.log_prob,
- use_reparameterization=use_reparameterization,
- )
-
- energy_score = E_1 - E_2 / 2
-
- # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
- energy_score = tf.abs(energy_score)
-
- return energy_score
-
-
-class MultivariateLoss(tf.keras.losses.Loss):
- """
- Compute losses for multivariate data.
-
- Facilitates computing losses for multivariate targets
- that may have different units. Allows rescaling the inputs
- and applying weights to each target variables.
-
- Parameters
- ----------
- metric: {"mse", "mae", "crps_energy"}
- The function used to compute the loss.
- scaling: {"minmax", "standard"}
- (Optional) A scaling to apply to the data, in order to address the differences
- in magnitude due to different units (if unit is not of the variables. Default is `None`.
- weights: array-like
- (Optional) Weights assigned to each variable in the computation of the loss. Default is `None`.
- **kwargs:
- (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
-
-
- """
-
- def mse_metric(y_true, y_pred):
- return tf.reduce_mean(tf.square(y_true - y_pred), axis=0)
-
- def mae_metric(y_true, y_pred):
- return tf.reduce_mean(tf.abs(y_true - y_pred), axis=0)
-
- avail_metrics = {
- "mse": mse_metric,
- "mae": mae_metric,
- "crps_energy": crps_energy,
- }
-
- avail_scaling = {
- "standard": "_standard_scaling",
- "minmax": "_minmax_scaling",
- None: "none",
- }
-
- def __init__(
- self,
- metric: Literal["mse", "mae"],
- scaling: Literal["minmax", "standard"] = None,
- weights: Union[list, np.ndarray] = None,
- **kwargs,
- ) -> None:
- super(MultivariateLoss, self).__init__(**kwargs)
-
- try:
- self.metric = self.avail_metrics[metric]
- except KeyError as err:
- raise NotImplementedError(
- f"`metric` argument must be one of {list(self.avail_metrics.keys())}"
- ) from err
-
try:
- if getattr(self.metric, "loss_type", None) == "probabilistic":
- method_name = self.avail_scaling[scaling] + "_probabilistic"
- else:
- method_name = self.avail_scaling[scaling]
- self.scaling = getattr(self, method_name, None)
- except KeyError as err:
- raise NotImplementedError(
- f"`scaling` argument must be one of {list(self.avail_scaling.keys())}"
- ) from err
-
- if weights:
- self.weights = tf.constant(weights)
- else:
- self.weights = None
-
- def get_config(self) -> None:
- config = {
- "metric": self.metric,
- "scaling": self.scaling,
- "weights": self.weights,
- }
- base_config = super().get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
- def call(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
- ) -> tf.Tensor:
- """
- Compute the loss.
-
- Parameters
- ----------
- y_true: array-like
- Values representing the ground truth.
- y_pred: array_like or tfp.Distribution
- Predicted values or distributions.
-
+ return SR_PARAM_ORDER[dist.name]
+ except KeyError:
+ raise ValueError(
+ "The order of the distribution parameters passed to scoringrules"
+ f"must be specified. Not found for {dist.name}")
+
+ @staticmethod
+ def _sr_reparametrization(dist: WrappingTorchDist)-> tp.Callable[..., tp.Tuple[torch.Tensor, ...]]:
+ """Given a distribution, returns a function to be
+ applied to every parameter of the distribution to match
+ the expected parametrization of scoringrules.
+
+ Args:
+ dist (torch.distributions.Distribution): the distribution
+
+ Raises:
+ ValueError: when the reparametrization function is not specified in
+ the SR_REPARAM mapping.
+
+ Returns:
+ tp.Callable[..., tp.Tuple[torch.Tensor, ...]]: a tuple with the reparametrized parameters.
"""
-
- assert y_true.shape[1:] == y_pred.shape[1:]
- if self.weights is not None:
- assert (
- len(self.weights) == y_true.shape[-1]
- ), "Number weights must match the number of target variables."
-
- if self.scaling:
- y_true, y_pred = self.scaling(y_true, y_pred)
-
- loss = self.metric(y_true, y_pred)
-
- if self.weights is not None:
- loss = tf.multiply(loss, self.weights)
-
- return loss
-
- def _minmax_scaling(
- self, y_true: Union[tf.Tensor, np.ndarray], y_pred: Union[tf.Tensor, np.ndarray]
- ) -> tuple[tf.Tensor]:
-
- y_true_min = tf.reduce_min(y_true, axis=0)
- y_true_max = tf.reduce_max(y_true, axis=0)
- y_true = (y_true - y_true_min) / (y_true_max - y_true_min)
- y_pred = (y_pred - y_true_min) / (y_true_max - y_true_min)
-
- return y_true, y_pred
-
- def _minmax_scaling_probabilistic(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: tfp.distributions.Distribution,
- ) -> tuple[tf.Tensor]:
-
- y_true_min = tf.reduce_min(y_true, axis=0)
- y_true_max = tf.reduce_max(y_true, axis=0)
-
- scale = tfp.bijectors.Scale(scale=1 / (y_true_max - y_true_min))
- shift = tfp.bijectors.Shift(shift=-y_true_min)
- y_true = (y_true - y_true_min) / (y_true_max - y_true_min)
- y_pred = scale(shift(y_pred))
-
- y_pred.shape = (*y_pred.batch_shape, *y_pred.event_shape)
-
- return y_true, y_pred
-
- def _standard_scaling(
- self, y_true: Union[tf.Tensor, np.ndarray], y_pred: Union[tf.Tensor, np.ndarray]
- ) -> tuple[tf.Tensor]:
-
- y_true_mean = tf.math.reduce_mean(y_true, axis=0)
- y_true_std = tf.math.reduce_std(y_true, axis=0)
- y_true = (y_true - y_true_mean) / y_true_std
- y_pred = (y_pred - y_true_mean) / y_true_std
-
- return y_true, y_pred
-
- def _standard_scaling_probabilistic(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: tfp.distributions.Distribution,
- ) -> tuple[tf.Tensor]:
-
- y_true_mean = tf.math.reduce_mean(y_true, axis=0)
- y_true_std = tf.math.reduce_std(y_true, axis=0)
-
- scale = tfp.bijectors.Scale(scale=1 / y_true_std)
- shift = tfp.bijectors.Shift(shift=-y_true_mean)
- y_true = (y_true - y_true_mean) / y_true_std
- y_pred = scale(shift(y_pred))
-
- y_pred.shape = (*y_pred.batch_shape, *y_pred.event_shape)
-
- return y_true, y_pred
-
-
-class BinaryClassifierLoss(tf.keras.losses.Loss):
+ try:
+ return SR_REPARAM[dist.name]
+ except KeyError:
+ raise ValueError(
+ f"The reparametrization function for the distribution parameters \
+ passed to scoringrules must be specified. None found for \
+ {dist.name}. If the parameters are the same,\
+ use the identity function.")
+
+# Mapping between distribution cls and parameter
+# order expected by scoringrules
+SR_PARAM_ORDER = {
+ "Normal": ["loc", "scale"],
+ "Exponential": ["rate"],
+ "Weibull": ["concentration", "scale"]
+}
+
+# Mapping between distribution cls and a reparametrization,
+# in case torch and scoringrules use different parameters format
+# such as 1/\beta vs \beta
+SR_REPARAM = {
+ "Normal": lambda loc, scale: (loc, scale),
+ "Exponential": lambda x: (x,),
+ "Weibull": lambda x: (x,)
+}
+
+
+
+class SampleLossWrapper(DistributionLoss, LossFunctionWrapper):
"""
- Compute binary classification loss from continuous predictions based on a threshold.
-
- Parameters
- ----------
- threshold: float
- loss_type: {"binary_crossentropy", "focal"}
- The type of loss to be used.
- n_samples: int, optional
- **kwargs:
- (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
-
+ Wraps a scoringrules ensamble-based estimation of a score function into a keras loss function,
+ such that it can be used with with y_true being a tensor and y_pred
+ being a torch.distributions.Distribution. Internally, num_samples samples will be sampled from
+ the predicted distribution and the loss value is computed with a MC approach.
+ For gradient-based optimization, this only makes sense if the underlying
+ torch.distributions.Distribution implements rsample(), ie the reparametrization of the sampling function.
"""
-
- def __init__(
- self,
- threshold: float,
- loss_type: Literal["binary_crossentropy", "focal"] = "binary_crossentropy",
- n_samples: int = 1000,
- **kwargs,
- ) -> None:
- super(BinaryClassifierLoss, self).__init__(**kwargs)
-
- self.threshold = float(threshold)
- self.n_samples = int(n_samples)
- if self.n_samples < 2:
- raise ValueError("n_samples must be > 1")
- self.loss_type = loss_type
-
- def get_config(self) -> dict:
- custom_config = {
- "threshold": self.threshold,
- "loss_type": self.loss_type,
- "n_samples": self.n_samples,
- }
- config = super().get_config()
- config.update(custom_config)
- return config
-
- def call(
- self,
- y_true: Union[tf.Tensor, np.ndarray],
- y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
- ) -> tf.Tensor:
- """
- Compute the loss.
-
- Parameters
- ----------
- y_true: array-like
- Values representing the ground truth.
- y_pred: array_like or tfp.Distribution
- Predicted values or distributions.
- """
- threshold = tf.constant(self.threshold, dtype=y_true.dtype)
- n_samples = self.n_samples
- y_true = tf.debugging.check_numerics(y_true, "Target values")
-
- if isinstance(y_pred, tfp.distributions.Distribution):
- y_pred_samples = y_pred.sample(n_samples)
- else:
- y_pred_samples = y_pred
-
- y_pred_samples = tf.debugging.check_numerics(y_pred_samples, "Predicted values")
-
- y_true_bool = tf.cast(y_true > threshold, dtype=y_true.dtype)
- y_pred_bool = tf.cast(y_pred_samples > threshold, dtype=y_true.dtype)
- y_pred_prob = tf.reduce_mean(y_pred_bool, axis=0)
-
- loss = tf.keras.losses.binary_crossentropy(y_true_bool, y_pred_prob, axis=1)
- if self.loss_type == "focal":
- loss = tf.pow(1 - tf.exp(-loss), 2)
-
- return loss
-
-
-class CombinedLoss(tf.keras.losses.Loss):
- def __init__(self, losses):
- # Local import to avoid circular dependency with mlpp_lib.utils
- from mlpp_lib.utils import get_loss
-
- super(CombinedLoss, self).__init__()
- self.losses = []
- self.weights = []
-
- # Initialize losses based on the input config dictionaries
- for loss_config in losses:
- self.weights.append(loss_config.get("weight", 1.0))
- self.losses.append(get_loss(loss_config))
-
+ def __init__(self, fn: tp.Callable[[torch.Tensor], torch.Tensor], num_samples: int=21, estimator: str = 'pwm', **kwargs):
+
+ kwargs = {'backend': backend.backend(), **kwargs}
+
+ def _extract_wrapper(y_true: torch.Tensor, y_pred: torch.Tensor, **kwargs):
+ if not y_pred.has_rsample:
+ raise MissingReparameterizationError(f"Gradient-based optimization will not work. {y_pred.__name__} does not implement rsample().")
+ # obtain num_samples samples from the distribution y_pred
+ y_pred_samples = y_pred.rsample(self.num_samples) # [Samples, Batch, Dim]
+ y_pred_samples = y_pred_samples.permute(1,0,2) # [Batch, Samples, Dim]
+ return fn(y_true, y_pred_samples, axis=1, estimator=estimator, **kwargs)
+
+ super().__init__(_extract_wrapper, **kwargs)
+ self.num_samples = (num_samples,)
+
def call(self, y_true, y_pred):
- total_loss = 0
- for loss, weight in zip(self.losses, self.weights):
- total_loss += weight * loss(y_true, y_pred)
- return total_loss
+ losses = self.fn(y_true, y_pred, **self._fn_kwargs)
+ if losses.numel() != y_true.shape[0]:
+ warnings.warn(
+ f"The number of elements in the losses tensor (shape {losses.shape}) is not as expected. There probably is an error.",
+ UserWarning,)
+ return losses
+
+# from typing import Literal, Optional, Union
+
+# import numpy as np
+# import tensorflow as tf
+# import tensorflow_probability as tfp
+# from tensorflow_probability import distributions as tfd
+
+# from mlpp_lib.decorators import with_attrs
+
+
+# @with_attrs(loss_type="probabilistic")
+# def crps_energy_ensemble(
+# obs: Union[tf.Tensor, np.ndarray],
+# fct_ens: Union[tf.Tensor, np.ndarray],
+# ) -> tf.Tensor:
+# """
+# Energy form of the Continuous Ranked Probability Score from Gneiting and Raftery (2007),
+# where the expectations terms are approximated from an ensemble.
+
+# .. math::
+# CRPS(F, y) = E_F|X - y| - 1/2 * E_F|X - X'|
+
+# Parameters
+# ----------
+# fct_ens: array-like
+# Ensemble forecasts, with ensemble members along the first dimension.
+# obs: array-like
+# Observations.
+
+# Return
+# ------
+# crps: tf.Tensor
+# The CRPS for each sample.
+# """
+
+# # first term
+# E_1 = tf.abs(fct_ens - obs[None, :])
+# E_1 = tf.reduce_mean(E_1, axis=0)
+
+# # second term
+# E_2 = tf.abs(fct_ens[None, :] - fct_ens[:, None])
+# E_2 = tf.reduce_mean(E_2, axis=(0, 1))
+# crps = E_1 - E_2 / 2
+
+# return crps
+
+
+# @with_attrs(loss_type="probabilistic")
+# def crps_energy(
+# obs: Union[tf.Tensor, np.ndarray],
+# fct_dist: tfp.distributions.Distribution,
+# ) -> tf.Tensor:
+# """
+# Energy form of the Continuous Ranked Probability Score from Gneiting and Raftery (2007),
+# where the expectation terms are approximated from the distribution using monte-carlo methods.
+
+# .. math::
+# CRPS(F, y) = E_F|X - y| - 1/2 * E_F|X - X'|
+
+# Parameters
+# ----------
+# obs: array-like
+# Array of observations.
+# fct_dist: tensorflow-probability Distribution
+# The predicted distribution.
+
+# Return
+# ------
+# crps: tf.Tensor
+# The CRPS for each sample.
+# """
+
+# n_samples = 1000
+
+# obs = tf.debugging.check_numerics(obs, "Target values")
+
+# use_reparameterization = (
+# fct_dist.reparameterization_type == tfd.FULLY_REPARAMETERIZED
+# )
+
+# samples_1 = fct_dist.sample(n_samples)
+# samples_2 = fct_dist.sample(n_samples)
+
+# # first term
+# E_1 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - obs[None, :], ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=fct_dist.log_prob,
+# use_reparameterization=use_reparameterization,
+# )
+
+# # second term
+# E_2 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=fct_dist.log_prob,
+# use_reparameterization=use_reparameterization,
+# )
+# crps = E_1 - E_2 / 2
+# # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
+# crps = tf.abs(crps)
+
+# return crps[..., None]
+
+
+# class WeightedCRPSEnergy(tf.keras.losses.Loss):
+# """
+# Compute threshold-weighted CRPS using its kernel score representation.
+
+# Parameters
+# ----------
+# threshold : float
+# The threshold for the weight function within the threshold-weighted CRPS.
+# Specifically, the weight function w(x) is 1{x > threshold}.
+# n_samples : int, optional
+# The number of Monte Carlo samples to be used for estimating expectations. Must be greater than 1.
+# Used only if `y_pred` is of type `tfp.Distribution`.
+# correct_crps : bool, optional
+# If True, applies a bias correction to the CRPS, as detailed in Eq. 4 of Fricker et al. (2013).
+# **kwargs : dict, optional
+# Additional keyword arguments to pass to the parent `Loss` class.
+
+# Methods
+# -------
+# call(y_true, y_pred, scale=None):
+# Compute the CRPS for predictions and ground truth data. Optionally, compute the
+# CRPS over maximum value for blocks of size `scale`.
+
+# Notes
+# -----
+# - The implemented weight function is w(x) = 1{x > threshold}.
+# - For computational stability, a small offset is added to the final CRPS value.
+# """
+
+# def __init__(
+# self,
+# threshold: float,
+# n_samples: int = 1000,
+# correct_crps: bool = True,
+# **kwargs,
+# ) -> None:
+# super(WeightedCRPSEnergy, self).__init__(**kwargs)
+
+# self.threshold = float(threshold)
+# self.n_samples = int(n_samples)
+# if self.n_samples < 2:
+# raise ValueError("n_samples must be > 1")
+# self.correct_crps = bool(correct_crps)
+# self.bias_correction = n_samples / (n_samples - 1) if correct_crps else 1.0
+
+# def get_config(self) -> dict:
+# custom_config = {
+# "threshold": self.threshold,
+# "n_samples": self.n_samples,
+# "correct_crps": self.correct_crps,
+# }
+# config = super().get_config()
+# config.update(custom_config)
+# return config
+
+# def call(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
+# scale: Optional[int] = None,
+# ) -> tf.Tensor:
+# """
+# Compute the loss.
+
+# Parameters
+# ----------
+# y_true: array-like
+# Values representing the ground truth.
+# y_pred: array_like or tfp.Distribution
+# Predicted values or distributions.
+# scale : int, optional
+# If provided, the CRPS is computed over the maximum values within blocks of
+# size `scale` in both `y_true` and `y_pred`. The input tensors' first dimension
+# (typically batch size) should be divisible by this scale.
+# """
+
+# threshold = tf.constant(self.threshold, dtype=y_true.dtype)
+# n_samples = self.n_samples
+# y_true = tf.debugging.check_numerics(y_true, "Target values")
+# v_obs = tf.math.maximum(y_true, threshold)
+
+# if tf.is_tensor(y_pred) or isinstance(y_pred, np.ndarray):
+
+# n_samples = y_pred.shape[0]
+
+# v_ens = tf.math.maximum(y_pred, threshold)
+
+# if scale is not None and scale > 1:
+# # Reshape the tensors and compute the block maxima
+# reshaped_true = tf.reshape(v_obs, (-1, scale, 1))
+# v_obs = tf.reduce_max(reshaped_true, axis=1)
+
+# reshaped_pred = tf.reshape(v_ens, (n_samples, -1, scale, 1))
+# v_ens = tf.reduce_max(reshaped_pred, axis=2)
+
+# # first term
+# E_1 = tf.abs(v_ens - v_obs[None, :])
+# E_1 = tf.reduce_mean(E_1, axis=0)
+
+# # second term
+# E_2 = tf.abs(v_ens[None, :] - v_ens[:, None])
+# E_2 = tf.reduce_mean(E_2, axis=(0, 1))
+
+# else:
+
+# use_reparameterization = (
+# y_pred.reparameterization_type == tfd.FULLY_REPARAMETERIZED
+# )
+
+# samples_1 = tf.math.maximum(y_pred.sample(n_samples), threshold)
+# samples_2 = tf.math.maximum(y_pred.sample(n_samples), threshold)
+
+# if scale is not None and scale > 1:
+# # Reshape the tensors and compute the block maxima
+# reshaped_true = tf.reshape(v_obs, (-1, scale, 1))
+# v_obs = tf.reduce_max(reshaped_true, axis=1)
+
+# reshaped_pred = tf.reshape(samples_1, (n_samples, -1, scale, 1))
+# samples_1 = tf.reduce_max(reshaped_pred, axis=2)
+
+# reshaped_pred = tf.reshape(samples_2, (n_samples, -1, scale, 1))
+# samples_2 = tf.reduce_max(reshaped_pred, axis=2)
+
+# # first term
+# E_1 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - v_obs[None, :], ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=y_pred.log_prob,
+# use_reparameterization=use_reparameterization,
+# )[..., None]
+
+# # second term
+# E_2 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=y_pred.log_prob,
+# use_reparameterization=use_reparameterization,
+# )[..., None]
+
+# twcrps = E_1 - self.bias_correction * E_2 / 2
+
+# # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
+# twcrps = tf.abs(twcrps)
+# # Add a small offset to ensure stability
+# twcrps += 1e-7
+
+# return twcrps
+
+
+# class MultiScaleCRPSEnergy(WeightedCRPSEnergy):
+# """
+# Compute threshold-weighted CRPS over multiple scales of data.
+
+# Parameters
+# ----------
+# scales: list[int]
+# List of scales (block sizes) over which CRPS is computed. The batch size used for
+# training must be divisible by all scales.
+# threshold: float
+# The threshold to be used within the weight function of the threshold-weighted CRPS.
+# n_samples: int, optional (default=1000)
+# Number of samples used to compute the Monte Carlo expectations.
+# correct_crps: bool, optional (default=True)
+# Whether to bias correct the CRPS following Eq. 4 in Fricker et al. (2013).
+# **kwargs:
+# (Optional) Additional keyword arguments to be passed to the parent `WeightedCRPSEnergy` class.
+
+# Notes
+# -----
+# The CRPS is computed over the maximum value in each block (scale) of `y_true`
+# and the samples sampled from `y_pred`.
+# """
+
+# def __init__(self, scales: list[int], **kwargs) -> None:
+# """Initialize the MultiScaleCRPSEnergy with scales and other parameters."""
+# super(MultiScaleCRPSEnergy, self).__init__(**kwargs)
+# self.scales = scales
+
+# def call(
+# self,
+# y_true: tf.Tensor,
+# y_pred: Union[tf.Tensor, tfp.distributions.Distribution],
+# ) -> tf.Tensor:
+# """
+# Compute the threshold-weighted CRPS over multiple scales of data.
+
+# Parameters
+# ----------
+# y_true: tf.Tensor
+# Ground truth tensor.
+# y_pred: Union[tf.Tensor, tfp.distributions.Distribution]
+# Predicted tensor or distribution.
+
+# Returns
+# -------
+# tf.Tensor
+# Loss tensor, computed as the average CRPS over the provided scales.
+# """
+
+# all_losses = []
+
+# for scale in self.scales:
+
+# if scale > 1:
+# tf.debugging.assert_equal(
+# tf.shape(y_true)[0] % scale,
+# 0,
+# message=f"Input tensor length ({tf.shape(y_true)[0]}) is not divisible by scale {scale}.",
+# )
+
+# scale_loss = super(MultiScaleCRPSEnergy, self).call(
+# y_true, y_pred, scale=scale
+# )
+
+# # Repeat loss to match the original shape for CRPS computation
+# if scale > 1:
+# scale_loss = tf.repeat(scale_loss, scale, axis=0)
+
+# all_losses.append(scale_loss)
+
+# # Average the losses over all scales
+# total_loss = tf.reduce_mean(tf.stack(all_losses, axis=0), axis=0)
+
+# return total_loss
+
+
+# class EnergyScore(tf.keras.losses.Loss):
+# """
+# Compute Energy Score.
+
+# Parameters
+# ----------
+# threshold: float
+# The threshold to be used within the weight function of the Energy Score.
+# n_samples: int
+# Number of samples used to compute the Monte Carlo expectations.
+# **kwargs:
+# (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
+# """
+
+# def __init__(
+# self,
+# n_samples: int = 1000,
+# **kwargs,
+# ) -> None:
+# super(EnergyScore, self).__init__(**kwargs)
+
+# self.n_samples = int(n_samples)
+
+# def get_config(self) -> None:
+# custom_config = {
+# "n_samples": self.n_samples,
+# }
+# config = super().get_config()
+# config.update(custom_config)
+# return config
+
+# def call(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: tfp.distributions.Distribution,
+# ) -> tf.Tensor:
+# """
+# Compute the loss.
+
+# Parameters
+# ----------
+# y_true: array-like
+# Values representing the ground truth.
+# y_pred: tfp.Distribution
+# Predicted distributions.
+# """
+
+# y_true = tf.debugging.check_numerics(y_true, "Target values")
+
+# use_reparameterization = (
+# y_pred.reparameterization_type == tfd.FULLY_REPARAMETERIZED
+# )
+
+# samples_1 = y_pred.sample(self.n_samples)
+# samples_2 = y_pred.sample(self.n_samples)
+
+# # first term
+# E_1 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - y_true[None, ...], ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=y_pred.log_prob,
+# use_reparameterization=use_reparameterization,
+# )
+
+# E_2 = tfp.monte_carlo.expectation(
+# f=lambda x: tf.norm(x - samples_2, ord=1, axis=-1),
+# samples=samples_1,
+# log_prob=y_pred.log_prob,
+# use_reparameterization=use_reparameterization,
+# )
+
+# energy_score = E_1 - E_2 / 2
+
+# # Avoid negative loss when E_2 >> E_1 caused by large values in `sample_2`
+# energy_score = tf.abs(energy_score)
+
+# return energy_score
+
+
+# class MultivariateLoss(tf.keras.losses.Loss):
+# """
+# Compute losses for multivariate data.
+
+# Facilitates computing losses for multivariate targets
+# that may have different units. Allows rescaling the inputs
+# and applying weights to each target variables.
+
+# Parameters
+# ----------
+# metric: {"mse", "mae", "crps_energy"}
+# The function used to compute the loss.
+# scaling: {"minmax", "standard"}
+# (Optional) A scaling to apply to the data, in order to address the differences
+# in magnitude due to different units (if unit is not of the variables. Default is `None`.
+# weights: array-like
+# (Optional) Weights assigned to each variable in the computation of the loss. Default is `None`.
+# **kwargs:
+# (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
+
+
+# """
+
+# def mse_metric(y_true, y_pred):
+# return tf.reduce_mean(tf.square(y_true - y_pred), axis=0)
+
+# def mae_metric(y_true, y_pred):
+# return tf.reduce_mean(tf.abs(y_true - y_pred), axis=0)
+
+# avail_metrics = {
+# "mse": mse_metric,
+# "mae": mae_metric,
+# "crps_energy": crps_energy,
+# }
+
+# avail_scaling = {
+# "standard": "_standard_scaling",
+# "minmax": "_minmax_scaling",
+# None: "none",
+# }
+
+# def __init__(
+# self,
+# metric: Literal["mse", "mae"],
+# scaling: Literal["minmax", "standard"] = None,
+# weights: Union[list, np.ndarray] = None,
+# **kwargs,
+# ) -> None:
+# super(MultivariateLoss, self).__init__(**kwargs)
+
+# try:
+# self.metric = self.avail_metrics[metric]
+# except KeyError as err:
+# raise NotImplementedError(
+# f"`metric` argument must be one of {list(self.avail_metrics.keys())}"
+# ) from err
+
+# try:
+# if getattr(self.metric, "loss_type", None) == "probabilistic":
+# method_name = self.avail_scaling[scaling] + "_probabilistic"
+# else:
+# method_name = self.avail_scaling[scaling]
+# self.scaling = getattr(self, method_name, None)
+# except KeyError as err:
+# raise NotImplementedError(
+# f"`scaling` argument must be one of {list(self.avail_scaling.keys())}"
+# ) from err
+
+# if weights:
+# self.weights = tf.constant(weights)
+# else:
+# self.weights = None
+
+# def get_config(self) -> None:
+# config = {
+# "metric": self.metric,
+# "scaling": self.scaling,
+# "weights": self.weights,
+# }
+# base_config = super().get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# def call(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
+# ) -> tf.Tensor:
+# """
+# Compute the loss.
+
+# Parameters
+# ----------
+# y_true: array-like
+# Values representing the ground truth.
+# y_pred: array_like or tfp.Distribution
+# Predicted values or distributions.
+
+# """
+
+# assert y_true.shape[1:] == y_pred.shape[1:]
+# if self.weights is not None:
+# assert (
+# len(self.weights) == y_true.shape[-1]
+# ), "Number weights must match the number of target variables."
+
+# if self.scaling:
+# y_true, y_pred = self.scaling(y_true, y_pred)
+
+# loss = self.metric(y_true, y_pred)
+
+# if self.weights is not None:
+# loss = tf.multiply(loss, self.weights)
+
+# return loss
+
+# def _minmax_scaling(
+# self, y_true: Union[tf.Tensor, np.ndarray], y_pred: Union[tf.Tensor, np.ndarray]
+# ) -> tuple[tf.Tensor]:
+
+# y_true_min = tf.reduce_min(y_true, axis=0)
+# y_true_max = tf.reduce_max(y_true, axis=0)
+# y_true = (y_true - y_true_min) / (y_true_max - y_true_min)
+# y_pred = (y_pred - y_true_min) / (y_true_max - y_true_min)
+
+# return y_true, y_pred
+
+# def _minmax_scaling_probabilistic(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: tfp.distributions.Distribution,
+# ) -> tuple[tf.Tensor]:
+
+# y_true_min = tf.reduce_min(y_true, axis=0)
+# y_true_max = tf.reduce_max(y_true, axis=0)
+
+# scale = tfp.bijectors.Scale(scale=1 / (y_true_max - y_true_min))
+# shift = tfp.bijectors.Shift(shift=-y_true_min)
+# y_true = (y_true - y_true_min) / (y_true_max - y_true_min)
+# y_pred = scale(shift(y_pred))
+
+# y_pred.shape = (*y_pred.batch_shape, *y_pred.event_shape)
+
+# return y_true, y_pred
+
+# def _standard_scaling(
+# self, y_true: Union[tf.Tensor, np.ndarray], y_pred: Union[tf.Tensor, np.ndarray]
+# ) -> tuple[tf.Tensor]:
+
+# y_true_mean = tf.math.reduce_mean(y_true, axis=0)
+# y_true_std = tf.math.reduce_std(y_true, axis=0)
+# y_true = (y_true - y_true_mean) / y_true_std
+# y_pred = (y_pred - y_true_mean) / y_true_std
+
+# return y_true, y_pred
+
+# def _standard_scaling_probabilistic(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: tfp.distributions.Distribution,
+# ) -> tuple[tf.Tensor]:
+
+# y_true_mean = tf.math.reduce_mean(y_true, axis=0)
+# y_true_std = tf.math.reduce_std(y_true, axis=0)
+
+# scale = tfp.bijectors.Scale(scale=1 / y_true_std)
+# shift = tfp.bijectors.Shift(shift=-y_true_mean)
+# y_true = (y_true - y_true_mean) / y_true_std
+# y_pred = scale(shift(y_pred))
+
+# y_pred.shape = (*y_pred.batch_shape, *y_pred.event_shape)
+
+# return y_true, y_pred
+
+
+# class BinaryClassifierLoss(tf.keras.losses.Loss):
+# """
+# Compute binary classification loss from continuous predictions based on a threshold.
+
+# Parameters
+# ----------
+# threshold: float
+# loss_type: {"binary_crossentropy", "focal"}
+# The type of loss to be used.
+# n_samples: int, optional
+# **kwargs:
+# (Optional) Additional keyword arguments to be passed to the parent `Loss` class.
+
+# """
+
+# def __init__(
+# self,
+# threshold: float,
+# loss_type: Literal["binary_crossentropy", "focal"] = "binary_crossentropy",
+# n_samples: int = 1000,
+# **kwargs,
+# ) -> None:
+# super(BinaryClassifierLoss, self).__init__(**kwargs)
+
+# self.threshold = float(threshold)
+# self.n_samples = int(n_samples)
+# if self.n_samples < 2:
+# raise ValueError("n_samples must be > 1")
+# self.loss_type = loss_type
+
+# def get_config(self) -> dict:
+# custom_config = {
+# "threshold": self.threshold,
+# "loss_type": self.loss_type,
+# "n_samples": self.n_samples,
+# }
+# config = super().get_config()
+# config.update(custom_config)
+# return config
+
+# def call(
+# self,
+# y_true: Union[tf.Tensor, np.ndarray],
+# y_pred: Union[tf.Tensor, np.ndarray, tfp.distributions.Distribution],
+# ) -> tf.Tensor:
+# """
+# Compute the loss.
+
+# Parameters
+# ----------
+# y_true: array-like
+# Values representing the ground truth.
+# y_pred: array_like or tfp.Distribution
+# Predicted values or distributions.
+# """
+# threshold = tf.constant(self.threshold, dtype=y_true.dtype)
+# n_samples = self.n_samples
+# y_true = tf.debugging.check_numerics(y_true, "Target values")
+
+# if isinstance(y_pred, tfp.distributions.Distribution):
+# y_pred_samples = y_pred.sample(n_samples)
+# else:
+# y_pred_samples = y_pred
+
+# y_pred_samples = tf.debugging.check_numerics(y_pred_samples, "Predicted values")
+
+# y_true_bool = tf.cast(y_true > threshold, dtype=y_true.dtype)
+# y_pred_bool = tf.cast(y_pred_samples > threshold, dtype=y_true.dtype)
+# y_pred_prob = tf.reduce_mean(y_pred_bool, axis=0)
+
+# loss = tf.keras.losses.binary_crossentropy(y_true_bool, y_pred_prob, axis=1)
+# if self.loss_type == "focal":
+# loss = tf.pow(1 - tf.exp(-loss), 2)
+
+# return loss
+
+
+# class CombinedLoss(tf.keras.losses.Loss):
+# def __init__(self, losses):
+# # Local import to avoid circular dependency with mlpp_lib.utils
+# from mlpp_lib.utils import get_loss
+
+# super(CombinedLoss, self).__init__()
+# self.losses = []
+# self.weights = []
+
+# # Initialize losses based on the input config dictionaries
+# for loss_config in losses:
+# self.weights.append(loss_config.get("weight", 1.0))
+# self.losses.append(get_loss(loss_config))
+
+# def call(self, y_true, y_pred):
+# total_loss = 0
+# for loss, weight in zip(self.losses, self.weights):
+# total_loss += weight * loss(y_true, y_pred)
+# return total_loss
diff --git a/mlpp_lib/metrics.py b/mlpp_lib/metrics.py
index 79beb85..fa67ac5 100644
--- a/mlpp_lib/metrics.py
+++ b/mlpp_lib/metrics.py
@@ -1,12 +1,13 @@
-import tensorflow as tf
-
+# import tensorflow as tf
+import keras
+import keras.ops as ops
def bias(y_true, y_pred):
return tf.reduce_mean(y_pred - y_true, axis=-1)
-class MAEBusts(tf.keras.metrics.Metric):
- """Compute frequency of occurrence of absolute errors > thr"""
+class MAEBusts(keras.metrics.Metric):
+ """Compute frequency of occurrence of absolute errors > threshold."""
def __init__(self, threshold, name="mae_busts", **kwargs):
super().__init__(name=name, **kwargs)
@@ -15,16 +16,16 @@ def __init__(self, threshold, name="mae_busts", **kwargs):
self.n_samples = self.add_weight(name="ns", initializer="zeros")
def update_state(self, y_true, y_pred, sample_weight=None):
- values = tf.cast(tf.abs(y_pred - y_true) > self.threshold, tf.float32)
+ values = ops.cast(ops.abs(y_pred - y_true) > self.threshold, self.dtype)
if sample_weight is not None:
- sample_weight = tf.cast(sample_weight, self.dtype)
- values = tf.multiply(values, sample_weight)
- self.n_samples.assign_add(tf.reduce_sum(sample_weight))
+ sample_weight = ops.cast(sample_weight, self.dtype)
+ values = ops.multiply(values, sample_weight)
+ self.n_samples.assign_add(ops.sum(sample_weight))
else:
- self.n_samples.assign_add(tf.cast(tf.size(values), tf.float32))
+ self.n_samples.assign_add(ops.cast(ops.size(values), self.dtype))
- self.n_busts.assign_add(tf.reduce_sum(values))
+ self.n_busts.assign_add(ops.sum(values))
def result(self):
return self.n_busts / self.n_samples
diff --git a/mlpp_lib/models.py b/mlpp_lib/models.py
index 592c6da..b7c4cbb 100644
--- a/mlpp_lib/models.py
+++ b/mlpp_lib/models.py
@@ -1,19 +1,23 @@
import logging
-from typing import Optional, Union, Any, Callable
+from typing import Optional, Union, Any, Literal
import numpy as np
-import tensorflow as tf
-from tensorflow.keras.layers import (
+# import tensorflow as tf
+import keras
+from keras.src.layers import (
Add,
Dense,
Dropout,
BatchNormalization,
Activation,
+ Concatenate
)
-from tensorflow.keras import Model, initializers
+from keras import Model, initializers
from mlpp_lib.physical_layers import *
-from mlpp_lib import probabilistic_layers
+# from mlpp_lib import probabilistic_layers
+from mlpp_lib.probabilistic_layers import BaseDistributionLayer, BaseParametricDistributionModule, distribution_to_layer
+from mlpp_lib.layers import FullyConnectedLayer, MultibranchLayer, CrossNetLayer, ParallelConcatenateLayer
try:
import tcn # type: ignore
@@ -25,38 +29,74 @@
_LOGGER = logging.getLogger(__name__)
-
-@tf.keras.saving.register_keras_serializable()
+class ProbabilisticModel(keras.Model):
+ """ A probabilistic model composed of an encoder layer
+ and a probabilistic layer predicting the output's distribution.
+ """
+ def __init__(self, encoder_layer: keras.Layer, probabilistic_layer: BaseDistributionLayer, default_output_type: Literal["distribution", "samples"] = "distribution"):
+ """_summary_
+
+ Args:
+ encoder_layer (keras.Layer): The encoder layer, transforming the inputs into
+ some latent dimension.
+ probabilistic_layer (BaseDistributionLayer): the output layer predicting the distribution.
+ default_output_type (Literal[distribution, samples], optional): Defines the defult behaviour of self.call(), where the model can either output a parametric
+ distribution, or samples obtained from it. This is important to when fitting the model, as the type of output defines what loss functions are suitable.
+ Defaults to "distribution".
+ """
+ super().__init__()
+
+ self.encoder_layer = encoder_layer
+ self.probabilistic_layer = probabilistic_layer
+ self.default_output_type = default_output_type
+
+
+ def call(self, inputs, output_type: Optional[Literal["distribution", "samples"]] = None):
+ if output_type is None:
+ output_type = self.default_output_type
+
+ enc = self.encoder_layer(inputs)
+ return self.probabilistic_layer(enc, output_type=output_type)
+
+
+
+
+@keras.saving.register_keras_serializable()
class MonteCarloDropout(Dropout):
def call(self, inputs):
return super().call(inputs, training=True)
-
-def get_probabilistic_layer(
- output_size,
- probabilistic_layer: Union[str, dict]
-) -> Callable:
- """Get the probabilistic layer."""
-
- if isinstance(probabilistic_layer, dict):
- probabilistic_layer_name = list(probabilistic_layer.keys())[0]
- probabilistic_layer_options = probabilistic_layer[probabilistic_layer_name]
- else:
- probabilistic_layer_name = probabilistic_layer
- probabilistic_layer_options = {}
-
- if hasattr(probabilistic_layers, probabilistic_layer_name):
- _LOGGER.info(f"Using custom probabilistic layer: {probabilistic_layer_name}")
- probabilistic_layer_obj = getattr(probabilistic_layers, probabilistic_layer_name)
- n_params = getattr(probabilistic_layers, probabilistic_layer_name).params_size(output_size)
- probabilistic_layer = (
- probabilistic_layer_obj(output_size, name="output", **probabilistic_layer_options) if isinstance(probabilistic_layer_obj, type)
- else probabilistic_layer_obj(output_size, name="output")
- )
- else:
- raise KeyError(f"The probabilistic layer {probabilistic_layer_name} is not available.")
-
- return probabilistic_layer, n_params
+def get_probabilistic_layer(distribution: str, bias_init, distribution_kwargs={},num_samples=21):
+ probabilistic_layer = distribution_to_layer[distribution](**distribution_kwargs)
+ return BaseDistributionLayer(distribution=probabilistic_layer,
+ num_samples=num_samples,
+ bias_init=bias_init)
+
+# def get_probabilistic_layer(
+# output_size,
+# probabilistic_layer: Union[str, dict]
+# ) -> Callable:
+# """Get the probabilistic layer."""
+
+# if isinstance(probabilistic_layer, dict):
+# probabilistic_layer_name = list(probabilistic_layer.keys())[0]
+# probabilistic_layer_options = probabilistic_layer[probabilistic_layer_name]
+# else:
+# probabilistic_layer_name = probabilistic_layer
+# probabilistic_layer_options = {}
+
+# if hasattr(probabilistic_layers, probabilistic_layer_name):
+# _LOGGER.info(f"Using custom probabilistic layer: {probabilistic_layer_name}")
+# probabilistic_layer_obj = getattr(probabilistic_layers, probabilistic_layer_name)
+# n_params = getattr(probabilistic_layers, probabilistic_layer_name).params_size(output_size)
+# probabilistic_layer = (
+# probabilistic_layer_obj(output_size, name="output", **probabilistic_layer_options) if isinstance(probabilistic_layer_obj, type)
+# else probabilistic_layer_obj(output_size, name="output")
+# )
+# else:
+# raise KeyError(f"The probabilistic layer {probabilistic_layer_name} is not available.")
+
+# return probabilistic_layer, n_params
def _build_fcn_block(
@@ -91,31 +131,17 @@ def _build_fcn_block(
return x
-def _build_fcn_output(x, output_size, probabilistic_layer, out_bias_init):
- # probabilistic prediction
- if probabilistic_layer:
- probabilistic_layer, n_params = get_probabilistic_layer(output_size, probabilistic_layer)
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init = np.hstack(
- [out_bias_init, [0.0] * (n_params - out_bias_init.shape[0])]
- )
- out_bias_init = initializers.Constant(out_bias_init)
-
- x = Dense(n_params, bias_initializer=out_bias_init, name="dist_params")(x)
- outputs = probabilistic_layer(x)
-
- # deterministic prediction
- else:
+def _build_fcn_output(output_size, out_bias_init, probabilistic_layer=None, **distribution_kwargs):
+ if probabilistic_layer is None:
if isinstance(out_bias_init, np.ndarray):
out_bias_init = initializers.Constant(out_bias_init)
-
- outputs = Dense(output_size, bias_initializer=out_bias_init, name="output")(x)
-
- return outputs
-
-
+ return Dense(output_size, name='output', bias_initializer=out_bias_init)
+
+
+ prob_layer = get_probabilistic_layer(distribution=probabilistic_layer, bias_init=out_bias_init,distribution_kwargs=distribution_kwargs)
+ return prob_layer
+
def fully_connected_network(
- input_shape: tuple[int],
output_size: int,
hidden_layers: list,
batchnorm: bool = False,
@@ -125,14 +151,13 @@ def fully_connected_network(
out_bias_init: Optional[Union[str, np.ndarray[Any, float]]] = "zeros",
probabilistic_layer: Optional[str] = None,
skip_connection: bool = False,
+ prob_layer_kwargs: dict = {}
) -> Model:
"""
- Build a Fully Connected Neural Network.
+ Get an unbuilt Fully Connected Neural Network.
Parameters
----------
- input_shape: tuple[int]
- Shape of the input samples (not including batch size)
output_size: int
Number of target predictants.
hidden_layers: list[int]
@@ -165,46 +190,30 @@ def fully_connected_network(
model: keras model
The built (but not yet compiled) model.
"""
+
+ ffnn = FullyConnectedLayer(hidden_layers=hidden_layers,
+ batchnorm=batchnorm,
+ activations=activations,
+ dropout=dropout,
+ mc_dropout=mc_dropout,
+ skip_connection=skip_connection)
+
+ output_layer = _build_fcn_output(out_bias_init=out_bias_init,
+ output_size=output_size,
+ probabilistic_layer=probabilistic_layer, **prob_layer_kwargs)
+
+ if probabilistic_layer is None:
+ return keras.models.Sequential([ffnn, output_layer])
+
+ return ProbabilisticModel(encoder_layer=ffnn,
+ probabilistic_layer=output_layer)
- if isinstance(dropout, list):
- assert len(dropout) == len(hidden_layers)
- elif isinstance(dropout, float):
- dropout = [dropout] * (len(hidden_layers) - 1)
- else:
- dropout = []
-
- if isinstance(activations, list):
- assert len(activations) == len(hidden_layers)
- elif isinstance(activations, str):
- activations = [activations] * len(hidden_layers)
-
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init_shape = out_bias_init.shape[-1]
- assert out_bias_init.shape[-1] == output_size, (
- f"Bias initialization array is shape {out_bias_init_shape}"
- f"but output size is {output_size}"
- )
-
- inputs = tf.keras.Input(shape=input_shape)
- x = _build_fcn_block(
- inputs,
- hidden_layers,
- batchnorm,
- activations,
- dropout,
- mc_dropout,
- skip_connection,
- )
- outputs = _build_fcn_output(x, output_size, probabilistic_layer, out_bias_init)
- model = Model(inputs=inputs, outputs=outputs)
-
- return model
def fully_connected_multibranch_network(
- input_shape: tuple[int],
output_size: int,
hidden_layers: list,
+ n_branches,
batchnorm: bool = False,
activations: Optional[Union[str, list[str]]] = "relu",
dropout: Optional[Union[float, list[float]]] = None,
@@ -212,9 +221,11 @@ def fully_connected_multibranch_network(
out_bias_init: Optional[Union[str, np.ndarray[Any, float]]] = "zeros",
probabilistic_layer: Optional[str] = None,
skip_connection: bool = False,
+ aggregation: Literal['sum', 'concat']='concat',
+ prob_layer_kwargs: dict = {}
) -> Model:
"""
- Build a multi-branch Fully Connected Neural Network.
+ Returns an unbuilt a multi-branch Fully Connected Neural Network.
Parameters
----------
@@ -225,6 +236,8 @@ def fully_connected_multibranch_network(
hidden_layers: list[int]
List that is used to define the fully connected block. Each element creates
a Dense layer with the corresponding units.
+ n_branches: int
+ The number of branches.
batchnorm: bool
Use batch normalization. Default is False.
activations: str or list[str]
@@ -246,74 +259,53 @@ def fully_connected_multibranch_network(
used as output layer of the keras `Model`. Default is None.
skip_connection: bool
Include a skip connection to the MLP architecture. Default is False.
+ aggregation: Literal['sum', 'concat']
+ The aggregation strategy to combine the branches' outputs.
Return
------
model: keras model
- The built (but not yet compiled) model.
+ The unbuilt and uncompiled model.
"""
-
- if isinstance(dropout, list):
- assert len(dropout) == len(hidden_layers)
- elif isinstance(dropout, float):
- dropout = [dropout] * (len(hidden_layers) - 1)
- else:
- dropout = []
-
- if isinstance(activations, list):
- assert len(activations) == len(hidden_layers)
- elif isinstance(activations, str):
- activations = [activations] * len(hidden_layers)
-
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init_shape = out_bias_init.shape[-1]
- assert out_bias_init.shape[-1] == output_size, (
- f"Bias initialization array is shape {out_bias_init_shape}"
- f"but output size is {output_size}"
- )
-
- if probabilistic_layer:
- _, n_params = get_probabilistic_layer(output_size, probabilistic_layer)
- n_branches = n_params
- else:
- n_branches = output_size
-
- inputs = tf.keras.Input(shape=input_shape)
- all_branch_outputs = []
+
+ branch_layers = []
for idx in range(n_branches):
- x = _build_fcn_block(
- inputs,
- hidden_layers,
- batchnorm,
- activations,
- dropout,
- mc_dropout,
- skip_connection,
- idx,
- )
- all_branch_outputs.append(x)
-
- concatenated_x = tf.keras.layers.Concatenate()(all_branch_outputs)
- outputs = _build_fcn_output(
- concatenated_x, output_size, probabilistic_layer, out_bias_init
- )
- model = Model(inputs=inputs, outputs=outputs)
-
- return model
+ branch_layers.append(FullyConnectedLayer(
+ hidden_layers=hidden_layers,
+ batchnorm=batchnorm,
+ activations=activations,
+ dropout=dropout,
+ mc_dropout=mc_dropout,
+ skip_connection=skip_connection,
+ indx=idx
+ ))
+
+ mb_ffnn = MultibranchLayer(branches=branch_layers, aggregation=aggregation)
+
+ output_layer = _build_fcn_output(out_bias_init=out_bias_init,
+ output_size=output_size,
+ probabilistic_layer=probabilistic_layer, **prob_layer_kwargs)
+
+ if probabilistic_layer is None:
+ return keras.models.Sequential([mb_ffnn, output_layer])
+
+ return ProbabilisticModel(encoder_layer=mb_ffnn,
+ probabilistic_layer=output_layer)
def deep_cross_network(
- input_shape: tuple[int],
output_size: int,
hidden_layers: list,
+ n_cross_layers: int,
+ cross_layers_hiddensize: int,
batchnorm: bool = True,
activations: Optional[Union[str, list[str]]] = "relu",
dropout: Optional[Union[float, list[float]]] = None,
mc_dropout: bool = False,
out_bias_init: Optional[Union[str, np.ndarray[Any, float]]] = "zeros",
probabilistic_layer: Optional[str] = None,
- skip_connection: bool = False,
+ prob_layer_kwargs: dict = {}
):
"""
Build a Deep and Cross Network (see https://arxiv.org/abs/1708.05123).
@@ -327,6 +319,10 @@ def deep_cross_network(
hidden_layers: list[int]
List that is used to define the fully connected block. Each element creates
a Dense layer with the corresponding units.
+ n_cross_layers: int
+ The number of cross layers
+ cross_layers_hiddensize: int
+ The hidden size to be used in the cross layers
batchnorm: bool
Use batch normalization. Default is True.
activations: str or list[str]
@@ -346,86 +342,38 @@ def deep_cross_network(
probabilistic_layer: str
(Optional) Name of a probabilistic layer defined in `mlpp_lib.probabilistic_layers`, which is
used as output layer of the keras `Model`. Default is None.
- skip_connection: bool
- Include a skip connection to the MLP architecture. Default is False.
Return
------
model: keras model
The built (but not yet compiled) model.
"""
- if isinstance(dropout, list):
- assert len(dropout) == len(hidden_layers)
- elif isinstance(dropout, float):
- dropout = [dropout] * (len(hidden_layers))
- else:
- dropout = []
-
- if isinstance(activations, list):
- assert len(activations) == len(hidden_layers)
- elif isinstance(activations, str):
- activations = [activations] * len(hidden_layers)
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init_shape = out_bias_init.shape[-1]
- assert out_bias_init.shape[-1] == output_size, (
- f"Bias initialization array is shape {out_bias_init_shape}"
- f"but output size is {output_size}"
- )
# cross part
- inputs = tf.keras.layers.Input(shape=input_shape)
- cross = inputs
- for _ in hidden_layers:
- units_ = cross.shape[-1]
- x = Dense(units_)(cross)
- cross = inputs * x + cross
- cross = BatchNormalization()(cross)
- # cross = tf.keras.Model(inputs=inputs, outputs=cross, name="crossblock")
-
+ cross_layer = CrossNetLayer(hidden_size=cross_layers_hiddensize,
+ depth=n_cross_layers)
+
# deep part
- deep = inputs
- deep = _build_fcn_block(
- deep,
- hidden_layers,
- batchnorm,
- activations,
- dropout,
- mc_dropout,
- skip_connection=False,
- )
+
+ deep_layer = FullyConnectedLayer(hidden_layers=hidden_layers,
+ batchnorm=batchnorm,
+ activations=activations,
+ dropout=dropout,
+ mc_dropout=mc_dropout)
- # merge
- merge = tf.keras.layers.Concatenate()([cross, deep])
+
+ encoder = ParallelConcatenateLayer([cross_layer, deep_layer])
- if skip_connection:
- merge = Dense(input_shape[0])(merge)
- merge = Add()([merge, inputs])
- merge = Activation(activation=activations[-1])(merge)
-
- # probabilistic prediction
- if probabilistic_layer:
- probabilistic_layer, n_params = get_probabilistic_layer(output_size, probabilistic_layer)
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init = np.hstack(
- [out_bias_init, [0.0] * (n_params - out_bias_init.shape[0])]
- )
- out_bias_init = initializers.Constant(out_bias_init)
+ output_layer = _build_fcn_output(out_bias_init=out_bias_init,
+ output_size=output_size,
+ probabilistic_layer=probabilistic_layer, **prob_layer_kwargs)
- x = Dense(n_params, bias_initializer=out_bias_init, name="dist_params")(merge)
- outputs = probabilistic_layer(x)
-
- # deterministic prediction
- else:
- if isinstance(out_bias_init, np.ndarray):
- out_bias_init = initializers.Constant(out_bias_init)
-
- outputs = Dense(output_size, bias_initializer=out_bias_init, name="output")(
- merge
- )
-
- model = Model(inputs=inputs, outputs=outputs, name="deep_cross_network")
- return model
+ if probabilistic_layer is None:
+ return keras.models.Sequential([encoder, output_layer])
+
+ return ProbabilisticModel(encoder_layer=encoder,
+ probabilistic_layer=output_layer)
def temporal_convolutional_network(
@@ -450,7 +398,7 @@ def temporal_convolutional_network(
if isinstance(out_bias_init, np.ndarray):
out_bias_init = initializers.Constant(out_bias_init)
- inputs = tf.keras.Input(shape=input_shape, name="input")
+ inputs = keras.Input(shape=input_shape, name="input")
x_tcn = tcn.TCN(
nb_filters=nb_filters,
kernel_size=kernel_size,
diff --git a/mlpp_lib/physical_layers.py b/mlpp_lib/physical_layers.py
index 2f4e8b5..c792a06 100644
--- a/mlpp_lib/physical_layers.py
+++ b/mlpp_lib/physical_layers.py
@@ -1,5 +1,5 @@
-import tensorflow as tf
-from tensorflow.keras.layers import Layer
+from keras.layers import Layer
+import keras.backend as K
class ThermodynamicLayer(Layer):
@@ -23,7 +23,7 @@ def __init__(self, **kwargs) -> None:
self.D_idx = 1 # dew_point_deficit
self.P_idx = 2 # surface_air_pressure
- self.EPSILON = tf.constant(622.0)
+ self.EPSILON = K.constant(622.0)
def build(self, input_shape: tuple[int]) -> None:
super().build(input_shape)
@@ -38,11 +38,11 @@ def call(self, inputs):
dew_point_deficit = inputs[..., self.D_idx]
surface_air_pressure = inputs[..., self.P_idx]
- dew_point_temperature = air_temperature - tf.nn.relu(dew_point_deficit)
- water_vapor_saturation_pressure = 6.112 * tf.exp(
+ dew_point_temperature = air_temperature - K.relu(dew_point_deficit)
+ water_vapor_saturation_pressure = 6.112 * K.exp(
(17.67 * air_temperature) / (air_temperature + 243.5)
)
- water_vapor_pressure = 6.112 * tf.exp(
+ water_vapor_pressure = 6.112 * K.exp(
(17.67 * dew_point_temperature) / (dew_point_temperature + 243.5)
)
relative_humidity = (
@@ -52,7 +52,7 @@ def call(self, inputs):
water_vapor_pressure / (surface_air_pressure - water_vapor_pressure)
)
- out = tf.concat(
+ out = K.concat(
[
air_temperature[..., None],
dew_point_temperature[..., None],
diff --git a/mlpp_lib/probabilistic_layers.py b/mlpp_lib/probabilistic_layers.py
index 838a5af..5cc15b3 100644
--- a/mlpp_lib/probabilistic_layers.py
+++ b/mlpp_lib/probabilistic_layers.py
@@ -1,1462 +1,1894 @@
-"""In this module, any custom built keras layers are included."""
-
+import torch
+import torch.nn as nn
+import keras
+from keras.layers import TorchModuleWrapper, Dense
+from keras import Layer
+from abc import ABC, abstractmethod
import numpy as np
-import tensorflow as tf
-import tensorflow_probability.python.layers as tfpl
-from tensorflow_probability.python import bijectors as tfb
-from tensorflow_probability.python import distributions as tfd
-from tensorflow_probability.python.layers.distribution_layer import (
- _event_size,
- _get_convert_to_tensor_fn,
- _serialize,
- dist_util,
- independent_lib,
-)
-
-
-# these almost work out of the box
-from tensorflow_probability.python.layers import (
- IndependentNormal,
- IndependentLogistic,
- IndependentBernoulli,
- IndependentPoisson,
-)
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentNormal(IndependentNormal):
- @property
- def output(self): # this is necessary to use the layer within shap
- return super().output[0]
+from keras import initializers
+from typing import Literal
+from inspect import getmembers, isclass
+import sys
+from mlpp_lib.custom_distributions import TruncatedNormalDistribution, CensoredNormalDistribution
+from mlpp_lib.exceptions import MissingReparameterizationError
+from mlpp_lib.layers import MeanAndTriLCovLayer
-@tf.keras.saving.register_keras_serializable()
-class IndependentLogistic(IndependentLogistic):
+class BaseParametricDistributionModule(nn.Module, ABC):
+ """ Base class for parametric distributions layers
+ """
@property
- def output(self):
- return super().output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentBernoulli(IndependentBernoulli):
+ @abstractmethod
+ def num_parameters(self):
+ '''The number of parameters that describe the distribution'''
+ pass
+
@property
- def output(self):
- return super().output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentPoisson(IndependentPoisson):
+ @abstractmethod
+ def name(self):
+ pass
+
@property
- def output(self):
- return super().output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentBeta(tfpl.DistributionLambda):
- """An independent 2-parameter Beta Keras layer"""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentBeta` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentBeta.new(t, event_shape, validate_args)
-
- super(IndependentBeta, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentBeta"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- alpha, beta = tf.split(params, 2, axis=-1)
-
- alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 1e-3
- beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 1e-3
- betad = tfd.Beta(alpha, beta, validate_args=validate_args)
-
- return independent_lib.Independent(
- betad,
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentBeta_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentBeta_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
+ def has_rsample(self):
+ return self._distribution.has_rsample
+
+ @abstractmethod
+ def process_params(self, **kwargs) -> torch.distributions.Distribution:
"""
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentBeta, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
- @property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentBeta, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class Independent4ParamsBeta(tfpl.DistributionLambda):
- """An independent 4-parameter Beta Keras layer allowing control over scale as well as a 'shift' parameter."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `Independent4ParamsBeta` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+ Given the distribution's parameters predicted by a previous layer,
+ ensures the constraints are met and the parametric distribution is returned.
"""
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return Independent4ParamsBeta.new(t, event_shape, validate_args)
-
- super(Independent4ParamsBeta, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "Independent4ParamsBeta"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- alpha, beta, shift, scale = tf.split(params, 4, axis=-1)
- # alpha > 2 and beta > 2 produce a concave downward Beta
- alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 1e-3
- beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 1e-3
- shift = tf.math.softplus(tf.reshape(shift, output_shape))
- scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
- betad = tfd.Beta(alpha, beta, validate_args=validate_args)
- transf_betad = tfd.TransformedDistribution(
- distribution=betad, bijector=tfb.Shift(shift)(tfb.Scale(scale))
- )
- return independent_lib.Independent(
- transf_betad,
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "Independent4ParamsBeta_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(4) * _event_size(
- event_shape, name=name or "Independent4ParamsBeta_params_size"
- )
+ pass
+
+ def forward(self, predicted_parameters, num_samples=1, return_dist=True, pattern: Literal['sbd', 'bsd'] = 'sbd', reparametrized=False):
+ parametric_dist = self.process_params(predicted_parameters)
+
+ dist = WrappingTorchDist(distribution=parametric_dist)
+
+ if return_dist:
+ return dist
+
+ if not reparametrized:
+ return dist.sample(num_samples, pattern=pattern)
+ return dist.rsample(num_samples, pattern)
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
+
+class WrappingTorchDist():
+ """
+ Wraps a torch.distributions.Distribution instance.
+ Unifies sample(torch.Size) and sample_n(int) in a single function and
+ allows to specify a pattern for the samples between [Batch, Samples, Dim]
+ and [Samples, Batch, Dim].
+ """
+ def __init__(self, distribution: torch.distributions.Distribution):
+ self._distribution = distribution
+
+ def _get_samples(self, sampling_fn, n: int, pattern: Literal['sbd', 'bsd'] = 'sbd'):
+ patterns_to_perm = {
+ "bsd": (1,0,2),
+ "sbd": (0,1,2)
}
- base_config = super(Independent4ParamsBeta, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ samples = sampling_fn((n,)) if isinstance(n, int) else sampling_fn(n)
+ return samples.permute(*patterns_to_perm[pattern])
+
+ def sample(self, n: int|tuple, pattern: Literal['sbd', 'bsd'] = 'sbd'):
+ return self._get_samples(sampling_fn=self._distribution.sample, n=n, pattern=pattern)
+
+ def rsample(self, n: int|tuple, pattern: Literal['sbd', 'bsd'] = 'sbd'):
+ if not self._distribution.has_rsample:
+ raise MissingReparameterizationError(f"{self._distribution.__class__} does not implement rsample.")
+
+ return self._get_samples(sampling_fn=self._distribution.rsample, n=n, pattern=pattern)
+
+ def __str__(self):
+ return f"Wrapper for {self._distribution.__class__.__name__} distribution."
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(Independent4ParamsBeta, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentDoublyCensoredNormal(tfpl.DistributionLambda):
- """An independent censored normal Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentDoublyCensoredNormal` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
- # get the clipping parameters and pop them
- _clip_low = kwargs.pop("clip_low", 0.0)
- _clip_high = kwargs.pop("clip_high", 1.0)
-
- def new_from_t(t):
- return IndependentDoublyCensoredNormal.new(
- t,
- event_shape,
- validate_args,
- clip_low=_clip_low,
- clip_high=_clip_high,
- )
-
- super(IndependentDoublyCensoredNormal, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(
- params,
- event_shape=(),
- validate_args=False,
- name=None,
- clip_low=0.0,
- clip_high=1.0,
- ):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentDoublyCensoredNormal"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- loc, scale = tf.split(params, 2, axis=-1)
- loc = tf.reshape(loc, output_shape)
- scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
- normal_dist = tfd.Normal(loc=loc, scale=scale, validate_args=validate_args)
-
- class CustomCensored(tfd.Distribution):
- def __init__(self, normal, clip_low=0.0, clip_high=1.0):
- self.normal = normal
- super(CustomCensored, self).__init__(
- dtype=normal.dtype,
- reparameterization_type=tfd.FULLY_REPARAMETERIZED,
- validate_args=validate_args,
- allow_nan_stats=True,
- )
- self.clip_low = clip_low
- self.clip_high = clip_high
-
- def _sample_n(self, n, seed=None):
-
- # Sample from normal distribution
- samples = self.normal.sample(sample_shape=(n,), seed=seed)
-
- # Clip values between 0 and 1
- chosen_samples = tf.clip_by_value(
- samples, self.clip_low, self.clip_high
- )
-
- return chosen_samples
-
- def _mean(self):
- """
- Original: X ~ N(mu, sigma)
- Censored: Y = X if clip_low <= X <= clip_high else clip_low if X < clip_low else clip_high
- Phi / phi: CDF / PDF of standard normal distribution
-
- Law of total expectations:
- E[Y] = E[Y | X > c_h] * P(X > c_h) + E[Y | X < c_l] * P(X < c_l) + E[Y | c_l <= X <= c_h] * P(c_l <= X <= c_h)
- = c_h * P(X > c_h) + P(X < c_l) * c_l + E[Y | c_l <= X <= c_h] * P(c_l <= X <= c_h)
- = c_h * P(X > c_h) + P(X < c_l) * c_l + E[Z ~ TruncNormal(mu, sigma, c_l, c_h)] * (Phi((c_h - mu) / sigma) - Phi(c_l - mu / sigma))
- = c_h * (1 - Phi((c_h - mu) / sigma))
- + c_l * Phi((c_l - mu) / sigma)
- + mu * (Phi((c_h - mu) / sigma) - Phi(c_l - mu / sigma))
- + sigma * (phi(c_l - mu / sigma) - phi((c_h - mu) / sigma))
- Ref for TruncatedNormal mean: https://en.wikipedia.org/wiki/Truncated_normal_distribution
- """
- mu, sigma = self.normal.mean(), self.normal.stddev()
- low_bound_standard = (self.clip_low - mu) / sigma
- high_bound_standard = (self.clip_high - mu) / sigma
-
- cdf = lambda x: tfd.Normal(0, 1).cdf(x)
- pdf = lambda x: tfd.Normal(0, 1).prob(x)
-
- return (
- self.clip_high * (1 - cdf(high_bound_standard))
- + self.clip_low * cdf(low_bound_standard)
- + mu * (cdf(high_bound_standard) - cdf(low_bound_standard))
- + sigma * (pdf(low_bound_standard) - pdf(high_bound_standard))
- )
-
- def _log_prob(self, value):
-
- mu, sigma = self.normal.mean(), self.normal.stddev()
- cdf = lambda x: tfd.Normal(0, 1).cdf(x)
- pdf = lambda x: tfd.Normal(0, 1).prob(x)
-
- logprob_left = lambda x: tf.math.log(
- cdf(self.clip_low - mu / sigma) + 1e-3
- )
- logprob_middle = lambda x: self.normal.log_prob(x)
- logprob_right = lambda x: tf.math.log(
- 1 - cdf((self.clip_high - mu) / sigma) + 1e-3
- )
-
- return (
- logprob_left(value)
- + logprob_middle(value)
- + logprob_right(value)
- )
-
- return independent_lib.Independent(
- CustomCensored(normal_dist, clip_low=clip_low, clip_high=clip_high),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentDoublyCensoredNormal_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentDoublyCensoredNormal_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentDoublyCensoredNormal, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ def name(self):
+ return self._distribution.__class__.__name__
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentDoublyCensoredNormal, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentConcaveBeta(tfpl.DistributionLambda):
- """An independent 4-parameter Beta Keras layer with enforced concavity"""
-
- # INdependent
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentConcaveBeta` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentConcaveBeta.new(t, event_shape, validate_args)
-
- super(IndependentConcaveBeta, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentConcaveBeta"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- alpha, beta, shift, scale = tf.split(params, 4, axis=-1)
- # alpha > 2 and beta > 2 produce a concave downward Beta
- alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 2.0
- beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 2.0
- shift = tf.math.softplus(tf.reshape(shift, output_shape))
- scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
- betad = tfd.Beta(alpha, beta, validate_args=validate_args)
- transf_betad = tfd.TransformedDistribution(
- distribution=betad, bijector=tfb.Shift(shift)(tfb.Scale(scale))
- )
- return independent_lib.Independent(
- transf_betad,
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentConcaveBeta_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(4) * _event_size(
- event_shape, name=name or "IndependentConcaveBeta_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentConcaveBeta, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ def has_rsample(self):
+ return self._distribution.has_rsample
+
+class UniveriateGaussianModule(BaseParametricDistributionModule):
+ '''
+ Torch implementation of a Gaussian sampling layer given mean and covariance
+ values of shape [None, 2]. This layer uses the reparametrization trick
+ to allow the flow of gradients.
+ '''
+ _name = 'IndependentNormal'
+ _distribution = torch.distributions.Normal # WrappingTorchDist(base_dist=torch.distributions.Normal).sample(2)
+ def __init__(self, **kwargs):
+ super(UniveriateGaussianModule, self).__init__()
+ self.get_positive_std = torch.nn.Softplus()
+
+ def process_params(self, moments):
+
+ # Create a copy of `moments` to avoid issues when using Softplus
+ # on tensor selections
+ new_moments = moments.clone()
+ new_moments[:, 1] = self.get_positive_std(moments[:, 1])
+
+ normal_dist = self._distribution(new_moments[:,0:1], new_moments[:,1:2])
+ return normal_dist
+
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentConcaveBeta, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentGamma(tfpl.DistributionLambda):
- """An independent gamma Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentGamma` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentGamma.new(t, event_shape, validate_args)
-
- super(IndependentGamma, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentGamma"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- concentration, rate = tf.split(params, 2, axis=-1)
- return independent_lib.Independent(
- tfd.Gamma(
- concentration=tf.math.softplus(
- tf.reshape(concentration, output_shape)
- ),
- rate=tf.math.softplus(tf.reshape(rate, output_shape)),
- validate_args=validate_args,
- ),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentGamma_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentGamma_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentGamma, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ def num_parameters(self):
+ return 2
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentGamma, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentLogNormal(tfpl.DistributionLambda):
- """An independent LogNormal Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentLogNormal` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentLogNormal.new(t, event_shape, validate_args)
-
- super(IndependentLogNormal, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentLogNormal"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- loc, scale = tf.split(params, 2, axis=-1)
- return independent_lib.Independent(
- tfd.LogNormal(
- loc=tf.reshape(loc, output_shape),
- scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
- validate_args=validate_args,
- ),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentLogNormal_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentLogNormal_params_size"
- )
+ def name(self):
+ return self._name
+
+class MultivariateGaussianTriLModule(BaseParametricDistributionModule):
+ """Multivariate Gaussian ~N(mu, L) where mu = E[x] is the mean vector, and L is a lower triangular
+ matrix such that LL^T = Cov(x). Matrix L is only required to be a lower triagular square matrix.
+ Internally, the values on the diagonal will be ensured positive with a softplus.
+ """
+ _name = 'multivariate_tril_gaussian'
+ _distribution = torch.distributions.MultivariateNormal
+
+ def __init__(self, dim, **kwargs):
+ super(MultivariateGaussianTriLModule, self).__init__()
+ self.dim = dim
+
+ def process_params(self, mean_and_tril_cov):
+ mean = mean_and_tril_cov[0]
+ tril_cov = mean_and_tril_cov[1]
+
+ tril_cov = self._ensure_lower_cholesky(tril_cov)
+
+ multivariate_normal = self._distribution(loc=mean, scale_tril=tril_cov)
+ return multivariate_normal
+
+
+ def _ensure_lower_cholesky(self, x):
+ """Ensures positive values on the diagonal.
+ The input is expected to be a lower triangular matrix.
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
"""
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentLogNormal, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ diag = torch.diagonal(x, dim1=-2, dim2=-1) # get diagonals
+ diag_fixed = torch.nn.functional.softplus(diag) # make them positive
+ # remove old diagonals and replace the new ones
+ return x - torch.diag_embed(diag) + torch.diag_embed(diag_fixed)
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentLogNormal, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentLogitNormal(tfpl.DistributionLambda):
- """An independent Logit-Normal Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.sample,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentLogitNormal` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentLogitNormal.new(t, event_shape, validate_args)
-
- super(IndependentLogitNormal, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentLogitNormal"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- loc, scale = tf.split(params, 2, axis=-1)
- return independent_lib.Independent(
- tfd.LogitNormal(
- loc=tf.reshape(loc, output_shape),
- scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
- validate_args=validate_args,
- ),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentLogitNormal_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentLogitNormal_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentLogitNormal, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ def num_parameters(self):
+ return (self.dim, self.dim * (self.dim + 1) // 2)
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentLogitNormal, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentMixtureNormal(tfpl.DistributionLambda):
- """A mixture of two normal distributions Keras layer.
- 5-parameters distribution: loc1, scale1, loc2, scale2, weight
- """
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentMixtureNormal` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
-
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentMixtureNormal.new(t, event_shape, validate_args)
-
- super(IndependentMixtureNormal, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentMixtureNormal"):
- params = tf.convert_to_tensor(params, name="params")
-
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
-
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
-
- loc1, scale1, loc2, scale2, weight = tf.split(params, 5, axis=-1)
- loc1 = tf.reshape(loc1, output_shape)
- scale1 = tf.math.softplus(tf.reshape(scale1, output_shape)) + 1e-3
- loc2 = tf.reshape(loc2, output_shape)
- scale2 = tf.math.softplus(tf.reshape(scale2, output_shape)) + 1e-3
- weight = tf.math.sigmoid(tf.reshape(weight, output_shape))
-
- # Create the component distributions
- normald1 = tfd.Normal(loc=loc1, scale=scale1)
- normald2 = tfd.Normal(loc=loc2, scale=scale2)
-
- # Create a categorical distribution for the weights
- cat = tfd.Categorical(
- probs=tf.concat(
- [tf.expand_dims(weight, -1), tf.expand_dims(1 - weight, -1)],
- axis=-1,
- )
- )
-
- class CustomMixture(tfd.Distribution):
- def __init__(self, cat, normald1, normald2):
- self.cat = cat
- self.normald1 = normald1
- self.normald2 = normald2
- super(CustomMixture, self).__init__(
- dtype=normald1.dtype,
- reparameterization_type=tfd.FULLY_REPARAMETERIZED,
- validate_args=validate_args,
- allow_nan_stats=True,
- )
-
- def _sample_n(self, n, seed=None):
- indices = self.cat.sample(sample_shape=(n,), seed=seed)
-
- # Sample from both truncated normal distributions
- samples1 = self.normald1.sample(sample_shape=(n,), seed=seed)
- samples2 = self.normald2.sample(sample_shape=(n,), seed=seed)
-
- # Stack the samples along a new axis
- samples = tf.stack([samples1, samples2], axis=-1)
-
- # Gather samples according to indices from the categorical distribution
- chosen_samples = tf.gather(
- samples,
- indices,
- batch_dims=tf.get_static_value(tf.rank(indices)),
- )
-
- return chosen_samples
-
- def _log_prob(self, value):
- log_prob1 = self.normald1.log_prob(value)
- log_prob2 = self.normald2.log_prob(value)
- log_probs = tf.stack([log_prob1, log_prob2], axis=-1)
- weighted_log_probs = log_probs + tf.math.log(
- tf.concat([weight, 1 - weight], axis=-1)
- )
- return tf.reduce_logsumexp(weighted_log_probs, axis=-1)
-
- def _mean(self):
- return (
- weight * self.normald1.mean()
- + (1 - weight) * self.normald2.mean()
- )
-
- mixtured = CustomMixture(cat, normald1, normald2)
-
- return independent_lib.Independent(
- mixtured,
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentMixtureNormal_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(5) * _event_size(
- event_shape, name=name or "IndependentMixtureNormal_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentMixtureNormal, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
+ def name(self):
+ return self._name
+
+class UnivariateTruncatedGaussianModule(BaseParametricDistributionModule):
+ _name = 'truncated_gaussian'
+ _distribution = TruncatedNormalDistribution
+
+ def __init__(self, a, b, **kwargs):
+ super(UnivariateTruncatedGaussianModule, self).__init__()
+ self.get_positive_std = torch.nn.Softplus()
+ if type(a) != torch.Tensor:
+ a = torch.tensor(a)
+ if type(b) != torch.Tensor:
+ b = torch.tensor(b)
+
+ self.a, self.b = a, b
+
+
+ def process_params(self, moments):
+
+ # Create a copy of `moments` to avoid issues when using Softplus
+ # on tensor selections
+ new_moments = moments.clone()
+ new_moments[:, 1] = self.get_positive_std(moments[:, 1])
+
+
+ trunc_normal_dist = self._distribution(mu_bar=new_moments[:,0:1], sigma_bar=new_moments[:,1:2], a=self.a, b=self.b)
+ return trunc_normal_dist
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentMixtureNormal, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentTruncatedNormal(tfpl.DistributionLambda):
- """An independent TruncatedNormal Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentTruncatedNormal` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentTruncatedNormal.new(t, event_shape, validate_args)
-
- super(IndependentTruncatedNormal, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentTruncatedNormal"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- loc, scale = tf.split(params, 2, axis=-1)
- return independent_lib.Independent(
- tfd.TruncatedNormal(
- loc=tf.reshape(loc, output_shape),
- scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
- low=0,
- high=np.inf,
- validate_args=validate_args,
- ),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentTruncatedNormal_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentTruncatedNormal_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentTruncatedNormal, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
+ def num_parameters(self):
+ return 2
+
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentTruncatedNormal, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class IndependentWeibull(tfpl.DistributionLambda):
- """An independent Weibull Keras layer."""
-
- def __init__(
- self,
- event_shape=(),
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the `IndependentWeibull` layer.
- Args:
- event_shape: integer vector `Tensor` representing the shape of single
- draw from this distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object.
- Default value: `tfd.Distribution.mean`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return IndependentWeibull.new(t, event_shape, validate_args)
-
- super(IndependentWeibull, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_shape = event_shape
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_shape=(), validate_args=False, name=None):
- """Create the distribution instance from a `params` vector."""
- with tf.name_scope(name or "IndependentWeibull"):
- params = tf.convert_to_tensor(params, name="params")
- event_shape = dist_util.expand_to_vector(
- tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- ),
- tensor_name="event_shape",
- )
- output_shape = tf.concat(
- [
- tf.shape(params)[:-1],
- event_shape,
- ],
- axis=0,
- )
- concentration, scale = tf.split(params, 2, axis=-1)
- return independent_lib.Independent(
- tfd.Weibull(
- concentration=tf.math.softplus(
- tf.reshape(concentration, output_shape)
- )
- + 1.0,
- scale=tf.math.softplus(tf.reshape(scale, output_shape)),
- validate_args=validate_args,
- ),
- reinterpreted_batch_ndims=tf.size(event_shape),
- validate_args=validate_args,
- )
-
- @staticmethod
- def params_size(event_shape=(), name=None):
- """The number of `params` needed to create a single distribution."""
- with tf.name_scope(name or "IndependentWeibull_params_size"):
- event_shape = tf.convert_to_tensor(
- event_shape, name="event_shape", dtype_hint=tf.int32
- )
- return np.int32(2) * _event_size(
- event_shape, name=name or "IndependentWeibull_params_size"
- )
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_shape": self._event_shape,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(IndependentWeibull, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
+ def name(self):
+ return self._name
+
+class UnivariateCensoredGaussianModule(BaseParametricDistributionModule):
+ _name = 'censored_gaussian'
+ _distribution = CensoredNormalDistribution
+ def __init__(self, a: torch.Tensor,b: torch.Tensor, **kwargs):
+ super(UnivariateCensoredGaussianModule, self).__init__()
+ self.get_positive_std = torch.nn.Softplus()
+ if type(a) != torch.Tensor:
+ a = torch.tensor(a)
+ if type(b) != torch.Tensor:
+ b = torch.tensor(b)
+ self.a, self.b = a, b
+
+ def process_params(self, moments):
+ new_moments = moments.clone()
+ new_moments[:, 1] = self.get_positive_std(moments[:, 1])
+
+ censored_normal_dist = self._distribution(mu_bar=new_moments[:,0:1], sigma_bar=new_moments[:,1:2], a=self.a, b=self.b)
+ return censored_normal_dist
@property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(IndependentWeibull, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class MultivariateNormalDiag(tfpl.DistributionLambda):
- """A `d`-variate normal Keras layer from `2* d` params,
- with a diagonal scale matrix.
+ def num_parameters(self):
+ return 2
+
+ @property
+ def name(self):
+ return self._name
+
+class UnivariateLogNormalModule(BaseParametricDistributionModule):
"""
-
- def __init__(
- self,
- event_size,
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- """Initialize the layer.
- Args:
- event_size: Scalar `int` representing the size of single draw from this
- distribution.
- convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
- instance and returns a `tf.Tensor`-like object. For examples, see
- `class` docstring.
- Default value: `tfd.Distribution.sample`.
- validate_args: Python `bool`, default `False`. When `True` distribution
- parameters are checked for validity despite possibly degrading runtime
- performance. When `False` invalid inputs may silently render incorrect
- outputs.
- Default value: `False`.
- **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
- """
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- def new_from_t(t):
- return MultivariateNormalDiag.new(t, event_size, validate_args)
-
- super(MultivariateNormalDiag, self).__init__(
- new_from_t, convert_to_tensor_fn, **kwargs
- )
-
- self._event_size = event_size
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- @staticmethod
- def new(params, event_size, validate_args=False, name=None):
- """Create the distribution instance from a 'params' vector."""
- with tf.name_scope(name or "MultivariateNormalDiag"):
- params = tf.convert_to_tensor(params, name="params")
- if event_size > 1:
- dist = tfd.MultivariateNormalDiag(
- loc=params[..., :event_size],
- scale_diag=1e-5 + tf.math.softplus(params[..., event_size:]),
- validate_args=validate_args,
- )
- else:
- dist = tfd.Normal(
- loc=params[..., :event_size],
- scale=1e-5 + tf.math.softplus(params[..., event_size:]),
- validate_args=validate_args,
- )
+ Module implementing Y such that
+ X ~ Normal(loc, scale)
+ Y = exp(X) ~ LogNormal(loc, scale)
+ """
+ _name = 'log_gaussian'
+ _distribution = torch.distributions.LogNormal
+
+ def __init__(self, **kwargs):
+ super(UnivariateLogNormalModule, self).__init__()
+ self.get_positive_std = torch.nn.Softplus()
+
+
+ def process_params(self, moments):
+ new_moments = moments.clone()
+ new_moments[:, 1] = self.get_positive_std(moments[:, 1])
+
+ log_normal_dist = self._distribution(new_moments[:,0:1], new_moments[:,1:2])
+ return log_normal_dist
+
+ @property
+ def num_parameters(self):
+ return 2
+
+ @property
+ def name(self):
+ return self._name
+
+class WeibullModule(BaseParametricDistributionModule):
+ """
+ Toch implementation of a 2-parameters Weibull distribution.
+ """
+ _name = 'weibull'
+ _distribution = torch.distributions.Weibull
+ def __init__(self, **kwargs):
+ super(WeibullModule, self).__init__()
+ self.get_positive_params = torch.nn.Softplus()
+
+ def process_params(self, params):
+ params = self.get_positive_params(params)
+
+ weibull_dist = self._distribution(scale=params[:,0:1],
+ concentration=params[:,1:2])
+ return weibull_dist
+
+ @property
+ def num_parameters(self):
+ return 2
+
+ @property
+ def name(self):
+ return self._name
+
+
+class ExponentialModule(BaseParametricDistributionModule):
+ _name = 'exponential'
+ _distribution = torch.distributions.Exponential
+ def __init__(self, **kwargs):
+ super(ExponentialModule, self).__init__()
+ self.get_positive_lambda = torch.nn.Softplus()
+
+ def process_params(self, params):
+ params = self.get_positive_lambda(params)
+
+ # return torch.distributions.Exponential(rate=params)
+ exp_dist = self._distribution(rate=params)
+ return exp_dist
+
+ @property
+ def num_parameters(self):
+ return 1
+
+ @property
+ def name(self):
+ return self._name
+
+class BetaModule(BaseParametricDistributionModule):
+ _name = 'beta'
+ _distribution = torch.distributions.Beta
+
+ def __init__(self, **kwargs):
+ super(BetaModule, self).__init__()
+
+ self.get_positive_concentrations = torch.nn.Softplus()
+
+ def process_params(self, params):
+ params = self.get_positive_concentrations(params)
+
+ beta_dist = self._distribution(concentration1=params[:,0:1], # c1 = alpha
+ concentration0=params[:,1:2]) # c0 = beta
+
+ return beta_dist
+
+ @property
+ def num_parameters(self):
+ return 2
+
+ @property
+ def name(self):
+ return self._name
+
+
+class GammaModule(BaseParametricDistributionModule):
+
+ _name = 'gamma'
+ _distribution = torch.distributions.Gamma
+ def __init__(self, **kwargs):
+ super(GammaModule, self).__init__()
+ self.get_positive_params = torch.nn.Softplus()
+
+ def process_params(self, params):
+ params = self.get_positive_params(params)
+
+ gamma_dist = self._distribution(concentration=params[:,0:1], # alpha
+ rate=params[:,1:2]) # beta or 1/scale
+
+ return gamma_dist
+
+ @property
+ def num_parameters(self):
+ return 2
+
+ @property
+ def name(self):
+ return self._name
+
+@keras.saving.register_keras_serializable()
+class BaseDistributionLayer(Layer):
+ '''
+ Keras layer implementing a sampling layer with reparametrization
+ trick, based on an underlying parametric distribution.
+ This layer is responsible of preparing the shape of the input to the probabilistic layer
+ to match the number of parameters. It does not assume anything about their properties as
+ it merely applies a linear layer. The underlying probabilistic layer needs to take care
+ of parameter constraints, e.g, the positiveness of the parameters.
+ '''
+ def __init__(self, distribution: BaseParametricDistributionModule,
+ num_samples: int=21,
+ bias_init = 'zeros',
+ **kwargs):
+ super(BaseDistributionLayer, self).__init__(**kwargs)
+
+ self.prob_layer = TorchModuleWrapper(distribution, name=distribution.name)
+ self.num_dist_params = distribution.num_parameters
+
+ if isinstance(bias_init, np.ndarray):
+ bias_init = np.hstack(
+ [bias_init, [0.0] * (distribution.num_parameters - bias_init.shape[0])]
+ )
+ bias_init = initializers.Constant(bias_init)
+ self.bias_init = bias_init
+ # linear layer to map any input size into the number of parameters of the underlying distribution.
+ self.num_samples=num_samples
+ self.is_multivariate_gaussian = isinstance(distribution, MultivariateGaussianTriLModule)
+
+ def build(self, input_shape):
+ if not self.is_multivariate_gaussian:
+ self.parameters_encoder = Dense(self.num_dist_params, name='parameters_encoder', bias_initializer=self.bias_init)
+ else:
+ self.parameters_encoder = MeanAndTriLCovLayer(d1=self.prob_layer.module.num_parameters[0])
+ super().build(input_shape)
+
+ def call(self, inputs, output_type: Literal["distribution", "samples"]='distribution', training=None, num_samples=1, pattern: Literal['sbd', 'bsd'] = 'sbd', reparametrized=False):
+ predicted_parameters = self.parameters_encoder(inputs)
+ if output_type == 'distribution':
+ dist = self.prob_layer(predicted_parameters, num_samples=0, return_dist=True, pattern=pattern, reparametrized=reparametrized)
return dist
+ elif output_type == 'samples':
+ if training and not self.prob_layer.module.has_rsample:
+ raise MissingReparameterizationError(f"Gradient-based optimization will not work, as the underlying {self.prob_layer.module._distribution.__name__} distribution does not have a reparametrized sampling function.")
+ samples = self.prob_layer(predicted_parameters, num_samples=num_samples, return_dist=False, pattern=pattern, reparametrized=reparametrized)
+ return samples
- @staticmethod
- def params_size(event_size, name=None):
- """The number of 'params' needed to create a single distribution."""
- with tf.name_scope(name or "MultivariateNormalDiag_params_size"):
- return 2 * event_size
+ def compute_output_shape(self, input_shape):
+ return (input_shape[0], self.num_samples, 1)
def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_size": self._event_size,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(MultivariateNormalDiag, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
- @property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(MultivariateNormalDiag, self).output[0]
-
-
-@tf.keras.saving.register_keras_serializable()
-class MultivariateNormalTriL(tfpl.MultivariateNormalTriL):
- def __init__(
- self,
- event_size,
- convert_to_tensor_fn=tfd.Distribution.mean,
- validate_args=False,
- **kwargs
- ):
- convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
-
- # If there is a 'make_distribution_fn' keyword argument (e.g., because we
- # are being called from a `from_config` method), remove it. We pass the
- # distribution function to `DistributionLambda.__init__` below as the first
- # positional argument.
- kwargs.pop("make_distribution_fn", None)
-
- super().__init__(event_size, convert_to_tensor_fn, validate_args, **kwargs)
- self._event_size = event_size
- self._convert_to_tensor_fn = convert_to_tensor_fn
- self._validate_args = validate_args
-
- def get_config(self):
- """Returns the config of this layer.
- NOTE: At the moment, this configuration can only be serialized if the
- Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
- implements `get_config`) or one of the standard values:
- - `Distribution.sample` (or `"sample"`)
- - `Distribution.mean` (or `"mean"`)
- - `Distribution.mode` (or `"mode"`)
- - `Distribution.stddev` (or `"stddev"`)
- - `Distribution.variance` (or `"variance"`)
- """
- config = {
- "event_size": self._event_size,
- "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
- "validate_args": self._validate_args,
- }
- base_config = super(MultivariateNormalTriL, self).get_config()
- return dict(list(base_config.items()) + list(config.items()))
-
- @property
- def output(self):
- """This allows the use of this layer with the shap package."""
- return super(MultivariateNormalTriL, self).output[0]
+ config = super(BaseDistributionLayer, self).get_config()
+ return config
+
+ @classmethod
+ def from_config(cls, config):
+ return cls(**config)
+
+ # @property
+ # def scoringrules_param_order(self):
+ # key = self.prob_layer.name
+
+ # _param_orders = {
+ # 'univariate_gaussian': ['loc', 'scale'],
+ # 'exponential': ['rate']
+ # }
+
+ # return _param_orders[key]
+
+
+
+
+
+distribution_to_layer = {obj[1]._name: obj[1] for obj in getmembers(sys.modules[__name__], isclass)
+ if issubclass(obj[1], BaseParametricDistributionModule) and obj[0] != 'BaseParametricDistributionModule'}
+
+
+
+
+
+# """In this module, any custom built keras layers are included."""
+
+# import numpy as np
+# import tensorflow as tf
+# import tensorflow_probability.python.layers as tfpl
+# from tensorflow_probability.python import bijectors as tfb
+# from tensorflow_probability.python import distributions as tfd
+# from tensorflow_probability.python.layers.distribution_layer import (
+# _event_size,
+# _get_convert_to_tensor_fn,
+# _serialize,
+# dist_util,
+# independent_lib,
+# )
+
+
+# # these almost work out of the box
+# from tensorflow_probability.python.layers import (
+# IndependentNormal,
+# IndependentLogistic,
+# IndependentBernoulli,
+# IndependentPoisson,
+# )
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentNormal(IndependentNormal):
+# @property
+# def output(self): # this is necessary to use the layer within shap
+# return super().output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentLogistic(IndependentLogistic):
+# @property
+# def output(self):
+# return super().output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentBernoulli(IndependentBernoulli):
+# @property
+# def output(self):
+# return super().output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentPoisson(IndependentPoisson):
+# @property
+# def output(self):
+# return super().output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentBeta(tfpl.DistributionLambda):
+# """An independent 2-parameter Beta Keras layer"""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentBeta` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentBeta.new(t, event_shape, validate_args)
+
+# super(IndependentBeta, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentBeta"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# alpha, beta = tf.split(params, 2, axis=-1)
+
+# alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 1e-3
+# beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 1e-3
+# betad = tfd.Beta(alpha, beta, validate_args=validate_args)
+
+# return independent_lib.Independent(
+# betad,
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentBeta_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentBeta_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentBeta, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentBeta, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class Independent4ParamsBeta(tfpl.DistributionLambda):
+# """An independent 4-parameter Beta Keras layer allowing control over scale as well as a 'shift' parameter."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `Independent4ParamsBeta` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return Independent4ParamsBeta.new(t, event_shape, validate_args)
+
+# super(Independent4ParamsBeta, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "Independent4ParamsBeta"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# alpha, beta, shift, scale = tf.split(params, 4, axis=-1)
+# # alpha > 2 and beta > 2 produce a concave downward Beta
+# alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 1e-3
+# beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 1e-3
+# shift = tf.math.softplus(tf.reshape(shift, output_shape))
+# scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
+# betad = tfd.Beta(alpha, beta, validate_args=validate_args)
+# transf_betad = tfd.TransformedDistribution(
+# distribution=betad, bijector=tfb.Shift(shift)(tfb.Scale(scale))
+# )
+# return independent_lib.Independent(
+# transf_betad,
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "Independent4ParamsBeta_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(4) * _event_size(
+# event_shape, name=name or "Independent4ParamsBeta_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(Independent4ParamsBeta, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(Independent4ParamsBeta, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentDoublyCensoredNormal(tfpl.DistributionLambda):
+# """An independent censored normal Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentDoublyCensoredNormal` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+# # get the clipping parameters and pop them
+# _clip_low = kwargs.pop("clip_low", 0.0)
+# _clip_high = kwargs.pop("clip_high", 1.0)
+
+# def new_from_t(t):
+# return IndependentDoublyCensoredNormal.new(
+# t,
+# event_shape,
+# validate_args,
+# clip_low=_clip_low,
+# clip_high=_clip_high,
+# )
+
+# super(IndependentDoublyCensoredNormal, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(
+# params,
+# event_shape=(),
+# validate_args=False,
+# name=None,
+# clip_low=0.0,
+# clip_high=1.0,
+# ):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentDoublyCensoredNormal"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# loc, scale = tf.split(params, 2, axis=-1)
+# loc = tf.reshape(loc, output_shape)
+# scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
+# normal_dist = tfd.Normal(loc=loc, scale=scale, validate_args=validate_args)
+
+# class CustomCensored(tfd.Distribution):
+# def __init__(self, normal, clip_low=0.0, clip_high=1.0):
+# self.normal = normal
+# super(CustomCensored, self).__init__(
+# dtype=normal.dtype,
+# reparameterization_type=tfd.FULLY_REPARAMETERIZED,
+# validate_args=validate_args,
+# allow_nan_stats=True,
+# )
+# self.clip_low = clip_low
+# self.clip_high = clip_high
+
+# def _sample_n(self, n, seed=None):
+
+# # Sample from normal distribution
+# samples = self.normal.sample(sample_shape=(n,), seed=seed)
+
+# # Clip values between 0 and 1
+# chosen_samples = tf.clip_by_value(
+# samples, self.clip_low, self.clip_high
+# )
+
+# return chosen_samples
+
+# def _mean(self):
+# """
+# Original: X ~ N(mu, sigma)
+# Censored: Y = X if clip_low <= X <= clip_high else clip_low if X < clip_low else clip_high
+# Phi / phi: CDF / PDF of standard normal distribution
+
+# Law of total expectations:
+# E[Y] = E[Y | X > c_h] * P(X > c_h) + E[Y | X < c_l] * P(X < c_l) + E[Y | c_l <= X <= c_h] * P(c_l <= X <= c_h)
+# = c_h * P(X > c_h) + P(X < c_l) * c_l + E[Y | c_l <= X <= c_h] * P(c_l <= X <= c_h)
+# = c_h * P(X > c_h) + P(X < c_l) * c_l + E[Z ~ TruncNormal(mu, sigma, c_l, c_h)] * (Phi((c_h - mu) / sigma) - Phi(c_l - mu / sigma))
+# = c_h * (1 - Phi((c_h - mu) / sigma))
+# + c_l * Phi((c_l - mu) / sigma)
+# + mu * (Phi((c_h - mu) / sigma) - Phi(c_l - mu / sigma))
+# + sigma * (phi(c_l - mu / sigma) - phi((c_h - mu) / sigma))
+# Ref for TruncatedNormal mean: https://en.wikipedia.org/wiki/Truncated_normal_distribution
+# """
+# mu, sigma = self.normal.mean(), self.normal.stddev()
+# low_bound_standard = (self.clip_low - mu) / sigma
+# high_bound_standard = (self.clip_high - mu) / sigma
+
+# cdf = lambda x: tfd.Normal(0, 1).cdf(x)
+# pdf = lambda x: tfd.Normal(0, 1).prob(x)
+
+# return (
+# self.clip_high * (1 - cdf(high_bound_standard))
+# + self.clip_low * cdf(low_bound_standard)
+# + mu * (cdf(high_bound_standard) - cdf(low_bound_standard))
+# + sigma * (pdf(low_bound_standard) - pdf(high_bound_standard))
+# )
+
+# def _log_prob(self, value):
+
+# mu, sigma = self.normal.mean(), self.normal.stddev()
+# cdf = lambda x: tfd.Normal(0, 1).cdf(x)
+# pdf = lambda x: tfd.Normal(0, 1).prob(x)
+
+# logprob_left = lambda x: tf.math.log(
+# cdf(self.clip_low - mu / sigma) + 1e-3
+# )
+# logprob_middle = lambda x: self.normal.log_prob(x)
+# logprob_right = lambda x: tf.math.log(
+# 1 - cdf((self.clip_high - mu) / sigma) + 1e-3
+# )
+
+# return (
+# logprob_left(value)
+# + logprob_middle(value)
+# + logprob_right(value)
+# )
+
+# return independent_lib.Independent(
+# CustomCensored(normal_dist, clip_low=clip_low, clip_high=clip_high),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentDoublyCensoredNormal_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentDoublyCensoredNormal_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentDoublyCensoredNormal, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentDoublyCensoredNormal, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentConcaveBeta(tfpl.DistributionLambda):
+# """An independent 4-parameter Beta Keras layer with enforced concavity"""
+
+# # INdependent
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentConcaveBeta` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentConcaveBeta.new(t, event_shape, validate_args)
+
+# super(IndependentConcaveBeta, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentConcaveBeta"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# alpha, beta, shift, scale = tf.split(params, 4, axis=-1)
+# # alpha > 2 and beta > 2 produce a concave downward Beta
+# alpha = tf.math.softplus(tf.reshape(alpha, output_shape)) + 2.0
+# beta = tf.math.softplus(tf.reshape(beta, output_shape)) + 2.0
+# shift = tf.math.softplus(tf.reshape(shift, output_shape))
+# scale = tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3
+# betad = tfd.Beta(alpha, beta, validate_args=validate_args)
+# transf_betad = tfd.TransformedDistribution(
+# distribution=betad, bijector=tfb.Shift(shift)(tfb.Scale(scale))
+# )
+# return independent_lib.Independent(
+# transf_betad,
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentConcaveBeta_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(4) * _event_size(
+# event_shape, name=name or "IndependentConcaveBeta_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentConcaveBeta, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentConcaveBeta, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentGamma(tfpl.DistributionLambda):
+# """An independent gamma Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentGamma` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentGamma.new(t, event_shape, validate_args)
+
+# super(IndependentGamma, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentGamma"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# concentration, rate = tf.split(params, 2, axis=-1)
+# return independent_lib.Independent(
+# tfd.Gamma(
+# concentration=tf.math.softplus(
+# tf.reshape(concentration, output_shape)
+# ),
+# rate=tf.math.softplus(tf.reshape(rate, output_shape)),
+# validate_args=validate_args,
+# ),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentGamma_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentGamma_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentGamma, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentGamma, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentLogNormal(tfpl.DistributionLambda):
+# """An independent LogNormal Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentLogNormal` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentLogNormal.new(t, event_shape, validate_args)
+
+# super(IndependentLogNormal, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentLogNormal"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# loc, scale = tf.split(params, 2, axis=-1)
+# return independent_lib.Independent(
+# tfd.LogNormal(
+# loc=tf.reshape(loc, output_shape),
+# scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
+# validate_args=validate_args,
+# ),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentLogNormal_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentLogNormal_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentLogNormal, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentLogNormal, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentLogitNormal(tfpl.DistributionLambda):
+# """An independent Logit-Normal Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.sample,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentLogitNormal` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentLogitNormal.new(t, event_shape, validate_args)
+
+# super(IndependentLogitNormal, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentLogitNormal"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# loc, scale = tf.split(params, 2, axis=-1)
+# return independent_lib.Independent(
+# tfd.LogitNormal(
+# loc=tf.reshape(loc, output_shape),
+# scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
+# validate_args=validate_args,
+# ),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentLogitNormal_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentLogitNormal_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentLogitNormal, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentLogitNormal, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentMixtureNormal(tfpl.DistributionLambda):
+# """A mixture of two normal distributions Keras layer.
+# 5-parameters distribution: loc1, scale1, loc2, scale2, weight
+# """
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentMixtureNormal` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentMixtureNormal.new(t, event_shape, validate_args)
+
+# super(IndependentMixtureNormal, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentMixtureNormal"):
+# params = tf.convert_to_tensor(params, name="params")
+
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+
+# loc1, scale1, loc2, scale2, weight = tf.split(params, 5, axis=-1)
+# loc1 = tf.reshape(loc1, output_shape)
+# scale1 = tf.math.softplus(tf.reshape(scale1, output_shape)) + 1e-3
+# loc2 = tf.reshape(loc2, output_shape)
+# scale2 = tf.math.softplus(tf.reshape(scale2, output_shape)) + 1e-3
+# weight = tf.math.sigmoid(tf.reshape(weight, output_shape))
+
+# # Create the component distributions
+# normald1 = tfd.Normal(loc=loc1, scale=scale1)
+# normald2 = tfd.Normal(loc=loc2, scale=scale2)
+
+# # Create a categorical distribution for the weights
+# cat = tfd.Categorical(
+# probs=tf.concat(
+# [tf.expand_dims(weight, -1), tf.expand_dims(1 - weight, -1)],
+# axis=-1,
+# )
+# )
+
+# class CustomMixture(tfd.Distribution):
+# def __init__(self, cat, normald1, normald2):
+# self.cat = cat
+# self.normald1 = normald1
+# self.normald2 = normald2
+# super(CustomMixture, self).__init__(
+# dtype=normald1.dtype,
+# reparameterization_type=tfd.FULLY_REPARAMETERIZED,
+# validate_args=validate_args,
+# allow_nan_stats=True,
+# )
+
+# def _sample_n(self, n, seed=None):
+# indices = self.cat.sample(sample_shape=(n,), seed=seed)
+
+# # Sample from both truncated normal distributions
+# samples1 = self.normald1.sample(sample_shape=(n,), seed=seed)
+# samples2 = self.normald2.sample(sample_shape=(n,), seed=seed)
+
+# # Stack the samples along a new axis
+# samples = tf.stack([samples1, samples2], axis=-1)
+
+# # Gather samples according to indices from the categorical distribution
+# chosen_samples = tf.gather(
+# samples,
+# indices,
+# batch_dims=tf.get_static_value(tf.rank(indices)),
+# )
+
+# return chosen_samples
+
+# def _log_prob(self, value):
+# log_prob1 = self.normald1.log_prob(value)
+# log_prob2 = self.normald2.log_prob(value)
+# log_probs = tf.stack([log_prob1, log_prob2], axis=-1)
+# weighted_log_probs = log_probs + tf.math.log(
+# tf.concat([weight, 1 - weight], axis=-1)
+# )
+# return tf.reduce_logsumexp(weighted_log_probs, axis=-1)
+
+# def _mean(self):
+# return (
+# weight * self.normald1.mean()
+# + (1 - weight) * self.normald2.mean()
+# )
+
+# mixtured = CustomMixture(cat, normald1, normald2)
+
+# return independent_lib.Independent(
+# mixtured,
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentMixtureNormal_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(5) * _event_size(
+# event_shape, name=name or "IndependentMixtureNormal_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentMixtureNormal, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentMixtureNormal, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentTruncatedNormal(tfpl.DistributionLambda):
+# """An independent TruncatedNormal Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentTruncatedNormal` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentTruncatedNormal.new(t, event_shape, validate_args)
+
+# super(IndependentTruncatedNormal, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentTruncatedNormal"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# loc, scale = tf.split(params, 2, axis=-1)
+# return independent_lib.Independent(
+# tfd.TruncatedNormal(
+# loc=tf.reshape(loc, output_shape),
+# scale=tf.math.softplus(tf.reshape(scale, output_shape)) + 1e-3,
+# low=0,
+# high=np.inf,
+# validate_args=validate_args,
+# ),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentTruncatedNormal_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentTruncatedNormal_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentTruncatedNormal, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentTruncatedNormal, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class IndependentWeibull(tfpl.DistributionLambda):
+# """An independent Weibull Keras layer."""
+
+# def __init__(
+# self,
+# event_shape=(),
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the `IndependentWeibull` layer.
+# Args:
+# event_shape: integer vector `Tensor` representing the shape of single
+# draw from this distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object.
+# Default value: `tfd.Distribution.mean`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return IndependentWeibull.new(t, event_shape, validate_args)
+
+# super(IndependentWeibull, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_shape = event_shape
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_shape=(), validate_args=False, name=None):
+# """Create the distribution instance from a `params` vector."""
+# with tf.name_scope(name or "IndependentWeibull"):
+# params = tf.convert_to_tensor(params, name="params")
+# event_shape = dist_util.expand_to_vector(
+# tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# ),
+# tensor_name="event_shape",
+# )
+# output_shape = tf.concat(
+# [
+# tf.shape(params)[:-1],
+# event_shape,
+# ],
+# axis=0,
+# )
+# concentration, scale = tf.split(params, 2, axis=-1)
+# return independent_lib.Independent(
+# tfd.Weibull(
+# concentration=tf.math.softplus(
+# tf.reshape(concentration, output_shape)
+# )
+# + 1.0,
+# scale=tf.math.softplus(tf.reshape(scale, output_shape)),
+# validate_args=validate_args,
+# ),
+# reinterpreted_batch_ndims=tf.size(event_shape),
+# validate_args=validate_args,
+# )
+
+# @staticmethod
+# def params_size(event_shape=(), name=None):
+# """The number of `params` needed to create a single distribution."""
+# with tf.name_scope(name or "IndependentWeibull_params_size"):
+# event_shape = tf.convert_to_tensor(
+# event_shape, name="event_shape", dtype_hint=tf.int32
+# )
+# return np.int32(2) * _event_size(
+# event_shape, name=name or "IndependentWeibull_params_size"
+# )
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_shape": self._event_shape,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(IndependentWeibull, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(IndependentWeibull, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class MultivariateNormalDiag(tfpl.DistributionLambda):
+# """A `d`-variate normal Keras layer from `2* d` params,
+# with a diagonal scale matrix.
+# """
+
+# def __init__(
+# self,
+# event_size,
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# """Initialize the layer.
+# Args:
+# event_size: Scalar `int` representing the size of single draw from this
+# distribution.
+# convert_to_tensor_fn: Python `callable` that takes a `tfd.Distribution`
+# instance and returns a `tf.Tensor`-like object. For examples, see
+# `class` docstring.
+# Default value: `tfd.Distribution.sample`.
+# validate_args: Python `bool`, default `False`. When `True` distribution
+# parameters are checked for validity despite possibly degrading runtime
+# performance. When `False` invalid inputs may silently render incorrect
+# outputs.
+# Default value: `False`.
+# **kwargs: Additional keyword arguments passed to `tf.keras.Layer`.
+# """
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# def new_from_t(t):
+# return MultivariateNormalDiag.new(t, event_size, validate_args)
+
+# super(MultivariateNormalDiag, self).__init__(
+# new_from_t, convert_to_tensor_fn, **kwargs
+# )
+
+# self._event_size = event_size
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# @staticmethod
+# def new(params, event_size, validate_args=False, name=None):
+# """Create the distribution instance from a 'params' vector."""
+# with tf.name_scope(name or "MultivariateNormalDiag"):
+# params = tf.convert_to_tensor(params, name="params")
+# if event_size > 1:
+# dist = tfd.MultivariateNormalDiag(
+# loc=params[..., :event_size],
+# scale_diag=1e-5 + tf.math.softplus(params[..., event_size:]),
+# validate_args=validate_args,
+# )
+# else:
+# dist = tfd.Normal(
+# loc=params[..., :event_size],
+# scale=1e-5 + tf.math.softplus(params[..., event_size:]),
+# validate_args=validate_args,
+# )
+# return dist
+
+# @staticmethod
+# def params_size(event_size, name=None):
+# """The number of 'params' needed to create a single distribution."""
+# with tf.name_scope(name or "MultivariateNormalDiag_params_size"):
+# return 2 * event_size
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_size": self._event_size,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(MultivariateNormalDiag, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(MultivariateNormalDiag, self).output[0]
+
+
+# @tf.keras.saving.register_keras_serializable()
+# class MultivariateNormalTriL(tfpl.MultivariateNormalTriL):
+# def __init__(
+# self,
+# event_size,
+# convert_to_tensor_fn=tfd.Distribution.mean,
+# validate_args=False,
+# **kwargs
+# ):
+# convert_to_tensor_fn = _get_convert_to_tensor_fn(convert_to_tensor_fn)
+
+# # If there is a 'make_distribution_fn' keyword argument (e.g., because we
+# # are being called from a `from_config` method), remove it. We pass the
+# # distribution function to `DistributionLambda.__init__` below as the first
+# # positional argument.
+# kwargs.pop("make_distribution_fn", None)
+
+# super().__init__(event_size, convert_to_tensor_fn, validate_args, **kwargs)
+# self._event_size = event_size
+# self._convert_to_tensor_fn = convert_to_tensor_fn
+# self._validate_args = validate_args
+
+# def get_config(self):
+# """Returns the config of this layer.
+# NOTE: At the moment, this configuration can only be serialized if the
+# Layer's `convert_to_tensor_fn` is a serializable Keras object (i.e.,
+# implements `get_config`) or one of the standard values:
+# - `Distribution.sample` (or `"sample"`)
+# - `Distribution.mean` (or `"mean"`)
+# - `Distribution.mode` (or `"mode"`)
+# - `Distribution.stddev` (or `"stddev"`)
+# - `Distribution.variance` (or `"variance"`)
+# """
+# config = {
+# "event_size": self._event_size,
+# "convert_to_tensor_fn": _serialize(self._convert_to_tensor_fn),
+# "validate_args": self._validate_args,
+# }
+# base_config = super(MultivariateNormalTriL, self).get_config()
+# return dict(list(base_config.items()) + list(config.items()))
+
+# @property
+# def output(self):
+# """This allows the use of this layer with the shap package."""
+# return super(MultivariateNormalTriL, self).output[0]
diff --git a/mlpp_lib/train.py b/mlpp_lib/train.py
index 519394a..be400ea 100644
--- a/mlpp_lib/train.py
+++ b/mlpp_lib/train.py
@@ -2,7 +2,8 @@
from pprint import pformat
from typing import Optional
-import tensorflow as tf
+# import tensorflow as tf
+import keras
from mlpp_lib.callbacks import TimeHistory, EnsembleMetrics
from mlpp_lib.datasets import DataLoader, DataModule
@@ -37,11 +38,11 @@ def get_log_params(param_run: dict) -> dict:
return log_params
-def get_lr(optimizer: tf.keras.optimizers.Optimizer) -> float:
+def get_lr(optimizer: keras.optimizers.Optimizer) -> float:
"""Get the learning rate of the optimizer"""
def lr(y_true, y_pred):
- return optimizer.lr
+ return optimizer.learning_rate
return lr
@@ -126,7 +127,7 @@ def train(
# we don't need to export loss and metric functions for deployments
model.compile(optimizer=optimizer, loss=None, metrics=None)
- custom_objects = tf.keras.layers.serialize(model)
+ custom_objects = keras.layers.serialize(model)
history = res.history
# for some reasons, 'lr' is provided as float32
diff --git a/mlpp_lib/utils.py b/mlpp_lib/utils.py
index 6ef69ce..f09ad25 100644
--- a/mlpp_lib/utils.py
+++ b/mlpp_lib/utils.py
@@ -3,8 +3,9 @@
import numpy as np
import xarray as xr
-import tensorflow as tf
-
+# import tensorflow as tf
+import keras
+import importlib
from mlpp_lib import callbacks, losses, metrics, models
@@ -29,9 +30,9 @@ def get_callback(callback: Union[str, dict]) -> Callable:
if isinstance(callback_obj, type)
else callback_obj
)
- elif hasattr(tf.keras.callbacks, callback_name):
+ elif hasattr(keras.callbacks, callback_name):
LOGGER.info(f"Using keras built-in callback: {callback_name}")
- callback_obj = getattr(tf.keras.callbacks, callback_name)
+ callback_obj = getattr(keras.callbacks, callback_name)
callback = (
callback_obj(**callback_options)
if isinstance(callback_obj, type)
@@ -47,7 +48,7 @@ def get_model(
input_shape: tuple[int],
output_shape: Union[int, tuple[int]],
model_config: dict[str, Any],
-) -> tf.keras.Model:
+) -> keras.Model:
"""Get the keras model."""
model_name = list(model_config.keys())[0]
@@ -58,7 +59,7 @@ def get_model(
LOGGER.debug(model_options)
if isinstance(output_shape, int):
output_shape = (output_shape,)
- model = getattr(models, model_name)(input_shape, output_shape[-1], **model_options)
+ model = getattr(models, model_name)(output_shape[-1], **model_options)
return model
@@ -67,24 +68,27 @@ def get_loss(loss: Union[str, dict]) -> Callable:
"""Get the loss function, either keras built-in or mlpp custom."""
if isinstance(loss, dict):
- loss_name = list(loss.keys())[0]
- loss_options = loss[loss_name]
- else:
- loss_name = loss
- loss_options = {}
-
- if hasattr(losses, loss_name):
- LOGGER.info(f"Using custom mlpp loss: {loss_name}")
- loss_obj = getattr(losses, loss_name)
- loss = loss_obj(**loss_options) if isinstance(loss_obj, type) else loss_obj
- elif hasattr(tf.keras.losses, loss_name):
- LOGGER.info(f"Using keras built-in loss: {loss_name}")
- loss_obj = getattr(tf.keras.losses, loss_name)
- loss = loss_obj(**loss_options) if isinstance(loss_obj, type) else loss_obj
+ wrapper = list(loss.keys())[0]
+ fn_ = loss[wrapper]
+ if isinstance(fn_, dict):
+ fn = list(fn_.keys())[0]
+ fn_args = fn_[fn]
+
+ module_name, fn_name = fn.rsplit(".", 1)
+
+ else:
+ module_name, fn_name = fn_.rsplit(".", 1)
+ fn_args = {}
+
+ module = importlib.import_module(module_name)
+ loss_fn = getattr(module, fn_name)
+ LOGGER.info(f"Using {fn_name} loss from {module_name}")
+ return getattr(losses, wrapper)(fn=loss_fn, **fn_args)
+
else:
- raise KeyError(f"The loss {loss_name} is not available.")
+ # TODO decide what to do
+ pass
- return loss
def get_metric(metric: Union[str, dict]) -> Callable:
@@ -103,9 +107,9 @@ def get_metric(metric: Union[str, dict]) -> Callable:
metric = (
metric_obj(**metric_options) if isinstance(metric_obj, type) else metric_obj
)
- elif hasattr(tf.keras.metrics, metric_name):
+ elif hasattr(keras.metrics, metric_name):
LOGGER.info(f"Using keras built-in metric: {metric_name}")
- metric_obj = getattr(tf.keras.metrics, metric_name)
+ metric_obj = getattr(keras.metrics, metric_name)
metric = (
metric_obj(**metric_options) if isinstance(metric_obj, type) else metric_obj
)
@@ -117,7 +121,7 @@ def get_metric(metric: Union[str, dict]) -> Callable:
def get_scheduler(
scheduler_config: Union[dict, None]
-) -> Optional[tf.keras.optimizers.schedules.LearningRateSchedule]:
+) -> Optional[keras.optimizers.schedules.LearningRateSchedule]:
"""Create a learning rate scheduler from a config dictionary."""
if not isinstance(scheduler_config, dict):
@@ -139,13 +143,13 @@ def get_scheduler(
f"Scheduler options for '{scheduler_name}' should be a dictionary."
)
- if hasattr(tf.keras.optimizers.schedules, scheduler_name):
+ if hasattr(keras.optimizers.schedules, scheduler_name):
LOGGER.info(f"Using keras built-in learning rate scheduler: {scheduler_name}")
- scheduler_cls = getattr(tf.keras.optimizers.schedules, scheduler_name)
+ scheduler_cls = getattr(keras.optimizers.schedules, scheduler_name)
scheduler = scheduler_cls(**scheduler_options)
else:
raise KeyError(
- f"The scheduler '{scheduler_name}' is not available in tf.keras.optimizers.schedules."
+ f"The scheduler '{scheduler_name}' is not available in keras.optimizers.schedules."
)
return scheduler
@@ -163,9 +167,9 @@ def get_optimizer(optimizer: Union[str, dict]) -> Callable:
optimizer_name = optimizer
optimizer_options = {}
- if hasattr(tf.keras.optimizers, optimizer_name):
+ if hasattr(keras.optimizers, optimizer_name):
LOGGER.info(f"Using keras built-in optimizer: {optimizer_name}")
- optimizer_obj = getattr(tf.keras.optimizers, optimizer_name)
+ optimizer_obj = getattr(keras.optimizers, optimizer_name)
optimizer = (
optimizer_obj(**optimizer_options)
if isinstance(optimizer_obj, type)
diff --git a/poetry.lock b/poetry.lock
index c71d03b..cd9a48c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
[[package]]
name = "absl-py"
@@ -13,13 +13,13 @@ files = [
[[package]]
name = "alembic"
-version = "1.13.3"
+version = "1.14.0"
description = "A database migration tool for SQLAlchemy."
optional = false
python-versions = ">=3.8"
files = [
- {file = "alembic-1.13.3-py3-none-any.whl", hash = "sha256:908e905976d15235fae59c9ac42c4c5b75cfcefe3d27c0fbf7ae15a37715d80e"},
- {file = "alembic-1.13.3.tar.gz", hash = "sha256:203503117415561e203aa14541740643a611f641517f0209fcae63e9fa09f1a2"},
+ {file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"},
+ {file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"},
]
[package.dependencies]
@@ -32,24 +32,24 @@ tz = ["backports.zoneinfo"]
[[package]]
name = "anyio"
-version = "4.6.2.post1"
+version = "4.7.0"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
optional = false
python-versions = ">=3.9"
files = [
- {file = "anyio-4.6.2.post1-py3-none-any.whl", hash = "sha256:6d170c36fba3bdd840c73d3868c1e777e33676a69c3a72cf0a0d5d6d8009b61d"},
- {file = "anyio-4.6.2.post1.tar.gz", hash = "sha256:4c8bc31ccdb51c7f7bd251f51c609e038d63e34219b44aa86e47576389880b4c"},
+ {file = "anyio-4.7.0-py3-none-any.whl", hash = "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"},
+ {file = "anyio-4.7.0.tar.gz", hash = "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48"},
]
[package.dependencies]
exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""}
idna = ">=2.8"
sniffio = ">=1.1"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""}
+typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
[package.extras]
-doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
-test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21.0b1)"]
+doc = ["Sphinx (>=7.4,<8.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
trio = ["trio (>=0.26.1)"]
[[package]]
@@ -151,36 +151,18 @@ files = [
[[package]]
name = "asttokens"
-version = "2.4.1"
+version = "3.0.0"
description = "Annotate AST trees with source code positions"
optional = false
-python-versions = "*"
+python-versions = ">=3.8"
files = [
- {file = "asttokens-2.4.1-py2.py3-none-any.whl", hash = "sha256:051ed49c3dcae8913ea7cd08e46a606dba30b79993209636c4875bc1d637bc24"},
- {file = "asttokens-2.4.1.tar.gz", hash = "sha256:b03869718ba9a6eb027e134bfdf69f38a236d681c83c160d510768af11254ba0"},
+ {file = "asttokens-3.0.0-py3-none-any.whl", hash = "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"},
+ {file = "asttokens-3.0.0.tar.gz", hash = "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7"},
]
-[package.dependencies]
-six = ">=1.12.0"
-
[package.extras]
-astroid = ["astroid (>=1,<2)", "astroid (>=2,<4)"]
-test = ["astroid (>=1,<2)", "astroid (>=2,<4)", "pytest"]
-
-[[package]]
-name = "astunparse"
-version = "1.6.3"
-description = "An AST unparser for Python"
-optional = false
-python-versions = "*"
-files = [
- {file = "astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8"},
- {file = "astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872"},
-]
-
-[package.dependencies]
-six = ">=1.6.1,<2.0"
-wheel = ">=0.23.0,<1.0"
+astroid = ["astroid (>=2,<4)"]
+test = ["astroid (>=2,<4)", "pytest", "pytest-cov", "pytest-xdist"]
[[package]]
name = "async-lru"
@@ -315,13 +297,13 @@ css = ["tinycss2 (>=1.1.0,<1.5)"]
[[package]]
name = "blinker"
-version = "1.8.2"
+version = "1.9.0"
description = "Fast, simple object-to-object and broadcast signaling"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "blinker-1.8.2-py3-none-any.whl", hash = "sha256:1779309f71bf239144b9399d06ae925637cf6634cf6bd131104184531bf67c01"},
- {file = "blinker-1.8.2.tar.gz", hash = "sha256:8f77b09d3bf7c795e969e9486f39c2c5e9c39d4ee07424be2bc594ece9642d83"},
+ {file = "blinker-1.9.0-py3-none-any.whl", hash = "sha256:ba0efaa9080b619ff2f3459d1d500c57bddea4a6b424b60a91141db6fd2f08bc"},
+ {file = "blinker-1.9.0.tar.gz", hash = "sha256:b4ce2265a7abece45e7cc896e98dbebe6cead56bcf805a3d23136d145f5445bf"},
]
[[package]]
@@ -594,76 +576,65 @@ test = ["pytest"]
[[package]]
name = "contourpy"
-version = "1.3.0"
+version = "1.3.1"
description = "Python library for calculating contours of 2D quadrilateral grids"
optional = false
-python-versions = ">=3.9"
-files = [
- {file = "contourpy-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:880ea32e5c774634f9fcd46504bf9f080a41ad855f4fef54f5380f5133d343c7"},
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@@ -1454,13 +1280,13 @@ numpy = ">=1.19.3"
[[package]]
name = "httpcore"
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description = "A minimal low-level HTTP client."
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python-versions = ">=3.8"
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[[package]]
name = "httpx"
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description = "The next generation HTTP client."
optional = false
python-versions = ">=3.8"
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certifi = "*"
httpcore = "==1.*"
idna = "*"
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brotli = ["brotli", "brotlicffi"]
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description = "Read metadata from Python packages"
optional = false
python-versions = ">=3.8"
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cover = ["pytest-cov"]
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@@ -1599,13 +1406,13 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio
[[package]]
name = "ipython"
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description = "IPython: Productive Interactive Computing"
optional = false
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matplotlib-inline = "*"
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pygments = ">=2.4.0"
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nbconvert = ["nbconvert"]
nbformat = ["nbformat"]
notebook = ["ipywidgets", "notebook"]
parallel = ["ipyparallel"]
qtconsole = ["qtconsole"]
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name = "ipywidgets"
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python-dateutil = ">=2.8.2"
pyzmq = ">=23.0"
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python-versions = ">=3.8"
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name = "matplotlib-inline"
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matplotlib = "<4"
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numpy = "<3"
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scikit-learn = "<2"
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sqlalchemy = ">=1.4.0,<3"
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sqlserver = ["mlflow-dbstore"]
xethub = ["mlflow-xethub"]
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+ {file = "wrapt-1.17.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:bc7f729a72b16ee21795a943f85c6244971724819819a41ddbaeb691b2dd85ad"},
+ {file = "wrapt-1.17.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:6ff02a91c4fc9b6a94e1c9c20f62ea06a7e375f42fe57587f004d1078ac86ca9"},
+ {file = "wrapt-1.17.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:2dfb7cff84e72e7bf975b06b4989477873dcf160b2fd89959c629535df53d4e0"},
+ {file = "wrapt-1.17.0-cp39-cp39-win32.whl", hash = "sha256:2399408ac33ffd5b200480ee858baa58d77dd30e0dd0cab6a8a9547135f30a88"},
+ {file = "wrapt-1.17.0-cp39-cp39-win_amd64.whl", hash = "sha256:4f763a29ee6a20c529496a20a7bcb16a73de27f5da6a843249c7047daf135977"},
+ {file = "wrapt-1.17.0-py3-none-any.whl", hash = "sha256:d2c63b93548eda58abf5188e505ffed0229bf675f7c3090f8e36ad55b8cbc371"},
+ {file = "wrapt-1.17.0.tar.gz", hash = "sha256:16187aa2317c731170a88ef35e8937ae0f533c402872c1ee5e6d079fcf320801"},
]
[[package]]
name = "xarray"
-version = "2024.7.0"
+version = "2024.11.0"
description = "N-D labeled arrays and datasets in Python"
optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
files = [
- {file = "xarray-2024.7.0-py3-none-any.whl", hash = "sha256:1b0fd51ec408474aa1f4a355d75c00cc1c02bd425d97b2c2e551fd21810e7f64"},
- {file = "xarray-2024.7.0.tar.gz", hash = "sha256:4cae512d121a8522d41e66d942fb06c526bc1fd32c2c181d5fe62fe65b671638"},
+ {file = "xarray-2024.11.0-py3-none-any.whl", hash = "sha256:6ee94f63ddcbdd0cf3909d1177f78cdac756640279c0e32ae36819a89cdaba37"},
+ {file = "xarray-2024.11.0.tar.gz", hash = "sha256:1ccace44573ddb862e210ad3ec204210654d2c750bec11bbe7d842dfc298591f"},
]
[package.dependencies]
-numpy = ">=1.23"
-packaging = ">=23.1"
-pandas = ">=2.0"
+numpy = ">=1.24"
+packaging = ">=23.2"
+pandas = ">=2.1"
[package.extras]
-accel = ["bottleneck", "flox", "numbagg", "opt-einsum", "scipy"]
-complete = ["xarray[accel,dev,io,parallel,viz]"]
-dev = ["hypothesis", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "xarray[complete]"]
+accel = ["bottleneck", "flox", "numba (>=0.54)", "numbagg", "opt_einsum", "scipy"]
+complete = ["xarray[accel,etc,io,parallel,viz]"]
+dev = ["hypothesis", "jinja2", "mypy", "pre-commit", "pytest", "pytest-cov", "pytest-env", "pytest-timeout", "pytest-xdist", "ruff", "sphinx", "sphinx_autosummary_accessors", "xarray[complete]"]
+etc = ["sparse"]
io = ["cftime", "fsspec", "h5netcdf", "netCDF4", "pooch", "pydap", "scipy", "zarr"]
parallel = ["dask[complete]"]
-viz = ["matplotlib", "nc-time-axis", "seaborn"]
+viz = ["cartopy", "matplotlib", "nc-time-axis", "seaborn"]
[[package]]
name = "zarr"
-version = "2.18.2"
+version = "2.18.3"
description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
optional = true
-python-versions = ">=3.9"
+python-versions = ">=3.10"
files = [
- {file = "zarr-2.18.2-py3-none-any.whl", hash = "sha256:a638754902f97efa99b406083fdc807a0e2ccf12a949117389d2a4ba9b05df38"},
- {file = "zarr-2.18.2.tar.gz", hash = "sha256:9bb393b8a0a38fb121dbb913b047d75db28de9890f6d644a217a73cf4ae74f47"},
+ {file = "zarr-2.18.3-py3-none-any.whl", hash = "sha256:b1f7dfd2496f436745cdd4c7bcf8d3b4bc1dceef5fdd0d589c87130d842496dd"},
+ {file = "zarr-2.18.3.tar.gz", hash = "sha256:2580d8cb6dd84621771a10d31c4d777dca8a27706a1a89b29f42d2d37e2df5ce"},
]
[package.dependencies]
asciitree = "*"
fasteners = {version = "*", markers = "sys_platform != \"emscripten\""}
numcodecs = ">=0.10.0"
-numpy = ">=1.23"
+numpy = ">=1.24"
[package.extras]
docs = ["numcodecs[msgpack]", "numpydoc", "pydata-sphinx-theme", "sphinx", "sphinx-automodapi", "sphinx-copybutton", "sphinx-design", "sphinx-issues"]
@@ -4994,13 +4879,13 @@ jupyter = ["ipytree (>=0.2.2)", "ipywidgets (>=8.0.0)", "notebook"]
[[package]]
name = "zipp"
-version = "3.20.2"
+version = "3.21.0"
description = "Backport of pathlib-compatible object wrapper for zip files"
optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
files = [
- {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"},
- {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"},
+ {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"},
+ {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"},
]
[package.extras]
@@ -5011,12 +4896,7 @@ enabler = ["pytest-enabler (>=2.2)"]
test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
type = ["pytest-mypy"]
-[extras]
-extended = ["numba", "tensorflow", "zarr"]
-tensorflow = ["tensorflow"]
-tensorflow-cpu = ["tensorflow-cpu"]
-
[metadata]
lock-version = "2.0"
-python-versions = ">=3.9, <3.11"
-content-hash = "a73fcce7ce161dfcb7576571b7d90229d2ce24af065016a80cada0eddfe8760f"
+python-versions = ">=3.10, <3.11"
+content-hash = "041814b095370fdfc93753c950a638e77d1b3ddc68d74c363c8f6e745912271c"
diff --git a/pyproject.toml b/pyproject.toml
index 3f0ed16..0c35bfd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,12 +6,11 @@ authors = ["Daniele Nerini "]
include = ["LICENSE", "README.md"]
[tool.poetry.dependencies]
-python = ">=3.9, <3.11"
+python = ">=3.10, <3.11"
numpy = "*"
-tensorflow = {version = "~2.16.1", optional = true}
-tensorflow-cpu = {version = "~2.16.1", optional = true}
-tensorflow-probability = "~0.24.0"
-tf-keras = "~2.16.0"
+keras = "^3.0.0"
+torch = "~2.5.0"
+scoringrules = "~0.7.0"
pandas = "*"
scipy = "*"
xarray = "*"
@@ -21,12 +20,9 @@ numba = {version = "*", optional = true}
zarr = {version = "*", optional = true}
[tool.poetry.extras]
-tensorflow = ["tensorflow"]
-tensorflow-cpu = ["tensorflow-cpu"]
-extended = ["zarr", "numba", "tensorflow"]
+
[tool.poetry.group.dev.dependencies]
-tensorflow = "~2.16.1"
pytest = "*"
black = "*"
mlflow = "*"
diff --git a/tests/__init__.py b/tests/__init__.py
index 7c96756..a3f6fc5 100644
--- a/tests/__init__.py
+++ b/tests/__init__.py
@@ -1,3 +1,5 @@
import os
-os.environ["TF_USE_LEGACY_KERAS"] = "1"
+import os
+
+os.environ["KERAS_BACKEND"] = "torch"
diff --git a/tests/conftest.py b/tests/conftest.py
index 13be07d..09bf450 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,9 +1,8 @@
import numpy as np
import pandas as pd
import pytest
-import tensorflow as tf
import xarray as xr
-from tensorflow import keras
+import keras
from mlpp_lib.utils import get_loss, get_model
@@ -146,12 +145,12 @@ def splits_train_val() -> dict:
@pytest.fixture
-def get_dummy_keras_model() -> tf.keras.Model:
+def get_dummy_keras_model() -> keras.Model:
def _model(n_inputs, n_outpus):
- inputs = tf.keras.Input(shape=(n_inputs,))
- x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
- outputs = tf.keras.layers.Dense(n_outpus, activation=tf.nn.softmax)(x)
- model = tf.keras.Model(inputs=inputs, outputs=outputs)
+ inputs = keras.Input(shape=(n_inputs,))
+ x = keras.layers.Dense(4, activation=keras.ops.relu)(inputs)
+ outputs = keras.layers.Dense(n_outpus, activation=keras.ops.softmax)(x)
+ model = keras.Model(inputs=inputs, outputs=outputs)
model.compile(
optimizer=keras.optimizers.RMSprop(learning_rate=1e-3),
loss=keras.losses.CategoricalCrossentropy(),
@@ -163,7 +162,7 @@ def _model(n_inputs, n_outpus):
@pytest.fixture
-def get_prob_model() -> tf.keras.Model:
+def get_prob_model() -> keras.Model:
def _model(n_inputs, n_outputs):
model_config = {
"fully_connected_network": {
@@ -172,7 +171,7 @@ def _model(n_inputs, n_outputs):
}
}
model = get_model(n_inputs, n_outputs, model_config)
- loss = get_loss("crps_energy")
+ loss = get_loss({'DistributionLossWrapper': 'scoringrules.crps_normal'})
model.compile(
optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
loss=loss,
diff --git a/tests/test_custom_distributions.py b/tests/test_custom_distributions.py
new file mode 100644
index 0000000..71d54e2
--- /dev/null
+++ b/tests/test_custom_distributions.py
@@ -0,0 +1,53 @@
+import pytest
+import torch
+
+from mlpp_lib.custom_distributions import TruncatedNormalDistribution, CensoredNormalDistribution
+
+@pytest.mark.parametrize("ab", [(4.8, 6.2), (4.0, 5.5), (4.2, 5.8)], ids=['left capped', 'right capped', 'centered'])
+def test_truncated_normal(ab):
+
+ a,b = ab
+
+ tn = TruncatedNormalDistribution(mu_bar=torch.tensor(5.0), sigma_bar=torch.tensor(0.5), a=torch.tensor(a), b=torch.tensor(b))
+
+
+ samples = tn.sample((50000,))
+
+ empirical_mean = samples.mean()
+ empirical_var = samples.var()
+
+
+ decimal_places = 2
+ tolerance = 10**-decimal_places
+
+ # test mean and variance
+ assert torch.allclose(empirical_mean, tn.mean(), atol=tolerance)
+ assert torch.allclose(empirical_var, tn.variance(), atol=tolerance)
+
+ # test first moment
+ decimal_places = 8
+ tolerance = 10**-decimal_places
+ assert torch.allclose(tn.mean(), tn.moment(1))
+
+
+@pytest.mark.parametrize("ab", [(4.8, 6.2), (4.0, 5.5), (4.2, 5.8)], ids=['left capped', 'right capped', 'centered'])
+def test_censored_normal(ab):
+
+ a,b = ab
+
+ cn = CensoredNormalDistribution(mu_bar=torch.tensor(5.0), sigma_bar=torch.tensor(0.5), a=torch.tensor(a), b=torch.tensor(b))
+
+
+ samples = cn.sample((50000,))
+
+ empirical_mean = samples.mean()
+ empirical_var = samples.var()
+
+
+ decimal_places = 2
+ tolerance = 10**-decimal_places
+
+ # test mean and variance
+ assert torch.allclose(empirical_mean, cn.mean(), atol=tolerance)
+ assert torch.allclose(empirical_var, cn.variance(), atol=tolerance)
+
diff --git a/tests/test_datasets.py b/tests/test_datasets.py
index ae1c217..6f50d95 100644
--- a/tests/test_datasets.py
+++ b/tests/test_datasets.py
@@ -1,303 +1,303 @@
-import os
-from pathlib import Path
-
-import pytest
-import xarray as xr
-import numpy as np
-
-from mlpp_lib.datasets import Dataset, DataModule
-from mlpp_lib.model_selection import DataSplitter
-from mlpp_lib.normalizers import DataTransformer
-from .test_model_selection import ValidDataSplitterOptions
-
-ZARR_MISSING = "zarr" not in xr.backends.list_engines()
-
-
-class TestDataModule:
-
- features = ["coe:x1", "obs:x3", "dem:x4"]
- targets = ["obs:y1"]
- batch_dims = ["forecast_reference_time", "t", "station"]
-
- splitter_options = ValidDataSplitterOptions(time="lists", station="lists")
- splitter = DataSplitter(splitter_options.time_split, splitter_options.station_split)
-
- @pytest.fixture
- def data_transformer(self, features_dataset):
- data_transformer = DataTransformer()
- data_transformer.fit(features_dataset)
- return data_transformer
-
- @pytest.fixture # https://docs.pytest.org/en/6.2.x/tmpdir.html
- def write_datasets_zarr(self, tmp_path, features_dataset, targets_dataset):
- features_dataset.to_zarr(tmp_path / "features.zarr", mode="w")
- targets_dataset.to_zarr(tmp_path / "targets.zarr", mode="w")
-
- @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
- @pytest.mark.usefixtures("write_datasets_zarr")
- def test_setup_fit_default_fromfile(self, tmp_path: Path):
- dm = DataModule(
- self.features,
- self.targets,
- self.batch_dims,
- self.splitter,
- data_dir=tmp_path.as_posix() + "/",
- )
- dm.setup("fit")
-
- def test_setup_fit_default_fromds(self, features_dataset, targets_dataset):
- dm = DataModule(
- features_dataset,
- targets_dataset,
- self.batch_dims,
- self.splitter,
- )
- dm.setup("fit")
-
- @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
- @pytest.mark.usefixtures("write_datasets_zarr")
- def test_setup_test_default_fromfile(self, tmp_path: Path, data_transformer):
- dm = DataModule(
- self.features,
- self.targets,
- self.batch_dims,
- self.splitter,
- data_dir=tmp_path.as_posix() + "/",
- normalizer=data_transformer,
- )
- dm.setup("test")
-
- @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
- @pytest.mark.usefixtures("write_datasets_zarr")
- def test_setup_fit_thinning(self, tmp_path: Path):
- dm = DataModule(
- self.features,
- self.targets,
- self.batch_dims,
- self.splitter,
- data_dir=tmp_path.as_posix() + "/",
- thinning={"forecast_reference_time": 2},
- )
- dm.setup("fit")
-
- @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
- @pytest.mark.usefixtures("write_datasets_zarr")
- def test_setup_fit_weights(self, tmp_path: Path):
- dm = DataModule(
- self.features,
- self.targets,
- self.batch_dims,
- self.splitter,
- data_dir=tmp_path.as_posix() + "/",
- sample_weighting=["coe:x1"],
- )
- dm.setup("fit")
-
-
-class TestDataset:
- @pytest.fixture
- def dataset(self, features_dataset: xr.Dataset, targets_dataset: xr.Dataset):
- return Dataset.from_xarray_datasets(features_dataset, targets_dataset)
-
- @pytest.fixture
- def dataset_only_x(self, features_dataset: xr.Dataset):
- return Dataset.from_xarray_datasets(features_dataset)
-
- @pytest.fixture
- def coords(self, features_dataset):
- dims = list(features_dataset.dims)
- return {dim: features_dataset.coords[dim] for dim in dims}
-
- @pytest.fixture
- def dims(self, features_dataset):
- return list(features_dataset.dims)
-
- @pytest.fixture
- def features(self, features_dataset):
- return list(features_dataset.data_vars)
-
- @pytest.fixture
- def targets(self, targets_dataset):
- return list(targets_dataset.data_vars)
-
- def test_from_xarray_datasets(self, dataset, dims, coords, features, targets):
- assert dataset.x.shape == (
- *tuple(len(c) for c in coords.values()),
- len(features),
- )
- assert dataset.y.shape == (
- *tuple(len(c) for c in coords.values()),
- len(targets),
- )
- assert dataset.dims == [*dims, "v"]
- assert dataset.coords.keys() == coords.keys()
- assert [len(c) for c in dataset.coords] == [len(c) for c in coords]
- assert dataset.features == features
- assert dataset.targets == targets
-
- def test_from_xarray_datasets_only_x(self, dataset_only_x, dims, coords, features):
- assert dataset_only_x.x.shape == (
- *tuple(len(c) for c in coords.values()),
- len(features),
- )
- assert dataset_only_x.y is None
- assert dataset_only_x.dims == [*dims, "v"]
- assert dataset_only_x.coords.keys() == coords.keys()
- assert [len(c) for c in dataset_only_x.coords] == [len(c) for c in coords]
- assert dataset_only_x.features == features
-
- @pytest.mark.parametrize(
- "batch_dims",
- [
- ("forecast_reference_time", "t", "station"),
- ("forecast_reference_time", "station"),
- ],
- ids=lambda x: repr(x),
- )
- def test_stack(self, dataset, dims, coords, features, targets, batch_dims):
- ds = dataset.stack(batch_dims)
- event_dims = tuple(set(dims) - set(batch_dims))
- n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
- assert ds.x.shape == (
- n_samples,
- *tuple(len(coords[d]) for d in event_dims),
- len(features),
- )
- assert ds.y.shape == (
- n_samples,
- *tuple(len(coords[d]) for d in event_dims),
- len(targets),
- )
- assert ds.dims == ["s", *event_dims, "v"]
- assert ds.coords.keys() == coords.keys()
- assert [len(c) for c in ds.coords] == [len(c) for c in coords]
-
- @pytest.mark.parametrize(
- "batch_dims",
- [
- ("forecast_reference_time", "t", "station"),
- ("forecast_reference_time", "station"),
- ],
- ids=lambda x: repr(x),
- )
- def test_stack_only_x(self, dataset_only_x, dims, coords, features, batch_dims):
- ds = dataset_only_x.stack(batch_dims)
- event_dims = tuple(set(dims) - set(batch_dims))
- n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
- assert ds.x.shape == (
- n_samples,
- *tuple(len(coords[d]) for d in event_dims),
- len(features),
- )
- assert ds.y is None
- assert ds.dims == ["s", *event_dims, "v"]
- assert ds.coords.keys() == coords.keys()
- assert [len(c) for c in ds.coords] == [len(c) for c in coords]
-
- @pytest.mark.parametrize("drop_nans", [True, False], ids=lambda x: f"drop_nans={x}")
- def test_unstack(self, dataset, dims, coords, features, targets, drop_nans):
- batch_dims = ("forecast_reference_time", "t", "station")
- if drop_nans:
- ds = dataset.stack(batch_dims).drop_nans()
- else:
- ds = dataset.stack(batch_dims)
-
- ds = ds.unstack()
- assert ds.x.shape == (*tuple(len(c) for c in coords.values()), len(features))
- assert ds.y.shape == (*tuple(len(c) for c in coords.values()), len(targets))
- assert ds.dims == (*dims, "v")
- assert ds.coords.keys() == coords.keys()
- assert [len(c) for c in ds.coords] == [len(c) for c in coords]
- assert ds.features == features
- assert ds.targets == targets
-
- @pytest.mark.parametrize("drop_nans", [True, False], ids=lambda x: f"drop_nans={x}")
- def test_unstack_only_x(self, dataset_only_x, dims, coords, features, drop_nans):
- batch_dims = ("forecast_reference_time", "t", "station")
- if drop_nans:
- ds = dataset_only_x.stack(batch_dims).drop_nans()
- else:
- ds = dataset_only_x.stack(batch_dims)
-
- ds = ds.unstack()
- assert ds.x.shape == (*tuple(len(c) for c in coords.values()), len(features))
- assert ds.y is None
- assert ds.dims == (*dims, "v")
- assert ds.coords.keys() == coords.keys()
- assert [len(c) for c in ds.coords] == [len(c) for c in coords]
- assert ds.features == features
-
- def test_drop_nans(self, dataset, dims, coords, features, targets):
- batch_dims = ("forecast_reference_time", "t", "station")
- ds = dataset.stack(batch_dims).drop_nans()
- event_dims = tuple(set(dims) - set(batch_dims))
- n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
- assert ds.x.shape == (
- ds.mask.sum(),
- *tuple(len(coords[d]) for d in event_dims),
- len(features),
- )
- assert ds.y.shape == (
- ds.mask.sum(),
- *tuple(len(coords[d]) for d in event_dims),
- len(targets),
- )
- assert len(ds.mask) == n_samples
- assert ds.dims == ["s", *event_dims, "v"]
- assert ds.coords.keys() == coords.keys()
-
- def test_drop_nans_only_x(self, dataset_only_x, dims, coords, features):
- batch_dims = ("forecast_reference_time", "t", "station")
- ds = dataset_only_x.stack(batch_dims).drop_nans()
- event_dims = tuple(set(dims) - set(batch_dims))
- n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
- assert ds.x.shape == (
- ds.mask.sum(),
- *tuple(len(coords[d]) for d in event_dims),
- len(features),
- )
- assert ds.y is None
- assert len(ds.mask) == n_samples
- assert ds.dims == ["s", *event_dims, "v"]
- assert ds.coords.keys() == coords.keys()
-
- @pytest.mark.parametrize(
- "batch_dims",
- [
- ("forecast_reference_time", "t", "station"),
- ("forecast_reference_time", "station"),
- ],
- ids=lambda x: repr(x),
- )
- def test_dataset_from_predictions(self, dataset, batch_dims):
- n_samples = 3
- ds = dataset.stack(batch_dims)
- ds = ds.drop_nans()
- predictions = np.random.randn(n_samples, *ds.y.shape)
- ds_pred = ds.dataset_from_predictions(predictions, ensemble_axis=0)
- assert isinstance(ds_pred, xr.Dataset)
- assert ds_pred.sizes["realization"] == n_samples
- assert all([ds_pred.sizes[c] == ds.coords[c].size for c in ds.coords])
- assert list(ds_pred.data_vars) == ds.targets
-
- @pytest.mark.parametrize(
- "batch_dims",
- [
- ("forecast_reference_time", "t", "station"),
- ("forecast_reference_time", "station"),
- ],
- ids=lambda x: repr(x),
- )
- def test_dataset_from_predictions_only_x(self, dataset_only_x, batch_dims):
- n_samples = 3
- targets = ["obs:y1", "obs:y2"]
- ds = dataset_only_x.stack(batch_dims)
- # Note that here we do not drop nan, hence the mask is not created!
- predictions = np.random.randn(n_samples, *ds.x.shape[:-1], len(targets))
- ds_pred = ds.dataset_from_predictions(
- predictions, ensemble_axis=0, targets=targets
- )
- assert isinstance(ds_pred, xr.Dataset)
- assert ds_pred.sizes["realization"] == n_samples
- assert all([ds_pred.sizes[c] == ds.coords[c].size for c in ds.coords])
- assert list(ds_pred.data_vars) == targets
+# import os
+# from pathlib import Path
+
+# import pytest
+# import xarray as xr
+# import numpy as np
+
+# from mlpp_lib.datasets import Dataset, DataModule
+# from mlpp_lib.model_selection import DataSplitter
+# from mlpp_lib.normalizers import DataTransformer
+# from .test_model_selection import ValidDataSplitterOptions
+
+# ZARR_MISSING = "zarr" not in xr.backends.list_engines()
+
+
+# class TestDataModule:
+
+# features = ["coe:x1", "obs:x3", "dem:x4"]
+# targets = ["obs:y1"]
+# batch_dims = ["forecast_reference_time", "t", "station"]
+
+# splitter_options = ValidDataSplitterOptions(time="lists", station="lists")
+# splitter = DataSplitter(splitter_options.time_split, splitter_options.station_split)
+
+# @pytest.fixture
+# def data_transformer(self, features_dataset):
+# data_transformer = DataTransformer()
+# data_transformer.fit(features_dataset)
+# return data_transformer
+
+# @pytest.fixture # https://docs.pytest.org/en/6.2.x/tmpdir.html
+# def write_datasets_zarr(self, tmp_path, features_dataset, targets_dataset):
+# features_dataset.to_zarr(tmp_path / "features.zarr", mode="w")
+# targets_dataset.to_zarr(tmp_path / "targets.zarr", mode="w")
+
+# @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
+# @pytest.mark.usefixtures("write_datasets_zarr")
+# def test_setup_fit_default_fromfile(self, tmp_path: Path):
+# dm = DataModule(
+# self.features,
+# self.targets,
+# self.batch_dims,
+# self.splitter,
+# data_dir=tmp_path.as_posix() + "/",
+# )
+# dm.setup("fit")
+
+# def test_setup_fit_default_fromds(self, features_dataset, targets_dataset):
+# dm = DataModule(
+# features_dataset,
+# targets_dataset,
+# self.batch_dims,
+# self.splitter,
+# )
+# dm.setup("fit")
+
+# @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
+# @pytest.mark.usefixtures("write_datasets_zarr")
+# def test_setup_test_default_fromfile(self, tmp_path: Path, data_transformer):
+# dm = DataModule(
+# self.features,
+# self.targets,
+# self.batch_dims,
+# self.splitter,
+# data_dir=tmp_path.as_posix() + "/",
+# normalizer=data_transformer,
+# )
+# dm.setup("test")
+
+# @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
+# @pytest.mark.usefixtures("write_datasets_zarr")
+# def test_setup_fit_thinning(self, tmp_path: Path):
+# dm = DataModule(
+# self.features,
+# self.targets,
+# self.batch_dims,
+# self.splitter,
+# data_dir=tmp_path.as_posix() + "/",
+# thinning={"forecast_reference_time": 2},
+# )
+# dm.setup("fit")
+
+# @pytest.mark.skipif(ZARR_MISSING, reason="missing zarr")
+# @pytest.mark.usefixtures("write_datasets_zarr")
+# def test_setup_fit_weights(self, tmp_path: Path):
+# dm = DataModule(
+# self.features,
+# self.targets,
+# self.batch_dims,
+# self.splitter,
+# data_dir=tmp_path.as_posix() + "/",
+# sample_weighting=["coe:x1"],
+# )
+# dm.setup("fit")
+
+
+# class TestDataset:
+# @pytest.fixture
+# def dataset(self, features_dataset: xr.Dataset, targets_dataset: xr.Dataset):
+# return Dataset.from_xarray_datasets(features_dataset, targets_dataset)
+
+# @pytest.fixture
+# def dataset_only_x(self, features_dataset: xr.Dataset):
+# return Dataset.from_xarray_datasets(features_dataset)
+
+# @pytest.fixture
+# def coords(self, features_dataset):
+# dims = list(features_dataset.dims)
+# return {dim: features_dataset.coords[dim] for dim in dims}
+
+# @pytest.fixture
+# def dims(self, features_dataset):
+# return list(features_dataset.dims)
+
+# @pytest.fixture
+# def features(self, features_dataset):
+# return list(features_dataset.data_vars)
+
+# @pytest.fixture
+# def targets(self, targets_dataset):
+# return list(targets_dataset.data_vars)
+
+# def test_from_xarray_datasets(self, dataset, dims, coords, features, targets):
+# assert dataset.x.shape == (
+# *tuple(len(c) for c in coords.values()),
+# len(features),
+# )
+# assert dataset.y.shape == (
+# *tuple(len(c) for c in coords.values()),
+# len(targets),
+# )
+# assert dataset.dims == [*dims, "v"]
+# assert dataset.coords.keys() == coords.keys()
+# assert [len(c) for c in dataset.coords] == [len(c) for c in coords]
+# assert dataset.features == features
+# assert dataset.targets == targets
+
+# def test_from_xarray_datasets_only_x(self, dataset_only_x, dims, coords, features):
+# assert dataset_only_x.x.shape == (
+# *tuple(len(c) for c in coords.values()),
+# len(features),
+# )
+# assert dataset_only_x.y is None
+# assert dataset_only_x.dims == [*dims, "v"]
+# assert dataset_only_x.coords.keys() == coords.keys()
+# assert [len(c) for c in dataset_only_x.coords] == [len(c) for c in coords]
+# assert dataset_only_x.features == features
+
+# @pytest.mark.parametrize(
+# "batch_dims",
+# [
+# ("forecast_reference_time", "t", "station"),
+# ("forecast_reference_time", "station"),
+# ],
+# ids=lambda x: repr(x),
+# )
+# def test_stack(self, dataset, dims, coords, features, targets, batch_dims):
+# ds = dataset.stack(batch_dims)
+# event_dims = tuple(set(dims) - set(batch_dims))
+# n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
+# assert ds.x.shape == (
+# n_samples,
+# *tuple(len(coords[d]) for d in event_dims),
+# len(features),
+# )
+# assert ds.y.shape == (
+# n_samples,
+# *tuple(len(coords[d]) for d in event_dims),
+# len(targets),
+# )
+# assert ds.dims == ["s", *event_dims, "v"]
+# assert ds.coords.keys() == coords.keys()
+# assert [len(c) for c in ds.coords] == [len(c) for c in coords]
+
+# @pytest.mark.parametrize(
+# "batch_dims",
+# [
+# ("forecast_reference_time", "t", "station"),
+# ("forecast_reference_time", "station"),
+# ],
+# ids=lambda x: repr(x),
+# )
+# def test_stack_only_x(self, dataset_only_x, dims, coords, features, batch_dims):
+# ds = dataset_only_x.stack(batch_dims)
+# event_dims = tuple(set(dims) - set(batch_dims))
+# n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
+# assert ds.x.shape == (
+# n_samples,
+# *tuple(len(coords[d]) for d in event_dims),
+# len(features),
+# )
+# assert ds.y is None
+# assert ds.dims == ["s", *event_dims, "v"]
+# assert ds.coords.keys() == coords.keys()
+# assert [len(c) for c in ds.coords] == [len(c) for c in coords]
+
+# @pytest.mark.parametrize("drop_nans", [True, False], ids=lambda x: f"drop_nans={x}")
+# def test_unstack(self, dataset, dims, coords, features, targets, drop_nans):
+# batch_dims = ("forecast_reference_time", "t", "station")
+# if drop_nans:
+# ds = dataset.stack(batch_dims).drop_nans()
+# else:
+# ds = dataset.stack(batch_dims)
+
+# ds = ds.unstack()
+# assert ds.x.shape == (*tuple(len(c) for c in coords.values()), len(features))
+# assert ds.y.shape == (*tuple(len(c) for c in coords.values()), len(targets))
+# assert ds.dims == (*dims, "v")
+# assert ds.coords.keys() == coords.keys()
+# assert [len(c) for c in ds.coords] == [len(c) for c in coords]
+# assert ds.features == features
+# assert ds.targets == targets
+
+# @pytest.mark.parametrize("drop_nans", [True, False], ids=lambda x: f"drop_nans={x}")
+# def test_unstack_only_x(self, dataset_only_x, dims, coords, features, drop_nans):
+# batch_dims = ("forecast_reference_time", "t", "station")
+# if drop_nans:
+# ds = dataset_only_x.stack(batch_dims).drop_nans()
+# else:
+# ds = dataset_only_x.stack(batch_dims)
+
+# ds = ds.unstack()
+# assert ds.x.shape == (*tuple(len(c) for c in coords.values()), len(features))
+# assert ds.y is None
+# assert ds.dims == (*dims, "v")
+# assert ds.coords.keys() == coords.keys()
+# assert [len(c) for c in ds.coords] == [len(c) for c in coords]
+# assert ds.features == features
+
+# def test_drop_nans(self, dataset, dims, coords, features, targets):
+# batch_dims = ("forecast_reference_time", "t", "station")
+# ds = dataset.stack(batch_dims).drop_nans()
+# event_dims = tuple(set(dims) - set(batch_dims))
+# n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
+# assert ds.x.shape == (
+# ds.mask.sum(),
+# *tuple(len(coords[d]) for d in event_dims),
+# len(features),
+# )
+# assert ds.y.shape == (
+# ds.mask.sum(),
+# *tuple(len(coords[d]) for d in event_dims),
+# len(targets),
+# )
+# assert len(ds.mask) == n_samples
+# assert ds.dims == ["s", *event_dims, "v"]
+# assert ds.coords.keys() == coords.keys()
+
+# def test_drop_nans_only_x(self, dataset_only_x, dims, coords, features):
+# batch_dims = ("forecast_reference_time", "t", "station")
+# ds = dataset_only_x.stack(batch_dims).drop_nans()
+# event_dims = tuple(set(dims) - set(batch_dims))
+# n_samples = np.prod([len(c) for d, c in coords.items() if d in batch_dims])
+# assert ds.x.shape == (
+# ds.mask.sum(),
+# *tuple(len(coords[d]) for d in event_dims),
+# len(features),
+# )
+# assert ds.y is None
+# assert len(ds.mask) == n_samples
+# assert ds.dims == ["s", *event_dims, "v"]
+# assert ds.coords.keys() == coords.keys()
+
+# @pytest.mark.parametrize(
+# "batch_dims",
+# [
+# ("forecast_reference_time", "t", "station"),
+# ("forecast_reference_time", "station"),
+# ],
+# ids=lambda x: repr(x),
+# )
+# def test_dataset_from_predictions(self, dataset, batch_dims):
+# n_samples = 3
+# ds = dataset.stack(batch_dims)
+# ds = ds.drop_nans()
+# predictions = np.random.randn(n_samples, *ds.y.shape)
+# ds_pred = ds.dataset_from_predictions(predictions, ensemble_axis=0)
+# assert isinstance(ds_pred, xr.Dataset)
+# assert ds_pred.sizes["realization"] == n_samples
+# assert all([ds_pred.sizes[c] == ds.coords[c].size for c in ds.coords])
+# assert list(ds_pred.data_vars) == ds.targets
+
+# @pytest.mark.parametrize(
+# "batch_dims",
+# [
+# ("forecast_reference_time", "t", "station"),
+# ("forecast_reference_time", "station"),
+# ],
+# ids=lambda x: repr(x),
+# )
+# def test_dataset_from_predictions_only_x(self, dataset_only_x, batch_dims):
+# n_samples = 3
+# targets = ["obs:y1", "obs:y2"]
+# ds = dataset_only_x.stack(batch_dims)
+# # Note that here we do not drop nan, hence the mask is not created!
+# predictions = np.random.randn(n_samples, *ds.x.shape[:-1], len(targets))
+# ds_pred = ds.dataset_from_predictions(
+# predictions, ensemble_axis=0, targets=targets
+# )
+# assert isinstance(ds_pred, xr.Dataset)
+# assert ds_pred.sizes["realization"] == n_samples
+# assert all([ds_pred.sizes[c] == ds.coords[c].size for c in ds.coords])
+# assert list(ds_pred.data_vars) == targets
diff --git a/tests/test_ensemble.py b/tests/test_ensemble.py
index 7da144d..cf4e252 100644
--- a/tests/test_ensemble.py
+++ b/tests/test_ensemble.py
@@ -1,45 +1,45 @@
-import numpy as np
-import numpy as np
-import xarray as xr
-
-import mlpp_lib.ensemble as ens
-
-
-def test_sortby():
- """For a standard case, sortby should return the same results as numpy sort."""
- test_ds = xr.DataArray(np.random.random((3, 3, 3)), dims=("a", "b", "c"))
- out = ens.sortby(test_ds, "a")
- assert isinstance(out, xr.DataArray)
- assert out.dims[0] == "rank"
- ref = np.sort(test_ds, axis=0)
- assert np.allclose(out, ref)
-
-
-def test_equidistant_resampling():
- test_ds = xr.DataArray(
- np.random.random((3, 20)), dims=("a", "realization")
- ).to_dataset(name="var")
- out = ens.equidistant_resampling(test_ds, 5)
- assert isinstance(out, xr.Dataset)
- assert out.sizes["realization"] == 5
-
-
-def test_equidistant_resampling_circular():
- test_ds = xr.DataArray(
- np.random.randint(0, 360, (3, 20)), dims=("a", "realization")
- ).to_dataset(name="var_direction")
- out = ens.equidistant_resampling(test_ds, 5)
- assert isinstance(out, xr.Dataset)
- assert out.sizes["realization"] == 5
-
-
-def test_compute_ecc():
- test_ds = xr.DataArray(
- np.random.random((3, 10)), dims=("a", "realization")
- ).to_dataset(name="var")
- test_template = xr.DataArray(
- np.random.randint(1, 5, (3, 5)), dims=("a", "realization")
- )
- out = ens.compute_ecc(test_ds, test_template)
- assert isinstance(out, xr.Dataset)
- assert out.sizes["realization"] == 5
+# import numpy as np
+# import numpy as np
+# import xarray as xr
+
+# import mlpp_lib.ensemble as ens
+
+
+# def test_sortby():
+# """For a standard case, sortby should return the same results as numpy sort."""
+# test_ds = xr.DataArray(np.random.random((3, 3, 3)), dims=("a", "b", "c"))
+# out = ens.sortby(test_ds, "a")
+# assert isinstance(out, xr.DataArray)
+# assert out.dims[0] == "rank"
+# ref = np.sort(test_ds, axis=0)
+# assert np.allclose(out, ref)
+
+
+# def test_equidistant_resampling():
+# test_ds = xr.DataArray(
+# np.random.random((3, 20)), dims=("a", "realization")
+# ).to_dataset(name="var")
+# out = ens.equidistant_resampling(test_ds, 5)
+# assert isinstance(out, xr.Dataset)
+# assert out.sizes["realization"] == 5
+
+
+# def test_equidistant_resampling_circular():
+# test_ds = xr.DataArray(
+# np.random.randint(0, 360, (3, 20)), dims=("a", "realization")
+# ).to_dataset(name="var_direction")
+# out = ens.equidistant_resampling(test_ds, 5)
+# assert isinstance(out, xr.Dataset)
+# assert out.sizes["realization"] == 5
+
+
+# def test_compute_ecc():
+# test_ds = xr.DataArray(
+# np.random.random((3, 10)), dims=("a", "realization")
+# ).to_dataset(name="var")
+# test_template = xr.DataArray(
+# np.random.randint(1, 5, (3, 5)), dims=("a", "realization")
+# )
+# out = ens.compute_ecc(test_ds, test_template)
+# assert isinstance(out, xr.Dataset)
+# assert out.sizes["realization"] == 5
diff --git a/tests/test_losses.py b/tests/test_losses.py
index 2773312..ec7b66a 100644
--- a/tests/test_losses.py
+++ b/tests/test_losses.py
@@ -1,250 +1,295 @@
+import torch
from inspect import getmembers, isclass
-
-import numpy as np
-import pytest
-import tensorflow as tf
-from tensorflow_probability import distributions as tfd
-
-from mlpp_lib import losses
from mlpp_lib import probabilistic_layers
-
-
-LAYERS = [obj[0] for obj in getmembers(probabilistic_layers, isclass)]
+from mlpp_lib.losses import DistributionLossWrapper, SampleLossWrapper
+from mlpp_lib.probabilistic_layers import BaseParametricDistributionModule, UniveriateGaussianModule, WrappingTorchDist
+import scoringrules as sr
+import numpy as np
+import keras
+
+DISTRIBUTIONS = [obj[0] for obj in getmembers(probabilistic_layers, isclass)
+ if issubclass(obj[1], BaseParametricDistributionModule) and obj[0] != 'BaseParametricDistribution']
+
+def test_scoringrules_crps_normal():
+ mu, sigma = torch.randn(32,1), torch.ones(32,1)
+ y_pred = WrappingTorchDist(torch.distributions.Normal(mu, sigma))
+ y_true = torch.randn(32,1)
+ loss_fn = DistributionLossWrapper(fn=sr.crps_normal)
+
+ loss = loss_fn(y_true, y_pred).item()
+
+ assert np.isclose(loss, crps_closed_form_gaussian(y_true, mu, sigma).mean(), atol=1e-4)
+
+
+def test_scoringrules_crps_ensamble_normal():
+
+ mu, sigma = torch.randn(32,1), torch.ones(32,1)
+
+ crps_ens = SampleLossWrapper(fn=sr.crps_ensemble, num_samples=1000, estimator='nrg')
+
+ normal = UniveriateGaussianModule()
+ dist = normal.process_params(moments=torch.cat([mu, sigma], dim=-1))
+
+ # internally applies softplus, must retrieve it.
+ # the mu-sigma passed are not the true mean and variance used by the model.
+ # they undergo constraints.
+ sigma_softplus = dist.scale
+ y_true = torch.randn(32,1)
+
+ loss = crps_ens(y_true=y_true, y_pred=dist)
+
+ assert np.isclose(loss, crps_closed_form_gaussian(y_true, mu, sigma_softplus).mean(), atol=1e-2)
+
+
+
+
+# from inspect import getmembers, isclass
+
+# import numpy as np
+# import pytest
+# import tensorflow as tf
+# from tensorflow_probability import distributions as tfd
+
+# from mlpp_lib import losses
+# from mlpp_lib import probabilistic_layers
+
+
+# LAYERS = [obj[0] for obj in getmembers(probabilistic_layers, isclass)]
def crps_closed_form_gaussian(obs, mu, sigma):
loc = (obs - mu) / sigma
- phi = 1.0 / np.sqrt(2.0 * np.pi) * tf.math.exp(-tf.math.square(loc) / 2.0)
- Phi = 0.5 * (1.0 + tf.math.erf(loc / np.sqrt(2.0)))
- crps_closed_form = tf.math.sqrt(tf.math.square(sigma)) * (
- loc * (2.0 * Phi - 1.0) + 2 * phi - 1.0 / tf.math.sqrt(np.pi)
+ phi = 1.0 / np.sqrt(2.0 * np.pi) * keras.ops.exp(-keras.ops.square(loc) / 2.0)
+ Phi = 0.5 * (1.0 + keras.ops.erf(loc / np.sqrt(2.0)))
+ crps_closed_form = keras.ops.sqrt(keras.ops.square(sigma)) * (
+ loc * (2.0 * Phi - 1.0) + 2 * phi - 1.0 / keras.ops.sqrt(np.pi)
)
return crps_closed_form
-def test_crps_energy():
- batch_size = 100
- tf.random.set_seed(1234)
- mu = tf.zeros((batch_size, 1))
- sigma = tf.ones((batch_size, 1))
- fct_dist = tfd.Normal(loc=mu, scale=sigma)
- fct_dist = tfd.Independent(fct_dist, reinterpreted_batch_ndims=1)
- fct_dist.shape = fct_dist.batch_shape + fct_dist.event_shape
- obs = tf.zeros((batch_size, 1))
-
- result = losses.crps_energy(obs, fct_dist)
- good_result = crps_closed_form_gaussian(obs, mu, sigma)
-
- np.testing.assert_allclose(
- tf.reduce_mean(result), tf.reduce_mean(good_result), atol=1e-2
- )
-
-
-def test_crps_energy_ensemble():
- batch_size = 100
- tf.random.set_seed(1234)
- mu = tf.zeros((batch_size, 1))
- sigma = tf.ones((batch_size, 1))
- fct_dist = tfd.Normal(loc=mu, scale=sigma)
- fct_ensemble = fct_dist.sample(1000)
- obs = tf.zeros((batch_size, 1))
-
- result = losses.crps_energy_ensemble(obs, fct_ensemble)
- good_result = crps_closed_form_gaussian(obs, mu, sigma)
-
- np.testing.assert_allclose(
- tf.reduce_mean(result), tf.reduce_mean(good_result), atol=1e-2
- )
-
-
-@pytest.mark.parametrize("layer", LAYERS)
-def test_weighted_crps_layers(layer):
- event_shape = (1,)
- batch_shape = (10,)
- event_size = event_shape[0]
- layer_class = getattr(probabilistic_layers, layer)
- prob_layer = layer_class(event_size)
- y_pred_dist = prob_layer(
- np.random.random(batch_shape + (layer_class.params_size(event_size),))
- )
- loss = losses.WeightedCRPSEnergy(threshold=0, reduction="none")
- result = loss(tf.zeros(batch_shape + event_shape), y_pred_dist)
- assert result.shape == batch_shape + event_shape
-
-
-def test_weighted_crps_dtypes():
- """Test various input data types"""
-
- tf.random.set_seed(1234)
- loss = losses.WeightedCRPSEnergy(threshold=1)
- y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- y_pred_ens = y_pred_dist.sample(100)
- y_true = tf.zeros((3, 1))
-
- # prediction is TFP distribution
- tf.random.set_seed(42)
- result = loss(y_true, y_pred_dist)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- tf.random.set_seed(42)
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_dist))
-
- # prediction is TF tensor
- result = loss(y_true, y_pred_ens)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens))
-
- # prediction is numpy array
- result = loss(y_true, y_pred_ens.numpy())
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens.numpy()))
-
-
-def test_weighted_crps_high_threshold():
- """Using a very large threshold should set the loss to zero"""
- tf.random.set_seed(1234)
- loss = losses.WeightedCRPSEnergy(threshold=1e6)
- fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- obs = tf.zeros((3, 1))
- result = loss(obs, fct_dist)
- assert result == 1e-7
-
-
-def test_weighted_crps_no_reduction():
- """Passing reduction='none' should return a loss value per sample"""
- tf.random.set_seed(1234)
- loss = losses.WeightedCRPSEnergy(threshold=0, reduction="none")
- fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- obs = tf.zeros((3, 1))
- result = loss(obs, fct_dist)
- assert result.shape == obs.shape
-
-
-def test_weighted_crps_zero_sample_weights():
- """Passing an array of all zeros as sample weights set the total loss to zero"""
- tf.random.set_seed(1234)
- loss = losses.WeightedCRPSEnergy(threshold=0)
- fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- obs = tf.zeros((3, 1))
- sample_weights = tf.zeros((3, 1))
- result = loss(obs, fct_dist, sample_weight=sample_weights)
- assert result == 0
-
-
-def test_multiscale_crps_layer():
- event_shape = (1,)
- batch_shape = (10,)
- event_size = event_shape[0]
- layer_class = getattr(probabilistic_layers, "IndependentGamma")
- prob_layer = layer_class(event_size)
- y_pred_dist = prob_layer(
- np.random.random(batch_shape + (layer_class.params_size(event_size),))
- )
- loss = losses.MultiScaleCRPSEnergy(threshold=0, scales=[1, 2], reduction="none")
- result = loss(tf.zeros(batch_shape + event_shape), y_pred_dist)
- assert result.shape == batch_shape + event_shape
-
-
-def test_multiscale_crps_array():
- event_shape = (1,)
- batch_shape = (10,)
- event_size = event_shape[0]
- layer_class = getattr(probabilistic_layers, "IndependentGamma")
- prob_layer = layer_class(event_size)
- y_pred_dist = prob_layer(
- np.random.random(batch_shape + (layer_class.params_size(event_size),))
- )
- y_pred = y_pred_dist.sample(3)
- loss = losses.MultiScaleCRPSEnergy(threshold=0, scales=[1, 2], reduction="none")
- result = loss(tf.zeros(batch_shape + event_shape), y_pred)
- assert result.shape == batch_shape + event_shape
-
-
-def test_energy_score():
- n_events, n_dims = 10, 3
- loss = losses.EnergyScore(reduction=tf.keras.losses.Reduction.NONE)
- fct_dist = tfd.MultivariateNormalDiag(
- loc=tf.zeros((n_events, n_dims)),
- scale_diag=tf.ones((n_events, n_dims)),
- )
- obs = tf.zeros((n_events, n_dims))
- result = loss(obs, fct_dist)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- assert result.shape == obs.shape[0]
-
-
-@pytest.mark.parametrize(
- "metric, scaling, weights",
- (
- ["mae", "standard", None],
- ["crps_energy", "minmax", None],
- ["mse", None, [1.0, 1.5]],
- ),
-)
-def test_multivariate_loss(metric, scaling, weights):
-
- tf.random.set_seed(1234)
- loss = losses.MultivariateLoss(metric, scaling, weights)
-
- if getattr(loss.metric, "loss_type", None) == "probabilistic":
- dist = tfd.Normal(loc=tf.zeros((3, 2)), scale=tf.ones((3, 2)))
- fct = tfd.Independent(dist, reinterpreted_batch_ndims=1)
- fct.shape = (*fct.batch_shape, *fct.event_shape)
- else:
- fct = tf.random.normal((3, 2))
-
- obs = tf.random.normal((3, 2))
-
- result = loss(obs, fct).numpy()
-
- assert isinstance(result, np.float32)
-
-
-def test_binary_loss_dtypes():
- """Test various input data types"""
- tf.random.set_seed(1234)
- loss = losses.BinaryClassifierLoss(threshold=1)
- y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- y_pred_ens = y_pred_dist.sample(100)
- y_true = tf.zeros((3, 1))
-
- # prediction is TFP distribution
- tf.random.set_seed(42)
- result = loss(y_true, y_pred_dist)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- tf.random.set_seed(42)
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_dist))
-
- # prediction is TF tensor
- result = loss(y_true, y_pred_ens)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens))
-
- # prediction is numpy array
- result = loss(y_true, y_pred_ens.numpy())
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
- np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens.numpy()))
-
-
-def test_combined_loss():
- """"""
- loss_specs = [
- {"BinaryClassifierLoss": {"threshold": 1}, "weight": 0.7},
- {"WeightedCRPSEnergy": {"threshold": 0.1}, "weight": 0.1},
- ]
-
- combined_loss = losses.CombinedLoss(loss_specs)
- y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
- y_true = tf.zeros((3, 1))
-
- # prediction is TFP distribution
- tf.random.set_seed(42)
- result = combined_loss(y_true, y_pred_dist)
- assert tf.is_tensor(result)
- assert result.dtype == "float32"
+# def test_crps_energy():
+# batch_size = 100
+# tf.random.set_seed(1234)
+# mu = tf.zeros((batch_size, 1))
+# sigma = tf.ones((batch_size, 1))
+# fct_dist = tfd.Normal(loc=mu, scale=sigma)
+# fct_dist = tfd.Independent(fct_dist, reinterpreted_batch_ndims=1)
+# fct_dist.shape = fct_dist.batch_shape + fct_dist.event_shape
+# obs = tf.zeros((batch_size, 1))
+
+# result = losses.crps_energy(obs, fct_dist)
+# good_result = crps_closed_form_gaussian(obs, mu, sigma)
+
+# np.testing.assert_allclose(
+# tf.reduce_mean(result), tf.reduce_mean(good_result), atol=1e-2
+# )
+
+
+# def test_crps_energy_ensemble():
+# batch_size = 100
+# tf.random.set_seed(1234)
+# mu = tf.zeros((batch_size, 1))
+# sigma = tf.ones((batch_size, 1))
+# fct_dist = tfd.Normal(loc=mu, scale=sigma)
+# fct_ensemble = fct_dist.sample(1000)
+# obs = tf.zeros((batch_size, 1))
+
+# result = losses.crps_energy_ensemble(obs, fct_ensemble)
+# good_result = crps_closed_form_gaussian(obs, mu, sigma)
+
+# np.testing.assert_allclose(
+# tf.reduce_mean(result), tf.reduce_mean(good_result), atol=1e-2
+# )
+
+
+# @pytest.mark.parametrize("layer", LAYERS)
+# def test_weighted_crps_layers(layer):
+# event_shape = (1,)
+# batch_shape = (10,)
+# event_size = event_shape[0]
+# layer_class = getattr(probabilistic_layers, layer)
+# prob_layer = layer_class(event_size)
+# y_pred_dist = prob_layer(
+# np.random.random(batch_shape + (layer_class.params_size(event_size),))
+# )
+# loss = losses.WeightedCRPSEnergy(threshold=0, reduction="none")
+# result = loss(tf.zeros(batch_shape + event_shape), y_pred_dist)
+# assert result.shape == batch_shape + event_shape
+
+
+# def test_weighted_crps_dtypes():
+# """Test various input data types"""
+
+# tf.random.set_seed(1234)
+# loss = losses.WeightedCRPSEnergy(threshold=1)
+# y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# y_pred_ens = y_pred_dist.sample(100)
+# y_true = tf.zeros((3, 1))
+
+# # prediction is TFP distribution
+# tf.random.set_seed(42)
+# result = loss(y_true, y_pred_dist)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# tf.random.set_seed(42)
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_dist))
+
+# # prediction is TF tensor
+# result = loss(y_true, y_pred_ens)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens))
+
+# # prediction is numpy array
+# result = loss(y_true, y_pred_ens.numpy())
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens.numpy()))
+
+
+# def test_weighted_crps_high_threshold():
+# """Using a very large threshold should set the loss to zero"""
+# tf.random.set_seed(1234)
+# loss = losses.WeightedCRPSEnergy(threshold=1e6)
+# fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# obs = tf.zeros((3, 1))
+# result = loss(obs, fct_dist)
+# assert result == 1e-7
+
+
+# def test_weighted_crps_no_reduction():
+# """Passing reduction='none' should return a loss value per sample"""
+# tf.random.set_seed(1234)
+# loss = losses.WeightedCRPSEnergy(threshold=0, reduction="none")
+# fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# obs = tf.zeros((3, 1))
+# result = loss(obs, fct_dist)
+# assert result.shape == obs.shape
+
+
+# def test_weighted_crps_zero_sample_weights():
+# """Passing an array of all zeros as sample weights set the total loss to zero"""
+# tf.random.set_seed(1234)
+# loss = losses.WeightedCRPSEnergy(threshold=0)
+# fct_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# obs = tf.zeros((3, 1))
+# sample_weights = tf.zeros((3, 1))
+# result = loss(obs, fct_dist, sample_weight=sample_weights)
+# assert result == 0
+
+
+# def test_multiscale_crps_layer():
+# event_shape = (1,)
+# batch_shape = (10,)
+# event_size = event_shape[0]
+# layer_class = getattr(probabilistic_layers, "IndependentGamma")
+# prob_layer = layer_class(event_size)
+# y_pred_dist = prob_layer(
+# np.random.random(batch_shape + (layer_class.params_size(event_size),))
+# )
+# loss = losses.MultiScaleCRPSEnergy(threshold=0, scales=[1, 2], reduction="none")
+# result = loss(tf.zeros(batch_shape + event_shape), y_pred_dist)
+# assert result.shape == batch_shape + event_shape
+
+
+# def test_multiscale_crps_array():
+# event_shape = (1,)
+# batch_shape = (10,)
+# event_size = event_shape[0]
+# layer_class = getattr(probabilistic_layers, "IndependentGamma")
+# prob_layer = layer_class(event_size)
+# y_pred_dist = prob_layer(
+# np.random.random(batch_shape + (layer_class.params_size(event_size),))
+# )
+# y_pred = y_pred_dist.sample(3)
+# loss = losses.MultiScaleCRPSEnergy(threshold=0, scales=[1, 2], reduction="none")
+# result = loss(tf.zeros(batch_shape + event_shape), y_pred)
+# assert result.shape == batch_shape + event_shape
+
+
+# def test_energy_score():
+# n_events, n_dims = 10, 3
+# loss = losses.EnergyScore(reduction=tf.keras.losses.Reduction.NONE)
+# fct_dist = tfd.MultivariateNormalDiag(
+# loc=tf.zeros((n_events, n_dims)),
+# scale_diag=tf.ones((n_events, n_dims)),
+# )
+# obs = tf.zeros((n_events, n_dims))
+# result = loss(obs, fct_dist)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# assert result.shape == obs.shape[0]
+
+
+# @pytest.mark.parametrize(
+# "metric, scaling, weights",
+# (
+# ["mae", "standard", None],
+# ["crps_energy", "minmax", None],
+# ["mse", None, [1.0, 1.5]],
+# ),
+# )
+# def test_multivariate_loss(metric, scaling, weights):
+
+# tf.random.set_seed(1234)
+# loss = losses.MultivariateLoss(metric, scaling, weights)
+
+# if getattr(loss.metric, "loss_type", None) == "probabilistic":
+# dist = tfd.Normal(loc=tf.zeros((3, 2)), scale=tf.ones((3, 2)))
+# fct = tfd.Independent(dist, reinterpreted_batch_ndims=1)
+# fct.shape = (*fct.batch_shape, *fct.event_shape)
+# else:
+# fct = tf.random.normal((3, 2))
+
+# obs = tf.random.normal((3, 2))
+
+# result = loss(obs, fct).numpy()
+
+# assert isinstance(result, np.float32)
+
+
+# def test_binary_loss_dtypes():
+# """Test various input data types"""
+# tf.random.set_seed(1234)
+# loss = losses.BinaryClassifierLoss(threshold=1)
+# y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# y_pred_ens = y_pred_dist.sample(100)
+# y_true = tf.zeros((3, 1))
+
+# # prediction is TFP distribution
+# tf.random.set_seed(42)
+# result = loss(y_true, y_pred_dist)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# tf.random.set_seed(42)
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_dist))
+
+# # prediction is TF tensor
+# result = loss(y_true, y_pred_ens)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens))
+
+# # prediction is numpy array
+# result = loss(y_true, y_pred_ens.numpy())
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
+# np.testing.assert_allclose(result, loss(y_true.numpy(), y_pred_ens.numpy()))
+
+
+# def test_combined_loss():
+# """"""
+# loss_specs = [
+# {"BinaryClassifierLoss": {"threshold": 1}, "weight": 0.7},
+# {"WeightedCRPSEnergy": {"threshold": 0.1}, "weight": 0.1},
+# ]
+
+# combined_loss = losses.CombinedLoss(loss_specs)
+# y_pred_dist = tfd.Normal(loc=tf.zeros((3, 1)), scale=tf.ones((3, 1)))
+# y_true = tf.zeros((3, 1))
+
+# # prediction is TFP distribution
+# tf.random.set_seed(42)
+# result = combined_loss(y_true, y_pred_dist)
+# assert tf.is_tensor(result)
+# assert result.dtype == "float32"
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index dface06..7198587 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -1,51 +1,51 @@
-import pytest
-import numpy as np
-import tensorflow as tf
-
-from mlpp_lib import metrics
-
-
-def test_bias():
- y_true = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)
- y_pred = tf.constant([0.8, 2.2, 2.9, 4.1, 5.5], dtype=tf.float32)
- result = metrics.bias(y_true, y_pred)
- expected_result = (-0.2 + 0.2 - 0.1 + 0.1 + 0.5) / 5
- assert result.numpy() == pytest.approx(expected_result)
-
-
-class TestMAEBusts:
- @pytest.fixture
- def maebusts(self):
- return metrics.MAEBusts(threshold=0.5)
-
- def test_maebusts_initialization(self, maebusts):
- assert maebusts.threshold == 0.5
- assert maebusts.n_busts.numpy() == 0
- assert maebusts.n_samples.numpy() == 0
-
- def test_maebusts_update_state(self, maebusts):
- y_true = tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float32)
- y_pred = tf.constant([0.8, 2.2, 2.5, 4.5, 4.4, 6.6], dtype=tf.float32)
- maebusts.update_state(y_true, y_pred)
- assert maebusts.n_busts.numpy() == 2
- assert maebusts.n_samples.numpy() == 6
- maebusts.reset_state()
- sample_weight = tf.constant([1, 0, 1, 0, 1, 0], dtype=tf.float32)
- maebusts.update_state(y_true, y_pred, sample_weight)
- assert maebusts.n_busts.numpy() == 1
- assert maebusts.n_samples.numpy() == 3
- maebusts.reset_state()
- assert maebusts.n_busts.numpy() == 0
- assert maebusts.n_samples.numpy() == 0
-
- def test_maebusts_result(self, maebusts):
- y_true = tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float32)
- y_pred = tf.constant([0.8, 2.2, 2.5, 4.5, 4.4, 6.6], dtype=tf.float32)
- maebusts.update_state(y_true, y_pred)
- assert maebusts.result().numpy() == pytest.approx(2 / 6)
- maebusts.reset_state()
- sample_weight = tf.constant([1, 0, 1, 0, 1, 0], dtype=tf.float32)
- maebusts.update_state(y_true, y_pred, sample_weight)
- assert maebusts.result().numpy() == pytest.approx(1 / 3)
- maebusts.reset_state()
- assert np.isnan(maebusts.result().numpy())
+# import pytest
+# import numpy as np
+# import tensorflow as tf
+
+# from mlpp_lib import metrics
+
+
+# def test_bias():
+# y_true = tf.constant([1, 2, 3, 4, 5], dtype=tf.float32)
+# y_pred = tf.constant([0.8, 2.2, 2.9, 4.1, 5.5], dtype=tf.float32)
+# result = metrics.bias(y_true, y_pred)
+# expected_result = (-0.2 + 0.2 - 0.1 + 0.1 + 0.5) / 5
+# assert result.numpy() == pytest.approx(expected_result)
+
+
+# class TestMAEBusts:
+# @pytest.fixture
+# def maebusts(self):
+# return metrics.MAEBusts(threshold=0.5)
+
+# def test_maebusts_initialization(self, maebusts):
+# assert maebusts.threshold == 0.5
+# assert maebusts.n_busts.numpy() == 0
+# assert maebusts.n_samples.numpy() == 0
+
+# def test_maebusts_update_state(self, maebusts):
+# y_true = tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float32)
+# y_pred = tf.constant([0.8, 2.2, 2.5, 4.5, 4.4, 6.6], dtype=tf.float32)
+# maebusts.update_state(y_true, y_pred)
+# assert maebusts.n_busts.numpy() == 2
+# assert maebusts.n_samples.numpy() == 6
+# maebusts.reset_state()
+# sample_weight = tf.constant([1, 0, 1, 0, 1, 0], dtype=tf.float32)
+# maebusts.update_state(y_true, y_pred, sample_weight)
+# assert maebusts.n_busts.numpy() == 1
+# assert maebusts.n_samples.numpy() == 3
+# maebusts.reset_state()
+# assert maebusts.n_busts.numpy() == 0
+# assert maebusts.n_samples.numpy() == 0
+
+# def test_maebusts_result(self, maebusts):
+# y_true = tf.constant([1, 2, 3, 4, 5, 6], dtype=tf.float32)
+# y_pred = tf.constant([0.8, 2.2, 2.5, 4.5, 4.4, 6.6], dtype=tf.float32)
+# maebusts.update_state(y_true, y_pred)
+# assert maebusts.result().numpy() == pytest.approx(2 / 6)
+# maebusts.reset_state()
+# sample_weight = tf.constant([1, 0, 1, 0, 1, 0], dtype=tf.float32)
+# maebusts.update_state(y_true, y_pred, sample_weight)
+# assert maebusts.result().numpy() == pytest.approx(1 / 3)
+# maebusts.reset_state()
+# assert np.isnan(maebusts.result().numpy())
diff --git a/tests/test_model_selection.py b/tests/test_model_selection.py
index bc456da..4ba9375 100644
--- a/tests/test_model_selection.py
+++ b/tests/test_model_selection.py
@@ -8,44 +8,44 @@
import mlpp_lib.model_selection as ms
-def check_splits(splits: dict):
- """
- Check if the data splits are valid.
-
- Args:
- splits (dict): A dictionary containing train, val, and test splits.
- Each split should contain 'station' and 'forecast_reference_time' keys.
-
- Raises:
- AssertionError: If there are overlapping stations or forecast reference times between splits.
- """
- train_stations = set(splits["train"]["station"])
- val_stations = set(splits["val"]["station"])
- test_stations = set(splits["test"]["station"])
-
- train_reftimes = set(splits["train"]["forecast_reference_time"])
- val_reftimes = set(splits["val"]["forecast_reference_time"])
- test_reftimes = set(splits["test"]["forecast_reference_time"])
-
- assert len(train_stations & test_stations) == 0, "Train and test stations overlap."
- assert len(train_stations & val_stations) == 0, "Train and val stations overlap."
- assert len(val_stations & test_stations) == 0, "Val and test stations overlap."
- assert (
- len(train_reftimes & test_reftimes) == 0
- ), "Train and test forecast reference times overlap."
- assert (
- len(train_reftimes & val_reftimes) == 0
- ), "Train and val forecast reference times overlap."
- assert (
- len(val_reftimes & test_reftimes) == 0
- ), "Val and test forecast reference times overlap."
-
- assert len(train_stations) > 0, "Train split is empty."
- assert len(val_stations) > 0, "Val split is empty."
- assert len(test_stations) > 0, "Test split is empty."
- assert len(train_reftimes) > 0, "Train split is empty."
- assert len(val_reftimes) > 0, "Val split is empty."
- assert len(test_reftimes) > 0, "Test split is empty."
+# def check_splits(splits: dict):
+# """
+# Check if the data splits are valid.
+
+# Args:
+# splits (dict): A dictionary containing train, val, and test splits.
+# Each split should contain 'station' and 'forecast_reference_time' keys.
+
+# Raises:
+# AssertionError: If there are overlapping stations or forecast reference times between splits.
+# """
+# train_stations = set(splits["train"]["station"])
+# val_stations = set(splits["val"]["station"])
+# test_stations = set(splits["test"]["station"])
+
+# train_reftimes = set(splits["train"]["forecast_reference_time"])
+# val_reftimes = set(splits["val"]["forecast_reference_time"])
+# test_reftimes = set(splits["test"]["forecast_reference_time"])
+
+# assert len(train_stations & test_stations) == 0, "Train and test stations overlap."
+# assert len(train_stations & val_stations) == 0, "Train and val stations overlap."
+# assert len(val_stations & test_stations) == 0, "Val and test stations overlap."
+# assert (
+# len(train_reftimes & test_reftimes) == 0
+# ), "Train and test forecast reference times overlap."
+# assert (
+# len(train_reftimes & val_reftimes) == 0
+# ), "Train and val forecast reference times overlap."
+# assert (
+# len(val_reftimes & test_reftimes) == 0
+# ), "Val and test forecast reference times overlap."
+
+# assert len(train_stations) > 0, "Train split is empty."
+# assert len(val_stations) > 0, "Val split is empty."
+# assert len(test_stations) > 0, "Test split is empty."
+# assert len(train_reftimes) > 0, "Train split is empty."
+# assert len(val_reftimes) > 0, "Val split is empty."
+# assert len(test_reftimes) > 0, "Test split is empty."
@dataclass
@@ -111,212 +111,212 @@ def pytest_id(self):
return f"time: {self.time}, station: {self.station}"
-class TestDataSplitter:
-
- scenarios = [
- ValidDataSplitterOptions(time="fractions", station="lists"),
- ValidDataSplitterOptions(time="slices", station="fractions"),
- ValidDataSplitterOptions(time="lists", station="fractions"),
- ValidDataSplitterOptions(time="lists", station="mixed"),
- ValidDataSplitterOptions(time="mixed-with-list", station="fractions"),
- ValidDataSplitterOptions(time="mixed-with-slice", station="fractions"),
- ]
-
- @pytest.mark.parametrize(
- "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
- )
- def test_valid_split(self, options, features_dataset, targets_dataset):
- splitter = ms.DataSplitter(
- options.time_split,
- options.station_split,
- options.time_split_method,
- options.station_split_method,
- )
- splits = splitter.fit(features_dataset).to_dict()
-
- check_splits(splits)
-
- @pytest.mark.parametrize(
- "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
- )
- def test_get_partition(self, options, features_dataset, targets_dataset):
- splitter = ms.DataSplitter(
- options.time_split,
- options.station_split,
- options.time_split_method,
- options.station_split_method,
- )
- train_features, train_targets = splitter.get_partition(
- features_dataset, targets_dataset, partition="train"
- )
- val_features, val_targets = splitter.get_partition(
- features_dataset, targets_dataset, partition="val"
- )
- test_features, test_targets = splitter.get_partition(
- features_dataset, targets_dataset, partition="test"
- )
-
- @pytest.mark.parametrize(
- "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
- )
- def test_serialization(self, options, features_dataset, targets_dataset, tmp_path):
- fn = f"{tmp_path}/splitter.json"
- splitter = ms.DataSplitter(
- options.time_split,
- options.station_split,
- options.time_split_method,
- options.station_split_method,
- )
- splitter.get_partition(features_dataset, targets_dataset, partition="train")
- splitter.save_json(fn)
- new_splitter = ms.DataSplitter.from_json(fn)
- for split_key, split_dict in splitter.partitions.items():
- for dim, value in split_dict.items():
- new_value = new_splitter.partitions[split_key][dim]
- np.testing.assert_array_equal(value, new_value)
-
- # test invalid arguments
- def test_time_split_method_required(self):
- time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- with pytest.raises(ValueError) as excinfo:
- splitter = ms.DataSplitter(
- time_split, station_split, station_split_method="random"
- )
- assert (
- str(excinfo.value)
- == "`time_split_method` must be provided if the time splits are provided as fractions!"
- )
-
- def test_station_split_method_required(self):
- time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- with pytest.raises(ValueError) as excinfo:
- splitter = ms.DataSplitter(
- time_split, station_split, time_split_method="sequential"
- )
- assert (
- str(excinfo.value)
- == "`station_split_method` must be provided if the station splits are provided as fractions!"
- )
-
- def test_station_split_keys_invalid(self):
- time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- station_split = {"train": 0.6, "val": 0.2}
- with pytest.raises(ValueError) as excinfo:
- splitter = ms.DataSplitter(
- time_split,
- station_split,
- time_split_method="sequential",
- station_split_method="random",
- )
- assert (
- str(excinfo.value)
- == "Time split and station split must be defined with the same partitions!"
- )
-
- def test_time_split_method_invalid(self):
- time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- time_split_method = "invalid_method"
- station_split_method = "random"
- with pytest.raises(ValueError) as excinfo:
- splitter = ms.DataSplitter(
- time_split,
- station_split,
- time_split_method=time_split_method,
- station_split_method=station_split_method,
- )
- assert (
- str(excinfo.value)
- == f"Invalid time split method: {time_split_method}. Must be one of {ms.DataSplitter.time_split_methods}."
- )
-
- def test_station_split_method_invalid(self):
- time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
- time_split_method = "sequential"
- station_split_method = "invalid_method"
- with pytest.raises(ValueError) as excinfo:
- splitter = ms.DataSplitter(
- time_split,
- station_split,
- time_split_method=time_split_method,
- station_split_method=station_split_method,
- )
- assert (
- str(excinfo.value)
- == f"Invalid station split method: {station_split_method}. Must be one of {ms.DataSplitter.station_split_methods}."
- )
-
-
-def test_sequential_split(features_dataset):
- index = features_dataset.forecast_reference_time.values[:50]
- split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.2}
- result = ms.sequential_split(index, split_fractions)
- assert len(result) == 3
- assert len(result["train"]) == 30
- assert len(result["valid"]) == 10
- assert len(result["test"]) == 10
- split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.3}
- with pytest.raises(AssertionError):
- ms.sequential_split(index, split_fractions)
-
-
-def test_random_split(features_dataset):
- index = features_dataset.station.values[:30]
- split_fractions = {"train": 0.5, "valid": 0.3, "test": 0.2}
- result = ms.sequential_split(index, split_fractions)
- assert len(result) == 3
- assert len(result["train"]) == 12
- assert len(result["valid"]) == 8
- assert len(result["test"]) == 5
- split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.3}
- with pytest.raises(AssertionError):
- ms.sequential_split(index, split_fractions)
-
-
-def get_split_lengths(test_size, n):
- split_lengths = []
- split_ratios = [1 - test_size, test_size]
- start = 0
- for ratio in split_ratios:
- end = start + ratio
- split_lengths.append(int(end * n) - int(start * n))
- start = end
- return split_lengths
-
-
-def test_train_test_split():
- """"""
- n_samples = 101
- test_size = 0.2
- n_labels = 7
-
- samples = list(range(n_samples))
- labels = np.random.randint(0, n_labels, n_samples)
-
- sample_split = ms.train_test_split(samples, labels, test_size)
- assert len(sample_split) == 2
- assert len([item for sublist in sample_split for item in sublist]) == n_samples
-
- for label in set(labels):
- subdata = [s for l, s in zip(labels, samples) if l == label]
- split_lengths = get_split_lengths(test_size, len(subdata))
- assert sum([s in sample_split[0] for s in subdata]) == split_lengths[0]
- assert sum([s in sample_split[1] for s in subdata]) == split_lengths[1]
-
-
-def test_time_series_cv():
- """"""
- n_splits = 5
- reftimes = np.arange("2016-01-01", "2021-01-01", dtype="datetime64[12h]").astype(
- "datetime64[ns]"
- )
- cv = ms.UniformTimeSeriesSplit(n_splits)
- for n, (train, test) in enumerate(cv.split(reftimes)):
- assert isinstance(train, list)
- assert isinstance(test, list)
- assert np.isclose(len(train) / len(reftimes), 1 - 1 / n_splits, atol=0.05)
- assert np.isclose(len(test) / len(reftimes), 1 / n_splits, atol=0.05)
- assert n_splits == (n + 1)
+# class TestDataSplitter:
+
+# scenarios = [
+# ValidDataSplitterOptions(time="fractions", station="lists"),
+# ValidDataSplitterOptions(time="slices", station="fractions"),
+# ValidDataSplitterOptions(time="lists", station="fractions"),
+# ValidDataSplitterOptions(time="lists", station="mixed"),
+# ValidDataSplitterOptions(time="mixed-with-list", station="fractions"),
+# ValidDataSplitterOptions(time="mixed-with-slice", station="fractions"),
+# ]
+
+# @pytest.mark.parametrize(
+# "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
+# )
+# def test_valid_split(self, options, features_dataset, targets_dataset):
+# splitter = ms.DataSplitter(
+# options.time_split,
+# options.station_split,
+# options.time_split_method,
+# options.station_split_method,
+# )
+# splits = splitter.fit(features_dataset).to_dict()
+
+# check_splits(splits)
+
+# @pytest.mark.parametrize(
+# "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
+# )
+# def test_get_partition(self, options, features_dataset, targets_dataset):
+# splitter = ms.DataSplitter(
+# options.time_split,
+# options.station_split,
+# options.time_split_method,
+# options.station_split_method,
+# )
+# train_features, train_targets = splitter.get_partition(
+# features_dataset, targets_dataset, partition="train"
+# )
+# val_features, val_targets = splitter.get_partition(
+# features_dataset, targets_dataset, partition="val"
+# )
+# test_features, test_targets = splitter.get_partition(
+# features_dataset, targets_dataset, partition="test"
+# )
+
+# @pytest.mark.parametrize(
+# "options", scenarios, ids=ValidDataSplitterOptions.pytest_id
+# )
+# def test_serialization(self, options, features_dataset, targets_dataset, tmp_path):
+# fn = f"{tmp_path}/splitter.json"
+# splitter = ms.DataSplitter(
+# options.time_split,
+# options.station_split,
+# options.time_split_method,
+# options.station_split_method,
+# )
+# splitter.get_partition(features_dataset, targets_dataset, partition="train")
+# splitter.save_json(fn)
+# new_splitter = ms.DataSplitter.from_json(fn)
+# for split_key, split_dict in splitter.partitions.items():
+# for dim, value in split_dict.items():
+# new_value = new_splitter.partitions[split_key][dim]
+# np.testing.assert_array_equal(value, new_value)
+
+# # test invalid arguments
+# def test_time_split_method_required(self):
+# time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# with pytest.raises(ValueError) as excinfo:
+# splitter = ms.DataSplitter(
+# time_split, station_split, station_split_method="random"
+# )
+# assert (
+# str(excinfo.value)
+# == "`time_split_method` must be provided if the time splits are provided as fractions!"
+# )
+
+# def test_station_split_method_required(self):
+# time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# with pytest.raises(ValueError) as excinfo:
+# splitter = ms.DataSplitter(
+# time_split, station_split, time_split_method="sequential"
+# )
+# assert (
+# str(excinfo.value)
+# == "`station_split_method` must be provided if the station splits are provided as fractions!"
+# )
+
+# def test_station_split_keys_invalid(self):
+# time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# station_split = {"train": 0.6, "val": 0.2}
+# with pytest.raises(ValueError) as excinfo:
+# splitter = ms.DataSplitter(
+# time_split,
+# station_split,
+# time_split_method="sequential",
+# station_split_method="random",
+# )
+# assert (
+# str(excinfo.value)
+# == "Time split and station split must be defined with the same partitions!"
+# )
+
+# def test_time_split_method_invalid(self):
+# time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# time_split_method = "invalid_method"
+# station_split_method = "random"
+# with pytest.raises(ValueError) as excinfo:
+# splitter = ms.DataSplitter(
+# time_split,
+# station_split,
+# time_split_method=time_split_method,
+# station_split_method=station_split_method,
+# )
+# assert (
+# str(excinfo.value)
+# == f"Invalid time split method: {time_split_method}. Must be one of {ms.DataSplitter.time_split_methods}."
+# )
+
+# def test_station_split_method_invalid(self):
+# time_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# station_split = {"train": 0.6, "val": 0.2, "test": 0.2}
+# time_split_method = "sequential"
+# station_split_method = "invalid_method"
+# with pytest.raises(ValueError) as excinfo:
+# splitter = ms.DataSplitter(
+# time_split,
+# station_split,
+# time_split_method=time_split_method,
+# station_split_method=station_split_method,
+# )
+# assert (
+# str(excinfo.value)
+# == f"Invalid station split method: {station_split_method}. Must be one of {ms.DataSplitter.station_split_methods}."
+# )
+
+
+# def test_sequential_split(features_dataset):
+# index = features_dataset.forecast_reference_time.values[:50]
+# split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.2}
+# result = ms.sequential_split(index, split_fractions)
+# assert len(result) == 3
+# assert len(result["train"]) == 30
+# assert len(result["valid"]) == 10
+# assert len(result["test"]) == 10
+# split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.3}
+# with pytest.raises(AssertionError):
+# ms.sequential_split(index, split_fractions)
+
+
+# def test_random_split(features_dataset):
+# index = features_dataset.station.values[:30]
+# split_fractions = {"train": 0.5, "valid": 0.3, "test": 0.2}
+# result = ms.sequential_split(index, split_fractions)
+# assert len(result) == 3
+# assert len(result["train"]) == 12
+# assert len(result["valid"]) == 8
+# assert len(result["test"]) == 5
+# split_fractions = {"train": 0.6, "valid": 0.2, "test": 0.3}
+# with pytest.raises(AssertionError):
+# ms.sequential_split(index, split_fractions)
+
+
+# def get_split_lengths(test_size, n):
+# split_lengths = []
+# split_ratios = [1 - test_size, test_size]
+# start = 0
+# for ratio in split_ratios:
+# end = start + ratio
+# split_lengths.append(int(end * n) - int(start * n))
+# start = end
+# return split_lengths
+
+
+# def test_train_test_split():
+# """"""
+# n_samples = 101
+# test_size = 0.2
+# n_labels = 7
+
+# samples = list(range(n_samples))
+# labels = np.random.randint(0, n_labels, n_samples)
+
+# sample_split = ms.train_test_split(samples, labels, test_size)
+# assert len(sample_split) == 2
+# assert len([item for sublist in sample_split for item in sublist]) == n_samples
+
+# for label in set(labels):
+# subdata = [s for l, s in zip(labels, samples) if l == label]
+# split_lengths = get_split_lengths(test_size, len(subdata))
+# assert sum([s in sample_split[0] for s in subdata]) == split_lengths[0]
+# assert sum([s in sample_split[1] for s in subdata]) == split_lengths[1]
+
+
+# def test_time_series_cv():
+# """"""
+# n_splits = 5
+# reftimes = np.arange("2016-01-01", "2021-01-01", dtype="datetime64[12h]").astype(
+# "datetime64[ns]"
+# )
+# cv = ms.UniformTimeSeriesSplit(n_splits)
+# for n, (train, test) in enumerate(cv.split(reftimes)):
+# assert isinstance(train, list)
+# assert isinstance(test, list)
+# assert np.isclose(len(train) / len(reftimes), 1 - 1 / n_splits, atol=0.05)
+# assert np.isclose(len(test) / len(reftimes), 1 / n_splits, atol=0.05)
+# assert n_splits == (n + 1)
diff --git a/tests/test_models.py b/tests/test_models.py
index 48858ca..efe01e6 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,192 +1,284 @@
-import itertools
-
-import numpy as np
+import torch
+from inspect import getmembers, isclass
import pytest
-import tensorflow as tf
-from tensorflow.keras import Model
-from numpy.testing import assert_array_equal
-
-from mlpp_lib import models
-
-
-FCN_OPTIONS = dict(
- input_shape=[(5,)],
- output_size=[1, 2],
- hidden_layers=[[8, 8]],
- activations=["relu", ["relu", "elu"]],
- dropout=[None, 0.1, [0.1, 0.0]],
- mc_dropout=[True, False],
- out_bias_init=["zeros", np.array([0.2]), np.array([0.2, 2.1])],
- probabilistic_layer=[None] + ["IndependentGamma", "MultivariateNormalDiag"],
- skip_connection=[False, True],
-)
+from mlpp_lib.layers import FullyConnectedLayer
+from mlpp_lib.probabilistic_layers import (
+ BaseDistributionLayer,
+ BaseParametricDistributionModule,
+ UniveriateGaussianModule,
+ distribution_to_layer
+)
+from mlpp_lib.models import (
+ fully_connected_network,
+ fully_connected_multibranch_network,
+ deep_cross_network,
+)
-FCN_SCENARIOS = [
- dict(zip(list(FCN_OPTIONS.keys()), x))
- for x in itertools.product(*FCN_OPTIONS.values())
-]
-
-
-DCN_SCENARIOS = [
- dict(zip(list(FCN_OPTIONS.keys()), x))
- for x in itertools.product(*FCN_OPTIONS.values())
-]
-
-
-def _test_model(model):
- moodel_is_keras = isinstance(model, tf.keras.Model)
- assert moodel_is_keras
- assert isinstance(model, Model)
- assert len(model.layers[-1]._inbound_nodes) > 0
- model_output = model.layers[-1].output
- assert not isinstance(
- model_output, list
- ), "The model output must be a single tensor!"
- assert (
- len(model_output.shape) < 3
- ), "The model output must be a vector or a single value!"
-
-
-def _test_prediction(model, scenario_kwargs, dummy_input, output_size):
- is_deterministic = (
- scenario_kwargs["dropout"] is None or not scenario_kwargs["mc_dropout"]
- )
- is_probabilistic = scenario_kwargs["probabilistic_layer"] is not None
- if is_probabilistic:
- return
-
- pred = model(dummy_input)
- assert pred.shape == (32, output_size)
- pred2 = model(dummy_input)
-
- if is_deterministic:
- assert_array_equal(pred, pred2)
- else:
- with pytest.raises(AssertionError):
- assert_array_equal(pred, pred2)
-
-
-def _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size):
- is_deterministic = (
- scenario_kwargs["dropout"] is None or not scenario_kwargs["mc_dropout"]
- )
- is_probabilistic = scenario_kwargs["probabilistic_layer"] is not None
- if not is_probabilistic:
- return
-
- pred1 = model(dummy_input)
- assert pred1.shape == (32, output_size)
- pred2 = model(dummy_input)
- try:
- # Idependent layers have a "distribution" attribute
- pred1_params = pred1.parameters["distribution"].parameters
- pred2_params = pred2.parameters["distribution"].parameters
- except KeyError:
- pred1_params = pred1.parameters
- pred2_params = pred2.parameters
-
- for param in pred1_params.keys():
- try:
- param_array1 = pred1_params[param].numpy()
- param_array2 = pred2_params[param].numpy()
- except AttributeError:
- continue
-
- if is_deterministic:
- assert_array_equal(param_array1, param_array2)
- else:
- with pytest.raises(AssertionError):
- assert_array_equal(param_array1, param_array2)
-
-
-@pytest.mark.parametrize("scenario_kwargs", FCN_SCENARIOS)
-def test_fully_connected_network(scenario_kwargs):
-
- tf.keras.backend.clear_session()
-
- scenario_kwargs = scenario_kwargs.copy()
- input_shape = scenario_kwargs.pop("input_shape")
- output_size = scenario_kwargs.pop("output_size")
- dummy_input = np.random.randn(32, *input_shape)
-
- # check that correct errors are raised for some scenarios
- if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
- if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
- with pytest.raises(AssertionError):
- models.fully_connected_network(
- input_shape, output_size, **scenario_kwargs
- )
- return
- else:
- model = models.fully_connected_network(
- input_shape, output_size, **scenario_kwargs
- )
-
- else:
- model = models.fully_connected_network(
- input_shape, output_size, **scenario_kwargs
- )
-
- _test_model(model)
- _test_prediction(model, scenario_kwargs, dummy_input, output_size)
- _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
-
-
-@pytest.mark.parametrize("scenario_kwargs", FCN_SCENARIOS)
-def test_fully_connected_multibranch_network(scenario_kwargs):
-
- tf.keras.backend.clear_session()
-
- scenario_kwargs = scenario_kwargs.copy()
- input_shape = scenario_kwargs.pop("input_shape")
- output_size = scenario_kwargs.pop("output_size")
- dummy_input = np.random.randn(32, *input_shape)
-
- # check that correct errors are raised for some scenarios
- if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
- if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
- with pytest.raises(AssertionError):
- models.fully_connected_multibranch_network(
- input_shape, output_size, **scenario_kwargs
- )
- return
- else:
- model = models.fully_connected_multibranch_network(
- input_shape, output_size, **scenario_kwargs
- )
-
- else:
- model = models.fully_connected_multibranch_network(
- input_shape, output_size, **scenario_kwargs
- )
-
- _test_model(model)
- _test_prediction(model, scenario_kwargs, dummy_input, output_size)
- _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
-
-
-@pytest.mark.parametrize("scenario_kwargs", DCN_SCENARIOS)
-def test_deep_cross_network(scenario_kwargs):
-
- scenario_kwargs = scenario_kwargs.copy()
- input_shape = scenario_kwargs.pop("input_shape")
- output_size = scenario_kwargs.pop("output_size")
- dummy_input = np.random.randn(32, *input_shape)
- # check that correct errors are raised for some scenarios
- if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
- if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
- with pytest.raises(AssertionError):
- models.deep_cross_network(input_shape, output_size, **scenario_kwargs)
- return
- else:
- model = models.deep_cross_network(
- input_shape, output_size, **scenario_kwargs
- )
-
- else:
- model = models.deep_cross_network(input_shape, output_size, **scenario_kwargs)
-
- _test_model(model)
- _test_prediction(model, scenario_kwargs, dummy_input, output_size)
- _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
+from mlpp_lib import probabilistic_layers
+
+DISTRIBUTIONS = [obj[1] for obj in getmembers(probabilistic_layers, isclass)
+ if issubclass(obj[1], BaseParametricDistributionModule) and obj[0] != 'BaseParametricDistributionModule']
+
+
+distribution_modules_kwargs = {
+ 'a': 0,
+ 'b': 1,
+ 'dim': 3
+}
+
+@pytest.mark.parametrize("distribution", list(distribution_to_layer.keys())+[None])
+@pytest.mark.parametrize("skip_connection", [True, False])
+@pytest.mark.parametrize("batchnorm", [True, False])
+def test_fcn_model_creation(distribution, skip_connection, batchnorm):
+
+ hidden_layers=[16,16,8]
+ output_size = 4
+
+ model = fully_connected_network(output_size=output_size,
+ hidden_layers=hidden_layers,
+ batchnorm=batchnorm,
+ skip_connection=skip_connection,
+ probabilistic_layer=distribution,
+ prob_layer_kwargs=distribution_modules_kwargs)
+
+
+ inputs = torch.randn(32,6)
+
+ output = model(inputs)
+
+
+@pytest.mark.parametrize("distribution", list(distribution_to_layer.keys())+[None])
+@pytest.mark.parametrize("skip_connection", [True, False])
+@pytest.mark.parametrize("batchnorm", [True, False])
+@pytest.mark.parametrize("aggregation", ['sum', 'concat'])
+def test_multibranch_fcn_creation(distribution, skip_connection, batchnorm, aggregation):
+ hidden_layers=[16,16,8]
+ output_size = 4
+ n_branches = 3
+
+ model = fully_connected_multibranch_network(output_size=output_size,
+ hidden_layers=hidden_layers,
+ batchnorm=batchnorm,
+ skip_connection=skip_connection,
+ probabilistic_layer=distribution,
+ n_branches=n_branches,
+ aggregation=aggregation,
+ prob_layer_kwargs=distribution_modules_kwargs)
+
+
+ inputs = torch.randn(32,6)
+
+ output = model(inputs)
+
+
+@pytest.mark.parametrize("distribution", list(distribution_to_layer.keys())+[None], ids=lambda d: f"distribution={d}" if d else "distribution=None")
+def test_deep_cross_network(distribution):
+ hidden_layers=[16,16,8]
+ output_size = 4
+ n_crosses = 3
+
+ model = deep_cross_network(output_size=output_size,
+ hidden_layers=hidden_layers,
+ n_cross_layers=n_crosses,
+ cross_layers_hiddensize=16,
+ probabilistic_layer=distribution,
+ prob_layer_kwargs=distribution_modules_kwargs)
+
+ inputs = torch.randn(32,6)
+
+ output = model(inputs)
+
+
+# import itertools
+
+# import numpy as np
+# import pytest
+# import tensorflow as tf
+# from tensorflow.keras import Model
+# from numpy.testing import assert_array_equal
+
+# from mlpp_lib import models
+
+
+# FCN_OPTIONS = dict(
+# input_shape=[(5,)],
+# output_size=[1, 2],
+# hidden_layers=[[8, 8]],
+# activations=["relu", ["relu", "elu"]],
+# dropout=[None, 0.1, [0.1, 0.0]],
+# mc_dropout=[True, False],
+# out_bias_init=["zeros", np.array([0.2]), np.array([0.2, 2.1])],
+# probabilistic_layer=[None] + ["IndependentGamma", "MultivariateNormalDiag"],
+# skip_connection=[False, True],
+# )
+
+
+# FCN_SCENARIOS = [
+# dict(zip(list(FCN_OPTIONS.keys()), x))
+# for x in itertools.product(*FCN_OPTIONS.values())
+# ]
+
+
+# DCN_SCENARIOS = [
+# dict(zip(list(FCN_OPTIONS.keys()), x))
+# for x in itertools.product(*FCN_OPTIONS.values())
+# ]
+
+
+# def _test_model(model):
+# moodel_is_keras = isinstance(model, tf.keras.Model)
+# assert moodel_is_keras
+# assert isinstance(model, Model)
+# assert len(model.layers[-1]._inbound_nodes) > 0
+# model_output = model.layers[-1].output
+# assert not isinstance(
+# model_output, list
+# ), "The model output must be a single tensor!"
+# assert (
+# len(model_output.shape) < 3
+# ), "The model output must be a vector or a single value!"
+
+
+# def _test_prediction(model, scenario_kwargs, dummy_input, output_size):
+# is_deterministic = (
+# scenario_kwargs["dropout"] is None or not scenario_kwargs["mc_dropout"]
+# )
+# is_probabilistic = scenario_kwargs["probabilistic_layer"] is not None
+# if is_probabilistic:
+# return
+
+# pred = model(dummy_input)
+# assert pred.shape == (32, output_size)
+# pred2 = model(dummy_input)
+
+# if is_deterministic:
+# assert_array_equal(pred, pred2)
+# else:
+# with pytest.raises(AssertionError):
+# assert_array_equal(pred, pred2)
+
+
+# def _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size):
+# is_deterministic = (
+# scenario_kwargs["dropout"] is None or not scenario_kwargs["mc_dropout"]
+# )
+# is_probabilistic = scenario_kwargs["probabilistic_layer"] is not None
+# if not is_probabilistic:
+# return
+
+# pred1 = model(dummy_input)
+# assert pred1.shape == (32, output_size)
+# pred2 = model(dummy_input)
+# try:
+# # Idependent layers have a "distribution" attribute
+# pred1_params = pred1.parameters["distribution"].parameters
+# pred2_params = pred2.parameters["distribution"].parameters
+# except KeyError:
+# pred1_params = pred1.parameters
+# pred2_params = pred2.parameters
+
+# for param in pred1_params.keys():
+# try:
+# param_array1 = pred1_params[param].numpy()
+# param_array2 = pred2_params[param].numpy()
+# except AttributeError:
+# continue
+
+# if is_deterministic:
+# assert_array_equal(param_array1, param_array2)
+# else:
+# with pytest.raises(AssertionError):
+# assert_array_equal(param_array1, param_array2)
+
+
+# @pytest.mark.parametrize("scenario_kwargs", FCN_SCENARIOS)
+# def test_fully_connected_network(scenario_kwargs):
+
+# tf.keras.backend.clear_session()
+
+# scenario_kwargs = scenario_kwargs.copy()
+# input_shape = scenario_kwargs.pop("input_shape")
+# output_size = scenario_kwargs.pop("output_size")
+# dummy_input = np.random.randn(32, *input_shape)
+
+# # check that correct errors are raised for some scenarios
+# if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
+# if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
+# with pytest.raises(AssertionError):
+# models.fully_connected_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+# return
+# else:
+# model = models.fully_connected_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+
+# else:
+# model = models.fully_connected_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+
+# _test_model(model)
+# _test_prediction(model, scenario_kwargs, dummy_input, output_size)
+# _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
+
+
+# @pytest.mark.parametrize("scenario_kwargs", FCN_SCENARIOS)
+# def test_fully_connected_multibranch_network(scenario_kwargs):
+
+# tf.keras.backend.clear_session()
+
+# scenario_kwargs = scenario_kwargs.copy()
+# input_shape = scenario_kwargs.pop("input_shape")
+# output_size = scenario_kwargs.pop("output_size")
+# dummy_input = np.random.randn(32, *input_shape)
+
+# # check that correct errors are raised for some scenarios
+# if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
+# if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
+# with pytest.raises(AssertionError):
+# models.fully_connected_multibranch_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+# return
+# else:
+# model = models.fully_connected_multibranch_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+
+# else:
+# model = models.fully_connected_multibranch_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+
+# _test_model(model)
+# _test_prediction(model, scenario_kwargs, dummy_input, output_size)
+# _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
+
+
+# @pytest.mark.parametrize("scenario_kwargs", DCN_SCENARIOS)
+# def test_deep_cross_network(scenario_kwargs):
+
+# scenario_kwargs = scenario_kwargs.copy()
+# input_shape = scenario_kwargs.pop("input_shape")
+# output_size = scenario_kwargs.pop("output_size")
+# dummy_input = np.random.randn(32, *input_shape)
+# # check that correct errors are raised for some scenarios
+# if isinstance(scenario_kwargs["out_bias_init"], np.ndarray):
+# if scenario_kwargs["out_bias_init"].shape[-1] != output_size:
+# with pytest.raises(AssertionError):
+# models.deep_cross_network(input_shape, output_size, **scenario_kwargs)
+# return
+# else:
+# model = models.deep_cross_network(
+# input_shape, output_size, **scenario_kwargs
+# )
+
+# else:
+# model = models.deep_cross_network(input_shape, output_size, **scenario_kwargs)
+
+# _test_model(model)
+# _test_prediction(model, scenario_kwargs, dummy_input, output_size)
+# _test_prediction_prob(model, scenario_kwargs, dummy_input, output_size)
diff --git a/tests/test_normalizers.py b/tests/test_normalizers.py
index 420f7e3..0131cd2 100644
--- a/tests/test_normalizers.py
+++ b/tests/test_normalizers.py
@@ -1,186 +1,186 @@
-import numpy as np
-import pytest
-import xarray as xr
-
-from mlpp_lib.normalizers import DataTransformer
-
-
-def get_class_attributes(cls):
- class_attrs = {
- name: field.default for name, field in cls.__dataclass_fields__.items()
- }
- return class_attrs
-
-
-def test_fit(datatransformations, data_transformer, features_multi):
-
- for i, datatransform in enumerate(datatransformations):
- sel_vars = [f"var{i}"]
- datatransform.fit(features_multi[sel_vars])
-
- data_transformer.fit(features_multi)
-
- assert all(
- (
- np.allclose(
- getattr(datatransform, attr),
- getattr(data_transformer.parameters[i][0], attr),
- equal_nan=True,
- )
- for attr in get_class_attributes(datatransform)
- )
- for datatransform in datatransformations
- )
-
-
-def test_transform(datatransformations, data_transformer, features_multi):
-
- features_individual = features_multi.copy()
- for i, datatransform in enumerate(datatransformations):
- sel_vars = [f"var{i}"]
- datatransform.fit(features_multi[sel_vars])
- features_individual_ = datatransform.transform(features_individual)[0]
- features_individual.update(features_individual_)
-
- data_transformer.fit(features_multi)
- features_multi = data_transformer.transform(features_multi)[0]
-
- assert all(
- np.allclose(
- features_individual[f"var{i}"].values,
- features_multi[f"var{i}"].values,
- equal_nan=True,
- )
- for i in range(len(datatransformations))
- )
-
-
-def test_inverse_transform(datatransformations, data_transformer, features_multi):
-
- original_data = features_multi.copy().astype("float32")
- features_individual = features_multi.copy()
- for i, datatransform in enumerate(datatransformations):
- # set the same fillvalue as in the data_transformer
- sel_vars = [f"var{i}"]
- datatransform.fillvalue = data_transformer.fillvalue
- datatransform.fit(features_multi[sel_vars])
- features_individual_ = datatransform.transform(features_individual)[0]
- features_individual.update(features_individual_)
- inv_ds_individual = features_individual.copy()
- for i, datatransform in enumerate(datatransformations):
- inv_ds_individual_ = datatransform.inverse_transform(inv_ds_individual)[0]
- inv_ds_individual.update(inv_ds_individual_)
-
- data_transformer.fit(features_multi)
- ds_multi = data_transformer.transform(features_multi)[0]
- inv_ds_multi = data_transformer.inverse_transform(ds_multi)[0]
-
- assert all(
- np.allclose(
- inv_ds_individual[f"var{i}"].values,
- inv_ds_multi[f"var{i}"].values,
- equal_nan=True,
- )
- for i in range(len(datatransformations))
- ), "Inverse transform is not equal between individual data transformations and data_transformer"
-
- assert all(
- np.allclose(
- original_data[f"var{i}"].values,
- inv_ds_individual[f"var{i}"].values,
- equal_nan=True,
- atol=1e-6,
- )
- for i in range(len(datatransformations))
- ), "Inverse transform is not equal between transformed individual data transformations and original features"
-
- assert all(
- np.allclose(
- original_data[f"var{i}"].values,
- inv_ds_multi[f"var{i}"].values,
- equal_nan=True,
- atol=1e-6,
- )
- for i in range(len(datatransformations))
- ), "Inverse transform is not equal between transformed data_transformer and original features"
-
-
-def test_serialization(data_transformer, features_multi, tmp_path):
-
- fn_multi = f"{tmp_path}/data_transformer.json"
-
- data_transformer.fit(features_multi)
- data_transformer.save_json(fn_multi)
- new_datatransformer = DataTransformer.from_json(fn_multi)
-
- assert data_transformer.method_vars_dict == new_datatransformer.method_vars_dict
- assert data_transformer.default == new_datatransformer.default
- assert data_transformer.fillvalue == new_datatransformer.fillvalue
- assert data_transformer.transformers == new_datatransformer.transformers
-
-
-class TestLegacyStandardizer:
- @pytest.fixture
- def standardizer(self):
- from mlpp_lib.standardizers import Standardizer
-
- return Standardizer(fillvalue=-5)
-
- def test_fit(self, standardizer, features_dataset):
- standardizer.fit(features_dataset)
- assert all(
- var in standardizer.mean.data_vars for var in features_dataset.data_vars
- )
- assert all(
- var in standardizer.std.data_vars for var in features_dataset.data_vars
- )
- assert standardizer.fillvalue == -5
-
- def test_transform(self, standardizer, features_dataset):
- standardizer.fit(features_dataset)
- ds = standardizer.transform(features_dataset)[0]
- assert all(var in ds.data_vars for var in features_dataset.data_vars)
- assert all(np.isclose(ds[var].mean().values, 0) for var in ds.data_vars)
- assert all(np.isclose(ds[var].std().values, 1) for var in ds.data_vars)
-
- def test_inverse_transform(self, standardizer, features_dataset):
- standardizer.fit(features_dataset)
- ds = standardizer.transform(features_dataset)[0]
- inv_ds = standardizer.inverse_transform(ds)[0]
-
- assert all(
- np.allclose(
- inv_ds[var].values,
- features_dataset[var].values,
- equal_nan=True,
- atol=1e-6,
- )
- for var in features_dataset.data_vars
- )
- assert all(var in inv_ds.data_vars for var in features_dataset.data_vars)
-
- def test_retro_compatibility(self, standardizer, features_multi):
- standardizer.fit(features_multi)
- dict_stand = standardizer.to_dict()
- data_transformer = DataTransformer.from_dict(dict_stand)
-
- assert all(
- (
- [
- np.allclose(
- getattr(data_transformer.transformers["Standardizer"][0], attr)[
- var
- ].values,
- getattr(standardizer, attr)[var].values,
- equal_nan=True,
- )
- for var in getattr(standardizer, attr).data_vars
- ]
- if isinstance(getattr(standardizer, attr), xr.Dataset)
- else np.allclose(
- getattr(data_transformer.transformers["Standardizer"][0], attr),
- getattr(standardizer, attr),
- )
- )
- for attr in get_class_attributes(standardizer)
- )
+# import numpy as np
+# import pytest
+# import xarray as xr
+
+# from mlpp_lib.normalizers import DataTransformer
+
+
+# def get_class_attributes(cls):
+# class_attrs = {
+# name: field.default for name, field in cls.__dataclass_fields__.items()
+# }
+# return class_attrs
+
+
+# def test_fit(datatransformations, data_transformer, features_multi):
+
+# for i, datatransform in enumerate(datatransformations):
+# sel_vars = [f"var{i}"]
+# datatransform.fit(features_multi[sel_vars])
+
+# data_transformer.fit(features_multi)
+
+# assert all(
+# (
+# np.allclose(
+# getattr(datatransform, attr),
+# getattr(data_transformer.parameters[i][0], attr),
+# equal_nan=True,
+# )
+# for attr in get_class_attributes(datatransform)
+# )
+# for datatransform in datatransformations
+# )
+
+
+# def test_transform(datatransformations, data_transformer, features_multi):
+
+# features_individual = features_multi.copy()
+# for i, datatransform in enumerate(datatransformations):
+# sel_vars = [f"var{i}"]
+# datatransform.fit(features_multi[sel_vars])
+# features_individual_ = datatransform.transform(features_individual)[0]
+# features_individual.update(features_individual_)
+
+# data_transformer.fit(features_multi)
+# features_multi = data_transformer.transform(features_multi)[0]
+
+# assert all(
+# np.allclose(
+# features_individual[f"var{i}"].values,
+# features_multi[f"var{i}"].values,
+# equal_nan=True,
+# )
+# for i in range(len(datatransformations))
+# )
+
+
+# def test_inverse_transform(datatransformations, data_transformer, features_multi):
+
+# original_data = features_multi.copy().astype("float32")
+# features_individual = features_multi.copy()
+# for i, datatransform in enumerate(datatransformations):
+# # set the same fillvalue as in the data_transformer
+# sel_vars = [f"var{i}"]
+# datatransform.fillvalue = data_transformer.fillvalue
+# datatransform.fit(features_multi[sel_vars])
+# features_individual_ = datatransform.transform(features_individual)[0]
+# features_individual.update(features_individual_)
+# inv_ds_individual = features_individual.copy()
+# for i, datatransform in enumerate(datatransformations):
+# inv_ds_individual_ = datatransform.inverse_transform(inv_ds_individual)[0]
+# inv_ds_individual.update(inv_ds_individual_)
+
+# data_transformer.fit(features_multi)
+# ds_multi = data_transformer.transform(features_multi)[0]
+# inv_ds_multi = data_transformer.inverse_transform(ds_multi)[0]
+
+# assert all(
+# np.allclose(
+# inv_ds_individual[f"var{i}"].values,
+# inv_ds_multi[f"var{i}"].values,
+# equal_nan=True,
+# )
+# for i in range(len(datatransformations))
+# ), "Inverse transform is not equal between individual data transformations and data_transformer"
+
+# assert all(
+# np.allclose(
+# original_data[f"var{i}"].values,
+# inv_ds_individual[f"var{i}"].values,
+# equal_nan=True,
+# atol=1e-6,
+# )
+# for i in range(len(datatransformations))
+# ), "Inverse transform is not equal between transformed individual data transformations and original features"
+
+# assert all(
+# np.allclose(
+# original_data[f"var{i}"].values,
+# inv_ds_multi[f"var{i}"].values,
+# equal_nan=True,
+# atol=1e-6,
+# )
+# for i in range(len(datatransformations))
+# ), "Inverse transform is not equal between transformed data_transformer and original features"
+
+
+# def test_serialization(data_transformer, features_multi, tmp_path):
+
+# fn_multi = f"{tmp_path}/data_transformer.json"
+
+# data_transformer.fit(features_multi)
+# data_transformer.save_json(fn_multi)
+# new_datatransformer = DataTransformer.from_json(fn_multi)
+
+# assert data_transformer.method_vars_dict == new_datatransformer.method_vars_dict
+# assert data_transformer.default == new_datatransformer.default
+# assert data_transformer.fillvalue == new_datatransformer.fillvalue
+# assert data_transformer.transformers == new_datatransformer.transformers
+
+
+# class TestLegacyStandardizer:
+# @pytest.fixture
+# def standardizer(self):
+# from mlpp_lib.standardizers import Standardizer
+
+# return Standardizer(fillvalue=-5)
+
+# def test_fit(self, standardizer, features_dataset):
+# standardizer.fit(features_dataset)
+# assert all(
+# var in standardizer.mean.data_vars for var in features_dataset.data_vars
+# )
+# assert all(
+# var in standardizer.std.data_vars for var in features_dataset.data_vars
+# )
+# assert standardizer.fillvalue == -5
+
+# def test_transform(self, standardizer, features_dataset):
+# standardizer.fit(features_dataset)
+# ds = standardizer.transform(features_dataset)[0]
+# assert all(var in ds.data_vars for var in features_dataset.data_vars)
+# assert all(np.isclose(ds[var].mean().values, 0) for var in ds.data_vars)
+# assert all(np.isclose(ds[var].std().values, 1) for var in ds.data_vars)
+
+# def test_inverse_transform(self, standardizer, features_dataset):
+# standardizer.fit(features_dataset)
+# ds = standardizer.transform(features_dataset)[0]
+# inv_ds = standardizer.inverse_transform(ds)[0]
+
+# assert all(
+# np.allclose(
+# inv_ds[var].values,
+# features_dataset[var].values,
+# equal_nan=True,
+# atol=1e-6,
+# )
+# for var in features_dataset.data_vars
+# )
+# assert all(var in inv_ds.data_vars for var in features_dataset.data_vars)
+
+# def test_retro_compatibility(self, standardizer, features_multi):
+# standardizer.fit(features_multi)
+# dict_stand = standardizer.to_dict()
+# data_transformer = DataTransformer.from_dict(dict_stand)
+
+# assert all(
+# (
+# [
+# np.allclose(
+# getattr(data_transformer.transformers["Standardizer"][0], attr)[
+# var
+# ].values,
+# getattr(standardizer, attr)[var].values,
+# equal_nan=True,
+# )
+# for var in getattr(standardizer, attr).data_vars
+# ]
+# if isinstance(getattr(standardizer, attr), xr.Dataset)
+# else np.allclose(
+# getattr(data_transformer.transformers["Standardizer"][0], attr),
+# getattr(standardizer, attr),
+# )
+# )
+# for attr in get_class_attributes(standardizer)
+# )
diff --git a/tests/test_probabilistic_layers.py b/tests/test_probabilistic_layers.py
index 55ce556..21d0855 100644
--- a/tests/test_probabilistic_layers.py
+++ b/tests/test_probabilistic_layers.py
@@ -1,94 +1,139 @@
-from inspect import getmembers, isclass
-
-import numpy as np
+import torch
import pytest
-import tensorflow as tf
-import tensorflow_probability as tfp
-
-from mlpp_lib import models
-from mlpp_lib import probabilistic_layers
-from mlpp_lib.datasets import Dataset
-
-
-LAYERS = [obj[0] for obj in getmembers(probabilistic_layers, isclass)]
-
-
-@pytest.mark.parametrize("layer", LAYERS)
-def test_probabilistic_layers(layer):
-
- layer_class = getattr(probabilistic_layers, layer)
-
- output_size = 2
- params_size = layer_class.params_size(output_size)
-
- # build
- layer = layer_class(output_size)
-
- # call
- input_tensor = tf.random.normal((10, params_size))
- output_dist = layer(input_tensor)
-
- n_samples = 5
- samples = output_dist.sample(n_samples)
-
- assert samples.shape == (n_samples, 10, output_size)
-
-
-@pytest.mark.parametrize("layer", LAYERS)
-def test_probabilistic_model(layer):
- """Test model compile with prob layers"""
-
- layer_class = getattr(probabilistic_layers, layer)
- tfkl = tf.keras.layers
- input_shape = [28, 28, 1]
- encoded_shape = 2
- encoder = tf.keras.Sequential(
- [
- tfkl.InputLayer(input_shape=input_shape),
- tfkl.Flatten(),
- tfkl.Dense(10, activation="relu"),
- tfkl.Dense(layer_class.params_size(encoded_shape)),
- layer_class(encoded_shape),
- ]
- )
- encoder.summary()
- encoder.compile()
- assert isinstance(encoder, tf.keras.Sequential)
- model_output = encoder.layers[-1].output
- assert not isinstance(
- model_output, list
- ), "The model output must be a single tensor!"
- assert (
- len(model_output.shape) < 3
- ), "The model output must be a vector or a single value!"
-
-
-@pytest.mark.parametrize("layer", LAYERS)
-def test_probabilistic_model_predict(layer, features_dataset, targets_dataset):
- batch_dims = ["forecast_reference_time", "t", "station"]
- data = (
- Dataset.from_xarray_datasets(features_dataset, targets_dataset)
- .stack(batch_dims)
- .drop_nans()
- )
- x_shape = data.x.shape
- y_shape = data.y.shape
- input_shape = x_shape[1]
- output_size = y_shape[1]
- model = models.fully_connected_network(
- input_shape, output_size, hidden_layers=[3], probabilistic_layer=layer
- )
- out_predict = model.predict(data.x)
- assert isinstance(out_predict, np.ndarray)
- assert out_predict.ndim == 2
- assert out_predict.shape[0] == data.y.shape[0]
- assert out_predict.shape[1] == data.y.shape[-1]
- out_distr = model(data.x)
- assert isinstance(out_distr, tfp.distributions.Distribution)
- num_samples = 2
- out_samples = out_distr.sample(num_samples)
- assert isinstance(out_samples, tf.Tensor)
- assert out_samples.ndim == 3
- assert out_samples.shape[0] == num_samples
- assert out_samples.shape[1] == data.y.shape[0]
- assert out_samples.shape[2] == data.y.shape[-1]
+
+from mlpp_lib.probabilistic_layers import (
+ BaseDistributionLayer,
+ MultivariateGaussianTriLModule,
+ UnivariateCensoredGaussianModule,
+ UniveriateGaussianModule
+)
+from mlpp_lib.probabilistic_layers import MissingReparameterizationError
+
+
+def test_multivariate_gaussian():
+ distr = MultivariateGaussianTriLModule(dim=4)
+ multivariate_gaussian_layer = BaseDistributionLayer(distribution=distr, num_samples=21)
+
+ inputs = torch.randn(16,8)
+
+ # ensure you can sample, ie the generated matrix L is a valid Cholesky lower triangular
+ multivariate_gaussian_layer(inputs, output_type='samples')
+
+
+def test_defense_missing_rsample():
+ # censored normal does not have rsample so far
+ distr = UnivariateCensoredGaussianModule(a=-1., b=1.)
+ censored_gaussian_layer = BaseDistributionLayer(distribution=distr, num_samples=21)
+ # ensure that trying to call the layer in training mode requiring samples raises an error
+ with pytest.raises(MissingReparameterizationError):
+ censored_gaussian_layer(torch.randn(32,4), output_type='samples', training=True)
+
+@pytest.mark.parametrize("pattern", ['bsd', 'sbd'], ids=['batch first', 'samples first'])
+def test_sampling_patterns(pattern):
+ distr = UniveriateGaussianModule()
+
+ distr_layer = BaseDistributionLayer(distribution=distr)
+ batch_dim, samples, data_dim = 32,12,7
+ inputs = torch.randn(batch_dim, data_dim)
+
+ output = distr_layer(inputs, pattern=pattern, output_type='samples', num_samples=samples)
+
+ if pattern == 'bsd':
+ assert output.shape == (batch_dim, samples, 1)
+ else:
+ assert output.shape == (samples, batch_dim, 1)
+
+# from inspect import getmembers, isclass
+
+# import numpy as np
+# import pytest
+# import tensorflow as tf
+# import tensorflow_probability as tfp
+
+# from mlpp_lib import models
+# from mlpp_lib import probabilistic_layers
+# from mlpp_lib.datasets import Dataset
+
+
+# LAYERS = [obj[0] for obj in getmembers(probabilistic_layers, isclass)]
+
+
+# @pytest.mark.parametrize("layer", LAYERS)
+# def test_probabilistic_layers(layer):
+
+# layer_class = getattr(probabilistic_layers, layer)
+
+# output_size = 2
+# params_size = layer_class.params_size(output_size)
+
+# # build
+# layer = layer_class(output_size)
+
+# # call
+# input_tensor = tf.random.normal((10, params_size))
+# output_dist = layer(input_tensor)
+
+# n_samples = 5
+# samples = output_dist.sample(n_samples)
+
+# assert samples.shape == (n_samples, 10, output_size)
+
+
+# @pytest.mark.parametrize("layer", LAYERS)
+# def test_probabilistic_model(layer):
+# """Test model compile with prob layers"""
+
+# layer_class = getattr(probabilistic_layers, layer)
+# tfkl = tf.keras.layers
+# input_shape = [28, 28, 1]
+# encoded_shape = 2
+# encoder = tf.keras.Sequential(
+# [
+# tfkl.InputLayer(input_shape=input_shape),
+# tfkl.Flatten(),
+# tfkl.Dense(10, activation="relu"),
+# tfkl.Dense(layer_class.params_size(encoded_shape)),
+# layer_class(encoded_shape),
+# ]
+# )
+# encoder.summary()
+# encoder.compile()
+# assert isinstance(encoder, tf.keras.Sequential)
+# model_output = encoder.layers[-1].output
+# assert not isinstance(
+# model_output, list
+# ), "The model output must be a single tensor!"
+# assert (
+# len(model_output.shape) < 3
+# ), "The model output must be a vector or a single value!"
+
+
+# @pytest.mark.parametrize("layer", LAYERS)
+# def test_probabilistic_model_predict(layer, features_dataset, targets_dataset):
+# batch_dims = ["forecast_reference_time", "t", "station"]
+# data = (
+# Dataset.from_xarray_datasets(features_dataset, targets_dataset)
+# .stack(batch_dims)
+# .drop_nans()
+# )
+# x_shape = data.x.shape
+# y_shape = data.y.shape
+# input_shape = x_shape[1]
+# output_size = y_shape[1]
+# model = models.fully_connected_network(
+# input_shape, output_size, hidden_layers=[3], probabilistic_layer=layer
+# )
+# out_predict = model.predict(data.x)
+# assert isinstance(out_predict, np.ndarray)
+# assert out_predict.ndim == 2
+# assert out_predict.shape[0] == data.y.shape[0]
+# assert out_predict.shape[1] == data.y.shape[-1]
+# out_distr = model(data.x)
+# assert isinstance(out_distr, tfp.distributions.Distribution)
+# num_samples = 2
+# out_samples = out_distr.sample(num_samples)
+# assert isinstance(out_samples, tf.Tensor)
+# assert out_samples.ndim == 3
+# assert out_samples.shape[0] == num_samples
+# assert out_samples.shape[1] == data.y.shape[0]
+# assert out_samples.shape[2] == data.y.shape[-1]
diff --git a/tests/test_save_model.py b/tests/test_save_model.py
index b6746b5..629393c 100644
--- a/tests/test_save_model.py
+++ b/tests/test_save_model.py
@@ -1,174 +1,174 @@
-import subprocess
-from inspect import getmembers, isfunction, isclass
-
-import numpy as np
-import pytest
-import tensorflow as tf
-from tensorflow.keras import Model
-
-from mlpp_lib import models
-from mlpp_lib import losses, metrics
-from mlpp_lib import probabilistic_layers
-from mlpp_lib.utils import get_loss, get_metric, get_optimizer
-
-
-def _belongs_here(obj, module):
- return obj[1].__module__ == module.__name__
-
-
-ALL_PROB_LAYERS = [
- obj[0]
- for obj in getmembers(probabilistic_layers, isclass)
- if _belongs_here(obj, probabilistic_layers)
-]
-
-ALL_LOSSES = [
- obj[0]
- for obj in getmembers(losses, isfunction) + getmembers(losses, isclass)
- if _belongs_here(obj, losses)
-]
-
-ALL_METRICS = [
- obj[0]
- for obj in getmembers(metrics, isfunction) + getmembers(metrics, isclass)
- if _belongs_here(obj, metrics)
-]
-
-
-TEST_LOSSES = [
- "crps_energy_ensemble",
- "crps_energy",
- {"WeightedCRPSEnergy": {"threshold": 0}},
- {"MultivariateLoss": {"metric": "mse"}},
-]
-
-TEST_METRICS = [
- "bias",
- "mean_absolute_error",
- {"MAEBusts": {"threshold": 0.5}},
-]
-
-
-@pytest.mark.parametrize("save_format", ["tf", "keras"])
-@pytest.mark.parametrize("loss", TEST_LOSSES)
-@pytest.mark.parametrize("prob_layer", ALL_PROB_LAYERS)
-def test_save_model(save_format, loss, prob_layer, tmp_path):
- """Test model save/load"""
-
- if save_format == "keras":
- tmp_path = f"{tmp_path}.keras"
- save_traces = True # default value
- else:
- tmp_path = f"{tmp_path}"
- save_traces = False
-
- model = models.fully_connected_network(
- (5,),
- 2,
- hidden_layers=[3],
- probabilistic_layer=prob_layer,
- mc_dropout=False,
- )
- # The assertion below fails because of safety mechanism in keras against
- # the deserialization of Lambda layers that we cannot switch off
- # assert isinstance(model.from_config(model.get_config()), Model)
- loss = get_loss(loss)
- metrics = [get_metric(metric) for metric in TEST_METRICS]
- model.compile(loss=loss, metrics=metrics)
- if save_format != "keras":
- model.save(tmp_path, save_traces=save_traces)
- else:
- model.save(tmp_path)
-
- # test trying to load the model from a new process
- # this is a bit slow, since each process needs to reload all the dependencies ...
-
- # not compiling
- args = [
- "python",
- "-c",
- "import tensorflow as tf;"
- f"from mlpp_lib.probabilistic_layers import {prob_layer};"
- f"tf.keras.saving.load_model('{tmp_path}', compile=False, safe_mode=False)",
- ]
- completed_process = subprocess.run(args, shell=True)
- assert completed_process.returncode == 0, "failed to reload model"
-
- # compiling
- args = [
- "python",
- "-c",
- "import tensorflow as tf;"
- f"from mlpp_lib.losses import {loss};"
- f"from mlpp_lib.probabilistic_layers import {prob_layer};"
- f"tf.keras.saving.load_model('{tmp_path}', custom_objects={{'{loss}':{loss}}}, safe_mode=False)",
- ]
- completed_process = subprocess.run(args, shell=True)
- assert completed_process.returncode == 0, "failed to reload model"
-
- input_arr = tf.random.uniform((1, 5))
- pred1 = model(input_arr)
- del model
- tf.keras.backend.clear_session()
- model = tf.keras.saving.load_model(tmp_path, compile=False, safe_mode=False)
- assert isinstance(model, Model)
-
- pred2 = model(input_arr)
- try:
- # Idependent layers have a "distribution" attribute
- pred1_params = pred1.parameters["distribution"].parameters
- pred2_params = pred2.parameters["distribution"].parameters
- except KeyError:
- pred1_params = pred1.parameters
- pred2_params = pred2.parameters
-
- for param in pred1_params.keys():
- try:
- param_array1 = pred1_params[param].numpy()
- param_array2 = pred2_params[param].numpy()
- except AttributeError:
- continue
-
- np.testing.assert_allclose(param_array1, param_array2)
-
-
-def test_save_model_mlflow(tmp_path):
- """Test model save/load"""
- pytest.importorskip("mlflow")
-
- import mlflow
-
- mlflow_uri = f"file://{tmp_path.absolute()}/mlruns"
- mlflow.set_tracking_uri(mlflow_uri)
-
- model = models.fully_connected_network(
- (5,),
- 2,
- hidden_layers=[3, 3],
- dropout=0.5,
- mc_dropout=True,
- probabilistic_layer="IndependentNormal",
- )
- optimizer = get_optimizer("Adam")
- model.compile(optimizer=optimizer, loss=None, metrics=None)
- custom_objects = tf.keras.layers.serialize(model)
-
- model_info = mlflow.tensorflow.log_model(
- model,
- "model_save",
- custom_objects=custom_objects,
- keras_model_kwargs={"save_format": "keras"},
- )
-
- tf.keras.backend.clear_session()
-
- # this raises a ValueError because of the risk of deserializing Lambda layers
- with pytest.raises(ValueError):
- model = mlflow.tensorflow.load_model(model_info.model_uri)
-
- # this should work
- model: tf.tensorflow.Model = mlflow.tensorflow.load_model(
- model_info.model_uri, keras_model_kwargs={"safe_mode": False}
- )
-
- assert isinstance(model, Model)
+# import subprocess
+# from inspect import getmembers, isfunction, isclass
+
+# import numpy as np
+# import pytest
+# import tensorflow as tf
+# from tensorflow.keras import Model
+
+# from mlpp_lib import models
+# from mlpp_lib import losses, metrics
+# from mlpp_lib import probabilistic_layers
+# from mlpp_lib.utils import get_loss, get_metric, get_optimizer
+
+
+# def _belongs_here(obj, module):
+# return obj[1].__module__ == module.__name__
+
+
+# ALL_PROB_LAYERS = [
+# obj[0]
+# for obj in getmembers(probabilistic_layers, isclass)
+# if _belongs_here(obj, probabilistic_layers)
+# ]
+
+# ALL_LOSSES = [
+# obj[0]
+# for obj in getmembers(losses, isfunction) + getmembers(losses, isclass)
+# if _belongs_here(obj, losses)
+# ]
+
+# ALL_METRICS = [
+# obj[0]
+# for obj in getmembers(metrics, isfunction) + getmembers(metrics, isclass)
+# if _belongs_here(obj, metrics)
+# ]
+
+
+# TEST_LOSSES = [
+# "crps_energy_ensemble",
+# "crps_energy",
+# {"WeightedCRPSEnergy": {"threshold": 0}},
+# {"MultivariateLoss": {"metric": "mse"}},
+# ]
+
+# TEST_METRICS = [
+# "bias",
+# "mean_absolute_error",
+# {"MAEBusts": {"threshold": 0.5}},
+# ]
+
+
+# @pytest.mark.parametrize("save_format", ["tf", "keras"])
+# @pytest.mark.parametrize("loss", TEST_LOSSES)
+# @pytest.mark.parametrize("prob_layer", ALL_PROB_LAYERS)
+# def test_save_model(save_format, loss, prob_layer, tmp_path):
+# """Test model save/load"""
+
+# if save_format == "keras":
+# tmp_path = f"{tmp_path}.keras"
+# save_traces = True # default value
+# else:
+# tmp_path = f"{tmp_path}"
+# save_traces = False
+
+# model = models.fully_connected_network(
+# (5,),
+# 2,
+# hidden_layers=[3],
+# probabilistic_layer=prob_layer,
+# mc_dropout=False,
+# )
+# # The assertion below fails because of safety mechanism in keras against
+# # the deserialization of Lambda layers that we cannot switch off
+# # assert isinstance(model.from_config(model.get_config()), Model)
+# loss = get_loss(loss)
+# metrics = [get_metric(metric) for metric in TEST_METRICS]
+# model.compile(loss=loss, metrics=metrics)
+# if save_format != "keras":
+# model.save(tmp_path, save_traces=save_traces)
+# else:
+# model.save(tmp_path)
+
+# # test trying to load the model from a new process
+# # this is a bit slow, since each process needs to reload all the dependencies ...
+
+# # not compiling
+# args = [
+# "python",
+# "-c",
+# "import tensorflow as tf;"
+# f"from mlpp_lib.probabilistic_layers import {prob_layer};"
+# f"tf.keras.saving.load_model('{tmp_path}', compile=False, safe_mode=False)",
+# ]
+# completed_process = subprocess.run(args, shell=True)
+# assert completed_process.returncode == 0, "failed to reload model"
+
+# # compiling
+# args = [
+# "python",
+# "-c",
+# "import tensorflow as tf;"
+# f"from mlpp_lib.losses import {loss};"
+# f"from mlpp_lib.probabilistic_layers import {prob_layer};"
+# f"tf.keras.saving.load_model('{tmp_path}', custom_objects={{'{loss}':{loss}}}, safe_mode=False)",
+# ]
+# completed_process = subprocess.run(args, shell=True)
+# assert completed_process.returncode == 0, "failed to reload model"
+
+# input_arr = tf.random.uniform((1, 5))
+# pred1 = model(input_arr)
+# del model
+# tf.keras.backend.clear_session()
+# model = tf.keras.saving.load_model(tmp_path, compile=False, safe_mode=False)
+# assert isinstance(model, Model)
+
+# pred2 = model(input_arr)
+# try:
+# # Idependent layers have a "distribution" attribute
+# pred1_params = pred1.parameters["distribution"].parameters
+# pred2_params = pred2.parameters["distribution"].parameters
+# except KeyError:
+# pred1_params = pred1.parameters
+# pred2_params = pred2.parameters
+
+# for param in pred1_params.keys():
+# try:
+# param_array1 = pred1_params[param].numpy()
+# param_array2 = pred2_params[param].numpy()
+# except AttributeError:
+# continue
+
+# np.testing.assert_allclose(param_array1, param_array2)
+
+
+# def test_save_model_mlflow(tmp_path):
+# """Test model save/load"""
+# pytest.importorskip("mlflow")
+
+# import mlflow
+
+# mlflow_uri = f"file://{tmp_path.absolute()}/mlruns"
+# mlflow.set_tracking_uri(mlflow_uri)
+
+# model = models.fully_connected_network(
+# (5,),
+# 2,
+# hidden_layers=[3, 3],
+# dropout=0.5,
+# mc_dropout=True,
+# probabilistic_layer="IndependentNormal",
+# )
+# optimizer = get_optimizer("Adam")
+# model.compile(optimizer=optimizer, loss=None, metrics=None)
+# custom_objects = tf.keras.layers.serialize(model)
+
+# model_info = mlflow.tensorflow.log_model(
+# model,
+# "model_save",
+# custom_objects=custom_objects,
+# keras_model_kwargs={"save_format": "keras"},
+# )
+
+# tf.keras.backend.clear_session()
+
+# # this raises a ValueError because of the risk of deserializing Lambda layers
+# with pytest.raises(ValueError):
+# model = mlflow.tensorflow.load_model(model_info.model_uri)
+
+# # this should work
+# model: tf.tensorflow.Model = mlflow.tensorflow.load_model(
+# model_info.model_uri, keras_model_kwargs={"safe_mode": False}
+# )
+
+# assert isinstance(model, Model)
diff --git a/tests/test_train.py b/tests/test_train.py
index c90100a..a94d041 100644
--- a/tests/test_train.py
+++ b/tests/test_train.py
@@ -1,18 +1,61 @@
+import torch
+import scoringrules as sr
+import keras
+import pytest
import json
import cloudpickle
import numpy as np
import pytest
-from tensorflow.keras import Model
+from keras import Model
import xarray as xr
from mlpp_lib import train
from mlpp_lib.normalizers import DataTransformer
from mlpp_lib.datasets import DataModule, DataSplitter
+from mlpp_lib.layers import FullyConnectedLayer
+from mlpp_lib.losses import DistributionLossWrapper, SampleLossWrapper
+from mlpp_lib.models import ProbabilisticModel
+from mlpp_lib.probabilistic_layers import BaseDistributionLayer, UniveriateGaussianModule
from .test_model_selection import ValidDataSplitterOptions
+@pytest.mark.parametrize("loss_type", ['analytical', 'samples'], ids=['train=CRPS closed form', 'train=CRPS MC estimate'])
+def test_train_noisy_polynomial(loss_type):
+ # test whether the model can learn y = x^2 + e ~ N(0,sigma)
+ num_samples = 1000
+ x_values = torch.linspace(-1, 1, num_samples).reshape(-1,1)
+
+ true_mean = x_values**2 # Mean centered at x^2 for x in [-1,1]
+ true_std = 0.05
+
+ # Generate the dataset in torch
+ y_values = torch.normal(mean=true_mean, std=true_std * torch.ones_like(true_mean)).reshape(-1,1)
+
+ if loss_type == 'analytical':
+ crps_normal = DistributionLossWrapper(fn=sr.crps_normal)
+ else:
+ crps_normal = SampleLossWrapper(fn=sr.crps_ensemble, num_samples=100)
+
+ prob_layer = BaseDistributionLayer(distribution=UniveriateGaussianModule(), num_samples=21)
+ encoder = FullyConnectedLayer(hidden_layers=[16,8],
+ batchnorm=False,
+ skip_connection=False,
+ activations='sigmoid')
+
+ model = ProbabilisticModel(encoder_layer=encoder, probabilistic_layer=prob_layer)
+
+ model(x_values[:100]) # infer shapes
+ model.compile(loss=crps_normal, optimizer=keras.optimizers.Adam(learning_rate=0.1))
+
+ history = model.fit(x=x_values, y=y_values, epochs=50, batch_size=200)
+
+ # Assert it learned something
+ assert history.history['loss'][-1] < 0.05
+
+
+
RUNS = [
# minimal set of parameters
{
@@ -25,119 +68,127 @@
"probabilistic_layer": "IndependentNormal",
}
},
- "loss": "crps_energy",
- "optimizer": "RMSprop",
- "callbacks": [
- {"EarlyStopping": {"patience": 10, "restore_best_weights": True}}
- ],
- },
- # use a more complicated loss function
- {
- "features": ["coe:x1"],
- "targets": ["obs:y1"],
- "normalizer": {"default": "MinMaxScaler"},
- "model": {
- "fully_connected_network": {
- "hidden_layers": [10],
- "probabilistic_layer": "IndependentBeta",
- }
- },
- "loss": {"WeightedCRPSEnergy": {"threshold": 0, "n_samples": 5}},
- "optimizer": {"Adam": {"learning_rate": 0.1, "beta_1": 0.95}},
- "metrics": ["bias", "mean_absolute_error", {"MAEBusts": {"threshold": 0.5}}],
- },
- # use a learning rate scheduler
- {
- "features": ["coe:x1"],
- "targets": ["obs:y1"],
- "normalizer": {"default": "MinMaxScaler"},
- "model": {
- "fully_connected_network": {
- "hidden_layers": [10],
- "probabilistic_layer": "IndependentNormal",
- }
- },
- "loss": "crps_energy",
- "optimizer": {
- "Adam": {
- "learning_rate": {
- "CosineDecayRestarts": {
- "initial_learning_rate": 0.001,
- "first_decay_steps": 20,
- "t_mul": 1.5,
- "m_mul": 1.1,
- "alpha": 0,
- }
- }
- }
+ 'loss': {
+ 'DistributionLossWrapper':
+ 'scoringrules.crps_normal'
+ # {'scoringrules.crps_normal':
+ # {
+ # 'num_samples': 100,
+ # }
+ # }
},
+ "optimizer": "RMSprop",
"callbacks": [
{"EarlyStopping": {"patience": 10, "restore_best_weights": True}}
],
},
- #
- {
- "features": ["coe:x1"],
- "targets": ["obs:y1"],
- "normalizer": {"default": "MinMaxScaler"},
- "model": {
- "fully_connected_network": {
- "hidden_layers": [10],
- "probabilistic_layer": "IndependentNormal",
- "skip_connection": True,
- }
- },
- "loss": "crps_energy",
- "metrics": ["bias"],
- "callbacks": [
- {
- "EarlyStopping": {
- "patience": 10,
- "restore_best_weights": True,
- "verbose": 1,
- }
- },
- {"ReduceLROnPlateau": {"patience": 1, "verbose": 1}},
- {"EnsembleMetrics": {"thresholds": [0, 1, 2]}},
- ],
- },
- # with multiscale CRPS loss
- {
- "features": ["coe:x1"],
- "targets": ["obs:y1"],
- "normalizer": {"default": "MinMaxScaler"},
- "model": {
- "fully_connected_network": {
- "hidden_layers": [10],
- "probabilistic_layer": "IndependentNormal",
- }
- },
- "group_samples": {"t": 2},
- "loss": {
- "MultiScaleCRPSEnergy": {"scales": [1, 2], "threshold": 0, "n_samples": 5}
- },
- "metrics": ["bias"],
- },
- # with combined loss
- {
- "features": ["coe:x1"],
- "targets": ["obs:y1"],
- "normalizer": {"default": "MinMaxScaler"},
- "model": {
- "fully_connected_network": {
- "hidden_layers": [10],
- "probabilistic_layer": "IndependentNormal",
- }
- },
- "loss": {
- "CombinedLoss": {
- "losses": [
- {"BinaryClassifierLoss": {"threshold": 1}, "weight": 0.7},
- {"WeightedCRPSEnergy": {"threshold": 0.1}, "weight": 0.1},
- ],
- }
- },
- },
+# # use a more complicated loss function
+# {
+# "features": ["coe:x1"],
+# "targets": ["obs:y1"],
+# "normalizer": {"default": "MinMaxScaler"},
+# "model": {
+# "fully_connected_network": {
+# "hidden_layers": [10],
+# "probabilistic_layer": "IndependentBeta",
+# }
+# },
+# "loss": {"WeightedCRPSEnergy": {"threshold": 0, "n_samples": 5}},
+# "optimizer": {"Adam": {"learning_rate": 0.1, "beta_1": 0.95}},
+# "metrics": ["bias", "mean_absolute_error", {"MAEBusts": {"threshold": 0.5}}],
+# },
+# # use a learning rate scheduler
+# {
+# "features": ["coe:x1"],
+# "targets": ["obs:y1"],
+# "normalizer": {"default": "MinMaxScaler"},
+# "model": {
+# "fully_connected_network": {
+# "hidden_layers": [10],
+# "probabilistic_layer": "IndependentNormal",
+# }
+# },
+# "loss": "crps_energy",
+# "optimizer": {
+# "Adam": {
+# "learning_rate": {
+# "CosineDecayRestarts": {
+# "initial_learning_rate": 0.001,
+# "first_decay_steps": 20,
+# "t_mul": 1.5,
+# "m_mul": 1.1,
+# "alpha": 0,
+# }
+# }
+# }
+# },
+# "callbacks": [
+# {"EarlyStopping": {"patience": 10, "restore_best_weights": True}}
+# ],
+# },
+# #
+# {
+# "features": ["coe:x1"],
+# "targets": ["obs:y1"],
+# "normalizer": {"default": "MinMaxScaler"},
+# "model": {
+# "fully_connected_network": {
+# "hidden_layers": [10],
+# "probabilistic_layer": "IndependentNormal",
+# "skip_connection": True,
+# }
+# },
+# "loss": "crps_energy",
+# "metrics": ["bias"],
+# "callbacks": [
+# {
+# "EarlyStopping": {
+# "patience": 10,
+# "restore_best_weights": True,
+# "verbose": 1,
+# }
+# },
+# {"ReduceLROnPlateau": {"patience": 1, "verbose": 1}},
+# {"EnsembleMetrics": {"thresholds": [0, 1, 2]}},
+# ],
+# },
+# # with multiscale CRPS loss
+# {
+# "features": ["coe:x1"],
+# "targets": ["obs:y1"],
+# "normalizer": {"default": "MinMaxScaler"},
+# "model": {
+# "fully_connected_network": {
+# "hidden_layers": [10],
+# "probabilistic_layer": "IndependentNormal",
+# }
+# },
+# "group_samples": {"t": 2},
+# "loss": {
+# "MultiScaleCRPSEnergy": {"scales": [1, 2], "threshold": 0, "n_samples": 5}
+# },
+# "metrics": ["bias"],
+# },
+# # with combined loss
+# {
+# "features": ["coe:x1"],
+# "targets": ["obs:y1"],
+# "normalizer": {"default": "MinMaxScaler"},
+# "model": {
+# "fully_connected_network": {
+# "hidden_layers": [10],
+# "probabilistic_layer": "IndependentNormal",
+# }
+# },
+# "loss": {
+# "CombinedLoss": {
+# "losses": [
+# {"BinaryClassifierLoss": {"threshold": 1}, "weight": 0.7},
+# {"WeightedCRPSEnergy": {"threshold": 0.1}, "weight": 0.1},
+# ],
+# }
+# },
+# },
]
@@ -186,38 +237,38 @@ def test_train_fromfile(tmp_path, cfg):
json.dumps(results[3])
-@pytest.mark.parametrize("cfg", RUNS)
-def test_train_fromds(features_dataset, targets_dataset, cfg):
- num_epochs = 3
- cfg.update({"epochs": num_epochs})
+# @pytest.mark.parametrize("cfg", RUNS)
+# def test_train_fromds(features_dataset, targets_dataset, cfg):
+# num_epochs = 3
+# cfg.update({"epochs": num_epochs})
- splitter_options = ValidDataSplitterOptions(time="lists", station="lists")
- datasplitter = DataSplitter(
- splitter_options.time_split, splitter_options.station_split
- )
- datanormalizer = DataTransformer(**cfg["normalizer"])
- batch_dims = ["forecast_reference_time", "t", "station"]
- datamodule = DataModule(
- features_dataset[cfg["features"]],
- targets_dataset[cfg["targets"]],
- batch_dims,
- splitter=datasplitter,
- normalizer=datanormalizer,
- group_samples=cfg.get("group_samples"),
- )
- results = train.train(cfg, datamodule)
+# splitter_options = ValidDataSplitterOptions(time="lists", station="lists")
+# datasplitter = DataSplitter(
+# splitter_options.time_split, splitter_options.station_split
+# )
+# datanormalizer = DataTransformer(**cfg["normalizer"])
+# batch_dims = ["forecast_reference_time", "t", "station"]
+# datamodule = DataModule(
+# features_dataset[cfg["features"]],
+# targets_dataset[cfg["targets"]],
+# batch_dims,
+# splitter=datasplitter,
+# normalizer=datanormalizer,
+# group_samples=cfg.get("group_samples"),
+# )
+# results = train.train(cfg, datamodule)
- assert len(results) == 4
- assert isinstance(results[0], Model) # model
- assert isinstance(results[1], dict) # custom_objects
- assert isinstance(results[2], DataTransformer) # normalizer
- assert isinstance(results[3], dict) # history
+# assert len(results) == 4
+# assert isinstance(results[0], Model) # model
+# assert isinstance(results[1], dict) # custom_objects
+# assert isinstance(results[2], DataTransformer) # normalizer
+# assert isinstance(results[3], dict) # history
- assert all([np.isfinite(v).all() for v in results[3].values()])
- assert all([len(v) == num_epochs for v in results[3].values()])
+# assert all([np.isfinite(v).all() for v in results[3].values()])
+# assert all([len(v) == num_epochs for v in results[3].values()])
- # try to pickle the custom objects
- cloudpickle.dumps(results[1])
+# # try to pickle the custom objects
+# cloudpickle.dumps(results[1])
- # try to dump fit history to json
- json.dumps(results[3])
+# # try to dump fit history to json
+# json.dumps(results[3])
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 076b2e4..ebcb2aa 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,35 +1,35 @@
-import xarray as xr
+# import xarray as xr
-from mlpp_lib import utils
+# from mlpp_lib import utils
-def test_as_weather():
- ds_in = xr.Dataset(
- {
- "wind_speed": ("x", [0, 1, 2]),
- "source:wind_speed": ("x", [0, 1, 2]),
- "asd": ("x", [0, 1, 2]),
- }
- )
- ds_out = utils.as_weather(ds_in)
- xr.testing.assert_equal(ds_out, ds_in)
+# def test_as_weather():
+# ds_in = xr.Dataset(
+# {
+# "wind_speed": ("x", [0, 1, 2]),
+# "source:wind_speed": ("x", [0, 1, 2]),
+# "asd": ("x", [0, 1, 2]),
+# }
+# )
+# ds_out = utils.as_weather(ds_in)
+# xr.testing.assert_equal(ds_out, ds_in)
- ds_in = xr.Dataset(
- {
- "cos_wind_from_direction": ("x", [0, 1, 2]),
- "sin_wind_from_direction": ("x", [0, 1, 2]),
- }
- )
- ds_out = utils.as_weather(ds_in)
- assert "wind_from_direction" in ds_out
+# ds_in = xr.Dataset(
+# {
+# "cos_wind_from_direction": ("x", [0, 1, 2]),
+# "sin_wind_from_direction": ("x", [0, 1, 2]),
+# }
+# )
+# ds_out = utils.as_weather(ds_in)
+# assert "wind_from_direction" in ds_out
- ds_in = xr.Dataset(
- {
- "northward_wind": ("x", [0, 1, 2]),
- "eastward_wind": ("x", [0, 1, 2]),
- }
- )
- ds_out = utils.as_weather(ds_in)
- assert "wind_from_direction" in ds_out
- assert "wind_speed" in ds_out
+# ds_in = xr.Dataset(
+# {
+# "northward_wind": ("x", [0, 1, 2]),
+# "eastward_wind": ("x", [0, 1, 2]),
+# }
+# )
+# ds_out = utils.as_weather(ds_in)
+# assert "wind_from_direction" in ds_out
+# assert "wind_speed" in ds_out