diff --git a/.gitignore b/.gitignore index 7bed6a7..36192a8 100644 --- a/.gitignore +++ b/.gitignore @@ -24,4 +24,6 @@ Thumbs.db *.html *.png mlruns -outputs \ No newline at end of file +outputs# pixi environments +.pixi/* +!.pixi/config.toml diff --git a/RunDNAm_small.sh b/RunDNAm_small.sh new file mode 100644 index 0000000..746fa8e --- /dev/null +++ b/RunDNAm_small.sh @@ -0,0 +1,33 @@ +#!/bin/bash +#SBATCH --job-name=Bulk_DNAmethylation +#SBATCH --account=project_2015212 +#SBATCH --partition=gpu +#SBATCH --gres=gpu:v100:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=160G +#SBATCH --time=05:00:00 +#SBATCH --output=/scratch/project_2015212/ceren/runs/bulk/%x-%j.out +#SBATCH --error=/scratch/project_2015212/ceren/runs/bulk/%x-%j.err + +set -euo pipefail + +module load tensorflow/2.18 +source /projappl/project_2015212/cavachon/envs/ceren/.venv/bin/activate + +export MLFLOW_TRACKING_URI="file:///scratch/project_2015212/ceren/mlruns" +export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export MKL_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export PYTHONUNBUFFERED=1 + +# Make sure key dirs exist +mkdir -p /scratch/project_2015212/ceren/runs/bulk/embeddings +mkdir -p /scratch/project_2015212/ceren/checkpoints + +cd /projappl/project_2015212/cavachon/CAVACHON + +python - << 'PY' +from cavachon.workflow import Workflow +CFG = "/projappl/project_2015212/cavachon/configs/ceren/DNAmethyl_small_run.yaml" +wf = Workflow(CFG) +wf.run() +PY \ No newline at end of file diff --git a/RunDNAm_small2.sh b/RunDNAm_small2.sh new file mode 100644 index 0000000..cf488ab --- /dev/null +++ b/RunDNAm_small2.sh @@ -0,0 +1,33 @@ +#!/bin/bash +#SBATCH --job-name=Bulk_DNAmethylation +#SBATCH --account=project_2015212 +#SBATCH --partition=gpu +#SBATCH --gres=gpu:v100:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=90G +#SBATCH --time=01:00:00 +#SBATCH --output=/scratch/project_2015212/ceren/runs/bulk2/%x-%j.out +#SBATCH --error=/scratch/project_2015212/ceren/runs/bulk2/%x-%j.err + +set -euo pipefail + +module load tensorflow/2.18 +source /projappl/project_2015212/cavachon/envs/ceren/.venv/bin/activate + +export MLFLOW_TRACKING_URI="file:///scratch/project_2015212/ceren/mlruns2" +export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export MKL_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export PYTHONUNBUFFERED=1 + +# Make sure key dirs exist +mkdir -p /scratch/project_2015212/ceren/runs/bulk2/embeddings +mkdir -p /scratch/project_2015212/ceren/checkpoints2 + +cd /projappl/project_2015212/cavachon/CAVACHON + +python - << 'PY' +from cavachon.workflow import Workflow +CFG = "/projappl/project_2015212/cavachon/configs/ceren/DNAm_second.yaml" +wf = Workflow(CFG) +wf.run() +PY \ No newline at end of file diff --git a/cavachon/dataloader/modifiers/__init__.py b/cavachon/dataloader/modifiers/__init__.py index 2420fea..fef33f4 100644 --- a/cavachon/dataloader/modifiers/__init__.py +++ b/cavachon/dataloader/modifiers/__init__.py @@ -1,6 +1,12 @@ -from .independent_bernoulli_data_modifier import ( - IndependentBernoulliDataModifier as IndependentBernoulliDataModifier, -) -from .independent_zero_inflated_negative_binomial_data_modifier import ( - IndependentZeroInflatedNegativeBinomialDataModifier as IndependentZeroInflatedNegativeBinomialDataModifier, -) +from .independent_bernoulli_data_modifier import ( + IndependentBernoulliDataModifier as IndependentBernoulliDataModifier, +) +from .independent_zero_inflated_negative_binomial_data_modifier import ( + IndependentZeroInflatedNegativeBinomialDataModifier as IndependentZeroInflatedNegativeBinomialDataModifier, +) +from .multivariate_normal_diag_data_modifier import ( + MultivariateNormalDiagDataModifier as MultivariateNormalDiagDataModifier, +) +from .studentt_data_modifier import ( + StudenttDataModifier as StudenttDataModifier, +) diff --git a/cavachon/dataloader/modifiers/multivariate_normal_diag_data_modifier.py b/cavachon/dataloader/modifiers/multivariate_normal_diag_data_modifier.py new file mode 100644 index 0000000..1081e2b --- /dev/null +++ b/cavachon/dataloader/modifiers/multivariate_normal_diag_data_modifier.py @@ -0,0 +1,74 @@ +import functools +from typing import Any, Mapping + +import tensorflow as tf + +from cavachon.environment.constants import Constants +from cavachon.layers.modifiers.to_dense import ToDense + + +class MultivariateNormalDiagDataModifier(tf.keras.Model): + """MultivariateNormalDiagDataModifier + + Modifiers for the modality which follows a MultivariateNormalDiag + distribution (Normal distribution with diagonal covariance). + The instance will be used right after the tf.data.Dataset is + created using the DataLoader. + + Attributes + ---------- + modality_names: str + modality name. + + modality_key: str + the key used to access the mapping of data created from + tf.data.Dataset. Defaults to `modality_name`_matrix. + + modifiers: List[tf.keras.layers.Layer] + list of modifiers that will be applied to the data created from + tf.data.Dataset. Defaults to [ToDense]. + + See Also + -------- + DataLoader: used to create tf.data.Dataset from MuData. + + """ + + def __init__(self, modality_name: str): + """Constructor for MultivariateNormalDiag data modifier + + Parameters + ---------- + modality_name: str + the name of modality that needs to be processed. + """ + super().__init__() + self.modality_name: str = modality_name + self.modality_key: str = f"{modality_name}_{Constants.TENSOR_NAME_X}" + # For continuous normalized data (CNV, normalized RNA, etc.) + # we only need to convert sparse matrices to dense tensors + self.modifiers = [ToDense(self.modality_key)] + + def call(self, inputs: Mapping[Any, tf.Tensor], training=None, mask=None): + """Process the data created from tf.data.Dataset. + + Parameters + ---------- + inputs: + Mapping of tf.Tensor, where the keys contain + self.modality_key. + + training: bool, optional + Not used (kept for tf.keras.Model API). + + mask: tf.Tensor, optional + Not used (kept for tf.keras.Model API). + + Returns + ------- + Mapping[Any, tf.Tensor] + processed data. + + """ + modifiers = self.modifiers + return functools.reduce(lambda x, modifier: modifier(x), modifiers, inputs) diff --git a/cavachon/dataloader/modifiers/studentt_data_modifier.py b/cavachon/dataloader/modifiers/studentt_data_modifier.py new file mode 100644 index 0000000..e011ffe --- /dev/null +++ b/cavachon/dataloader/modifiers/studentt_data_modifier.py @@ -0,0 +1,17 @@ +import functools +from typing import Any, Mapping + +import tensorflow as tf + +from cavachon.environment.constants import Constants +from cavachon.layers.modifiers.to_dense import ToDense + + +class StudenttDataModifier(tf.keras.Model): + def __init__(self, modality_name: str): + super().__init__() + self.modality_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + self.modifiers = [ToDense(self.modality_key)] + + def call(self, inputs: Mapping[Any, tf.Tensor], **kwargs): + return functools.reduce(lambda x, mod: mod(x), self.modifiers, inputs) diff --git a/cavachon/distributions/__init__.py b/cavachon/distributions/__init__.py index cfeda9a..e775189 100644 --- a/cavachon/distributions/__init__.py +++ b/cavachon/distributions/__init__.py @@ -8,3 +8,6 @@ from .multivariate_normal_diag_distribution import ( MultivariateNormalDiagDistribution as MultivariateNormalDiagDistribution, ) +from .studentt_distribution import ( + StudenttDistribution as StudenttDistribution, +) diff --git a/cavachon/distributions/distribution.py b/cavachon/distributions/distribution.py index 7d20187..7e5fe86 100644 --- a/cavachon/distributions/distribution.py +++ b/cavachon/distributions/distribution.py @@ -1,4 +1,4 @@ -from abc import ABC, abstractclassmethod +from abc import ABC, abstractmethod from typing import Mapping, Union import tensorflow as tf @@ -11,7 +11,8 @@ class Distribution(ABC): """ - @abstractclassmethod + @classmethod + @abstractmethod def from_parameterizer_output( cls, params: Union[tf.Tensor, Mapping[str, tf.Tensor]], **kwargs ) -> tfp.distributions.Distribution: diff --git a/cavachon/distributions/studentt_distribution.py b/cavachon/distributions/studentt_distribution.py new file mode 100644 index 0000000..7add8f1 --- /dev/null +++ b/cavachon/distributions/studentt_distribution.py @@ -0,0 +1,29 @@ +import tensorflow as tf +import tensorflow_probability as tfp + +from cavachon.distributions.distribution import Distribution + + +class StudenttDistribution(Distribution, tfp.distributions.StudentT): + """StudentT distribution for continuous data with heavy tails (e.g. CNV).""" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + @classmethod + def from_parameterizer_output(cls, params: tf.Tensor, **kwargs): + """ + Creates distribution from a single tensor. + The last dimension is split into 3: loc, scale, and df. + """ + # Split into 3 equal parts + loc, scale_raw, df_raw = tf.split(params, 3, axis=-1) + + # Scale (sigma) must be positive + scale = tf.math.softplus(scale_raw) + 1e-7 + + # Degrees of Freedom (nu) must be > 0. + # Adding 2.0 ensures the variance is mathematically defined (> 2). + df = tf.math.softplus(df_raw) + 2.0 + + return cls(df=df, loc=loc, scale=scale, **kwargs) diff --git a/cavachon/layers/parameterizers/__init__.py b/cavachon/layers/parameterizers/__init__.py index c348864..e002ec7 100644 --- a/cavachon/layers/parameterizers/__init__.py +++ b/cavachon/layers/parameterizers/__init__.py @@ -13,3 +13,9 @@ from .multivariate_normal_diag_sampler import ( MultivariateNormalDiagSampler as MultivariateNormalDiagSampler, ) +from .studentt_parameterizer_layer import ( + StudenttParameterizerLayer as StudenttParameterizerLayer, +) +from .studentt_sampler import ( + StudenttSampler as StudenttSampler, +) diff --git a/cavachon/layers/parameterizers/independent_zero_inflated_negative_binomial_parameterizer_layer.py b/cavachon/layers/parameterizers/independent_zero_inflated_negative_binomial_parameterizer_layer.py index fd0d162..f49a455 100644 --- a/cavachon/layers/parameterizers/independent_zero_inflated_negative_binomial_parameterizer_layer.py +++ b/cavachon/layers/parameterizers/independent_zero_inflated_negative_binomial_parameterizer_layer.py @@ -1,121 +1,121 @@ -import tensorflow as tf - - -class IndependentZeroInflatedNegativeBinomialParameterizerLayer(tf.keras.layers.Layer): - """IndependentZeroInflatedNegativeBinomialParameterizerLayer - - Parameterizer for IndependentZeroInflatedNegativeBinomialLayer - distributions (logits, mean and dispersion). - - """ - - def __init__( - self, - event_dims: int, - use_shared_dispersion: bool = True, - name: str = "independent_zero_inflated_negative_binomial_parameterizer_layer", - ): - """Constructor for - IndependentZeroInflatedNegativeBinomialParameterizerLayer - - Parameters - ---------- - event_dims: int - number of event dimensions for the independent zero-inflated - negative binomial distribution. - - use_shared_dispersion: bool - use shared dispersion across all samples instead of modeling - the dispersions independently for each sample (see Rybkin - et al., 2021) - - name: str, optional - Name for the tensorflow layer. Defaults to - 'independent_zero_inflated_negative_binomial_parameterizer_layer'. - """ - super().__init__(name=name) - self.event_dims: int = event_dims - self.use_shared_dispersion: bool = use_shared_dispersion - return - - def build(self, input_shape: tf.TensorShape) -> None: - """Create necessary tf.Variable for the first time being - called. (see tf.keras.layers.Layer) - - Parameters - ---------- - input_shape: tf.TensorShape - input shape of tf.Tensor. - - """ - self.logits_weight = self.add_weight( - name=f"{self.name}_logits_weight", - shape=(int(input_shape[-1]), self.event_dims), - ) - self.logits_bias = self.add_weight( - name=f"{self.name}_logits_bias", shape=(1, self.event_dims) - ) - self.mean_weight = self.add_weight( - name=f"{self.name}_mean_weight", - shape=(int(input_shape[-1]), self.event_dims), - ) - self.mean_bias = self.add_weight( - name=f"{self.name}_mean_bias", shape=(1, self.event_dims) - ) - if self.use_shared_dispersion: - self.dispersion_weight = self.add_weight( - name=f"{self.name}_dispersion_weight", shape=(1, self.event_dims) - ) - else: - self.dispersion_weight = self.add_weight( - name=f"{self.name}_dispersion_weight", - shape=(int(input_shape[-1]), self.event_dims), - ) - self.dispersion_bias = self.add_weight( - name=f"{self.name}_dispersion_bias", shape=(1, self.event_dims) - ) - - return - - def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: - """Parameterize independent zero-inflated negative binomial - distributions with logits, mean and dispersion using the given - tf.Tensor. - - Parameters - ---------- - inputs: tf.Tensor - inputs Tensor. - - Returns - ------- - tf.Tensor - logits, mean and dispersion for zero-inflated negative - binomial distributions, with shape (batch, event_dims * 3), - where - 1. results[..., 0:event_dims] are the logits - 2. results[..., event_dims:2*event_dims] are the means - 3. results[..., 2*event_dims:3*event_dims] are the - dispersions - - """ - if self.use_shared_dispersion: - batch_size = tf.shape(inputs)[0] - dispersion = tf.math.sigmoid(self.dispersion_weight + self.dispersion_bias) - dispersion = tf.repeat(dispersion, repeats=batch_size, axis=0) - else: - dispersion = tf.math.sigmoid( - tf.matmul(inputs, self.dispersion_weight) + self.dispersion_bias - ) - - dispersion = tf.where( - dispersion <= 0.1, 0.1 * tf.ones_like(dispersion), dispersion - ) - - result = ( - tf.matmul(inputs, self.logits_weight) + self.logits_bias, - tf.math.softmax(tf.matmul(inputs, self.mean_weight) + self.mean_bias), - dispersion, - ) - # shape: (batch, event_dims * 3) - return tf.concat(result, axis=-1) +import tensorflow as tf + + +class IndependentZeroInflatedNegativeBinomialParameterizerLayer(tf.keras.layers.Layer): + """IndependentZeroInflatedNegativeBinomialParameterizerLayer + + Parameterizer for IndependentZeroInflatedNegativeBinomialLayer + distributions (logits, mean and dispersion). + + """ + + def __init__( + self, + event_dims: int, + use_shared_dispersion: bool = False, + name: str = "independent_zero_inflated_negative_binomial_parameterizer_layer", + ): + """Constructor for + IndependentZeroInflatedNegativeBinomialParameterizerLayer + + Parameters + ---------- + event_dims: int + number of event dimensions for the independent zero-inflated + negative binomial distribution. + + use_shared_dispersion: bool + use shared dispersion across all samples instead of modeling + the dispersions independently for each sample (see Rybkin + et al., 2021) + + name: str, optional + Name for the tensorflow layer. Defaults to + 'independent_zero_inflated_negative_binomial_parameterizer_layer'. + """ + super().__init__(name=name) + self.event_dims: int = event_dims + self.use_shared_dispersion: bool = use_shared_dispersion + return + + def build(self, input_shape: tf.TensorShape) -> None: + """Create necessary tf.Variable for the first time being + called. (see tf.keras.layers.Layer) + + Parameters + ---------- + input_shape: tf.TensorShape + input shape of tf.Tensor. + + """ + self.logits_weight = self.add_weight( + name=f"{self.name}_logits_weight", + shape=(int(input_shape[-1]), self.event_dims), + ) + self.logits_bias = self.add_weight( + name=f"{self.name}_logits_bias", shape=(1, self.event_dims) + ) + self.mean_weight = self.add_weight( + name=f"{self.name}_mean_weight", + shape=(int(input_shape[-1]), self.event_dims), + ) + self.mean_bias = self.add_weight( + name=f"{self.name}_mean_bias", shape=(1, self.event_dims) + ) + if self.use_shared_dispersion: + self.dispersion_weight = self.add_weight( + name=f"{self.name}_dispersion_weight", shape=(1, self.event_dims) + ) + else: + self.dispersion_weight = self.add_weight( + name=f"{self.name}_dispersion_weight", + shape=(int(input_shape[-1]), self.event_dims), + ) + self.dispersion_bias = self.add_weight( + name=f"{self.name}_dispersion_bias", shape=(1, self.event_dims) + ) + + return + + def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: + """Parameterize independent zero-inflated negative binomial + distributions with logits, mean and dispersion using the given + tf.Tensor. + + Parameters + ---------- + inputs: tf.Tensor + inputs Tensor. + + Returns + ------- + tf.Tensor + logits, mean and dispersion for zero-inflated negative + binomial distributions, with shape (batch, event_dims * 3), + where + 1. results[..., 0:event_dims] are the logits + 2. results[..., event_dims:2*event_dims] are the means + 3. results[..., 2*event_dims:3*event_dims] are the + dispersions + + """ + if self.use_shared_dispersion: + batch_size = tf.shape(inputs)[0] + dispersion = tf.math.sigmoid(self.dispersion_weight + self.dispersion_bias) + dispersion = tf.repeat(dispersion, repeats=batch_size, axis=0) + else: + dispersion = tf.math.sigmoid( + tf.matmul(inputs, self.dispersion_weight) + self.dispersion_bias + ) + + dispersion = tf.where( + dispersion <= 0.1, 0.1 * tf.ones_like(dispersion), dispersion + ) + + result = ( + tf.matmul(inputs, self.logits_weight) + self.logits_bias, + tf.math.softmax(tf.matmul(inputs, self.mean_weight) + self.mean_bias), + dispersion, + ) + # shape: (batch, event_dims * 3) + return tf.concat(result, axis=-1) diff --git a/cavachon/layers/parameterizers/mixture_multivariate_normal_diag_parameterizer_layer.py b/cavachon/layers/parameterizers/mixture_multivariate_normal_diag_parameterizer_layer.py index 0ea8622..d29f7f0 100644 --- a/cavachon/layers/parameterizers/mixture_multivariate_normal_diag_parameterizer_layer.py +++ b/cavachon/layers/parameterizers/mixture_multivariate_normal_diag_parameterizer_layer.py @@ -1,176 +1,225 @@ -import tensorflow as tf - - -class MixtureMultivariateNormalDiagParameterizerLayer(tf.keras.layers.Layer): - """MixtureMultivariateNormalDiagParameterizerLayer - - Parameterizer for mixture of multivariate normal distributions with - diagonal covariance matrix (logits, loc and scale_diag). - - """ - - def __init__( - self, - event_dims: int, - n_components: int, - unit_variance: bool = False, - name: str = "mixture_multivariate_normal_diag_parameterizer_layer", - ): - """Constructor for MultivariateNormalDiagParameterizerLayer - - Parameters - ---------- - event_dims: int - number of event dimensions for the multivariate normal - distributions with diagonal covariance matrix. - - n_components: int - number of components in the mixture distributions. - - unit_variance: bool, optional - use unit variance. Defaults to False. - - name: str, optional - Name for the tensorflow layer. Defaults to - 'mixture_multivariate_normal_diag_parameterizer_layer'. - - """ - super().__init__(name=name) - self.event_dims: int = event_dims - self.n_components: int = n_components - self.unit_variance: bool = unit_variance - - return - - def build(self, input_shape: tf.TensorShape) -> None: - """Create necessary tf.Variable for the first time being called. - (see tf.keras.layers.Layer) - - Parameters - ---------- - input_shape: tf.TensorShape - input shape of tf.Tensor. - - """ - self.logits_weight = self.add_weight( - name=f"{self.name}_logits_weight", - shape=(int(input_shape[-1]), self.n_components), - initializer=tf.keras.initializers.Constant(0.0), - ) - self.logits_bias = self.add_weight( - name=f"{self.name}_logits_bias", - shape=(1, self.n_components), - initializer=tf.keras.initializers.Constant(0.0), - ) - - self.loc_weight = [] - self.loc_bias = [] - if not self.unit_variance: - self.scale_diag_weight = [] - self.scale_diag_bias = [] - - for i in range(self.n_components): - self.loc_weight.append( - self.add_weight( - name=f"{self.name}_loc_weight_{i}", - shape=(int(input_shape[-1]), self.event_dims), - ) - ) - self.loc_bias.append( - self.add_weight( - name=f"{self.name}_loc_bias_{i}", shape=(1, self.event_dims) - ) - ) - if not self.unit_variance: - self.scale_diag_weight.append( - self.add_weight( - name=f"{self.name}_scale_diag_weight_{i}", - shape=(int(input_shape[-1]), self.event_dims), - ) - ) - self.scale_diag_bias.append( - self.add_weight( - name=f"{self.name}_scale_diag_bias_{i}", - shape=(1, self.event_dims), - ) - ) - - return - - def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: - """Parameterize mixture of multivariate normal distributions - with diagonal covariance matrix with loc and scale_diag using - the given tf.Tensor. - - Parameters - ---------- - inputs: tf.Tensor - inputs Tensor. - - Returns - ------- - tf.Tensor - logits, loc and scale_diag for normal distributions with - diagonal covariance matrix, with shape - (batch, n_components, event_dims * 2 + 1), where - 1. results[..., n_components, 0] are the logits - 2. results[..., n_components, 1:event_dims+1] are the loc - (mean) - 3. results[..., n_components, event_dims+1:] are the - scale_diag (std) - - """ - - # shape: (batch, n_components, 1) - logits = tf.expand_dims( - tf.matmul(inputs, self.logits_weight) + self.logits_bias, -1 - ) - means = [] - scale_diag = [] - for i in range(self.n_components): - loc_weight = self.loc_weight[i] - loc_bias = self.loc_bias[i] - if not self.unit_variance: - scale_diag_weight = self.scale_diag_weight[i] - scale_diag_bias = self.scale_diag_bias[i] - - mean = tf.matmul(inputs, loc_weight) + loc_bias - - (means.append(mean),) - if not self.unit_variance: - scale_diag.append( - tf.math.softplus( - tf.matmul(inputs, scale_diag_weight) + scale_diag_bias - ) - + 1e-7 - ) - else: - scale_diag.append(tf.ones_like(mean)) - - # shape: (batch, n_components, event_dims) - means = tf.stack(means, axis=1) - scale_diag = tf.stack(scale_diag, axis=1) - - # shape: (batch, n_components, event_dims * 2 + 1) - return tf.concat([logits, means, scale_diag], axis=-1) - - @property - def parameters(self) -> tf.Tensor: - """Getter function for parameters, return the parameters of the - priors. Used when the parameterizer is trained without the use - of observed data (each parameter is itself a trainable - parameters) - - Returns - ------- - tf.Tensor - logits, loc and scale_diag for normal distributions with - diagonal covariance matrix, with shape - (n_components, event_dims * 2 + 1), where - 1. results[n_components, 0] are the logits - 2. results[n_components, 1:event_dims+1] are the loc (mean) - 3. results[n_components, event_dims+1:] are the scale_diag - (std) - - """ - return tf.squeeze(self(tf.ones((1, 1)))) +import numpy as np +import tensorflow as tf + + +class MixtureMultivariateNormalDiagParameterizerLayer(tf.keras.layers.Layer): + """MixtureMultivariateNormalDiagParameterizerLayer + + Parameterizer for mixture of multivariate normal distributions with + diagonal covariance matrix (logits, loc and scale_diag). + + """ + + def __init__( + self, + event_dims: int, + n_components: int, + unit_variance: bool = False, + name: str = "mixture_multivariate_normal_diag_parameterizer_layer", + ): + """Constructor for MultivariateNormalDiagParameterizerLayer + + Parameters + ---------- + event_dims: int + number of event dimensions for the multivariate normal + distributions with diagonal covariance matrix. + + n_components: int + number of components in the mixture distributions. + + unit_variance: bool, optional + use unit variance. Defaults to False. + + name: str, optional + Name for the tensorflow layer. Defaults to + 'mixture_multivariate_normal_diag_parameterizer_layer'. + + """ + super().__init__(name=name) + self.event_dims: int = event_dims + self.n_components: int = n_components + self.unit_variance: bool = unit_variance + + return + + def _make_grid_positions(self, radius: float = 1.5) -> np.ndarray: + K = self.n_components + D = self.event_dims + # side length of the grid along each dimension (hypercube) + side = int(np.ceil(K ** (1.0 / D))) + if side < 1: + side = 1 + + coords = [] + for idx in range(K): + # represent idx in base `side` with D digits + digits = [] + tmp = idx + for _ in range(D): + digits.append(tmp % side) + tmp //= side + # digits is in reverse order; reverse back + digits = digits[::-1] + digits = np.array(digits, dtype=np.float32) + + # center the grid around 0 and scale to roughly [-1, 1]* radius (depends on radius) + center = (side - 1) / 2.0 + if center > 0: + coord = (digits - center) / center + else: + coord = np.zeros_like(digits) + + coord = coord * radius + coords.append(coord) + + coords = np.stack(coords, axis=0) # (K, D) + return coords + + def build(self, input_shape: tf.TensorShape) -> None: + """Create necessary tf.Variable for the first time being called. + (see tf.keras.layers.Layer) + + Parameters + ---------- + input_shape: tf.TensorShape + input shape of tf.Tensor. + + """ + self.logits_weight = self.add_weight( + name=f"{self.name}_logits_weight", + shape=(int(input_shape[-1]), self.n_components), + initializer=tf.keras.initializers.Constant(0.0), + ) + self.logits_bias = self.add_weight( + name=f"{self.name}_logits_bias", + shape=(1, self.n_components), + initializer=tf.keras.initializers.Constant(0.0), + ) + + self.loc_weight = [] + self.loc_bias = [] + if not self.unit_variance: + self.scale_diag_weight = [] + self.scale_diag_bias = [] + # --- NEW: compute grid coordinates for all components --- + grid_coords = self._make_grid_positions() # shape (K, event_dims) + # -------------------------------------------------------- + + for i in range(self.n_components): + # means should not depend on input -> weights = 0 + self.loc_weight.append( + self.add_weight( + name=f"{self.name}_loc_weight_{i}", + shape=(int(input_shape[-1]), self.event_dims), + initializer=tf.keras.initializers.Constant(0.0), + ) + ) + + # bias = initial mean for component i (grid position) + self.loc_bias.append( + self.add_weight( + name=f"{self.name}_loc_bias_{i}", + shape=(1, self.event_dims), + initializer=tf.keras.initializers.Constant( + grid_coords[i][None, :] # shape (1, D) + ), + ) + ) + #### + + if not self.unit_variance: + self.scale_diag_weight.append( + self.add_weight( + name=f"{self.name}_scale_diag_weight_{i}", + shape=(int(input_shape[-1]), self.event_dims), + initializer=tf.keras.initializers.Constant(0.0), # test + ) + ) + self.scale_diag_bias.append( + self.add_weight( + name=f"{self.name}_scale_diag_bias_{i}", + shape=(1, self.event_dims), + initializer=tf.keras.initializers.Constant(0.02), # it was 0.5 + ) + ) + + return + + def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: + """Parameterize mixture of multivariate normal distributions + with diagonal covariance matrix with loc and scale_diag using + the given tf.Tensor. + + Parameters + ---------- + inputs: tf.Tensor + inputs Tensor. + + Returns + ------- + tf.Tensor + logits, loc and scale_diag for normal distributions with + diagonal covariance matrix, with shape + (batch, n_components, event_dims * 2 + 1), where + 1. results[..., n_components, 0] are the logits + 2. results[..., n_components, 1:event_dims+1] are the loc + (mean) + 3. results[..., n_components, event_dims+1:] are the + scale_diag (std) + + """ + + # shape: (batch, n_components, 1) + logits = tf.expand_dims( + tf.matmul(inputs, self.logits_weight) + self.logits_bias, -1 + ) + means = [] + scale_diag = [] + for i in range(self.n_components): + loc_weight = self.loc_weight[i] + loc_bias = self.loc_bias[i] + if not self.unit_variance: + scale_diag_weight = self.scale_diag_weight[i] + scale_diag_bias = self.scale_diag_bias[i] + + mean = tf.matmul(inputs, loc_weight) + loc_bias + + (means.append(mean),) + if not self.unit_variance: + scale_diag.append( + tf.math.softplus( + tf.matmul(inputs, scale_diag_weight) + scale_diag_bias + ) + + 1e-7 + ) + else: + scale_diag.append(tf.ones_like(mean)) + + # shape: (batch, n_components, event_dims) + means = tf.stack(means, axis=1) + scale_diag = tf.stack(scale_diag, axis=1) + + # shape: (batch, n_components, event_dims * 2 + 1) + return tf.concat([logits, means, scale_diag], axis=-1) + + @property + def parameters(self) -> tf.Tensor: + """Getter function for parameters, return the parameters of the + priors. Used when the parameterizer is trained without the use + of observed data (each parameter is itself a trainable + parameters) + + Returns + ------- + tf.Tensor + logits, loc and scale_diag for normal distributions with + diagonal covariance matrix, with shape + (n_components, event_dims * 2 + 1), where + 1. results[n_components, 0] are the logits + 2. results[n_components, 1:event_dims+1] are the loc (mean) + 3. results[n_components, event_dims+1:] are the scale_diag + (std) + + """ + return tf.squeeze(self(tf.ones((1, 1)))) diff --git a/cavachon/layers/parameterizers/studentt_parameterizer_layer.py b/cavachon/layers/parameterizers/studentt_parameterizer_layer.py new file mode 100644 index 0000000..92f3e07 --- /dev/null +++ b/cavachon/layers/parameterizers/studentt_parameterizer_layer.py @@ -0,0 +1,26 @@ +import tensorflow as tf + + +class StudenttParameterizerLayer(tf.keras.layers.Layer): + def __init__( + self, + event_dims: int, + name: str = "studentt_parameterizer_layer", + ): + super().__init__(name=name) + self.event_dims = event_dims + + def build(self, input_shape: tf.TensorShape) -> None: + # We need event_dims * 3 outputs: one set each for loc, scale, and df + self.weight = self.add_weight( + name=f"{self.name}_weight", + shape=(int(input_shape[-1]), self.event_dims * 3), + ) + self.bias = self.add_weight( + name=f"{self.name}_bias", shape=(1, self.event_dims * 3) + ) + + def call(self, inputs: tf.Tensor, **kwargs) -> tf.Tensor: + # Simple linear transformation. The activation (softplus) + # happens inside the Distribution class. + return tf.matmul(inputs, self.weight) + self.bias diff --git a/cavachon/layers/parameterizers/studentt_sampler.py b/cavachon/layers/parameterizers/studentt_sampler.py new file mode 100644 index 0000000..4b5bc2d --- /dev/null +++ b/cavachon/layers/parameterizers/studentt_sampler.py @@ -0,0 +1,28 @@ +import tensorflow as tf +import tensorflow_probability as tfp + + +class StudenttSampler(tf.keras.layers.Layer): + """Sampler for Student-T distribution.""" + + def __init__(self, name: str = "studentt_sampler"): + super().__init__(name=name) + + def call(self, inputs: tf.Tensor, training: bool = False, **kwargs) -> tf.Tensor: + """ + Samples from the Student-T distribution using the parameters. + """ + # We split the 3 parameters (loc, scale, df) + loc, raw_scale, raw_df = tf.split(inputs, 3, axis=-1) + + # Apply the same constraints as in the Distribution class + scale = tf.math.softplus(raw_scale) + 1e-7 + df = tf.math.softplus(raw_df) + 2.0 + + if training: + # During training, we use TFP's sample method (Reparameterization trick) + dist = tfp.distributions.StudentT(df=df, loc=loc, scale=scale) + return dist.sample() + else: + # During inference/prediction, we usually just return the mean (loc) + return loc diff --git a/cavachon/losses/kl_divergence.py b/cavachon/losses/kl_divergence.py index 668845f..166875b 100644 --- a/cavachon/losses/kl_divergence.py +++ b/cavachon/losses/kl_divergence.py @@ -1,139 +1,148 @@ -import tensorflow as tf - -from cavachon.distributions.mixture_multivariate_normal_diag_distribution import ( - MixtureMultivariateNormalDiagDistribution, -) -from cavachon.distributions.multivariate_normal_diag_distribution import ( - MultivariateNormalDiagDistribution, -) - - -class KLDivergence(tf.keras.losses.Loss): - """KLDivergence - - KLDivergence loss adapted from Falck et al., 2021. Computes: - logpx_z + 𝚺_j𝚺_y[py_z(logpz_y + logpy)] - 𝚺_j[logqz_x] - - 𝚺_j𝚺_y[py_z(logpc_z)] - """ - - def __init__(self, weight: float = 1.0, name: str = "kl_divergence", **kwargs): - """Constructor for KLDivergence - - Parameters - ---------- - weight: float, optional - the scaling factor for the loss. The output will be - weight * loss. Defaults to 1.0. - - name: str, optional - name for the tf.keras.losses.Loss (will be used when - reporting the loss during training_step in Component and - Model). Defaults to 'kl_divergence'. - - kwargs: Mapping[str, Any] - additional parameters for tf.keras.losses.Loss - - """ - self.weight = weight - super().__init__( - name=name, reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, **kwargs - ) - - def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: - """Compute the KLDivergence loss - - Parameters - ---------- - y_true: tf.Tensor - The outputs of - layers.parameterizers.MixtureMultivariateNormalDiag with a - inputs of tf.ones((1, 1)), which outputs a tf.Tensor with a - shape of (1, n_components, event_dims * 2 + 1), where: - 1. y_true[..., 0] is the logits for mixture distribution, - 2. y_true[..., 1:event_dims+1] is the locs for each - distribution. - 3. y_true[..., event_dims+1:] is the scale_diag for each - distribution. - Note that this special requirement is designed to follow - the API tf.keras.losses.Loss provides. Can be ignored if - the developers wish to use custom eager training. - - y_pred: tf.Tensor - The outputs of layers.parameterizers.MultivariateNormalDiag, - which outputs a tf.Tensor with a shape of - (batch, event_dims * 2), where - 1. y_pred[..., 0:event_dim] is the loc. - 2. y_pred[..., event_dim:2*event_dims] is the scale_diag. - Note that this special requirement is designed to follow - the API tf.keras.losses.Loss provides. Can be ignored if - the developers wish to use custom eager training. - - Returns - ------- - tf.Tensor: - The computed KLDivergence loss - """ - # Based on eq (C.48) from Falck et al., 2021. Here, we use y to denote c_j - # logpx_z + 𝚺_j𝚺_y[py_z(logpz_y + logpy)] - 𝚺_j[logqz_x] - 𝚺_j𝚺_y[py_z(logpc_z)] - # logpx_z + 𝚺_j𝚺_y[py_z(logpz_y)] + 𝚺_j𝚺_y[py_z(logpy)] - 𝚺_j[logqz_x] - 𝚺_j𝚺_y[py_z(logpy_z)] - # can be written as: - # (a) + (b) + (c) + (d) + (e) - # or - # LogDataLikelihood - NegativeKLDivergence (maximizing the ELBO) - # or - # NegativeLogDataLikelihood + KLDivergence (minimizing the loss) - - event_dims = y_pred.shape[1] // 3 - z = y_pred[..., 0:event_dims] - dist_z_x_params = y_pred[..., event_dims:] - logits_prior = y_true[..., 0] - - # batch_shape: (batch, ), event_shape: (event_dims, ) - dist_z_x = MultivariateNormalDiagDistribution.from_parameterizer_output( - dist_z_x_params - ) - # the 1 here for dist_z_y and dist_z depends on the dimensionality of input tensor to the - # parameterizers of MixtureMultivariateNormalDiag. In MFCVAE, this value should be one (one - # set of priors with n_componetns for each layer) - # batch_shape: (1, n_components), event_shape: (event_dims, ) - dist_z_y = MultivariateNormalDiagDistribution.from_parameterizer_output( - y_true[..., 1:] - ) - # batch_shape: (1, ), event_shape: (event_dims, ) - dist_z = MixtureMultivariateNormalDiagDistribution.from_parameterizer_output( - y_true - ) - - # change the shape of z from (batch, event_dims) to (batch, 1, event_dims) to make the - # operation broadcastable with batch shape (1, n_components) of dist_z_y - # shape: (batch, n_components) - logpz_y = dist_z_y.log_prob(tf.expand_dims(z, -2)) - # shape: (1, n_components) - logpy = tf.math.log(tf.math.softmax(logits_prior) + 1e-7) - # shape: (batch, 1) - logpz = tf.expand_dims(dist_z.log_prob(z), -1) - - # shape: (batch, n_components) - py_z = tf.math.softmax(logpz_y + logpy - logpz) - logpy_z = tf.math.log(py_z + 1e-7) - # logpy_z = logpz_y + logpy - logpz - # py_z = tf.exp(logpy_z) - - # term (b): 𝚺_j𝚺_y[py_z(logpz_y)] - py_z_logpz_y = tf.reduce_sum(py_z * logpz_y, axis=-1) - - # term (c): 𝚺_j𝚺_y[py_z(logpy)] - py_z_logpy = tf.reduce_sum(py_z * logpy, axis=-1) - - # term (d): 𝚺_j[logqz_x] - logqz_x = dist_z_x.log_prob(z) - - # term (e): 𝚺_j𝚺_y[py_z(logpy_z)] - py_z_logpy_z = tf.reduce_sum(py_z * logpy_z, axis=-1) - - kl_divergence = -py_z_logpz_y - py_z_logpy + py_z_logpy_z + logqz_x - kl_divergence = tf.where( - kl_divergence < 0, tf.zeros_like(kl_divergence), kl_divergence - ) - - return self.weight * kl_divergence +import tensorflow as tf + +from cavachon.distributions.mixture_multivariate_normal_diag_distribution import ( + MixtureMultivariateNormalDiagDistribution, +) +from cavachon.distributions.multivariate_normal_diag_distribution import ( + MultivariateNormalDiagDistribution, +) + + +class KLDivergence(tf.keras.losses.Loss): + """KLDivergence + + KLDivergence loss adapted from Falck et al., 2021. Computes: + logpx_z + 𝚺_j𝚺_y[py_z(logpz_y + logpy)] - 𝚺_j[logqz_x] - + 𝚺_j𝚺_y[py_z(logpc_z)] + """ + + def __init__( + self, + weight_var=None, # ← Accept a tf.Variable + name: str = "kl_divergence", + **kwargs + ): + """Constructor for KLDivergence + + Parameters + ---------- + weight_var: float, optional + the scaling factor for the loss. The output will be + weight * loss. Defaults to 1.0. + + name: str, optional + name for the tf.keras.losses.Loss (will be used when + reporting the loss during training_step in Component and + Model). Defaults to 'kl_divergence'. + + kwargs: Mapping[str, Any] + additional parameters for tf.keras.losses.Loss + + """ + super().__init__( + name=name, reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, **kwargs + ) + # Use the provided variable, or create a constant + if weight_var is not None: + self.weight = weight_var + else: + self.weight = tf.constant(1.0, dtype=tf.float32) + + def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: + """Compute the KLDivergence loss + + Parameters + ---------- + y_true: tf.Tensor + The outputs of + layers.parameterizers.MixtureMultivariateNormalDiag with a + inputs of tf.ones((1, 1)), which outputs a tf.Tensor with a + shape of (1, n_components, event_dims * 2 + 1), where: + 1. y_true[..., 0] is the logits for mixture distribution, + 2. y_true[..., 1:event_dims+1] is the locs for each + distribution. + 3. y_true[..., event_dims+1:] is the scale_diag for each + distribution. + Note that this special requirement is designed to follow + the API tf.keras.losses.Loss provides. Can be ignored if + the developers wish to use custom eager training. + + y_pred: tf.Tensor + The outputs of layers.parameterizers.MultivariateNormalDiag, + which outputs a tf.Tensor with a shape of + (batch, event_dims * 2), where + 1. y_pred[..., 0:event_dim] is the loc. + 2. y_pred[..., event_dim:2*event_dims] is the scale_diag. + Note that this special requirement is designed to follow + the API tf.keras.losses.Loss provides. Can be ignored if + the developers wish to use custom eager training. + + Returns + ------- + tf.Tensor: + The computed KLDivergence loss + """ + # Based on eq (C.48) from Falck et al., 2021. Here, we use y to denote c_j + # logpx_z + 𝚺_j𝚺_y[py_z(logpz_y + logpy)] - 𝚺_j[logqz_x] - 𝚺_j𝚺_y[py_z(logpc_z)] + # logpx_z + 𝚺_j𝚺_y[py_z(logpz_y)] + 𝚺_j𝚺_y[py_z(logpy)] - 𝚺_j[logqz_x] - 𝚺_j𝚺_y[py_z(logpy_z)] + # can be written as: + # (a) + (b) + (c) + (d) + (e) + # or + # LogDataLikelihood - NegativeKLDivergence (maximizing the ELBO) + # or + # NegativeLogDataLikelihood + KLDivergence (minimizing the loss) + + event_dims = y_pred.shape[1] // 3 + z = y_pred[..., 0:event_dims] + dist_z_x_params = y_pred[..., event_dims:] + logits_prior = y_true[..., 0] + + # batch_shape: (batch, ), event_shape: (event_dims, ) + dist_z_x = MultivariateNormalDiagDistribution.from_parameterizer_output( + dist_z_x_params + ) + # the 1 here for dist_z_y and dist_z depends on the dimensionality of input tensor to the + # parameterizers of MixtureMultivariateNormalDiag. In MFCVAE, this value should be one (one + # set of priors with n_componetns for each layer) + # batch_shape: (1, n_components), event_shape: (event_dims, ) + dist_z_y = MultivariateNormalDiagDistribution.from_parameterizer_output( + y_true[..., 1:] + ) + # batch_shape: (1, ), event_shape: (event_dims, ) + dist_z = MixtureMultivariateNormalDiagDistribution.from_parameterizer_output( + y_true + ) + + # change the shape of z from (batch, event_dims) to (batch, 1, event_dims) to make the + # operation broadcastable with batch shape (1, n_components) of dist_z_y + # shape: (batch, n_components) + logpz_y = dist_z_y.log_prob(tf.expand_dims(z, -2)) + # shape: (1, n_components) + logpy = tf.math.log(tf.math.softmax(logits_prior) + 1e-7) + # shape: (batch, 1) + logpz = tf.expand_dims(dist_z.log_prob(z), -1) + + # shape: (batch, n_components) + py_z = tf.math.softmax(logpz_y + logpy - logpz) + logpy_z = tf.math.log(py_z + 1e-7) + # logpy_z = logpz_y + logpy - logpz + # py_z = tf.exp(logpy_z) + + # term (b): 𝚺_j𝚺_y[py_z(logpz_y)] + py_z_logpz_y = tf.reduce_sum(py_z * logpz_y, axis=-1) + + # term (c): 𝚺_j𝚺_y[py_z(logpy)] + py_z_logpy = tf.reduce_sum(py_z * logpy, axis=-1) + + # term (d): 𝚺_j[logqz_x] + logqz_x = dist_z_x.log_prob(z) + + # term (e): 𝚺_j𝚺_y[py_z(logpy_z)] + py_z_logpy_z = tf.reduce_sum(py_z * logpy_z, axis=-1) + + kl_divergence = -py_z_logpz_y - py_z_logpy + py_z_logpy_z + logqz_x + kl_divergence = tf.where( + kl_divergence < 0, tf.zeros_like(kl_divergence), kl_divergence + ) + + return self.weight * kl_divergence diff --git a/cavachon/losses/vanilla_kl_divergence.py b/cavachon/losses/vanilla_kl_divergence.py new file mode 100644 index 0000000..28ef9be --- /dev/null +++ b/cavachon/losses/vanilla_kl_divergence.py @@ -0,0 +1,97 @@ +import tensorflow as tf + + +class VanillaKLDivergence(tf.keras.losses.Loss): + """VanillaKLDivergence + + KL divergence between encoder's N(μ, σ²) and standard normal N(0, 1). + KL(N(μ, σ²) || N(0, 1)) = -0.5 * sum[1 + log(σ²) - μ² - σ²] + + - In order to avoid exploding graients, let log(σ²) be the direct output 's' + - Then σ² = exp(s) + + """ + + def __init__( + self, + weight_var=None, # ← Accept a tf.Variable, + name: str = "vanilla_kl_divergence", + **kwargs + ): + """Constructor for VanillaKLDivergence + + Parameters + ---------- + weight_var: float, optional + Scaling factor for the loss. Defaults to 1.0. + + name: str, optional + Name for the loss (shows up in training logs). Defaults to 'vanilla_kl_divergence'. + """ + + super().__init__( + name=name, + reduction=tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE, # Average over batch + **kwargs, + ) + # Use the provided variable, or create a constant + if weight_var is not None: + self.weight = weight_var + else: + self.weight = tf.constant(3.0, dtype=tf.float32) + + def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: + """Compute the vanilla KL divergence loss + + Parameters + ---------- + y_true: tf.Tensor + Not used (prior is fixed N(0,1)), but kept for TensorFlow API compatibility. + + y_pred: tf.Tensor + Shape: (batch, event_dims * 3) + Contains: [sampled_z, mean, std] concatenated + - y_pred[..., 0:event_dims] = sampled z (we ignore this) + - y_pred[..., event_dims:2*event_dims] = mean (μ) from encoder + - y_pred[..., 2*event_dims:3*event_dims] = std (σ) from encoder + + Returns + ------- + tf.Tensor: + The KL divergence value (positive scalar, averaged over batch) + """ + + # Step 1: Figure out dimensions + # y_pred has 3 parts concatenated, so divide by 3 + # y_pred layout: [sampled_z, mean, log_var] + event_dims = y_pred.shape[1] // 3 + + # Step 2: Extract mean (μ) and std (σ) from encoder output + # We skip the first part (sampled z) and only use mean and std + mean = y_pred[..., event_dims : 2 * event_dims] # μ + log_var = y_pred[..., 2 * event_dims : 3 * event_dims] # log(σ²) + + # Step 3: calculate variance + # use exp(), so that var is guaranteed to be positive + var = tf.exp(log_var) + # var = tf.square(std) # σ² = std² + # log_var = tf.math.log(var + 1e-7) # log(σ²), add small number to avoid log(0) + + # Step 4: Apply the closed-form KL formula + # KL(N(μ, σ²) || N(0, 1)) = -0.5 * sum[1 + log(σ²) - μ² - σ²] + kl_divergence = -0.5 * tf.reduce_sum( + 1.0 + log_var - tf.square(mean) - var, + axis=-1, # Sum over latent dimensions + ) + # Result shape: (batch,) - one KL value per sample + + # Step 5: Ensure KL is non-negative + # (Sometimes numerical errors make it slightly negative) + kl_divergence = tf.maximum(kl_divergence, 0.0) + # kl_divergence = tf.where( + # kl_divergence < 0, tf.zeros_like(kl_divergence), kl_divergence + # ) + + # Step 6: Scale by weight and return + # The reduction=SUM_OVER_BATCH_SIZE averages this automatically + return self.weight * kl_divergence diff --git a/cavachon/model/model.py b/cavachon/model/model.py index a01c110..2258b83 100644 --- a/cavachon/model/model.py +++ b/cavachon/model/model.py @@ -1,760 +1,1099 @@ -import warnings -from typing import Any, Dict, Iterable, List, Mapping, Tuple, Union - -import muon as mu -import numpy as np -import tensorflow as tf -from tqdm import tqdm - -from cavachon.config.config_mapping.component_config_mapping import ( - ComponentConfigMapping, -) -from cavachon.dataloader.dataloader import DataLoader -from cavachon.environment.constants import Constants -from cavachon.layers.modifiers import ToDense -from cavachon.losses.kl_divergence import KLDivergence -from cavachon.losses.negative_log_data_likelihood import NegativeLogDataLikelihood -from cavachon.modules.components.component import Component -from cavachon.utils.general_utils import GeneralUtils -from cavachon.utils.tensor_utils import TensorUtils - - -class Model(tf.keras.Model): - """Model - - Main CAVACHON model. It consists of multiple Components and the - dependency between them. - - Attributes - ---------- - components: Mapping[str, Component] - the components which makes up the model. - - component_configs: List[ComponentConfigMapping] - the config used to create the components in the model. - - """ - - def __init__( - self, - inputs: Mapping[Any, tf.keras.Input], - outputs: Mapping[Any, tf.Tensor], - components: Mapping[str, Component], - component_configs: List[ComponentConfigMapping], - name: str = "model", - **kwargs, - ): - """Constuctor for Model. Should not be called directly most of - the time. Please use make() to create the model. - - Parameters - ---------- - inputs: Mapping[Any, tf.keras.Input]): - inputs for building tf.keras.Model using Tensorflow - functional API. By defaults, expect to have keys - 'z_hat_conditional', `modality_name`_matrix, and - `modality_name`_libsize (if applicable). - - outputs: Mapping[Any, tf.keras.Input]): - outputs for building tf.keras.Model using Tensorflow - functional API. By defaults, the keys are: - 1. `component_names`_z - 2. `component_names`_z_hat - 3. `component_names`_z_parameters - 4. `component_names`_`modality_nanes`_x_parameters. - - components: Mapping[str, Component] - the components which makes up the model. - - component_configs: List[ComponentConfigMapping] - the config used to create the components in the model. - - name: str, optional: - Name for the tensorflow model. Defaults to 'model'. - - kwargs: Mapping[str, Any] - additional parameters for custom models. - - """ - super().__init__(inputs=inputs, outputs=outputs, name=name) - self.components: List[Component] = components - self.component_configs: List[ComponentConfigMapping] = component_configs - - @classmethod - def setup_inputs( - cls, - modality_names: List[str], - n_vars: Mapping[str, int], - n_vars_batch_effect: Mapping[str, int], - **kwargs, - ) -> Mapping[Any, tf.keras.Input]: - """Builder function for setting up inputs. Developers can - overwrite this function to create custom Model. - - Parameters - ---------- - modality_names: str - names of the modalities used in the model. - - n_vars: Mapping[str, int] - number of variables for the inputs data distribution. It - should be the size of last dimensions of inputs Tensor. The - keys are the modality names, and the values are the - corresponding number of variables. - - n_vars_batch_effect: Mapping[str, int] - number of variables for the batch effect tensor. It should - be the size of last dimensions of batch effect Tensor. The - keys are the modality names, and the values are the - corresponding number of variables. - - kwargs: Mapping[str, Any] - additional parameters used for custom setup_inputs() - - Returns - ------- - Mapping[Any, tf.keras.Input]: - inputs for building tf.keras.Model using Tensorflow - functional API, where keys are `modality_name`_matrix, - values are the tf.keras.Input. - - """ - inputs = dict() - for modality_name in modality_names: - modality_matrix_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" - modality_batch_key = f"{modality_name}_{Constants.TENSOR_NAME_BATCH}" - inputs.setdefault( - modality_matrix_key, - tf.keras.Input( - shape=(n_vars.get(modality_name),), - name=f"{modality_name}_{Constants.TENSOR_NAME_X}", - ), - ) - inputs.setdefault( - modality_batch_key, - tf.keras.Input( - shape=(n_vars_batch_effect.get(modality_name),), - name=f"{modality_name}_{Constants.TENSOR_NAME_BATCH}", - ), - ) - - return inputs - - @classmethod - def setup_components( - cls, component_configs: List[ComponentConfigMapping], **kwargs - ) -> Tuple: - """Builder function for setting up components. Developers can - overwrite this function to create custom Model. - - Parameters - ---------- - component_configs: List[ComponentConfigMapping] - the config used to create the components in the model. - - kwargs: Mapping[str, Any] - additional parameters used for custom setup_components() - - Returns - ------- - Tuple - 1. The first element is the mapping of created components, - where the keys are the component names, values are the - created components. - 2. The second element is the component configs but - reordered based on the number of breadth first search - successors (topological sort) in the dependency direct - acyclic graph. - 3. The third element is the list of names of all - modalities used in the model. The last element is the - Mapping of number of variables for each modality, where - the keys are the modality names. - - """ - component_configs = GeneralUtils.order_components(component_configs) - components = dict() - modality_names = set() - distributions = dict() - n_vars = dict() - n_vars_batch_effect = dict() - for component_config in component_configs: - modality_names = modality_names.union( - set( - component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES - ) - ) - ) - distributions.update( - component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_DIST_NAMES - ) - ) - n_vars.update(component_config.get(Constants.CONFIG_FIELD_COMPONENT_N_VARS)) - n_vars_batch_effect.update(component_config.get("n_vars_batch_effect")) - - component_name = component_config.get("name") - conditional_dims_config = Model.prepare_conditional_dims_config( - component_config, components - ) - component_config.update(conditional_dims_config) - - components.setdefault(component_name, Component.make(**component_config)) - - return ( - components, - component_configs, - modality_names, - n_vars, - n_vars_batch_effect, - ) - - @classmethod - def setup_outputs( - cls, - inputs: Mapping[Any, tf.keras.Input], - components: List[Component], - component_configs: List[ComponentConfigMapping], - **kwargs, - ) -> Mapping[Any, tf.Tensor]: - """Builder function for setting up outputs. Developers can - overwrite this function to create custom Model. - - Parameters - ---------- - inputs: Mapping[Any, tf.keras.Input] - inputs created using setup_inputs() - - components: Mapping[str, Component] - components created by setup_components(). - - component_configs: List[ComponentConfigMapping] - the config used to create the components in the model. - - kwargs: Mapping[str, Any] - additional parameters used for custom setup_outputs() - - Returns - ------- - Mapping[Any, tf.Tensor] - outputs for building tf.keras.Model using Tensorflow - functional API. - - """ - z_conditional = dict() - z_hat_conditional = dict() - outputs = dict() - for component_config in component_configs: - component_name = component_config.get("name") - component = components.get(component_name) - component_inputs = Model.prepare_component_inputs( - inputs, - component_config, - component_name, - components, - z_conditional, - z_hat_conditional, - ) - - results = component(component_inputs) - for key, result in results.items(): - outputs.setdefault(f"{component_name}_{key}", result) - - z_conditional.setdefault( - component_name, results.get(Constants.MODEL_OUTPUTS_Z) - ) - z_hat_conditional.setdefault( - component_name, results.get(Constants.MODEL_OUTPUTS_Z_HAT) - ) - - return outputs - - @classmethod - def make( - cls, - component_configs: List[ComponentConfigMapping], - name: str = "cavachon", - **kwargs, - ) -> tf.keras.Model: - """Make the tf.keras.Model using the functional API of - Tensorflow. - - Parameters - ---------- - component_configs: Union[Iterable[Dict[str, Any]], Dict[str, Dict[str, Any]]] - the config used to create the components in the model. - - name: str, optional: - name for the tensorflow model. Defaults to 'component'. - - kwargs: Mapping[str, Any] - additional parameters used for the builder functions. - - Returns - ------- - tf.keras.Model - created model using Tensorflow functional API. - - """ - components, component_configs, modality_names, n_vars, n_vars_batch_effect = ( - cls.setup_components(component_configs=component_configs, **kwargs) - ) - - inputs = cls.setup_inputs( - modality_names=modality_names, - n_vars=n_vars, - n_vars_batch_effect=n_vars_batch_effect, - **kwargs, - ) - - outputs = cls.setup_outputs( - inputs=inputs, - components=components, - component_configs=component_configs, - **kwargs, - ) - - return cls( - inputs=inputs, - outputs=outputs, - name=name, - components=components, - component_configs=component_configs, - ) - - def predict( - self, - x: Union[Mapping[str, tf.Tensor], mu.MuData], - batch_size: int = None, - **kwargs, - ): - """Predict based on Mapping[str, tf.Tensor] (with the same - format constructed by the dataset of DataLoader) or mu.MuData. - If provided with mu.MuData, the predicted z and x_parameters - will be stored in the obsm of each modality. - - Parameters - ---------- - x: Union[Mapping[str, tf.Tensor], mu.MuData] - inputs. - - batch_size: int, optional - batch size. If provided with None, will automatically set - to 1. Defaults to None. - - kwargs: Mapping[str, Any] - Additional parameters used to compile the model. - - """ - if batch_size is None: - batch_size = 1 - if issubclass(type(x), mu.MuData): - outputs = dict() - use_which_component = dict() - field_save_x = Constants.CONFIG_FIELD_COMPONENT_MODALITY_SAVE_X - field_save_z = Constants.CONFIG_FIELD_COMPONENT_MODALITY_SAVE_Z - save_x = dict() - save_z = dict() - save_z_hat = dict() - for component_config in self.component_configs: - component_name = component_config.name - outputs.setdefault(f"{component_name}_z", list()) - outputs.setdefault(f"{component_name}_z_hat", list()) - modality_names = component_config.get( - Constants.CONFIG_FIELD_COMPONENT_N_VARS - ).keys() - predict_x = False - - for modality_name in modality_names: - if component_config.get(field_save_x).get(modality_name): - predict_x = True - - use_which_component.setdefault(modality_name, []) - use_which_component.get(modality_name).append(component_name) - save_x.setdefault( - f"{component_name}_{modality_name}", - component_config.get(field_save_x).get(modality_name), - ) - save_z.setdefault( - f"{component_name}_{modality_name}", - component_config.get(field_save_z).get(modality_name), - ) - save_z_hat.setdefault( - f"{component_name}_{modality_name}", - component_config.get(field_save_z).get(modality_name), - ) - if predict_x: - outputs.setdefault( - f"{component_name}_{modality_name}_x_parameters", list() - ) - - dataloader = DataLoader(x, batch_size=batch_size) - for batch in tqdm(dataloader): - result = self.predict_on_batch(batch) - for key in outputs: - outputs[key].append(result.get(key)) - for key in outputs: - outputs[key] = np.vstack(outputs[key]) - - for modality_name, component_names in use_which_component.items(): - for component_name in component_names: - if save_z.get(f"{component_name}_{modality_name}"): - x.mod[modality_name].obsm[f"z_{component_name}"] = outputs.get( - f"{component_name}_z" - ) - if save_z.get(f"{component_name}_{modality_name}"): - x.mod[modality_name].obsm[f"z_hat_{component_name}"] = ( - outputs.get(f"{component_name}_z_hat") - ) - if save_x.get(f"{component_name}_{modality_name}"): - x.mod[modality_name].obsm[f"x_parameters_{component_name}"] = ( - outputs.get( - f"{component_name}_{modality_name}_x_parameters" - ) - ) - - return outputs - else: - return super.__predict__(x=x, batch_size=batch_size, **kwargs) - - def compile(self, **kwargs) -> None: - """Compile the model before training. Note that the 'metrics' - will be ignored in Model because of the incompatibility with - Tensorflow API. The 'loss' will be setup automatically if not - provided. - - Parameters - ---------- - kwargs: Mapping[str, Any] - additional parameters used to compile the model. - - """ - loss_weights = kwargs.get("loss_weights", dict()) - kwargs.pop("loss_weights", None) - - if "loss" not in kwargs: - loss = dict() - for component_config in self.component_configs: - component_name = component_config.get("name") - kl_divergence_name = ( - f"{component_name}_{Constants.MODEL_LOSS_KL_POSTFIX}" - ) - loss.setdefault( - kl_divergence_name, - KLDivergence( - loss_weights.get(kl_divergence_name, 1.0), - name=kl_divergence_name, - ), - ) - - for modality_name in component_config.get("modality_names"): - nldl_name = f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}" - distribution_names = component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_DIST_NAMES - ) - loss.setdefault( - nldl_name, - NegativeLogDataLikelihood( - distribution_names.get(modality_name), - loss_weights.get(nldl_name, 1.0), - name=nldl_name, - ), - ) - kwargs.setdefault("loss", loss) - else: - message = "".join( - ( - "Please make sure the provided custom losses are properly used in ", - f"train_step() of {self.__class__.__name__}.", - ) - ) - warnings.warn(message, RuntimeWarning) - - if "metrics" in kwargs: - message = "".join( - ( - f"{self.__class__.__name__} directly uses the loss as evaluation metrics. ", - "The custom metrics provided to compile() will be ignored.", - ) - ) - warnings.warn(message, RuntimeWarning) - kwargs.pop("metrics") - - super().compile(**kwargs) - - def train_step(self, data: Mapping[Any, tf.Tensor]) -> Mapping[str, float]: - """Training step for one iteration. The trainable variables in - the Model will be trained once after calling this function. - - Parameters - ---------- - data: Mapping[Any, tf.Tensor] - input data with structure specified with self.inputs. - - Returns - ------- - Mapping[str, float] - losses trained in the training iteration, where the keys - are the names of the losses. - - """ - with tf.GradientTape() as tape: - results = self(data, training=True) - y_true = dict() - y_pred = dict() - - for component_config in self.component_configs: - component_name = component_config.get("name") - kl_divergence_name = ( - f"{component_name}_{Constants.MODEL_LOSS_KL_POSTFIX}" - ) - component = self.components.get(component_name) - - modality_names = component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES - ) - y_true.setdefault( - kl_divergence_name, - results.get( - f"{component_name}_{Constants.MODEL_OUTPUTS_Z_PRIOR_PARAMS}" - ), - ) - - z_key = f"{component_name}_{Constants.MODEL_OUTPUTS_Z}" - z_params_key = f"{component_name}_{Constants.MODEL_OUTPUTS_Z_PARAMS}" - - y_pred.setdefault( - kl_divergence_name, - tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=-1))( - [results.get(z_key), results.get(z_params_key)] - ), - ) - for modality_name in modality_names: - nldl_name = f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}" - modality_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" - data = ToDense(modality_key)(data) - y_true.setdefault(nldl_name, data.get(modality_key)) - y_pred.setdefault( - nldl_name, - results.get( - f"{component_name}_{modality_name}_{Constants.MODEL_OUTPUTS_X_PARAMS}" - ), - ) - - loss = self.compute_loss(x=None, y=y_true, y_pred=y_pred) - t = self.components["ATAC"].z_prior_parameterizer(tf.ones((1, 1)))[:, 1:] - print(tf.split(t, 2, 1)[0]) - gradients = tape.gradient(loss, self.trainable_variables) - # print(gradients) - gradients = TensorUtils.remove_nan_gradients(gradients) - self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) - - loss_metrics = {"loss": loss} - for key in y_true: - loss_fn = self.loss.get(key) - if loss_fn: - loss_value = loss_fn(y_true[key], y_pred[key]) - loss_metrics[key] = loss_value - - return loss_metrics - - def __setattr__(self, name: str, value: Any) -> None: - """Overwrite __setattr__ function, so that every time setting - trainable to False, it automatically set alpha in the - progressive_scaler of every components to 1.0. - - Parameters - ---------- - name: str - name of the attributes - - value: Any - new value of the attributes. - - """ - super().__setattr__(name, value) - if name == "trainable": - if not value: - for component_name in self.components.keys(): - self.components[component_name].trainable = value - - @staticmethod - def prepare_conditionals( - for_dims: bool = True, - z_conditional: Mapping[str, tf.Tensor] = None, - z_hat_conditional: Mapping[str, tf.Tensor] = None, - ) -> Iterable: - """Prepare iterable conditionals used in - `prepare_conditional_dims_config` and - `prepare_component_inputs`. This function should not be used - directly by the user. - - Parameters - ---------- - for_dims: bool, optional - whether the function is called by - `prepare_conditional_dims_config`. Defaults to True. - - z_conditional: Mapping[str, tf.Tensor], optional - Tensor of z_conditional, keys should be the component names - that the current component condition on (z), value is the - corresponding z Tensor. Ignored if `for_dims=True`. Default - to None. - - z_hat_conditional: Mapping[str, tf.Tensor], optional - Tensor of z_hat_conditional, keys should be the component - names that the current component condition on (z_hat), - value is the corresponding z_hat Tensor. Ignored if - `for_dims=True`. Default to None. - - Returns - ------- - Iterable - if `for_dims=True`, return zip( - ['conditioned_on_z', 'conditioned_on_z_hat'], - ['z_conditional_dims', 'z_hat_conditonal_dims']) - else, return zip( - ['z_conditional', 'z_hat_conditional'], - ['conditioned_on_z', 'conditioned_on_z_hat'], - [{`modality_names`: z}, {`modality_names`: z_hat}]) - - """ - - conditional_input_keys = [ - Constants.MODULE_INPUTS_CONDITIONED_Z, - Constants.MODULE_INPUTS_CONDITIONED_Z_HAT, - ] - - conditional_config_keys = [ - Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z, - Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, - ] - - conditional_dims_keys = [ - Constants.MODEL_INPUTS_Z_CONDITIONAL_DIMS, - Constants.MODEL_INPUTS_Z_HAT_CONDITIONAL_DIMS, - ] - - conditional_tensor_dicts = [z_conditional, z_hat_conditional] - if for_dims: - conditionals = zip(conditional_config_keys, conditional_dims_keys) - else: - conditionals = zip( - conditional_input_keys, - conditional_config_keys, - conditional_tensor_dicts, - ) - - return conditionals - - @staticmethod - def prepare_conditional_dims_config( - component_config: ComponentConfigMapping, components: Mapping[str, Component] - ) -> Dict[str, int]: - """Prepare the config for conditional dimensions used in - `setup_components`. This function should not be used directly - by the user. - - Parameters - ---------- - component_config: List[ComponentConfigMapping] - the config used to create the current component. - - components: Mapping[str, Component] - the components which makes up the model. - - Returns - ------- - Dict[str, int] - keys are the `z_conditional_dims` and - `z_hat_conditional_dims`, values are the corresponding - Tensor dimensions. - - """ - conditional_dims_config = dict() - - conditionals = Model.prepare_conditionals() - for config_key, dims_key in conditionals: - conditional_component_names = component_config.get(config_key, []) - if len(conditional_component_names) == 0: - conditional_dims_config.setdefault(dims_key, None) - else: - conditional_dims = 0 - for conditional_component_name in conditional_component_names: - component = components.get(conditional_component_name) - conditional_dims += component.z_prior_parameterizer.event_dims - conditional_dims_config.setdefault(dims_key, conditional_dims) - - return conditional_dims_config - - @staticmethod - def prepare_component_inputs( - batch: Mapping[str, tf.Tensor], - component_config: ComponentConfigMapping, - target_component: str, - components: Mapping[str, Component], - z_conditional: Mapping[str, tf.Tensor] = dict(), - z_hat_conditional: Mapping[str, tf.Tensor] = dict(), - ) -> Dict[str, tf.Tensor]: - """Prepare the inputs for the component used in `setup_outputs`. - - Parameters - ---------- - batch: Mapping[str, tf.Tensor] - batch inputs. - - component_config: List[ComponentConfigMapping] - the config used to create the current component. - - target_component: str - the target component name. - - components: Mapping[str, Component] - the components which makes up the model. - - z_conditional: Mapping[str, tf.Tensor], optional - the keys are the name of the conditioned component (z), the - values are the corresponding z Tensor. Defaults to {}. - - z_hat_conditional: Mapping[str, tf.Tensor], optional - the keys are the name of the conditioned component (z_hat), - the values are the corresponding z_hat Tensor. Defaults to - {}. - - Returns - ------- - Dict[str, tf.Tensors] - keys are the `{modality_name}_matrix`, - `{modality_name}_batch_effect`, `z_conditional`, - `z_hat_conditional`, values are the corresponding Tensors. - - """ - component_inputs = dict() - for modality_name in components.get(target_component).modality_names: - modality_matrix_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" - modality_batch_key = f"{modality_name}_{Constants.TENSOR_NAME_BATCH}" - component_inputs.setdefault( - modality_matrix_key, batch.get(modality_matrix_key) - ) - component_inputs.setdefault( - modality_batch_key, batch.get(modality_batch_key) - ) - - conditionals = Model.prepare_conditionals( - False, z_conditional, z_hat_conditional - ) - - for input_key, config_key, tensor_dict in conditionals: - conditional_tensors = [] - conditional_component_names = component_config.get(config_key, []) - if len(conditional_component_names) != 0: - for conditional_component_name in conditional_component_names: - conditional_tensors.append( - tensor_dict.get(conditional_component_name) - ) - conditional_tensors = tf.keras.layers.Lambda( - lambda x: tf.concat(x, axis=-1) - )(conditional_tensors) - component_inputs.setdefault(input_key, conditional_tensors) - - return component_inputs +import warnings +from typing import Any, Dict, Iterable, List, Mapping, Optional, Tuple, Union + +import muon as mu +import numpy as np +import tensorflow as tf +from tqdm import tqdm + +from cavachon.config.config_mapping.component_config_mapping import ( + ComponentConfigMapping, +) +from cavachon.dataloader.dataloader import DataLoader +from cavachon.environment.constants import Constants +from cavachon.layers.modifiers import ToDense +from cavachon.losses.kl_divergence import KLDivergence +from cavachon.losses.negative_log_data_likelihood import NegativeLogDataLikelihood +from cavachon.losses.vanilla_kl_divergence import VanillaKLDivergence +from cavachon.modules.components.component import Component +from cavachon.utils.general_utils import GeneralUtils +from cavachon.utils.tensor_utils import TensorUtils + + +class Model(tf.keras.Model): + """Model + + Main CAVACHON model. It consists of multiple Components and the + dependency between them. + + Attributes + ---------- + components: Mapping[str, Component] + the components which makes up the model. + + component_configs: List[ComponentConfigMapping] + the config used to create the components in the model. + + """ + + def __init__( + self, + inputs: Mapping[Any, tf.keras.Input], + outputs: Mapping[Any, tf.Tensor], + components: Mapping[str, Component], + component_configs: List[ComponentConfigMapping], + name: str = "model", + **kwargs, + ): + """Constuctor for Model. Should not be called directly most of + the time. Please use make() to create the model. + + Parameters + ---------- + inputs: Mapping[Any, tf.keras.Input]): + inputs for building tf.keras.Model using Tensorflow + functional API. By defaults, expect to have keys + 'z_hat_conditional', `modality_name`_matrix, and + `modality_name`_libsize (if applicable). + + outputs: Mapping[Any, tf.keras.Input]): + outputs for building tf.keras.Model using Tensorflow + functional API. By defaults, the keys are: + 1. `component_names`_z + 2. `component_names`_z_hat + 3. `component_names`_z_parameters + 4. `component_names`_`modality_nanes`_x_parameters. + + components: Mapping[str, Component] + the components which makes up the model. + + component_configs: List[ComponentConfigMapping] + the config used to create the components in the model. + + name: str, optional: + Name for the tensorflow model. Defaults to 'model'. + + kwargs: Mapping[str, Any] + additional parameters for custom models. + + """ + super().__init__(inputs=inputs, outputs=outputs, name=name) + self.components: List[Component] = components + self.component_configs: List[ComponentConfigMapping] = component_configs + + @classmethod + def setup_inputs( + cls, + modality_names: List[str], + n_vars: Mapping[str, int], + n_vars_batch_effect: Mapping[str, int], + **kwargs, + ) -> Mapping[Any, tf.keras.Input]: + """Builder function for setting up inputs. Developers can + overwrite this function to create custom Model. + + Parameters + ---------- + modality_names: str + names of the modalities used in the model. + + n_vars: Mapping[str, int] + number of variables for the inputs data distribution. It + should be the size of last dimensions of inputs Tensor. The + keys are the modality names, and the values are the + corresponding number of variables. + + n_vars_batch_effect: Mapping[str, int] + number of variables for the batch effect tensor. It should + be the size of last dimensions of batch effect Tensor. The + keys are the modality names, and the values are the + corresponding number of variables. + + kwargs: Mapping[str, Any] + additional parameters used for custom setup_inputs() + + Returns + ------- + Mapping[Any, tf.keras.Input]: + inputs for building tf.keras.Model using Tensorflow + functional API, where keys are `modality_name`_matrix, + values are the tf.keras.Input. + + """ + inputs = dict() + for modality_name in modality_names: + modality_matrix_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + modality_batch_key = f"{modality_name}_{Constants.TENSOR_NAME_BATCH}" + inputs.setdefault( + modality_matrix_key, + tf.keras.Input( + shape=(n_vars.get(modality_name),), + name=f"{modality_name}_{Constants.TENSOR_NAME_X}", + ), + ) + inputs.setdefault( + modality_batch_key, + tf.keras.Input( + shape=(n_vars_batch_effect.get(modality_name),), + name=f"{modality_name}_{Constants.TENSOR_NAME_BATCH}", + ), + ) + + return inputs + + @classmethod + def setup_components( + cls, component_configs: List[ComponentConfigMapping], **kwargs + ) -> Tuple: + """Builder function for setting up components. Developers can + overwrite this function to create custom Model. + + Parameters + ---------- + component_configs: List[ComponentConfigMapping] + the config used to create the components in the model. + + kwargs: Mapping[str, Any] + additional parameters used for custom setup_components() + + Returns + ------- + Tuple + 1. The first element is the mapping of created components, + where the keys are the component names, values are the + created components. + 2. The second element is the component configs but + reordered based on the number of breadth first search + successors (topological sort) in the dependency direct + acyclic graph. + 3. The third element is the list of names of all + modalities used in the model. The last element is the + Mapping of number of variables for each modality, where + the keys are the modality names. + + """ + component_configs = GeneralUtils.order_components(component_configs) + components = dict() + modality_names = set() + distributions = dict() + n_vars = dict() + n_vars_batch_effect = dict() + for component_config in component_configs: + modality_names = modality_names.union( + set( + component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES + ) + ) + ) + distributions.update( + component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_DIST_NAMES + ) + ) + n_vars.update(component_config.get(Constants.CONFIG_FIELD_COMPONENT_N_VARS)) + n_vars_batch_effect.update(component_config.get("n_vars_batch_effect")) + + component_name = component_config.get("name") + conditional_dims_config = Model.prepare_conditional_dims_config( + component_config, components + ) + component_config.update(conditional_dims_config) + + components.setdefault(component_name, Component.make(**component_config)) + + return ( + components, + component_configs, + modality_names, + n_vars, + n_vars_batch_effect, + ) + + @classmethod + def setup_outputs( + cls, + inputs: Mapping[Any, tf.keras.Input], + components: List[Component], + component_configs: List[ComponentConfigMapping], + **kwargs, + ) -> Mapping[Any, tf.Tensor]: + """Builder function for setting up outputs. Developers can + overwrite this function to create custom Model. + + Parameters + ---------- + inputs: Mapping[Any, tf.keras.Input] + inputs created using setup_inputs() + + components: Mapping[str, Component] + components created by setup_components(). + + component_configs: List[ComponentConfigMapping] + the config used to create the components in the model. + + kwargs: Mapping[str, Any] + additional parameters used for custom setup_outputs() + + Returns + ------- + Mapping[Any, tf.Tensor] + outputs for building tf.keras.Model using Tensorflow + functional API. + + """ + z_conditional = dict() + z_hat_conditional = dict() + outputs = dict() + for component_config in component_configs: + component_name = component_config.get("name") + component = components.get(component_name) + component_inputs = Model.prepare_component_inputs( + inputs, + component_config, + component_name, + components, + z_conditional, + z_hat_conditional, + ) + + results = component(component_inputs) + for key, result in results.items(): + outputs.setdefault(f"{component_name}_{key}", result) + + z_conditional.setdefault( + component_name, results.get(Constants.MODEL_OUTPUTS_Z) + ) + z_hat_conditional.setdefault( + component_name, results.get(Constants.MODEL_OUTPUTS_Z_HAT) + ) + + return outputs + + @classmethod + def make( + cls, + component_configs: List[ComponentConfigMapping], + name: str = "cavachon", + **kwargs, + ) -> tf.keras.Model: + """Make the tf.keras.Model using the functional API of + Tensorflow. + + Parameters + ---------- + component_configs: Union[Iterable[Dict[str, Any]], Dict[str, Dict[str, Any]]] + the config used to create the components in the model. + + name: str, optional: + name for the tensorflow model. Defaults to 'component'. + + kwargs: Mapping[str, Any] + additional parameters used for the builder functions. + + Returns + ------- + tf.keras.Model + created model using Tensorflow functional API. + + """ + components, component_configs, modality_names, n_vars, n_vars_batch_effect = ( + cls.setup_components(component_configs=component_configs, **kwargs) + ) + + inputs = cls.setup_inputs( + modality_names=modality_names, + n_vars=n_vars, + n_vars_batch_effect=n_vars_batch_effect, + **kwargs, + ) + + outputs = cls.setup_outputs( + inputs=inputs, + components=components, + component_configs=component_configs, + **kwargs, + ) + + return cls( + inputs=inputs, + outputs=outputs, + name=name, + components=components, + component_configs=component_configs, + ) + + def predict( + self, + x: Union[Mapping[str, tf.Tensor], mu.MuData], + batch_size: int = None, + **kwargs, + ): + """Predict based on Mapping[str, tf.Tensor] (with the same + format constructed by the dataset of DataLoader) or mu.MuData. + If provided with mu.MuData, the predicted z and x_parameters + will be stored in the obsm of each modality. + + Parameters + ---------- + x: Union[Mapping[str, tf.Tensor], mu.MuData] + inputs. + + batch_size: int, optional + batch size. If provided with None, will automatically set + to 1. Defaults to None. + + kwargs: Mapping[str, Any] + Additional parameters used to compile the model. + + """ + if batch_size is None: + batch_size = 1 + if issubclass(type(x), mu.MuData): + outputs = dict() + use_which_component = dict() + field_save_x = Constants.CONFIG_FIELD_COMPONENT_MODALITY_SAVE_X + field_save_z = Constants.CONFIG_FIELD_COMPONENT_MODALITY_SAVE_Z + save_x = dict() + save_z = dict() + save_z_hat = dict() + for component_config in self.component_configs: + component_name = component_config.name + outputs.setdefault(f"{component_name}_z", list()) + outputs.setdefault(f"{component_name}_z_hat", list()) + modality_names = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_N_VARS + ).keys() + predict_x = False + + for modality_name in modality_names: + if component_config.get(field_save_x).get(modality_name): + predict_x = True + + use_which_component.setdefault(modality_name, []) + use_which_component.get(modality_name).append(component_name) + save_x.setdefault( + f"{component_name}_{modality_name}", + component_config.get(field_save_x).get(modality_name), + ) + save_z.setdefault( + f"{component_name}_{modality_name}", + component_config.get(field_save_z).get(modality_name), + ) + save_z_hat.setdefault( + f"{component_name}_{modality_name}", + component_config.get(field_save_z).get(modality_name), + ) + if predict_x: + outputs.setdefault( + f"{component_name}_{modality_name}_x_parameters", list() + ) + + dataloader = DataLoader(x, batch_size=batch_size) + for batch in tqdm(dataloader): + result = self.predict_on_batch(batch) + for key in outputs: + outputs[key].append(result.get(key)) + for key in outputs: + outputs[key] = np.vstack(outputs[key]) + + for modality_name, component_names in use_which_component.items(): + for component_name in component_names: + if save_z.get(f"{component_name}_{modality_name}"): + x.mod[modality_name].obsm[f"z_{component_name}"] = outputs.get( + f"{component_name}_z" + ) + if save_z.get(f"{component_name}_{modality_name}"): + x.mod[modality_name].obsm[f"z_hat_{component_name}"] = ( + outputs.get(f"{component_name}_z_hat") + ) + if save_x.get(f"{component_name}_{modality_name}"): + x.mod[modality_name].obsm[f"x_parameters_{component_name}"] = ( + outputs.get( + f"{component_name}_{modality_name}_x_parameters" + ) + ) + + return outputs + else: + return super.__predict__(x=x, batch_size=batch_size, **kwargs) + + def compile(self, use_vanilla_kl=False, use_both_kl=False, **kwargs) -> None: + """Compile the model before training. Note that the 'metrics' + will be ignored in Model because of the incompatibility with + Tensorflow API. The 'loss' will be setup automatically if not + provided. + + Parameters + ---------- + use_vanilla_kl: bool, optional + If True, uses vanilla N(0,1) KL divergence instead of GMM KL. + Used for progressive training phase. Defaults to False. + + use_both_kl: bool, optional + If True, use BOTH vanilla and GMM KL losses (Phase 2 - Transition) + If both False, use only GMM KL loss (Phase 3) + + kwargs: Mapping[str, Any] + additional parameters used to compile the model. + + """ + # Create two separate weight variables + if not hasattr(self, "_vanilla_kl_weights"): + self._vanilla_kl_weights = {} + if not hasattr(self, "_gmm_kl_weights"): + self._gmm_kl_weights = {} + + # Create one variable per component + for component_config in self.component_configs: + component_name = component_config.get("name") + + if component_name not in self._vanilla_kl_weights: + self._vanilla_kl_weights[component_name] = tf.Variable( + 3.0, + trainable=False, + dtype=tf.float32, + name=f"{component_name}_vanilla_kl_weight", + ) + + if component_name not in self._gmm_kl_weights: + self._gmm_kl_weights[component_name] = tf.Variable( + 0.0, + trainable=False, + dtype=tf.float32, + name=f"{component_name}_gmm_kl_weight", + ) + + loss_weights = kwargs.get("loss_weights", dict()) + kwargs.pop("loss_weights", None) + + if "loss" not in kwargs: + loss = dict() + for component_config in self.component_configs: + component_name = component_config.get("name") + + kl_divergence_name = ( + f"{component_name}_{Constants.MODEL_LOSS_KL_POSTFIX}" + ) + + # Three way logic for KL loss + if use_both_kl: + # PHASE 2 (TRANSITION): Both losses active with different names + loss.setdefault( + f"{component_name}_vanilla_kl_divergence", # Different name for vanilla + VanillaKLDivergence( + weight_var=self._vanilla_kl_weights[component_name], + name=f"{component_name}_vanilla_kl_divergence", + ), + ) + loss.setdefault( + f"{component_name}_gmm_kl_divergence", # Different name for gmm + KLDivergence( + weight_var=self._gmm_kl_weights[component_name], + name=f"{component_name}_gmm_kl_divergence", + ), + ) + elif use_vanilla_kl: + # PHASE 1: Vanilla KL only + loss.setdefault( + kl_divergence_name, + VanillaKLDivergence( + weight_var=self._vanilla_kl_weights[component_name], + name=kl_divergence_name, + ), + ) + else: + # PHASE 3: Use GMM KL only + loss.setdefault( + kl_divergence_name, + KLDivergence( + weight_var=self._gmm_kl_weights[component_name], + name=kl_divergence_name, + ), + ) + + for modality_name in component_config.get("modality_names"): + nldl_name = f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}" + distribution_names = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_DIST_NAMES + ) + loss.setdefault( + nldl_name, + NegativeLogDataLikelihood( + distribution_names.get(modality_name), + loss_weights.get(nldl_name, 1.0), + name=nldl_name, + ), + ) + kwargs.setdefault("loss", loss) + else: + message = "".join( + ( + "Please make sure the provided custom losses are properly used in ", + f"train_step() of {self.__class__.__name__}.", + ) + ) + warnings.warn(message, RuntimeWarning) + + if "metrics" in kwargs: + message = "".join( + ( + f"{self.__class__.__name__} directly uses the loss as evaluation metrics. ", + "The custom metrics provided to compile() will be ignored.", + ) + ) + warnings.warn(message, RuntimeWarning) + kwargs.pop("metrics") + + super().compile(**kwargs) + + def train_step(self, data: Mapping[Any, tf.Tensor]) -> Mapping[str, float]: + """Training step for one iteration. The trainable variables in + the Model will be trained once after calling this function. + + Parameters + ---------- + data: Mapping[Any, tf.Tensor] + input data with structure specified with self.inputs. + + Returns + ------- + Mapping[str, float] + losses trained in the training iteration, where the keys + are the names of the losses. + + """ + with tf.GradientTape() as tape: + results = self(data, training=True) + y_true = dict() + y_pred = dict() + + for component_config in self.component_configs: + component_name = component_config.get("name") + + kl_divergence_name = ( + f"{component_name}_{Constants.MODEL_LOSS_KL_POSTFIX}" + ) + + modality_names = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES + ) + + # Get the prior parameters and z data (same for all phases) + prior_params = results.get( + f"{component_name}_{Constants.MODEL_OUTPUTS_Z_PRIOR_PARAMS}" + ) + z_key = f"{component_name}_{Constants.MODEL_OUTPUTS_Z}" + z_params_key = f"{component_name}_{Constants.MODEL_OUTPUTS_Z_PARAMS}" + z_concat = tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=-1))( + [results.get(z_key), results.get(z_params_key)] + ) + # Check which KL losses are compiled + vanilla_kl_name = f"{component_name}_vanilla_kl_divergence" + gmm_kl_name = f"{component_name}_gmm_kl_divergence" + + if vanilla_kl_name in self.loss and gmm_kl_name in self.loss: + # PHASE 2: Both losses active + y_true.setdefault(vanilla_kl_name, prior_params) + y_pred.setdefault(vanilla_kl_name, z_concat) + y_true.setdefault(gmm_kl_name, prior_params) + y_pred.setdefault(gmm_kl_name, z_concat) + else: + # PHASE 1 or 3: Single loss (standard name) + y_true.setdefault(kl_divergence_name, prior_params) + y_pred.setdefault(kl_divergence_name, z_concat) + + for modality_name in modality_names: + nldl_name = f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}" + modality_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + data = ToDense(modality_key)(data) + y_true.setdefault(nldl_name, data.get(modality_key)) + y_pred.setdefault( + nldl_name, + results.get( + f"{component_name}_{modality_name}_{Constants.MODEL_OUTPUTS_X_PARAMS}" + ), + ) + + loss = self.compute_loss(x=None, y=y_true, y_pred=y_pred) + gradients = tape.gradient(loss, self.trainable_variables) + gradients = TensorUtils.remove_nan_gradients(gradients) + self.optimizer.apply_gradients(zip(gradients, self.trainable_variables)) + + loss_metrics = {"loss": loss} + for key in y_true: + loss_fn = self.loss.get(key) + if loss_fn: + loss_value = loss_fn(y_true[key], y_pred[key]) + loss_metrics[key] = loss_value + + return loss_metrics + + def __setattr__(self, name: str, value: Any) -> None: + """Overwrite __setattr__ function, so that every time setting + trainable to False, it automatically set alpha in the + progressive_scaler of every components to 1.0. + + Parameters + ---------- + name: str + name of the attributes + + value: Any + new value of the attributes. + + """ + super().__setattr__(name, value) + if name == "trainable": + if not value: + for component_name in self.components.keys(): + self.components[component_name].trainable = value + + @staticmethod + def prepare_conditionals( + for_dims: bool = True, + z_conditional: Mapping[str, tf.Tensor] = None, + z_hat_conditional: Mapping[str, tf.Tensor] = None, + ) -> Iterable: + """Prepare iterable conditionals used in + `prepare_conditional_dims_config` and + `prepare_component_inputs`. This function should not be used + directly by the user. + + Parameters + ---------- + for_dims: bool, optional + whether the function is called by + `prepare_conditional_dims_config`. Defaults to True. + + z_conditional: Mapping[str, tf.Tensor], optional + Tensor of z_conditional, keys should be the component names + that the current component condition on (z), value is the + corresponding z Tensor. Ignored if `for_dims=True`. Default + to None. + + z_hat_conditional: Mapping[str, tf.Tensor], optional + Tensor of z_hat_conditional, keys should be the component + names that the current component condition on (z_hat), + value is the corresponding z_hat Tensor. Ignored if + `for_dims=True`. Default to None. + + Returns + ------- + Iterable + if `for_dims=True`, return zip( + ['conditioned_on_z', 'conditioned_on_z_hat'], + ['z_conditional_dims', 'z_hat_conditonal_dims']) + else, return zip( + ['z_conditional', 'z_hat_conditional'], + ['conditioned_on_z', 'conditioned_on_z_hat'], + [{`modality_names`: z}, {`modality_names`: z_hat}]) + + """ + + conditional_input_keys = [ + Constants.MODULE_INPUTS_CONDITIONED_Z, + Constants.MODULE_INPUTS_CONDITIONED_Z_HAT, + ] + + conditional_config_keys = [ + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z, + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, + ] + + conditional_dims_keys = [ + Constants.MODEL_INPUTS_Z_CONDITIONAL_DIMS, + Constants.MODEL_INPUTS_Z_HAT_CONDITIONAL_DIMS, + ] + + conditional_tensor_dicts = [z_conditional, z_hat_conditional] + if for_dims: + conditionals = zip(conditional_config_keys, conditional_dims_keys) + else: + conditionals = zip( + conditional_input_keys, + conditional_config_keys, + conditional_tensor_dicts, + ) + + return conditionals + + @staticmethod + def prepare_conditional_dims_config( + component_config: ComponentConfigMapping, components: Mapping[str, Component] + ) -> Dict[str, int]: + """Prepare the config for conditional dimensions used in + `setup_components`. This function should not be used directly + by the user. + + Parameters + ---------- + component_config: List[ComponentConfigMapping] + the config used to create the current component. + + components: Mapping[str, Component] + the components which makes up the model. + + Returns + ------- + Dict[str, int] + keys are the `z_conditional_dims` and + `z_hat_conditional_dims`, values are the corresponding + Tensor dimensions. + + """ + conditional_dims_config = dict() + + conditionals = Model.prepare_conditionals() + for config_key, dims_key in conditionals: + conditional_component_names = component_config.get(config_key, []) + if len(conditional_component_names) == 0: + conditional_dims_config.setdefault(dims_key, None) + else: + conditional_dims = 0 + for conditional_component_name in conditional_component_names: + component = components.get(conditional_component_name) + conditional_dims += component.z_prior_parameterizer.event_dims + conditional_dims_config.setdefault(dims_key, conditional_dims) + + return conditional_dims_config + + @staticmethod + def prepare_component_inputs( + batch: Mapping[str, tf.Tensor], + component_config: ComponentConfigMapping, + target_component: str, + components: Mapping[str, Component], + z_conditional: Mapping[str, tf.Tensor] = dict(), + z_hat_conditional: Mapping[str, tf.Tensor] = dict(), + ) -> Dict[str, tf.Tensor]: + """Prepare the inputs for the component used in `setup_outputs`. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + batch inputs. + + component_config: List[ComponentConfigMapping] + the config used to create the current component. + + target_component: str + the target component name. + + components: Mapping[str, Component] + the components which makes up the model. + + z_conditional: Mapping[str, tf.Tensor], optional + the keys are the name of the conditioned component (z), the + values are the corresponding z Tensor. Defaults to {}. + + z_hat_conditional: Mapping[str, tf.Tensor], optional + the keys are the name of the conditioned component (z_hat), + the values are the corresponding z_hat Tensor. Defaults to + {}. + + Returns + ------- + Dict[str, tf.Tensors] + keys are the `{modality_name}_matrix`, + `{modality_name}_batch_effect`, `z_conditional`, + `z_hat_conditional`, values are the corresponding Tensors. + + """ + component_inputs = dict() + for modality_name in components.get(target_component).modality_names: + modality_matrix_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + modality_batch_key = f"{modality_name}_{Constants.TENSOR_NAME_BATCH}" + component_inputs.setdefault( + modality_matrix_key, batch.get(modality_matrix_key) + ) + component_inputs.setdefault( + modality_batch_key, batch.get(modality_batch_key) + ) + + conditionals = Model.prepare_conditionals( + False, z_conditional, z_hat_conditional + ) + + for input_key, config_key, tensor_dict in conditionals: + conditional_tensors = [] + conditional_component_names = component_config.get(config_key, []) + if len(conditional_component_names) != 0: + for conditional_component_name in conditional_component_names: + conditional_tensors.append( + tensor_dict.get(conditional_component_name) + ) + conditional_tensors = tf.keras.layers.Lambda( + lambda x: tf.concat(x, axis=-1) + )(conditional_tensors) + component_inputs.setdefault(input_key, conditional_tensors) + + return component_inputs + + def encode( + self, + batch: Mapping[str, tf.Tensor], + components: Optional[List[str]] = None, + training: bool = False, + ) -> Mapping[str, Mapping[str, tf.Tensor]]: + """Encode requested components into latent outputs. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Raw input batch. + + components: List[str], optional + Component names to encode. Defaults to all components. + + training: bool + Whether to run the sublayers in training mode. + + Returns + ------- + Mapping[str, Mapping[str, tf.Tensor]] + Mapping with ``z_parameters`` and ``z`` outputs keyed by + component name. + + Raises + ------ + ValueError + Raised when unknown component names are requested. + + """ + requested_components = set(components or self.components.keys()) + unknown_components = requested_components.difference(self.components.keys()) + if unknown_components: + raise ValueError( + f"Unknown component names: {sorted(unknown_components)}" + ) + + outputs = { + Constants.MODEL_OUTPUTS_Z_PARAMS: dict(), + Constants.MODEL_OUTPUTS_Z: dict(), + } + for component_config in self.component_configs: + component_name = component_config.get("name") + if component_name not in requested_components: + continue + + component_input_config = dict(component_config) + component_input_config.update( + { + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z: [], + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT: [], + } + ) + component_batch = Model.prepare_component_inputs( + batch, + component_input_config, + component_name, + self.components, + {}, + {}, + ) + component_outputs = self.components.get(component_name).encode( + component_batch, training=training + ) + outputs[Constants.MODEL_OUTPUTS_Z_PARAMS][component_name] = ( + component_outputs.get(Constants.MODEL_OUTPUTS_Z_PARAMS) + ) + outputs[Constants.MODEL_OUTPUTS_Z][component_name] = component_outputs.get( + Constants.MODEL_OUTPUTS_Z + ) + + return outputs + + def hierarchical_encode( + self, + batch: Mapping[str, tf.Tensor], + z: Mapping[str, tf.Tensor], + z_hat_seed: Optional[Mapping[str, tf.Tensor]] = None, + components: Optional[List[str]] = None, + strict: bool = True, + training: bool = False, + ) -> Mapping[str, Mapping[str, tf.Tensor]]: + """Hierarchically encode requested latents into z_hat. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Raw input batch used for conditional inputs. + + z: Mapping[str, tf.Tensor] + Component-keyed latent samples from ``encode``. + + z_hat_seed: Mapping[str, tf.Tensor], optional + Pre-seeded ``z_hat`` values. Defaults to ``{}``. + + components: List[str], optional + Component names to process. Defaults to all components. + + strict: bool + If ``True``, missing parent ``z`` or ``z_hat`` raises + ``ValueError``. If ``False``, missing parents are skipped. + + training: bool + Whether to run the sublayers in training mode. + + Returns + ------- + Mapping[str, Mapping[str, tf.Tensor]] + Mapping with ``z_hat`` outputs keyed by component name. + + Raises + ------ + ValueError + Raised when unknown components, missing ``z``, or missing + required parent values are requested in strict mode. + + """ + requested_components = set(components or self.components.keys()) + unknown_components = requested_components.difference(self.components.keys()) + if unknown_components: + raise ValueError( + f"Unknown component names: {sorted(unknown_components)}" + ) + + accumulated_z_hat = dict(z_hat_seed or {}) + outputs = {Constants.MODEL_OUTPUTS_Z_HAT: dict()} + for component_config in self.component_configs: + component_name = component_config.get("name") + if component_name not in requested_components: + continue + + if component_name not in z: + raise ValueError(f"Missing z for component '{component_name}'.") + + z_conditional = dict() + for parent_name in component_config.get( + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z, [] + ): + if parent_name in z: + z_conditional[parent_name] = z.get(parent_name) + elif strict: + raise ValueError( + "Missing required parent z for component " + f"'{component_name}': '{parent_name}'." + ) + + z_hat_conditional = dict() + for parent_name in component_config.get( + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, [] + ): + if parent_name in accumulated_z_hat: + z_hat_conditional[parent_name] = accumulated_z_hat.get(parent_name) + elif strict: + raise ValueError( + "Missing required parent z_hat for component " + f"'{component_name}': '{parent_name}'." + ) + + component_batch = Model.prepare_component_inputs( + batch, + component_config, + component_name, + self.components, + z_conditional, + z_hat_conditional, + ) + component_outputs = self.components.get(component_name).hierarchical_encode( + component_batch, + z.get(component_name), + training=training, + ) + component_z_hat = component_outputs.get(Constants.MODEL_OUTPUTS_Z_HAT) + accumulated_z_hat[component_name] = component_z_hat + outputs[Constants.MODEL_OUTPUTS_Z_HAT][component_name] = component_z_hat + + return outputs + + def decode( + self, + batch: Mapping[str, tf.Tensor], + z_hat: Mapping[str, tf.Tensor], + components: Optional[List[str]] = None, + strict: bool = True, + training: bool = False, + ) -> Mapping[str, Mapping[str, tf.Tensor]]: + """Decode requested component ``z_hat`` tensors into ``x`` params. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Raw input batch used for conditional inputs. + + z_hat: Mapping[str, tf.Tensor] + Component-keyed hierarchical latents. Pass ``z_hat`` here, + not raw ``z``. + + components: List[str], optional + Component names to decode. Defaults to all components. + + strict: bool + If ``True``, missing ``z_hat`` raises ``ValueError``. If + ``False``, missing components are skipped. + + training: bool + Whether to run the sublayers in training mode. + + Returns + ------- + Mapping[str, Mapping[str, tf.Tensor]] + Mapping with ``x_parameters`` outputs keyed by component and + modality. + + Raises + ------ + ValueError + Raised when unknown components or required ``z_hat`` values + are missing. + + """ + requested_components = set(components or self.components.keys()) + unknown_components = requested_components.difference(self.components.keys()) + if unknown_components: + raise ValueError( + f"Unknown component names: {sorted(unknown_components)}" + ) + + outputs = {Constants.MODEL_OUTPUTS_X_PARAMS: dict()} + for component_config in self.component_configs: + component_name = component_config.get("name") + if component_name not in requested_components: + continue + + component_z_hat = z_hat.get(component_name) + if component_z_hat is None: + if strict: + raise ValueError( + f"Missing z_hat for component '{component_name}'." + ) + continue + + component_input_config = dict(component_config) + component_input_config.update( + { + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z: [], + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT: [], + } + ) + component_batch = Model.prepare_component_inputs( + batch, + component_input_config, + component_name, + self.components, + {}, + {}, + ) + component_outputs = self.components.get(component_name).decode( + component_batch, + component_z_hat, + training=training, + ) + for key, value in component_outputs.items(): + outputs[Constants.MODEL_OUTPUTS_X_PARAMS][ + f"{component_name}_{key}" + ] = value + + return outputs diff --git a/cavachon/modules/base/decoder_data_parameterizer.py b/cavachon/modules/base/decoder_data_parameterizer.py index c693940..4965295 100644 --- a/cavachon/modules/base/decoder_data_parameterizer.py +++ b/cavachon/modules/base/decoder_data_parameterizer.py @@ -1,130 +1,130 @@ -from typing import Optional - -import tensorflow as tf - -from cavachon.environment.constants import Constants -from cavachon.utils.reflection_handler import ReflectionHandler -from cavachon.utils.tensor_utils import TensorUtils - - -class DecoderDataParameterizer(tf.keras.Model): - """DecoderDataParameterizer - - Decoder and parameterizer for data distributions. This base module - is implemented with Tensorflow sequential API. - - Attributes - ---------- - backbone_network: tf.keras.Model - backbone network for decoder. - - x_parameterizer: tf.keras.Model - parameterizer for data distribution. - - """ - - def __init__( - self, - distribution_name: str, - n_vars: int = 5, - n_layers: int = 3, - *args, - **kwargs, - ): - """Constructor DecoderDataParameterizer - - Parameters - ---------- - distribution_name: str - distribution name for the modality to be decoded. By - default, this is used to automatically find the - parameterizer in modules.parameterizers - - n_vars: int, optional: - number of event dimension for data distributions. Defaults - to 5. - - n_layers: int, optional - number of layers constructed in the backbone network. - Defaults to 3. - - """ - super().__init__(*args, **kwargs) - - distribution_parameterizer = ReflectionHandler.get_class_by_name( - distribution_name, "modules/parameterizers", "Parameterizer" - ) - - self.backbone_network = TensorUtils.create_backbone_layers( - n_layers=1, - base_n_neurons=512, - activation="linear", - name=Constants.MODULE_BACKBONE, - ) - - input_dims = 0 - for layer in self.backbone_network.layers[::-1]: - if isinstance(layer, tf.keras.layers.Dense): - input_dims = layer.units - break - - self.x_parameterizer = distribution_parameterizer.make( - input_dims=input_dims, - event_dims=n_vars, - name=Constants.MODULE_X_PARAMETERIZER, - ) - - def compute_attribution_target(self, inputs: tf.Tensor): - result = self.backbone_network( - inputs.get(Constants.TENSOR_NAME_X), training=False - ) - x_parameterizer_inputs = dict() - x_parameterizer_inputs.setdefault(Constants.TENSOR_NAME_X, result) - - return self.x_parameterizer.compute_attribution_target(x_parameterizer_inputs) - - def call( - self, - inputs: tf.Tensor, - training: bool = False, - mask: Optional[tf.Tensor] = None, - ) -> tf.Tensor: - """Forward pass for DecoderDataParameterizer. - - Parameters - ---------- - inputs: Mapping[str, tf.Tensor] - inputs Tensors for the decoder, where keys are 'matrix' and - 'libsize' (if applicable). (by defaults, expect the outputs - by HierarchicalEncoder) - - training: bool, optional - whether to run the network in training mode. Defaults to - False. - - mask: tf.Tensor, optional - a mask or list of masks. Defaults to None. - - Returns - ------- - tf.Tensor - parameters for the data distributions. - - """ - result = self.backbone_network( - inputs.get(Constants.TENSOR_NAME_X), training=training, mask=mask - ) - x_parameterizer_inputs = dict() - x_parameterizer_inputs.setdefault(Constants.TENSOR_NAME_X, result) - if self.x_parameterizer.libsize_scaling: - x_parameterizer_inputs.setdefault( - Constants.TENSOR_NAME_LIBSIZE, inputs.get(Constants.TENSOR_NAME_LIBSIZE) - ) - result = self.x_parameterizer( - x_parameterizer_inputs, training=training, mask=mask - ) - - return result - - def train_step(self, data): - raise NotImplementedError() +from typing import Optional + +import tensorflow as tf + +from cavachon.environment.constants import Constants +from cavachon.utils.reflection_handler import ReflectionHandler +from cavachon.utils.tensor_utils import TensorUtils + + +class DecoderDataParameterizer(tf.keras.Model): + """DecoderDataParameterizer + + Decoder and parameterizer for data distributions. This base module + is implemented with Tensorflow sequential API. + + Attributes + ---------- + backbone_network: tf.keras.Model + backbone network for decoder. + + x_parameterizer: tf.keras.Model + parameterizer for data distribution. + + """ + + def __init__( + self, + distribution_name: str, + n_vars: int = 5, + n_layers: int = 3, + *args, + **kwargs, + ): + """Constructor DecoderDataParameterizer + + Parameters + ---------- + distribution_name: str + distribution name for the modality to be decoded. By + default, this is used to automatically find the + parameterizer in modules.parameterizers + + n_vars: int, optional: + number of event dimension for data distributions. Defaults + to 5. + + n_layers: int, optional + number of layers constructed in the backbone network. + Defaults to 3. + + """ + super().__init__(*args, **kwargs) + + distribution_parameterizer = ReflectionHandler.get_class_by_name( + distribution_name, "modules/parameterizers", "Parameterizer" + ) + + self.backbone_network = TensorUtils.create_backbone_layers( + n_layers=2, + base_n_neurons=32, + activation="swish", + name=Constants.MODULE_BACKBONE, + ) + + input_dims = 0 + for layer in self.backbone_network.layers[::-1]: + if isinstance(layer, tf.keras.layers.Dense): + input_dims = layer.units + break + + self.x_parameterizer = distribution_parameterizer.make( + input_dims=input_dims, + event_dims=n_vars, + name=Constants.MODULE_X_PARAMETERIZER, + ) + + def compute_attribution_target(self, inputs: tf.Tensor): + result = self.backbone_network( + inputs.get(Constants.TENSOR_NAME_X), training=False + ) + x_parameterizer_inputs = dict() + x_parameterizer_inputs.setdefault(Constants.TENSOR_NAME_X, result) + + return self.x_parameterizer.compute_attribution_target(x_parameterizer_inputs) + + def call( + self, + inputs: tf.Tensor, + training: bool = False, + mask: Optional[tf.Tensor] = None, + ) -> tf.Tensor: + """Forward pass for DecoderDataParameterizer. + + Parameters + ---------- + inputs: Mapping[str, tf.Tensor] + inputs Tensors for the decoder, where keys are 'matrix' and + 'libsize' (if applicable). (by defaults, expect the outputs + by HierarchicalEncoder) + + training: bool, optional + whether to run the network in training mode. Defaults to + False. + + mask: tf.Tensor, optional + a mask or list of masks. Defaults to None. + + Returns + ------- + tf.Tensor + parameters for the data distributions. + + """ + result = self.backbone_network( + inputs.get(Constants.TENSOR_NAME_X), training=training, mask=mask + ) + x_parameterizer_inputs = dict() + x_parameterizer_inputs.setdefault(Constants.TENSOR_NAME_X, result) + if self.x_parameterizer.libsize_scaling: + x_parameterizer_inputs.setdefault( + Constants.TENSOR_NAME_LIBSIZE, inputs.get(Constants.TENSOR_NAME_LIBSIZE) + ) + result = self.x_parameterizer( + x_parameterizer_inputs, training=training, mask=mask + ) + + return result + + def train_step(self, data): + raise NotImplementedError() diff --git a/cavachon/modules/base/hierarchical_encoder.py b/cavachon/modules/base/hierarchical_encoder.py index 8c1bf5e..ef80cfe 100644 --- a/cavachon/modules/base/hierarchical_encoder.py +++ b/cavachon/modules/base/hierarchical_encoder.py @@ -1,115 +1,117 @@ -from typing import Optional - -import tensorflow as tf - -from cavachon.environment.constants import Constants -from cavachon.layers.progressive_scaler import ProgressiveScaler - - -class HierarchicalEncoder(tf.keras.Model): - """HierarchicalEncoder - - HierarchicalEncoder used to encode z_hat hierarchically through - the dependency between components. It expects multiple tf.Tensor - as inputs. The key of the inputs are 'z', 'z_conditional' (if - applicable) and 'z_hat_conditional' (if applicable). This base - module is implemented using Tensorflow sequential API. - - """ - - def __init__( - self, - n_latent_dims: int = 5, - is_conditioned_on_z: bool = False, - is_conditioned_on_z_hat: bool = False, - progressive_iterations: int = 5000, - name: str = "hierarchical_encoder", - **kwargs, - ): - """Constructor for HierarchicalEncoder. Should not be called - directly most of the time. Please use make() to create the - model. - - Parameters - ---------- - n_latent_dims: int, optional - number of latent dimensions for the input z. Defaults to 5. - - is_conditioned_on_z: bool, optional - use latent representation from the conditioned components. - Defaults to False. - - is_conditioned_on_z_hat: bool, optional - use transformed latent representation (contains information - of all ancestor of conditioned components) from the - conditioned components. Defaults to False. - - progressive_iterations: int, optional - total iterations for progressive training. Defaults to 5000. - - name: str, optional: - Name for the tensorflow model. Defaults to - 'hierarchical_encoder'. - """ - super().__init__(name=name) - self.is_conditioned_on_z = is_conditioned_on_z - self.is_conditioned_on_z_hat = is_conditioned_on_z_hat - self.progressive_scaler = ProgressiveScaler(progressive_iterations) - self.r_network = tf.keras.Sequential( - [tf.keras.layers.Dense(n_latent_dims)], name=Constants.MODULE_R_NETWORK - ) - self.b_network = tf.keras.Sequential( - [tf.keras.layers.Dense(n_latent_dims)], name=Constants.MODULE_B_NETWORK - ) - - def call( - self, - inputs: tf.Tensor, - training: bool = False, - mask: Optional[tf.Tensor] = None, - ) -> tf.Tensor: - """Forward pass for HierarchicalEncoder. - - Parameters - ---------- - inputs: Mapping[str, tf.Tensor] - inputs Tensors for the HierarchicalEncoder, where keys are - 'z', 'z_conditional' (if applicable) and 'z_hat_conditional' - (if applicable). (if applicable). (by defaults, expect the - outputs by HierarchicalEncoder) - - training: bool, optional - whether to run the network in training mode. Defaults to - False. - - mask: tf.Tensor, optional - a mask or list of masks. Defaults to None. - - Returns - ------- - tf.Tensor - z_hat (contains information of latent representation and - the conditioned components) - - """ - z_hat = self.r_network(inputs.get(Constants.MODEL_OUTPUTS_Z)) - z_hat = self.progressive_scaler(z_hat) - concat_inputs = [] - if self.is_conditioned_on_z or self.is_conditioned_on_z_hat: - if self.is_conditioned_on_z: - concat_inputs.append( - inputs.get(Constants.MODULE_INPUTS_CONDITIONED_Z, None) - ) - if self.is_conditioned_on_z_hat: - concat_inputs.append( - inputs.get(Constants.MODULE_INPUTS_CONDITIONED_Z_HAT, None) - ) - - concat_inputs.append(z_hat) - z_hat = tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=-1))(concat_inputs) - z_hat = self.b_network(z_hat) - - return z_hat - - def train_step(self, data): - raise NotImplementedError() +from typing import Optional + +import tensorflow as tf + +from cavachon.environment.constants import Constants +from cavachon.layers.progressive_scaler import ProgressiveScaler + + +class HierarchicalEncoder(tf.keras.Model): + """HierarchicalEncoder + + HierarchicalEncoder used to encode z_hat hierarchically through + the dependency between components. It expects multiple tf.Tensor + as inputs. The key of the inputs are 'z', 'z_conditional' (if + applicable) and 'z_hat_conditional' (if applicable). This base + module is implemented using Tensorflow sequential API. + + """ + + def __init__( + self, + n_latent_dims: int = 5, + is_conditioned_on_z: bool = False, + is_conditioned_on_z_hat: bool = False, + progressive_iterations: int = 5000, + name: str = "hierarchical_encoder", + **kwargs, + ): + """Constructor for HierarchicalEncoder. Should not be called + directly most of the time. Please use make() to create the + model. + + Parameters + ---------- + n_latent_dims: int, optional + number of latent dimensions for the input z. Defaults to 5. + + is_conditioned_on_z: bool, optional + use latent representation from the conditioned components. + Defaults to False. + + is_conditioned_on_z_hat: bool, optional + use transformed latent representation (contains information + of all ancestor of conditioned components) from the + conditioned components. Defaults to False. + + progressive_iterations: int, optional + total iterations for progressive training. Defaults to 5000. + + name: str, optional: + Name for the tensorflow model. Defaults to + 'hierarchical_encoder'. + """ + super().__init__(name=name) + self.is_conditioned_on_z = is_conditioned_on_z + self.is_conditioned_on_z_hat = is_conditioned_on_z_hat + self.progressive_scaler = ProgressiveScaler(progressive_iterations) + self.r_network = tf.keras.Sequential( + [tf.keras.layers.Dense(n_latent_dims, use_bias=False)], + name=Constants.MODULE_R_NETWORK + ) + self.b_network = tf.keras.Sequential( + [tf.keras.layers.Dense(n_latent_dims, use_bias=False)], + name=Constants.MODULE_B_NETWORK + ) + + def call( + self, + inputs: tf.Tensor, + training: bool = False, + mask: Optional[tf.Tensor] = None, + ) -> tf.Tensor: + """Forward pass for HierarchicalEncoder. + + Parameters + ---------- + inputs: Mapping[str, tf.Tensor] + inputs Tensors for the HierarchicalEncoder, where keys are + 'z', 'z_conditional' (if applicable) and 'z_hat_conditional' + (if applicable). (if applicable). (by defaults, expect the + outputs by HierarchicalEncoder) + + training: bool, optional + whether to run the network in training mode. Defaults to + False. + + mask: tf.Tensor, optional + a mask or list of masks. Defaults to None. + + Returns + ------- + tf.Tensor + z_hat (contains information of latent representation and + the conditioned components) + + """ + z_hat = self.r_network(inputs.get(Constants.MODEL_OUTPUTS_Z)) + z_hat = self.progressive_scaler(z_hat) + concat_inputs = [] + if self.is_conditioned_on_z or self.is_conditioned_on_z_hat: + if self.is_conditioned_on_z: + concat_inputs.append( + inputs.get(Constants.MODULE_INPUTS_CONDITIONED_Z, None) + ) + if self.is_conditioned_on_z_hat: + concat_inputs.append( + inputs.get(Constants.MODULE_INPUTS_CONDITIONED_Z_HAT, None) + ) + + concat_inputs.append(z_hat) + z_hat = tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=-1))(concat_inputs) + z_hat = self.b_network(z_hat) + + return z_hat + + def train_step(self, data): + raise NotImplementedError() diff --git a/cavachon/modules/components/component.py b/cavachon/modules/components/component.py index 8f5ea93..e4c104c 100644 --- a/cavachon/modules/components/component.py +++ b/cavachon/modules/components/component.py @@ -65,21 +65,22 @@ class Component(tf.keras.Model): Name for the tensorflow model. Defaults to 'component'. """ - def __init__( - self, - inputs: Mapping[Any, tf.keras.Input], - outputs: Mapping[Any, tf.Tensor], - modality_names: List[Any], + def __init__( + self, + inputs: Mapping[Any, tf.keras.Input], + outputs: Mapping[Any, tf.Tensor], + modality_names: List[Any], distribution_names: Mapping[str, str], preprocessor: tf.keras.Model, - encoder: tf.keras.Model, - z_prior_parameterizer: tf.keras.layers.Layer, - hierarchical_encoder: tf.keras.Model, - decoders: Mapping[str, tf.keras.Model], - conditioned_on_z: List[str] = [], - conditioned_on_z_hat: List[str] = [], - name: str = "component", - **kwargs, + encoder: tf.keras.Model, + z_prior_parameterizer: tf.keras.layers.Layer, + hierarchical_encoder: tf.keras.Model, + z_sampler: Union[tf.keras.layers.Layer, tf.keras.Model], + decoders: Mapping[str, tf.keras.Model], + conditioned_on_z: List[str] = [], + conditioned_on_z_hat: List[str] = [], + name: str = "component", + **kwargs, ): """Constructor for Component. Should not be called directly most of the time. Please use make() to create the model. @@ -117,14 +118,17 @@ def __init__( parameterizer used for the priors in latent distributions. Used when computing the KLDivergence. - hierarchical_encoder: tf.keras.Model - hierarchical encoder used to encode z_hat hierarchically - through the dependency between components. - - decoders: Mapping[str, tf.keras.Model] - decoder neural networks. The keys are the name of the - modality, the values are the corresponding decoder neural - network. + hierarchical_encoder: tf.keras.Model + hierarchical encoder used to encode z_hat hierarchically + through the dependency between components. + + z_sampler: Union[tf.keras.layers.Layer, tf.keras.Model] + sampler used to transform z_parameters into z. + + decoders: Mapping[str, tf.keras.Model] + decoder neural networks. The keys are the name of the + modality, the values are the corresponding decoder neural + network. conditioend_on_z: str, optional The current component will be conditionally independent @@ -151,12 +155,13 @@ def __init__( self.modality_names = modality_names self.preprocessor = preprocessor self.distribution_names = distribution_names - self.encoder = encoder - self.z_prior_parameterizer = z_prior_parameterizer - self.hierarchical_encoder = hierarchical_encoder - self.decoders = decoders - self.conditioned_on_z = conditioned_on_z - self.conditioned_on_z_hat = conditioned_on_z_hat + self.encoder = encoder + self.z_prior_parameterizer = z_prior_parameterizer + self.hierarchical_encoder = hierarchical_encoder + self.z_sampler = z_sampler + self.decoders = decoders + self.conditioned_on_z = conditioned_on_z + self.conditioned_on_z_hat = conditioned_on_z_hat @classmethod def setup_inputs( @@ -781,26 +786,193 @@ def make( **kwargs, ) - return cls( - inputs=inputs, - outputs=outputs, - modality_names=modality_names, - distribution_names=distribution_names, - preprocessor=preprocessor, - encoder=encoder, - z_prior_parameterizer=z_prior_parameterizer, - hierarchical_encoder=hierarchical_encoder, - decoders=decoders, - conditioned_on_z=conditioned_on_z, - conditioned_on_z_hat=conditioned_on_z_hat, - name=name, - **kwargs, + return cls( + inputs=inputs, + outputs=outputs, + modality_names=modality_names, + distribution_names=distribution_names, + preprocessor=preprocessor, + encoder=encoder, + z_prior_parameterizer=z_prior_parameterizer, + hierarchical_encoder=hierarchical_encoder, + z_sampler=z_sampler, + decoders=decoders, + conditioned_on_z=conditioned_on_z, + conditioned_on_z_hat=conditioned_on_z_hat, + name=name, + **kwargs, + ) + + def encode( + self, + batch: Mapping[str, tf.Tensor], + training: bool = False, + ) -> Mapping[str, tf.Tensor]: + """Encode a batch into latent parameters and samples. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Input tensors. Each modality must provide + ``{modality_name}_matrix``. + + training: bool, optional + Whether to run the encoder path in training mode. Defaults + to False. + + Returns + ------- + Mapping[str, tf.Tensor] + Mapping with keys ``z_parameters`` and ``z``. + + Raises + ------ + ValueError + If a required modality matrix key is missing from ``batch``. + """ + for modality_name in self.modality_names: + modality_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + if modality_key not in batch: + raise ValueError( + f"Missing required input key '{modality_key}' in batch." + ) + + preprocessor_inputs = Component.prepare_preprocessor_inputs( + batch, self.modality_names + ) + preprocessor_outputs = self.preprocessor( + preprocessor_inputs, training=training + ) + z_parameters = self.encoder( + preprocessor_outputs.get(self.preprocessor.matrix_key), + training=training, + ) + z = self.z_sampler(z_parameters, training=training) + + return { + Constants.MODEL_OUTPUTS_Z_PARAMS: z_parameters, + Constants.MODEL_OUTPUTS_Z: z, + } + + def hierarchical_encode( + self, + batch: Mapping[str, tf.Tensor], + z: tf.Tensor, + training: bool = False, + ) -> Mapping[str, tf.Tensor]: + """Transform z into z_hat using the hierarchical encoder. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Input tensors. Must contain z_conditional and/or + z_hat_conditional keys if the component is conditioned. + + z: tf.Tensor + Latent sample from encode(). Expected shape + (batch_size, n_latent_dims). + + training: bool, optional + Whether to run in training mode. Defaults to False. + + Returns + ------- + Mapping[str, tf.Tensor] + Mapping with key ``z_hat``. + + Raises + ------ + tf.errors.InvalidArgumentError + If ``z`` does not have rank 2. + """ + tf.debugging.assert_rank(z, 2, message="Expected 'z' to have rank 2.") + hierarchical_encoder_inputs = Component.prepare_hierarchical_encoder_inputs( + batch, z + ) + z_hat = self.hierarchical_encoder( + hierarchical_encoder_inputs, training=training + ) + return {Constants.MODEL_OUTPUTS_Z_HAT: z_hat} + + def decode( + self, + batch: Mapping[str, tf.Tensor], + z_hat: tf.Tensor, + training: bool = False, + ) -> Mapping[str, tf.Tensor]: + """Decode a transformed latent sample into modality parameters. + + Parameters + ---------- + batch: Mapping[str, tf.Tensor] + Input tensors. Each modality must provide + ``{modality_name}_batch_effect``. + + z_hat: tf.Tensor + Hierarchically transformed latent tensor to decode. + Expected shape is + ``(batch_size, n_latent_dims)``. + + training: bool, optional + Whether to run the decoder path in training mode. Defaults + to False. + + Returns + ------- + Mapping[str, tf.Tensor] + Mapping with keys + ``{modality_name}_x_parameters`` for each modality. + + Raises + ------ + ValueError + If a required modality batch-effect key is missing from + ``batch``. + + ValueError + If no decoder is registered for a modality name. + + tf.errors.InvalidArgumentError + If ``z_hat`` does not have rank 2. + """ + for modality_name in self.modality_names: + modality_batch_key = f"{modality_name}_{Constants.TENSOR_NAME_BATCH}" + if modality_batch_key not in batch: + raise ValueError( + f"Missing required input key '{modality_batch_key}' in batch." + ) + + tf.debugging.assert_rank( + z_hat, 2, message="Expected 'z_hat' to have rank 2." + ) + preprocessor_inputs = Component.prepare_preprocessor_inputs( + batch, self.modality_names + ) + preprocessor_outputs = self.preprocessor( + preprocessor_inputs, training=training ) - def compile(self, **kwargs) -> None: - """Compile the model before training. Note that the 'metrics' - will be ignored in Model because of the incompatibility with - Tensorflow API. The 'loss' will be setup automatically if not + outputs = dict() + for modality_name in self.modality_names: + decoder = self.decoders.get(modality_name) + if decoder is None: + raise ValueError( + f"No decoder found for modality '{modality_name}'." + ) + decoder_inputs = Component.prepare_decoder_inputs( + batch, modality_name, z_hat, preprocessor_outputs + ) + x_parameters = decoder(decoder_inputs, training=training) + outputs[f"{modality_name}_{Constants.MODEL_OUTPUTS_X_PARAMS}"] = ( + x_parameters + ) + + return outputs + + def compile(self, **kwargs) -> None: + """Compile the model before training. Note that the 'metrics' + will be ignored in Model because of the incompatibility with + Tensorflow API. The 'loss' will be setup automatically if not provided. Parameters diff --git a/cavachon/modules/parameterizers/__init__.py b/cavachon/modules/parameterizers/__init__.py index 63d1810..d9a716e 100644 --- a/cavachon/modules/parameterizers/__init__.py +++ b/cavachon/modules/parameterizers/__init__.py @@ -11,3 +11,6 @@ MultivariateNormalDiagParameterizer as MultivariateNormalDiagParameterizer, ) from .parameterizer import Parameterizer as Parameterizer +from .studentt_parameterizer import ( + StudenttParameterizer as StudenttParameterizer, +) diff --git a/cavachon/modules/parameterizers/studentt_parameterizer.py b/cavachon/modules/parameterizers/studentt_parameterizer.py new file mode 100644 index 0000000..aad669a --- /dev/null +++ b/cavachon/modules/parameterizers/studentt_parameterizer.py @@ -0,0 +1,18 @@ +from cavachon.modules.parameterizers.parameterizer import Parameterizer + + +class StudenttParameterizer(Parameterizer): + """Module for Student-T parameterization.""" + + default_libsize_scaling = False + + @classmethod + def make(cls, input_dims: int, event_dims: int, name: str = "student_t", **kwargs): + # We call the parent make, which handles the Keras functional setup + return super().make( + input_dims=input_dims, + event_dims=event_dims, + name=name, + libsize_scaling=False, + exp_transform=False, + ) diff --git a/cavachon/modules/preprocessors/modifiers/__init__.py b/cavachon/modules/preprocessors/modifiers/__init__.py index 64ddbb9..cb2b23f 100644 --- a/cavachon/modules/preprocessors/modifiers/__init__.py +++ b/cavachon/modules/preprocessors/modifiers/__init__.py @@ -7,3 +7,6 @@ from .multivariate_normal_diag_modifier import ( MultivariateNormalDiagModifier as MultivariateNormalDiagModifier, ) +from .studentt_modifier import ( + StudenttModifier as StudenttModifier, +) diff --git a/cavachon/modules/preprocessors/modifiers/studentt_modifier.py b/cavachon/modules/preprocessors/modifiers/studentt_modifier.py new file mode 100644 index 0000000..81e8980 --- /dev/null +++ b/cavachon/modules/preprocessors/modifiers/studentt_modifier.py @@ -0,0 +1,28 @@ +import functools + +import tensorflow as tf + +from cavachon.environment.constants import Constants +from cavachon.layers.modifiers.to_dense import ToDense + + +class StudenttModifier(tf.keras.Model): + """ + Modifier for Student-T modalities used during the model's preprocessing + step. This ensures that the data is converted to a Dense tensor + immediately before the model call. + """ + + def __init__(self, modality_name: str): + super().__init__() + self.modality_name = modality_name + self.modality_key = f"{modality_name}_{Constants.TENSOR_NAME_X}" + # For CNV data, we just need the ToDense conversion. + self.modifiers = [ToDense(self.modality_key)] + + def call(self, inputs, training=None, mask=None): + """ + Applies the sequence of modifiers to the input mapping. + """ + modifiers = self.modifiers + return functools.reduce(lambda x, modifier: modifier(x), modifiers, inputs) diff --git a/cavachon/scheduler/sequential_training_scheduler.py b/cavachon/scheduler/sequential_training_scheduler.py index 12c5b1c..f8de655 100644 --- a/cavachon/scheduler/sequential_training_scheduler.py +++ b/cavachon/scheduler/sequential_training_scheduler.py @@ -1,338 +1,907 @@ -import itertools -from collections import defaultdict -from copy import deepcopy -from typing import Any, List, Mapping, Optional, Tuple - -import mlflow -import tensorflow as tf - -from cavachon.environment.constants import Constants - - -class SequentialTrainingScheduler: - """SequentialTrainingScheduler - - Training scheduler that sets the loss weight and stop the gradient - of trained components sequentially during training process. - - Attributes - ---------- - model : tf.keras.Model - input model that needs to be trained. - - component_configs: List[ComponentConfigMapping] - the config used to create the components in the model. - - optimizer: str - optimizer used to train the model (only the attributes of the - optimizer will be used) - - early_stopping: bool - whether or not to use early stopping when training the model. - - training_order: Mapping[int, List[str]] - the training order of the components, the keys are the training - order, the values are lists of the components trained in the - corresponding order. - - modality_weight: Mapping[str, Mapping[str, int]] - the weight of the data distribution by component. The keys are - the component names, the values are the mapping where the keys - are the modality names and the values are the weight. - - """ - - def __init__( - self, - model: tf.keras.Model, - optimizer: str = "adam", - learning_rate: float = 1e-4, - early_stopping: bool = True, - ): - """Constructor for SequentialTrainingScheduler. - - Parameters - ---------- - model: tf.keras.Model - input model that needs to be trained. - - optimizer: tf.keras.optimizers.Optimizer, optional - optimizer used to train the model (only the attributes of - the optimizer will be used if provided with Optimizer). - Defaults to 'adam'. - - learning_rate: float, optional - learning rate for the optimizer. Defaults to 1e-4. - - early_stopping: bool, optional - whether or not to use early stopping when training the model. - - """ - self.model = model - self.component_configs = self.model.component_configs - self.optimizer = optimizer - self.learning_rate = learning_rate - self.early_stopping = early_stopping - self.training_order = self.compute_component_training_order() - self.modality_weight = self.compute_modality_weight() - - def compute_component_training_order(self) -> Mapping[int, List[str]]: - """Compute the training order of the components based on the - order of topological sort of the input dependency graph. - - Returns - ------- - Mapping[int, List[str]] - the training order of the components, the keys are the - training order, the values are lists of the components - trained in the corresponding order. - """ - training_order = list() - component_order = dict() - self.run_progressive_training = dict() - component_configs = self.component_configs - training_order.append([]) - for component_config in component_configs: - component_name = component_config.get("name") - conditioned_on_z = component_config.get( - Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z, [] - ) - conditioned_on_z_hat = component_config.get( - Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, [] - ) - if len(conditioned_on_z) == 0 and len(conditioned_on_z_hat) == 0: - self.run_progressive_training[component_name] = False - else: - self.run_progressive_training[component_name] = True - - conditioned_on = itertools.chain(conditioned_on_z, conditioned_on_z_hat) - order = max([0] + [component_order[x] + 1 for x in conditioned_on]) - if order > len(training_order) - 1: - training_order.append([]) - training_order[order].append(component_name) - component_order.setdefault(component_name, order) - - return [[x] for xs in training_order for x in xs] # training_order - - def compute_modality_weight( - self, constant: bool = False - ) -> Mapping[str, Mapping[str, int]]: - """Compute the weight of the data distribution for each - modality. - - Parameters - ---------- - constant : bool, optional - whether or not to use constant weight for the data - distribution. Defaults to False. - - Returns - ------- - Mapping[str, Mapping[str, int]] - the weight of the data distribution by component. The keys - are the component names, the values are the mapping where - the keys are the modality names and the values are the - weight. - """ - modality_weight_by_component = defaultdict(dict) - for component_config in self.component_configs: - component_name = component_config.get("name") - modality_weight = dict() - if not constant: - n_vars = component_config.get(Constants.CONFIG_FIELD_COMPONENT_N_VARS) - total_vars = 0 - total_scaled_weight = 0 - for modality_name, n_var in n_vars.items(): - total_vars += n_var - for modality_name, n_var in n_vars.items(): - scaled_weight = total_vars / n_var - total_scaled_weight += scaled_weight - modality_weight.setdefault(modality_name, scaled_weight) - for modality_name, scaled_weight in modality_weight.items(): - modality_weight[modality_name] = scaled_weight / total_scaled_weight - else: - for modality_name in n_vars.keys(): - modality_weight.setdefault(modality_name, 1.0) - modality_weight_by_component.setdefault(component_name, modality_weight) - - return modality_weight_by_component - - def fit(self, x: tf.data.Dataset, **kwargs) -> List[tf.keras.callbacks.History]: - """Fit self.model sequentially. - - Parameters - ---------- - x: tf.data.Dataset - input dataset created by DataLoader. - - **kwargs: Mapping[str, Any] - additional arguments passed to self.model.fit. - - Returns - ------- - List[tf.keras.callbacks.History] - history of model.fit in each step. - """ - - n_batches = len(x) - learning_rate = self.learning_rate - history = [] - - experiment_name = self.model.name - mlflow.set_experiment(experiment_name) - experiment = mlflow.get_experiment_by_name(experiment_name) - - for component_order, train_components in enumerate(self.training_order): - # progressive training - if self.run_progressive_training.get(train_components[0]): - loss_weights, max_n_progressive_epochs = ( - self.setup_component_and_loss_weights( - train_components=train_components, - n_batches=n_batches, - initial_iteration=0.0, - ) - ) - - if max_n_progressive_epochs != 0: - run_name = f"Training/{component_order}/Progressive/{'/'.join(train_components)}" - mlflow.start_run( - experiment_id=experiment.experiment_id, run_name=run_name - ) - mlflow.tensorflow.autolog( - log_every_n_steps=1, - log_every_epoch=False, - log_models=False, - checkpoint=False, - checkpoint_save_best_only=False, - registered_model_name=f"Model/{run_name}", - ) - optimizer = tf.keras.optimizers.get(self.optimizer).__class__( - learning_rate=learning_rate - ) - self.model.compile(optimizer=optimizer, loss_weights=loss_weights) - kwargs_progressive = deepcopy(kwargs) - kwargs_progressive.pop("epochs", None) - history.append( - self.model.fit( - x, epochs=max_n_progressive_epochs, **kwargs_progressive - ) - ) - mlflow.end_run() - - # non-progressive training - run_name = f"Training/{component_order}/{'/'.join(train_components)}" - mlflow.start_run(experiment_id=experiment.experiment_id, run_name=run_name) - mlflow.tensorflow.autolog( - log_every_epoch=True, - log_models=False, - checkpoint=False, - checkpoint_save_best_only=False, - registered_model_name=f"Model/{run_name}", - ) - loss_weights, max_n_progressive_epochs = ( - self.setup_component_and_loss_weights( - train_components=train_components, - n_batches=n_batches, - initial_iteration=None, - ) - ) - - optimizer = tf.keras.optimizers.get(self.optimizer).__class__( - learning_rate=learning_rate - ) - self.model.compile(optimizer=optimizer, loss_weights=loss_weights) - callbacks = deepcopy(kwargs.get("callbacks", [])) - if self.early_stopping: - callbacks.append( - tf.keras.callbacks.EarlyStopping( - monitor="loss", - min_delta=5, - patience=max(10, int(kwargs.get("epochs", 1) / 20)), - restore_best_weights=True, - verbose=1, - ) - ) - history.append(self.model.fit(x, callbacks=callbacks, **kwargs)) - mlflow.end_run() - - return history - - def setup_component_and_loss_weights( - self, - train_components: List[str], - n_batches=int, - initial_iteration: Optional[int] = None, - ) -> Tuple[Any]: - """Setup the trainable attributes for each component and the - alpha (with current_iteration and total_iterations) in - progressive scaler and set the loss weights properly (for - components that take more than one modality). - - Parameters - ---------- - train_components : List[str] - list of component names for components that need to be - trained in this sequential step. - - n_batches: int - number of batches needed to processed in one epoch. - - initial_iteration : Optional[int], optional - initial iteration for progressive scaler when the training - starts. The progressive training will be turn off if - provided with None. Defaults to None. - - Returns - ------- - Tuple[Any] - The first element in the tuple is the loss weights, the - second element is the maximum number of progressive epochs. - """ - loss_weights = dict() - max_n_progressive_epochs = 0 - for component_config in self.component_configs: - component_name = component_config.get("name") - component = self.model.components.get(component_name) - if component_name in train_components: - component.trainable = True - weight_scale = 1.0 - - n_progressive_epochs = float( - component_config.get( - Constants.CONFIG_FIELD_COMPONENT_N_PROGRESSIVE_EPOCHS - ) - ) - progressive_iterations = n_batches * n_progressive_epochs - max_n_progressive_epochs = max( - max_n_progressive_epochs, n_progressive_epochs - ) - - # For non-progressive training - if initial_iteration is None or initial_iteration <= 0: - initial_iteration = progressive_iterations - - component.set_progressive_scaler_iteration( - current_iteration=initial_iteration, - total_iterations=progressive_iterations, - ) - else: - component.trainable = False - weight_scale = 0.0 - component.set_progressive_scaler_iteration( - current_iteration=1.0, total_iterations=1.0 - ) - - loss_weights.setdefault( - f"{component_name}_{Constants.MODEL_LOSS_KL_POSTFIX}", - 1.0 * weight_scale, - ) - for modality_name in component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES - ): - loss_weights.setdefault( - f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}", - self.modality_weight.get(component_name).get(modality_name) - * weight_scale, - ) - - return loss_weights, int(max_n_progressive_epochs) +import itertools +import os +from collections import defaultdict +from copy import deepcopy +from typing import Any, List, Mapping, Optional, Tuple + +import mlflow +import muon as mu +import numpy as np +import tensorflow as tf +from tqdm import tqdm + +from cavachon.dataloader.dataloader import DataLoader +from cavachon.distributions.mixture_multivariate_normal_diag_distribution import ( + MixtureMultivariateNormalDiagDistribution, +) +from cavachon.distributions.multivariate_normal_diag_distribution import ( + MultivariateNormalDiagDistribution, +) +from cavachon.environment.constants import Constants + + +class PeriodicTSNECallback(tf.keras.callbacks.Callback): + """ + new test callback to save logpy_z and z per n epoch + then append the callback in fit() + """ + + def __init__( + self, + mdata: mu.MuData, + component: str, + outdir: str, + batch_size=int, + batch_effect_colnames: Optional[Mapping[str, List[str]]] = None, + distribution_names: Optional[Mapping[str, str]] = None, + ): + super().__init__() + self.mdata = mdata + self.component = component + self.batch_effect_colnames = batch_effect_colnames + self.distribution_names = distribution_names + self.batch_size = batch_size + self.output_dir = outdir + os.makedirs(self.output_dir, exist_ok=True) + + def on_epoch_end(self, epoch, logs=None): + # EDIT HERE TO SPECIFY WHICH EPOCHS TO SAVE: + save_epochs = set() # Empty set = never save (disabled) + # save_epochs = {0, 349, 699, 840, 1199} # Example: save at these epochs + + if epoch not in save_epochs: + return + + component = self.component + + # First temporarily freeze the model + # this prevents layers from updating internal states (like Batch Norm) during the predict call + original_trainable_state = self.model.trainable + self.model.trainable = False + + # Then, extract mean latent z + # By calling predict with verbose=0 and the model's training state as False, + # Model will return the Mean (μ) and skip the sampling (ϵ) step. + # This gives the stable "Mean Z" + outputs = self.model.predict(self.mdata, batch_size=self.batch_size, verbose=0) + # outputs = self.model.predict(self.mdata, batch_size=self.batch_size) + z_full = outputs[f"{self.component}_z"] + + # compute logpy_z (copied from cluster_analysis.py) + z_prior_parameterizer = self.model.components[component].z_prior_parameterizer + z_prior_parameters = tf.squeeze(z_prior_parameterizer(tf.ones((1, 1)))) + + dist_z_y = MultivariateNormalDiagDistribution.from_parameterizer_output( + z_prior_parameters[..., 1:] + ) + dist_z = MixtureMultivariateNormalDiagDistribution.from_parameterizer_output( + z_prior_parameters + ) + logpy = tf.math.log(tf.math.softmax(z_prior_parameters[..., 0]) + 1e-7) + + logpy_z_parts = [] + dataloader = DataLoader( + self.mdata, + self.batch_size, + self.batch_effect_colnames, + self.distribution_names, + ) + for batch_data in tqdm(dataloader, desc=f"Epoch {epoch}: computing logpy_z"): + # use training=False here as well to ensure we get the MEAN Z + # and don't accidentally update any model weights. + outs = self.model(batch_data, training=False) + z = outs[f"{self.component}_z"] + logpz_y = dist_z_y.log_prob(tf.expand_dims(z, -2)) + logpz = tf.expand_dims(dist_z.log_prob(z), -1) + logpy_z_parts.append(logpy + logpz_y - logpz) + + logpy_z = np.vstack([x.numpy() for x in logpy_z_parts]) + + # restore the model state back + self.model.trainable = original_trainable_state + + # Save z and logpy_z in the configured results directory + np.save( + os.path.join(self.output_dir, f"{epoch + 1}_z.h5"), z_full + ) # because of zero indexing + np.save(os.path.join(self.output_dir, f"{epoch + 1}_logpy_z.h5"), logpy_z) + np.save( + os.path.join(self.output_dir, f"{epoch + 1}_prior_params.npy"), + z_prior_parameters.numpy(), + ) + + +class KLAnnealingCallback(tf.keras.callbacks.Callback): + """3-phase KL annealing for a single component. + Phase 1: Vanilla KL only (β=3.0) + Phase 2: Crossfade (vanilla 3→0, GMM 0→1) + Phase 3: GMM KL only (β=1.0) + + K-means initialization triggers at start of Phase 2. + """ + + def __init__( + self, + total_epochs: int, + scheduler, + component_name: str, + phase_boundaries: Tuple[int, int], + vanilla_beta_max: float = 3.0, + gmm_beta_max: float = 1.0, + run_kmeans: bool = True, + ): + super().__init__() + self.total_epochs = total_epochs + self.scheduler = scheduler + self.component_name = component_name + self.phase1_end, self.phase2_end = phase_boundaries + self.vanilla_beta_max = vanilla_beta_max + self.gmm_beta_max = gmm_beta_max + self.run_kmeans = run_kmeans + self.kmeans_executed = False + + print(f"\n{'=' * 70}") + print(f"KL ANNEALING SCHEDULE - {component_name}") + print(f" Total epochs: {total_epochs}") + print(f" Phase 1 (Vanilla only): epochs 0-{self.phase1_end}") + print(f" Phase 2 (Crossfade): epochs {self.phase1_end}-{self.phase2_end}") + print(f" Phase 3 (GMM only): epochs {self.phase2_end}-{total_epochs}") + if run_kmeans: + print(f" K-means trigger: epoch {self.phase1_end}") + print(f"{'=' * 70}\n") + + def on_epoch_begin(self, epoch, logs=None): + """Update beta weights and trigger K-means.""" + vanilla_beta, gmm_beta = self._calculate_betas(epoch) + + # COMPONENT-SPECIFIC assignment + self.model._vanilla_kl_weights[self.component_name].assign(vanilla_beta) + self.model._gmm_kl_weights[self.component_name].assign(gmm_beta) + + # Trigger K-means at start of Phase 2 + if self.run_kmeans and epoch == self.phase1_end and not self.kmeans_executed: + print(f"\n{'=' * 70}") + print(f"K-MEANS INITIALIZATION AT EPOCH {epoch} - {self.component_name}") + print(f"{'=' * 70}\n") + + self.model.trainable = False + self.scheduler.initialize_gmm_priors_with_kmeans( + component_name=self.component_name, + seed=42, + add_noise=True, + noise_std=0.1, + ) + self.model.trainable = True + self.kmeans_executed = True + + def _calculate_betas(self, epoch): + """Calculate vanilla and GMM beta values.""" + if epoch < self.phase1_end: + return self.vanilla_beta_max, 0.0 + elif epoch < self.phase2_end: + progress = (epoch - self.phase1_end) / (self.phase2_end - self.phase1_end) + vanilla_beta = self.vanilla_beta_max * (1.0 - progress) + gmm_beta = self.gmm_beta_max * progress + return vanilla_beta, gmm_beta + else: + return 0.0, self.gmm_beta_max + + +class ComponentWeightTransferCallback(tf.keras.callbacks.Callback): + """Gradual weight transfer from parent to child component. + + Parent: GMM KL (β=1.0 frozen), weight fades 1.0 → 0.0 + Child: Vanilla KL (β=3.0 frozen), weight fades 0.0 → 1.0 + """ + + def __init__(self, total_epochs: int, parent_names: List[str], child_name: str): + super().__init__() + self.total_epochs = total_epochs + self.parent_names = parent_names + self.child_name = child_name + + print(f"\n{'=' * 70}") + print("COMPONENT WEIGHT TRANSFER") + print(f" Total epochs: {total_epochs}") + print(f" Parents: {', '.join(parent_names)} (weight 1.0 → 0.0)") + print(f" Child: {child_name} (Vanilla β=3.0, weight 0.0 → 1.0)") + print(f"{'=' * 70}\n") + + def on_epoch_begin(self, epoch, logs=None): + """Fade component loss weights.""" + progress = epoch / self.total_epochs + parent_weight = 1.0 - progress + child_weight = progress + + # Apply to ALL losses for each component + for loss_name, loss_fn in self.model.loss.items(): + if any(parent in loss_name for parent in self.parent_names): + if hasattr(loss_fn, "weight"): + loss_fn.weight = parent_weight + elif self.child_name in loss_name: + if hasattr(loss_fn, "weight"): + loss_fn.weight = child_weight + + if epoch % 50 == 0: + print( + f"Epoch {epoch}/{self.total_epochs}: " + f"Parent={parent_weight:.3f}, Child={child_weight:.3f}" + ) + + +class SequentialTrainingScheduler: + """SequentialTrainingScheduler + + Training scheduler that sets the loss weight and stop the gradient + of trained components sequentially during training process. + + Attributes + ---------- + model : tf.keras.Model + input model that needs to be trained. + + component_configs: List[ComponentConfigMapping] + the config used to create the components in the model. + + optimizer: str + optimizer used to train the model (only the attributes of the + optimizer will be used) + + early_stopping: bool + whether or not to use early stopping when training the model. + + training_order: Mapping[int, List[str]] + the training order of the components, the keys are the training + order, the values are lists of the components trained in the + corresponding order. + + modality_weight: Mapping[str, Mapping[str, int]] + the weight of the data distribution by component. The keys are + the component names, the values are the mapping where the keys + are the modality names and the values are the weight. + + """ + + def __init__( + self, + model: tf.keras.Model, + mdata, + optimizer: str = "adam", + learning_rate: float = 1e-4, + early_stopping: bool = True, + batch_size: int = 128, + outdir: Optional[str] = None, + distribution_names: Optional[Mapping[str, str]] = None, + batch_effect_colnames: Optional[Mapping[str, List[str]]] = None, + ): + """Constructor for SequentialTrainingScheduler. + + Parameters + ---------- + model: tf.keras.Model + input model that needs to be trained. + + optimizer: tf.keras.optimizers.Optimizer, optional + optimizer used to train the model (only the attributes of + the optimizer will be used if provided with Optimizer). + Defaults to 'adam'. + + learning_rate: float, optional + learning rate for the optimizer. Defaults to 1e-4. + + early_stopping: bool, optional + whether or not to use early stopping when training the model. + + """ + self.model = model + self.mdata = mdata + self.component_configs = self.model.component_configs + self.optimizer = optimizer + self.learning_rate = learning_rate + self.early_stopping = early_stopping + self.training_order = self.compute_component_training_order() + self.modality_weight = self.compute_modality_weight() + self.batch_size = batch_size + self.output_dir = outdir + self.distribution_names = distribution_names + self.batch_effect_colnames = batch_effect_colnames + + def compute_component_training_order(self) -> Mapping[int, List[str]]: + """Compute the training order of the components based on the + order of topological sort of the input dependency graph. + + Returns + ------- + Mapping[int, List[str]] + the training order of the components, the keys are the + training order, the values are lists of the components + trained in the corresponding order. + """ + training_order = list() + component_order = dict() + self.run_progressive_training = dict() + component_configs = self.component_configs + training_order.append([]) + for component_config in component_configs: + component_name = component_config.get("name") + conditioned_on_z = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z, [] + ) + conditioned_on_z_hat = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, [] + ) + if len(conditioned_on_z) == 0 and len(conditioned_on_z_hat) == 0: + self.run_progressive_training[component_name] = False + else: + self.run_progressive_training[component_name] = True + + conditioned_on = itertools.chain(conditioned_on_z, conditioned_on_z_hat) + order = max([0] + [component_order[x] + 1 for x in conditioned_on]) + if order > len(training_order) - 1: + training_order.append([]) + training_order[order].append(component_name) + component_order.setdefault(component_name, order) + + return [[x] for xs in training_order for x in xs] # training_order + + def compute_modality_weight( + self, constant: bool = False + ) -> Mapping[str, Mapping[str, int]]: + """Compute the weight of the data distribution for each + modality. + + Parameters + ---------- + constant : bool, optional + whether or not to use constant weight for the data + distribution. Defaults to False. + + Returns + ------- + Mapping[str, Mapping[str, int]] + the weight of the data distribution by component. The keys + are the component names, the values are the mapping where + the keys are the modality names and the values are the + weight. + """ + modality_weight_by_component = defaultdict(dict) + for component_config in self.component_configs: + component_name = component_config.get("name") + modality_weight = dict() + if not constant: + n_vars = component_config.get(Constants.CONFIG_FIELD_COMPONENT_N_VARS) + total_vars = 0 + total_scaled_weight = 0 + for modality_name, n_var in n_vars.items(): + total_vars += n_var + for modality_name, n_var in n_vars.items(): + scaled_weight = total_vars / n_var + total_scaled_weight += scaled_weight + modality_weight.setdefault(modality_name, scaled_weight) + for modality_name, scaled_weight in modality_weight.items(): + modality_weight[modality_name] = scaled_weight / total_scaled_weight + else: + for modality_name in n_vars.keys(): + modality_weight.setdefault(modality_name, 1.0) + modality_weight_by_component.setdefault(component_name, modality_weight) + + return modality_weight_by_component + + def _kmeans_plus_plus( + self, X: tf.Tensor, n_clusters: int, seed: int = None + ) -> tf.Tensor: + """K-Means++ seeding using TensorFlow ops. + + Selects n_clusters initial centers from data X using the K-means++ + algorithm, which spreads centers probabilistically to cover all + natural clusters. + + Parameters + ---------- + X: tf.Tensor + Data tensor of shape (n_samples, n_dimensions) + n_clusters: int + Number of centers to select + seed: int, optional + Random seed for reproducibility + + Returns + ------- + tf.Tensor + Centers of shape (n_clusters, n_dimensions) + """ + n_samples = tf.shape(X)[0] + + # 1. Choose first center uniformly at random + first_idx = tf.random.uniform( + [], minval=0, maxval=n_samples, dtype=tf.int32, seed=seed + ) + centers = [tf.gather(X, first_idx)] + # 2. Iteratively choose remaining centers + for i in range(1, n_clusters): + # Stack current centers: (num_current_centers, n_dims) + current_centers = tf.stack(centers) + # Calculate squared distances to nearest center + # Expand for broadcasting: (n_samples, 1, n_dims) - (1, num_centers, n_dims) + distances_sq = tf.reduce_sum( + tf.square(tf.expand_dims(X, 1) - tf.expand_dims(current_centers, 0)), + axis=2, + ) # Shape: (n_samples, num_centers) + # For each point, find distance to NEAREST center + min_distances_sq = tf.reduce_min( + distances_sq, axis=1 + ) # Shape: (n_samples,) + + # 3. Probabilistic selection: P(x) ∝ D(x)² + # Points far from existing centers are more likely to be chosen + logits = tf.math.log( + tf.expand_dims(min_distances_sq, 0) + ) # Shape: (1, n_samples) + next_idx = tf.random.categorical(logits, num_samples=1, seed=seed)[0, 0] + + centers.append(tf.gather(X, next_idx)) + return tf.stack(centers) # Shape: (n_clusters, n_dimensions) + + def _compute_cluster_assignments( + self, X: tf.Tensor, centers: tf.Tensor + ) -> tf.Tensor: + """Assign each data point to its nearest center. + + Parameters + ---------- + X: tf.Tensor + Data tensor of shape (n_samples, n_dimensions) + centers: tf.Tensor + Cluster centers of shape (n_clusters, n_dimensions) + + Returns + ------- + tf.Tensor + Cluster assignments of shape (n_samples,) + Each value is an integer in [0, n_clusters-1] + """ + # Calculate squared Euclidean distances + # (n_samples, 1, n_dims) - (1, n_clusters, n_dims) + distances_sq = tf.reduce_sum( + tf.square(tf.expand_dims(X, 1) - tf.expand_dims(centers, 0)), axis=2 + ) # Shape: (n_samples, n_clusters) + + # Assign to nearest center + assignments = tf.argmin( + distances_sq, axis=1, output_type=tf.int32 + ) # Shape: (n_samples,) + + return assignments + + def initialize_gmm_priors_with_kmeans( + self, + component_name: str, + seed: int = 42, + add_noise: bool = True, + noise_std: float = 0.1, + ): + """ + Initialize GMM prior means and logits using K-means++ on vanilla latent space. + + This should be called after vanilla phase and before transition phase. + Uses K-means++ to find cluster centers and initializes: + - loc_bias (means): at cluster centers + - logits_bias: based on cluster sizes + + Parameters + ---------- + component_name: str + Name of the component (e.g., "RNA") + seed: int + Random seed for reproducibility + add_noise: bool + Whether to add small noise to centers (prevents identical initialization) + noise_std: float + Standard deviation of noise to add to centers + """ + # 1. Extract latent representations from end of vanilla phase + self.model.trainable = False + outputs = self.model.predict(self.mdata, batch_size=self.batch_size, verbose=0) + z = outputs[f"{component_name}_z"] # Shape: (n_samples, event_dims) + z_tensor = tf.constant(z, dtype=tf.float32) + + n_samples = z.shape[0] + event_dims = z.shape[1] + + # 2. Get number of GMM components + prior_layer = self.model.components[component_name].z_prior_parameterizer + n_clusters = prior_layer.n_components + + # 3. Run K-means++ to find cluster centers + centers = self._kmeans_plus_plus(z_tensor, n_clusters, seed=seed) + + # 4. Compute cluster assignments and sizes + assignments = self._compute_cluster_assignments(z_tensor, centers) + assignments_np = assignments.numpy() + + # Count points in each cluster + cluster_counts = np.zeros(n_clusters, dtype=np.int32) + for k in range(n_clusters): + cluster_counts[k] = np.sum(assignments_np == k) + for k in range(n_clusters): + pct = 100.0 * cluster_counts[k] / n_samples + + # 5. Optionally add small noise to centers + if add_noise: + noise = tf.random.normal( + shape=centers.shape, + mean=0.0, + stddev=noise_std, + seed=seed, + dtype=tf.float32, + ) + centers = centers + noise + print(f" Added Gaussian noise (std={noise_std}) to centers") + else: + print(" No noise added to centers") + + # Initialize GMM prior parameters + # 6a. Initialize means (loc_bias) + for k in range(n_clusters): + center = centers[k : k + 1, :] # Shape: (1, event_dims) + prior_layer.loc_bias[k].assign(center) + + # 6b. Initialize logits based on cluster sizes + # Convert counts to log-probabilities + cluster_proportions = cluster_counts / n_samples + # Avoid log(0) for empty clusters (shouldn't happen with k-means++) + cluster_proportions = np.maximum(cluster_proportions, 1e-7) + logits = np.log(cluster_proportions).astype(np.float32) + logits_tensor = tf.constant(logits.reshape(1, n_clusters), dtype=tf.float32) + prior_layer.logits_bias.assign(logits_tensor) + + # 6c. Initialize stds based on cluster spreads + # Compute std for each cluster + cluster_stds = np.zeros((n_clusters, event_dims), dtype=np.float32) + for k in range(n_clusters): + points_in_cluster = z[assignments_np == k] + if len(points_in_cluster) > 1: + # Compute std per dimension + cluster_stds[k] = np.std(points_in_cluster, axis=0) + else: + # Fallback for empty/tiny clusters + cluster_stds[k] = 0.5 + # Add minimum threshold to prevent collapse + # cluster_stds[k] = np.maximum(cluster_stds[k], 0.02) + + # Convert std to scale_diag_bias value (inverse of softplus) + # softplus(x) = log(1 + exp(x)) + # Inverse: x = log(exp(std) - 1) + # But simpler approximation for std > 0.5: x ≈ std + for k in range(n_clusters): + std_value = cluster_stds[k] + # Inverse softplus (approximate) + # For std > 1: bias ≈ std + # For std < 1: bias ≈ log(exp(std) - 1) + bias_value = np.where( + std_value > 1.0, + std_value - 0.5, # Approximation + np.log(np.exp(std_value) - 1 + 1e-7), # Exact inverse + ) + bias_tensor = tf.constant( + bias_value.reshape(1, event_dims), dtype=tf.float32 + ) + prior_layer.scale_diag_bias[k].assign(bias_tensor) + + self.model.trainable = True + + def _get_parent_component(self, child_name: str) -> Optional[str]: + """Get parent component name for the child.""" + for component_config in self.component_configs: + if component_config.get("name") == child_name: + conditioned_on_z_hat = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_CONDITION_Z_HAT, [] + ) + if len(conditioned_on_z_hat) > 0: + return conditioned_on_z_hat + return [] + + def _get_phase_boundaries( + self, total_epochs: int, ratios: Tuple[float, float, float] = (0.5, 0.2, 0.3) + ) -> Tuple[int, int]: + """Calculate epoch boundaries for 3-phase training.""" + phase1_end = int(total_epochs * ratios[0]) + phase2_end = int(total_epochs * (ratios[0] + ratios[1])) + return phase1_end, phase2_end + + def fit(self, x: tf.data.Dataset, **kwargs) -> List[tf.keras.callbacks.History]: + """Fit model with multi-phase hierarchical training.""" + n_batches = len(x) + learning_rate = self.learning_rate + history = [] + max_n_epochs = kwargs.get("epochs", 100) + + experiment_name = self.model.name + mlflow.set_experiment(experiment_name) + experiment = mlflow.get_experiment_by_name(experiment_name) + + # Check if single component (for PeriodicTSNECallback decision) + is_single_component = len(self.training_order) == 1 + + for component_order, train_components in enumerate(self.training_order): + component_name = train_components[0] + + # PROGRESSIVE SECTION - Phase 2 (Child Only) + if self.run_progressive_training.get(component_name): + parent_names = self._get_parent_component(component_name) + + if len(parent_names) > 0: + component_config = [ + c + for c in self.component_configs + if c.get("name") == component_name + ][0] + n_progressive_epochs = component_config.get( + Constants.CONFIG_FIELD_COMPONENT_N_PROGRESSIVE_EPOCHS, 0 + ) + + if n_progressive_epochs > 0: + # EDIT HERE LATER: Could split into two sub-phases + # phase2a_epochs = n_progressive_epochs // 2 + # phase2b_epochs = n_progressive_epochs - phase2a_epochs + print(f"\n{'═' * 70}") + print("PHASE 2: COMPONENT WEIGHT TRANSFER") + print(f" Parents: {', '.join(parent_names)} → Child: {component_name}") + print(f" Epochs: {n_progressive_epochs}") + print(f"{'═' * 70}\n") + + run_name = f"Training/{component_order}/WeightTransfer/{'-'.join(parent_names)}_to_{component_name}" + mlflow.start_run( + experiment_id=experiment.experiment_id, run_name=run_name + ) + mlflow.tensorflow.autolog( + log_every_n_steps=None, + log_every_epoch=True, + log_models=False, + checkpoint=False, + ) + + loss_weights, _ = self.setup_component_and_loss_weights( + train_components=parent_names + [component_name], + n_batches=n_batches, + initial_iteration=None, + ) + + optimizer = tf.keras.optimizers.get(self.optimizer).__class__( + learning_rate=learning_rate + ) + + # Compile for phase 2 + self.model.compile( + use_vanilla_kl=True, # Child uses Vanilla + use_both_kl=False, # Noth both types + optimizer=optimizer, + loss_weights=loss_weights, + ) + + # Set initial KL weights after compile + # inside loop for each parent, outside for child + for parent_name in parent_names: + self.model._vanilla_kl_weights[parent_name].assign(0.0) + self.model._gmm_kl_weights[parent_name].assign(1.0) + + # Child: vanilla β = 3.0 (frozen during weight transfer) + self.model._vanilla_kl_weights[component_name].assign(3.0) + + kwargs_progressive = deepcopy(kwargs) + kwargs_progressive.pop("epochs", None) + callbacks_progressive = deepcopy(kwargs.get("callbacks", [])) + + callbacks_progressive.append( + ComponentWeightTransferCallback( + total_epochs=n_progressive_epochs, + parent_names=parent_names, + child_name=component_name, + ) + ) + + history.append( + self.model.fit( + x, + epochs=n_progressive_epochs, + callbacks=callbacks_progressive, + **kwargs_progressive, + ) + ) + + mlflow.end_run() + + # Freeze parent after weight transfer + print(f"FREEZING PARENT: {parent_name}") + for parent_name in parent_names: + self.model.components[parent_name].trainable = False + + # NON-PROGRESSIVE SECTION + # Phase 1 (parent) or Phase 3 (child) - KL Annealing + print(f"\n{'═' * 70}") + if self.run_progressive_training.get(component_name): + print(f"PHASE 3: CHILD KL ANNEALING - {component_name}") + else: + print(f"PHASE 1: PARENT KL ANNEALING - {component_name}") + print(f" Epochs: {max_n_epochs}") + print(f"{'═' * 70}\n") + + run_name = f"Training/{component_order}/KLAnnealing/{component_name}" + mlflow.start_run(experiment_id=experiment.experiment_id, run_name=run_name) + mlflow.tensorflow.autolog( + log_every_epoch=True, + log_models=False, + checkpoint=False, + ) + + loss_weights, _ = self.setup_component_and_loss_weights( + train_components=train_components, + n_batches=n_batches, + initial_iteration=None, + ) + + optimizer = tf.keras.optimizers.get(self.optimizer).__class__( + learning_rate=learning_rate + ) + self.model.compile( + use_vanilla_kl=False, + use_both_kl=True, + optimizer=optimizer, + loss_weights=loss_weights, + ) + + # Set KL weights immediately after compile + self.model._vanilla_kl_weights[component_name].assign(3.0) + self.model._gmm_kl_weights[component_name].assign(0.0) + + # to zero out parent loss at phase3: + if self.run_progressive_training.get(component_name): + parent_names = self._get_parent_component(component_name) + if len(parent_names) > 0: + for parent_name in parent_names: + for loss_name, loss_fn in self.model.loss.items(): + if parent_name in loss_name: + if hasattr(loss_fn, "weight"): + loss_fn.weight = 0.0 + print(f" Set {loss_name}.weight = 0.0") + + phase_boundaries = self._get_phase_boundaries(max_n_epochs) + callbacks = deepcopy(kwargs.get("callbacks", [])) + + callbacks.append( + KLAnnealingCallback( + total_epochs=max_n_epochs, + scheduler=self, + component_name=component_name, + phase_boundaries=phase_boundaries, + vanilla_beta_max=3.0, + gmm_beta_max=1.0, + run_kmeans=True, + ) + ) + + # Only add PeriodicTSNECallback for SINGLE component + if is_single_component and self.output_dir: + callbacks.append( + PeriodicTSNECallback( + mdata=self.mdata, + component=component_name, + outdir=os.path.join(self.output_dir, "snapshots"), + batch_size=self.batch_size, + batch_effect_colnames=self.batch_effect_colnames, + distribution_names=self.distribution_names, + ) + ) + + if self.early_stopping: + callbacks.append( + tf.keras.callbacks.EarlyStopping( + monitor="loss", + min_delta=5, + patience=max(10, int(kwargs.get("epochs", 1) / 20)), + restore_best_weights=True, + verbose=1, + ) + ) + + kwargs_copy = deepcopy(kwargs) + kwargs_copy.pop("epochs", None) + history.append( + self.model.fit( + x, + epochs=max_n_epochs, + callbacks=callbacks, + **kwargs_copy, + ) + ) + + mlflow.end_run() + + return history + + def setup_component_and_loss_weights( + self, + train_components: List[str], + n_batches: int, + initial_iteration: Optional[int] = None, + ) -> Tuple[Any]: + """Setup the trainable attributes for each component and the + alpha (with current_iteration and total_iterations) in + progressive scaler and set the loss weights properly (for + components that take more than one modality). + + Parameters + ---------- + train_components : List[str] + list of component names for components that need to be + trained in this sequential step. + + n_batches: int + number of batches needed to processed in one epoch. + + initial_iteration : Optional[int], optional + initial iteration for progressive scaler when the training + starts. The progressive training will be turn off if + provided with None. Defaults to None. + + Returns + ------- + Tuple[Any] + The first element in the tuple is the loss weights, the + second element is the maximum number of progressive epochs. + """ + loss_weights = dict() + max_n_progressive_epochs = 0 + + for component_config in self.component_configs: + component_name = component_config.get("name") + component = self.model.components.get(component_name) + + if component_name in train_components: + component.trainable = True + weight_scale = 1.0 + + n_progressive_epochs = float( + component_config.get( + Constants.CONFIG_FIELD_COMPONENT_N_PROGRESSIVE_EPOCHS, 0 + ) + ) + progressive_iterations = n_batches * n_progressive_epochs + max_n_progressive_epochs = max( + max_n_progressive_epochs, n_progressive_epochs + ) + + if initial_iteration is None or initial_iteration <= 0: + initial_iteration = progressive_iterations + + component.set_progressive_scaler_iteration( + current_iteration=initial_iteration, + total_iterations=progressive_iterations, + ) + else: + component.trainable = False + weight_scale = 0.0 + component.set_progressive_scaler_iteration( + current_iteration=1.0, total_iterations=1.0 + ) + + # MODIFIED: Component-specific KL loss names + loss_weights.setdefault( + f"{component_name}_vanilla_kl_divergence", + 1.0 * weight_scale, + ) + loss_weights.setdefault( + f"{component_name}_gmm_kl_divergence", + 1.0 * weight_scale, + ) + + for modality_name in component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES + ): + loss_weights.setdefault( + f"{component_name}_{modality_name}_{Constants.MODEL_LOSS_DATA_POSTFIX}", + self.modality_weight.get(component_name).get(modality_name) + * weight_scale, + ) + + return loss_weights, int(max_n_progressive_epochs) diff --git a/cavachon/tools/cluster_analysis.py b/cavachon/tools/cluster_analysis.py index 421b977..244e0d8 100644 --- a/cavachon/tools/cluster_analysis.py +++ b/cavachon/tools/cluster_analysis.py @@ -1,361 +1,361 @@ -import warnings -from typing import Dict, List, Optional, Sequence, Union - -import muon as mu -import numpy as np -import pandas as pd -import scanpy -import tensorflow as tf -from sklearn.metrics.cluster import contingency_matrix -from sklearn.preprocessing import LabelEncoder -from tqdm import tqdm - -from cavachon.dataloader.dataloader import DataLoader -from cavachon.distributions.mixture_multivariate_normal_diag_distribution import ( - MixtureMultivariateNormalDiagDistribution, -) -from cavachon.distributions.multivariate_normal_diag_distribution import ( - MultivariateNormalDiagDistribution, -) - - -class ClusterAnalysis: - """ClusterAnalysis - - Cluster analysis including online multi-facet (soft) clustering, - K-nearest neighbor analysis. - - Attributes - ---------- - mdata: muon.MuData - the MuData for analysis. - - model: tf.keras.Model - the trained generative model. - - """ - - def __init__(self, mdata: mu.MuData, model: tf.keras.Model): - """Constructor for ClusterAnalysis - - Parameters - ---------- - mdata: muon.MuData - the MuData for analysis. - - model: tf.keras.Model - the trained generative model. - - """ - self.mdata = mdata - self.model = model - - def compute_cluster_log_probability( - self, - modality: str, - component: str, - batch_effect_colnames: Optional[Dict[str, List[str]]] = None, - distribution_names: Optional[Dict[str, str]] = None, - batch_size: int = 128, - min_n_obs=50, - ) -> np.array: - """Compute the log probability of a sample being assigned to - each cluster in the latent space of the specified component. - - Parameters - ---------- - modality: str - the result will be stored in the obs and obsm of this - modality in the self.mdata. - - component : str - which latent space of the component to used for the - clustering. - - batch_effect_colnames: Dict[str, List[str]], optional - the batch effect columns for each modality. Defaults to - None. - - distribution_names: Dict[str, str], optional - the distribution names for each modality. Defaults to - None. - - batch_size : int, optional - batch size used for the forward pass. Defaults to 128 - - Returns - ------- - np.array - logpy_z, the log probability of a sample `i` being assigned - to each cluster `j`. - """ - z_prior_parameterizer = self.model.components[component].z_prior_parameterizer - z_prior_parameters = tf.squeeze(z_prior_parameterizer(tf.ones((1, 1)))) - dist_z_y = MultivariateNormalDiagDistribution.from_parameterizer_output( - z_prior_parameters[..., 1:] - ) - dist_z = MixtureMultivariateNormalDiagDistribution.from_parameterizer_output( - z_prior_parameters - ) - logpy = tf.math.log(tf.math.softmax(z_prior_parameters[..., 0]) + 1e-7) - - logpy_z = list() - dataloader = DataLoader( - self.mdata, batch_size, batch_effect_colnames, distribution_names - ) - for batch_data in tqdm(dataloader): - outputs = self.model(batch_data, training=False) - z = outputs[f"{component}_z"] - logpz_y = dist_z_y.log_prob(tf.expand_dims(z, -2)) - logpz = tf.expand_dims(dist_z.log_prob(z), -1) - logpy_z.append(logpy + logpz_y - logpz) - - logpy_z = np.vstack(logpy_z) - cluster = tf.argmax(logpy_z, axis=-1).numpy() - self.mdata.mod[modality].obs[f"cluster_{component}"] = [ - f"Cluster {x:03d}" for x in cluster - ] - _keep = self.mdata.mod[modality].obs[f"cluster_{component}"].value_counts() - - while (_keep < min_n_obs).sum() > 0: - keep = [] - for i, x in zip(_keep.index[:-1], _keep): - if x > min_n_obs: - keep.append(int(i.split(" ")[1])) - logpy_z = logpy_z[:, keep] - cluster = tf.argmax(logpy_z, axis=-1).numpy() - self.mdata.mod[modality].obsm[f"logpy_z_{component}"] = logpy_z - self.mdata.mod[modality].obs[f"cluster_{component}"] = [ - f"Cluster {x:03d}" for x in cluster - ] - _keep = self.mdata.mod[modality].obs[f"cluster_{component}"].value_counts() - - return logpy_z - - def compute_neighbors_with_same_annotations( - self, - modality: str, - use_cluster: str, - use_rep: Union[str, np.array], - n_neighbors: Union[int, Sequence[int]] = list(range(5, 25)), - ) -> pd.DataFrame: - """Perform K-nearest neighbor analysis. - - Parameters - ---------- - modality: str - the modality to used. - - use_cluster: str - the column name of the clusters in the obs of modality. - - use_rep: Union[str, np.array] - the key of obsm of modality to used to compute the distance - within and between clusters. Alternatively, the array will - be used if provided with np.array, - - n_neighbors: Union[int, Sequence[int]], optional - the number of neighbors to be analyzed, Defaults to - list(range(5, 25)) - - Returns - ------- - pd.DataFrame - analysis result for K-nearest neighbor. The DataFrame - contains 3 columns, the first column is the number of - neighbors (K), the second column is the cluster - identifiers, the third column specify the proportion of KNN - samples with the same cluster, - - Raises - ------ - KeyError - if use_cluster is not in the obs of the modality in - self.mdata. (please perform compute_cluster_log_probability - first for unsupervised clustering). - """ - if use_cluster not in self.mdata.mod[modality].obs: - message = f"{use_cluster} not in obs DataFrame of the modality." - raise KeyError(message) - - proportions_series = list() - clusters_series = list() - n_neighbors_series = list() - if isinstance(n_neighbors, (int, float)): - n_neighbors = [n_neighbors] - - if isinstance(use_rep, np.ndarray): - self.mdata[modality].obsm["z_custom"] = use_rep - use_rep = "z_custom" - - for k in tqdm(n_neighbors): - if isinstance(k, float): - k = int(k) - message = ( - "Expect int for element in n_neighbors, transform float to int." - ) - warnings.warn(message, RuntimeWarning) - - scanpy.pp.neighbors( - self.mdata[modality], n_neighbors=k + 1, use_rep=use_rep - ) - proportions_k = list() - for i, j in enumerate(self.mdata[modality].obsp["distances"]): - cluster = self.mdata[modality].obs.iloc[i][use_cluster] - neighbor_clusters = self.mdata[modality].obs.iloc[j.indices][ - use_cluster - ] - proportions_k.append((neighbor_clusters == cluster).sum() / k) - clusters_series += [cluster] - - proportions_series.append(np.array(proportions_k)) - n_neighbors_series.append(np.array([k] * len(proportions_k))) - - analysis_result = pd.DataFrame( - { - "Number of Neighbors": np.concatenate(n_neighbors_series), - "Cluster": clusters_series, - "% of KNN Cells with the Same Cluster": np.concatenate( - proportions_series - ), - } - ) - - analysis_result["KNN Cells with the Same Cluster"] = ( - analysis_result["% of KNN Cells with the Same Cluster"] - * analysis_result["Number of Neighbors"] - ) - num_cells = self.mdata[modality].obs["cell_type"].value_counts() - total_num_cells = num_cells.sum() - analysis_result["Number of Cells"] = analysis_result["Cluster"].map(num_cells) - analysis_result["Cell Enrichment Score"] = ( - analysis_result["% of KNN Cells with the Same Cluster"] - - analysis_result["Number of Cells"] / total_num_cells - ).clip(lower=0) / (1 - analysis_result["Number of Cells"] / total_num_cells) - - return analysis_result - - def compute_contingency_matrix( - self, modality: str, cluster_colname_a: str, cluster_colname_b: str - ) -> pd.DataFrame: - """Compute the contingency matrix of two clustering results. - - Parameters - ---------- - modality: str - the modality to used. - - cluster_colname_a: str - column name for the first clustering assignments in var. - - cluster_colname_b: str - column name for the second clustering assignments in var. - - Returns - ------- - pd.DataFrame - contingency matrix where the indices are the cluster names - in the first clustering assignment, the column names are - the second clustering assignment. - - """ - encoder_cluster_a = LabelEncoder() - encoder_cluster_b = LabelEncoder() - - encoded_array_a = encoder_cluster_a.fit_transform( - self.mdata[modality].obs[cluster_colname_a] - ) - encoded_array_b = encoder_cluster_b.fit_transform( - self.mdata[modality].obs[cluster_colname_b] - ) - - contigency_matrix = pd.DataFrame( - contingency_matrix(encoded_array_a, encoded_array_b) - ) - contigency_matrix.index = encoder_cluster_a.classes_ - contigency_matrix.columns = encoder_cluster_b.classes_ - - return contigency_matrix - - def compute_classification_metrics( - self, modality: str, cluster_colname_a: str, cluster_colname_b: str - ) -> pd.DataFrame: - """Compute the classification metrics if using the second - clustering assignment to predict the first cluster assignment. - - Parameters - ---------- - modality: str - the modality to used. - - cluster_colname_a: str - column name for the first clustering assignments in var. - - cluster_colname_b: str - column name for the second clustering assignments in var. - - Returns - ------- - pd.DataFrame - classification metrics where the column names are: - 1. Cluster A - 2. Cluster B - 3. F1 Score - 4. Accuracy - 5. Sensitivity - 6. Specificity - 7. Precision - - """ - contingency_matrix = self.compute_contingency_matrix( - modality, cluster_colname_a, cluster_colname_b - ) - - result = [] - for target_a in contingency_matrix.index: - for target_b in contingency_matrix.columns: - FP = ( - contingency_matrix[target_b].sum() - - contingency_matrix[target_b][target_a] - ) - TP = contingency_matrix[target_b][target_a] - FN = ( - contingency_matrix.loc[target_a].sum() - - contingency_matrix[target_b][target_a] - ) - TN = ( - np.sum(contingency_matrix.values) - - contingency_matrix[target_b].sum() - - contingency_matrix.loc[target_a].sum() - + contingency_matrix[target_b][target_a] - ) - - sensitivity = TP / (TP + FN) - specificity = TN / (TN + FP) - precision = TP / (TP + FP) - accuracy = (TP + TN) / (TP + TN + FP + FN) - f1 = 2 * precision * sensitivity / (precision + sensitivity + 1e-3) - - result.append( - [ - target_a, - target_b, - f1, - accuracy, - sensitivity, - specificity, - precision, - ] - ) - - colnames = [ - "Cluster A", - "Cluster B", - "F1 Score", - "Accuracy", - "Sensitivity", - "Specificity", - "Precision", - ] - - return pd.DataFrame(result, columns=colnames) +import warnings +from typing import Dict, List, Optional, Sequence, Union + +import muon as mu +import numpy as np +import pandas as pd +import scanpy +import tensorflow as tf +from sklearn.metrics.cluster import contingency_matrix +from sklearn.preprocessing import LabelEncoder +from tqdm import tqdm + +from cavachon.dataloader.dataloader import DataLoader +from cavachon.distributions.mixture_multivariate_normal_diag_distribution import ( + MixtureMultivariateNormalDiagDistribution, +) +from cavachon.distributions.multivariate_normal_diag_distribution import ( + MultivariateNormalDiagDistribution, +) + + +class ClusterAnalysis: + """ClusterAnalysis + + Cluster analysis including online multi-facet (soft) clustering, + K-nearest neighbor analysis. + + Attributes + ---------- + mdata: muon.MuData + the MuData for analysis. + + model: tf.keras.Model + the trained generative model. + + """ + + def __init__(self, mdata: mu.MuData, model: tf.keras.Model): + """Constructor for ClusterAnalysis + + Parameters + ---------- + mdata: muon.MuData + the MuData for analysis. + + model: tf.keras.Model + the trained generative model. + + """ + self.mdata = mdata + self.model = model + + def compute_cluster_log_probability( + self, + modality: str, + component: str, + batch_effect_colnames: Optional[Dict[str, List[str]]] = None, + distribution_names: Optional[Dict[str, str]] = None, + batch_size: int = 128, + min_n_obs=36, + ) -> np.array: + """Compute the log probability of a sample being assigned to + each cluster in the latent space of the specified component. + + Parameters + ---------- + modality: str + the result will be stored in the obs and obsm of this + modality in the self.mdata. + + component : str + which latent space of the component to used for the + clustering. + + batch_effect_colnames: Dict[str, List[str]], optional + the batch effect columns for each modality. Defaults to + None. + + distribution_names: Dict[str, str], optional + the distribution names for each modality. Defaults to + None. + + batch_size : int, optional + batch size used for the forward pass. Defaults to 128 + + Returns + ------- + np.array + logpy_z, the log probability of a sample `i` being assigned + to each cluster `j`. + """ + z_prior_parameterizer = self.model.components[component].z_prior_parameterizer + z_prior_parameters = tf.squeeze(z_prior_parameterizer(tf.ones((1, 1)))) + dist_z_y = MultivariateNormalDiagDistribution.from_parameterizer_output( + z_prior_parameters[..., 1:] + ) + dist_z = MixtureMultivariateNormalDiagDistribution.from_parameterizer_output( + z_prior_parameters + ) + logpy = tf.math.log(tf.math.softmax(z_prior_parameters[..., 0]) + 1e-7) + + logpy_z = list() + dataloader = DataLoader( + self.mdata, batch_size, batch_effect_colnames, distribution_names + ) + for batch_data in tqdm(dataloader): + outputs = self.model(batch_data, training=False) + z = outputs[f"{component}_z"] + logpz_y = dist_z_y.log_prob(tf.expand_dims(z, -2)) + logpz = tf.expand_dims(dist_z.log_prob(z), -1) + logpy_z.append(logpy + logpz_y - logpz) + + logpy_z = np.vstack(logpy_z) + cluster = tf.argmax(logpy_z, axis=-1).numpy() + self.mdata.mod[modality].obs[f"cluster_{component}"] = [ + f"Cluster {x:03d}" for x in cluster + ] + _keep = self.mdata.mod[modality].obs[f"cluster_{component}"].value_counts() + + while (_keep < min_n_obs).sum() > 0: + keep = [] + for i, x in zip(_keep.index[:-1], _keep): + if x > min_n_obs: + keep.append(int(i.split(" ")[1])) + logpy_z = logpy_z[:, keep] + cluster = tf.argmax(logpy_z, axis=-1).numpy() + self.mdata.mod[modality].obsm[f"logpy_z_{component}"] = logpy_z + self.mdata.mod[modality].obs[f"cluster_{component}"] = [ + f"Cluster {x:03d}" for x in cluster + ] + _keep = self.mdata.mod[modality].obs[f"cluster_{component}"].value_counts() + + return logpy_z + + def compute_neighbors_with_same_annotations( + self, + modality: str, + use_cluster: str, + use_rep: Union[str, np.array], + n_neighbors: Union[int, Sequence[int]] = list(range(5, 25)), + ) -> pd.DataFrame: + """Perform K-nearest neighbor analysis. + + Parameters + ---------- + modality: str + the modality to used. + + use_cluster: str + the column name of the clusters in the obs of modality. + + use_rep: Union[str, np.array] + the key of obsm of modality to used to compute the distance + within and between clusters. Alternatively, the array will + be used if provided with np.array, + + n_neighbors: Union[int, Sequence[int]], optional + the number of neighbors to be analyzed, Defaults to + list(range(5, 25)) + + Returns + ------- + pd.DataFrame + analysis result for K-nearest neighbor. The DataFrame + contains 3 columns, the first column is the number of + neighbors (K), the second column is the cluster + identifiers, the third column specify the proportion of KNN + samples with the same cluster, + + Raises + ------ + KeyError + if use_cluster is not in the obs of the modality in + self.mdata. (please perform compute_cluster_log_probability + first for unsupervised clustering). + """ + if use_cluster not in self.mdata.mod[modality].obs: + message = f"{use_cluster} not in obs DataFrame of the modality." + raise KeyError(message) + + proportions_series = list() + clusters_series = list() + n_neighbors_series = list() + if isinstance(n_neighbors, (int, float)): + n_neighbors = [n_neighbors] + + if isinstance(use_rep, np.ndarray): + self.mdata[modality].obsm["z_custom"] = use_rep + use_rep = "z_custom" + + for k in tqdm(n_neighbors): + if isinstance(k, float): + k = int(k) + message = ( + "Expect int for element in n_neighbors, transform float to int." + ) + warnings.warn(message, RuntimeWarning) + + scanpy.pp.neighbors( + self.mdata[modality], n_neighbors=k + 1, use_rep=use_rep + ) + proportions_k = list() + for i, j in enumerate(self.mdata[modality].obsp["distances"]): + cluster = self.mdata[modality].obs.iloc[i][use_cluster] + neighbor_clusters = self.mdata[modality].obs.iloc[j.indices][ + use_cluster + ] + proportions_k.append((neighbor_clusters == cluster).sum() / k) + clusters_series += [cluster] + + proportions_series.append(np.array(proportions_k)) + n_neighbors_series.append(np.array([k] * len(proportions_k))) + + analysis_result = pd.DataFrame( + { + "Number of Neighbors": np.concatenate(n_neighbors_series), + "Cluster": clusters_series, + "% of KNN Cells with the Same Cluster": np.concatenate( + proportions_series + ), + } + ) + + analysis_result["KNN Cells with the Same Cluster"] = ( + analysis_result["% of KNN Cells with the Same Cluster"] + * analysis_result["Number of Neighbors"] + ) + num_cells = self.mdata[modality].obs["cell_type"].value_counts() + total_num_cells = num_cells.sum() + analysis_result["Number of Cells"] = analysis_result["Cluster"].map(num_cells) + analysis_result["Cell Enrichment Score"] = ( + analysis_result["% of KNN Cells with the Same Cluster"] + - analysis_result["Number of Cells"] / total_num_cells + ).clip(lower=0) / (1 - analysis_result["Number of Cells"] / total_num_cells) + + return analysis_result + + def compute_contingency_matrix( + self, modality: str, cluster_colname_a: str, cluster_colname_b: str + ) -> pd.DataFrame: + """Compute the contingency matrix of two clustering results. + + Parameters + ---------- + modality: str + the modality to used. + + cluster_colname_a: str + column name for the first clustering assignments in var. + + cluster_colname_b: str + column name for the second clustering assignments in var. + + Returns + ------- + pd.DataFrame + contingency matrix where the indices are the cluster names + in the first clustering assignment, the column names are + the second clustering assignment. + + """ + encoder_cluster_a = LabelEncoder() + encoder_cluster_b = LabelEncoder() + + encoded_array_a = encoder_cluster_a.fit_transform( + self.mdata[modality].obs[cluster_colname_a] + ) + encoded_array_b = encoder_cluster_b.fit_transform( + self.mdata[modality].obs[cluster_colname_b] + ) + + contigency_matrix = pd.DataFrame( + contingency_matrix(encoded_array_a, encoded_array_b) + ) + contigency_matrix.index = encoder_cluster_a.classes_ + contigency_matrix.columns = encoder_cluster_b.classes_ + + return contigency_matrix + + def compute_classification_metrics( + self, modality: str, cluster_colname_a: str, cluster_colname_b: str + ) -> pd.DataFrame: + """Compute the classification metrics if using the second + clustering assignment to predict the first cluster assignment. + + Parameters + ---------- + modality: str + the modality to used. + + cluster_colname_a: str + column name for the first clustering assignments in var. + + cluster_colname_b: str + column name for the second clustering assignments in var. + + Returns + ------- + pd.DataFrame + classification metrics where the column names are: + 1. Cluster A + 2. Cluster B + 3. F1 Score + 4. Accuracy + 5. Sensitivity + 6. Specificity + 7. Precision + + """ + contingency_matrix = self.compute_contingency_matrix( + modality, cluster_colname_a, cluster_colname_b + ) + + result = [] + for target_a in contingency_matrix.index: + for target_b in contingency_matrix.columns: + FP = ( + contingency_matrix[target_b].sum() + - contingency_matrix[target_b][target_a] + ) + TP = contingency_matrix[target_b][target_a] + FN = ( + contingency_matrix.loc[target_a].sum() + - contingency_matrix[target_b][target_a] + ) + TN = ( + np.sum(contingency_matrix.values) + - contingency_matrix[target_b].sum() + - contingency_matrix.loc[target_a].sum() + + contingency_matrix[target_b][target_a] + ) + + sensitivity = TP / (TP + FN) + specificity = TN / (TN + FP) + precision = TP / (TP + FP) + accuracy = (TP + TN) / (TP + TN + FP + FN) + f1 = 2 * precision * sensitivity / (precision + sensitivity + 1e-3) + + result.append( + [ + target_a, + target_b, + f1, + accuracy, + sensitivity, + specificity, + precision, + ] + ) + + colnames = [ + "Cluster A", + "Cluster B", + "F1 Score", + "Accuracy", + "Sensitivity", + "Specificity", + "Precision", + ] + + return pd.DataFrame(result, columns=colnames) diff --git a/cavachon/utils/tensor_utils.py b/cavachon/utils/tensor_utils.py index e50e8db..c38a263 100644 --- a/cavachon/utils/tensor_utils.py +++ b/cavachon/utils/tensor_utils.py @@ -1,305 +1,305 @@ -from typing import Any, Dict, Iterable, List, Optional, Tuple - -import numpy as np -import pandas as pd -import scipy -import tensorflow as tf -from sklearn.preprocessing import LabelEncoder - -from cavachon.utils.dataframe_utils import DataFrameUtils - - -class TensorUtils: - """TensorUtils - - Class containing multiple utility functions for tf.Tensor. - - """ - - @staticmethod - def is_sparse_tensor(input: Any) -> bool: - """Check if an input is a sparse tensor. - - Parameters - ---------- - input : Any - the input variable - - Returns - ------- - bool - if the input is a sparse tensor - """ - if isinstance(input, tf.SparseTensor): - return True - if isinstance(input, tf.keras.KerasTensor) and input.sparse: - return True - return False - - @staticmethod - def max_n_neurons(layers: Iterable[tf.keras.layers.Layer]) -> int: - """Get the maximum number of neurons (of tf.keras.layers.Dense) - in layers. - - Parameters - ---------- - layers: Iterable[tf.keras.layers.Layer] - layers in tf.keras.Model. - - Returns - ------- - int: - the maximum number of neurons (of tf.keras.layers.Dense) in - layers. Return 0 if no Dense layer in the layers. - - """ - current_max = 0 - for layer in layers: - if isinstance(layer, tf.keras.layers.Dense) and current_max < layer.units: - current_max = layer.units - return current_max - - @staticmethod - def remove_nan_gradients( - gradients: List[tf.Tensor], clip_value=10 - ) -> List[tf.Tensor]: - """Replace nan, inf with 0 and perform gradient clipping for - the gradients computed by tf.GradientTape.gradient(). - - Parameters - ---------- - gradients: List[tf.Tensor] - gradients computed by tf.GradientTape.gradient() - - clip_value: float, optional - clip values for gradient clipping. The resulting gradient - will be in the range of [-clip_value, clip_value] - - Returns - ------- - List[tf.Tensor] - processed gradients. - - """ - for i, g in enumerate(gradients): - if gradients[i] is None: - continue - gradients[i] = tf.keras.layers.Lambda( - lambda x: tf.where(tf.math.is_nan(x), tf.zeros_like(x), x) - )(gradients[i]) - gradients[i] = tf.keras.layers.Lambda( - lambda x: tf.where(tf.math.is_inf(x), tf.zeros_like(x), x) - )(gradients[i]) - gradients[i] = tf.keras.layers.Lambda( - lambda x: tf.where(x > clip_value, clip_value * tf.ones_like(x), x) - )(gradients[i]) - gradients[i] = tf.keras.layers.Lambda( - lambda x: tf.where( - x < -1 * clip_value, -1 * clip_value * tf.ones_like(x), x - ) - )(gradients[i]) - - return gradients - - @staticmethod - def create_backbone_layers( - n_layers: int = 3, - base_n_neurons: int = 128, - max_n_neurons: int = 2048, - rate: int = 2, - activation: str = "swish", - reverse: bool = False, - name: Optional[str] = "backbone_network", - ) -> tf.keras.Model: - """Create tf.keras.Sequential models with tf.keras.layers.Dense - and tf.keras.layers.BatchNormalization(). The created dense - layers would have number of neurons - [`base_n_neurons`, `base_n_neurons`*`rate`, ...]. For instance, - with default parameters, it creates layers of: - 1. tf.keras.layers.Dense(128, activation='elu') - 2. tf.keras.layers.BatchNormalization() - 3. tf.keras.layers.Dense(256, activation='elu') - 4. tf.keras.layers.BatchNormalization() - 5. tf.keras.layers.Dense(512, activation='elu') - 6. tf.keras.layers.BatchNormalization() - - Parameters - ---------- - n_layers: int, optional - number of layers. Defaults to 3. - - base_n_neurons: int, optional - base number of neurons. Defaults to 128. - - max_n_neurons: int, optional - maximum number of neurons. Defaults to 1024. - - rate: int, optional - increasing rate of number of neurons (see description of - the function for more details). Defaults to 2. - - activation: str, optional - activation functions in tf.keras.layers.Dense layer. - Defaults to 'elu'. - - reverse: bool, optional - whether to decrease the number of neurons in later layers. - Defaults to False. - - name: str, optional - name of the created tf.keras.Model. Defaults to - 'backbone_network'. - - Returns - ------- - tf.keras.Model - created tf.keras.Sequential model. - - """ - - layers = [] - for no_layer in range(0, n_layers): - n_neurons = min(base_n_neurons * rate**no_layer, max_n_neurons) - layers.append(tf.keras.layers.Dense(n_neurons, activation=activation)) - layers.append(tf.keras.layers.LayerNormalization()) - - if reverse: - layers.reverse() - - return tf.keras.Sequential(layers, name=name) - - @staticmethod - def create_tensor_from_df( - df: pd.DataFrame, - colnames: List[str] = [], - encoder_dict: Dict[str, LabelEncoder] = dict(), - ) -> Tuple[tf.Tensor, Dict[str, LabelEncoder]]: - """Create a Tensorflow Tensor from column data (specified with - `colnames`) in the provided DataFrame. If the column data is a - categorical variable, transform it with one-hot encoded Tensor. - If it is a continuous variable, simply transform it into a - Tensorflow Tensor. If no valid column data is provided, return - Tensor which is a zero vector. - - - Parameters - ---------- - df: pd.DataFrame - input DataFrame. - - colnames: List[str], optional - columns of the DataFrame that are used to create the - Tensor. Defaults to []. - - Returns - ------- - Tuple[tf.Tensor, Dict[str, LabelEncoder]] - the first element is the one-hot encoded Tensor. The second - element is the dictionary of LabelEncoder used to map the - categorical variable into scalar representation, where the - keys are the column names and the values are the - corresponding LabelEncoder. The value will be None if the - column data is not a continuous variable. - """ - # if no valid batch effect column is provided, use zero vector for batch effect - n_obs, n_features = df.shape - tensor_list = [] - - for colname in colnames: - if colname not in df.columns: - continue - coldata = df[colname] - if DataFrameUtils.check_is_categorical(coldata): - # if the column is a categorical variable, use one hot encoded tensor - encoded_tensor, encoder = TensorUtils.create_one_hot_encoded_tensor( - coldata, encoder_dict.get(colname, None) - ) - encoder_dict.setdefault(colname, encoder) - tensor_list.append(encoded_tensor) - else: - # if the column is a continous variable, - tensor = tf.reshape(tf.convert_to_tensor(coldata, tf.float32), (-1, 1)) - encoder_dict.setdefault(colname, None) - tensor_list.append(tensor) - - if len(tensor_list) == 0: - tensor_list.append(tf.zeros((n_obs, 1))) - - return tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=1))( - tensor_list - ), encoder_dict - - @staticmethod - def create_one_hot_encoded_tensor( - data: pd.Series, encoder: Optional[LabelEncoder] = None - ) -> Tuple[tf.Tensor, LabelEncoder]: - """Create a one hot encoded Tensor from a Series variable. - - Parameters - ---------- - data: pd.Series - pd.Series of categorical variables to be transformed to - one-hot encoded tf.Tensor. - - Returns - ------- - Tuple[tf.Tensor, LabelEncoder]: - the first element is the one-hot encoded Tensor, the second - element is the LabelEncoder used to map the categorical - variable into scalar representation. - - """ - if encoder is None: - encoder = LabelEncoder() - encoded_array = encoder.fit_transform(data) - else: - encoded_array = encoder.transform(data) - n_class = len(encoder.classes_) - encoded_tensor = tf.cast(tf.one_hot(encoded_array, n_class), tf.float32) - - return encoded_tensor, encoder - - @staticmethod - def spmatrix_to_sparse_tensor(spmatrix: scipy.sparse.spmatrix) -> tf.SparseTensor: - """Create a SparseTensor out of a scipy sparse matrix. - - Parameters - ---------- - spmatrix: sparse matrix - the provided matrix - - Returns - ------- - tf.SparseTensor: - the created SparseTensor. - - """ - coo_matrix = spmatrix.tocoo() - indices = np.mat([coo_matrix.row, coo_matrix.col]).transpose() - sparse_tensor = tf.SparseTensor(indices, coo_matrix.data, coo_matrix.shape) - return tf.sparse.reorder(tf.cast(sparse_tensor, tf.float32)) - - @staticmethod - def split(x: tf.Tensor, batch_size: int = 128) -> List[tf.Tensor]: - """Split the tensor on the first dimension (batch), with - batch_size. - - Parameters - ---------- - x: tf.Tensor - input tensor. - - batch_size: int, optional - the batch size to split. Defaults to 128 - - Returns - ------- - List[tf.Tensor] - List of splitted tensors. - """ - n_obs = x.shape[0] - # if batch_size = 128, n_obs = 1000 - # split_batch = [128, 128, 128, 128, 128, 128, 128, 104] - split_batch = [batch_size] * (n_obs // batch_size) + [n_obs % batch_size] - - return tf.keras.layers.Lambda(lambda x: tf.split(x, split_batch))(x) +from typing import Any, Dict, Iterable, List, Optional, Tuple + +import numpy as np +import pandas as pd +import scipy +import tensorflow as tf +from sklearn.preprocessing import LabelEncoder + +from cavachon.utils.dataframe_utils import DataFrameUtils + + +class TensorUtils: + """TensorUtils + + Class containing multiple utility functions for tf.Tensor. + + """ + + @staticmethod + def is_sparse_tensor(input: Any) -> bool: + """Check if an input is a sparse tensor. + + Parameters + ---------- + input : Any + the input variable + + Returns + ------- + bool + if the input is a sparse tensor + """ + if isinstance(input, tf.SparseTensor): + return True + if isinstance(input, tf.keras.KerasTensor) and input.sparse: + return True + return False + + @staticmethod + def max_n_neurons(layers: Iterable[tf.keras.layers.Layer]) -> int: + """Get the maximum number of neurons (of tf.keras.layers.Dense) + in layers. + + Parameters + ---------- + layers: Iterable[tf.keras.layers.Layer] + layers in tf.keras.Model. + + Returns + ------- + int: + the maximum number of neurons (of tf.keras.layers.Dense) in + layers. Return 0 if no Dense layer in the layers. + + """ + current_max = 0 + for layer in layers: + if isinstance(layer, tf.keras.layers.Dense) and current_max < layer.units: + current_max = layer.units + return current_max + + @staticmethod + def remove_nan_gradients( + gradients: List[tf.Tensor], clip_value=10 + ) -> List[tf.Tensor]: + """Replace nan, inf with 0 and perform gradient clipping for + the gradients computed by tf.GradientTape.gradient(). + + Parameters + ---------- + gradients: List[tf.Tensor] + gradients computed by tf.GradientTape.gradient() + + clip_value: float, optional + clip values for gradient clipping. The resulting gradient + will be in the range of [-clip_value, clip_value] + + Returns + ------- + List[tf.Tensor] + processed gradients. + + """ + for i, g in enumerate(gradients): + if gradients[i] is None: + continue + gradients[i] = tf.keras.layers.Lambda( + lambda x: tf.where(tf.math.is_nan(x), tf.zeros_like(x), x) + )(gradients[i]) + gradients[i] = tf.keras.layers.Lambda( + lambda x: tf.where(tf.math.is_inf(x), tf.zeros_like(x), x) + )(gradients[i]) + gradients[i] = tf.keras.layers.Lambda( + lambda x: tf.where(x > clip_value, clip_value * tf.ones_like(x), x) + )(gradients[i]) + gradients[i] = tf.keras.layers.Lambda( + lambda x: tf.where( + x < -1 * clip_value, -1 * clip_value * tf.ones_like(x), x + ) + )(gradients[i]) + + return gradients + + @staticmethod + def create_backbone_layers( + n_layers: int = 2, #3 + base_n_neurons: int = 32, #128 + max_n_neurons: int = 64, #2048 + rate: int = 2, + activation: str = "swish", #"linear" + reverse: bool = False, + name: Optional[str] = "backbone_network", + ) -> tf.keras.Model: + """Create tf.keras.Sequential models with tf.keras.layers.Dense + and tf.keras.layers.BatchNormalization(). The created dense + layers would have number of neurons + [`base_n_neurons`, `base_n_neurons`*`rate`, ...]. For instance, + with default parameters, it creates layers of: + 1. tf.keras.layers.Dense(128, activation='elu') + 2. tf.keras.layers.BatchNormalization() + 3. tf.keras.layers.Dense(256, activation='elu') + 4. tf.keras.layers.BatchNormalization() + 5. tf.keras.layers.Dense(512, activation='elu') + 6. tf.keras.layers.BatchNormalization() + + Parameters + ---------- + n_layers: int, optional + number of layers. Defaults to 3. + + base_n_neurons: int, optional + base number of neurons. Defaults to 128. + + max_n_neurons: int, optional + maximum number of neurons. Defaults to 1024. + + rate: int, optional + increasing rate of number of neurons (see description of + the function for more details). Defaults to 2. + + activation: str, optional + activation functions in tf.keras.layers.Dense layer. + Defaults to 'elu'. + + reverse: bool, optional + whether to decrease the number of neurons in later layers. + Defaults to False. + + name: str, optional + name of the created tf.keras.Model. Defaults to + 'backbone_network'. + + Returns + ------- + tf.keras.Model + created tf.keras.Sequential model. + + """ + + layers = [] + for no_layer in range(0, n_layers): + n_neurons = min(base_n_neurons * rate**no_layer, max_n_neurons) + layers.append(tf.keras.layers.Dense(n_neurons, activation=activation)) + layers.append(tf.keras.layers.LayerNormalization()) + + if reverse: + layers.reverse() + + return tf.keras.Sequential(layers, name=name) + + @staticmethod + def create_tensor_from_df( + df: pd.DataFrame, + colnames: List[str] = [], + encoder_dict: Dict[str, LabelEncoder] = dict(), + ) -> Tuple[tf.Tensor, Dict[str, LabelEncoder]]: + """Create a Tensorflow Tensor from column data (specified with + `colnames`) in the provided DataFrame. If the column data is a + categorical variable, transform it with one-hot encoded Tensor. + If it is a continuous variable, simply transform it into a + Tensorflow Tensor. If no valid column data is provided, return + Tensor which is a zero vector. + + + Parameters + ---------- + df: pd.DataFrame + input DataFrame. + + colnames: List[str], optional + columns of the DataFrame that are used to create the + Tensor. Defaults to []. + + Returns + ------- + Tuple[tf.Tensor, Dict[str, LabelEncoder]] + the first element is the one-hot encoded Tensor. The second + element is the dictionary of LabelEncoder used to map the + categorical variable into scalar representation, where the + keys are the column names and the values are the + corresponding LabelEncoder. The value will be None if the + column data is not a continuous variable. + """ + # if no valid batch effect column is provided, use zero vector for batch effect + n_obs, n_features = df.shape + tensor_list = [] + + for colname in colnames: + if colname not in df.columns: + continue + coldata = df[colname] + if DataFrameUtils.check_is_categorical(coldata): + # if the column is a categorical variable, use one hot encoded tensor + encoded_tensor, encoder = TensorUtils.create_one_hot_encoded_tensor( + coldata, encoder_dict.get(colname, None) + ) + encoder_dict.setdefault(colname, encoder) + tensor_list.append(encoded_tensor) + else: + # if the column is a continous variable, + tensor = tf.reshape(tf.convert_to_tensor(coldata, tf.float32), (-1, 1)) + encoder_dict.setdefault(colname, None) + tensor_list.append(tensor) + + if len(tensor_list) == 0: + tensor_list.append(tf.zeros((n_obs, 1))) + + return tf.keras.layers.Lambda(lambda x: tf.concat(x, axis=1))( + tensor_list + ), encoder_dict + + @staticmethod + def create_one_hot_encoded_tensor( + data: pd.Series, encoder: Optional[LabelEncoder] = None + ) -> Tuple[tf.Tensor, LabelEncoder]: + """Create a one hot encoded Tensor from a Series variable. + + Parameters + ---------- + data: pd.Series + pd.Series of categorical variables to be transformed to + one-hot encoded tf.Tensor. + + Returns + ------- + Tuple[tf.Tensor, LabelEncoder]: + the first element is the one-hot encoded Tensor, the second + element is the LabelEncoder used to map the categorical + variable into scalar representation. + + """ + if encoder is None: + encoder = LabelEncoder() + encoded_array = encoder.fit_transform(data) + else: + encoded_array = encoder.transform(data) + n_class = len(encoder.classes_) + encoded_tensor = tf.cast(tf.one_hot(encoded_array, n_class), tf.float32) + + return encoded_tensor, encoder + + @staticmethod + def spmatrix_to_sparse_tensor(spmatrix: scipy.sparse.spmatrix) -> tf.SparseTensor: + """Create a SparseTensor out of a scipy sparse matrix. + + Parameters + ---------- + spmatrix: sparse matrix + the provided matrix + + Returns + ------- + tf.SparseTensor: + the created SparseTensor. + + """ + coo_matrix = spmatrix.tocoo() + indices = np.mat([coo_matrix.row, coo_matrix.col]).transpose() + sparse_tensor = tf.SparseTensor(indices, coo_matrix.data, coo_matrix.shape) + return tf.sparse.reorder(tf.cast(sparse_tensor, tf.float32)) + + @staticmethod + def split(x: tf.Tensor, batch_size: int = 128) -> List[tf.Tensor]: + """Split the tensor on the first dimension (batch), with + batch_size. + + Parameters + ---------- + x: tf.Tensor + input tensor. + + batch_size: int, optional + the batch size to split. Defaults to 128 + + Returns + ------- + List[tf.Tensor] + List of splitted tensors. + """ + n_obs = x.shape[0] + # if batch_size = 128, n_obs = 1000 + # split_batch = [128, 128, 128, 128, 128, 128, 128, 104] + split_batch = [batch_size] * (n_obs // batch_size) + [n_obs % batch_size] + + return tf.keras.layers.Lambda(lambda x: tf.split(x, split_batch))(x) diff --git a/cavachon/workflow/workflow.py b/cavachon/workflow/workflow.py index ec6bfd8..7cdafb0 100644 --- a/cavachon/workflow/workflow.py +++ b/cavachon/workflow/workflow.py @@ -1,487 +1,719 @@ -import os -import warnings -from copy import deepcopy -from typing import Dict, List, MutableMapping, Optional, Tuple - -import anndata -import muon as mu -import pandas as pd -import tensorflow as tf - -from cavachon.config.application_config import ApplicationConfig -from cavachon.dataloader.dataloader import DataLoader -from cavachon.environment.constants import Constants -from cavachon.filter.anndata_filter_handler import AnnDataFilterHandler -from cavachon.io.file_reader import FileReader -from cavachon.modality.modality import Modality -from cavachon.modality.multi_modality import MultiModality -from cavachon.model.model import Model -from cavachon.scheduler.sequential_training_scheduler import SequentialTrainingScheduler -from cavachon.tools.cluster_analysis import ClusterAnalysis -from cavachon.tools.differential_analysis import DifferentialAnalysis -from cavachon.tools.interactive_visualization import InteractiveVisualization -from cavachon.utils.anndata_utils import AnnDataUtils - - -class Workflow: - """Workflow - - The configured workflow to perform analysis. - - Attributes - --------- - application_config: ApplicationConfig - configuration for the workflow. - - mdata: mu.MuData - the multi-modality data. - - dataloader: DataLoader - data loader used to create tf.data.Dataset from the input data. - - model: tf.keras.Model - generative model created and trained as configured. - - train_scheduler: SequentialTrainingScheduler - sequential training scheduler for each component in the model. - - train_history: List[tf.keras.callbacks.History] - history of training process in each step. - - outputs: MutableMapping[str, tf.Tensor] - outputs latent representations and reconstructed data from the - trained generative model. - - """ - - def __init__(self, filename: str): - """Constructor for Workflow. - - Parameters - ---------- - filename: str - path to the configuration file (config.yaml) - - """ - self.config: ApplicationConfig = ApplicationConfig(filename) - self.mdata: Optional[mu.Mudata] = None - self.dataloader: Optional[DataLoader] = None - self.anndata_filters: AnnDataFilterHandler = AnnDataFilterHandler.from_config( - self.config - ) - self.model: Optional[Model] = None - self.train_scheduler: Optional[SequentialTrainingScheduler] = None - self.train_history: List[tf.keras.callbacks.History] = list() - self.outputs: MutableMapping[str, tf.Tensor] = dict() - self.differential_analysis_results: MutableMapping[ - Tuple[str], Dict[str, pd.DataFrame] - ] = dict() - - return - - def run(self) -> None: - """Run the specified workflow configured in self.config""" - self.setup_mdata() - self.setup_dataloader() - self.model = Model.make( - component_configs=self.config.components, name=self.config.model.name - ) - - self.setup_train_scheduler() - if self.config.model.load_weights: - self.predict() - self.load_model_weights() - if self.config.training.train: - self.train_model() - self.predict() - - self.perform_clustering_analysis() - self.visualize_embedding() - - outdir = os.path.join(self.config.io.outdir, "mdata") - os.makedirs(outdir, exist_ok=True) - self.mdata.write(f"{outdir}/mdata.h5mu") - - self.visualize_conditional_attribution_scores() - self.perform_differential_analysis() - - return - - def setup_mdata(self) -> None: - """Setup mdata and update n_vars in the component config.""" - adata_dict = Workflow.read_modalities(self.config) - adata_dict = self.anndata_filters(adata_dict) - self.mdata = MultiModality(adata_dict) - self.update_config_nvars() - - @staticmethod - def read_modalities( - config: ApplicationConfig, - ) -> MutableMapping[str, anndata.AnnData]: - """Read the modality files from the configuration. - - Returns - ------- - MutableMapping[str, anndata.AnnData] - the keys are the names of the modality, the values are the - corresponding AnnData. - - """ - modalities = dict() - for modality_name in config.modality_names: - modality_config = config.modality[modality_name] - h5ad = modality_config.get(Constants.CONFIG_FIELD_MODALITY_H5AD) - if h5ad: - adata = anndata.read_h5ad(os.path.join(config.io.datadir, h5ad)) - else: - adata = FileReader.read_multiomics_data(config, modality_name) - modalities.setdefault( - modality_name, - Modality( - adata, - name=modality_name, - modality_type=modality_config.get( - Constants.CONFIG_FIELD_MODALITY_TYPE - ), - distribution_name=modality_config.get( - Constants.CONFIG_FIELD_MODALITY_DIST - ), - batch_effect_colnames=modality_config.get( - Constants.CONFIG_FIELD_MODALITY_BATCH_COLNAMES - ), - ), - ) - - return modalities - - def filter_adata_mapping( - self, adata_mapping: MutableMapping[str, anndata.AnnData] - ) -> MutableMapping[str, anndata.AnnData]: - """Filter the mapping of provided AnnData with - self.anndata_filters. - - Parameters - ---------- - adata_mapping: MutableMapping[str, anndata.AnnData] - provided mapping of AnnData. - - Returns - ------- - MutableMapping[str, anndata.AnnData] - mapping of filtered AnnData. - - """ - return self.anndata_filters(adata_mapping) - - def update_config_nvars(self) -> None: - """Update the number of variables in the config after filtering AnnData""" - processed_component_configs = list() - for component_config in self.config.components: - component_vars = dict() - for modality_name in component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES - ): - component_vars.setdefault( - modality_name, self.mdata[modality_name].n_vars - ) - - component_config[Constants.CONFIG_FIELD_COMPONENT_N_VARS] = component_vars - processed_component_configs.append(component_config) - - self.config.components = processed_component_configs - self.config.model[Constants.CONFIG_FIELD_MODEL_COMPONENT] = ( - processed_component_configs - ) - - return - - def setup_dataloader(self) -> None: - """Setup mdata and update n_vars_batch_effect in the component config.""" - batch_size = self.config.dataset.get( - Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE - ) - self.distribution_names = dict() - self.batch_effect_colnames = dict() - for modality_name, modality_config in self.config.modality.items(): - self.distribution_names.setdefault( - modality_name, modality_config.get(Constants.CONFIG_FIELD_MODALITY_DIST) - ) - self.batch_effect_colnames.setdefault( - modality_name, - modality_config.get(Constants.CONFIG_FIELD_MODALITY_BATCH_COLNAMES), - ) - - self.dataloader = DataLoader( - self.mdata, batch_size, self.batch_effect_colnames, self.distribution_names - ) - self.update_nvars_batch_effect() - - return - - def update_nvars_batch_effect(self) -> None: - """Update the number of batch effect variables after creating DataLoader""" - processed_component_configs = list() - nvars = self.dataloader.n_vars_batch_effect - for component_config in self.config.components: - component_vars = dict() - for modality_name in component_config.get( - Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES - ): - component_vars[modality_name] = nvars.get(modality_name) - component_config[Constants.CONFIG_FIELD_COMPONENT_N_VARS_BATCH] = ( - component_vars - ) - processed_component_configs.append(component_config) - - self.config.components = processed_component_configs - self.config.model[Constants.CONFIG_FIELD_MODEL_COMPONENT] = ( - processed_component_configs - ) - - return - - def setup_train_scheduler(self) -> None: - """Setup the training scheduler""" - optimizer_config = self.config.training.get( - Constants.CONFIG_FIELD_MODEL_TRAINING_OPTIMIZER - ) - optimizer = optimizer_config.get("name") - learning_rate = optimizer_config.get( - Constants.CONFIG_FIELD_MODEL_TRAINING_LEARNING_RATE - ) - early_stopping = self.config.training.get( - Constants.CONFIG_FIELD_MODEL_TRAINING_EARLY_STOPPING - ) - self.train_scheduler = SequentialTrainingScheduler( - self.model, optimizer, learning_rate, early_stopping - ) - - return - - def load_model_weights(self) -> None: - """Load the pretrained model weights""" - try: - self.model.load_weights( - os.path.join( - f"{self.config.io.checkpointdir}", f"{self.model.name}.weights.h5" - ) - ) - except OSError: - message = "".join( - ( - f"Cannot load the pretrained weights in {self.config.io.checkpointdir}." - ) - ) - warnings.warn(message, RuntimeWarning) - - return None - - def train_model(self) -> None: - """Train the model and save the weights if model.save_weights is - set to True. - - """ - batch_size = self.config.dataset.get( - Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE - ) - max_epochs = self.config.training.get( - Constants.CONFIG_FIELD_MODEL_TRAINING_N_EPOCHS - ) - - # shuffle dataset if needed - if self.config.dataset.get(Constants.CONFIG_FIELD_MODEL_DATASET_SHUFFLE): - train_dataset = self.dataloader.dataset.shuffle(self.mdata.n_obs).batch( - batch_size - ) - else: - train_dataset = self.dataloader.dataset.batch(batch_size) - - # train the model - self.train_history = self.train_scheduler.fit(train_dataset, epochs=max_epochs) - - # save the weights if needed - if self.config.model.save_weights: - os.makedirs(self.config.io.checkpointdir, exist_ok=True) - self.model.save_weights( - os.path.join( - f"{self.config.io.checkpointdir}", f"{self.model.name}.weights.h5" - ) - ) - - # change the training states to False - for component in self.model.components.values(): - component.trainable = False - component.set_progressive_scaler_iteration( - current_iteration=1.0, total_iterations=1.0 - ) - self.model.compile() - - return - - def predict(self) -> None: - """Predict generative process for self.mdata.""" - self.model.trainable = False - self.model.compile() - batch_size = self.config.dataset.get( - Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE - ) - self.outputs = self.model.predict(self.mdata, batch_size=batch_size) - - return - - def perform_clustering_analysis(self) -> None: - """Perform clustering analsis of each modality""" - batch_size = self.config.dataset.get( - Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE - ) - analysis = ClusterAnalysis(self.mdata, self.model) - for clustering_config in self.config.analysis.clustering: - component = clustering_config.component - modality = clustering_config.modality - analysis.compute_cluster_log_probability( - modality=modality, - component=component, - batch_size=batch_size, - batch_effect_colnames=self.batch_effect_colnames, - distribution_names=self.distribution_names, - ) - - return - - def visualize_embedding(self) -> None: - """Create visualization of posterior distribution""" - outdir = os.path.join(self.config.io.outdir, "embeddings") - os.makedirs(outdir, exist_ok=True) - - # to void dictionary changed during iteration - for config in self.config.analysis.visualize_embedding: - embedding_method = config.embedding_method - color = config.color_by - modality_name = config.modality - use_rep = config.use_rep - interactive = config.interactive - adata = self.mdata[modality_name] - title = ( - f"{use_rep} of {modality_name} colored with {color} {embedding_method}" - ) - extension = "html" if interactive else "png" - InteractiveVisualization.embedding( - adata=adata, - title=title, - method=embedding_method, - use_rep=use_rep, - color=color, - width=800, - height=760, - filename=f"{outdir}/{title}.{extension}".lower().replace(" ", "_"), - ) - - def visualize_knn(self, use_cluster, n_neighbors) -> None: - """Visualize k-nearest neighbors""" - outdir = os.path.join(self.config.io.outdir, "knn") - os.makedirs(outdir, exist_ok=True) - - targets = list() - for modality_name in self.mdata.mod.keys(): - adata = self.mdata[modality_name] - for latent_representation in adata.obsm.keys(): - if latent_representation.startswith("z_"): - targets.append((modality_name, latent_representation)) - - for modality_name, latent_representation in targets: - outdir = os.path.join(self.config.io.outdir, "knn") - os.makedirs(outdir, exist_ok=True) - title = f"{latent_representation} of {modality_name} colored with {use_cluster} {n_neighbors} neighbors" - InteractiveVisualization.neighbors_with_same_annotations( - self.mdata, - self.model, - modality=modality_name, - title=title, - use_cluster=use_cluster, - use_rep=latent_representation, - group_by_cluster=True, - n_neighbors=n_neighbors, - filename=f"{outdir}/{title}.html".lower().replace(" ", "_"), - width=800, - height=760, - ) - - def perform_differential_analysis(self) -> None: - """Perform differential analysis across clusters""" - targets = list() - outdir = os.path.join(self.config.io.outdir, "differential_analysis") - os.makedirs(outdir, exist_ok=True) - for analysis_config in self.config.analysis.differential_analysis: - colors = deepcopy(self.config.analysis.annotation_colnames) - # colors.append(f'cluster_{self.config.analysis.clustering.get(modality_name)}') - for color in colors: - targets.append( - (analysis_config.modality, analysis_config.component, color) - ) - - analysis = DifferentialAnalysis( - self.mdata, - self.model, - self.batch_effect_colnames, - self.distribution_names, - self.dataloader.batch_effect_encoders, - ) - for target in targets: - modality_name, component, use_cluster = target - results = analysis.across_clusters_pairwise( - component, modality_name, use_cluster - ) - self.differential_analysis_results[target] = results - - for target in self.differential_analysis_results.keys(): - for cluster, degs in self.differential_analysis_results[target].items(): - cluster = cluster.lower().replace("/", "_") - degs.to_csv( - f"{outdir}/{'_'.join(target).lower().replace(' ', '_')}_{cluster}.tsv", - sep="\t", - ) - - return - - def visualize_conditional_attribution_scores(self) -> None: - """Create visualization of conditional attribution score""" - outdir = os.path.join(self.config.io.outdir, "attribution") - os.makedirs(outdir, exist_ok=True) - batch_size = self.config.dataset.get( - Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE - ) - - targets = list() - for attribution_config in self.config.analysis.conditional_attribution_scores: - modality_name = attribution_config.modality - component = attribution_config.component - with_respect_to = attribution_config.with_respect_to - use_cluster = attribution_config.use_cluster - targets.append((modality_name, component, with_respect_to, use_cluster)) - - # to avoid dictionary changed during iteration - for target in targets: - modality_name, component, with_respect_to, use_cluster = target - title = "".join( - ( - f"{component} {modality_name} regulatory score colored with {use_cluster}" - ) - ) - InteractiveVisualization.attribution_score( - mdata=AnnDataUtils.merge_mdata_on_obs_annotation( - self.mdata, use_cluster, self.batch_effect_colnames - ), - model=self.model, - component=component, - modality=modality_name, - with_respect_to=with_respect_to, - use_cluster=use_cluster, - batch_size=batch_size, - title=title, - batch_effect_colnames=self.batch_effect_colnames, - distribution_names=self.distribution_names, - batch_effect_encoders=self.dataloader.batch_effect_encoders, - width=800, - height=760, - filename=f"{outdir}/{title}.html".lower().replace(" ", "_"), - ) +import os +import warnings +from copy import deepcopy +from itertools import product +from typing import Dict, List, MutableMapping, Optional, Tuple + +import anndata +import muon as mu +import numpy as np +import pandas as pd +import tensorflow as tf + +from cavachon.config.application_config import ApplicationConfig +from cavachon.dataloader.dataloader import DataLoader +from cavachon.environment.constants import Constants +from cavachon.filter.anndata_filter_handler import AnnDataFilterHandler +from cavachon.io.file_reader import FileReader +from cavachon.modality.modality import Modality +from cavachon.modality.multi_modality import MultiModality +from cavachon.model.model import Model +from cavachon.scheduler.sequential_training_scheduler import SequentialTrainingScheduler +from cavachon.tools.cluster_analysis import ClusterAnalysis +from cavachon.tools.differential_analysis import DifferentialAnalysis +from cavachon.tools.interactive_visualization import InteractiveVisualization +from cavachon.utils.anndata_utils import AnnDataUtils + + +class Workflow: + """Workflow + + The configured workflow to perform analysis. + + Attributes + --------- + application_config: ApplicationConfig + configuration for the workflow. + + mdata: mu.MuData + the multi-modality data. + + dataloader: DataLoader + data loader used to create tf.data.Dataset from the input data. + + model: tf.keras.Model + generative model created and trained as configured. + + train_scheduler: SequentialTrainingScheduler + sequential training scheduler for each component in the model. + + train_history: List[tf.keras.callbacks.History] + history of training process in each step. + + outputs: MutableMapping[str, tf.Tensor] + outputs latent representations and reconstructed data from the + trained generative model. + + """ + + def __init__(self, filename: str): + """Constructor for Workflow. + + Parameters + ---------- + filename: str + path to the configuration file (config.yaml) + + """ + self.config: ApplicationConfig = ApplicationConfig(filename) + self.mdata: Optional[mu.MuData] = None + self.dataloader: Optional[DataLoader] = None + self.anndata_filters: AnnDataFilterHandler = AnnDataFilterHandler.from_config( + self.config + ) + self.model: Optional[Model] = None + self.train_scheduler: Optional[SequentialTrainingScheduler] = None + self.train_history: List[tf.keras.callbacks.History] = list() + self.outputs: MutableMapping[str, tf.Tensor] = dict() + self.differential_analysis_results: MutableMapping[ + Tuple[str], Dict[str, pd.DataFrame] + ] = dict() + + return + + def run(self) -> None: + """Run the specified workflow configured in self.config""" + self.setup_mdata() + self.setup_dataloader() + self.model = Model.make( + component_configs=self.config.components, name=self.config.model.name + ) + # print(f"LATENT OTOTJEDLKJSLFDSJ: {self.model.n_latent_dims}") + + self.setup_train_scheduler() + if self.config.model.load_weights: + self.predict() + self.load_model_weights() + if self.config.training.train: + self.train_model() + self.predict() + + self.perform_clustering_analysis() + self.compute_integrated_clusters() + self.visualize_embedding() + + outdir = os.path.join(self.config.io.outdir, "mdata") + os.makedirs(outdir, exist_ok=True) + self.mdata.write(f"{outdir}/mdata.h5mu") + + self.visualize_conditional_attribution_scores() + self.perform_differential_analysis() + + return + + def setup_mdata(self) -> None: + """Setup mdata and update n_vars in the component config.""" + adata_dict = Workflow.read_modalities(self.config) + adata_dict = self.anndata_filters(adata_dict) + self.mdata = MultiModality(adata_dict) + self.update_config_nvars() + + @staticmethod + def read_modalities( + config: ApplicationConfig, + ) -> MutableMapping[str, anndata.AnnData]: + """Read the modality files from the configuration. + + Returns + ------- + MutableMapping[str, anndata.AnnData] + the keys are the names of the modality, the values are the + corresponding AnnData. + + """ + modalities = dict() + for modality_name in config.modality_names: + modality_config = config.modality[modality_name] + h5ad = modality_config.get(Constants.CONFIG_FIELD_MODALITY_H5AD) + if h5ad: + adata = anndata.read_h5ad(os.path.join(config.io.datadir, h5ad)) + else: + adata = FileReader.read_multiomics_data(config, modality_name) + modalities.setdefault( + modality_name, + Modality( + adata, + name=modality_name, + modality_type=modality_config.get( + Constants.CONFIG_FIELD_MODALITY_TYPE + ), + distribution_name=modality_config.get( + Constants.CONFIG_FIELD_MODALITY_DIST + ), + batch_effect_colnames=modality_config.get( + Constants.CONFIG_FIELD_MODALITY_BATCH_COLNAMES + ), + ), + ) + + return modalities + + def filter_adata_mapping( + self, adata_mapping: MutableMapping[str, anndata.AnnData] + ) -> MutableMapping[str, anndata.AnnData]: + """Filter the mapping of provided AnnData with + self.anndata_filters. + + Parameters + ---------- + adata_mapping: MutableMapping[str, anndata.AnnData] + provided mapping of AnnData. + + Returns + ------- + MutableMapping[str, anndata.AnnData] + mapping of filtered AnnData. + + """ + return self.anndata_filters(adata_mapping) + + def update_config_nvars(self) -> None: + """Update the number of variables in the config after filtering AnnData""" + processed_component_configs = list() + for component_config in self.config.components: + component_vars = dict() + for modality_name in component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES + ): + component_vars.setdefault( + modality_name, self.mdata[modality_name].n_vars + ) + + component_config[Constants.CONFIG_FIELD_COMPONENT_N_VARS] = component_vars + processed_component_configs.append(component_config) + + self.config.components = processed_component_configs + self.config.model[Constants.CONFIG_FIELD_MODEL_COMPONENT] = ( + processed_component_configs + ) + + return + + def setup_dataloader(self) -> None: + """Setup mdata and update n_vars_batch_effect in the component config.""" + batch_size = self.config.dataset.get( + Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE + ) + self.distribution_names = dict() + self.batch_effect_colnames = dict() + for modality_name, modality_config in self.config.modality.items(): + self.distribution_names.setdefault( + modality_name, modality_config.get(Constants.CONFIG_FIELD_MODALITY_DIST) + ) + self.batch_effect_colnames.setdefault( + modality_name, + modality_config.get(Constants.CONFIG_FIELD_MODALITY_BATCH_COLNAMES), + ) + + self.dataloader = DataLoader( + self.mdata, batch_size, self.batch_effect_colnames, self.distribution_names + ) + self.update_nvars_batch_effect() + + return + + def update_nvars_batch_effect(self) -> None: + """Update the number of batch effect variables after creating DataLoader""" + processed_component_configs = list() + nvars = self.dataloader.n_vars_batch_effect + for component_config in self.config.components: + component_vars = dict() + for modality_name in component_config.get( + Constants.CONFIG_FIELD_COMPONENT_MODALITY_NAMES + ): + component_vars[modality_name] = nvars.get(modality_name) + component_config[Constants.CONFIG_FIELD_COMPONENT_N_VARS_BATCH] = ( + component_vars + ) + processed_component_configs.append(component_config) + + self.config.components = processed_component_configs + self.config.model[Constants.CONFIG_FIELD_MODEL_COMPONENT] = ( + processed_component_configs + ) + + return + + def setup_train_scheduler(self) -> None: + """Setup the training scheduler""" + optimizer_config = self.config.training.get( + Constants.CONFIG_FIELD_MODEL_TRAINING_OPTIMIZER + ) + optimizer = optimizer_config.get("name") + learning_rate = optimizer_config.get( + Constants.CONFIG_FIELD_MODEL_TRAINING_LEARNING_RATE + ) + early_stopping = self.config.training.get( + Constants.CONFIG_FIELD_MODEL_TRAINING_EARLY_STOPPING + ) + self.train_scheduler = SequentialTrainingScheduler( + self.model, + self.mdata, + optimizer, + learning_rate, + early_stopping, + self.dataloader.batch_size, + self.config.io.outdir, + self.batch_effect_colnames, + self.distribution_names, + ) + + return + + def load_model_weights(self) -> None: + """Load the pretrained model weights""" + try: + self.model.load_weights( + os.path.join( + f"{self.config.io.checkpointdir}", f"{self.model.name}.weights.h5" + ) + ) + except OSError: + message = "".join( + ( + f"Cannot load the pretrained weights in {self.config.io.checkpointdir}." + ) + ) + warnings.warn(message, RuntimeWarning) + + return None + + def train_model(self) -> None: + """Train the model and save the weights if model.save_weights is + set to True. + + """ + batch_size = self.config.dataset.get( + Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE + ) + max_epochs = self.config.training.get( + Constants.CONFIG_FIELD_MODEL_TRAINING_N_EPOCHS + ) + + # shuffle dataset if needed + if self.config.dataset.get(Constants.CONFIG_FIELD_MODEL_DATASET_SHUFFLE): + train_dataset = self.dataloader.dataset.shuffle(self.mdata.n_obs).batch( + batch_size + ) + else: + train_dataset = self.dataloader.dataset.batch(batch_size) + + # train the model + self.train_history = self.train_scheduler.fit(train_dataset, epochs=max_epochs) + + # save the weights if needed + if self.config.model.save_weights: + os.makedirs(self.config.io.checkpointdir, exist_ok=True) + self.model.save_weights( + os.path.join( + f"{self.config.io.checkpointdir}", f"{self.model.name}.weights.h5" + ) + ) + + # change the training states to False + for component in self.model.components.values(): + component.trainable = False + component.set_progressive_scaler_iteration( + current_iteration=1.0, total_iterations=1.0 + ) + self.model.compile() + + return + + def predict(self) -> None: + """Predict generative process for self.mdata.""" + self.model.trainable = False + self.model.compile() + batch_size = self.config.dataset.get( + Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE + ) + self.outputs = self.model.predict(self.mdata, batch_size=batch_size) + + return + + def perform_clustering_analysis(self) -> None: + """Perform clustering analsis of each modality""" + batch_size = self.config.dataset.get( + Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE + ) + analysis = ClusterAnalysis(self.mdata, self.model) + for clustering_config in self.config.analysis.clustering: + component = clustering_config.component + modality = clustering_config.modality + analysis.compute_cluster_log_probability( + modality=modality, + component=component, + batch_size=batch_size, + batch_effect_colnames=self.batch_effect_colnames, + distribution_names=self.distribution_names, + ) + + return + + def visualize_embedding(self) -> None: + """Create visualization of posterior distribution""" + outdir = os.path.join(self.config.io.outdir, "embeddings") + os.makedirs(outdir, exist_ok=True) + + # to void dictionary changed during iteration + for config in self.config.analysis.visualize_embedding: + embedding_method = config.embedding_method + color = config.color_by + modality_name = config.modality + use_rep = config.use_rep + interactive = config.interactive + adata = self.mdata[modality_name] + title = ( + f"{use_rep} of {modality_name} colored with {color} {embedding_method}" + ) + extension = "html" if interactive else "png" + InteractiveVisualization.embedding( + adata=adata, + title=title, + method=embedding_method, + use_rep=use_rep, + color=color, + width=800, + height=760, + filename=f"{outdir}/{title}.{extension}".lower().replace(" ", "_"), + ) + + def compute_integrated_clusters(self) -> None: + """Compute integrated z_hat clusters for hierarchical components. + + Computes integrated cluster assignments by transforming parent and child + GMM priors through the learned b_network weights. + Creates cluster labels like 'cluster_RNA_integrated' and saves to mdata. + """ + # Detect hierarchical components + for component_config in self.config.components: + component_name = component_config.get("name") + parent_names = component_config.get("conditioned_on_z_hat", []) + + if not parent_names: + continue # Skip non-hierarchical components + + # Handle multiple modalities per component + modality_names = component_config.get("modality_names", []) + modalities_to_save = modality_names if modality_names else [component_name] + + # Use first modality for z_hat extraction + first_modality = modalities_to_save[0] + + # Extract GMM parameters for all parent component(s) + parent_params = [] + parent_dims = [] + + for parent_name in parent_names: + # Get the learned GMM prior parameterizer + prior_parameterizer = self.model.components[ + parent_name + ].z_prior_parameterizer + prior_parameters = tf.squeeze(prior_parameterizer(tf.ones((1, 1)))) + + # Extract from the output tensor + # Structure: [logits, loc_cluster0, scale_cluster0, loc_cluster1, scale_cluster1, ...] + logits = prior_parameters[:, 0].numpy() + n_clusters = len(logits) + params = prior_parameters[:, 1:].numpy() # [n_clusters, params] + n_dims = params.shape[1] // 2 + + # Extract loc (mean)and scale (std) for each cluster + loc = params[:, :n_dims] # [n_clusters, n_dims] + scale = params[:, n_dims:] # [n_clusters, n_dims] + + parent_params.append( + { + "pi": tf.nn.softmax(logits).numpy(), + "mu": loc, + "sigma2": scale**2, + "K": n_clusters, + "D": n_dims, + } + ) + parent_dims.append(n_dims) + + # Extract GMM parameters for child component + child_parameterizer = self.model.components[ + component_name + ].z_prior_parameterizer + child_parameters = tf.squeeze(child_parameterizer(tf.ones((1, 1)))) + + child_logits = child_parameters[:, 0].numpy() + n_clusters = len(child_logits) + + params = child_parameters[:, 1:].numpy() + n_dims = params.shape[1] // 2 + + child_mu = params[:, :n_dims] + child_scale = params[:, n_dims:] + + child_pi = tf.nn.softmax(child_logits).numpy() + child_sigma2 = child_scale**2 + + # Extract both r_network and b_network weights + component_hierarchical_encoder = self.model.components[ + component_name + ].hierarchical_encoder + + # r_network: transforms z_child before concatenation + # r_network weights (no bias since use_bias=False) + W_r = component_hierarchical_encoder.r_network.get_weights()[0] + + # b_network: combines [z_parent, r_network(z_child)] + # b_network weights (no bias since use_bias=False) + W_b = component_hierarchical_encoder.b_network.get_weights()[0] + + # Split W_b (b_network weights) by parent dimensions + W_parent_parts = [] + offset = 0 + for dim in parent_dims: + W_parent_parts.append(W_b[offset : offset + dim, :]) + offset += dim + W_child_raw = W_b[offset:, :] + + # Compose W_child (child weights) with W_r (r network) + # This accounts for: z_hat = W_b @ [z_parent, W_r @ z_child] + W_child_effective = W_child_raw @ W_r + + # Compute all integrated cluster parameters (vectorized) + # Create all combinations of parent×child clusters + cluster_ranges = [range(p["K"]) for p in parent_params] + [ + range(len(child_pi)) + ] + n_clusters = np.prod([len(r) for r in cluster_ranges]) + D_out = W_b.shape[1] # Output dim is second axis + + mu_zhat = np.zeros((n_clusters, D_out)) + sigma2_zhat = np.zeros((n_clusters, D_out)) + pi_zhat = np.zeros(n_clusters) + + # Compute parameters for each integrated cluster + for idx, indices in enumerate(product(*cluster_ranges)): + parent_idx = indices[:-1] + child_idx = indices[-1] + + # Integrated mean: μ_zhat = W_parent @ μ_parent + W_child_effective @ μ_child + mu_zhat[idx] = np.zeros(D_out) + for p_idx, W_p, p_params in zip( + parent_idx, W_parent_parts, parent_params + ): + mu_zhat[idx] += W_p.T @ p_params["mu"][p_idx] + mu_zhat[idx] += W_child_effective.T @ child_mu[child_idx] + + # Integrated variance: σ²_zhat = W²_parent @ σ²_parent + W²_child_effective @ σ²_child + sigma2_zhat[idx] = np.zeros(D_out) + for p_idx, W_p, p_params in zip( + parent_idx, W_parent_parts, parent_params + ): + sigma2_zhat[idx] += (W_p**2).T @ p_params["sigma2"][p_idx] + sigma2_zhat[idx] += (W_child_effective**2).T @ child_sigma2[child_idx] + + # Integrated prior: π_zhat = π_parent × π_child (product of priors) + pi_zhat[idx] = child_pi[child_idx] + for p_idx, p_params in zip(parent_idx, parent_params): + pi_zhat[idx] *= p_params["pi"][p_idx] + + print(f"\n{'=' * 70}") + print(f"DEBUG: Integrated clustering for {component_name}") + print(f" Parent clusters: {[p['K'] for p in parent_params]}") + print(f" Child clusters: {len(child_pi)}") + print(f" Total integrated clusters created: {n_clusters}") + print("\nIntegrated cluster parameters:") + print(f" pi_zhat (cluster priors): {pi_zhat}") + print(" mu_zhat sample (first 3 clusters, first 3 dims):") + for i in range(min(3, n_clusters)): + print(f" Cluster {i}: {mu_zhat[i, :3]}") + print(" sigma2_zhat sample (first 3 clusters, first 3 dims):") + for i in range(min(3, n_clusters)): + print(f" Cluster {i}: {sigma2_zhat[i, :3]}") + print(f"{'=' * 70}") + + # Compute MAP assignment for each sample + z_hat = self.mdata.mod[first_modality].obsm[f"z_hat_{component_name}"] + N = z_hat.shape[0] + print(f" Sample count: {N}") + + # Compute log posterior for each sample-cluster pair + logpy = np.log(pi_zhat + 1e-7) + logpy_zhat = np.zeros((N, n_clusters)) + + for k in range(n_clusters): + # Compute log likelihood: log N(z_hat | μ_k, σ²_k) + diff = z_hat - mu_zhat[k] + log_likelihood = -0.5 * np.sum( + np.log(2 * np.pi * sigma2_zhat[k]) + (diff**2) / sigma2_zhat[k], + axis=1, + ) + # Log posterior = log prior + log likelihood + logpy_zhat[:, k] = logpy[k] + log_likelihood + + # Assign each sample to cluster with highest posterior + cluster = tf.argmax(logpy_zhat, axis=-1).numpy() + + print("\nInitial assignment:") + unique, counts = np.unique(cluster, return_counts=True) + for u, c in zip(unique, counts): + print(f" Cluster {u}: {c} samples") + + # Cleanup - remove clusters with too few samples + min_n_obs = 36 + cluster_labels = [f"Cluster {x:03d}" for x in cluster] + + temp_series = pd.Series(cluster_labels) + _keep = temp_series.value_counts() + while (_keep < min_n_obs).sum() > 0: + keep = [] + for i, x in zip(_keep.index, _keep): + if x >= min_n_obs: + keep.append(int(i.split(" ")[1])) + if len(keep) == 0: + break + + logpy_zhat = logpy_zhat[:, keep] + cluster = tf.argmax(logpy_zhat, axis=-1).numpy() + + # Recompute counts + cluster_labels = [f"Cluster {x:03d}" for x in cluster] + temp_series = pd.Series(cluster_labels) + _keep = temp_series.value_counts() + + unique_clusters = np.unique(cluster) + remap = {old: new for new, old in enumerate(unique_clusters)} + cluster_final = np.array([remap[c] for c in cluster]) + + print("\nAfter cleanup and remapping:") + print(f" Final clusters: {len(unique_clusters)}") + unique, counts = np.unique(cluster_final, return_counts=True) + for u, c in zip(unique, counts): + print(f" Cluster {u:03d}: {c} samples") + print(f"{'=' * 70}\n") + + cluster_key = f"cluster_{component_name}_integrated" + final_labels = [f"Cluster {x:03d}" for x in cluster_final] + + for modality in modalities_to_save: + self.mdata.mod[modality].obs[cluster_key] = final_labels + self.mdata.mod[modality].obsm[ + f"logpy_zhat_{component_name}_integrated" + ] = logpy_zhat + + def visualize_knn(self, use_cluster, n_neighbors) -> None: + """Visualize k-nearest neighbors""" + outdir = os.path.join(self.config.io.outdir, "knn") + os.makedirs(outdir, exist_ok=True) + + targets = list() + for modality_name in self.mdata.mod.keys(): + adata = self.mdata[modality_name] + for latent_representation in adata.obsm.keys(): + if latent_representation.startswith("z_"): + targets.append((modality_name, latent_representation)) + + for modality_name, latent_representation in targets: + outdir = os.path.join(self.config.io.outdir, "knn") + os.makedirs(outdir, exist_ok=True) + title = f"{latent_representation} of {modality_name} colored with {use_cluster} {n_neighbors} neighbors" + InteractiveVisualization.neighbors_with_same_annotations( + self.mdata, + self.model, + modality=modality_name, + title=title, + use_cluster=use_cluster, + use_rep=latent_representation, + group_by_cluster=True, + n_neighbors=n_neighbors, + filename=f"{outdir}/{title}.html".lower().replace(" ", "_"), + width=800, + height=760, + ) + + def perform_differential_analysis(self) -> None: + """Perform differential analysis across clusters""" + targets = list() + outdir = os.path.join(self.config.io.outdir, "differential_analysis") + os.makedirs(outdir, exist_ok=True) + for analysis_config in self.config.analysis.differential_analysis: + colors = deepcopy(self.config.analysis.annotation_colnames) + # colors.append(f'cluster_{self.config.analysis.clustering.get(modality_name)}') + for color in colors: + targets.append( + (analysis_config.modality, analysis_config.component, color) + ) + + analysis = DifferentialAnalysis( + self.mdata, + self.model, + self.batch_effect_colnames, + self.distribution_names, + self.dataloader.batch_effect_encoders, + ) + for target in targets: + modality_name, component, use_cluster = target + results = analysis.across_clusters_pairwise( + component, modality_name, use_cluster + ) + self.differential_analysis_results[target] = results + + for target in self.differential_analysis_results.keys(): + for cluster, degs in self.differential_analysis_results[target].items(): + cluster = cluster.lower().replace("/", "_") + degs.to_csv( + f"{outdir}/{'_'.join(target).lower().replace(' ', '_')}_{cluster}.tsv", + sep="\t", + ) + + return + + def visualize_conditional_attribution_scores(self) -> None: + """Create visualization of conditional attribution score""" + outdir = os.path.join(self.config.io.outdir, "attribution") + os.makedirs(outdir, exist_ok=True) + batch_size = self.config.dataset.get( + Constants.CONFIG_FIELD_MODEL_DATASET_BATCHSIZE + ) + + targets = list() + for attribution_config in self.config.analysis.conditional_attribution_scores: + modality_name = attribution_config.modality + component = attribution_config.component + with_respect_to = attribution_config.with_respect_to + use_cluster = attribution_config.use_cluster + targets.append((modality_name, component, with_respect_to, use_cluster)) + + # to avoid dictionary changed during iteration + for target in targets: + modality_name, component, with_respect_to, use_cluster = target + title = "".join( + ( + f"{component} {modality_name} regulatory score colored with {use_cluster}" + ) + ) + InteractiveVisualization.attribution_score( + mdata=AnnDataUtils.merge_mdata_on_obs_annotation( + self.mdata, use_cluster, self.batch_effect_colnames + ), + model=self.model, + component=component, + modality=modality_name, + with_respect_to=with_respect_to, + use_cluster=use_cluster, + batch_size=batch_size, + title=title, + batch_effect_colnames=self.batch_effect_colnames, + distribution_names=self.distribution_names, + batch_effect_encoders=self.dataloader.batch_effect_encoders, + width=800, + height=760, + filename=f"{outdir}/{title}.html".lower().replace(" ", "_"), + ) diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..376072d --- /dev/null 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--git a/pyproject.toml b/pyproject.toml index b744da1..2b746f2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,28 +1,29 @@ -[project] +[project] name = "cavachon" version = "0.1.0" description = "" authors = [ - {name = "dn070017",email = "dn070017@gmail.com"} +{name = "dn070017",email = "dn070017@gmail.com"} ] license = {text = "MIT"} readme = "README.md" requires-python = ">=3.11,<4" dependencies = [ - "tensorflow-probability[tf] (==0.24.0)", - "mlflow (==2.20)", - "muon (>=0.1.7,<0.2.0)", - "pandas (>=2.2.3,<3.0.0)", - "plotly (>=6.0.0,<7.0.0)", - "scikit-learn (>=1.1,<1.6.0)", - "seaborn (>=0.13.2,<0.14.0)", - "tensorflow[and-cuda] (==2.16.1)", - "networkx (>=3.4.2,<4.0.0)", - "gseapy (>=1.1.5,<2.0.0)", - "scanpy (>=1.11.0,<2.0.0)", - "ipywidgets (>=8.1.5,<9.0.0)", - "nbformat (>=4.2.0)", - "kaleido (==0.2.1)" + "tensorflow-probability[tf] (==0.24.0)", + "numpy (>=1.23.5,<2.0.0)", + "mlflow (==2.20)", + "muon (>=0.1.7,<0.2.0)", + "pandas (>=2.2.3,<3.0.0)", + "plotly (>=6.0.0,<7.0.0)", + "scikit-learn (>=1.1,<1.6.0)", + "seaborn (>=0.13.2,<0.14.0)", + "tensorflow[and-cuda] (==2.16.1)", + "networkx (>=3.4.2,<4.0.0)", + "gseapy (>=1.1.5,<2.0.0)", + "scanpy (>=1.11.0,<2.0.0)", + "ipywidgets (>=8.1.5,<9.0.0)", + "nbformat (>=4.2.0)", + "kaleido (==0.2.1)" ] @@ -35,3 +36,21 @@ ipykernel = "^6.29.5" ruff = "^0.9.7" pytest = "^8.3.4" +[tool.pixi.activation] +env = { LD_LIBRARY_PATH = "$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cudnn/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cuda_runtime/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cublas/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cufft/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/curand/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cusolver/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/cusparse/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/nccl/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib/python3.11/site-packages/nvidia/nvtx/lib:$PIXI_PROJECT_ROOT/.pixi/envs/default/lib" } + +[tool.pixi.workspace] +channels = ["conda-forge", "nvidia"] +platforms = ["linux-64"] + +[tool.pixi.dependencies] +python = "3.11.*" +poetry = ">=2.0" +packaging = "<25" +numpy = ">=1.23.5,<2.0.0" + +[tool.pixi.tasks] +setup = "poetry install" +setup-dev = "poetry install --with dev" +check-gpu = "python -c 'import tensorflow as tf; print(\"Devices:\", tf.config.list_physical_devices(\"GPU\"))'" + diff --git a/runSepData.sh b/runSepData.sh new file mode 100644 index 0000000..81dede0 --- /dev/null +++ b/runSepData.sh @@ -0,0 +1,33 @@ +#!/bin/bash +#SBATCH --job-name=Bulk_test_separated +#SBATCH --account=project_2015212 +#SBATCH --partition=gpu +#SBATCH --gres=gpu:v100:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32G +#SBATCH --time=01:00:00 +#SBATCH --output=/scratch/project_2015212/ceren/runs/bulk/%x-%j.out +#SBATCH --error=/scratch/project_2015212/ceren/runs/bulk/%x-%j.err + +set -euo pipefail + +module load tensorflow/2.18 +source /projappl/project_2015212/cavachon/envs/ceren/.venv/bin/activate + +export MLFLOW_TRACKING_URI="file:///scratch/project_2015212/ceren/mlruns" +export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export MKL_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export PYTHONUNBUFFERED=1 + +# Make sure key dirs exist +mkdir -p /scratch/project_2015212/ceren/runs/bulk/embeddings +mkdir -p /scratch/project_2015212/ceren/checkpoints + +cd /projappl/project_2015212/cavachon/CAVACHON-old + +python - << 'PY' +from cavachon.workflow import Workflow +CFG = "/projappl/project_2015212/cavachon/configs/ceren/sparse_bulk_sep.yaml" +wf = Workflow(CFG) +wf.run() +PY diff --git a/testOption2.sh b/testOption2.sh new file mode 100644 index 0000000..5a9f1c0 --- /dev/null +++ b/testOption2.sh @@ -0,0 +1,58 @@ +#!/bin/bash +#SBATCH --job-name=Bulk_test_separated +#SBATCH --account=project_2015212 +#SBATCH --partition=gpu +#SBATCH --gres=gpu:v100:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32G +#SBATCH --time=01:00:00 +#SBATCH --output=/scratch/project_2015212/ceren/runs/bulk/%x-%j.out +#SBATCH --error=/scratch/project_2015212/ceren/runs/bulk/%x-%j.err + +set -euo pipefail + +module load tensorflow/2.18 +source /projappl/project_2015212/cavachon/envs/ceren/.venv/bin/activate + +export MLFLOW_TRACKING_URI="file:///scratch/project_2015212/ceren/mlruns" +export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export MKL_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export PYTHONUNBUFFERED=1 + +# Make sure key dirs exist +mkdir -p /scratch/project_2015212/ceren/runs/bulk/embeddings +mkdir -p /scratch/project_2015212/ceren/checkpoints + +cd /projappl/project_2015212/cavachon/CAVACHON + +python - << 'PY' +from cavachon.workflow import Workflow +from cavachon.io.file_reader import FileReader +from cavachon.model import Model + +CFG = "/projappl/project_2015212/cavachon/configs/ceren/sparse_bulk_v5.yaml" +wf = Workflow(CFG) + +#wf.self.setup_io() +#wf.self.setup_modality() +#wf.self.setup_sample() +#wf.self.setup_training() +#wf.self.setup_dataset() +#wf.self.setup_model() +#wf.self.setup_analysis() + +wf.setup_mdata() +wf.setup_dataloader() +wf.model = Model.make( + component_configs=wf.config.components, + name=wf.config.model.name, +) + +wf.setup_train_scheduler() +wf.model.compile() + +for data in wf.dataloader.dataset.batch(10): + wf.model.train_step(data) + break + +PY \ No newline at end of file diff --git a/testThis.sh b/testThis.sh new file mode 100644 index 0000000..9a1a6aa --- /dev/null +++ b/testThis.sh @@ -0,0 +1,33 @@ +#!/bin/bash +#SBATCH --job-name=Bulk_test_separated +#SBATCH --account=project_2015212 +#SBATCH --partition=gpu +#SBATCH --gres=gpu:v100:1 +#SBATCH --cpus-per-task=8 +#SBATCH --mem=32G +#SBATCH --time=01:00:00 +#SBATCH --output=/scratch/project_2015212/ceren/runs/bulk/%x-%j.out +#SBATCH --error=/scratch/project_2015212/ceren/runs/bulk/%x-%j.err + +set -euo pipefail + +module load tensorflow/2.18 +source /projappl/project_2015212/cavachon/envs/ceren/.venv/bin/activate + +export MLFLOW_TRACKING_URI="file:///scratch/project_2015212/ceren/mlruns" +export OMP_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export MKL_NUM_THREADS=${SLURM_CPUS_PER_TASK} +export PYTHONUNBUFFERED=1 + +# Make sure key dirs exist +mkdir -p /scratch/project_2015212/ceren/runs/bulk/embeddings +mkdir -p /scratch/project_2015212/ceren/checkpoints + +cd /projappl/project_2015212/cavachon/CAVACHON + +python - << 'PY' +from cavachon.workflow import Workflow +CFG = "/projappl/project_2015212/cavachon/configs/ceren/sparse_bulk_v5.yaml" +wf = Workflow(CFG) +wf.run() +PY