From 0e2242ef90f0d445e6736b1916fa3d87d7f158d0 Mon Sep 17 00:00:00 2001 From: qgh533 Date: Thu, 18 Apr 2024 17:55:45 +0200 Subject: [PATCH 01/94] Add files for multiprocessing --- analysis/__init__.py | 6 + analysis/metrics.py | 99 + conf/__init__.py | 3 + conf/data/base_data.yaml | 38 + conf/main.yaml | 27 + conf/schema.py | 268 +++ conf/task/analyze_latent.yaml | 31 + conf/task/identify_associations_bayes.yaml | 38 + conf/task/identify_associations_ttest.yaml | 39 + conf/task/tune_model_reconstruction.yaml | 29 + conf/task/tune_model_stability.yaml | 30 + core/__init__.py | 4 + core/logging.py | 44 + core/seed.py | 24 + core/typing.py | 14 + data/__init__.py | 3 + data/dataloaders.py | 210 ++ data/io.py | 164 ++ data/perturbations.py | 207 ++ data/preprocessing.py | 103 + isoforms_first_try/config/data/isoforms.yaml | 25 + .../config/task/isoforms__id_assoc_bayes.yaml | 24 + isoforms_first_try/encode_data_iso.py | 7 + isoforms_first_try/encode_data_iso.sh | 23 + isoforms_first_try/encode_iso.err | 14 + .../identify_assoc_bayes_iso_multiprocess.py | 6 + .../identify_assoc_bayes_iso_multiprocess.sh | 23 + .../identify_assoc_bayes_iso_notmulti.py | 6 + .../identify_assoc_bayes_normal.sh | 23 + isoforms_first_try/interim_data/DFI.txt | 1 + isoforms_first_try/interim_data/DSS.txt | 1 + isoforms_first_try/interim_data/OS.txt | 1 + isoforms_first_try/interim_data/PFI.txt | 1 + .../ajcc_pathologic_tumor_stage.txt | 1 + .../interim_data/cancer_type_abbreviation.txt | 1 + isoforms_first_try/interim_data/gender.txt | 1 + .../interim_data/gene_tpm_100.txt | 99 + .../interim_data/gene_tpm_2000_data.txt | 1991 +++++++++++++++++ .../interim_data/histological_type.txt | 1 + .../interim_data/iso_IF_100.txt | 99 + .../interim_data/iso_IF_2000_data.txt | 1985 ++++++++++++++++ .../interim_data/iso_tpm_100.txt | 99 + .../interim_data/iso_tpm_2000_data.txt | 1987 ++++++++++++++++ isoforms_first_try/interim_data/mappings.json | 88 + .../interim_data/pheno_continuous_float.txt | 6 + .../interim_data/vital_status.txt | 1 + isoforms_first_try/multi_bayes_iso.err | 5 + isoforms_first_try/normal_bayes.err | 8 + ...on_age_at_initial_pathologic_diagnosis.png | Bin 0 -> 97362 bytes ...itial_pathologic_diagnosis_unprocessed.png | Bin 0 -> 104558 bytes .../Value_distribution_gene_tpm_100.png | Bin 0 -> 227354 bytes ..._distribution_gene_tpm_100_unprocessed.png | Bin 0 -> 230737 bytes .../Value_distribution_gene_tpm_2000_data.png | Bin 0 -> 266049 bytes ...ibution_gene_tpm_2000_data_unprocessed.png | Bin 0 -> 241248 bytes .../Value_distribution_iso_IF_100.png | Bin 0 -> 217554 bytes ...ue_distribution_iso_IF_100_unprocessed.png | Bin 0 -> 241450 bytes .../Value_distribution_iso_IF_2000_data.png | Bin 0 -> 249943 bytes ...tribution_iso_IF_2000_data_unprocessed.png | Bin 0 -> 294526 bytes .../Value_distribution_iso_tpm_100.png | Bin 0 -> 228243 bytes ...e_distribution_iso_tpm_100_unprocessed.png | Bin 0 -> 228402 bytes 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tasks/encode_data.py | 66 + tasks/identify_associations.py | 1208 ++++++++++ tasks/identify_associations_ORIGINAL.py | 877 ++++++++ tasks/identify_associations_based_on_multi.py | 1212 ++++++++++ .../identify_associations_first_efficient.py | 1263 +++++++++++ tasks/identify_associations_multiprocess.py | 832 +++++++ tasks/tune_model.py | 256 +++ training/__init__.py | 0 training/training_loop.py | 92 + visualization/__init__.py | 24 + visualization/dataset_distributions.py | 284 +++ visualization/feature_importance.py | 188 ++ visualization/latent_space.py | 219 ++ visualization/loss_curves.py | 44 + visualization/metrics.py | 58 + visualization/style.py | 48 + visualization/vae_visualization.py | 268 +++ 95 files changed, 17180 insertions(+) create mode 100644 analysis/__init__.py create mode 100644 analysis/metrics.py create mode 100644 conf/__init__.py create mode 100644 conf/data/base_data.yaml create mode 100644 conf/main.yaml create mode 100644 conf/schema.py create mode 100644 conf/task/analyze_latent.yaml create mode 100644 conf/task/identify_associations_bayes.yaml create mode 100644 conf/task/identify_associations_ttest.yaml create mode 100644 conf/task/tune_model_reconstruction.yaml create mode 100644 conf/task/tune_model_stability.yaml create mode 100644 core/__init__.py create mode 100644 core/logging.py create mode 100644 core/seed.py create mode 100644 core/typing.py create mode 100644 data/__init__.py create mode 100644 data/dataloaders.py create mode 100644 data/io.py create mode 100644 data/perturbations.py create mode 100644 data/preprocessing.py create mode 100644 isoforms_first_try/config/data/isoforms.yaml create mode 100644 isoforms_first_try/config/task/isoforms__id_assoc_bayes.yaml create mode 100755 isoforms_first_try/encode_data_iso.py create mode 100755 isoforms_first_try/encode_data_iso.sh create mode 100755 isoforms_first_try/encode_iso.err create mode 100755 isoforms_first_try/identify_assoc_bayes_iso_multiprocess.py create mode 100755 isoforms_first_try/identify_assoc_bayes_iso_multiprocess.sh create mode 100755 isoforms_first_try/identify_assoc_bayes_iso_notmulti.py create mode 100755 isoforms_first_try/identify_assoc_bayes_normal.sh create mode 100755 isoforms_first_try/interim_data/DFI.txt create mode 100755 isoforms_first_try/interim_data/DSS.txt create mode 100755 isoforms_first_try/interim_data/OS.txt create mode 100755 isoforms_first_try/interim_data/PFI.txt create mode 100755 isoforms_first_try/interim_data/ajcc_pathologic_tumor_stage.txt create mode 100755 isoforms_first_try/interim_data/cancer_type_abbreviation.txt create mode 100755 isoforms_first_try/interim_data/gender.txt create mode 100755 isoforms_first_try/interim_data/gene_tpm_100.txt create mode 100755 isoforms_first_try/interim_data/gene_tpm_2000_data.txt create mode 100755 isoforms_first_try/interim_data/histological_type.txt create mode 100755 isoforms_first_try/interim_data/iso_IF_100.txt create mode 100755 isoforms_first_try/interim_data/iso_IF_2000_data.txt create mode 100755 isoforms_first_try/interim_data/iso_tpm_100.txt create mode 100755 isoforms_first_try/interim_data/iso_tpm_2000_data.txt create mode 100755 isoforms_first_try/interim_data/mappings.json create mode 100755 isoforms_first_try/interim_data/pheno_continuous_float.txt create mode 100755 isoforms_first_try/interim_data/vital_status.txt create mode 100755 isoforms_first_try/multi_bayes_iso.err create mode 100755 isoforms_first_try/normal_bayes.err create mode 100644 isoforms_first_try/results/encoded_datasets/Value_distribution_age_at_initial_pathologic_diagnosis.png create mode 100644 isoforms_first_try/results/encoded_datasets/Value_distribution_age_at_initial_pathologic_diagnosis_unprocessed.png create mode 100644 isoforms_first_try/results/encoded_datasets/Value_distribution_gene_tpm_100.png create mode 100644 isoforms_first_try/results/encoded_datasets/Value_distribution_gene_tpm_100_unprocessed.png create mode 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isoforms_first_try/results/encoded_datasets/Value_distribution_trans_tcga_gene_tpm2_unprocessed.png create mode 100755 isoforms_first_try/try_encode_data.py create mode 100644 models/__init__.py create mode 100644 models/vae.py create mode 100755 tasks/__init__.py create mode 100755 tasks/analyze_latent.py create mode 100644 tasks/analyze_latent_efficient.py create mode 100644 tasks/analyze_latent_fast.py create mode 100644 tasks/analyze_latent_original.py create mode 100755 tasks/encode_data.py create mode 100755 tasks/identify_associations.py create mode 100755 tasks/identify_associations_ORIGINAL.py create mode 100644 tasks/identify_associations_based_on_multi.py create mode 100644 tasks/identify_associations_first_efficient.py create mode 100755 tasks/identify_associations_multiprocess.py create mode 100755 tasks/tune_model.py create mode 100644 training/__init__.py create mode 100644 training/training_loop.py create mode 100644 visualization/__init__.py create mode 100644 visualization/dataset_distributions.py create mode 100644 visualization/feature_importance.py create mode 100644 visualization/latent_space.py create mode 100644 visualization/loss_curves.py create mode 100644 visualization/metrics.py create mode 100644 visualization/style.py create mode 100644 visualization/vae_visualization.py diff --git a/analysis/__init__.py b/analysis/__init__.py new file mode 100644 index 00000000..681aa857 --- /dev/null +++ b/analysis/__init__.py @@ -0,0 +1,6 @@ +__all__ = ["calculate_accuracy", "calculate_cosine_similarity"] + +from move.analysis.metrics import ( + calculate_accuracy, + calculate_cosine_similarity, +) diff --git a/analysis/metrics.py b/analysis/metrics.py new file mode 100644 index 00000000..611bbb67 --- /dev/null +++ b/analysis/metrics.py @@ -0,0 +1,99 @@ +__all__ = ["calculate_accuracy", "calculate_cosine_similarity"] + +import numpy as np + +from move.core.typing import FloatArray + + +def calculate_accuracy( + original_input: FloatArray, reconstruction: FloatArray +) -> FloatArray: + """Compute accuracy per sample. + + Args: + original_input: Original labels (one-hot encoded as a 3D array). + reconstruction: Reconstructed labels (2D array). + + Returns: + Array of accuracy scores. + """ + if original_input.ndim != 3: + raise ValueError("Expected original input to have three dimensions.") + if reconstruction.ndim != 2: + raise ValueError("Expected reconstruction to have two dimensions.") + if original_input[:, :, 0].shape != reconstruction.shape: + raise ValueError( + f"Original input {original_input.shape} and reconstruction " + f"{reconstruction.shape} shapes do not match." + ) + + is_nan = original_input.sum(axis=2) == 0 + original_input = np.argmax(original_input, axis=2) # 3D => 2D + y_true = np.ma.masked_array(original_input, mask=is_nan) + y_pred = np.ma.masked_array(reconstruction, mask=is_nan) + + num_features = np.ma.count(y_true, axis=1) + scores = np.sum(y_true == y_pred, axis=1) / num_features + + return np.ma.filled(scores, 0) + + +def calculate_cosine_similarity( + original_input: FloatArray, reconstruction: FloatArray +) -> FloatArray: + """Compute cosine similarity per sample. + + Args: + original_input: Original values (2D array). + reconstruction: Reconstructed values (2D array). + + Returns: + Array of similarities. + """ + if any((original_input.ndim != 2, reconstruction.ndim != 2)): + raise ValueError("Expected both inputs to have two dimensions.") + if original_input.shape != reconstruction.shape: + raise ValueError( + f"Original input {original_input.shape} and reconstruction " + f"{reconstruction.shape} shapes do not match." + ) + + is_nan = original_input == 0 + x = np.ma.masked_array(original_input, mask=is_nan) + y = np.ma.masked_array(reconstruction, mask=is_nan) + + # Equivalent to `np.diag(sklearn.metrics.pairwise.cosine_similarity(x, y))` + # But can handle masked arrays + scores = np.sum(x * y, axis=1) / (norm(x) * norm(y)) + + return np.ma.filled(scores, 0) + + +def norm(x: np.ma.MaskedArray, axis: int = 1) -> np.ma.MaskedArray: + """Return Euclidean norm. This function is equivalent to `np.linalg.norm`, + but it can handle masked arrays. + + Args: + x: 2D masked array + axis: Axis along which to the operation is performed. Defaults to 1. + + Returns: + 1D array with the specified axis removed. + """ + return np.sqrt(np.sum(x**2, axis=axis)) + + +def get_2nd_order_polynomial(x_array, y_array, n_points=100): + """ + Given a set of x an y values, find the 2nd oder polynomial fitting best the data. + + Returns: + x_pol: x coordinates for the polynomial function evaluation. + y_pol: y coordinates for the polynomial function evaluation. + """ + a2, a1, a = np.polyfit(x_array, y_array, deg=2) + + x_pol = np.linspace(np.min(x_array), np.max(x_array), n_points) + y_pol = np.array([a2 * x * x + a1 * x + a for x in x_pol]) + + return x_pol, y_pol, (a2, a1, a) diff --git a/conf/__init__.py b/conf/__init__.py new file mode 100644 index 00000000..7669b5c3 --- /dev/null +++ b/conf/__init__.py @@ -0,0 +1,3 @@ +__all__ = ["MOVEConfig"] + +from move.conf.schema import MOVEConfig diff --git a/conf/data/base_data.yaml b/conf/data/base_data.yaml new file mode 100644 index 00000000..27904c3d --- /dev/null +++ b/conf/data/base_data.yaml @@ -0,0 +1,38 @@ + +# raw - for raw data (e.g., TSVs, text) +# interim - for intermediate data (e.g., NPYs, trained models, latents) +# processed - for results (e.g., TSVs) +raw_data_path: data/ +interim_data_path: interim_data/ +results_path: processed_data/ + +sample_names: baseline_ids + +categorical_inputs: + - name: diabetes_genotypes + weight: 1 + - name: baseline_drugs + weight: 1 + - name: baseline_categorical + weight: 1 + +continuous_inputs: + - name: baseline_continuous + weight: 2 + - name: baseline_transcriptomics + weight: 1 + - name: baseline_diet_wearables + weight: 1 + - name: baseline_proteomic_antibodies + weight: 1 + - name: baseline_target_metabolomics + weight: 1 + - name: baseline_untarget_metabolomics + weight: 1 + - name: baseline_metagenomics + weight: 1 + +categorical_names: ${names:${data.categorical_inputs}} +continuous_names: ${names:${data.continuous_inputs}} +categorical_weights: ${weights:${data.categorical_inputs}} +continuous_weights: ${weights:${data.continuous_inputs}} diff --git a/conf/main.yaml b/conf/main.yaml new file mode 100644 index 00000000..8246ad18 --- /dev/null +++ b/conf/main.yaml @@ -0,0 +1,27 @@ +# @package _global_ + +defaults: + - _self_ + - data: base_data + - task: null + - experiment: null + - override /hydra/job_logging: none + - override /hydra/hydra_logging: none + +hydra: + output_subdir: null + run: + dir: ./ + searchpath: + - file://config/ + sweep: + dir: ./ + subdir: ./ + job: + config: + override_dirname: + item_sep: ";" + exclude_keys: + - experiment + +seed: null diff --git a/conf/schema.py b/conf/schema.py new file mode 100644 index 00000000..08c3acb3 --- /dev/null +++ b/conf/schema.py @@ -0,0 +1,268 @@ +__all__ = [ + "MOVEConfig", + "EncodeDataConfig", + "AnalyzeLatentConfig", + "TuneModelReconstructionConfig", + "TuneModelStabilityConfig", + "IdentifyAssociationsConfig", + "IdentifyAssociationsBayesConfig", + "IdentifyAssociationsTTestConfig", +] + +from dataclasses import dataclass, field +from typing import Any, Optional + +from hydra.core.config_store import ConfigStore +from omegaconf import MISSING, OmegaConf + +from move.models.vae import VAE +from move.training.training_loop import training_loop + + +def get_fully_qualname(sth: Any) -> str: + return ".".join((sth.__module__, sth.__qualname__)) + + +@dataclass +class InputConfig: + name: str + weight: int = 1 + + +@dataclass +class DataConfig: + raw_data_path: str = MISSING + interim_data_path: str = MISSING + results_path: str = MISSING + sample_names: str = MISSING + categorical_inputs: list[InputConfig] = MISSING + continuous_inputs: list[InputConfig] = MISSING + categorical_names: list[str] = MISSING + continuous_names: list[str] = MISSING + categorical_weights: list[int] = MISSING + continuous_weights: list[int] = MISSING + + +@dataclass +class ModelConfig: + _target_: str = MISSING + cuda: bool = MISSING + + +@dataclass +class VAEConfig(ModelConfig): + """Configuration for the VAE module.""" + + _target_: str = get_fully_qualname(VAE) + categorical_weights: list[int] = MISSING + continuous_weights: list[int] = MISSING + num_hidden: list[int] = MISSING + num_latent: int = MISSING + beta: float = MISSING + dropout: float = MISSING + cuda: bool = False + + +@dataclass +class TrainingLoopConfig: + _target_: str = get_fully_qualname(training_loop) + num_epochs: int = MISSING + lr: float = MISSING + kld_warmup_steps: list[int] = MISSING + batch_dilation_steps: list[int] = MISSING + early_stopping: bool = MISSING + patience: int = MISSING + + +@dataclass +class TaskConfig: + """Configuration for a MOVE task. + + Attributes: + batch_size: Number of samples in a training batch. + model: Configuration for a model. + training_loop: Configuration for a training loop. + """ + + batch_size: Optional[int] + model: Optional[VAEConfig] + training_loop: Optional[TrainingLoopConfig] + + +@dataclass +class EncodeDataConfig(TaskConfig): + """Configuration for a data-encoding task.""" + + batch_size = None + model = None + training_loop = None + + +@dataclass +class TuneModelConfig(TaskConfig): + """Configure the "tune model" task.""" + + ... + + +@dataclass +class TuneModelStabilityConfig(TuneModelConfig): + """Configure the "tune model" task.""" + + num_refits: int = MISSING + + +@dataclass +class TuneModelReconstructionConfig(TuneModelConfig): + """Configure the "tune model" task.""" + + ... + + +@dataclass +class AnalyzeLatentConfig(TaskConfig): + """Configure the "analyze latents" task. + + Attributes: + feature_names: + Names of features to visualize.""" + + feature_names: list[str] = field(default_factory=list) + reducer: dict[str, Any] = MISSING + fast: bool = False # Default value is False + + + +@dataclass +class IdentifyAssociationsConfig(TaskConfig): + """Configure the "identify associations" task. + + Attributes: + target_dataset: + Name of categorical dataset to perturb. + target_value: + The value to change to. It should be a category name. + num_refits: + Number of times to refit the model. + sig_threshold: + Threshold used to determine whether an association is significant. + In the t-test approach, this is called significance level (alpha). + In the probabilistc approach, significant associations are selected + if their FDR is below this threshold. + + This value should be within the range [0, 1]. + save_models: + Whether to save the weights of each refit. If weights are saved, + rerunning the task will load them instead of training. + """ + + target_dataset: str = MISSING + target_value: str = MISSING + num_refits: int = MISSING + sig_threshold: float = 0.05 + save_refits: bool = False + + +@dataclass +class IdentifyAssociationsBayesConfig(IdentifyAssociationsConfig): + """Configure the probabilistic approach to identify associations.""" + multiprocess: bool = False # Default value is False + + ... + + +@dataclass +class IdentifyAssociationsTTestConfig(IdentifyAssociationsConfig): + """Configure the t-test approach to identify associations. + + Args: + num_latent: + List of latent space dimensions to train. It should contain four + elements. + """ + + num_latent: list[int] = MISSING + + +@dataclass +class IdentifyAssociationsKSConfig(IdentifyAssociationsConfig): + """Configure the Kolmogorov-Smirnov approach to identify associations. + + Args: + perturbed_feature_names: names of the perturbed features of interest. + target_feature_names: names of the target features of interest. + + Description: + For each perturbed feature - target feature pair, we will plot: + - Input vs. reconstruction correlation plot: to assess reconstruction + quality of both target and perturbed features. + - Distribution of reconstruction values for the target feature before + and after the perturbation of the perturbed feature. + + """ + + perturbed_feature_names: list[str] = field(default_factory=list) + target_feature_names: list[str] = field(default_factory=list) + + +@dataclass +class MOVEConfig: + defaults: list[Any] = field(default_factory=lambda: [dict(data="base_data")]) + data: DataConfig = MISSING + task: TaskConfig = MISSING + seed: Optional[int] = None + + +def extract_weights(configs: list[InputConfig]) -> list[int]: + """Extracts the weights from a list of input configs.""" + return [1 if not hasattr(item, "weight") else item.weight for item in configs] + + +def extract_names(configs: list[InputConfig]) -> list[str]: + """Extracts the weights from a list of input configs.""" + return [item.name for item in configs] + + +# Store config schema +cs = ConfigStore.instance() +cs.store(name="config_schema", node=MOVEConfig) +cs.store( + group="task", + name="encode_data", + node=EncodeDataConfig, +) +cs.store( + group="task", + name="tune_model_reconstruction_schema", + node=TuneModelReconstructionConfig, +) + +cs.store( + group="task", + name="tune_model_stability_schema", + node=TuneModelStabilityConfig, +) +cs.store( + group="task", + name="analyze_latent_schema", + node=AnalyzeLatentConfig, +) +cs.store( + group="task", + name="identify_associations_bayes_schema", + node=IdentifyAssociationsBayesConfig, +) +cs.store( + group="task", + name="identify_associations_ttest_schema", + node=IdentifyAssociationsTTestConfig, +) +cs.store( + group="task", + name="identify_associations_ks_schema", + node=IdentifyAssociationsKSConfig, +) + +# Register custom resolvers +OmegaConf.register_new_resolver("weights", extract_weights) +OmegaConf.register_new_resolver("names", extract_names) diff --git a/conf/task/analyze_latent.yaml b/conf/task/analyze_latent.yaml new file mode 100644 index 00000000..b652819f --- /dev/null +++ b/conf/task/analyze_latent.yaml @@ -0,0 +1,31 @@ +defaults: + - analyze_latent_schema + +batch_size: 10 + +reducer: + _target_: sklearn.manifold.TSNE + +model: + categorical_weights: ${weights:${data.categorical_inputs}} + continuous_weights: ${weights:${data.continuous_inputs}} + num_hidden: + - 1000 + num_latent: 150 + beta: 0.0001 + dropout: 0.1 + cuda: False + +training_loop: + lr: 1e-4 + num_epochs: 40 + batch_dilation_steps: + - 50 + - 100 + - 150 + kld_warmup_steps: + - 15 + - 20 + - 25 + early_stopping: false + patience: 0 diff --git a/conf/task/identify_associations_bayes.yaml b/conf/task/identify_associations_bayes.yaml new file mode 100644 index 00000000..c54d1724 --- /dev/null +++ b/conf/task/identify_associations_bayes.yaml @@ -0,0 +1,38 @@ +defaults: + - identify_associations_bayes_schema + +multiprocess: false + +batch_size: 10 + +num_refits: 10 +sig_threshold: 0.05 + +target_dataset: baseline_drugs +target_value: 1 + +model: + categorical_weights: ${weights:${data.categorical_inputs}} + continuous_weights: ${weights:${data.continuous_inputs}} + num_hidden: + - 2000 + num_latent: 150 + beta: 0.0001 + dropout: 0.1 + cuda: false + +training_loop: + lr: 1e-4 + num_epochs: 30 + batch_dilation_steps: + - 50 + - 100 + - 150 + kld_warmup_steps: + - 15 + - 20 + - 25 + early_stopping: false + patience: 0 + + diff --git a/conf/task/identify_associations_ttest.yaml b/conf/task/identify_associations_ttest.yaml new file mode 100644 index 00000000..99e78706 --- /dev/null +++ b/conf/task/identify_associations_ttest.yaml @@ -0,0 +1,39 @@ +defaults: + - identify_associations_ttest_schema + +batch_size: 10 + +num_refits: 10 +num_latent: + - 150 + - 200 + - 250 + - 300 + +target_dataset: baseline_drugs +target_value: 1 + +model: + categorical_weights: ${weights:${data.categorical_inputs}} + continuous_weights: ${weights:${data.continuous_inputs}} + num_hidden: + - 2000 + beta: 0.0001 + dropout: 0.1 + cuda: false + +training_loop: + lr: 1e-4 + num_epochs: 30 + batch_dilation_steps: + - 50 + - 100 + - 150 + kld_warmup_steps: + - 15 + - 20 + - 25 + early_stopping: false + patience: 0 + + diff --git a/conf/task/tune_model_reconstruction.yaml b/conf/task/tune_model_reconstruction.yaml new file mode 100644 index 00000000..677dc052 --- /dev/null +++ b/conf/task/tune_model_reconstruction.yaml @@ -0,0 +1,29 @@ +defaults: + - tune_model_reconstruction_schema + +batch_size: 10 + +model: + categorical_weights: ${weights:${data.categorical_inputs}} + continuous_weights: ${weights:${data.continuous_inputs}} + num_hidden: + - 1000 + num_latent: 150 + beta: 0.0001 + dropout: 0.1 + cuda: false + +training_loop: + lr: 1e-4 + num_epochs: 40 + batch_dilation_steps: + - 50 + - 100 + - 150 + kld_warmup_steps: + - 15 + - 20 + - 25 + early_stopping: false + patience: 0 + \ No newline at end of file diff --git a/conf/task/tune_model_stability.yaml b/conf/task/tune_model_stability.yaml new file mode 100644 index 00000000..3df9f3cf --- /dev/null +++ b/conf/task/tune_model_stability.yaml @@ -0,0 +1,30 @@ +defaults: + - tune_model_stability_schema + +batch_size: 10 +num_refits: 3 + +model: + categorical_weights: ${weights:${data.categorical_inputs}} + continuous_weights: ${weights:${data.continuous_inputs}} + num_hidden: + - 1000 + num_latent: 150 + beta: 0.0001 + dropout: 0.1 + cuda: false + +training_loop: + lr: 1e-4 + num_epochs: 40 + batch_dilation_steps: + - 50 + - 100 + - 150 + kld_warmup_steps: + - 15 + - 20 + - 25 + early_stopping: false + patience: 0 + \ No newline at end of file diff --git a/core/__init__.py b/core/__init__.py new file mode 100644 index 00000000..a776a73f --- /dev/null +++ b/core/__init__.py @@ -0,0 +1,4 @@ +__all__ = ["logging", "set_global_seed", "typing"] + +from move.core import logging, typing +from move.core.seed import set_global_seed diff --git a/core/logging.py b/core/logging.py new file mode 100644 index 00000000..e665dfab --- /dev/null +++ b/core/logging.py @@ -0,0 +1,44 @@ +import logging +from pathlib import Path + + +def get_logger(name: str): + """Return a logger with the specified name. The logger writes messages to + a log file and the console. + + Args: + name: + Name of the logger. If it contains a dot, only the succeeding + substring is used (e.g., `foo.bar` => `bar`). + + Returns: + Logger + """ + + if "." in name: + name = name.split(".")[-1] + + logging.captureWarnings(True) + logger = logging.getLogger(name) + + if not logger.hasHandlers(): + logger.setLevel(logging.DEBUG) + + path = Path.cwd() / "logs" + path.mkdir(exist_ok=True) + fmt = "[%(asctime)s] [%(levelname)-5s - %(name)s]: %(message)s" + formatter = logging.Formatter(fmt, datefmt="%Y-%m-%d %H:%M:%S") + file_handler = logging.FileHandler(path / f"{name}.log", encoding="utf-8") + file_handler.setFormatter(formatter) + file_handler.setLevel(logging.DEBUG) + + fmt = "[%(levelname)-5s - %(name)s]: %(message)s" + formatter = logging.Formatter(fmt) + stream_handler = logging.StreamHandler() + stream_handler.setFormatter(formatter) + stream_handler.setLevel(logging.INFO) + + logger.addHandler(file_handler) + logger.addHandler(stream_handler) + + return logger diff --git a/core/seed.py b/core/seed.py new file mode 100644 index 00000000..d5be426f --- /dev/null +++ b/core/seed.py @@ -0,0 +1,24 @@ +__all__ = ["set_global_seed"] + +import os +import random +from typing import Optional + +import numpy as np +import torch + + +def set_global_seed(seed: Optional[int]) -> None: + """Seed the random number generators. + + Args: + seed: Seed for global random state. + """ + if seed is None: + return + random.seed(seed) # + np.random.seed(seed) # + os.environ["PYTHONASSEED"] = str(seed) + torch.manual_seed(seed) # + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) diff --git a/core/typing.py b/core/typing.py new file mode 100644 index 00000000..9fc16ced --- /dev/null +++ b/core/typing.py @@ -0,0 +1,14 @@ +__all__ = ["BoolArray", "FloatArray", "IntArray", "ObjectArray", "PathLike"] + +import os +from typing import Union + +import numpy as np +from numpy.typing import NDArray + +PathLike = Union[str, os.PathLike] + +BoolArray = NDArray[np.bool_] +IntArray = NDArray[np.int_] +FloatArray = NDArray[np.float_] +ObjectArray = NDArray[np.object_] diff --git a/data/__init__.py b/data/__init__.py new file mode 100644 index 00000000..4d43b968 --- /dev/null +++ b/data/__init__.py @@ -0,0 +1,3 @@ +__all__ = ["io", "preprocessing"] + +from move.data import io, preprocessing diff --git a/data/dataloaders.py b/data/dataloaders.py new file mode 100644 index 00000000..d5c8f49f --- /dev/null +++ b/data/dataloaders.py @@ -0,0 +1,210 @@ +__all__ = ["MOVEDataset", "make_dataset", "make_dataloader", "split_samples"] + +from typing import Optional + +import numpy as np +import torch +from torch.utils.data import DataLoader, TensorDataset + +from move.core.typing import BoolArray, FloatArray + + +class MOVEDataset(TensorDataset): + """ + Characterizes a dataset for PyTorch + + Args: + cat_all: + categorical input matrix (N_samples, N_variables x N_max-classes. + con_all: + normalized continuous input matrix (N_samples, N_variables). + cat_shapes: + list of tuples corresponding to number of features (N_variables, + N_max-classes) of each categorical class. + con_shapes: + list of tuples corresponding to number of features + (N_variables) of each continuous class. + + Raises: + ValueError: + Number of samples between categorical and continuous datasets must + match. + ValueError: + Categorical and continuous data cannot be both empty. + """ + + def __init__( + self, + cat_all: Optional[torch.Tensor] = None, + con_all: Optional[torch.Tensor] = None, + cat_shapes: Optional[list[tuple[int, ...]]] = None, + con_shapes: Optional[list[int]] = None, + ) -> None: + # Check + num_samples = None if cat_all is None else cat_all.shape[0] + if con_all is not None: + if num_samples is None: + num_samples = con_all.shape[0] + elif num_samples != con_all.shape[0]: + raise ValueError( + "Number of samples between categorical and continuous " + "datasets must match." + ) + elif num_samples is None: + raise ValueError("Categorical and continuous data cannot be both empty.") + self.num_samples = num_samples + self.cat_all = cat_all + self.cat_shapes = cat_shapes + self.con_all = con_all + self.con_shapes = con_shapes + + def __len__(self) -> int: + return self.num_samples + + def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]: + cat_slice = torch.empty(0) if self.cat_all is None else self.cat_all[idx] + con_slice = torch.empty(0) if self.con_all is None else self.con_all[idx] + return cat_slice, con_slice + +def concat_cat_list( + cat_list: list[FloatArray], +) -> tuple[list[tuple[int, ...]], FloatArray]: + """ + Concatenate a list of categorical data + Args: + cat_list: list with each categorical class data + Returns: + (tuple): a tuple containing: + cat_shapes: + list of categorical data classes shapes (N_variables, + N_max-classes) + cat_all (FloatArray): + 2D array of concatenated patients categorical data + """ + + cat_shapes = [] + cat_flat = [] + for cat in cat_list: + cat_shape = (cat.shape[1], cat.shape[2]) + cat_shapes.append(cat_shape) + cat_flat.append(cat.reshape(cat.shape[0], -1)) + cat_all = np.concatenate(cat_flat, axis=1) + return cat_shapes, cat_all + + +def concat_con_list( + con_list: list[FloatArray], +) -> tuple[list[int], FloatArray]: + """ + Concatenate a list of continuous data + Args: + con_list: list with each continuous class data + Returns: + (tuple): a tuple containing: + n_con_shapes: + list of continuous data classes shapes (in 1D) (N_variables) + con_all: + 2D array of concatenated patients continuous data + """ + con_shapes = [con.shape[1] for con in con_list] + con_all: FloatArray = np.concatenate(con_list, axis=1) + return con_shapes, con_all + + +def make_dataset( + cat_list: Optional[list[FloatArray]] = None, + con_list: Optional[list[FloatArray]] = None, + mask: Optional[BoolArray] = None, +) -> MOVEDataset: + """Creates a dataset that combines categorical and continuous datasets. + + Args: + cat_list: + List of categorical datasets (`num_samples` x `num_features` + x `num_categories`). Defaults to None. + con_list: + List of continuous datasets (`num_samples` x `num_features`). + Defaults to None. + mask: + Boolean array to mask samples. Defaults to None. + + Raises: + ValueError: If both inputs are None + + Returns: + MOVEDataset + """ + if not cat_list and not con_list: + raise ValueError("At least one type of data must be in the input") + + cat_shapes, cat_all = [], None + if cat_list: + cat_shapes, cat_all = concat_cat_list(cat_list) + + con_shapes, con_all = [], None + if con_list: + con_shapes, con_all = concat_con_list(con_list) + + if cat_all is not None: + cat_all = torch.from_numpy(cat_all) + if mask is not None: + cat_all = cat_all[mask] + + if con_all is not None: + con_all = torch.from_numpy(con_all) + if mask is not None: + con_all = con_all[mask] + + return MOVEDataset(cat_all, con_all, cat_shapes, con_shapes) + + +def make_dataloader( + cat_list: Optional[list[FloatArray]] = None, + con_list: Optional[list[FloatArray]] = None, + mask: Optional[BoolArray] = None, + **kwargs +) -> DataLoader: + """Creates a DataLoader that combines categorical and continuous datasets. + + Args: + cat_list: + List of categorical datasets (`num_samples` x `num_features` + x `num_categories`). Defaults to None. + con_list: + List of continuous datasets (`num_samples` x `num_features`). + Defaults to None. + mask: + Boolean array to mask samples. Defaults to None. + **kwargs: + Arguments to pass to the DataLoader (e.g., batch size) + + Raises: + ValueError: If both inputs are None + + Returns: + DataLoader + """ + dataset = make_dataset(cat_list, con_list, mask) + return DataLoader(dataset, **kwargs) + + +def split_samples( + num_samples: int, + train_frac: float, +) -> BoolArray: + """Generate mask to randomly split samples into training and test sets. + + Args: + num_samples: Number of samples to split. + train_frac: Fraction of samples corresponding to training set. + + Returns: + Boolean array to mask test samples. + """ + sample_ids = np.arange(num_samples) + train_size = int(train_frac * num_samples) + + rng = np.random.default_rng() + train_ids = rng.permutation(sample_ids)[:train_size] + + return np.isin(sample_ids, train_ids) diff --git a/data/io.py b/data/io.py new file mode 100644 index 00000000..309fbd04 --- /dev/null +++ b/data/io.py @@ -0,0 +1,164 @@ +__all__ = [ + "dump_names", + "dump_mappings", + "load_mappings", + "load_preprocessed_data", + "read_config", + "read_names", + "read_tsv", +] + +import json +from pathlib import Path +from typing import Optional + +import hydra +import numpy as np +import pandas as pd +from omegaconf import DictConfig + +from move import HYDRA_VERSION_BASE, conf +from move.core.typing import BoolArray, FloatArray, ObjectArray, PathLike + + +def read_config( + data_config_name: Optional[str], task_config_name: Optional[str], *args +) -> DictConfig: + """Composes configuration for the MOVE framework. + + Args: + data_config_name: Name of data configuration file + task_config_name: Name of task configuration file + *args: Additional overrides + + Returns: + Merged configuration + """ + overrides = [] + if data_config_name is not None: + overrides.append(f"data={data_config_name}") + if task_config_name is not None: + overrides.append(f"task={task_config_name}") + overrides.extend(args) + with hydra.initialize_config_module(conf.__name__, version_base=HYDRA_VERSION_BASE): + return hydra.compose("main", overrides=overrides) + + +def load_categorical_dataset(filepath: PathLike) -> FloatArray: + """Loads categorical data in a NumPy file. + + Args: + filepath: Path to NumPy file containing a categorical dataset + + Returns: + NumPy array containing categorical data + """ + return np.load(filepath).astype(np.float32) + + +def load_continuous_dataset(filepath: PathLike) -> tuple[FloatArray, BoolArray]: + """Loads continuous data from a NumPy file and filters out columns + (features) whose sum is zero. Additionally, encodes NaN values as zeros. + + Args: + filepath: Path to NumPy file containing a continuous dataset + + Returns: + Tuple containing (1) the NumPy dataset and (2) a mask marking columns + (i.e., features) that were not filtered out + """ + data = np.load(filepath).astype(np.float32) + data[np.isnan(data)] = 0 + mask_col = np.abs(data).sum(axis=0) != 0 + data = data[:, mask_col] + return data, mask_col + + +def load_preprocessed_data( + path: Path, + categorical_dataset_names: list[str], + continuous_dataset_names: list[str], +) -> tuple[list[FloatArray], list[list[str]], list[FloatArray], list[list[str]]]: + """Loads the pre-processed categorical and continuous data. + + Args: + path: Where the data is saved + categorical_dataset_names: List of names of the categorical datasets + continuous_dataset_names: List of names of the continuous datasets + + Returns: + Returns two pairs of list containing (1, 3) the pre-processed data and + (2, 4) the lists of names of each feature + """ + + categorical_data, categorical_var_names = [], [] + for dataset_name in categorical_dataset_names: + data = load_categorical_dataset(path / f"{dataset_name}.npy") + categorical_data.append(data) + var_names = read_names(path / f"{dataset_name}.txt") + categorical_var_names.append(var_names) + + continuous_data, continuous_var_names = [], [] + for dataset_name in continuous_dataset_names: + data, keep = load_continuous_dataset(path / f"{dataset_name}.npy") + continuous_data.append(data) + var_names = read_names(path / f"{dataset_name}.txt") + var_names = [name for i, name in enumerate(var_names) if keep[i]] + continuous_var_names.append(var_names) + + return ( + categorical_data, + categorical_var_names, + continuous_data, + continuous_var_names, + ) + + +def read_names(path: PathLike) -> list[str]: + """Reads sample names from a text file. The text file should have one line + per sample name. + + Args: + path: Path to the text file + + Returns: + A list of sample names + """ + with open(path, "r", encoding="utf-8") as file: + return [i.strip() for i in file.readlines()] + + +def read_tsv( + path: PathLike, sample_names: Optional[list[str]] = None +) -> tuple[ObjectArray, np.ndarray]: + """Read a dataset from a TSV file. The TSV is expected to have an index + column (0th index). + + Args: + path: Path to TSV + index: List of sample names used to sort/filter samples + + Returns: + Tuple containing (1) feature names and (2) 2D matrix (samples x + features) + """ + data = pd.read_csv(path, index_col=0, sep="\t") + if sample_names is not None: + data.index = data.index.astype(str, False) + data = data.loc[sample_names] + return data.columns.values, data.values + + +def load_mappings(path: PathLike) -> dict[str, dict[str, int]]: + with open(path, "r", encoding="utf-8") as file: + return json.load(file) + + +def dump_mappings(path: PathLike, mappings: dict[str, dict[str, int]]) -> None: + with open(path, "w", encoding="utf-8") as file: + json.dump(mappings, file, indent=4, ensure_ascii=False) + + +def dump_names(path: PathLike, names: np.ndarray) -> None: + with open(path, "w", encoding="utf-8") as file: + file.writelines([f"{name}\n" for name in names]) diff --git a/data/perturbations.py b/data/perturbations.py new file mode 100644 index 00000000..f8642742 --- /dev/null +++ b/data/perturbations.py @@ -0,0 +1,207 @@ +__all__ = ["perturb_categorical_data", "perturb_continuous_data"] + +from pathlib import Path +from typing import Literal, Optional, cast + +import numpy as np +import torch +from torch.utils.data import DataLoader + +from move.data.dataloaders import MOVEDataset +from move.data.preprocessing import feature_stats +from move.visualization.dataset_distributions import plot_value_distributions + + +ContinuousPerturbationType = Literal["minimum", "maximum", "plus_std", "minus_std"] + + +def perturb_categorical_data( + baseline_dataloader: DataLoader, + cat_dataset_names: list[str], + target_dataset_name: str, + target_value: np.ndarray, +) -> list[DataLoader]: + """Add perturbations to categorical data. For each feature in the target + dataset, change its value to target. + + Args: + baseline_dataloader: Baseline dataloader + cat_dataset_names: List of categorical dataset names + target_dataset_name: Target categorical dataset to perturb + target_value: Target value + + Returns: + List of dataloaders containing all perturbed datasets + """ + + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.cat_shapes is not None + assert baseline_dataset.cat_all is not None + + target_idx = cat_dataset_names.index(target_dataset_name) + splits = np.cumsum( + [0] + [int.__mul__(*shape) for shape in baseline_dataset.cat_shapes] + ) + slice_ = slice(*splits[target_idx : target_idx + 2]) + + target_shape = baseline_dataset.cat_shapes[target_idx] + num_features = target_shape[0] # CHANGE + + dataloaders = [] + for i in range(num_features): + perturbed_cat = baseline_dataset.cat_all.clone() + target_dataset = perturbed_cat[:, slice_].view( + baseline_dataset.num_samples, *target_shape + ) + target_dataset[:, i, :] = torch.FloatTensor(target_value) + perturbed_dataset = MOVEDataset( + perturbed_cat, + baseline_dataset.con_all, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + dataloaders.append(perturbed_dataloader) + return dataloaders + + +def perturb_continuous_data( + baseline_dataloader: DataLoader, + con_dataset_names: list[str], + target_dataset_name: str, + target_value: float, +) -> list[DataLoader]: + """Add perturbations to continuous data. For each feature in the target + dataset, change its value to target. + + Args: + baseline_dataloader: Baseline dataloader + con_dataset_names: List of continuous dataset names + target_dataset_name: Target continuous dataset to perturb + target_value: Target value + + Returns: + List of dataloaders containing all perturbed datasets + """ + + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.con_shapes is not None + assert baseline_dataset.con_all is not None + + target_idx = con_dataset_names.index(target_dataset_name) + splits = np.cumsum([0] + baseline_dataset.con_shapes) + slice_ = slice(*splits[target_idx : target_idx + 2]) + + num_features = baseline_dataset.con_shapes[target_idx] + + dataloaders = [] + for i in range(num_features): + perturbed_con = baseline_dataset.con_all.clone() + target_dataset = perturbed_con[:, slice_] + target_dataset[:, i] = torch.FloatTensor([target_value]) + perturbed_dataset = MOVEDataset( + baseline_dataset.cat_all, + perturbed_con, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + dataloaders.append(perturbed_dataloader) + + return dataloaders + + +def perturb_continuous_data_extended( + baseline_dataloader: DataLoader, + con_dataset_names: list[str], + target_dataset_name: str, + perturbation_type: ContinuousPerturbationType, + output_subpath: Optional[Path] = None, +) -> list[DataLoader]: + + """Add perturbations to continuous data. For each feature in the target + dataset, change the feature's value in all samples (in rows): + 1,2) substituting this feature in all samples by the feature's minimum/maximum value. + 3,4) Adding/Substracting one standard deviation to the sample's feature value. + + Args: + baseline_dataloader: Baseline dataloader + con_dataset_names: List of continuous dataset names + target_dataset_name: Target continuous dataset to perturb + perturbation_type: 'minimum', 'maximum', 'plus_std' or 'minus_std'. + output_subpath: path where the figure showing the perturbation will be saved + + Returns: + - List of dataloaders containing all perturbed datasets + - Plot of the feature value distribution after the perturbation. Note that + all perturbations are collapsed into one single plot. + + Note: + This function was created so that it could generalize to non-normalized + datasets. Scaling is done per dataset, not per feature -> slightly different stds + feature to feature. + """ + + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.con_shapes is not None + assert baseline_dataset.con_all is not None + + target_idx = con_dataset_names.index(target_dataset_name) # dataset index + splits = np.cumsum([0] + baseline_dataset.con_shapes) + slice_ = slice(*splits[target_idx : target_idx + 2]) + + num_features = baseline_dataset.con_shapes[target_idx] + dataloaders = [] + perturbations_list = [] + # Change below. + #num_features = 10 + + for i in range(num_features): + perturbed_con = baseline_dataset.con_all.clone() + target_dataset = perturbed_con[:, slice_] + # Change the desired feature value by: + min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( + target_dataset + ) + if perturbation_type == "minimum": + target_dataset[:, i] = torch.FloatTensor([min_feat_val_list[i]]) + elif perturbation_type == "maximum": + target_dataset[:, i] = torch.FloatTensor([max_feat_val_list[i]]) + elif perturbation_type == "plus_std": + target_dataset[:, i] += torch.FloatTensor([std_feat_val_list[i]]) + elif perturbation_type == "minus_std": + target_dataset[:, i] -= torch.FloatTensor([std_feat_val_list[i]]) + + perturbations_list.append(target_dataset[:, i].numpy()) + + perturbed_dataset = MOVEDataset( + baseline_dataset.cat_all, + perturbed_con, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + dataloaders.append(perturbed_dataloader) + + # Plot the perturbations for all features, collapsed in one plot: + if output_subpath is not None: + fig = plot_value_distributions(np.array(perturbations_list).transpose()) + fig_path = str( + output_subpath / f"perturbation_distribution_{target_dataset_name}.png" + ) + fig.savefig(fig_path) + + return dataloaders diff --git a/data/preprocessing.py b/data/preprocessing.py new file mode 100644 index 00000000..ad184879 --- /dev/null +++ b/data/preprocessing.py @@ -0,0 +1,103 @@ +__all__ = ["one_hot_encode", "one_hot_encode_single", "scale"] + +from typing import Any, Optional + +import numpy as np +import pandas as pd +from numpy.typing import ArrayLike +from sklearn.preprocessing import scale as standardize + +from move.core.typing import BoolArray, FloatArray, IntArray + + +def _category_name(value: Any) -> str: + return value if isinstance(value, str) else str(int(value)) + + +def one_hot_encode(x_: ArrayLike) -> tuple[IntArray, dict[str, int]]: + """One-hot encode a matrix with samples in its rows and features in its + columns. Columns share number of classes. + + Args: + x: a 1D or 2D matrix, can be numerical or contain strings + + Returns: + A 3D one-hot encoded matrix (extra dim corresponds to number of + classes) and a mapping between classes and corresponding codes + """ + x: np.ndarray = np.copy(x_) + if x.ndim == 1: + x = x[:, np.newaxis] + shape = x.shape + has_na = np.any(pd.isna(x)) + if x.dtype == object: + x = x.astype(str) + categories, codes = np.unique(x, return_inverse=True) + num_classes = len(categories) + encoded_x = np.zeros((x.size, num_classes), dtype=np.uint8) + encoded_x[np.arange(x.size), codes.astype(np.uint8).ravel()] = 1 + encoded_x = encoded_x.reshape(*shape, num_classes) + if has_na: + # remove NaN column + categories = categories[:-1] + encoded_x = encoded_x[:, :, :-1] + mapping = { + _category_name(category): code for code, category in enumerate(categories) + } + return encoded_x, mapping + + +def one_hot_encode_single(mapping: dict[str, int], value: Optional[str]) -> IntArray: + """One-hot encode a single value given an existing mapping. + + Args: + mapping: cateogry-to-code lookup dictionary + value: category + + Returns: + 2D array + """ + encoded_value = np.zeros((1, len(mapping)), dtype=int) + if not pd.isna(value): + code = mapping[str(value)] + encoded_value[0, code] = 1 + return encoded_value + + +def scale(x: np.ndarray) -> tuple[FloatArray, BoolArray]: + """Center to mean and scale to unit variance. Convert NaN values to 0. + + Args: + x: 2D array with samples in its rows and features in its columns + + Returns: + Tuple containing (1) scaled output and (2) a 1D mask marking columns + (i.e., features) without zero variance + """ + #CHANGES: Allow for negative values, so only standardization + #logx = np.log2(x + 1) + logx = x + mask_1d = ~np.isclose(np.nanstd(logx, axis=0), 0.0) + scaled_x = standardize(logx[:, mask_1d], axis=0) + scaled_x[np.isnan(scaled_x)] = 0 + return scaled_x, mask_1d + + +def feature_stats(x: ArrayLike) -> tuple[FloatArray, FloatArray, FloatArray]: + """ + Read an array of continuous values and extract the + minimum, maximum and standard deviation per column (feature). + + Args: + x: 2D array with samples in its rows and features in its columns + + Returns: + minimum: list with minimum value per feature (column) + maximum: list with maximum " " + std: list with std " " + """ + + minimum = np.nanmin(x, axis=0) + maximum = np.nanmax(x, axis=0) + std = np.nanstd(x, axis=0) + return minimum, maximum, std diff --git a/isoforms_first_try/config/data/isoforms.yaml b/isoforms_first_try/config/data/isoforms.yaml new file mode 100644 index 00000000..d7619dbd --- /dev/null +++ b/isoforms_first_try/config/data/isoforms.yaml @@ -0,0 +1,25 @@ +defaults: + - base_data + +raw_data_path: data/ +interim_data_path: interim_data/ +results_path: results/ + +sample_names: common_samples_500 + +categorical_inputs: + - name: OS + - name: DSS + - name: PFI + - name: DFI + - name: ajcc_pathologic_tumor_stage + - name: gender + - name: cancer_type_abbreviation + - name: histological_type + - name: vital_status + +continuous_inputs: + - name: pheno_continuous_float + - name: iso_IF_100 + - name: iso_tpm_100 + - name: gene_tpm_100 \ No newline at end of file diff --git a/isoforms_first_try/config/task/isoforms__id_assoc_bayes.yaml b/isoforms_first_try/config/task/isoforms__id_assoc_bayes.yaml new file mode 100644 index 00000000..fcb13ce1 --- /dev/null +++ b/isoforms_first_try/config/task/isoforms__id_assoc_bayes.yaml @@ -0,0 +1,24 @@ +defaults: + - identify_associations_bayes + +multiprocess: True + +batch_size: 250 + +num_refits: 2 + +target_dataset: iso_tpm_100 # Dataset to perturb +target_value: maximum # We perturb in all samples for the maximum value of that feature across all samples +save_refits: True + +model: + num_hidden: + - 150 + num_latent: 10 + beta: 0.001 + dropout: .1 + cuda: false + +training_loop: + lr: 1e-4 + num_epochs: 20 \ No newline at end of file diff --git a/isoforms_first_try/encode_data_iso.py b/isoforms_first_try/encode_data_iso.py new file mode 100755 index 00000000..f73d3127 --- /dev/null +++ b/isoforms_first_try/encode_data_iso.py @@ -0,0 +1,7 @@ +from move.tasks import encode_data +from move.data import io +from move.data import preprocessing + +config = io.read_config("isoforms", "encode_data") +encode_data(config.data) + diff --git a/isoforms_first_try/encode_data_iso.sh b/isoforms_first_try/encode_data_iso.sh new file mode 100755 index 00000000..ed32f55a --- /dev/null +++ b/isoforms_first_try/encode_data_iso.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# The following are commonly used options for running jobs. Remove one +# "#" from the "##SBATCH" lines (changing them to "#SBATCH") to enable +# a given option. + +#SBATCH --job-name=encode500 +# The number of CPUs (cores) used by your task. Defaults to 1. +###SBATCH --cpus-per-task=10 +# The amount of RAM used by your task. Tasks are automatically assigned 15G +# per CPU (set above) if this option is not set. +#SBATCH --mem=800G +# Request a GPU on the GPU code. Use `--gres=gpu:a100:2` to request both GPUs. +##SBATCH --partition=gpuqueue --gres=gpu:a100:1 +# Send notifications when job ends. Remember to update the email address! +##SBATCH --mail-user=qgh533@ku.dk --mail-type=END,FAIL +# Set an error file +#SBATCH --error=encode_iso.err +#SBATCH --out=encode_iso.log + + +module load python/3.11.3 +python encode_data_iso.py diff --git a/isoforms_first_try/encode_iso.err b/isoforms_first_try/encode_iso.err new file mode 100755 index 00000000..539b605a --- /dev/null +++ b/isoforms_first_try/encode_iso.err @@ -0,0 +1,14 @@ +[INFO - encode_data]: Beginning task: encode data +[INFO - encode_data]: Encoding 'OS' +[INFO - encode_data]: Encoding 'DSS' +[INFO - encode_data]: Encoding 'PFI' +[INFO - encode_data]: Encoding 'DFI' +[INFO - encode_data]: Encoding 'ajcc_pathologic_tumor_stage' +[INFO - encode_data]: Encoding 'gender' +[INFO - encode_data]: Encoding 'cancer_type_abbreviation' +[INFO - encode_data]: Encoding 'histological_type' +[INFO - encode_data]: Encoding 'vital_status' +[INFO - encode_data]: Encoding 'pheno_continuous_float' +[INFO - encode_data]: Encoding 'iso_IF_2000_data' +[INFO - encode_data]: Encoding 'iso_tpm_2000_data' +[INFO - encode_data]: Encoding 'gene_tpm_2000_data' diff --git a/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.py b/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.py new file mode 100755 index 00000000..36491108 --- /dev/null +++ b/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.py @@ -0,0 +1,6 @@ +from move.tasks import identify_associations_multiprocess +from move.data import io + +config = io.read_config("isoforms", "isoforms__id_assoc_bayes") +identify_associations_multiprocess(config) + diff --git a/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.sh b/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.sh new file mode 100755 index 00000000..9342a9bb --- /dev/null +++ b/isoforms_first_try/identify_assoc_bayes_iso_multiprocess.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# The following are commonly used options for running jobs. Remove one +# "#" from the "##SBATCH" lines (changing them to "#SBATCH") to enable +# a given option. + +#SBATCH --job-name=500multi + +# The number of CPUs (cores) used by your task. Defaults to 1. +#SBATCH --cpus-per-task=50 +# The amount of RAM used by your task. Tasks are automatically assigned 15G +# per CPU (set above) if this option is not set. +#SBATCH --mem=1000G +# Request a GPU on the GPU code. Use `--gres=gpu:a100:2` to request both GPUs. +##SBATCH --partition=gpuqueue --gres=gpu:a100:1 +# Send notifications when job ends. Remember to update the email address! +##SBATCH --mail-user=qgh533@ku.dk --mail-type=END,FAIL +# Set an error file +#SBATCH --error=multi_bayes_iso.err + + +module load python/3.11.3 +python identify_assoc_bayes_iso_multiprocess.py diff --git a/isoforms_first_try/identify_assoc_bayes_iso_notmulti.py b/isoforms_first_try/identify_assoc_bayes_iso_notmulti.py new file mode 100755 index 00000000..9e5b2d77 --- /dev/null +++ b/isoforms_first_try/identify_assoc_bayes_iso_notmulti.py @@ -0,0 +1,6 @@ +from move.tasks import identify_associations +from move.data import io + +config = io.read_config("isoforms", "isoforms__id_assoc_bayes") +identify_associations(config) + diff --git a/isoforms_first_try/identify_assoc_bayes_normal.sh b/isoforms_first_try/identify_assoc_bayes_normal.sh new file mode 100755 index 00000000..ca1470d8 --- /dev/null +++ b/isoforms_first_try/identify_assoc_bayes_normal.sh @@ -0,0 +1,23 @@ +#!/bin/bash + +# The following are commonly used options for running jobs. Remove one +# "#" from the "##SBATCH" lines (changing them to "#SBATCH") to enable +# a given option. + +#SBATCH --job-name=500notmulti_bayes + +# The number of CPUs (cores) used by your task. Defaults to 1. +###SBATCH --cpus-per-task=1 +# The amount of RAM used by your task. Tasks are automatically assigned 15G +# per CPU (set above) if this option is not set. +#SBATCH --mem=1000G +# Request a GPU on the GPU code. Use `--gres=gpu:a100:2` to request both GPUs. +##SBATCH --partition=gpuqueue --gres=gpu:a100:1 +# Send notifications when job ends. Remember to update the email address! +###SBATCH --mail-user=qgh533@ku.dk --mail-type=END,FAIL +# Set an error file +#SBATCH --error=normal_bayes.err + + +module load python/3.11.3 +python identify_assoc_bayes_iso_notmulti.py diff --git a/isoforms_first_try/interim_data/DFI.txt b/isoforms_first_try/interim_data/DFI.txt new file mode 100755 index 00000000..a45045bb --- /dev/null +++ b/isoforms_first_try/interim_data/DFI.txt @@ -0,0 +1 @@ +DFI diff --git a/isoforms_first_try/interim_data/DSS.txt b/isoforms_first_try/interim_data/DSS.txt new file mode 100755 index 00000000..f42987f9 --- /dev/null +++ b/isoforms_first_try/interim_data/DSS.txt @@ -0,0 +1 @@ +DSS diff --git a/isoforms_first_try/interim_data/OS.txt b/isoforms_first_try/interim_data/OS.txt new file mode 100755 index 00000000..5734d1c9 --- /dev/null +++ b/isoforms_first_try/interim_data/OS.txt @@ -0,0 +1 @@ +OS diff --git a/isoforms_first_try/interim_data/PFI.txt b/isoforms_first_try/interim_data/PFI.txt new file mode 100755 index 00000000..11b4bf90 --- /dev/null +++ b/isoforms_first_try/interim_data/PFI.txt @@ -0,0 +1 @@ +PFI diff --git a/isoforms_first_try/interim_data/ajcc_pathologic_tumor_stage.txt b/isoforms_first_try/interim_data/ajcc_pathologic_tumor_stage.txt new file mode 100755 index 00000000..20ac1403 --- /dev/null +++ b/isoforms_first_try/interim_data/ajcc_pathologic_tumor_stage.txt @@ -0,0 +1 @@ +ajcc_pathologic_tumor_stage diff --git a/isoforms_first_try/interim_data/cancer_type_abbreviation.txt b/isoforms_first_try/interim_data/cancer_type_abbreviation.txt new file mode 100755 index 00000000..32f6d30c --- /dev/null +++ b/isoforms_first_try/interim_data/cancer_type_abbreviation.txt @@ -0,0 +1 @@ +cancer type abbreviation diff --git a/isoforms_first_try/interim_data/gender.txt b/isoforms_first_try/interim_data/gender.txt new file mode 100755 index 00000000..e6105169 --- /dev/null +++ b/isoforms_first_try/interim_data/gender.txt @@ -0,0 +1 @@ +gender diff --git a/isoforms_first_try/interim_data/gene_tpm_100.txt b/isoforms_first_try/interim_data/gene_tpm_100.txt new file mode 100755 index 00000000..5da6ad91 --- /dev/null +++ b/isoforms_first_try/interim_data/gene_tpm_100.txt @@ -0,0 +1,99 @@ +ENSG00000005302.17 +ENSG00000006327.13 +ENSG00000006432.15 +ENSG00000017260.19 +ENSG00000018699.11 +ENSG00000026508.16 +ENSG00000047249.16 +ENSG00000051180.16 +ENSG00000052841.14 +ENSG00000060069.16 +ENSG00000064666.14 +ENSG00000065457.10 +ENSG00000066044.13 +ENSG00000066136.19 +ENSG00000066468.20 +ENSG00000069345.11 +ENSG00000073605.18 +ENSG00000074054.17 +ENSG00000075415.12 +ENSG00000075702.16 +ENSG00000078747.12 +ENSG00000080845.17 +ENSG00000084636.17 +ENSG00000090266.12 +ENSG00000099899.14 +ENSG00000099917.17 +ENSG00000100129.17 +ENSG00000100142.14 +ENSG00000100599.15 +ENSG00000100731.15 +ENSG00000102226.9 +ENSG00000104517.12 +ENSG00000104613.11 +ENSG00000104814.12 +ENSG00000104824.16 +ENSG00000105245.9 +ENSG00000105323.16 +ENSG00000106392.10 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"1": 1 + }, + "PFI": { + "0": 0, + "1": 1 + }, + "DFI": { + "0": 0, + "1": 1 + }, + "ajcc_pathologic_tumor_stage": { + "IS": 0, + "Stage I": 1, + "Stage IA": 2, + "Stage IB": 3, + "Stage II": 4, + "Stage IIA": 5, + "Stage IIB": 6, + "Stage III": 7, + "Stage IIIA": 8, + "Stage IIIB": 9, + "Stage IIIC": 10, + "Stage IV": 11, + "Stage IVA": 12, + "[Discrepancy]": 13 + }, + "gender": { + "FEMALE": 0, + "MALE": 1 + }, + "cancer_type_abbreviation": { + "BLCA": 0, + "ESCA": 1, + "GBM": 2, + "LUAD": 3, + "LUSC": 4, + "OV": 5, + "PRAD": 6, + "TGCT": 7, + "UCEC": 8 + }, + "histological_type": { + "Endometrioid endometrial adenocarcinoma": 0, + "Esophagus Adenocarcinoma, NOS": 1, + "Glioblastoma Multiforme (GBM)": 2, + "Lung Adenocarcinoma": 3, + "Lung Squamous Cell Carcinoma": 4, + "Muscle invasive urothelial carcinoma (pT2 or above)": 5, + "Non-Seminoma; Embryonal Carcinoma": 6, + "Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Choriocarcinoma|Non-Seminoma; Yolk Sac Tumor|Seminoma; NOS": 7, + "Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor": 8, + "Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Yolk Sac Tumor": 9, + "Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Teratoma (Mature)": 10, + "Non-Seminoma; Embryonal Carcinoma|Seminoma; NOS": 11, + "Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor": 12, + "Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Mature)": 13, + "Non-Seminoma; Teratoma (Mature)": 14, + "Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor": 15, + "Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Embryonal Carcinoma": 16, + "Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Yolk Sac Tumor": 17, + "Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Embryonal Carcinoma|Seminoma; NOS": 18, + "Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Yolk Sac Tumor": 19, + "Non-Seminoma; Teratoma (Mature)|Seminoma; NOS|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Yolk Sac Tumor": 20, + "Non-Seminoma; Teratoma (Mature)|[Not Available]|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Yolk Sac Tumor": 21, + "Non-Seminoma; Yolk Sac Tumor": 22, + "Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Embryonal Carcinoma": 23, + "Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)": 24, + "Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Embryonal Carcinoma|Non-Seminoma; Teratoma (Mature)|Seminoma; NOS": 25, + "Non-Seminoma; Yolk Sac Tumor|Non-Seminoma; Teratoma (Mature)": 26, + "Prostate Adenocarcinoma Acinar Type": 27, + "Seminoma; NOS": 28, + "Seminoma; NOS|Non-Seminoma; Embryonal Carcinoma": 29, + "Seminoma; NOS|Non-Seminoma; Teratoma (Mature)|Non-Seminoma; Teratoma (Immature)|Non-Seminoma; Yolk Sac Tumor": 30, + "Serous Cystadenocarcinoma": 31, + "Untreated primary (de novo) GBM": 32 + }, + "vital_status": { + "Alive": 0, + "Dead": 1 + } +} \ No newline at end of file diff --git a/isoforms_first_try/interim_data/pheno_continuous_float.txt b/isoforms_first_try/interim_data/pheno_continuous_float.txt new file mode 100755 index 00000000..be6da2a9 --- /dev/null +++ b/isoforms_first_try/interim_data/pheno_continuous_float.txt @@ -0,0 +1,6 @@ +OS.time +age_at_initial_pathologic_diagnosis +DFI.time +DSS.time +initial_pathologic_dx_year +PFI.time diff --git a/isoforms_first_try/interim_data/vital_status.txt b/isoforms_first_try/interim_data/vital_status.txt new file mode 100755 index 00000000..45b26698 --- /dev/null +++ b/isoforms_first_try/interim_data/vital_status.txt @@ -0,0 +1 @@ +vital_status diff --git a/isoforms_first_try/multi_bayes_iso.err b/isoforms_first_try/multi_bayes_iso.err new file mode 100755 index 00000000..d149874a --- /dev/null +++ b/isoforms_first_try/multi_bayes_iso.err @@ -0,0 +1,5 @@ +[INFO - identify_associations_multiprocess]: Perturbing dataset: 'iso_tpm_100' +[INFO - identify_associations_multiprocess]: Beginning task: identify associations continuous (bayes) +[INFO - identify_associations_multiprocess]: Perturbation type: maximum +[INFO - identify_associations_multiprocess]: Training or reloading models +slurmstepd: error: *** JOB 216877 ON esrumcmpn01fl CANCELLED AT 2024-04-03T19:06:12 *** diff --git a/isoforms_first_try/normal_bayes.err b/isoforms_first_try/normal_bayes.err new file mode 100755 index 00000000..6ccaf8c8 --- /dev/null +++ b/isoforms_first_try/normal_bayes.err @@ -0,0 +1,8 @@ +[INFO - identify_associations]: Perturbing dataset: 'iso_tpm_100' +[INFO - identify_associations]: Beginning task: identify associations continuous (bayes) +[INFO - 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zLS3vFX8A8hFyN&}o1(KMMcXxKOGyn=YUu4S!eoLd_IJ$JgIO4Y^R4|z4PwA(u zEG!m2{a+rZMC#AR*bt>;V<;<#o#Dtx9nwX_ptG|xL5xgl0$;y-w;72SKdA;9kN7|f z=$!ELO>i5CCYa!sEZ*}Z=b6?+;)=42or}v4-8sUfMheC6>YoP?pwxb$wbfFSCUF7( zjoI_xh=fDdLe#|3~NuV@s1KY2@!0{O|RpOFn05l0E1QzHD2E|0v3xk Date: Wed, 3 Jul 2024 11:02:10 +0200 Subject: [PATCH 42/94] :construction: multiprocess only defined for bayes factors --- tutorial/config/task/random_small__id_assoc_bayes.yaml | 2 ++ tutorial/config/task/random_small__id_assoc_ttest.yaml | 2 -- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tutorial/config/task/random_small__id_assoc_bayes.yaml b/tutorial/config/task/random_small__id_assoc_bayes.yaml index ad7e67f7..c1252251 100644 --- a/tutorial/config/task/random_small__id_assoc_bayes.yaml +++ b/tutorial/config/task/random_small__id_assoc_bayes.yaml @@ -1,6 +1,8 @@ defaults: - identify_associations_bayes +multiprocess: False + batch_size: 10 num_refits: 40 diff --git a/tutorial/config/task/random_small__id_assoc_ttest.yaml b/tutorial/config/task/random_small__id_assoc_ttest.yaml index f4b29a20..0a88162e 100644 --- a/tutorial/config/task/random_small__id_assoc_ttest.yaml +++ b/tutorial/config/task/random_small__id_assoc_ttest.yaml @@ -1,8 +1,6 @@ defaults: - identify_associations_ttest -multiprocess: False - batch_size: 10 # number of samples per batch in training loop num_refits: 10 # number of times to refit (retrain) model From fbbeb190a817779b0f1d646e6545304b1f2f6259 Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 3 Jul 2024 11:23:01 +0200 Subject: [PATCH 43/94] :bug: remove non-existing, intermediate tasks (used for developing), no multiprocessing for t-tests - document that t-test is not available for multiprocessing -> key is still needed in Schema - do not set key in tutorial config as it it not implemented for t-test --- src/move/__main__.py | 12 +----------- src/move/conf/schema.py | 1 + 2 files changed, 2 insertions(+), 11 deletions(-) diff --git a/src/move/__main__.py b/src/move/__main__.py index 12d21b6a..7c4822a9 100644 --- a/src/move/__main__.py +++ b/src/move/__main__.py @@ -6,8 +6,6 @@ import move.tasks import move.tasks.analyze_latent_multiprocess import move.tasks.identify_associations_multiprocess - -# import move.tasks.identify_associations_selected # no such module from move import HYDRA_VERSION_BASE from move.conf.schema import ( AnalyzeLatentConfig, @@ -43,23 +41,15 @@ def main(config: MOVEConfig) -> None: # the fast version does not calcuate feature importance, # it is only to look at the reconstruction metrics elif task_type is AnalyzeLatentConfig: - if config.task.fast: - move.tasks.analyze_latent_fast(config) - elif config.task.multiprocess: + if config.task.multiprocess: move.tasks.analyze_latent_multiprocess(config) else: move.tasks.analyze_latent(config) elif issubclass(task_type, IdentifyAssociationsConfig): if config.task.multiprocess: move.tasks.identify_associations_multiprocess(config) - elif config.task.selected: - move.tasks.identify_associations_selected(config) - elif config.task.efficient: - move.tasks.identify_associations_efficient(config) else: move.tasks.identify_associations(config) - # What we had before - # move.tasks.identify_associations(config) else: raise ValueError("Unsupported type of task.") diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index 50fc0c69..b0c34105 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -193,6 +193,7 @@ class IdentifyAssociationsTTestConfig(IdentifyAssociationsConfig): """ num_latent: list[int] = MISSING + multiprocess: bool = False # Multiprocessiong not implemented for t-test approach @dataclass From cb3ad3014ad94e97788bc580e72c8daf0bd657dd Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 3 Jul 2024 13:19:27 +0200 Subject: [PATCH 44/94] :bug: also deactivate mutliprocessing for KS as it's not implemented --- src/move/conf/schema.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index b0c34105..85af9e0e 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -176,7 +176,7 @@ class IdentifyAssociationsBayesConfig(IdentifyAssociationsConfig): multiprocess: bool = False # Default value is False selected: bool = False pert_type: str = MISSING - efficient: bool = False + # efficient: bool = False # not needed? multiloop: bool = False ... @@ -215,6 +215,7 @@ class IdentifyAssociationsKSConfig(IdentifyAssociationsConfig): perturbed_feature_names: list[str] = field(default_factory=list) target_feature_names: list[str] = field(default_factory=list) + multiprocess: bool = False # Multiprocessiong not implemented for KS approach @dataclass From 8c61d35c440cea3078f4f6889b46eebe9509890c Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 3 Jul 2024 13:34:20 +0200 Subject: [PATCH 45/94] :art: fix flake8-bugbear issues except missing multiprocessing of t-test and KS approach --- src/move/conf/schema.py | 1 - src/move/data/perturbations.py | 29 ++++++++++--------- src/move/tasks/analyze_latent.py | 5 ++-- src/move/tasks/analyze_latent_multiprocess.py | 21 +++++++------- src/move/tasks/identify_associations.py | 18 ++++++------ .../identify_associations_multiprocess.py | 21 ++++++++------ 6 files changed, 50 insertions(+), 45 deletions(-) diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index 85af9e0e..ee6a6524 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -176,7 +176,6 @@ class IdentifyAssociationsBayesConfig(IdentifyAssociationsConfig): multiprocess: bool = False # Default value is False selected: bool = False pert_type: str = MISSING - # efficient: bool = False # not needed? multiloop: bool = False ... diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index ba3a2c3a..12675bcf 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -49,7 +49,7 @@ def perturb_continuous_data_one( splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) - num_features = baseline_dataset.con_shapes[target_idx] + # num_features = baseline_dataset.con_shapes[target_idx] # dataloaders = [] i = index_pert_feat # Instead of the loop, we do it only for one @@ -103,7 +103,7 @@ def perturb_categorical_data_one( slice_ = slice(*splits[target_idx : target_idx + 2]) target_shape = baseline_dataset.cat_shapes[target_idx] - num_features = target_shape[0] + # num_features = target_shape[0] i = index_pert_feat # dataloaders = [] @@ -128,9 +128,12 @@ def perturb_categorical_data_one( return perturbed_dataloader -def perturb_continuous_data_extended_one( # We will keep the input almost the same, to make everything easier - # However, I have to introduce a variable that allows me to index the specific dataloader I want to create (index_pert_feat) - # And I eliminate the output directory, an image is not generated now (too many features) +# We will keep the input almost the same, to make everything easier +# However, I have to introduce a variable that allows me to index the specific +# dataloader I want to create (index_pert_feat) +# And I eliminate the output directory, an image is not generated now +# (too many features) +def perturb_continuous_data_extended_one( baseline_dataloader: DataLoader, con_dataset_names: list[str], target_dataset_name: str, @@ -142,7 +145,7 @@ def perturb_continuous_data_extended_one( # We will keep the input almost the s logger = get_logger(__name__) """Add perturbations to continuous data. For each feature in the target dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value. + 1,2) substituting this feature in all samples by the feature's minimum/maximum value 3,4) Adding/Substracting one standard deviation to the sample's feature value. Args: @@ -158,8 +161,8 @@ def perturb_continuous_data_extended_one( # We will keep the input almost the s Note: This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different stds - feature to feature. + datasets. Scaling is done per dataset, not per feature -> slightly different + stds feature to feature. """ logger.debug( f"Inside perturb_continuous_data_extended_one for feature {index_pert_feat}" @@ -174,10 +177,10 @@ def perturb_continuous_data_extended_one( # We will keep the input almost the s slice_ = slice(*splits[target_idx : target_idx + 2]) # Use it only if we want to perturb all features in the target dataset - num_features = baseline_dataset.con_shapes[target_idx] + # num_features = baseline_dataset.con_shapes[target_idx] - # Now, instead of the for loop that iterates over all the features we want to perturb, we do it only for one feature, the one - # indicated in index_pert_feat + # Now, instead of the for loop that iterates over all the features we want to + # perturb, we do it only for one feature, the one indicated in index_pert_feat logger.debug(f"Setting up perturbed_con for feature {index_pert_feat}") perturbed_con = baseline_dataset.con_all.clone() @@ -365,8 +368,8 @@ def perturb_continuous_data_extended( Note: This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different stds - feature to feature. + datasets. Scaling is done per dataset, not per feature -> slightly different + stds feature to feature. """ logger = get_logger(__name__) logger.debug("Inside perturb_extended, creating baseline dataset") diff --git a/src/move/tasks/analyze_latent.py b/src/move/tasks/analyze_latent.py index eb5c4d1a..5de71d95 100644 --- a/src/move/tasks/analyze_latent.py +++ b/src/move/tasks/analyze_latent.py @@ -59,7 +59,7 @@ def perturb_continuous_data_one( splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) - num_features = baseline_dataset.con_shapes[target_idx] + # num_features = baseline_dataset.con_shapes[target_idx] # dataloaders = [] i = index_pert_feat # Instead of the loop, we do it only for one @@ -364,7 +364,8 @@ def analyze_latent(config: MOVEConfig) -> None: na_value, index_pert_feat, ) - # We calculate the difference for each of the perturbed features, and store it in an object + # We calculate the difference for each of the perturbed features, + # and store it in an object z_perturb = model.project(dataloader) diffs[:, j] = np.sum(z_perturb - z, axis=1) diff --git a/src/move/tasks/analyze_latent_multiprocess.py b/src/move/tasks/analyze_latent_multiprocess.py index 1551e6db..4a519e56 100644 --- a/src/move/tasks/analyze_latent_multiprocess.py +++ b/src/move/tasks/analyze_latent_multiprocess.py @@ -11,7 +11,9 @@ import numpy as np import pandas as pd import torch +import torch.multiprocessing from sklearn.base import TransformerMixin +from torch.multiprocessing import Pool import move.visualization as viz from move.analysis.metrics import ( @@ -23,19 +25,14 @@ from move.core.typing import FloatArray from move.data import io from move.data.dataloaders import MOVEDataset, make_dataloader +# from move.data.perturbations import perturb_categorical_data, perturb_continuous_data, from move.data.perturbations import ( - # perturb_categorical_data, - # perturb_continuous_data, - perturb_continuous_data_one, perturb_categorical_data_one, + perturb_continuous_data_one, ) from move.data.preprocessing import one_hot_encode_single from move.models.vae import VAE from move.training.training_loop import TrainingLoopOutput -from torch.utils.data import DataLoader - -import torch.multiprocessing -from torch.multiprocessing import Pool def find_feature_values( @@ -58,9 +55,10 @@ def find_feature_values( values corresponding to the feature """ dataset_index, feature_index = [None] * 2 - for dataset_index, feature_names in enumerate(feature_names_lists): + for _dataset_index, _feature_names in enumerate(feature_names_lists): try: - feature_index = feature_names.index(feature_name) + feature_index = _feature_names.index(feature_name) + dataset_index = _dataset_index except ValueError: continue break @@ -96,7 +94,8 @@ def _categorical_importance_worker(args): z, ) = args - # Diff will store the differences between z and z_perturb for the perturbed feature index_pert_feat + # Diff will store the differences between z and + # z_perturb for the perturbed feature index_pert_feat diff = np.empty((num_features)) logger.debug(f"Perturbing feature {index_pert_feat} for {dataset_name}") @@ -197,7 +196,7 @@ def analyze_latent_multiprocess(config: MOVEConfig) -> None: df_index = pd.Index(sample_names, name="sample") assert task_config.model is not None - device = torch.device("cuda" if task_config.model.cuda == True else "cpu") + device = torch.device("cuda" if task_config.model.cuda else "cpu") model: VAE = hydra.utils.instantiate( task_config.model, continuous_shapes=test_dataset.con_shapes, diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 7737422f..1a52b02a 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -25,10 +25,9 @@ from move.core.typing import BoolArray, FloatArray, IntArray from move.data import io from move.data.dataloaders import MOVEDataset, make_dataloader -from move.data.perturbations import ( +from move.data.perturbations import ( # perturb_continuous_data_extended, ContinuousPerturbationType, perturb_categorical_data, - perturb_continuous_data_extended, ) from move.data.preprocessing import feature_stats, one_hot_encode_single from move.models.vae import VAE @@ -54,7 +53,7 @@ def perturb_continuous_data_extended( """Add perturbations to continuous data. For each feature in the target dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value. + 1,2) substituting this feature in all samples by the feature's minimum/maximum value 3,4) Adding/Substracting one standard deviation to the sample's feature value. Args: @@ -71,8 +70,8 @@ def perturb_continuous_data_extended( Note: This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different stds - feature to feature. + datasets. Scaling is done per dataset, not per feature -> slightly different + stds feature to feature. """ logger.debug("Inside perturb_extended, creating baseline dataset") baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) @@ -89,7 +88,7 @@ def perturb_continuous_data_extended( num_features = 5 logger.debug(f"number of feature to perturb is {num_features}") dataloaders = [] - perturbations_list = [] + # perturbations_list = [] # Change below. # num_features = 10 @@ -244,7 +243,7 @@ def perturb_continuous_data_extended_one( logger = get_logger(__name__) """Add perturbations to continuous data. For each feature in the target dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value. + 1,2) substituting this feature in all samples by the feature's minimum/maximum value 3,4) Adding/Substracting one standard deviation to the sample's feature value. Args: @@ -278,7 +277,7 @@ def perturb_continuous_data_extended_one( slice_ = slice(*splits[target_idx : target_idx + 2]) # Use it only if we want to perturb all features in the target dataset - num_features = baseline_dataset.con_shapes[target_idx] + # num_features = baseline_dataset.con_shapes[target_idx] # Change below. # num_features = 10 @@ -394,7 +393,8 @@ def prepare_for_continuous_perturbation( # con_dataset_names=config.data.continuous_names, # target_dataset_name=task_config.target_dataset, # perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), - # index_pert_feat=i,) # Like this, I get only one perturbed dataloader, and the nan and feature masks + # index_pert_feat=i,) # Like this, I get only one perturbed dataloader, + # and the nan and feature masks # logger.debug # dataloaders.append(perturbed_dataloader) diff --git a/src/move/tasks/identify_associations_multiprocess.py b/src/move/tasks/identify_associations_multiprocess.py index ff2ea9bc..cc183d1a 100644 --- a/src/move/tasks/identify_associations_multiprocess.py +++ b/src/move/tasks/identify_associations_multiprocess.py @@ -33,6 +33,7 @@ perturb_continuous_data_extended_one, ) from move.models.vae import VAE +from move.tasks import prepare_for_categorical_perturbation # ! Is this used? from move.visualization.dataset_distributions import ( plot_feature_association_graph, ) @@ -162,7 +163,7 @@ def _bayes_approach_worker(args): continuous_shapes=continuous_shapes, categorical_shapes=categorical_shapes, ) - device = torch.device("cuda" if task_config.model.cuda == True else "cpu") + device = torch.device("cuda" if task_config.model.cuda else "cpu") logger.debug(f"Loading model from {model_path}") model.load_state_dict(torch.load(model_path)) logger.debug(f"Loaded model from {model_path}") @@ -259,13 +260,15 @@ def _bayes_approach_parallel( logger = get_logger(__name__) logger.debug("Inside the bayes_parallel function") - # First, I train or reload the models (number of refits), and save the baseline reconstruction. - # We train and get the reconstruction outside to make sure that we use the same model and use the same + # First, I train or reload the models (number of refits), + # and save the baseline reconstruction. + # We train and get the reconstruction outside to make sure + # that we use the same model and use the same # baseline reconstruction for all the worker functions baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) assert task_config.model is not None - device = torch.device("cuda" if task_config.model.cuda == True else "cpu") + device = torch.device("cuda" if task_config.model.cuda else "cpu") logger.debug("Model moved to device in bayes_approach_parallel") # Train or reload models @@ -594,8 +597,8 @@ def identify_associations_multiprocess(config: MOVEConfig) -> None: 2) Evaluate associations using bayes or ttest approach. 3) Save results. """ - #################### DATA PREPARATION ###################### - ####### Read original data and create perturbed datasets#### + # DATA PREPARATION ###################### + # Read original data and create perturbed datasets #### logger = get_logger(__name__) task_config = cast(IdentifyAssociationsConfig, config.task) @@ -639,7 +642,7 @@ def identify_associations_multiprocess(config: MOVEConfig) -> None: baseline_dataloader = make_dataloader( cat_list, con_list, shuffle=False, batch_size=task_config.batch_size ) - logger.debug(f"Baseline dataloader created") + logger.debug("Baseline dataloader created") # Indentify associations between continuous features: logger.info(f"Perturbing dataset: '{task_config.target_dataset}'") @@ -670,7 +673,7 @@ def identify_associations_multiprocess(config: MOVEConfig) -> None: num_perturbed = baseline_dataset.con_shapes[target_idx] logger.debug(f"# perturbed features: {num_perturbed}") - ################# APPROACH EVALUATION ########################## + # APPROACH EVALUATION ########################## if task_type == "bayes": task_config = cast(IdentifyAssociationsBayesConfig, task_config) @@ -733,7 +736,7 @@ def identify_associations_multiprocess(config: MOVEConfig) -> None: else: raise ValueError() - ###################### RESULTS ################################ + # RESULTS ################################ save_results( config, con_shapes, From 5aa03ffd7631252ca63de5759246777ee93178d4 Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 3 Jul 2024 13:42:55 +0200 Subject: [PATCH 46/94] :bug: format and fix import --- src/move/tasks/analyze_latent_multiprocess.py | 3 ++- src/move/tasks/identify_associations_multiprocess.py | 4 +++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/src/move/tasks/analyze_latent_multiprocess.py b/src/move/tasks/analyze_latent_multiprocess.py index 4a519e56..aeed923e 100644 --- a/src/move/tasks/analyze_latent_multiprocess.py +++ b/src/move/tasks/analyze_latent_multiprocess.py @@ -25,6 +25,7 @@ from move.core.typing import FloatArray from move.data import io from move.data.dataloaders import MOVEDataset, make_dataloader + # from move.data.perturbations import perturb_categorical_data, perturb_continuous_data, from move.data.perturbations import ( perturb_categorical_data_one, @@ -61,7 +62,7 @@ def find_feature_values( dataset_index = _dataset_index except ValueError: continue - break + break # ! why? if dataset_index is not None and feature_index is not None: return ( dataset_index, diff --git a/src/move/tasks/identify_associations_multiprocess.py b/src/move/tasks/identify_associations_multiprocess.py index cc183d1a..7eb63682 100644 --- a/src/move/tasks/identify_associations_multiprocess.py +++ b/src/move/tasks/identify_associations_multiprocess.py @@ -33,7 +33,9 @@ perturb_continuous_data_extended_one, ) from move.models.vae import VAE -from move.tasks import prepare_for_categorical_perturbation # ! Is this used? +from move.tasks.identify_associations import ( + prepare_for_categorical_perturbation, +) # ! Is this used? from move.visualization.dataset_distributions import ( plot_feature_association_graph, ) From 8b06298eabbf908cffb49ac62582d7155fdb8c56 Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 3 Jul 2024 17:01:09 +0200 Subject: [PATCH 47/94] :bug: use perturb_continuous_data_extended from perturbations (as defined on developer branch) - remove debug log statements here - the version in "identify_associations.py" had a hardcorded number of parameters (5): num_features = 5 --- src/move/data/perturbations.py | 16 +--- src/move/tasks/identify_associations.py | 97 +------------------------ 2 files changed, 4 insertions(+), 109 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 12675bcf..b837f735 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -371,13 +371,11 @@ def perturb_continuous_data_extended( datasets. Scaling is done per dataset, not per feature -> slightly different stds feature to feature. """ - logger = get_logger(__name__) - logger.debug("Inside perturb_extended, creating baseline dataset") + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) assert baseline_dataset.con_shapes is not None assert baseline_dataset.con_all is not None - logger.debug("Creating target_ics, splits, and slice") target_idx = con_dataset_names.index(target_dataset_name) # dataset index splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) @@ -387,7 +385,6 @@ def perturb_continuous_data_extended( perturbations_list = [] for i in range(num_features): - logger.debug(f"Getting perturbed dataset for feature {i}") perturbed_con = baseline_dataset.con_all.clone() target_dataset = perturbed_con[:, slice_] # Change the desired feature value by: @@ -403,7 +400,7 @@ def perturb_continuous_data_extended( elif perturbation_type == "minus_std": target_dataset[:, i] -= torch.FloatTensor([std_feat_val_list[i]]) - # perturbations_list.append(target_dataset[:, i].numpy()) + perturbations_list.append(target_dataset[:, i].numpy()) perturbed_dataset = MOVEDataset( baseline_dataset.cat_all, @@ -418,15 +415,6 @@ def perturb_continuous_data_extended( batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) - logger.debug("Finished perturb_continuous_data_extended function") - - # Plot the perturbations for all features, collapsed in one plot: - # if output_subpath is not None: - # fig = plot_value_distributions(np.array(perturbations_list).transpose()) - # fig_path = str( - # output_subpath / f"perturbation_distribution_{target_dataset_name}.png" - # ) - # fig.savefig(fig_path) # Plot the perturbations for all features, collapsed in one plot: if output_subpath is not None: diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 1a52b02a..a172b433 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -25,9 +25,10 @@ from move.core.typing import BoolArray, FloatArray, IntArray from move.data import io from move.data.dataloaders import MOVEDataset, make_dataloader -from move.data.perturbations import ( # perturb_continuous_data_extended, +from move.data.perturbations import ( ContinuousPerturbationType, perturb_categorical_data, + perturb_continuous_data_extended, ) from move.data.preprocessing import feature_stats, one_hot_encode_single from move.models.vae import VAE @@ -42,100 +43,6 @@ CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] -def perturb_continuous_data_extended( - baseline_dataloader: DataLoader, - con_dataset_names: list[str], - target_dataset_name: str, - perturbation_type: ContinuousPerturbationType, - output_subpath: Optional[Path] = None, -) -> list[DataLoader]: - logger = get_logger(__name__) - - """Add perturbations to continuous data. For each feature in the target - dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value - 3,4) Adding/Substracting one standard deviation to the sample's feature value. - - Args: - baseline_dataloader: Baseline dataloader - con_dataset_names: List of continuous dataset names - target_dataset_name: Target continuous dataset to perturb - perturbation_type: 'minimum', 'maximum', 'plus_std' or 'minus_std'. - output_subpath: path where the figure showing the perturbation will be saved - - Returns: - - List of dataloaders containing all perturbed datasets - - Plot of the feature value distribution after the perturbation. Note that - all perturbations are collapsed into one single plot. - - Note: - This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different - stds feature to feature. - """ - logger.debug("Inside perturb_extended, creating baseline dataset") - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.con_shapes is not None - assert baseline_dataset.con_all is not None - - logger.debug("Creating target_ics, splits, and slice") - target_idx = con_dataset_names.index(target_dataset_name) # dataset index - splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - # num_features = baseline_dataset.con_shapes[target_idx] - # CHANGED THIS TO TRY IT. CHANGE LATER - num_features = 5 - logger.debug(f"number of feature to perturb is {num_features}") - dataloaders = [] - # perturbations_list = [] - # Change below. - # num_features = 10 - - for i in range(num_features): - logger.debug(f"Getting perturbed dataset for feature {i}") - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - # Change the desired feature value by: - min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( - target_dataset - ) - if perturbation_type == "minimum": - target_dataset[:, i] = torch.FloatTensor([min_feat_val_list[i]]) - elif perturbation_type == "maximum": - target_dataset[:, i] = torch.FloatTensor([max_feat_val_list[i]]) - elif perturbation_type == "plus_std": - target_dataset[:, i] += torch.FloatTensor([std_feat_val_list[i]]) - elif perturbation_type == "minus_std": - target_dataset[:, i] -= torch.FloatTensor([std_feat_val_list[i]]) - - # perturbations_list.append(target_dataset[:, i].numpy()) - - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - dataloaders.append(perturbed_dataloader) - logger.debug("Finished perturb_continuous_data_extended function") - - # Plot the perturbations for all features, collapsed in one plot: - # if output_subpath is not None: - # fig = plot_value_distributions(np.array(perturbations_list).transpose()) - # fig_path = str( - # output_subpath / f"perturbation_distribution_{target_dataset_name}.png" - # ) - # fig.savefig(fig_path) - - return dataloaders - def _get_task_type( task_config: IdentifyAssociationsConfig, From 6cfd1f804bbb3991151b35d2d5875c5e4db65949 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 4 Jul 2024 10:15:35 +0200 Subject: [PATCH 48/94] :fire: comments and old configurations; format --- src/move/__main__.py | 2 -- src/move/conf/schema.py | 2 -- src/move/tasks/identify_associations.py | 3 +-- 3 files changed, 1 insertion(+), 6 deletions(-) diff --git a/src/move/__main__.py b/src/move/__main__.py index 7c4822a9..f904a3c5 100644 --- a/src/move/__main__.py +++ b/src/move/__main__.py @@ -38,8 +38,6 @@ def main(config: MOVEConfig) -> None: move.tasks.encode_data(config.data) elif issubclass(task_type, TuneModelConfig): move.tasks.tune_model(config) - # the fast version does not calcuate feature importance, - # it is only to look at the reconstruction metrics elif task_type is AnalyzeLatentConfig: if config.task.multiprocess: move.tasks.analyze_latent_multiprocess(config) diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index ee6a6524..4f2323c0 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -134,9 +134,7 @@ class AnalyzeLatentConfig(TaskConfig): feature_names: list[str] = field(default_factory=list) reducer: dict[str, Any] = MISSING - fast: bool = False # Default value is False multiprocess: bool = False - number_al: int = 1 @dataclass diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index a172b433..9991192b 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -3,7 +3,7 @@ from functools import reduce from os.path import exists from pathlib import Path -from typing import Literal, Optional, Sized, Union, cast +from typing import Literal, Sized, Union, cast import hydra import numpy as np @@ -43,7 +43,6 @@ CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] - def _get_task_type( task_config: IdentifyAssociationsConfig, ) -> TaskType: From 82778914e7b2bcfafb8bffbc6a6d9614bf01167c Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 4 Jul 2024 11:07:56 +0200 Subject: [PATCH 49/94] :fire: remove duplicated functionality - use only one version in extra module --- src/move/data/perturbations.py | 10 +-- src/move/tasks/analyze_latent.py | 114 ++----------------------------- 2 files changed, 5 insertions(+), 119 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index b837f735..2e756449 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -20,7 +20,6 @@ ContinuousPerturbationType = Literal["minimum", "maximum", "plus_std", "minus_std"] -# Also in analyze_latent.py def perturb_continuous_data_one( baseline_dataloader: DataLoader, con_dataset_names: list[str], @@ -35,7 +34,7 @@ def perturb_continuous_data_one( baseline_dataloader: Baseline dataloader con_dataset_names: List of continuous dataset names target_dataset_name: Target continuous dataset to perturb - target_value: Target value. In analyze_latent, it will be 0 + target_value: Target value. Returns: One dataloader, with the ith dataset perturbed @@ -49,11 +48,7 @@ def perturb_continuous_data_one( splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) - # num_features = baseline_dataset.con_shapes[target_idx] - # dataloaders = [] i = index_pert_feat - # Instead of the loop, we do it only for one - # for i in range(num_features): perturbed_con = baseline_dataset.con_all.clone() target_dataset = perturbed_con[:, slice_] target_dataset[:, i] = torch.FloatTensor([target_value]) @@ -103,11 +98,8 @@ def perturb_categorical_data_one( slice_ = slice(*splits[target_idx : target_idx + 2]) target_shape = baseline_dataset.cat_shapes[target_idx] - # num_features = target_shape[0] i = index_pert_feat - # dataloaders = [] - # for i in range(num_features): perturbed_cat = baseline_dataset.cat_all.clone() target_dataset = perturbed_cat[:, slice_].view( baseline_dataset.num_samples, *target_shape diff --git a/src/move/tasks/analyze_latent.py b/src/move/tasks/analyze_latent.py index 5de71d95..fc4350d4 100644 --- a/src/move/tasks/analyze_latent.py +++ b/src/move/tasks/analyze_latent.py @@ -12,7 +12,6 @@ import pandas as pd import torch from sklearn.base import TransformerMixin -from torch.utils.data import DataLoader import move.visualization as viz from move.analysis.metrics import ( @@ -24,119 +23,14 @@ from move.core.typing import FloatArray from move.data import io from move.data.dataloaders import MOVEDataset, make_dataloader +from move.data.perturbations import ( + perturb_categorical_data_one, + perturb_continuous_data_one, +) from move.data.preprocessing import one_hot_encode_single from move.models.vae import VAE from move.training.training_loop import TrainingLoopOutput -# Define perturb_continuous_data_one (not extended) - - -def perturb_continuous_data_one( - baseline_dataloader: DataLoader, - con_dataset_names: list[str], - target_dataset_name: str, - target_value: float, - index_pert_feat: int, # Index of the datasetto perturb -) -> DataLoader: # change list(DataLoader) to just one DataLoader - """Add perturbations to continuous data. For each feature in the target - dataset, change its value to target. - - Args: - baseline_dataloader: Baseline dataloader - con_dataset_names: List of continuous dataset names - target_dataset_name: Target continuous dataset to perturb - target_value: Target value - - Returns: - One dataloader, with the ith dataset perturbed - """ - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.con_shapes is not None - assert baseline_dataset.con_all is not None - - target_idx = con_dataset_names.index(target_dataset_name) - splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - # num_features = baseline_dataset.con_shapes[target_idx] - # dataloaders = [] - i = index_pert_feat - # Instead of the loop, we do it only for one - # for i in range(num_features): - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - target_dataset[:, i] = torch.FloatTensor([target_value]) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - - return perturbed_dataloader - - -def perturb_categorical_data_one( - baseline_dataloader: DataLoader, - cat_dataset_names: list[str], - target_dataset_name: str, - target_value: np.ndarray, - index_pert_feat: int, -) -> DataLoader: - """Add perturbations to categorical data. For each feature in the target - dataset, change its value to target. - - Args: - baseline_dataloader: Baseline dataloader - cat_dataset_names: List of categorical dataset names - target_dataset_name: Target categorical dataset to perturb - target_value: Target value - - Returns: - List of dataloaders containing all perturbed datasets - """ - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.cat_shapes is not None - assert baseline_dataset.cat_all is not None - - target_idx = cat_dataset_names.index(target_dataset_name) - splits = np.cumsum( - [0] + [int.__mul__(*shape) for shape in baseline_dataset.cat_shapes] - ) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - target_shape = baseline_dataset.cat_shapes[target_idx] - # num_features = target_shape[0] # CHANGE - - i = index_pert_feat - # dataloaders = [] - # for i in range(num_features): - perturbed_cat = baseline_dataset.cat_all.clone() - target_dataset = perturbed_cat[:, slice_].view( - baseline_dataset.num_samples, *target_shape - ) - target_dataset[:, i, :] = torch.FloatTensor(target_value) - perturbed_dataset = MOVEDataset( - perturbed_cat, - baseline_dataset.con_all, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - - return perturbed_dataloader - def find_feature_values( feature_name: str, From 44802ebc9a23521227d00dba0760348a54097815 Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 4 Jul 2024 12:34:17 +0200 Subject: [PATCH 50/94] :sparkles: integrate multiprocessing into analyze_latent.py - add test with multiprocessing activated - unify modules into one (with the option for both approaches) --- .github/workflows/release.yaml | 20 + src/move/__main__.py | 6 +- src/move/tasks/__init__.py | 4 +- src/move/tasks/analyze_latent.py | 227 +++++++-- src/move/tasks/analyze_latent_multiprocess.py | 435 ------------------ 5 files changed, 214 insertions(+), 478 deletions(-) delete mode 100644 src/move/tasks/analyze_latent_multiprocess.py diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 84c077c5..c40506e0 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -54,6 +54,26 @@ jobs: cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=20 + run-tutorial-w-multiprocessing: + name: Run tutorial - random_small + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install dependencies + run: pip install . + - name: Prepare tutorial data + run: | + cd tutorial + move-dl data=random_small task=encode_data --cfg job + move-dl data=random_small task=encode_data + - name: Train model and analyze latent space + run: | + cd tutorial + move-dl data=random_small task=random_small__latent task.multiprocess=true --cfg job + move-dl data=random_small task=random_small__latent task.multiprocess=true run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest diff --git a/src/move/__main__.py b/src/move/__main__.py index f904a3c5..ec6fb666 100644 --- a/src/move/__main__.py +++ b/src/move/__main__.py @@ -4,7 +4,6 @@ from omegaconf import OmegaConf import move.tasks -import move.tasks.analyze_latent_multiprocess import move.tasks.identify_associations_multiprocess from move import HYDRA_VERSION_BASE from move.conf.schema import ( @@ -39,10 +38,7 @@ def main(config: MOVEConfig) -> None: elif issubclass(task_type, TuneModelConfig): move.tasks.tune_model(config) elif task_type is AnalyzeLatentConfig: - if config.task.multiprocess: - move.tasks.analyze_latent_multiprocess(config) - else: - move.tasks.analyze_latent(config) + move.tasks.analyze_latent(config) elif issubclass(task_type, IdentifyAssociationsConfig): if config.task.multiprocess: move.tasks.identify_associations_multiprocess(config) diff --git a/src/move/tasks/__init__.py b/src/move/tasks/__init__.py index 7e23e9dd..576c4e64 100644 --- a/src/move/tasks/__init__.py +++ b/src/move/tasks/__init__.py @@ -4,14 +4,12 @@ "identify_associations", "tune_model", "identify_associations_multiprocess", - "analyze_latent_multiprocess", ] from move.tasks.analyze_latent import analyze_latent from move.tasks.encode_data import encode_data from move.tasks.identify_associations import identify_associations -from move.tasks.tune_model import tune_model from move.tasks.identify_associations_multiprocess import ( identify_associations_multiprocess, ) -from move.tasks.analyze_latent_multiprocess import analyze_latent_multiprocess +from move.tasks.tune_model import tune_model diff --git a/src/move/tasks/analyze_latent.py b/src/move/tasks/analyze_latent.py index fc4350d4..53481a42 100644 --- a/src/move/tasks/analyze_latent.py +++ b/src/move/tasks/analyze_latent.py @@ -11,7 +11,9 @@ import numpy as np import pandas as pd import torch +import torch.multiprocessing from sklearn.base import TransformerMixin +from torch.multiprocessing import Pool import move.visualization as viz from move.analysis.metrics import ( @@ -51,17 +53,18 @@ def find_feature_values( Tuple containing (1) index of dataset containing feature and (2) values corresponding to the feature """ - _dataset_index, feature_index = [None] * 2 - for _dataset_index, feature_names in enumerate(feature_names_lists): + dataset_index, feature_index = [None] * 2 + for _dataset_index, _feature_names in enumerate(feature_names_lists): try: - feature_index = feature_names.index(feature_name) + feature_index = _feature_names.index(feature_name) + dataset_index = _dataset_index except ValueError: continue break - if _dataset_index is not None and feature_index is not None: + if dataset_index is not None and feature_index is not None: return ( - _dataset_index, - np.take(feature_values[_dataset_index], feature_index, axis=1), + dataset_index, + np.take(feature_values[dataset_index], feature_index, axis=1), ) raise KeyError(f"Feature '{feature_name}' not in any dataset.") @@ -71,6 +74,97 @@ def _validate_task_config(task_config: AnalyzeLatentConfig) -> None: raise ValueError("Reducer class not specified properly.") +def _categorical_importance_worker(args): + """ + Worker function to calculate the importance of categorical features + """ + torch.set_num_threads(1) + + logger = get_logger(__name__) + + ( + test_dataloader, + categorical_names, + dataset_name, + na_value, + index_pert_feat, + num_features, + model, + z, + ) = args + + # Diff will store the differences between z and + # z_perturb for the perturbed feature index_pert_feat + diff = np.empty((num_features)) + + logger.debug(f"Perturbing feature {index_pert_feat} for {dataset_name}") + dataloader = perturb_categorical_data_one( + test_dataloader, + categorical_names, + dataset_name, + na_value, + index_pert_feat, + ) + logger.debug( + "Perturbation completed. Projecting perturbation on latent space for " + f"feature {index_pert_feat}, {dataset_name}" + ) + z_perturb = model.project(dataloader) + logger.debug(f"Calculating diff for feature {index_pert_feat}, {dataset_name}") + diff = np.sum(z_perturb - z, axis=1) + + logger.debug( + "Finished catagorical worker function for " + f"feature {index_pert_feat}, {dataset_name}" + ) + return index_pert_feat, diff + + +def _continuous_importance_worker(args): + """ + Worker function to calculate the importance of continuous features + """ + torch.set_num_threads(1) + + logger = get_logger(__name__) + + ( + test_dataloader, + continuous_names, + dataset_name, + index_pert_feat, + num_features, + model, + z, + ) = args + + # Diff will store the differences between z and z_perturb for the perturbed + # feature index_pert_feat + diff = np.empty((num_features)) + + logger.debug(f"Perturbing feature {index_pert_feat} for {dataset_name}") + dataloader = perturb_continuous_data_one( + test_dataloader, + continuous_names, + dataset_name, + 0.0, + index_pert_feat, + ) + logger.debug( + "Perturbation completed. Projecting perturbation on latent space for " + f"feature {index_pert_feat}, {dataset_name}" + ) + z_perturb = model.project(dataloader) + logger.debug(f"Calculating diff for feature {index_pert_feat}, {dataset_name}") + diff = np.sum(z_perturb - z, axis=1) + + logger.debug( + "Finished continuous worker function for " + f"feature {index_pert_feat}, {dataset_name}" + ) + return index_pert_feat, diff + + def analyze_latent(config: MOVEConfig) -> None: """Train one model to inspect its latent space projections.""" @@ -110,9 +204,10 @@ def analyze_latent(config: MOVEConfig) -> None: logger.debug(f"Model: {model}") + # If we already have a model, we reload it. Otherwise, we train it. model_path = output_path / "model.pt" if model_path.exists(): - logger.debug("Re-loading model") + logger.debug(f"Re-loading model from {model_path}") model.load_state_dict(torch.load(model_path)) model.to(device) else: @@ -248,23 +343,49 @@ def analyze_latent(config: MOVEConfig) -> None: z = model.project(test_dataloader) diffs = np.empty((num_samples, num_features)) - j = 0 # Index to keep count of the perturbed feature we are in + if config.task.multiprocess: + args = [ + ( + test_dataloader, + config.data.categorical_names, + dataset_name, + na_value, + index_pert_feat, + num_features, + model, + z, + ) + for index_pert_feat in range(num_features) + ] - for index_pert_feat in range(num_features): - dataloader = perturb_categorical_data_one( - test_dataloader, - config.data.categorical_names, - dataset_name, - na_value, - index_pert_feat, - ) - # We calculate the difference for each of the perturbed features, - # and store it in an object + with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: + logger.debug("Inside the pool loop for categorical features") + # Map worker function to arguments + # We get the bayes_k matrix, filled for all the perturbed features + results = pool.map(_categorical_importance_worker, args) + + # Unpack results + for j, diff in results: + diffs[:, j] = diff - z_perturb = model.project(dataloader) - diffs[:, j] = np.sum(z_perturb - z, axis=1) + else: + j = 0 # Index to keep count of the perturbed feature we are in + + for index_pert_feat in range(num_features): + dataloader = perturb_categorical_data_one( + test_dataloader, + config.data.categorical_names, + dataset_name, + na_value, + index_pert_feat, + ) + # We calculate the difference for each of the perturbed features, + # and store it in an object + + z_perturb = model.project(dataloader) + diffs[:, j] = np.sum(z_perturb - z, axis=1) - j = j + 1 # Increase j for the next iteration + j = j + 1 # Increase j for the next iteration feature_mapping = { str(code): category for category, code in mappings[dataset_name].items() @@ -276,6 +397,10 @@ def analyze_latent(config: MOVEConfig) -> None: fig.savefig(fig_path, bbox_inches="tight") fig_df = pd.DataFrame(diffs, columns=cat_names[i], index=df_index) fig_df.to_csv(output_path / f"feat_importance_{dataset_name}.tsv", sep="\t") + logger.info( + "Analysis for categorical features completed. " + "Starting analysis for continuous features" + ) # NOW, THE SAME BUT FOR CONTINUOUS DATA @@ -295,25 +420,57 @@ def analyze_latent(config: MOVEConfig) -> None: # We create one diff per dataset, to not store all of them in memory z = model.project(test_dataloader) diffs = np.empty((num_samples, num_features)) - # Index to check the number of perturbed feature we are in now - j = 0 - - for index_pert_feat in range(num_features): - dataloader = perturb_continuous_data_one( - test_dataloader, - config.data.continuous_names, - dataset_name, - 0.0, - index_pert_feat, - ) - z_perturb = model.project(dataloader) - diffs[:, j] = np.sum(z_perturb - z, axis=1) + if config.task.multiprocess: + args = [ + ( + test_dataloader, + config.data.continuous_names, + dataset_name, + index_pert_feat, + num_features, + model, + z, + ) + for index_pert_feat in range(num_features) + ] + + with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: + logger.debug("Inside the pool loop for continuous features") + # Map worker function to arguments + # We get the bayes_k matrix, filled for all the perturbed features + results = pool.map(_continuous_importance_worker, args) + + # Unpack results + + logger.debug(f"Unpacking results for dataset {dataset_name}") + for j, diff in results: + diffs[:, j] = diff + + logger.debug(f"Generating plot for {dataset_name}") - j = j + 1 + else: + # Index to check the number of perturbed feature we are in now + j = 0 + + for index_pert_feat in range(num_features): + dataloader = perturb_continuous_data_one( + test_dataloader, + config.data.continuous_names, + dataset_name, + 0.0, + index_pert_feat, + ) + + z_perturb = model.project(dataloader) + diffs[:, j] = np.sum(z_perturb - z, axis=1) + + j = j + 1 fig = viz.plot_continuous_feature_importance(diffs, con_list[i], con_names[i]) fig_path = str(output_path / f"feat_importance_{dataset_name}.png") fig.savefig(fig_path, bbox_inches="tight") fig_df = pd.DataFrame(diffs, columns=con_names[i], index=df_index) fig_df.to_csv(output_path / f"feat_importance_{dataset_name}.tsv", sep="\t") + + logger.info("Continuous features finished for all datasets") diff --git a/src/move/tasks/analyze_latent_multiprocess.py b/src/move/tasks/analyze_latent_multiprocess.py deleted file mode 100644 index aeed923e..00000000 --- a/src/move/tasks/analyze_latent_multiprocess.py +++ /dev/null @@ -1,435 +0,0 @@ -# Now it is analyze_latent_efficient.py - - -__all__ = ["analyze_latent_multiprocess"] - -import re -from pathlib import Path -from typing import Sized, cast - -import hydra -import numpy as np -import pandas as pd -import torch -import torch.multiprocessing -from sklearn.base import TransformerMixin -from torch.multiprocessing import Pool - -import move.visualization as viz -from move.analysis.metrics import ( - calculate_accuracy, - calculate_cosine_similarity, -) -from move.conf.schema import AnalyzeLatentConfig, MOVEConfig -from move.core.logging import get_logger -from move.core.typing import FloatArray -from move.data import io -from move.data.dataloaders import MOVEDataset, make_dataloader - -# from move.data.perturbations import perturb_categorical_data, perturb_continuous_data, -from move.data.perturbations import ( - perturb_categorical_data_one, - perturb_continuous_data_one, -) -from move.data.preprocessing import one_hot_encode_single -from move.models.vae import VAE -from move.training.training_loop import TrainingLoopOutput - - -def find_feature_values( - feature_name: str, - feature_names_lists: list[list[str]], - feature_values: list[FloatArray], -) -> tuple[int, FloatArray]: - """Look for the feature in the list of datasets and returns its values. - - Args: - feature_name: Look-up key - feature_names_lists: List of lists with feature names for each dataset - feature_values: List of data arrays, each representing a dataset - - Raises: - KeyError: If feature does not exist in any dataset - - Returns: - Tuple containing (1) index of dataset containing feature and (2) - values corresponding to the feature - """ - dataset_index, feature_index = [None] * 2 - for _dataset_index, _feature_names in enumerate(feature_names_lists): - try: - feature_index = _feature_names.index(feature_name) - dataset_index = _dataset_index - except ValueError: - continue - break # ! why? - if dataset_index is not None and feature_index is not None: - return ( - dataset_index, - np.take(feature_values[dataset_index], feature_index, axis=1), - ) - raise KeyError(f"Feature '{feature_name}' not in any dataset.") - - -def _validate_task_config(task_config: AnalyzeLatentConfig) -> None: - if "_target_" not in task_config.reducer: - raise ValueError("Reducer class not specified properly.") - - -def _categorical_importance_worker(args): - """ - Worker function to calculate the importance of categorical features - """ - torch.set_num_threads(1) - - logger = get_logger(__name__) - - ( - test_dataloader, - categorical_names, - dataset_name, - na_value, - index_pert_feat, - num_features, - model, - z, - ) = args - - # Diff will store the differences between z and - # z_perturb for the perturbed feature index_pert_feat - diff = np.empty((num_features)) - - logger.debug(f"Perturbing feature {index_pert_feat} for {dataset_name}") - dataloader = perturb_categorical_data_one( - test_dataloader, - categorical_names, - dataset_name, - na_value, - index_pert_feat, - ) - logger.debug( - "Perturbation completed. Projecting perturbation on latent space for " - f"feature {index_pert_feat}, {dataset_name}" - ) - z_perturb = model.project(dataloader) - logger.debug(f"Calculating diff for feature {index_pert_feat}, {dataset_name}") - diff = np.sum(z_perturb - z, axis=1) - - logger.debug( - "Finished catagorical worker function for " - f"feature {index_pert_feat}, {dataset_name}" - ) - return index_pert_feat, diff - - -def _continuous_importance_worker(args): - """ - Worker function to calculate the importance of continuous features - """ - torch.set_num_threads(1) - - logger = get_logger(__name__) - - ( - test_dataloader, - continuous_names, - dataset_name, - index_pert_feat, - num_features, - model, - z, - ) = args - - # Diff will store the differences between z and z_perturb for the perturbed - # feature index_pert_feat - diff = np.empty((num_features)) - - logger.debug(f"Perturbing feature {index_pert_feat} for {dataset_name}") - dataloader = perturb_continuous_data_one( - test_dataloader, - continuous_names, - dataset_name, - 0.0, - index_pert_feat, - ) - logger.debug( - "Perturbation completed. Projecting perturbation on latent space for " - f"feature {index_pert_feat}, {dataset_name}" - ) - z_perturb = model.project(dataloader) - logger.debug(f"Calculating diff for feature {index_pert_feat}, {dataset_name}") - diff = np.sum(z_perturb - z, axis=1) - - logger.debug( - "Finished continuous worker function for " - f"feature {index_pert_feat}, {dataset_name}" - ) - return index_pert_feat, diff - - -def analyze_latent_multiprocess(config: MOVEConfig) -> None: - """Train one model to inspect its latent space projections.""" - - logger = get_logger(__name__) - logger.info("Beginning task: analyze latent space") - task_config = cast(AnalyzeLatentConfig, config.task) - _validate_task_config(task_config) - - raw_data_path = Path(config.data.raw_data_path) - interim_path = Path(config.data.interim_data_path) - output_path = Path(config.data.results_path) / "latent_space_multiprocess" - output_path.mkdir(exist_ok=True, parents=True) - - logger.debug("Reading data") - sample_names = io.read_names(raw_data_path / f"{config.data.sample_names}.txt") - cat_list, cat_names, con_list, con_names = io.load_preprocessed_data( - interim_path, - config.data.categorical_names, - config.data.continuous_names, - ) - test_dataloader = make_dataloader( - cat_list, - con_list, - shuffle=False, - batch_size=task_config.batch_size, - ) - test_dataset = cast(MOVEDataset, test_dataloader.dataset) - df_index = pd.Index(sample_names, name="sample") - - assert task_config.model is not None - device = torch.device("cuda" if task_config.model.cuda else "cpu") - model: VAE = hydra.utils.instantiate( - task_config.model, - continuous_shapes=test_dataset.con_shapes, - categorical_shapes=test_dataset.cat_shapes, - ) - - logger.debug(f"Model: {model}") - - # If we already have a model, we reload it. Otherwise, we train it. - model_path = output_path / "model.pt" - if model_path.exists(): - logger.debug(f"Re-loading model from {model_path}") - model.load_state_dict(torch.load(model_path)) - model.to(device) - else: - logger.debug("Training model") - - model.to(device) - train_dataloader = make_dataloader( - cat_list, - con_list, - shuffle=True, - batch_size=task_config.batch_size, - drop_last=True, - ) - output: TrainingLoopOutput = hydra.utils.call( - task_config.training_loop, - model=model, - train_dataloader=train_dataloader, - ) - losses = output[:-1] - torch.save(model.state_dict(), model_path) - logger.info("Generating visualizations") - logger.debug("Generating plot: loss curves") - fig = viz.plot_loss_curves(losses) - fig_path = str(output_path / "loss_curve.png") - fig.savefig(fig_path, bbox_inches="tight") - fig_df = pd.DataFrame(dict(zip(viz.LOSS_LABELS, losses))) - fig_df.index.name = "epoch" - fig_df.to_csv(output_path / "loss_curve.tsv", sep="\t") - - model.eval() - - logger.info("Projecting into latent space") - latent_space = model.project(test_dataloader) - reducer: TransformerMixin = hydra.utils.instantiate(task_config.reducer) - embedding = reducer.fit_transform(latent_space) - - mappings_path = interim_path / "mappings.json" - if mappings_path.exists(): - mappings = io.load_mappings(mappings_path) - else: - mappings = {} - - fig_df = pd.DataFrame( - np.take(embedding, [0, 1], axis=1), - columns=["dim0", "dim1"], - index=df_index, - ) - - for feature_name in task_config.feature_names: - logger.debug(f"Generating plot: latent space + '{feature_name}'") - is_categorical = False - try: - dataset_index, feature_values = find_feature_values( - feature_name, cat_names, cat_list - ) - is_categorical = True - except KeyError: - try: - dataset_index, feature_values = find_feature_values( - feature_name, con_names, con_list - ) - except KeyError: - logger.warning(f"Feature '{feature_name}' not found in any dataset.") - continue - - if is_categorical: - # Convert one-hot encoding to category codes - is_nan = feature_values.sum(axis=1) == 0 - feature_values = np.argmax(feature_values, axis=1) - - dataset_name = config.data.categorical_names[dataset_index] - feature_mapping = { - str(code): category for category, code in mappings[dataset_name].items() - } - fig = viz.plot_latent_space_with_cat( - embedding, - feature_name, - feature_values, - feature_mapping, - is_nan, - ) - fig_df[feature_name] = np.where(is_nan, np.nan, feature_values) - else: - feature_values = feature_values - fig = viz.plot_latent_space_with_con( - embedding, feature_name, feature_values - ) - fig_df[feature_name] = np.where(feature_values == 0, np.nan, feature_values) - - # Remove non-alpha characters - safe_feature_name = re.sub(r"[^\w\s]", "", feature_name) - fig_path = str(output_path / f"latent_space_{safe_feature_name}.png") - fig.savefig(fig_path, bbox_inches="tight") - - fig_df.to_csv(output_path / "latent_space.tsv", sep="\t") - - logger.info("Reconstructing") - cat_recons, con_recons = model.reconstruct(test_dataloader) - con_recons = np.split(con_recons, np.cumsum(model.continuous_shapes[:-1]), axis=1) - logger.info("Computing reconstruction metrics") - scores = [] - labels = config.data.categorical_names + config.data.continuous_names - for cat, cat_recon in zip(cat_list, cat_recons): - accuracy = calculate_accuracy(cat, cat_recon) - scores.append(accuracy) - for con, con_recon in zip(con_list, con_recons): - cosine_sim = calculate_cosine_similarity(con, con_recon) - scores.append(cosine_sim) - - logger.debug("Generating plot: reconstruction metrics") - - plot_scores = [np.ma.compressed(np.ma.masked_equal(each, 0)) for each in scores] - fig = viz.plot_metrics_boxplot(plot_scores, labels) - fig_path = str(output_path / "reconstruction_metrics.png") - fig.savefig(fig_path, bbox_inches="tight") - fig_df = pd.DataFrame(dict(zip(labels, scores)), index=df_index) - fig_df.to_csv(output_path / "reconstruction_metrics.tsv", sep="\t") - - ################################################################################# - # PARALLELIZATION IN THIS PART - ################################################################################# - logger.info("Computing feature importance") - num_samples = len(cast(Sized, test_dataloader.sampler)) - - # START WITH IMPORTANCE FOR CATEGORICAL FEATURES. - for i, dataset_name in enumerate(config.data.categorical_names): - logger.debug(f"Generating plot: feature importance '{dataset_name}'") - na_value = one_hot_encode_single(mappings[dataset_name], None) - cat_dataset_names = config.data.categorical_names - target_idx = cat_dataset_names.index(dataset_name) - target_shape = test_dataset.cat_shapes[target_idx] - num_features = target_shape[0] # Number of features in the current dataset - - # We will use this inside the loop that iterates over all features: - # We create one diff per dataset, to not store all of them in memory - z = model.project(test_dataloader) - diffs = np.empty((num_samples, num_features)) - - args = [ - ( - test_dataloader, - config.data.categorical_names, - dataset_name, - na_value, - index_pert_feat, - num_features, - model, - z, - ) - for index_pert_feat in range(num_features) - ] - - with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: - logger.debug("Inside the pool loop for categorical features") - # Map worker function to arguments - # We get the bayes_k matrix, filled for all the perturbed features - results = pool.map(_categorical_importance_worker, args) - - # Unpack results - for j, diff in results: - diffs[:, j] = diff - - feature_mapping = { - str(code): category for category, code in mappings[dataset_name].items() - } - fig = viz.plot_categorical_feature_importance( - diffs, cat_list[i], cat_names[i], feature_mapping - ) - fig_path = str(output_path / f"feat_importance_{dataset_name}.png") - fig.savefig(fig_path, bbox_inches="tight") - fig_df = pd.DataFrame(diffs, columns=cat_names[i], index=df_index) - fig_df.to_csv(output_path / f"feat_importance_{dataset_name}.tsv", sep="\t") - - logger.info( - "Pool multiprocess for categorical features completed. " - "Starting pool process for continuous features" - ) - - # NOW, THE SAME BUT FOR CONTINUOUS DATA - for i, dataset_name in enumerate(config.data.continuous_names): - logger.debug(f"Generating plot: feature importance '{dataset_name}'") - con_dataset_names = config.data.continuous_names - target_idx = con_dataset_names.index(dataset_name) - num_features = test_dataset.con_shapes[target_idx] - - # We will use this inside the loop that iterates over all features: - # We create one diff per dataset, to not store all of them in memory - z = model.project(test_dataloader) - diffs = np.empty((num_samples, num_features)) - - args = [ - ( - test_dataloader, - config.data.continuous_names, - dataset_name, - index_pert_feat, - num_features, - model, - z, - ) - for index_pert_feat in range(num_features) - ] - - with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: - logger.debug("Inside the pool loop for continuous features") - # Map worker function to arguments - # We get the bayes_k matrix, filled for all the perturbed features - results = pool.map(_continuous_importance_worker, args) - - # Unpack results - - logger.debug(f"Unpacking results for dataset {dataset_name}") - for j, diff in results: - diffs[:, j] = diff - - logger.debug(f"Generating plot for {dataset_name}") - fig = viz.plot_continuous_feature_importance(diffs, con_list[i], con_names[i]) - fig_path = str(output_path / f"feat_importance_{dataset_name}.png") - fig.savefig(fig_path, bbox_inches="tight") - fig_df = pd.DataFrame(diffs, columns=con_names[i], index=df_index) - fig_df.to_csv(output_path / f"feat_importance_{dataset_name}.tsv", sep="\t") - - logger.info("Continuous features finished for all datasets") From f64d7799051a77f9b23b44fca3abd7f62834432c Mon Sep 17 00:00:00 2001 From: Henry Date: Thu, 4 Jul 2024 15:43:25 +0200 Subject: [PATCH 51/94] :sparkles: merge multiprocessing bayes factors into identify_associations - add possibility to multiprocess to itdentify associations --- .github/workflows/release.yaml | 9 +- src/move/__main__.py | 6 +- src/move/data/perturbations.py | 4 - src/move/tasks/__init__.py | 4 - src/move/tasks/identify_associations.py | 556 +++++++++---- .../identify_associations_multiprocess.py | 765 ------------------ .../random_continuous__id_assoc_bayes.yaml | 2 + 7 files changed, 421 insertions(+), 925 deletions(-) delete mode 100644 src/move/tasks/identify_associations_multiprocess.py diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index c40506e0..805ca731 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -55,7 +55,7 @@ jobs: move-dl data=random_small task=random_small__id_assoc_bayes --cfg job move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=20 run-tutorial-w-multiprocessing: - name: Run tutorial - random_small + name: Run - random_small - multiprocess runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -69,11 +69,16 @@ jobs: cd tutorial move-dl data=random_small task=encode_data --cfg job move-dl data=random_small task=encode_data - - name: Train model and analyze latent space + - name: Train model and analyze latent space - multiprocess run: | cd tutorial move-dl data=random_small task=random_small__latent task.multiprocess=true --cfg job move-dl data=random_small task=random_small__latent task.multiprocess=true + - name: Identify associations - bayes factors - multiprocess + run: | + cd tutorial + move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job + move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=30 task.num_refits=20 run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest diff --git a/src/move/__main__.py b/src/move/__main__.py index ec6fb666..485e5bac 100644 --- a/src/move/__main__.py +++ b/src/move/__main__.py @@ -4,7 +4,6 @@ from omegaconf import OmegaConf import move.tasks -import move.tasks.identify_associations_multiprocess from move import HYDRA_VERSION_BASE from move.conf.schema import ( AnalyzeLatentConfig, @@ -40,10 +39,7 @@ def main(config: MOVEConfig) -> None: elif task_type is AnalyzeLatentConfig: move.tasks.analyze_latent(config) elif issubclass(task_type, IdentifyAssociationsConfig): - if config.task.multiprocess: - move.tasks.identify_associations_multiprocess(config) - else: - move.tasks.identify_associations(config) + move.tasks.identify_associations(config) else: raise ValueError("Unsupported type of task.") diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 2e756449..d8567f8a 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -123,8 +123,6 @@ def perturb_categorical_data_one( # We will keep the input almost the same, to make everything easier # However, I have to introduce a variable that allows me to index the specific # dataloader I want to create (index_pert_feat) -# And I eliminate the output directory, an image is not generated now -# (too many features) def perturb_continuous_data_extended_one( baseline_dataloader: DataLoader, con_dataset_names: list[str], @@ -202,8 +200,6 @@ def perturb_continuous_data_extended_one( [std_feat_val_list[index_pert_feat]] ) logger.debug(f"Perturbation succesful for feature {index_pert_feat}") - # We used this for a plot, so no need to create it now - # perturbations_list.append(target_dataset[:, i].numpy()) logger.debug( f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" diff --git a/src/move/tasks/__init__.py b/src/move/tasks/__init__.py index 576c4e64..324027c9 100644 --- a/src/move/tasks/__init__.py +++ b/src/move/tasks/__init__.py @@ -3,13 +3,9 @@ "encode_data", "identify_associations", "tune_model", - "identify_associations_multiprocess", ] from move.tasks.analyze_latent import analyze_latent from move.tasks.encode_data import encode_data from move.tasks.identify_associations import identify_associations -from move.tasks.identify_associations_multiprocess import ( - identify_associations_multiprocess, -) from move.tasks.tune_model import tune_model diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 9991192b..cf31dd47 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -9,8 +9,10 @@ import numpy as np import pandas as pd import torch +import torch.multiprocessing from omegaconf import OmegaConf from scipy.stats import ks_2samp, pearsonr # type: ignore +from torch.multiprocessing import Pool from torch.utils.data import DataLoader from move.analysis.metrics import get_2nd_order_polynomial @@ -29,8 +31,9 @@ ContinuousPerturbationType, perturb_categorical_data, perturb_continuous_data_extended, + perturb_continuous_data_extended_one, ) -from move.data.preprocessing import feature_stats, one_hot_encode_single +from move.data.preprocessing import one_hot_encode_single from move.models.vae import VAE from move.visualization.dataset_distributions import ( plot_correlations, @@ -39,7 +42,11 @@ plot_reconstruction_movement, ) +# We can do three types of statistical tests. Multiprocessing is only implemented +# for bayes at the moment TaskType = Literal["bayes", "ttest", "ks"] + +# Possible values for continuous pertrubation CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] @@ -132,122 +139,6 @@ def prepare_for_categorical_perturbation( ) -def perturb_continuous_data_extended_one( - # We will keep the input almost the same, - # to make everything easier - # However, I have to introduce a variable that allows me to index the specific - # dataloader I want to create (index_pert_feat) - # And I eliminate the output directory, because I am not going to save any image - baseline_dataloader: DataLoader, - con_dataset_names: list[str], - target_dataset_name: str, - perturbation_type: ContinuousPerturbationType, - index_pert_feat: int, -) -> ( - DataLoader -): # But we change the output from list[DataLoader] to just one DataLoader - logger = get_logger(__name__) - """Add perturbations to continuous data. For each feature in the target - dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value - 3,4) Adding/Substracting one standard deviation to the sample's feature value. - - Args: - baseline_dataloader: Baseline dataloader - con_dataset_names: List of continuous dataset names - target_dataset_name: Target continuous dataset to perturb - perturbation_type: 'minimum', 'maximum', 'plus_std' or 'minus_std'. - #output_subpath: path where the figure showing the perturbation will be saved - index_pert_feat: Index we want to perturb - - Returns: - - Dataloader with the ith feature (index_pert_feat) perturbed. - - Plot of the feature value distribution after the perturbation. Note that - all perturbations are collapsed into one single plot. - - Note: - This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different - stds feature to feature. - """ - logger.debug( - f"Inside perturb_continuous_data_extended_one for feature {index_pert_feat}" - ) - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.con_shapes is not None - assert baseline_dataset.con_all is not None - - target_idx = con_dataset_names.index(target_dataset_name) # dataset index - splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - # Use it only if we want to perturb all features in the target dataset - # num_features = baseline_dataset.con_shapes[target_idx] - # Change below. - # num_features = 10 - - # Now, instead of the for loop that iterates over all the features we want to - # perturb, we do it only for one feature, the one indicated in index_pert_feat - - # for i in range(num_features): - logger.debug(f"Setting up perturbed_con for feature {index_pert_feat}") - - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - - logger.debug( - f"Changing to desired perturbation value for feature {index_pert_feat}" - ) - # Change the desired feature value by: - min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( - target_dataset - ) - if perturbation_type == "minimum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [min_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "maximum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [max_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "plus_std": - target_dataset[:, index_pert_feat] += torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "minus_std": - target_dataset[:, index_pert_feat] -= torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - logger.debug(f"Perturbation succesful for feature {index_pert_feat}") - # We used this for a plot I have removed, so no need to use it - # perturbations_list.append(target_dataset[:, i].numpy()) - - logger.debug( - f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" - ) - - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - # dataloaders.append(perturbed_dataloader) - - logger.debug( - f"Finished perturb_continuous_data_extended_one for feature {index_pert_feat}" - ) - - return perturbed_dataloader - - def prepare_for_continuous_perturbation( config: MOVEConfig, output_subpath: Path, @@ -291,19 +182,6 @@ def prepare_for_continuous_perturbation( ) dataloaders.append(baseline_dataloader) - # dataloaders =[] - - # for i in range(5): - # perturbed_dataloader = perturb_continuous_data_extended_one( - # baseline_dataloader=baseline_dataloader, - # con_dataset_names=config.data.continuous_names, - # target_dataset_name=task_config.target_dataset, - # perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), - # index_pert_feat=i,) # Like this, I get only one perturbed dataloader, - # and the nan and feature masks - # logger.debug - - # dataloaders.append(perturbed_dataloader) logger.debug(f"Dataloaders length is {len(dataloaders)}") baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) @@ -317,6 +195,371 @@ def prepare_for_continuous_perturbation( return (dataloaders, nan_mask, feature_mask) +def _bayes_approach_worker(args): + """ + Worker function to calculate mean differences and Bayes factors for one feature. + """ + # Set the number of threads available: + # VERY IMPORTANT, TO AVOID CPU OVERSUBSCRIPTION + torch.set_num_threads(1) + + # Unpack arguments. + ( + config, + task_config, + baseline_dataloader, + num_samples, + num_continuous, + i, + models_path, + continuous_shapes, + categorical_shapes, + nan_mask, + ) = args + # Initialize logging + logger = get_logger(__name__) + logger.debug(f"Inside the worker function for num_perturbed {i}") + + # Now we are inside the num_perturbed loop, we will do this for each of the + # perturbed features + # Now, mean_diff will not have a first dimension for num_perturbed, because we will + # not store it for each perturbed feature + # we will use it in each loop for calculating the bayes factors, and then overwrite + # its content with a new perturbed feature + # mean_diff will contain the differences between the baseline and the perturbed + # reconstruction for feature i, taking into account + # all refits (all refits have the same importance) + # We also set up bayes_k, which has the same dimensions as mean_diff + mean_diff = np.zeros((num_samples, num_continuous)) + bayes_k_worker = np.zeros( + (num_continuous) + ) # This will be what we will put in bayes_k[i,:] + # Set the normalizer + normalizer = ( + 1 / task_config.num_refits + ) # Divide by the number of refits. All the refits will have the same importance + + # Create perturbed dataloader for the current feature (i) + logger.debug(f"Creating perturbed dataloader for feature {i}") + perturbed_dataloader = perturb_continuous_data_extended_one( + baseline_dataloader=baseline_dataloader, + con_dataset_names=config.data.categorical_names, # ! was an error: continuous_names + target_dataset_name=task_config.target_dataset, + perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), + index_pert_feat=i, + ) + logger.debug(f"created perturbed dataloader for feature {i}") + + # For each refit, reload baseline reconstruction (obtained in bayes_parallel + # function). Also, get the reconstruction for the perturbed dataloader + for j in range(task_config.num_refits): + + model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" + reconstruction_path = ( + models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" + ) + if reconstruction_path.exists(): + logger.debug( + f"Loading baseline reconstruction from {reconstruction_path}, " + "in the worker function" + ) + baseline_recon = torch.load(reconstruction_path) + + logger.debug(f"Loading model {model_path}, using load function") + model: VAE = hydra.utils.instantiate( + task_config.model, + continuous_shapes=continuous_shapes, + categorical_shapes=categorical_shapes, + ) + device = torch.device("cuda" if task_config.model.cuda else "cpu") + logger.debug(f"Loading model from {model_path}") + model.load_state_dict(torch.load(model_path)) + logger.debug(f"Loaded model from {model_path}") + model.to(device) + model.eval() + + logger.debug(f"Reconstructing num_perturbed {i}, with model {model_path}") + _, perturb_recon = model.reconstruct( + perturbed_dataloader + ) # Instead of dataloaders[i], create the perturbed one here and + # use it only here + logger.debug( + f"Perturbed reconstruction succesful for feature {i}, model {model}" + ) + + # diff is a matrix with the same dimensions as perturb_recon and baseline_recon + # (rows are samples and columns all the continuous features) + # We calculate diff for each refit, and add it to mean_diff after dividing by + # the number of refits + logger.debug(f"Calculating diff for num_perturbed {i}, with model {model}") + diff = perturb_recon - baseline_recon + logger.debug( + f"Calculating mean_diff for num_perturbed {i}, with model {model}" + ) + mean_diff += diff * normalizer + logger.debug(f"Deleting model {model_path}, to see if I can free up space?") + del model + logger.debug(f"Deleted model {model_path} in worker {i} to save some space") + + logger.debug(f"mean_diff for feature {i}, calculated, using all refits") + mean_diff_shape = mean_diff.shape + logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") + + # Apply nan_mask to the result in mean_diff + diff_mask = np.ma.masked_array(mean_diff, mask=nan_mask) + diff_mask_shape = diff_mask.shape + logger.debug( + f"Calculated diff_masked for feature {i}. Its shape is {diff_mask_shape}" + ) + prob = np.ma.compressed(np.mean(diff_mask > 1e-8, axis=0)) + logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") + + # Calculate bayes factor + bayes_k_worker = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + + logger.debug( + f"bayes factor calculated for feature {i}. Woker function {i} finished" + ) + + # Return bayes_k_worker and the index of the feature + return i, bayes_k_worker + + +def _bayes_approach_parallel( + config: MOVEConfig, + task_config: IdentifyAssociationsBayesConfig, + train_dataloader: DataLoader, + baseline_dataloader: DataLoader, + num_perturbed: int, + num_samples: int, + num_continuous: int, + models_path: Path, + # indexes, +): + logger = get_logger(__name__) + logger.debug("Inside the bayes_parallel function") + + # First, I train or reload the models (number of refits), + # and save the baseline reconstruction. + # We train and get the reconstruction outside to make sure + # that we use the same model and use the same + # baseline reconstruction for all the worker functions + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + + assert task_config.model is not None + device = torch.device("cuda" if task_config.model.cuda else "cpu") + logger.debug("Model moved to device in bayes_approach_parallel") + + # Train or reload models + logger = get_logger(__name__) + logger.info("Training or reloading models") + + for j in range( + task_config.num_refits + ): # We create as many models (refits) as indicated in the config file + # For each j (number of refits) we train a different model, but on the same data + # Initialize model + model: VAE = hydra.utils.instantiate( + task_config.model, + continuous_shapes=baseline_dataset.con_shapes, + categorical_shapes=baseline_dataset.cat_shapes, + ) + if ( + j == 0 + ): # First, we see if the models are already created (if we trained them + # before). for each j, we check if model number j has already been created. + logger.debug(f"Model: {model}") + + # Define paths for the baseline reconstruction and for the model + reconstruction_path = ( + models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" + ) + model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" + + if ( + model_path.exists() + ): # If the models were already created, we load them only if we need to get a + # baseline reconstruction. Otherwise, nothing needs to be done at this point + logger.debug(f"Model {model_path} already exists") + if not reconstruction_path.exists(): + logger.debug(f"Re-loading refit {j + 1}/{task_config.num_refits}") + model.load_state_dict(torch.load(model_path)) + model.to(device) + logger.debug(f"Model {j} reloaded") + else: + logger.debug( + f"Baseline reconstruction for {reconstruction_path} already exists" + f", no need to load model {model_path} " + ) + else: # If the models are not created yet, he have to train them, with the + # parameters we indicated in the config file + logger.debug(f"Training refit {j + 1}/{task_config.num_refits}") + model.to(device) + hydra.utils.call( + task_config.training_loop, + model=model, + train_dataloader=train_dataloader, + ) + # Save the refits, to use them later + if task_config.save_refits: + # pickle_protocol=4 is necessary for very big models + torch.save(model.state_dict(), model_path, pickle_protocol=4) + + # Calculate baseline reconstruction + # For each model j, we get a different reconstruction for the baseline. + # We haven't perturbed anything yet, we are just + # getting the reconstruction for the baseline, to make sure that we get + # the same reconstruction for each refit, we cannot + # do it inside each process because the results might be different + + if reconstruction_path.exists(): + logger.debug(f"Baseline reconstruction for model {j} already created") + else: + model.eval() + _, baseline_recon = model.reconstruct(baseline_dataloader) + + # Save the baseline reconstruction for each model + logger.debug(f"Saving baseline reconstruction {j}") + torch.save(baseline_recon, reconstruction_path, pickle_protocol=4) + logger.debug(f"Saved baseline reconstruction {j}") + del model + + # Get NaN mask ouside the worker function, it is the same for all. + logger.debug("Creating NaN mask in bayes_parallel") + orig_con = ( + baseline_dataset.con_all + ) # Original continuous data of the baseline dataset (all data) + nan_mask = ( + orig_con == 0 + ).numpy() # Creates a mask to identify NaN values in the original data. + + # Define more arguments that are needed for the worker functions + continuous_shapes = baseline_dataset.con_shapes + categorical_shapes = baseline_dataset.cat_shapes + + """ + Perform parallelized bayes approach. + """ + logger.debug("Starting parallelization") + + # Define arguments for each worker, and iterate over models and perturbed features + args = [ + ( + config, + task_config, + baseline_dataloader, + num_samples, + num_continuous, + i, + models_path, + continuous_shapes, + categorical_shapes, + nan_mask, + ) + for i in range(num_perturbed) + ] + # for i in range(100)] # Possible to change to less perturbations if needed + + # logger.debug(f"Arguments for workers are {args}") + + # Create a Pool with multiprocessing.cpu_count() - 1 processes + # with Pool(processes=torch.multiprocessing.cpu_count() - 1, maxtasksperchild=1 + # ) as pool: + with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: + logger.debug("Inside the pool loops") + # Map worker function to arguments + # We get the bayes_k matrix, filled for all the perturbed features + results = pool.map(_bayes_approach_worker, args) + + logger.info("Pool multiprocess completed. Calculating bayes_abs and bayes_p") + + bayes_k = np.empty((num_perturbed, num_continuous)) + + # Get results in the correct order + for i, computed_bayes_k in results: + logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") + bayes_k[i, :] = computed_bayes_k + + # Once we have the Bayes factors for all features, we can calculate Bayes + # probabilities + bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) + bayes_max = np.max(bayes_abs) + bayes_min = np.min(bayes_abs) + bayes_abs_shape = bayes_abs.shape + logger.debug( + f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " + f"Bayes_abs shape is {bayes_abs_shape}" + ) + # file_path = output_path / "bayes_abs_multi.tsv" + # logger.debug(f"Saving bayes_abs to {file_path}") + # np.savetxt(file_path, bayes_abs, delimiter='\t') + + bayes_p = np.exp(bayes_abs) / ( + 1 + np.exp(bayes_abs) + ) # 2D: N x C (perturbed features as rows, all continuous features as columns) + bayes_p_shape = bayes_p.shape + logger.debug(f"bayes_p shape is {bayes_p_shape}") + # file_path = output_path / "bayes_p_multi.tsv" + # logger.debug(f"Saving bayes_p to {file_path}") + # np.savetxt(file_path, bayes_p, delimiter='\t') + + # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE + # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results + + # Get only the significant associations: + # This will flatten the array,so we get all bayes_abs for all perturbed features + # vs all continuous features in one 1D array + # Then, we sort them, and get the indexes in the flattened array. So, we get an + # list of sorted indexes in the flatenned array + sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C + # file_path = output_path / "sort_ids_multi.tsv" + logger.debug(f"sort_ids are {sort_ids}") + # logger.debug(f"Saving sort_ids to {file_path}") + # np.savetxt(file_path, sort_ids, delimiter='\t') + + prob = np.take(bayes_p, sort_ids) # 1D: N x C + # bayes_p is the array from which elements will be taken. + # sort_ids contains the indices that determine the order in which elements should + # be taken from bayes_p. + # This operation essentially rearranges the elements of bayes_p based on the + # sorting order specified by sort_ids + # np.take considers the input array as if it were flattened when extracting + # elements using the provided indices. + # So, even though sort_ids is obtained from a flattened version of bayes_abs, + # np.take understands how to map these indices + # correctly to the original shape of bayes_p. We get a flattened array? + logger.debug(f"prob is {prob}") + logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") + + # Sort bayes_k in descending order, aligning with the sorted bayes_abs. + bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C + logger.debug(f"bayes k, after sorting with the ids, is {bayes_k}") + + logger.debug("Calculating fdr") + # Calculate FDR + fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? + logger.debug(f"fdr is {fdr}") + logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") + idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) + logger.debug(f"Index is {idx}") + # idx will contain the index of the element in fdr that is closest + # to task_config.sig_threshold. + # This line essentially finds the index where the False Discovery Rate (fdr) is + # closest to the significance threshold + # (task_config.sig_threshold). + + logger.debug(f"Sig ids sorted is {sort_ids}") + logger.debug(f"Sigids[idx] is {sort_ids[:idx]}") + + # Return elements only up to idx. They will be the significant findings + # sort_ids[:idx]: Indices of features sorted by significance. + # prob[:idx]: Probabilities of significant associations for selected features. + # fdr[:idx]: False Discovery Rate values for selected features. + # bayes_k[:idx]: Bayes Factors indicating the strength of evidence for selected + # associations. + return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] + + def _bayes_approach( config: MOVEConfig, task_config: IdentifyAssociationsBayesConfig, @@ -907,11 +1150,15 @@ def save_results( target_dataset_idx = config.data.continuous_names.index( task_config.target_dataset ) + # This creates a DataFrame named a_df with one column + # named "feature_a_name". The values in this column are + # taken from con_names using the target_dataset_idx index. a_df = pd.DataFrame(dict(feature_a_name=con_names[target_dataset_idx])) else: target_dataset_idx = config.data.categorical_names.index( task_config.target_dataset ) + a_df = pd.DataFrame(dict(feature_a_name=cat_names[target_dataset_idx])) a_df.index.name = "feature_a_id" a_df.reset_index(inplace=True) @@ -968,9 +1215,12 @@ def identify_associations(config: MOVEConfig) -> None: config.data.continuous_names, ) + logger.debug( + "Making train dataloader in main function identify_associations_selected" + ) train_dataloader = make_dataloader( - cat_list, - con_list, + cat_list, # List of categorical datasets + con_list, # List of continuous datasets shuffle=True, batch_size=task_config.batch_size, drop_last=True, @@ -1019,19 +1269,35 @@ def identify_associations(config: MOVEConfig) -> None: if task_type == "bayes": task_config = cast(IdentifyAssociationsBayesConfig, task_config) - sig_ids, *extra_cols = _bayes_approach( - config, - task_config, - train_dataloader, - baseline_dataloader, - dataloaders, - models_path, - num_perturbed, - num_samples, - num_continuous, - nan_mask, - feature_mask, - ) + if task_config.multiprocess: + sig_ids, *extra_cols = _bayes_approach_parallel( + config, + task_config, + train_dataloader, + baseline_dataloader, + num_perturbed, + num_samples, + num_continuous, + models_path, + ) + logger.debug( + "Completed bayes task (parallel function in main function " + "(identify_associations_selected))" + ) + else: + sig_ids, *extra_cols = _bayes_approach( + config, + task_config, + train_dataloader, + baseline_dataloader, + dataloaders, + models_path, + num_perturbed, + num_samples, + num_continuous, + nan_mask, + feature_mask, + ) extra_colnames = ["proba", "fdr", "bayes_k"] diff --git a/src/move/tasks/identify_associations_multiprocess.py b/src/move/tasks/identify_associations_multiprocess.py deleted file mode 100644 index 7eb63682..00000000 --- a/src/move/tasks/identify_associations_multiprocess.py +++ /dev/null @@ -1,765 +0,0 @@ -__all__ = ["identify_associations_multiprocess"] - - -from functools import reduce -from os.path import exists -from pathlib import Path -from typing import Literal, Sized, cast - -import hydra -import numpy as np -import pandas as pd -import torch -import torch.multiprocessing -from omegaconf import OmegaConf -from torch.multiprocessing import Pool - -from move.conf.schema import ( - IdentifyAssociationsBayesConfig, - IdentifyAssociationsConfig, - IdentifyAssociationsKSConfig, - IdentifyAssociationsTTestConfig, - MOVEConfig, -) -from move.core.logging import get_logger -from move.data import io -from move.data.dataloaders import MOVEDataset, make_dataloader - -# Later, change this to import the _one options from these files, -# rather than defining them here -# CHANGE -from move.data.perturbations import ( - ContinuousPerturbationType, - perturb_continuous_data_extended_one, -) -from move.models.vae import VAE -from move.tasks.identify_associations import ( - prepare_for_categorical_perturbation, -) # ! Is this used? -from move.visualization.dataset_distributions import ( - plot_feature_association_graph, -) - -# from scipy.stats import ks_2samp, pearsonr # type: ignore - - -# from move.visualization.dataset_distributions import plot_value_distributions - - -# from move.analysis.metrics import get_2nd_order_polynomial - - -# make_dataloader creates a dataloader with both continuous and categorical datasets - - -# We can do three types of statistical tests. Multiprocessing is only implemented -# for bayes at the moment -TaskType = Literal["bayes", "ttest", "ks"] - -# Possible values for continuous pertrubation -CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] - - -def _get_task_type( - task_config: IdentifyAssociationsConfig, -) -> TaskType: - task_type = OmegaConf.get_type(task_config) - if task_type is IdentifyAssociationsBayesConfig: - return "bayes" - if task_type is IdentifyAssociationsTTestConfig: - return "ttest" - if task_type is IdentifyAssociationsKSConfig: - return "ks" - raise ValueError("Unsupported type of task!") - - -def _validate_task_config( - task_config: IdentifyAssociationsConfig, task_type: TaskType -) -> None: - if not (0.0 <= task_config.sig_threshold <= 1.0): - raise ValueError("Significance threshold must be within [0, 1].") - if task_type == "ttest": - task_config = cast(IdentifyAssociationsTTestConfig, task_config) - if len(task_config.num_latent) != 4: - raise ValueError("4 latent space dimensions required.") - - -# def _worker_process(queue, args): -# result = _bayes_approach_worker(args) -# queue.put(result) - - -def _bayes_approach_worker(args): - """ - Worker function to calculate mean differences and Bayes factors for one feature. - """ - # Set the number of threads available: - # VERY IMPORTANT, TO AVOID CPU OVERSUBSCRIPTION - torch.set_num_threads(1) - - # Unpack arguments. - ( - config, - task_config, - baseline_dataloader, - num_samples, - num_continuous, - i, - models_path, - continuous_shapes, - categorical_shapes, - nan_mask, - ) = args - # Initialize logging - logger = get_logger(__name__) - logger.debug(f"Inside the worker function for num_perturbed {i}") - - # Now we are inside the num_perturbed loop, we will do this for each of the - # perturbed features - # Now, mean_diff will not have a first dimension for num_perturbed, because we will - # not store it for each perturbed feature - # we will use it in each loop for calculating the bayes factors, and then overwrite - # its content with a new perturbed feature - # mean_diff will contain the differences between the baseline and the perturbed - # reconstruction for feature i, taking into account - # all refits (all refits have the same importance) - # We also set up bayes_k, which has the same dimensions as mean_diff - mean_diff = np.zeros((num_samples, num_continuous)) - bayes_k_worker = np.zeros( - (num_continuous) - ) # This will be what we will put in bayes_k[i,:] - # Set the normalizer - normalizer = ( - 1 / task_config.num_refits - ) # Divide by the number of refits. All the refits will have the same importance - - # Create perturbed dataloader for the current feature (i) - logger.debug(f"Creating perturbed dataloader for feature {i}") - perturbed_dataloader = perturb_continuous_data_extended_one( - baseline_dataloader=baseline_dataloader, - con_dataset_names=config.data.continuous_names, - target_dataset_name=task_config.target_dataset, - perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), - index_pert_feat=i, - ) - logger.debug(f"created perturbed dataloader for feature {i}") - - # For each refit, reload baseline reconstruction (obtained in bayes_parallel - # function). Also, get the reconstruction for the perturbed dataloader - for j in range(task_config.num_refits): - - model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" - reconstruction_path = ( - models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" - ) - if reconstruction_path.exists(): - logger.debug( - f"Loading baseline reconstruction from {reconstruction_path}, " - "in the worker function" - ) - baseline_recon = torch.load(reconstruction_path) - - logger.debug(f"Loading model {model_path}, using load function") - model: VAE = hydra.utils.instantiate( - task_config.model, - continuous_shapes=continuous_shapes, - categorical_shapes=categorical_shapes, - ) - device = torch.device("cuda" if task_config.model.cuda else "cpu") - logger.debug(f"Loading model from {model_path}") - model.load_state_dict(torch.load(model_path)) - logger.debug(f"Loaded model from {model_path}") - model.to(device) - model.eval() - - logger.debug(f"Reconstructing num_perturbed {i}, with model {model_path}") - _, perturb_recon = model.reconstruct( - perturbed_dataloader - ) # Instead of dataloaders[i], create the perturbed one here and - # use it only here - logger.debug( - f"Perturbed reconstruction succesful for feature {i}, model {model}" - ) - - # diff is a matrix with the same dimensions as perturb_recon and baseline_recon - # (rows are samples and columns all the continuous features) - # We calculate diff for each refit, and add it to mean_diff after dividing by - # the number of refits - logger.debug(f"Calculating diff for num_perturbed {i}, with model {model}") - diff = perturb_recon - baseline_recon - logger.debug( - f"Calculating mean_diff for num_perturbed {i}, with model {model}" - ) - mean_diff += diff * normalizer - logger.debug(f"Deleting model {model_path}, to see if I can free up space?") - del model - logger.debug(f"Deleted model {model_path} in worker {i} to save some space") - - logger.debug(f"mean_diff for feature {i}, calculated, using all refits") - mean_diff_shape = mean_diff.shape - logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") - - # Apply nan_mask to the result in mean_diff - diff_mask = np.ma.masked_array(mean_diff, mask=nan_mask) - diff_mask_shape = diff_mask.shape - logger.debug( - f"Calculated diff_masked for feature {i}. Its shape is {diff_mask_shape}" - ) - prob = np.ma.compressed(np.mean(diff_mask > 1e-8, axis=0)) - logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") - - # The code below is to save results, in case we want to compare results - # data = diff_mask.data # Extract the data from the masked array - # mask = diff_mask.mask # Extract the mask from the masked array - # Replace masked values with a placeholder (e.g., np.nan) - # data[mask] = np.nan - # Define the file path to save the TSV file - # output_path = Path(config.data.results_path - # ) / "identify_associations_multiprocess" - # file_path = output_path / "diff_multi.tsv" - # Save the data to the TSV fil - # logger.debug(f"Saving diff to {file_path}") - # np.savetxt(file_path, diff_mask, delimiter='\t') - - # data = prob.data # Extract the data from the masked array - # mask = prob.mask # Extract the mask from the masked array - # Replace masked values with a placeholder (e.g., np.nan) - # data[mask] = np.nan - # Define the file path to save the TSV file - # output_path = Path(config.data.results_path) / "identify_associations_selected" - # file_path = output_path / "prob_multi_script.tsv" - # Save the data to the TSV fil - # logger.debug(f"Saving prob to {file_path}") - # np.savetxt(file_path, prob, delimiter='\t') - # logger.debug(f"prob is {prob}") - - # Calculate bayes factor - bayes_k_worker = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) - # Code below is to save file - # file_path = output_path / f"bayes_k_multi_worker_{i}.tsv" - # logger.debug(f"Saving bayes_k_worker {i} to {file_path}") - # np.savetxt(file_path, bayes_k_worker, delimiter='\t') - - logger.debug( - f"bayes factor calculated for feature {i}. Woker function {i} finished" - ) - - # Return bayes_k_worker and the index of the feature - return i, bayes_k_worker - - -def _bayes_approach_parallel( - config, - task_config, - train_dataloader, - baseline_dataloader, - num_perturbed, - num_samples, - num_continuous, - models_path, - # indexes, -): - logger = get_logger(__name__) - logger.debug("Inside the bayes_parallel function") - - # First, I train or reload the models (number of refits), - # and save the baseline reconstruction. - # We train and get the reconstruction outside to make sure - # that we use the same model and use the same - # baseline reconstruction for all the worker functions - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - - assert task_config.model is not None - device = torch.device("cuda" if task_config.model.cuda else "cpu") - logger.debug("Model moved to device in bayes_approach_parallel") - - # Train or reload models - logger = get_logger(__name__) - logger.info("Training or reloading models") - - for j in range( - task_config.num_refits - ): # We create as many models (refits) as indicated in the config file - # For each j (number of refits) we train a different model, but on the same data - # Initialize model - model: VAE = hydra.utils.instantiate( - task_config.model, - continuous_shapes=baseline_dataset.con_shapes, - categorical_shapes=baseline_dataset.cat_shapes, - ) - if ( - j == 0 - ): # First, we see if the models are already created (if we trained them - # before). for each j, we check if model number j has already been created. - logger.debug(f"Model: {model}") - - # Define paths for the baseline reconstruction and for the model - reconstruction_path = ( - models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" - ) - model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" - - if ( - model_path.exists() - ): # If the models were already created, we load them only if we need to get a - # baseline reconstruction. Otherwise, nothing needs to be done at this point - logger.debug(f"Model {model_path} already exists") - if not reconstruction_path.exists(): - logger.debug(f"Re-loading refit {j + 1}/{task_config.num_refits}") - model.load_state_dict(torch.load(model_path)) - model.to(device) - logger.debug(f"Model {j} reloaded") - else: - logger.debug( - f"Baseline reconstruction for {reconstruction_path} already exists" - f", no need to load model {model_path} " - ) - else: # If the models are not created yet, he have to train them, with the - # parameters we indicated in the config file - logger.debug(f"Training refit {j + 1}/{task_config.num_refits}") - model.to(device) - hydra.utils.call( - task_config.training_loop, - model=model, - train_dataloader=train_dataloader, - ) - # Save the refits, to use them later - if task_config.save_refits: - # pickle_protocol=4 is necessary for very big models - torch.save(model.state_dict(), model_path, pickle_protocol=4) - - # Calculate baseline reconstruction - # For each model j, we get a different reconstruction for the baseline. - # We haven't perturbed anything yet, we are just - # getting the reconstruction for the baseline, to make sure that we get - # the same reconstruction for each refit, we cannot - # do it inside each process because the results might be different - - if reconstruction_path.exists(): - logger.debug(f"Baseline reconstruction for model {j} already created") - else: - model.eval() - _, baseline_recon = model.reconstruct(baseline_dataloader) - - # Save the baseline reconstruction for each model - logger.debug(f"Saving baseline reconstruction {j}") - torch.save(baseline_recon, reconstruction_path, pickle_protocol=4) - logger.debug(f"Saved baseline reconstruction {j}") - del model - - # Get NaN mask ouside the worker function, it is the same for all. - logger.debug("Creating NaN mask in bayes_parallel") - orig_con = ( - baseline_dataset.con_all - ) # Original continuous data of the baseline dataset (all data) - nan_mask = ( - orig_con == 0 - ).numpy() # Creates a mask to identify NaN values in the original data. - - # Define more arguments that are needed for the worker functions - continuous_shapes = baseline_dataset.con_shapes - categorical_shapes = baseline_dataset.cat_shapes - - """ - Perform parallelized bayes approach. - """ - logger.debug("Starting parallelization") - - # Define arguments for each worker, and iterate over models and perturbed features - args = [ - ( - config, - task_config, - baseline_dataloader, - num_samples, - num_continuous, - i, - models_path, - continuous_shapes, - categorical_shapes, - nan_mask, - ) - for i in range(num_perturbed) - ] - # for i in range(100)] # Possible to change to less perturbations if needed - - # logger.debug(f"Arguments for workers are {args}") - - # Create a Pool with multiprocessing.cpu_count() - 1 processes - # with Pool(processes=torch.multiprocessing.cpu_count() - 1, maxtasksperchild=1 - # ) as pool: - with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: - logger.debug("Inside the pool loops") - # Map worker function to arguments - # We get the bayes_k matrix, filled for all the perturbed features - results = pool.map(_bayes_approach_worker, args) - - logger.info("Pool multiprocess completed. Calculating bayes_abs and bayes_p") - - bayes_k = np.empty((num_perturbed, num_continuous)) - - # Get results in the correct order - for i, computed_bayes_k in results: - logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") - bayes_k[i, :] = computed_bayes_k - - # output_path = Path(config.data.results_path - # ) / "identify_associations_multiprocess" - # file_path = output_path / "bayes_k_multi_all.tsv" - # ogger.debug(f"Saving bayes_k (all, not the worker) to {file_path}") - # np.savetxt(file_path, bayes_k, delimiter='\t') - - # Once we have the Bayes factors for all features, we can calculate Bayes - # probabilities - bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) - bayes_max = np.max(bayes_abs) - bayes_min = np.min(bayes_abs) - bayes_abs_shape = bayes_abs.shape - logger.debug( - f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " - f"Bayes_abs shape is {bayes_abs_shape}" - ) - # file_path = output_path / "bayes_abs_multi.tsv" - # logger.debug(f"Saving bayes_abs to {file_path}") - # np.savetxt(file_path, bayes_abs, delimiter='\t') - - bayes_p = np.exp(bayes_abs) / ( - 1 + np.exp(bayes_abs) - ) # 2D: N x C (perturbed features as rows, all continuous features as columns) - bayes_p_shape = bayes_p.shape - logger.debug(f"bayes_p shape is {bayes_p_shape}") - # file_path = output_path / "bayes_p_multi.tsv" - # logger.debug(f"Saving bayes_p to {file_path}") - # np.savetxt(file_path, bayes_p, delimiter='\t') - - # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE - # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results - - # Get only the significant associations: - # This will flatten the array,so we get all bayes_abs for all perturbed features - # vs all continuous features in one 1D array - # Then, we sort them, and get the indexes in the flattened array. So, we get an - # list of sorted indexes in the flatenned array - sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C - # file_path = output_path / "sort_ids_multi.tsv" - logger.debug(f"sort_ids are {sort_ids}") - # logger.debug(f"Saving sort_ids to {file_path}") - # np.savetxt(file_path, sort_ids, delimiter='\t') - - prob = np.take(bayes_p, sort_ids) # 1D: N x C - # file_path = output_path / "prob_final_multi.tsv" - # logger.debug(f"Saving prob to {file_path}") - # np.savetxt(file_path, prob, delimiter='\t') - # bayes_p is the array from which elements will be taken. - # sort_ids contains the indices that determine the order in which elements should - # be taken from bayes_p. - # This operation essentially rearranges the elements of bayes_p based on the - # sorting order specified by sort_ids - # np.take considers the input array as if it were flattened when extracting - # elements using the provided indices. - # So, even though sort_ids is obtained from a flattened version of bayes_abs, - # np.take understands how to map these indices - # correctly to the original shape of bayes_p. We get a flattened array? - logger.debug(f"prob is {prob}") - logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") - - # Sort bayes_k in descending order, aligning with the sorted bayes_abs. - bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C - logger.debug(f"bayes k, after sorting with the ids, is {bayes_k}") - # file_path = output_path / "sorted_bayes_k_multi.tsv" - # logger.debug(f"Saving sorted_bayes_k to {file_path}") - # np.savetxt(file_path, bayes_k, delimiter='\t') - - logger.debug("Calculating fdr") - # Calculate FDR - fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? - logger.debug(f"fdr is {fdr}") - logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") - # file_path = output_path / "fdr_multi.tsv" - # logger.debug(f"Saving fdr to {file_path}") - # np.savetxt(file_path, fdr, delimiter='\t') - - idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) - # file_path = output_path / "idx_multi.tsv" - # logger.debug(f"Saving idx to {file_path}") - # np.savetxt(file_path, idx, delimiter='\t') - logger.debug(f"Index is {idx}") - # idx will contain the index of the element in fdr that is closest - # to task_config.sig_threshold. - # This line essentially finds the index where the False Discovery Rate (fdr) is - # closest to the significance threshold - # (task_config.sig_threshold). - - logger.debug(f"Sig ids sorted is {sort_ids}") - logger.debug(f"Sigids[idx] is {sort_ids[:idx]}") - - # Return elements only up to idx. They will be the significant findings - return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] - # sort_ids[:idx]: Indices of features sorted by significance. - # prob[:idx]: Probabilities of significant associations for selected features. - # fdr[:idx]: False Discovery Rate values for selected features. - # bayes_k[:idx]: Bayes Factors indicating the strength of evidence for selected - # associations. - - -def save_results( - config: MOVEConfig, - con_shapes: list[int], - cat_names: list[list[str]], - con_names: list[list[str]], - output_path: Path, - sig_ids, - extra_cols, - extra_colnames, -) -> None: - """ - This function saves the obtained associations in a TSV file containing - the following columns: - feature_a_id - feature_b_id - feature_a_name - feature_b_name - feature_b_dataset - proba/p_value: number quantifying the significance of the association - - Args: - config: main config - con_shapes: tuple with the number of features per continuous dataset - cat_names: list of lists of names for the categorical features. - Each inner list corresponds to a separate dataset. - con_names: list of lists of names for the continuous features. - Each inner list corresponds to a separate dataset. - output_path: path where the results will be saved - sig_ids: ids for the significat features - extra_cols: extra data when calling the approach function - extra_colnames: names for the extra data columns - """ - logger = get_logger(__name__) - logger.info(f"Significant hits found: {sig_ids.size}") - task_config = cast(IdentifyAssociationsConfig, config.task) - task_type = _get_task_type(task_config) - pert_type = task_config.pert_type - - num_continuous = sum(con_shapes) # C - - if sig_ids.size > 0: - sig_ids = np.vstack((sig_ids // num_continuous, sig_ids % num_continuous)).T - # sig_pairs = [np.vstack((sig_id // num_continuous, sig_id % num_continuous)) - # for sig_id in sig_ids] - - logger.info("Writing results") - results = pd.DataFrame(sig_ids, columns=["feature_a_id", "feature_b_id"]) - - # Check if the task is for continuous or categorical data - if task_config.target_value in CONTINUOUS_TARGET_VALUE: - target_dataset_idx = config.data.continuous_names.index( - task_config.target_dataset - ) - # This creates a DataFrame named a_df with one column - # named "feature_a_name". The values in this column are - # taken from con_names using the target_dataset_idx index. - a_df = pd.DataFrame(dict(feature_a_name=con_names[target_dataset_idx])) - else: - target_dataset_idx = config.data.categorical_names.index( - task_config.target_dataset - ) - - a_df = pd.DataFrame(dict(feature_a_name=cat_names[target_dataset_idx])) - a_df.index.name = "feature_a_id" - a_df.reset_index(inplace=True) - feature_names = reduce(list.__add__, con_names) - b_df = pd.DataFrame(dict(feature_b_name=feature_names)) - b_df.index.name = "feature_b_id" - b_df.reset_index(inplace=True) - results = results.merge(a_df, on="feature_a_id", how="left").merge( - b_df, on="feature_b_id", how="left" - ) - results["feature_b_dataset"] = pd.cut( - results["feature_b_id"], - bins=cast(list[int], np.cumsum([0] + con_shapes)), - right=False, - labels=config.data.continuous_names, - ) - for col, colname in zip(extra_cols, extra_colnames): - results[colname] = col - results.to_csv( - output_path - / f"results_sig_assoc_{task_type}_multiprocess_all_{pert_type}.tsv", - sep="\t", - index=False, - ) - - -def identify_associations_multiprocess(config: MOVEConfig) -> None: - """ - Leads to the execution of the appropriate association - identification tasks. The function is organized in three - blocks: - 1) Prepare the data and create the dataloaders with their masks. - 2) Evaluate associations using bayes or ttest approach. - 3) Save results. - """ - # DATA PREPARATION ###################### - # Read original data and create perturbed datasets #### - - logger = get_logger(__name__) - task_config = cast(IdentifyAssociationsConfig, config.task) - task_type = _get_task_type(task_config) - _validate_task_config(task_config, task_type) - - interim_path = Path(config.data.interim_data_path) - - models_path = interim_path / "models" - if task_config.save_refits: - models_path.mkdir(exist_ok=True) - - output_path = Path(config.data.results_path) / "identify_associations_multiprocess" - output_path.mkdir(exist_ok=True, parents=True) - - # Load datasets: - cat_list, cat_names, con_list, con_names = io.load_preprocessed_data( - interim_path, - config.data.categorical_names, - config.data.continuous_names, - ) - - logger.debug( - "Making train dataloader in main function identify_associations_selected" - ) - train_dataloader = make_dataloader( - cat_list, # List of categorical datasets - con_list, # List of continuous datasets - shuffle=True, - batch_size=task_config.batch_size, - drop_last=True, - ) - - con_shapes = [con.shape[1] for con in con_list] - - num_samples = len(cast(Sized, train_dataloader.sampler)) # N - num_continuous = sum(con_shapes) # C - logger.debug(f"# continuous features: {num_continuous}") - - # Creating the baseline dataloader: - baseline_dataloader = make_dataloader( - cat_list, con_list, shuffle=False, batch_size=task_config.batch_size - ) - logger.debug("Baseline dataloader created") - - # Indentify associations between continuous features: - logger.info(f"Perturbing dataset: '{task_config.target_dataset}'") - if task_config.target_value in CONTINUOUS_TARGET_VALUE: - logger.info(f"Beginning task: identify associations continuous ({task_type})") - logger.info(f"Perturbation type: {task_config.target_value}") - # output_subpath = Path(output_path) / "perturbation_visualization" - # output_subpath.mkdir(exist_ok=True, parents=True) - # Identify associations between categorical and continuous features: - else: - logger.info("Beginning task: identify associations categorical") - ( - dataloaders, - nan_mask, - feature_mask, - ) = prepare_for_categorical_perturbation( - config, interim_path, baseline_dataloader, cat_list - ) - - baseline_dataset = baseline_dataloader.dataset - con_shapes = [con.shape[1] for con in con_list] - logger.debug(f"con_shapes is {con_shapes}") - target_dataset_name = task_config.target_dataset - con_dataset_names = config.data.continuous_names - target_idx = con_dataset_names.index(target_dataset_name) - logger.debug(f"Target idx is {target_idx}") - - num_perturbed = baseline_dataset.con_shapes[target_idx] - logger.debug(f"# perturbed features: {num_perturbed}") - - # APPROACH EVALUATION ########################## - - if task_type == "bayes": - task_config = cast(IdentifyAssociationsBayesConfig, task_config) - sig_ids, *extra_cols = _bayes_approach_parallel( - config, - task_config, - train_dataloader, - baseline_dataloader, - # dataloaders, I will create it inside - num_perturbed, - num_samples, - num_continuous, - models_path, - # indexes, - ) - logger.debug( - "Completed bayes task (parallel function in main function " - "(identify_associations_selected))" - ) - - extra_colnames = ["proba", "fdr", "bayes_k"] - - elif task_type == "ttest": - task_config = cast(IdentifyAssociationsTTestConfig, task_config) - sig_ids, *extra_cols = _ttest_approach( - config, - task_config, - train_dataloader, - baseline_dataloader, - # dataloaders, I will create it inside - models_path, - interim_path, - num_perturbed, - num_samples, - num_continuous, - nan_mask, - feature_mask, - ) - - extra_colnames = ["p_value"] - - elif task_type == "ks": - task_config = cast(IdentifyAssociationsKSConfig, task_config) - sig_ids, *extra_cols = _ks_approach( - config, - task_config, - train_dataloader, - baseline_dataloader, - dataloaders, - models_path, - num_perturbed, - num_samples, - num_continuous, - con_names, - output_path, - ) - - extra_colnames = ["ks_distance"] - - else: - raise ValueError() - - # RESULTS ################################ - save_results( - config, - con_shapes, - cat_names, - con_names, - output_path, - sig_ids, - extra_cols, - extra_colnames, - ) - logger.debug("Results tsv created") - pert_type = task_config.pert_type - logger.debug(f"pert type is {pert_type}") - - if exists( - output_path / f"results_sig_assoc_{task_type}_multiprocess_all_{pert_type}.tsv" - ): - association_df = pd.read_csv( - output_path - / f"results_sig_assoc_{task_type}_multiprocess_all_{pert_type}.tsv", - sep="\t", - ) - plot_feature_association_graph(association_df, output_path) - plot_feature_association_graph(association_df, output_path, layout="spring") diff --git a/tutorial/config/task/random_continuous__id_assoc_bayes.yaml b/tutorial/config/task/random_continuous__id_assoc_bayes.yaml index 6a004992..78b3f788 100644 --- a/tutorial/config/task/random_continuous__id_assoc_bayes.yaml +++ b/tutorial/config/task/random_continuous__id_assoc_bayes.yaml @@ -1,6 +1,8 @@ defaults: - identify_associations_bayes +multiprocess: False + batch_size: 10 num_refits: 10 From 6a17110eee66c7397786934cfb323ed80e88dc46 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 10:10:08 +0200 Subject: [PATCH 52/94] :fire: remove old schema entries, increase run time --- .github/workflows/release.yaml | 14 +++++++------- src/move/conf/schema.py | 3 --- 2 files changed, 7 insertions(+), 10 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 805ca731..f20c104b 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -53,7 +53,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=20 + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=40 task.num_refits=25 run-tutorial-w-multiprocessing: name: Run - random_small - multiprocess runs-on: ubuntu-latest @@ -78,7 +78,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=30 task.num_refits=20 + move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=40 task.num_refits=25 run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest @@ -98,11 +98,11 @@ jobs: cd tutorial move-dl data=random_continuous task=random_continuous__latent --cfg job move-dl data=random_continuous task=random_continuous__latent - - name: Identify associations - t-test - run: | - cd tutorial - move-dl data=random_continuous task=random_continuous__id_assoc_ttest --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 + # - name: Identify associations - t-test + # run: | + # cd tutorial + # move-dl data=random_continuous task=random_continuous__id_assoc_ttest --cfg job + # move-dl data=random_continuous task=random_continuous__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index 4f2323c0..92d4dce5 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -172,9 +172,6 @@ class IdentifyAssociationsBayesConfig(IdentifyAssociationsConfig): """Configure the probabilistic approach to identify associations.""" multiprocess: bool = False # Default value is False - selected: bool = False - pert_type: str = MISSING - multiloop: bool = False ... From b8b4769137a6b32316f91df4e7594c09eafff64e Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 10:50:15 +0200 Subject: [PATCH 53/94] :zip: do no save intermediate files for single-process bayes_approach --- src/move/tasks/identify_associations.py | 82 ++++++++++++------------- 1 file changed, 40 insertions(+), 42 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index cf31dd47..f997a104 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -243,7 +243,7 @@ def _bayes_approach_worker(args): logger.debug(f"Creating perturbed dataloader for feature {i}") perturbed_dataloader = perturb_continuous_data_extended_one( baseline_dataloader=baseline_dataloader, - con_dataset_names=config.data.categorical_names, # ! was an error: continuous_names + con_dataset_names=config.data.categorical_names, # ! error: continuous_names target_dataset_name=task_config.target_dataset, perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), index_pert_feat=i, @@ -458,13 +458,7 @@ def _bayes_approach_parallel( ) for i in range(num_perturbed) ] - # for i in range(100)] # Possible to change to less perturbations if needed - # logger.debug(f"Arguments for workers are {args}") - - # Create a Pool with multiprocessing.cpu_count() - 1 processes - # with Pool(processes=torch.multiprocessing.cpu_count() - 1, maxtasksperchild=1 - # ) as pool: with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: logger.debug("Inside the pool loops") # Map worker function to arguments @@ -669,68 +663,68 @@ def _bayes_approach( # Replace masked values with a placeholder (e.g., np.nan) data[mask] = np.nan # Define the file path to save the TSV file - output_path = Path(config.data.results_path) / "identify_associations" - file_path = output_path / "diff_normal.tsv" + # output_path = Path(config.data.results_path) / "identify_associations" + # file_path = output_path / "diff_normal.tsv" # Save the data to the TSV fil # logger.debug(f"Saving diff to {file_path}") - np.savetxt(file_path, diff, delimiter="\t") + # np.savetxt(file_path, diff, delimiter="\t") # data = prob.data # Extract the data from the masked array # mask = prob.mask # Extract the mask from the masked array # Replace masked values with a placeholder (e.g., np.nan) # data[mask] = np.nan # Define the file path to save the TSV file - output_path = Path(config.data.results_path) / "identify_associations" - file_path = output_path / "prob_original_script.tsv" - # Save the data to the TSV fil - logger.debug(f"Saving prob to {file_path}") - np.savetxt(file_path, prob, delimiter="\t") - logger.debug(f"prob is {prob}") - - file_path = output_path / "bayes_k_original_all.tsv" - logger.debug(f"Saving bayes_k (not worker, all) to {file_path}") - np.savetxt(file_path, bayes_k, delimiter="\t") - + # output_path = Path(config.data.results_path) / "identify_associations" + # file_path = output_path / "prob_original_script.tsv" + # # Save the data to the TSV fil + # logger.debug(f"Saving prob to {file_path}") + # np.savetxt(file_path, prob, delimiter="\t") + # logger.debug(f"prob is {prob}") + + # file_path = output_path / "bayes_k_original_all.tsv" + # logger.debug(f"Saving bayes_k (not worker, all) to {file_path}") + # np.savetxt(file_path, bayes_k, delimiter="\t") bayes_mask[bayes_mask != 0] = 1 bayes_mask = np.array(bayes_mask, dtype=bool) # Calculate Bayes probabilities bayes_abs = np.abs(bayes_k) - file_path = output_path / "bayes_abs_original.tsv" - logger.debug(f"Saving bayes_abs to {file_path}") - np.savetxt(file_path, bayes_abs, delimiter="\t") + # file_path = output_path / "bayes_abs_original.tsv" + # logger.debug(f"Saving bayes_abs to {file_path}") + # np.savetxt(file_path, bayes_abs, delimiter="\t") bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C - file_path = output_path / "bayes_p_original.tsv" - logger.debug(f"Saving bayes_p to {file_path}") - np.savetxt(file_path, bayes_p, delimiter="\t") + # file_path = output_path / "bayes_p_original.tsv" + # logger.debug(f"Saving bayes_p to {file_path}") + # np.savetxt(file_path, bayes_p, delimiter="\t") - # bayes_abs[bayes_mask] = np.min( - # bayes_abs - # ) # Bring feature_i feature_i associations to minimum + # ! Marc said this is needed to not identify the features with themselves + bayes_abs[bayes_mask] = np.min( + bayes_abs + ) # Bring feature_i feature_i associations to minimum sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C - file_path = output_path / "sort_ids_original_script.tsv" - logger.debug(f"Saving sort_ids to {file_path}") - np.savetxt(file_path, sort_ids, delimiter="\t") + # file_path = output_path / "sort_ids_original_script.tsv" + # logger.debug(f"Saving sort_ids to {file_path}") + # np.savetxt(file_path, sort_ids, delimiter="\t") logger.debug(f"sort_ids are {sort_ids}") prob = np.take(bayes_p, sort_ids) # 1D: N x C - file_path = output_path / "prob_original_final.tsv" - logger.debug(f"Saving prob to {file_path}") - np.savetxt(file_path, prob, delimiter="\t") + # file_path = output_path / "prob_original_final.tsv" + # logger.debug(f"Saving prob to {file_path}") + # np.savetxt(file_path, prob, delimiter="\t") logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") # Sort Bayes bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C - file_path = output_path / "sorted_bayes_k_original_script.tsv" - logger.debug(f"Saving sorted_bayes_k to {file_path}") - np.savetxt(file_path, bayes_k, delimiter="\t") + # file_path = output_path / "sorted_bayes_k_original_script.tsv" + # logger.debug(f"Saving sorted_bayes_k to {file_path}") + # np.savetxt(file_path, bayes_k, delimiter="\t") # Calculate FDR fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D - file_path = output_path / "fdr_original_script.tsv" - logger.debug(f"Saving fdr to {file_path}") - np.savetxt(file_path, fdr, delimiter="\t") + # file_path = output_path / "fdr_original_script.tsv" + # logger.debug(f"Saving fdr to {file_path}") + # np.savetxt(file_path, fdr, delimiter="\t") idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) logger.debug(f"Index is {idx}") # file_path = output_path / "idx_original_script.tsv" @@ -1303,6 +1297,8 @@ def identify_associations(config: MOVEConfig) -> None: elif task_type == "ttest": task_config = cast(IdentifyAssociationsTTestConfig, task_config) + if task_config.multiprocess: + logger.warning("Multiprocessing is not supported for ttest approach.") sig_ids, *extra_cols = _ttest_approach( task_config, train_dataloader, @@ -1321,6 +1317,8 @@ def identify_associations(config: MOVEConfig) -> None: elif task_type == "ks": task_config = cast(IdentifyAssociationsKSConfig, task_config) + if task_config.multiprocess: + logger.warning("Multiprocessing is not supported for KS approach.") sig_ids, *extra_cols = _ks_approach( config, task_config, From 2927d3f9027745882e4cd72ef94fbc4ed43bbf90 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 11:29:05 +0200 Subject: [PATCH 54/94] :fire: remove comments --- src/move/tasks/identify_associations.py | 47 +------------------------ 1 file changed, 1 insertion(+), 46 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index f997a104..ce346d30 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -484,9 +484,6 @@ def _bayes_approach_parallel( f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " f"Bayes_abs shape is {bayes_abs_shape}" ) - # file_path = output_path / "bayes_abs_multi.tsv" - # logger.debug(f"Saving bayes_abs to {file_path}") - # np.savetxt(file_path, bayes_abs, delimiter='\t') bayes_p = np.exp(bayes_abs) / ( 1 + np.exp(bayes_abs) @@ -662,74 +659,32 @@ def _bayes_approach( mask = diff.mask # Extract the mask from the masked array # Replace masked values with a placeholder (e.g., np.nan) data[mask] = np.nan - # Define the file path to save the TSV file - # output_path = Path(config.data.results_path) / "identify_associations" - # file_path = output_path / "diff_normal.tsv" - # Save the data to the TSV fil - # logger.debug(f"Saving diff to {file_path}") - # np.savetxt(file_path, diff, delimiter="\t") - - # data = prob.data # Extract the data from the masked array - # mask = prob.mask # Extract the mask from the masked array - # Replace masked values with a placeholder (e.g., np.nan) - # data[mask] = np.nan - # Define the file path to save the TSV file - # output_path = Path(config.data.results_path) / "identify_associations" - # file_path = output_path / "prob_original_script.tsv" - # # Save the data to the TSV fil - # logger.debug(f"Saving prob to {file_path}") - # np.savetxt(file_path, prob, delimiter="\t") - # logger.debug(f"prob is {prob}") - - # file_path = output_path / "bayes_k_original_all.tsv" - # logger.debug(f"Saving bayes_k (not worker, all) to {file_path}") - # np.savetxt(file_path, bayes_k, delimiter="\t") + bayes_mask[bayes_mask != 0] = 1 bayes_mask = np.array(bayes_mask, dtype=bool) # Calculate Bayes probabilities bayes_abs = np.abs(bayes_k) - # file_path = output_path / "bayes_abs_original.tsv" - # logger.debug(f"Saving bayes_abs to {file_path}") - # np.savetxt(file_path, bayes_abs, delimiter="\t") bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C - # file_path = output_path / "bayes_p_original.tsv" - # logger.debug(f"Saving bayes_p to {file_path}") - # np.savetxt(file_path, bayes_p, delimiter="\t") # ! Marc said this is needed to not identify the features with themselves bayes_abs[bayes_mask] = np.min( bayes_abs ) # Bring feature_i feature_i associations to minimum sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C - # file_path = output_path / "sort_ids_original_script.tsv" - # logger.debug(f"Saving sort_ids to {file_path}") - # np.savetxt(file_path, sort_ids, delimiter="\t") logger.debug(f"sort_ids are {sort_ids}") prob = np.take(bayes_p, sort_ids) # 1D: N x C - # file_path = output_path / "prob_original_final.tsv" - # logger.debug(f"Saving prob to {file_path}") - # np.savetxt(file_path, prob, delimiter="\t") logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") # Sort Bayes bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C - # file_path = output_path / "sorted_bayes_k_original_script.tsv" - # logger.debug(f"Saving sorted_bayes_k to {file_path}") - # np.savetxt(file_path, bayes_k, delimiter="\t") # Calculate FDR fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D - # file_path = output_path / "fdr_original_script.tsv" - # logger.debug(f"Saving fdr to {file_path}") - # np.savetxt(file_path, fdr, delimiter="\t") idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) logger.debug(f"Index is {idx}") - # file_path = output_path / "idx_original_script.tsv" - # logger.debug(f"Saving idx to {file_path}") - # np.savetxt(file_path, idx, delimiter='\t') logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] From 202eb74b414a46ceb9dfa31de7853fc0e138b2ed Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 13:46:01 +0200 Subject: [PATCH 55/94] :fire: remove unused code --- src/move/tasks/identify_associations.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index ce346d30..fb78c1b7 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -653,12 +653,6 @@ def _bayes_approach( baseline_dataloader.dataset.con_all[0, :] - dataloaders[i].dataset.con_all[0, :] ) - # Save diff as a file - # Extract the data and mask separately - data = diff.data # Extract the data from the masked array - mask = diff.mask # Extract the mask from the masked array - # Replace masked values with a placeholder (e.g., np.nan) - data[mask] = np.nan bayes_mask[bayes_mask != 0] = 1 bayes_mask = np.array(bayes_mask, dtype=bool) From d6bc896b8505d374485056871585281761d5ba2d Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 13:56:32 +0200 Subject: [PATCH 56/94] :art: reorder functions --- src/move/data/perturbations.py | 388 ++++++++++++++++----------------- 1 file changed, 194 insertions(+), 194 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index d8567f8a..17c0f33c 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -20,13 +20,66 @@ ContinuousPerturbationType = Literal["minimum", "maximum", "plus_std", "minus_std"] -def perturb_continuous_data_one( +def perturb_categorical_data( + baseline_dataloader: DataLoader, + cat_dataset_names: list[str], + target_dataset_name: str, + target_value: np.ndarray, +) -> list[DataLoader]: + """Add perturbations to categorical data. For each feature in the target + dataset, change its value to target. + + Args: + baseline_dataloader: Baseline dataloader + cat_dataset_names: List of categorical dataset names + target_dataset_name: Target categorical dataset to perturb + target_value: Target value + + Returns: + List of dataloaders containing all perturbed datasets + """ + + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.cat_shapes is not None + assert baseline_dataset.cat_all is not None + + target_idx = cat_dataset_names.index(target_dataset_name) + splits = np.cumsum( + [0] + [int.__mul__(*shape) for shape in baseline_dataset.cat_shapes] + ) + slice_ = slice(*splits[target_idx : target_idx + 2]) + + target_shape = baseline_dataset.cat_shapes[target_idx] + num_features = target_shape[0] + + dataloaders = [] + for i in range(num_features): + perturbed_cat = baseline_dataset.cat_all.clone() + target_dataset = perturbed_cat[:, slice_].view( + baseline_dataset.num_samples, *target_shape + ) + target_dataset[:, i, :] = torch.FloatTensor(target_value) + perturbed_dataset = MOVEDataset( + perturbed_cat, + baseline_dataset.con_all, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + dataloaders.append(perturbed_dataloader) + return dataloaders + + +def perturb_continuous_data( baseline_dataloader: DataLoader, con_dataset_names: list[str], target_dataset_name: str, target_value: float, - index_pert_feat: int, # Index of the datasetto perturb -) -> DataLoader: # change list(DataLoader) to just one DataLoader +) -> list[DataLoader]: """Add perturbations to continuous data. For each feature in the target dataset, change its value to target. @@ -34,10 +87,10 @@ def perturb_continuous_data_one( baseline_dataloader: Baseline dataloader con_dataset_names: List of continuous dataset names target_dataset_name: Target continuous dataset to perturb - target_value: Target value. + target_value: Target value Returns: - One dataloader, with the ith dataset perturbed + List of dataloaders containing all perturbed datasets """ baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) @@ -48,23 +101,27 @@ def perturb_continuous_data_one( splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) - i = index_pert_feat - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - target_dataset[:, i] = torch.FloatTensor([target_value]) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) + num_features = baseline_dataset.con_shapes[target_idx] - return perturbed_dataloader + dataloaders = [] + for i in range(num_features): + perturbed_con = baseline_dataset.con_all.clone() + target_dataset = perturbed_con[:, slice_] + target_dataset[:, i] = torch.FloatTensor([target_value]) + perturbed_dataset = MOVEDataset( + baseline_dataset.cat_all, + perturbed_con, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + dataloaders.append(perturbed_dataloader) + + return dataloaders def perturb_categorical_data_one( @@ -120,216 +177,53 @@ def perturb_categorical_data_one( return perturbed_dataloader -# We will keep the input almost the same, to make everything easier -# However, I have to introduce a variable that allows me to index the specific -# dataloader I want to create (index_pert_feat) -def perturb_continuous_data_extended_one( +def perturb_continuous_data_one( baseline_dataloader: DataLoader, con_dataset_names: list[str], target_dataset_name: str, - perturbation_type: ContinuousPerturbationType, - index_pert_feat: int, -) -> ( - DataLoader -): # But we change the output from list[DataLoader] to just one DataLoader - logger = get_logger(__name__) + target_value: float, + index_pert_feat: int, # Index of the datasetto perturb +) -> DataLoader: # change list(DataLoader) to just one DataLoader """Add perturbations to continuous data. For each feature in the target - dataset, change the feature's value in all samples (in rows): - 1,2) substituting this feature in all samples by the feature's minimum/maximum value - 3,4) Adding/Substracting one standard deviation to the sample's feature value. + dataset, change its value to target. Args: baseline_dataloader: Baseline dataloader con_dataset_names: List of continuous dataset names target_dataset_name: Target continuous dataset to perturb - perturbation_type: 'minimum', 'maximum', 'plus_std' or 'minus_std'. - #output_subpath: path where the figure showing the perturbation will be saved - index_pert_feat: Index we want to perturb + target_value: Target value. Returns: - - Dataloader with the ith feature (index_pert_feat) perturbed. - - Note: - This function was created so that it could generalize to non-normalized - datasets. Scaling is done per dataset, not per feature -> slightly different - stds feature to feature. + One dataloader, with the ith dataset perturbed """ - logger.debug( - f"Inside perturb_continuous_data_extended_one for feature {index_pert_feat}" - ) baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) assert baseline_dataset.con_shapes is not None assert baseline_dataset.con_all is not None - target_idx = con_dataset_names.index(target_dataset_name) # dataset index + target_idx = con_dataset_names.index(target_dataset_name) splits = np.cumsum([0] + baseline_dataset.con_shapes) slice_ = slice(*splits[target_idx : target_idx + 2]) - # Use it only if we want to perturb all features in the target dataset - # num_features = baseline_dataset.con_shapes[target_idx] - - # Now, instead of the for loop that iterates over all the features we want to - # perturb, we do it only for one feature, the one indicated in index_pert_feat - logger.debug(f"Setting up perturbed_con for feature {index_pert_feat}") - + i = index_pert_feat perturbed_con = baseline_dataset.con_all.clone() target_dataset = perturbed_con[:, slice_] - - logger.debug( - f"Changing to desired perturbation value for feature {index_pert_feat}" - ) - # Change the desired feature value by: - min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( - target_dataset - ) - if perturbation_type == "minimum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [min_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "maximum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [max_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "plus_std": - target_dataset[:, index_pert_feat] += torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "minus_std": - target_dataset[:, index_pert_feat] -= torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - logger.debug(f"Perturbation succesful for feature {index_pert_feat}") - - logger.debug( - f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" - ) - + target_dataset[:, i] = torch.FloatTensor([target_value]) perturbed_dataset = MOVEDataset( baseline_dataset.cat_all, perturbed_con, baseline_dataset.cat_shapes, baseline_dataset.con_shapes, ) - perturbed_dataloader = DataLoader( perturbed_dataset, shuffle=False, batch_size=baseline_dataloader.batch_size, ) - # dataloaders.append(perturbed_dataloader) - - logger.debug( - f"Finished perturb_continuous_data_extended_one for feature {index_pert_feat}" - ) return perturbed_dataloader -def perturb_categorical_data( - baseline_dataloader: DataLoader, - cat_dataset_names: list[str], - target_dataset_name: str, - target_value: np.ndarray, -) -> list[DataLoader]: - """Add perturbations to categorical data. For each feature in the target - dataset, change its value to target. - - Args: - baseline_dataloader: Baseline dataloader - cat_dataset_names: List of categorical dataset names - target_dataset_name: Target categorical dataset to perturb - target_value: Target value - - Returns: - List of dataloaders containing all perturbed datasets - """ - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.cat_shapes is not None - assert baseline_dataset.cat_all is not None - - target_idx = cat_dataset_names.index(target_dataset_name) - splits = np.cumsum( - [0] + [int.__mul__(*shape) for shape in baseline_dataset.cat_shapes] - ) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - target_shape = baseline_dataset.cat_shapes[target_idx] - num_features = target_shape[0] - - dataloaders = [] - for i in range(num_features): - perturbed_cat = baseline_dataset.cat_all.clone() - target_dataset = perturbed_cat[:, slice_].view( - baseline_dataset.num_samples, *target_shape - ) - target_dataset[:, i, :] = torch.FloatTensor(target_value) - perturbed_dataset = MOVEDataset( - perturbed_cat, - baseline_dataset.con_all, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - dataloaders.append(perturbed_dataloader) - return dataloaders - - -def perturb_continuous_data( - baseline_dataloader: DataLoader, - con_dataset_names: list[str], - target_dataset_name: str, - target_value: float, -) -> list[DataLoader]: - """Add perturbations to continuous data. For each feature in the target - dataset, change its value to target. - - Args: - baseline_dataloader: Baseline dataloader - con_dataset_names: List of continuous dataset names - target_dataset_name: Target continuous dataset to perturb - target_value: Target value - - Returns: - List of dataloaders containing all perturbed datasets - """ - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.con_shapes is not None - assert baseline_dataset.con_all is not None - - target_idx = con_dataset_names.index(target_dataset_name) - splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) - - num_features = baseline_dataset.con_shapes[target_idx] - - dataloaders = [] - for i in range(num_features): - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - target_dataset[:, i] = torch.FloatTensor([target_value]) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, - batch_size=baseline_dataloader.batch_size, - ) - dataloaders.append(perturbed_dataloader) - - return dataloaders - - def perturb_continuous_data_extended( baseline_dataloader: DataLoader, con_dataset_names: list[str], @@ -413,3 +307,109 @@ def perturb_continuous_data_extended( fig.savefig(fig_path) return dataloaders + + +# We will keep the input almost the same, to make everything easier +# However, I have to introduce a variable that allows me to index the specific +# dataloader I want to create (index_pert_feat) +def perturb_continuous_data_extended_one( + baseline_dataloader: DataLoader, + con_dataset_names: list[str], + target_dataset_name: str, + perturbation_type: ContinuousPerturbationType, + index_pert_feat: int, +) -> ( + DataLoader +): # But we change the output from list[DataLoader] to just one DataLoader + logger = get_logger(__name__) + """Add perturbations to continuous data. For each feature in the target + dataset, change the feature's value in all samples (in rows): + 1,2) substituting this feature in all samples by the feature's minimum/maximum value + 3,4) Adding/Substracting one standard deviation to the sample's feature value. + + Args: + baseline_dataloader: Baseline dataloader + con_dataset_names: List of continuous dataset names + target_dataset_name: Target continuous dataset to perturb + perturbation_type: 'minimum', 'maximum', 'plus_std' or 'minus_std'. + #output_subpath: path where the figure showing the perturbation will be saved + index_pert_feat: Index we want to perturb + + Returns: + - Dataloader with the ith feature (index_pert_feat) perturbed. + + Note: + This function was created so that it could generalize to non-normalized + datasets. Scaling is done per dataset, not per feature -> slightly different + stds feature to feature. + """ + logger.debug( + f"Inside perturb_continuous_data_extended_one for feature {index_pert_feat}" + ) + + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.con_shapes is not None + assert baseline_dataset.con_all is not None + + target_idx = con_dataset_names.index(target_dataset_name) # dataset index + splits = np.cumsum([0] + baseline_dataset.con_shapes) + slice_ = slice(*splits[target_idx : target_idx + 2]) + + # Use it only if we want to perturb all features in the target dataset + # num_features = baseline_dataset.con_shapes[target_idx] + + # Now, instead of the for loop that iterates over all the features we want to + # perturb, we do it only for one feature, the one indicated in index_pert_feat + logger.debug(f"Setting up perturbed_con for feature {index_pert_feat}") + + perturbed_con = baseline_dataset.con_all.clone() + target_dataset = perturbed_con[:, slice_] + + logger.debug( + f"Changing to desired perturbation value for feature {index_pert_feat}" + ) + # Change the desired feature value by: + min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( + target_dataset + ) + if perturbation_type == "minimum": + target_dataset[:, index_pert_feat] = torch.FloatTensor( + [min_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "maximum": + target_dataset[:, index_pert_feat] = torch.FloatTensor( + [max_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "plus_std": + target_dataset[:, index_pert_feat] += torch.FloatTensor( + [std_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "minus_std": + target_dataset[:, index_pert_feat] -= torch.FloatTensor( + [std_feat_val_list[index_pert_feat]] + ) + logger.debug(f"Perturbation succesful for feature {index_pert_feat}") + + logger.debug( + f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" + ) + + perturbed_dataset = MOVEDataset( + baseline_dataset.cat_all, + perturbed_con, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=baseline_dataloader.batch_size, + ) + # dataloaders.append(perturbed_dataloader) + + logger.debug( + f"Finished perturb_continuous_data_extended_one for feature {index_pert_feat}" + ) + + return perturbed_dataloader From b99ce97989a12d1331d79dee2dec33a44945eb21 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 14:02:04 +0200 Subject: [PATCH 57/94] :construction: move bayes_parallel to own module - start rewriting _bayes_approach to contain more well defined steps (define separate functions for each step) - check that the results will match roughly --- src/move/tasks/bayes_parallel.py | 381 ++++++++++++++++++++++++ src/move/tasks/identify_associations.py | 359 +--------------------- 2 files changed, 382 insertions(+), 358 deletions(-) create mode 100644 src/move/tasks/bayes_parallel.py diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py new file mode 100644 index 00000000..27681a27 --- /dev/null +++ b/src/move/tasks/bayes_parallel.py @@ -0,0 +1,381 @@ +from pathlib import Path +from typing import Literal, cast + +import hydra +import numpy as np +import torch +import torch.multiprocessing +from torch.multiprocessing import Pool +from torch.utils.data import DataLoader + +from move.conf.schema import IdentifyAssociationsBayesConfig, MOVEConfig +from move.core.logging import get_logger +from move.data.dataloaders import MOVEDataset +from move.data.perturbations import ( + ContinuousPerturbationType, + perturb_continuous_data_extended_one, +) +from move.models.vae import VAE + +# We can do three types of statistical tests. Multiprocessing is only implemented +# for bayes at the moment +TaskType = Literal["bayes", "ttest", "ks"] + +# Possible values for continuous pertrubation +CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] + + +def _bayes_approach_worker(args): + """ + Worker function to calculate mean differences and Bayes factors for one feature. + """ + # Set the number of threads available: + # VERY IMPORTANT, TO AVOID CPU OVERSUBSCRIPTION + torch.set_num_threads(1) + + # Unpack arguments. + ( + config, + task_config, + baseline_dataloader, + num_samples, + num_continuous, + i, + models_path, + continuous_shapes, + categorical_shapes, + nan_mask, + ) = args + # Initialize logging + logger = get_logger(__name__) + logger.debug(f"Inside the worker function for num_perturbed {i}") + + # Now we are inside the num_perturbed loop, we will do this for each of the + # perturbed features + # Now, mean_diff will not have a first dimension for num_perturbed, because we will + # not store it for each perturbed feature + # we will use it in each loop for calculating the bayes factors, and then overwrite + # its content with a new perturbed feature + # mean_diff will contain the differences between the baseline and the perturbed + # reconstruction for feature i, taking into account + # all refits (all refits have the same importance) + # We also set up bayes_k, which has the same dimensions as mean_diff + mean_diff = np.zeros((num_samples, num_continuous)) + bayes_k_worker = np.zeros( + (num_continuous) + ) # This will be what we will put in bayes_k[i,:] + # Set the normalizer + normalizer = ( + 1 / task_config.num_refits + ) # Divide by the number of refits. All the refits will have the same importance + + # Create perturbed dataloader for the current feature (i) + logger.debug(f"Creating perturbed dataloader for feature {i}") + perturbed_dataloader = perturb_continuous_data_extended_one( + baseline_dataloader=baseline_dataloader, + con_dataset_names=config.data.categorical_names, # ! error: continuous_names + target_dataset_name=task_config.target_dataset, + perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), + index_pert_feat=i, + ) + logger.debug(f"created perturbed dataloader for feature {i}") + + # For each refit, reload baseline reconstruction (obtained in bayes_parallel + # function). Also, get the reconstruction for the perturbed dataloader + for j in range(task_config.num_refits): + + model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" + reconstruction_path = ( + models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" + ) + if reconstruction_path.exists(): + logger.debug( + f"Loading baseline reconstruction from {reconstruction_path}, " + "in the worker function" + ) + baseline_recon = torch.load(reconstruction_path) + + logger.debug(f"Loading model {model_path}, using load function") + model: VAE = hydra.utils.instantiate( + task_config.model, + continuous_shapes=continuous_shapes, + categorical_shapes=categorical_shapes, + ) + device = torch.device("cuda" if task_config.model.cuda else "cpu") + logger.debug(f"Loading model from {model_path}") + model.load_state_dict(torch.load(model_path)) + logger.debug(f"Loaded model from {model_path}") + model.to(device) + model.eval() + + logger.debug(f"Reconstructing num_perturbed {i}, with model {model_path}") + _, perturb_recon = model.reconstruct( + perturbed_dataloader + ) # Instead of dataloaders[i], create the perturbed one here and + # use it only here + logger.debug( + f"Perturbed reconstruction succesful for feature {i}, model {model}" + ) + + # diff is a matrix with the same dimensions as perturb_recon and baseline_recon + # (rows are samples and columns all the continuous features) + # We calculate diff for each refit, and add it to mean_diff after dividing by + # the number of refits + logger.debug(f"Calculating diff for num_perturbed {i}, with model {model}") + diff = perturb_recon - baseline_recon + logger.debug( + f"Calculating mean_diff for num_perturbed {i}, with model {model}" + ) + mean_diff += diff * normalizer + logger.debug(f"Deleting model {model_path}, to see if I can free up space?") + del model + logger.debug(f"Deleted model {model_path} in worker {i} to save some space") + + logger.debug(f"mean_diff for feature {i}, calculated, using all refits") + mean_diff_shape = mean_diff.shape + logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") + + # Apply nan_mask to the result in mean_diff + diff_mask = np.ma.masked_array(mean_diff, mask=nan_mask) + diff_mask_shape = diff_mask.shape + logger.debug( + f"Calculated diff_masked for feature {i}. Its shape is {diff_mask_shape}" + ) + prob = np.ma.compressed(np.mean(diff_mask > 1e-8, axis=0)) + logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") + + # Calculate bayes factor + bayes_k_worker = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + + logger.debug( + f"bayes factor calculated for feature {i}. Woker function {i} finished" + ) + + # Return bayes_k_worker and the index of the feature + return i, bayes_k_worker + + +def _bayes_approach_parallel( + config: MOVEConfig, + task_config: IdentifyAssociationsBayesConfig, + train_dataloader: DataLoader, + baseline_dataloader: DataLoader, + num_perturbed: int, + num_samples: int, + num_continuous: int, + models_path: Path, + # indexes, +): + logger = get_logger(__name__) + logger.debug("Inside the bayes_parallel function") + + # First, I train or reload the models (number of refits), + # and save the baseline reconstruction. + # We train and get the reconstruction outside to make sure + # that we use the same model and use the same + # baseline reconstruction for all the worker functions + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + + assert task_config.model is not None + device = torch.device("cuda" if task_config.model.cuda else "cpu") + logger.debug("Model moved to device in bayes_approach_parallel") + + # Train or reload models + logger = get_logger(__name__) + logger.info("Training or reloading models") + + for j in range( + task_config.num_refits + ): # We create as many models (refits) as indicated in the config file + # For each j (number of refits) we train a different model, but on the same data + # Initialize model + model: VAE = hydra.utils.instantiate( + task_config.model, + continuous_shapes=baseline_dataset.con_shapes, + categorical_shapes=baseline_dataset.cat_shapes, + ) + if ( + j == 0 + ): # First, we see if the models are already created (if we trained them + # before). for each j, we check if model number j has already been created. + logger.debug(f"Model: {model}") + + # Define paths for the baseline reconstruction and for the model + reconstruction_path = ( + models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" + ) + model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" + + if ( + model_path.exists() + ): # If the models were already created, we load them only if we need to get a + # baseline reconstruction. Otherwise, nothing needs to be done at this point + logger.debug(f"Model {model_path} already exists") + if not reconstruction_path.exists(): + logger.debug(f"Re-loading refit {j + 1}/{task_config.num_refits}") + model.load_state_dict(torch.load(model_path)) + model.to(device) + logger.debug(f"Model {j} reloaded") + else: + logger.debug( + f"Baseline reconstruction for {reconstruction_path} already exists" + f", no need to load model {model_path} " + ) + else: # If the models are not created yet, he have to train them, with the + # parameters we indicated in the config file + logger.debug(f"Training refit {j + 1}/{task_config.num_refits}") + model.to(device) + hydra.utils.call( + task_config.training_loop, + model=model, + train_dataloader=train_dataloader, + ) + # Save the refits, to use them later + if task_config.save_refits: + # pickle_protocol=4 is necessary for very big models + torch.save(model.state_dict(), model_path, pickle_protocol=4) + + # Calculate baseline reconstruction + # For each model j, we get a different reconstruction for the baseline. + # We haven't perturbed anything yet, we are just + # getting the reconstruction for the baseline, to make sure that we get + # the same reconstruction for each refit, we cannot + # do it inside each process because the results might be different + + if reconstruction_path.exists(): + logger.debug(f"Baseline reconstruction for model {j} already created") + else: + model.eval() + _, baseline_recon = model.reconstruct(baseline_dataloader) + + # Save the baseline reconstruction for each model + logger.debug(f"Saving baseline reconstruction {j}") + torch.save(baseline_recon, reconstruction_path, pickle_protocol=4) + logger.debug(f"Saved baseline reconstruction {j}") + del model + + # Get NaN mask ouside the worker function, it is the same for all. + logger.debug("Creating NaN mask in bayes_parallel") + orig_con = ( + baseline_dataset.con_all + ) # Original continuous data of the baseline dataset (all data) + nan_mask = ( + orig_con == 0 + ).numpy() # Creates a mask to identify NaN values in the original data. + + # Define more arguments that are needed for the worker functions + continuous_shapes = baseline_dataset.con_shapes + categorical_shapes = baseline_dataset.cat_shapes + + """ + Perform parallelized bayes approach. + """ + logger.debug("Starting parallelization") + + # Define arguments for each worker, and iterate over models and perturbed features + args = [ + ( + config, + task_config, + baseline_dataloader, + num_samples, + num_continuous, + i, + models_path, + continuous_shapes, + categorical_shapes, + nan_mask, + ) + for i in range(num_perturbed) + ] + + with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: + logger.debug("Inside the pool loops") + # Map worker function to arguments + # We get the bayes_k matrix, filled for all the perturbed features + results = pool.map(_bayes_approach_worker, args) + + logger.info("Pool multiprocess completed. Calculating bayes_abs and bayes_p") + + bayes_k = np.empty((num_perturbed, num_continuous)) + + # Get results in the correct order + for i, computed_bayes_k in results: + logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") + bayes_k[i, :] = computed_bayes_k + + # Once we have the Bayes factors for all features, we can calculate Bayes + # probabilities + bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) + bayes_max = np.max(bayes_abs) + bayes_min = np.min(bayes_abs) + bayes_abs_shape = bayes_abs.shape + logger.debug( + f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " + f"Bayes_abs shape is {bayes_abs_shape}" + ) + + bayes_p = np.exp(bayes_abs) / ( + 1 + np.exp(bayes_abs) + ) # 2D: N x C (perturbed features as rows, all continuous features as columns) + bayes_p_shape = bayes_p.shape + logger.debug(f"bayes_p shape is {bayes_p_shape}") + # file_path = output_path / "bayes_p_multi.tsv" + # logger.debug(f"Saving bayes_p to {file_path}") + # np.savetxt(file_path, bayes_p, delimiter='\t') + + # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE + # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results + + # Get only the significant associations: + # This will flatten the array,so we get all bayes_abs for all perturbed features + # vs all continuous features in one 1D array + # Then, we sort them, and get the indexes in the flattened array. So, we get an + # list of sorted indexes in the flatenned array + sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C + # file_path = output_path / "sort_ids_multi.tsv" + logger.debug(f"sort_ids are {sort_ids}") + # logger.debug(f"Saving sort_ids to {file_path}") + # np.savetxt(file_path, sort_ids, delimiter='\t') + + prob = np.take(bayes_p, sort_ids) # 1D: N x C + # bayes_p is the array from which elements will be taken. + # sort_ids contains the indices that determine the order in which elements should + # be taken from bayes_p. + # This operation essentially rearranges the elements of bayes_p based on the + # sorting order specified by sort_ids + # np.take considers the input array as if it were flattened when extracting + # elements using the provided indices. + # So, even though sort_ids is obtained from a flattened version of bayes_abs, + # np.take understands how to map these indices + # correctly to the original shape of bayes_p. We get a flattened array? + logger.debug(f"prob is {prob}") + logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") + + # Sort bayes_k in descending order, aligning with the sorted bayes_abs. + bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C + logger.debug(f"bayes k, after sorting with the ids, is {bayes_k}") + + logger.debug("Calculating fdr") + # Calculate FDR + fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? + logger.debug(f"fdr is {fdr}") + logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") + idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) + logger.debug(f"Index is {idx}") + # idx will contain the index of the element in fdr that is closest + # to task_config.sig_threshold. + # This line essentially finds the index where the False Discovery Rate (fdr) is + # closest to the significance threshold + # (task_config.sig_threshold). + + logger.debug(f"Sig ids sorted is {sort_ids}") + logger.debug(f"Sigids[idx] is {sort_ids[:idx]}") + + # Return elements only up to idx. They will be the significant findings + # sort_ids[:idx]: Indices of features sorted by significance. + # prob[:idx]: Probabilities of significant associations for selected features. + # fdr[:idx]: False Discovery Rate values for selected features. + # bayes_k[:idx]: Bayes Factors indicating the strength of evidence for selected + # associations. + return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index fb78c1b7..926ec834 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -12,7 +12,6 @@ import torch.multiprocessing from omegaconf import OmegaConf from scipy.stats import ks_2samp, pearsonr # type: ignore -from torch.multiprocessing import Pool from torch.utils.data import DataLoader from move.analysis.metrics import get_2nd_order_polynomial @@ -31,10 +30,10 @@ ContinuousPerturbationType, perturb_categorical_data, perturb_continuous_data_extended, - perturb_continuous_data_extended_one, ) from move.data.preprocessing import one_hot_encode_single from move.models.vae import VAE +from move.tasks.bayes_parallel import _bayes_approach_parallel from move.visualization.dataset_distributions import ( plot_correlations, plot_cumulative_distributions, @@ -195,362 +194,6 @@ def prepare_for_continuous_perturbation( return (dataloaders, nan_mask, feature_mask) -def _bayes_approach_worker(args): - """ - Worker function to calculate mean differences and Bayes factors for one feature. - """ - # Set the number of threads available: - # VERY IMPORTANT, TO AVOID CPU OVERSUBSCRIPTION - torch.set_num_threads(1) - - # Unpack arguments. - ( - config, - task_config, - baseline_dataloader, - num_samples, - num_continuous, - i, - models_path, - continuous_shapes, - categorical_shapes, - nan_mask, - ) = args - # Initialize logging - logger = get_logger(__name__) - logger.debug(f"Inside the worker function for num_perturbed {i}") - - # Now we are inside the num_perturbed loop, we will do this for each of the - # perturbed features - # Now, mean_diff will not have a first dimension for num_perturbed, because we will - # not store it for each perturbed feature - # we will use it in each loop for calculating the bayes factors, and then overwrite - # its content with a new perturbed feature - # mean_diff will contain the differences between the baseline and the perturbed - # reconstruction for feature i, taking into account - # all refits (all refits have the same importance) - # We also set up bayes_k, which has the same dimensions as mean_diff - mean_diff = np.zeros((num_samples, num_continuous)) - bayes_k_worker = np.zeros( - (num_continuous) - ) # This will be what we will put in bayes_k[i,:] - # Set the normalizer - normalizer = ( - 1 / task_config.num_refits - ) # Divide by the number of refits. All the refits will have the same importance - - # Create perturbed dataloader for the current feature (i) - logger.debug(f"Creating perturbed dataloader for feature {i}") - perturbed_dataloader = perturb_continuous_data_extended_one( - baseline_dataloader=baseline_dataloader, - con_dataset_names=config.data.categorical_names, # ! error: continuous_names - target_dataset_name=task_config.target_dataset, - perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), - index_pert_feat=i, - ) - logger.debug(f"created perturbed dataloader for feature {i}") - - # For each refit, reload baseline reconstruction (obtained in bayes_parallel - # function). Also, get the reconstruction for the perturbed dataloader - for j in range(task_config.num_refits): - - model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" - reconstruction_path = ( - models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" - ) - if reconstruction_path.exists(): - logger.debug( - f"Loading baseline reconstruction from {reconstruction_path}, " - "in the worker function" - ) - baseline_recon = torch.load(reconstruction_path) - - logger.debug(f"Loading model {model_path}, using load function") - model: VAE = hydra.utils.instantiate( - task_config.model, - continuous_shapes=continuous_shapes, - categorical_shapes=categorical_shapes, - ) - device = torch.device("cuda" if task_config.model.cuda else "cpu") - logger.debug(f"Loading model from {model_path}") - model.load_state_dict(torch.load(model_path)) - logger.debug(f"Loaded model from {model_path}") - model.to(device) - model.eval() - - logger.debug(f"Reconstructing num_perturbed {i}, with model {model_path}") - _, perturb_recon = model.reconstruct( - perturbed_dataloader - ) # Instead of dataloaders[i], create the perturbed one here and - # use it only here - logger.debug( - f"Perturbed reconstruction succesful for feature {i}, model {model}" - ) - - # diff is a matrix with the same dimensions as perturb_recon and baseline_recon - # (rows are samples and columns all the continuous features) - # We calculate diff for each refit, and add it to mean_diff after dividing by - # the number of refits - logger.debug(f"Calculating diff for num_perturbed {i}, with model {model}") - diff = perturb_recon - baseline_recon - logger.debug( - f"Calculating mean_diff for num_perturbed {i}, with model {model}" - ) - mean_diff += diff * normalizer - logger.debug(f"Deleting model {model_path}, to see if I can free up space?") - del model - logger.debug(f"Deleted model {model_path} in worker {i} to save some space") - - logger.debug(f"mean_diff for feature {i}, calculated, using all refits") - mean_diff_shape = mean_diff.shape - logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") - - # Apply nan_mask to the result in mean_diff - diff_mask = np.ma.masked_array(mean_diff, mask=nan_mask) - diff_mask_shape = diff_mask.shape - logger.debug( - f"Calculated diff_masked for feature {i}. Its shape is {diff_mask_shape}" - ) - prob = np.ma.compressed(np.mean(diff_mask > 1e-8, axis=0)) - logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") - - # Calculate bayes factor - bayes_k_worker = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) - - logger.debug( - f"bayes factor calculated for feature {i}. Woker function {i} finished" - ) - - # Return bayes_k_worker and the index of the feature - return i, bayes_k_worker - - -def _bayes_approach_parallel( - config: MOVEConfig, - task_config: IdentifyAssociationsBayesConfig, - train_dataloader: DataLoader, - baseline_dataloader: DataLoader, - num_perturbed: int, - num_samples: int, - num_continuous: int, - models_path: Path, - # indexes, -): - logger = get_logger(__name__) - logger.debug("Inside the bayes_parallel function") - - # First, I train or reload the models (number of refits), - # and save the baseline reconstruction. - # We train and get the reconstruction outside to make sure - # that we use the same model and use the same - # baseline reconstruction for all the worker functions - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - - assert task_config.model is not None - device = torch.device("cuda" if task_config.model.cuda else "cpu") - logger.debug("Model moved to device in bayes_approach_parallel") - - # Train or reload models - logger = get_logger(__name__) - logger.info("Training or reloading models") - - for j in range( - task_config.num_refits - ): # We create as many models (refits) as indicated in the config file - # For each j (number of refits) we train a different model, but on the same data - # Initialize model - model: VAE = hydra.utils.instantiate( - task_config.model, - continuous_shapes=baseline_dataset.con_shapes, - categorical_shapes=baseline_dataset.cat_shapes, - ) - if ( - j == 0 - ): # First, we see if the models are already created (if we trained them - # before). for each j, we check if model number j has already been created. - logger.debug(f"Model: {model}") - - # Define paths for the baseline reconstruction and for the model - reconstruction_path = ( - models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" - ) - model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" - - if ( - model_path.exists() - ): # If the models were already created, we load them only if we need to get a - # baseline reconstruction. Otherwise, nothing needs to be done at this point - logger.debug(f"Model {model_path} already exists") - if not reconstruction_path.exists(): - logger.debug(f"Re-loading refit {j + 1}/{task_config.num_refits}") - model.load_state_dict(torch.load(model_path)) - model.to(device) - logger.debug(f"Model {j} reloaded") - else: - logger.debug( - f"Baseline reconstruction for {reconstruction_path} already exists" - f", no need to load model {model_path} " - ) - else: # If the models are not created yet, he have to train them, with the - # parameters we indicated in the config file - logger.debug(f"Training refit {j + 1}/{task_config.num_refits}") - model.to(device) - hydra.utils.call( - task_config.training_loop, - model=model, - train_dataloader=train_dataloader, - ) - # Save the refits, to use them later - if task_config.save_refits: - # pickle_protocol=4 is necessary for very big models - torch.save(model.state_dict(), model_path, pickle_protocol=4) - - # Calculate baseline reconstruction - # For each model j, we get a different reconstruction for the baseline. - # We haven't perturbed anything yet, we are just - # getting the reconstruction for the baseline, to make sure that we get - # the same reconstruction for each refit, we cannot - # do it inside each process because the results might be different - - if reconstruction_path.exists(): - logger.debug(f"Baseline reconstruction for model {j} already created") - else: - model.eval() - _, baseline_recon = model.reconstruct(baseline_dataloader) - - # Save the baseline reconstruction for each model - logger.debug(f"Saving baseline reconstruction {j}") - torch.save(baseline_recon, reconstruction_path, pickle_protocol=4) - logger.debug(f"Saved baseline reconstruction {j}") - del model - - # Get NaN mask ouside the worker function, it is the same for all. - logger.debug("Creating NaN mask in bayes_parallel") - orig_con = ( - baseline_dataset.con_all - ) # Original continuous data of the baseline dataset (all data) - nan_mask = ( - orig_con == 0 - ).numpy() # Creates a mask to identify NaN values in the original data. - - # Define more arguments that are needed for the worker functions - continuous_shapes = baseline_dataset.con_shapes - categorical_shapes = baseline_dataset.cat_shapes - - """ - Perform parallelized bayes approach. - """ - logger.debug("Starting parallelization") - - # Define arguments for each worker, and iterate over models and perturbed features - args = [ - ( - config, - task_config, - baseline_dataloader, - num_samples, - num_continuous, - i, - models_path, - continuous_shapes, - categorical_shapes, - nan_mask, - ) - for i in range(num_perturbed) - ] - - with Pool(processes=torch.multiprocessing.cpu_count() - 1) as pool: - logger.debug("Inside the pool loops") - # Map worker function to arguments - # We get the bayes_k matrix, filled for all the perturbed features - results = pool.map(_bayes_approach_worker, args) - - logger.info("Pool multiprocess completed. Calculating bayes_abs and bayes_p") - - bayes_k = np.empty((num_perturbed, num_continuous)) - - # Get results in the correct order - for i, computed_bayes_k in results: - logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") - bayes_k[i, :] = computed_bayes_k - - # Once we have the Bayes factors for all features, we can calculate Bayes - # probabilities - bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) - bayes_max = np.max(bayes_abs) - bayes_min = np.min(bayes_abs) - bayes_abs_shape = bayes_abs.shape - logger.debug( - f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " - f"Bayes_abs shape is {bayes_abs_shape}" - ) - - bayes_p = np.exp(bayes_abs) / ( - 1 + np.exp(bayes_abs) - ) # 2D: N x C (perturbed features as rows, all continuous features as columns) - bayes_p_shape = bayes_p.shape - logger.debug(f"bayes_p shape is {bayes_p_shape}") - # file_path = output_path / "bayes_p_multi.tsv" - # logger.debug(f"Saving bayes_p to {file_path}") - # np.savetxt(file_path, bayes_p, delimiter='\t') - - # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE - # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results - - # Get only the significant associations: - # This will flatten the array,so we get all bayes_abs for all perturbed features - # vs all continuous features in one 1D array - # Then, we sort them, and get the indexes in the flattened array. So, we get an - # list of sorted indexes in the flatenned array - sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C - # file_path = output_path / "sort_ids_multi.tsv" - logger.debug(f"sort_ids are {sort_ids}") - # logger.debug(f"Saving sort_ids to {file_path}") - # np.savetxt(file_path, sort_ids, delimiter='\t') - - prob = np.take(bayes_p, sort_ids) # 1D: N x C - # bayes_p is the array from which elements will be taken. - # sort_ids contains the indices that determine the order in which elements should - # be taken from bayes_p. - # This operation essentially rearranges the elements of bayes_p based on the - # sorting order specified by sort_ids - # np.take considers the input array as if it were flattened when extracting - # elements using the provided indices. - # So, even though sort_ids is obtained from a flattened version of bayes_abs, - # np.take understands how to map these indices - # correctly to the original shape of bayes_p. We get a flattened array? - logger.debug(f"prob is {prob}") - logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") - - # Sort bayes_k in descending order, aligning with the sorted bayes_abs. - bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C - logger.debug(f"bayes k, after sorting with the ids, is {bayes_k}") - - logger.debug("Calculating fdr") - # Calculate FDR - fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? - logger.debug(f"fdr is {fdr}") - logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") - idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) - logger.debug(f"Index is {idx}") - # idx will contain the index of the element in fdr that is closest - # to task_config.sig_threshold. - # This line essentially finds the index where the False Discovery Rate (fdr) is - # closest to the significance threshold - # (task_config.sig_threshold). - - logger.debug(f"Sig ids sorted is {sort_ids}") - logger.debug(f"Sigids[idx] is {sort_ids[:idx]}") - - # Return elements only up to idx. They will be the significant findings - # sort_ids[:idx]: Indices of features sorted by significance. - # prob[:idx]: Probabilities of significant associations for selected features. - # fdr[:idx]: False Discovery Rate values for selected features. - # bayes_k[:idx]: Bayes Factors indicating the strength of evidence for selected - # associations. - return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] - - def _bayes_approach( config: MOVEConfig, task_config: IdentifyAssociationsBayesConfig, From 171c91506a6878e98d1c27bace4d75f40a8fd261 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 15:10:33 +0200 Subject: [PATCH 58/94] :construction: unify interface --- src/move/tasks/bayes_parallel.py | 6 +++-- src/move/tasks/identify_associations.py | 36 +++++++++++++------------ 2 files changed, 23 insertions(+), 19 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 27681a27..69d67369 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -10,6 +10,7 @@ from move.conf.schema import IdentifyAssociationsBayesConfig, MOVEConfig from move.core.logging import get_logger +from move.core.typing import BoolArray from move.data.dataloaders import MOVEDataset from move.data.perturbations import ( ContinuousPerturbationType, @@ -160,11 +161,12 @@ def _bayes_approach_parallel( task_config: IdentifyAssociationsBayesConfig, train_dataloader: DataLoader, baseline_dataloader: DataLoader, + models_path: Path, num_perturbed: int, num_samples: int, num_continuous: int, - models_path: Path, - # indexes, + nan_mask: BoolArray, + feature_mask: BoolArray, ): logger = get_logger(__name__) logger.debug("Inside the bayes_parallel function") diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 926ec834..e8d11690 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -857,14 +857,16 @@ def identify_associations(config: MOVEConfig) -> None: task_config = cast(IdentifyAssociationsBayesConfig, task_config) if task_config.multiprocess: sig_ids, *extra_cols = _bayes_approach_parallel( - config, - task_config, - train_dataloader, - baseline_dataloader, - num_perturbed, - num_samples, - num_continuous, - models_path, + config=config, + task_config=task_config, + train_dataloader=train_dataloader, + baseline_dataloader=baseline_dataloader, + models_path=models_path, + num_perturbed=num_perturbed, + num_samples=num_samples, + num_continuous=num_continuous, + nan_mask=nan_mask, + feature_mask=feature_mask, ) logger.debug( "Completed bayes task (parallel function in main function " @@ -874,15 +876,15 @@ def identify_associations(config: MOVEConfig) -> None: sig_ids, *extra_cols = _bayes_approach( config, task_config, - train_dataloader, - baseline_dataloader, - dataloaders, - models_path, - num_perturbed, - num_samples, - num_continuous, - nan_mask, - feature_mask, + train_dataloader=train_dataloader, + baseline_dataloader=baseline_dataloader, + dataloaders=dataloaders, + models_path=models_path, + num_perturbed=num_perturbed, + num_samples=num_samples, + num_continuous=num_continuous, + nan_mask=nan_mask, + feature_mask=feature_mask, ) extra_colnames = ["proba", "fdr", "bayes_k"] From 95c9f407b1fbf8f5d8740f6b0edbbe9265102721 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 16:50:42 +0200 Subject: [PATCH 59/94] :fire: remove not-used code --- src/move/tasks/identify_associations.py | 15 --------------- 1 file changed, 15 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index e8d11690..cc2cf4bb 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -261,27 +261,12 @@ def _bayes_approach( else: _, baseline_recon = model.reconstruct(baseline_dataloader) - min_feat, max_feat = ( - np.zeros((num_perturbed, num_continuous)), - np.zeros((num_perturbed, num_continuous)), - ) - min_baseline, max_baseline = np.min(baseline_recon, axis=0), np.max( - baseline_recon, axis=0 - ) - # Calculate perturb reconstruction => keep track of mean difference for i in range(num_perturbed): _, perturb_recon = model.reconstruct(dataloaders[i]) diff = perturb_recon - baseline_recon # 2D: N x C mean_diff[i, :, :] += diff * normalizer - min_perturb, max_perturb = np.min(perturb_recon, axis=0), np.max( - perturb_recon, axis=0 - ) - min_feat[i, :], max_feat[i, :] = np.min( - [min_baseline, min_perturb], axis=0 - ), np.max([max_baseline, max_perturb], axis=0) - # Calculate Bayes factors logger.info("Identifying significant features") bayes_k = np.empty((num_perturbed, num_continuous)) From e556fae8f8bc9f1e5ffd00817c4579b827860033 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 16:54:28 +0200 Subject: [PATCH 60/94] :art: start separating recurrent code into fcts --- src/move/data/perturbations.py | 159 ++++++++++++++++----------------- 1 file changed, 77 insertions(+), 82 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 17c0f33c..5e6f4320 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -19,6 +19,58 @@ ContinuousPerturbationType = Literal["minimum", "maximum", "plus_std", "minus_std"] +logger = get_logger(__name__) + + +def _build_perturbed_dataloader(baseline_dataset, perturbed, batch_size): + # currently for continuous data only + perturbed_dataset = MOVEDataset( + baseline_dataset.cat_all, + perturbed, + baseline_dataset.cat_shapes, + baseline_dataset.con_shapes, + ) + + perturbed_dataloader = DataLoader( + perturbed_dataset, + shuffle=False, + batch_size=batch_size, + ) + return perturbed_dataloader + + +def _pertub_cont_feat_col(baseline_dataset, slice_, index_pert_feat, perturbation_type): + + logger.debug(f"Slice: %s {slice_}") + perturbed_con = baseline_dataset.con_all.clone() + target_dataset = perturbed_con[:, slice_] + logger.debug(f"Target dataset shape: {target_dataset.shape}") + logger.debug( + f"Changing to desired perturbation value for feature {index_pert_feat}" + ) + # Change the desired feature value by: + min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( + target_dataset + ) + if perturbation_type == "minimum": + target_dataset[:, index_pert_feat] = torch.FloatTensor( + [min_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "maximum": + target_dataset[:, index_pert_feat] = torch.FloatTensor( + [max_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "plus_std": + target_dataset[:, index_pert_feat] += torch.FloatTensor( + [std_feat_val_list[index_pert_feat]] + ) + elif perturbation_type == "minus_std": + target_dataset[:, index_pert_feat] -= torch.FloatTensor( + [std_feat_val_list[index_pert_feat]] + ) + logger.debug(f"Perturbation succesful for feature {index_pert_feat}") + return perturbed_con + def perturb_categorical_data( baseline_dataloader: DataLoader, @@ -74,6 +126,7 @@ def perturb_categorical_data( return dataloaders +# not used anymore def perturb_continuous_data( baseline_dataloader: DataLoader, con_dataset_names: list[str], @@ -99,7 +152,7 @@ def perturb_continuous_data( target_idx = con_dataset_names.index(target_dataset_name) splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) + slice_ = slice(*splits[target_idx : target_idx + 2]) # start, stop, step num_features = baseline_dataset.con_shapes[target_idx] @@ -108,15 +161,9 @@ def perturb_continuous_data( perturbed_con = baseline_dataset.con_all.clone() target_dataset = perturbed_con[:, slice_] target_dataset[:, i] = torch.FloatTensor([target_value]) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_perturbed_dataloader( + baseline_dataset=baseline_dataset, + perturbed=perturbed_con, batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) @@ -209,15 +256,9 @@ def perturb_continuous_data_one( perturbed_con = baseline_dataset.con_all.clone() target_dataset = perturbed_con[:, slice_] target_dataset[:, i] = torch.FloatTensor([target_value]) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_perturbed_dataloader( + baseline_dataset=baseline_dataset, + perturbed=perturbed_con, batch_size=baseline_dataloader.batch_size, ) @@ -267,33 +308,17 @@ def perturb_continuous_data_extended( perturbations_list = [] for i in range(num_features): - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - # Change the desired feature value by: - min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( - target_dataset - ) - if perturbation_type == "minimum": - target_dataset[:, i] = torch.FloatTensor([min_feat_val_list[i]]) - elif perturbation_type == "maximum": - target_dataset[:, i] = torch.FloatTensor([max_feat_val_list[i]]) - elif perturbation_type == "plus_std": - target_dataset[:, i] += torch.FloatTensor([std_feat_val_list[i]]) - elif perturbation_type == "minus_std": - target_dataset[:, i] -= torch.FloatTensor([std_feat_val_list[i]]) - - perturbations_list.append(target_dataset[:, i].numpy()) - - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, + perturbed_con = _pertub_cont_feat_col( + baseline_dataset=baseline_dataset, + slice_=slice_, + index_pert_feat=i, + perturbation_type=perturbation_type, ) + perturbations_list.append(perturbed_con[:, slice_][:, i].numpy()) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_perturbed_dataloader( + baseline_dataset=baseline_dataset, + perturbed=perturbed_con, batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) @@ -321,7 +346,6 @@ def perturb_continuous_data_extended_one( ) -> ( DataLoader ): # But we change the output from list[DataLoader] to just one DataLoader - logger = get_logger(__name__) """Add perturbations to continuous data. For each feature in the target dataset, change the feature's value in all samples (in rows): 1,2) substituting this feature in all samples by the feature's minimum/maximum value @@ -362,51 +386,22 @@ def perturb_continuous_data_extended_one( # perturb, we do it only for one feature, the one indicated in index_pert_feat logger.debug(f"Setting up perturbed_con for feature {index_pert_feat}") - perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - - logger.debug( - f"Changing to desired perturbation value for feature {index_pert_feat}" - ) - # Change the desired feature value by: - min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( - target_dataset + perturbed_con = _pertub_cont_feat_col( + baseline_dataset=baseline_dataset, + slice_=slice_, + index_pert_feat=index_pert_feat, + perturbation_type=perturbation_type, ) - if perturbation_type == "minimum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [min_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "maximum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( - [max_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "plus_std": - target_dataset[:, index_pert_feat] += torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - elif perturbation_type == "minus_std": - target_dataset[:, index_pert_feat] -= torch.FloatTensor( - [std_feat_val_list[index_pert_feat]] - ) - logger.debug(f"Perturbation succesful for feature {index_pert_feat}") logger.debug( f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" ) - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed_con, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_perturbed_dataloader( + baseline_dataset=baseline_dataset, + perturbed=perturbed_con, batch_size=baseline_dataloader.batch_size, ) - # dataloaders.append(perturbed_dataloader) logger.debug( f"Finished perturb_continuous_data_extended_one for feature {index_pert_feat}" From 1a2774d6644d9fd3127b77733707f35f1ba8a11f Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 16:59:11 +0200 Subject: [PATCH 61/94] :art: adapt to look more similar to single-core bayes factor fct --- src/move/tasks/bayes_parallel.py | 43 ++++++-------------------------- 1 file changed, 7 insertions(+), 36 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 69d67369..40d58134 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -62,13 +62,11 @@ def _bayes_approach_worker(args): # all refits (all refits have the same importance) # We also set up bayes_k, which has the same dimensions as mean_diff mean_diff = np.zeros((num_samples, num_continuous)) - bayes_k_worker = np.zeros( - (num_continuous) - ) # This will be what we will put in bayes_k[i,:] + # This will be what we will put in bayes_k[i,:] + bayes_k_worker = np.zeros((num_continuous)) # Set the normalizer - normalizer = ( - 1 / task_config.num_refits - ) # Divide by the number of refits. All the refits will have the same importance + # Divide by the number of refits. All the refits will have the same importance + normalizer = 1 / task_config.num_refits # Create perturbed dataloader for the current feature (i) logger.debug(f"Creating perturbed dataloader for feature {i}") @@ -306,25 +304,8 @@ def _bayes_approach_parallel( logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") bayes_k[i, :] = computed_bayes_k - # Once we have the Bayes factors for all features, we can calculate Bayes - # probabilities bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) - bayes_max = np.max(bayes_abs) - bayes_min = np.min(bayes_abs) - bayes_abs_shape = bayes_abs.shape - logger.debug( - f"bayes_abs max is {bayes_max}. Bayes_abs min is {bayes_min}. " - f"Bayes_abs shape is {bayes_abs_shape}" - ) - - bayes_p = np.exp(bayes_abs) / ( - 1 + np.exp(bayes_abs) - ) # 2D: N x C (perturbed features as rows, all continuous features as columns) - bayes_p_shape = bayes_p.shape - logger.debug(f"bayes_p shape is {bayes_p_shape}") - # file_path = output_path / "bayes_p_multi.tsv" - # logger.debug(f"Saving bayes_p to {file_path}") - # np.savetxt(file_path, bayes_p, delimiter='\t') + bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results @@ -335,12 +316,8 @@ def _bayes_approach_parallel( # Then, we sort them, and get the indexes in the flattened array. So, we get an # list of sorted indexes in the flatenned array sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C - # file_path = output_path / "sort_ids_multi.tsv" logger.debug(f"sort_ids are {sort_ids}") - # logger.debug(f"Saving sort_ids to {file_path}") - # np.savetxt(file_path, sort_ids, delimiter='\t') - prob = np.take(bayes_p, sort_ids) # 1D: N x C # bayes_p is the array from which elements will be taken. # sort_ids contains the indices that determine the order in which elements should # be taken from bayes_p. @@ -351,19 +328,16 @@ def _bayes_approach_parallel( # So, even though sort_ids is obtained from a flattened version of bayes_abs, # np.take understands how to map these indices # correctly to the original shape of bayes_p. We get a flattened array? - logger.debug(f"prob is {prob}") + prob = np.take(bayes_p, sort_ids) # 1D: N x C logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") # Sort bayes_k in descending order, aligning with the sorted bayes_abs. bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C - logger.debug(f"bayes k, after sorting with the ids, is {bayes_k}") - logger.debug("Calculating fdr") # Calculate FDR fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? - logger.debug(f"fdr is {fdr}") - logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) + logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") logger.debug(f"Index is {idx}") # idx will contain the index of the element in fdr that is closest # to task_config.sig_threshold. @@ -371,9 +345,6 @@ def _bayes_approach_parallel( # closest to the significance threshold # (task_config.sig_threshold). - logger.debug(f"Sig ids sorted is {sort_ids}") - logger.debug(f"Sigids[idx] is {sort_ids[:idx]}") - # Return elements only up to idx. They will be the significant findings # sort_ids[:idx]: Indices of features sorted by significance. # prob[:idx]: Probabilities of significant associations for selected features. From 680c164c0a89bd9ea3b1f63e5b084da50028fbdf Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 5 Jul 2024 17:30:50 +0200 Subject: [PATCH 62/94] :sparkles: add back masking of self-perturbed feat. --- src/move/tasks/bayes_parallel.py | 27 +++++++++++++++++++++------ 1 file changed, 21 insertions(+), 6 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 40d58134..32005aed 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -121,7 +121,7 @@ def _bayes_approach_worker(args): # We calculate diff for each refit, and add it to mean_diff after dividing by # the number of refits logger.debug(f"Calculating diff for num_perturbed {i}, with model {model}") - diff = perturb_recon - baseline_recon + diff = perturb_recon - baseline_recon # 2D: N x C logger.debug( f"Calculating mean_diff for num_perturbed {i}, with model {model}" ) @@ -129,7 +129,13 @@ def _bayes_approach_worker(args): logger.debug(f"Deleting model {model_path}, to see if I can free up space?") del model logger.debug(f"Deleted model {model_path} in worker {i} to save some space") - + # Marc's masking approach (for subset of perturbed cont. features?) + # difference for only perturbed feature? + bayes_mask = ( + baseline_dataloader.dataset.con_all[0, :] + - perturbed_dataloader.dataset.con_all[0, :] + ) + bayes_mask[bayes_mask != 0] = 1 logger.debug(f"mean_diff for feature {i}, calculated, using all refits") mean_diff_shape = mean_diff.shape logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") @@ -151,7 +157,7 @@ def _bayes_approach_worker(args): ) # Return bayes_k_worker and the index of the feature - return i, bayes_k_worker + return i, bayes_k_worker, bayes_mask def _bayes_approach_parallel( @@ -298,18 +304,27 @@ def _bayes_approach_parallel( logger.info("Pool multiprocess completed. Calculating bayes_abs and bayes_p") bayes_k = np.empty((num_perturbed, num_continuous)) - + bayes_mask = np.zeros(np.shape(bayes_k)) # Get results in the correct order - for i, computed_bayes_k in results: + for i, computed_bayes_k, mask_k in results: logger.debug(f"{i} has bayes_k worker {computed_bayes_k}") + # computed_bayes_k: already normalized probability + # (log differences, i.e. Bayes factors) bayes_k[i, :] = computed_bayes_k + if task_config.target_value in CONTINUOUS_TARGET_VALUE: + bayes_mask[i, :] = mask_k + + # mask already created in worker function + bayes_mask = np.array(bayes_mask, dtype=bool) bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results - + bayes_abs[bayes_mask] = np.min( + bayes_abs + ) # Bring feature_i feature_i associations to minimum # Get only the significant associations: # This will flatten the array,so we get all bayes_abs for all perturbed features # vs all continuous features in one 1D array From 8f43c9003b4e207a6e9589bcf34615fb74556201 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 11:19:20 +0200 Subject: [PATCH 63/94] :art: initiailize logger at the top of the module --- src/move/tasks/bayes_parallel.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 32005aed..a267d6b6 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -25,6 +25,8 @@ # Possible values for continuous pertrubation CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] +logger = get_logger(__name__) + def _bayes_approach_worker(args): """ @@ -172,7 +174,6 @@ def _bayes_approach_parallel( nan_mask: BoolArray, feature_mask: BoolArray, ): - logger = get_logger(__name__) logger.debug("Inside the bayes_parallel function") # First, I train or reload the models (number of refits), @@ -187,7 +188,6 @@ def _bayes_approach_parallel( logger.debug("Model moved to device in bayes_approach_parallel") # Train or reload models - logger = get_logger(__name__) logger.info("Training or reloading models") for j in range( From 6178c8f78cbaceae8300697a9eedb96709f0ed0d Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 12:30:53 +0200 Subject: [PATCH 64/94] :sparkles: pass feature_mask to bayes_parallell - pass feature mask --- src/move/tasks/bayes_parallel.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index a267d6b6..3860461c 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -48,9 +48,9 @@ def _bayes_approach_worker(args): continuous_shapes, categorical_shapes, nan_mask, + feature_mask, ) = args # Initialize logging - logger = get_logger(__name__) logger.debug(f"Inside the worker function for num_perturbed {i}") # Now we are inside the num_perturbed loop, we will do this for each of the @@ -143,12 +143,11 @@ def _bayes_approach_worker(args): logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") # Apply nan_mask to the result in mean_diff - diff_mask = np.ma.masked_array(mean_diff, mask=nan_mask) - diff_mask_shape = diff_mask.shape - logger.debug( - f"Calculated diff_masked for feature {i}. Its shape is {diff_mask_shape}" - ) - prob = np.ma.compressed(np.mean(diff_mask > 1e-8, axis=0)) + mask = feature_mask | nan_mask # 2D: N x C + diff = np.ma.masked_array(mean_diff, mask=mask) + diff_shape = diff.shape + logger.debug(f"Calculated diff (masked) for feature {i}. Its shape is {diff_shape}") + prob = np.ma.compressed(np.mean(diff > 1e-8, axis=0)) logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") # Calculate bayes factor @@ -291,6 +290,7 @@ def _bayes_approach_parallel( continuous_shapes, categorical_shapes, nan_mask, + feature_mask[:, [i]], ) for i in range(num_perturbed) ] From 60ed227face196f59001ac70c4addf93b5ebc68f Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 12:44:48 +0200 Subject: [PATCH 65/94] :bug: add condition for masking - finish aligning variable names (bayes_k) --- src/move/tasks/bayes_parallel.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 3860461c..7ddbd9c7 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -64,8 +64,6 @@ def _bayes_approach_worker(args): # all refits (all refits have the same importance) # We also set up bayes_k, which has the same dimensions as mean_diff mean_diff = np.zeros((num_samples, num_continuous)) - # This will be what we will put in bayes_k[i,:] - bayes_k_worker = np.zeros((num_continuous)) # Set the normalizer # Divide by the number of refits. All the refits will have the same importance normalizer = 1 / task_config.num_refits @@ -131,12 +129,15 @@ def _bayes_approach_worker(args): logger.debug(f"Deleting model {model_path}, to see if I can free up space?") del model logger.debug(f"Deleted model {model_path} in worker {i} to save some space") + # Marc's masking approach (for subset of perturbed cont. features?) # difference for only perturbed feature? - bayes_mask = ( - baseline_dataloader.dataset.con_all[0, :] - - perturbed_dataloader.dataset.con_all[0, :] - ) + bayes_mask = np.zeros(np.shape(mean_diff.shape)) + if task_config.target_value in CONTINUOUS_TARGET_VALUE: + bayes_mask = ( + baseline_dataloader.dataset.con_all[0, :] + - perturbed_dataloader.dataset.con_all[0, :] + ) bayes_mask[bayes_mask != 0] = 1 logger.debug(f"mean_diff for feature {i}, calculated, using all refits") mean_diff_shape = mean_diff.shape @@ -151,14 +152,14 @@ def _bayes_approach_worker(args): logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") # Calculate bayes factor - bayes_k_worker = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + bayes_k = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) logger.debug( f"bayes factor calculated for feature {i}. Woker function {i} finished" ) - # Return bayes_k_worker and the index of the feature - return i, bayes_k_worker, bayes_mask + # Return bayes_k and the index of the feature + return i, bayes_k, bayes_mask def _bayes_approach_parallel( From f5da671572e6b1882cde5681849fb64dfa5f61db Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 13:28:07 +0200 Subject: [PATCH 66/94] :art: align masking strategies --- src/move/tasks/bayes_parallel.py | 24 +++++++++++------------- src/move/tasks/identify_associations.py | 1 - 2 files changed, 11 insertions(+), 14 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 7ddbd9c7..dbebe3a7 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -130,15 +130,6 @@ def _bayes_approach_worker(args): del model logger.debug(f"Deleted model {model_path} in worker {i} to save some space") - # Marc's masking approach (for subset of perturbed cont. features?) - # difference for only perturbed feature? - bayes_mask = np.zeros(np.shape(mean_diff.shape)) - if task_config.target_value in CONTINUOUS_TARGET_VALUE: - bayes_mask = ( - baseline_dataloader.dataset.con_all[0, :] - - perturbed_dataloader.dataset.con_all[0, :] - ) - bayes_mask[bayes_mask != 0] = 1 logger.debug(f"mean_diff for feature {i}, calculated, using all refits") mean_diff_shape = mean_diff.shape logger.debug(f"Returning mean_diff for feature {i}. Its shape is {mean_diff_shape}") @@ -154,6 +145,15 @@ def _bayes_approach_worker(args): # Calculate bayes factor bayes_k = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + # Marc's masking approach (for subset of perturbed cont. features?) + # difference for only perturbed feature? + bayes_mask = np.zeros(np.shape(bayes_k.shape)) + if task_config.target_value in CONTINUOUS_TARGET_VALUE: + bayes_mask = ( + baseline_dataloader.dataset.con_all[0, :] + - perturbed_dataloader.dataset.con_all[0, :] + ) + logger.debug( f"bayes factor calculated for feature {i}. Woker function {i} finished" ) @@ -312,17 +312,15 @@ def _bayes_approach_parallel( # computed_bayes_k: already normalized probability # (log differences, i.e. Bayes factors) bayes_k[i, :] = computed_bayes_k - if task_config.target_value in CONTINUOUS_TARGET_VALUE: - bayes_mask[i, :] = mask_k + bayes_mask[i, :] = mask_k # mask already created in worker function + bayes_mask[bayes_mask != 0] = 1 bayes_mask = np.array(bayes_mask, dtype=bool) bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C - # NOTE_ : I AM SKIPPING THE MASK STEP, SO I WILL HAVE TO REMOVE - # FEATURE I - FEATURE I ASSOCIATIONS LATER, in the results bayes_abs[bayes_mask] = np.min( bayes_abs ) # Bring feature_i feature_i associations to minimum diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index cc2cf4bb..6e19cbec 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -290,7 +290,6 @@ def _bayes_approach( bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C - # ! Marc said this is needed to not identify the features with themselves bayes_abs[bayes_mask] = np.min( bayes_abs ) # Bring feature_i feature_i associations to minimum From 7eae82d2fbc588219359755b3f8c609c5da1cd6b Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 14:42:13 +0200 Subject: [PATCH 67/94] :bug: fix cont perturbation - before more than one feat was changed, and probably not the desired one. - out of index slice in numpy returns just an empty array! see: https://stackoverflow.com/questions/54613753/why-does-python-allow-out-of-range-slice-indexes-for-sequences --- src/move/data/perturbations.py | 42 +++++++++++++++++----------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 5e6f4320..56d8f938 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -39,33 +39,35 @@ def _build_perturbed_dataloader(baseline_dataset, perturbed, batch_size): return perturbed_dataloader -def _pertub_cont_feat_col(baseline_dataset, slice_, index_pert_feat, perturbation_type): +def _pertub_cont_feat_col( + baseline_dataset, start_idx, num_features, index_pert_feat, perturbation_type +): - logger.debug(f"Slice: %s {slice_}") perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] + target_dataset = perturbed_con[:, start_idx : start_idx + num_features] logger.debug(f"Target dataset shape: {target_dataset.shape}") logger.debug( f"Changing to desired perturbation value for feature {index_pert_feat}" ) # Change the desired feature value by: + # ! one would only need the stats for a single feature? min_feat_val_list, max_feat_val_list, std_feat_val_list = feature_stats( target_dataset ) if perturbation_type == "minimum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( + perturbed_con[:, start_idx + index_pert_feat] = torch.FloatTensor( [min_feat_val_list[index_pert_feat]] ) elif perturbation_type == "maximum": - target_dataset[:, index_pert_feat] = torch.FloatTensor( + perturbed_con[:, start_idx + index_pert_feat] = torch.FloatTensor( [max_feat_val_list[index_pert_feat]] ) elif perturbation_type == "plus_std": - target_dataset[:, index_pert_feat] += torch.FloatTensor( + perturbed_con[:, start_idx + index_pert_feat] += torch.FloatTensor( [std_feat_val_list[index_pert_feat]] ) elif perturbation_type == "minus_std": - target_dataset[:, index_pert_feat] -= torch.FloatTensor( + perturbed_con[:, start_idx + index_pert_feat] -= torch.FloatTensor( [std_feat_val_list[index_pert_feat]] ) logger.debug(f"Perturbation succesful for feature {index_pert_feat}") @@ -152,15 +154,13 @@ def perturb_continuous_data( target_idx = con_dataset_names.index(target_dataset_name) splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) # start, stop, step - + start_idx = splits[target_idx] num_features = baseline_dataset.con_shapes[target_idx] dataloaders = [] for i in range(num_features): perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - target_dataset[:, i] = torch.FloatTensor([target_value]) + perturbed_con[:, start_idx + i] = torch.FloatTensor([target_value]) perturbed_dataloader = _build_perturbed_dataloader( baseline_dataset=baseline_dataset, perturbed=perturbed_con, @@ -250,12 +250,10 @@ def perturb_continuous_data_one( target_idx = con_dataset_names.index(target_dataset_name) splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) + start_idx = splits[target_idx] - i = index_pert_feat perturbed_con = baseline_dataset.con_all.clone() - target_dataset = perturbed_con[:, slice_] - target_dataset[:, i] = torch.FloatTensor([target_value]) + perturbed_con[:, start_idx + index_pert_feat] = torch.FloatTensor([target_value]) perturbed_dataloader = _build_perturbed_dataloader( baseline_dataset=baseline_dataset, perturbed=perturbed_con, @@ -301,7 +299,7 @@ def perturb_continuous_data_extended( target_idx = con_dataset_names.index(target_dataset_name) # dataset index splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) + start_idx = splits[target_idx] num_features = baseline_dataset.con_shapes[target_idx] dataloaders = [] @@ -310,11 +308,12 @@ def perturb_continuous_data_extended( for i in range(num_features): perturbed_con = _pertub_cont_feat_col( baseline_dataset=baseline_dataset, - slice_=slice_, + start_idx=start_idx, + num_features=num_features, index_pert_feat=i, perturbation_type=perturbation_type, ) - perturbations_list.append(perturbed_con[:, slice_][:, i].numpy()) + perturbations_list.append(perturbed_con[:, start_idx + i].numpy()) perturbed_dataloader = _build_perturbed_dataloader( baseline_dataset=baseline_dataset, @@ -377,10 +376,10 @@ def perturb_continuous_data_extended_one( target_idx = con_dataset_names.index(target_dataset_name) # dataset index splits = np.cumsum([0] + baseline_dataset.con_shapes) - slice_ = slice(*splits[target_idx : target_idx + 2]) + start_idx = splits[target_idx] # Use it only if we want to perturb all features in the target dataset - # num_features = baseline_dataset.con_shapes[target_idx] + num_features = baseline_dataset.con_shapes[target_idx] # Now, instead of the for loop that iterates over all the features we want to # perturb, we do it only for one feature, the one indicated in index_pert_feat @@ -388,7 +387,8 @@ def perturb_continuous_data_extended_one( perturbed_con = _pertub_cont_feat_col( baseline_dataset=baseline_dataset, - slice_=slice_, + start_idx=start_idx, + num_features=num_features, index_pert_feat=index_pert_feat, perturbation_type=perturbation_type, ) From 50a623e8e18fd9ca5342b066827be6c4a93d00ab Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 15:38:11 +0200 Subject: [PATCH 68/94] :bug: remove redefintion of nan_mask --- src/move/tasks/bayes_parallel.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index dbebe3a7..7fec4b30 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -260,15 +260,6 @@ def _bayes_approach_parallel( logger.debug(f"Saved baseline reconstruction {j}") del model - # Get NaN mask ouside the worker function, it is the same for all. - logger.debug("Creating NaN mask in bayes_parallel") - orig_con = ( - baseline_dataset.con_all - ) # Original continuous data of the baseline dataset (all data) - nan_mask = ( - orig_con == 0 - ).numpy() # Creates a mask to identify NaN values in the original data. - # Define more arguments that are needed for the worker functions continuous_shapes = baseline_dataset.con_shapes categorical_shapes = baseline_dataset.cat_shapes From 2df057b5ecdea1e5b9b324a69faebd434cfb48d4 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 16:09:52 +0200 Subject: [PATCH 69/94] :art: only define logger once in module --- src/move/tasks/identify_associations.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 6e19cbec..7b0efd13 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -48,6 +48,8 @@ # Possible values for continuous pertrubation CONTINUOUS_TARGET_VALUE = ["minimum", "maximum", "plus_std", "minus_std"] +logger = get_logger(__name__) + def _get_task_type( task_config: IdentifyAssociationsConfig, @@ -101,7 +103,6 @@ def prepare_for_categorical_perturbation( # Read original data and create perturbed datasets task_config = cast(IdentifyAssociationsConfig, config.task) - logger = get_logger(__name__) # Loading mappings: mappings = io.load_mappings(interim_path / "mappings.json") @@ -169,7 +170,6 @@ def prepare_for_continuous_perturbation( """ # Read original data and create perturbed datasets - logger = get_logger(__name__) task_config = cast(IdentifyAssociationsConfig, config.task) dataloaders = perturb_continuous_data_extended( @@ -211,9 +211,8 @@ def _bayes_approach( assert task_config.model is not None device = torch.device("cuda" if task_config.model.cuda else "cpu") - # Train models - logger = get_logger(__name__) - logger.info("Training models") + # Train or reload models + logger.info("Training or reloading models") mean_diff = np.zeros((num_perturbed, num_samples, num_continuous)) normalizer = 1 / task_config.num_refits @@ -331,7 +330,6 @@ def _ttest_approach( device = torch.device("cuda" if task_config.model.cuda else "cpu") # Train models - logger = get_logger(__name__) logger.info("Training models") pvalues = np.empty( ( @@ -495,7 +493,6 @@ def _ks_approach( baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) # Train models - logger = get_logger(__name__) logger.info("Training models") target_dataset_idx = config.data.continuous_names.index(task_config.target_dataset) @@ -703,7 +700,6 @@ def save_results( extra_cols: extra data when calling the approach function extra_colnames: names for the extra data columns """ - logger = get_logger(__name__) logger.info(f"Significant hits found: {sig_ids.size}") task_config = cast(IdentifyAssociationsConfig, config.task) task_type = _get_task_type(task_config) @@ -764,7 +760,6 @@ def identify_associations(config: MOVEConfig) -> None: # DATA PREPARATION ###################### # Read original data and create perturbed datasets#### - logger = get_logger(__name__) task_config = cast(IdentifyAssociationsConfig, config.task) task_type = _get_task_type(task_config) _validate_task_config(task_config, task_type) From 2e87e12f476f254e3b12128438510817c9361840 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 16:17:05 +0200 Subject: [PATCH 70/94] :art: align single process bayes and multiprocess bayes fct - allows easier comparison of functionality --- src/move/tasks/bayes_parallel.py | 72 ++++++++++++------------- src/move/tasks/identify_associations.py | 54 +++++++++++++++---- 2 files changed, 81 insertions(+), 45 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 7fec4b30..a5f170ee 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -88,10 +88,7 @@ def _bayes_approach_worker(args): models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" ) if reconstruction_path.exists(): - logger.debug( - f"Loading baseline reconstruction from {reconstruction_path}, " - "in the worker function" - ) + logger.debug(f"Loading baseline reconstruction from {reconstruction_path}.") baseline_recon = torch.load(reconstruction_path) logger.debug(f"Loading model {model_path}, using load function") @@ -173,26 +170,27 @@ def _bayes_approach_parallel( num_continuous: int, nan_mask: BoolArray, feature_mask: BoolArray, -): - logger.debug("Inside the bayes_parallel function") +) -> tuple[Union[IntArray, FloatArray], ...]: + """ + Calculate Bayes factors for all perturbed features in parallel. - # First, I train or reload the models (number of refits), - # and save the baseline reconstruction. - # We train and get the reconstruction outside to make sure - # that we use the same model and use the same - # baseline reconstruction for all the worker functions - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + First, I train or reload the models (number of refits), and save the baseline + reconstruction. We train and get the reconstruction outside to make sure + that we use the same model and use the same baseline reconstruction for all + the worker functions. + """ + logger.debug("Inside the bayes_parallel function") assert task_config.model is not None device = torch.device("cuda" if task_config.model.cuda else "cpu") - logger.debug("Model moved to device in bayes_approach_parallel") # Train or reload models logger.info("Training or reloading models") + # non-perturbed baseline dataset + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - for j in range( - task_config.num_refits - ): # We create as many models (refits) as indicated in the config file + for j in range(task_config.num_refits): + # We create as many models (refits) as indicated in the config file # For each j (number of refits) we train a different model, but on the same data # Initialize model model: VAE = hydra.utils.instantiate( @@ -200,9 +198,8 @@ def _bayes_approach_parallel( continuous_shapes=baseline_dataset.con_shapes, categorical_shapes=baseline_dataset.cat_shapes, ) - if ( - j == 0 - ): # First, we see if the models are already created (if we trained them + if j == 0: + # First, we see if the models are already created (if we trained them # before). for each j, we check if model number j has already been created. logger.debug(f"Model: {model}") @@ -212,9 +209,8 @@ def _bayes_approach_parallel( ) model_path = models_path / f"model_{task_config.model.num_latent}_{j}.pt" - if ( - model_path.exists() - ): # If the models were already created, we load them only if we need to get a + if model_path.exists(): + # If the models were already created, we load them only if we need to get a # baseline reconstruction. Otherwise, nothing needs to be done at this point logger.debug(f"Model {model_path} already exists") if not reconstruction_path.exists(): @@ -227,7 +223,8 @@ def _bayes_approach_parallel( f"Baseline reconstruction for {reconstruction_path} already exists" f", no need to load model {model_path} " ) - else: # If the models are not created yet, he have to train them, with the + else: + # If the models are not created yet, he have to train them, with the # parameters we indicated in the config file logger.debug(f"Training refit {j + 1}/{task_config.num_refits}") model.to(device) @@ -240,6 +237,7 @@ def _bayes_approach_parallel( if task_config.save_refits: # pickle_protocol=4 is necessary for very big models torch.save(model.state_dict(), model_path, pickle_protocol=4) + model.eval() # Calculate baseline reconstruction # For each model j, we get a different reconstruction for the baseline. @@ -249,9 +247,12 @@ def _bayes_approach_parallel( # do it inside each process because the results might be different if reconstruction_path.exists(): - logger.debug(f"Baseline reconstruction for model {j} already created") + logger.debug( + f"Loading baseline reconstruction from {reconstruction_path}, " + "in the worker function" + ) + # baseline_recon = torch.load(reconstruction_path) else: - model.eval() _, baseline_recon = model.reconstruct(baseline_dataloader) # Save the baseline reconstruction for each model @@ -260,13 +261,13 @@ def _bayes_approach_parallel( logger.debug(f"Saved baseline reconstruction {j}") del model + # Calculate Bayes factors + logger.info("Identifying significant features") + # Define more arguments that are needed for the worker functions continuous_shapes = baseline_dataset.con_shapes categorical_shapes = baseline_dataset.cat_shapes - """ - Perform parallelized bayes approach. - """ logger.debug("Starting parallelization") # Define arguments for each worker, and iterate over models and perturbed features @@ -304,25 +305,24 @@ def _bayes_approach_parallel( # (log differences, i.e. Bayes factors) bayes_k[i, :] = computed_bayes_k bayes_mask[i, :] = mask_k - - # mask already created in worker function bayes_mask[bayes_mask != 0] = 1 bayes_mask = np.array(bayes_mask, dtype=bool) + # Calculate Bayes probabilities bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) + bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C bayes_abs[bayes_mask] = np.min( bayes_abs ) # Bring feature_i feature_i associations to minimum # Get only the significant associations: - # This will flatten the array,so we get all bayes_abs for all perturbed features + # This will flatten the array, so we get all bayes_abs for all perturbed features # vs all continuous features in one 1D array # Then, we sort them, and get the indexes in the flattened array. So, we get an # list of sorted indexes in the flatenned array sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C logger.debug(f"sort_ids are {sort_ids}") - # bayes_p is the array from which elements will be taken. # sort_ids contains the indices that determine the order in which elements should # be taken from bayes_p. @@ -332,7 +332,7 @@ def _bayes_approach_parallel( # elements using the provided indices. # So, even though sort_ids is obtained from a flattened version of bayes_abs, # np.take understands how to map these indices - # correctly to the original shape of bayes_p. We get a flattened array? + # correctly to the original shape of bayes_p. prob = np.take(bayes_p, sort_ids) # 1D: N x C logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") @@ -340,15 +340,15 @@ def _bayes_approach_parallel( bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C # Calculate FDR - fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D ??? + fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) - logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") - logger.debug(f"Index is {idx}") # idx will contain the index of the element in fdr that is closest # to task_config.sig_threshold. # This line essentially finds the index where the False Discovery Rate (fdr) is # closest to the significance threshold # (task_config.sig_threshold). + logger.debug(f"Index is {idx}") + logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") # Return elements only up to idx. They will be the significant findings # sort_ids[:idx]: Indices of features sorted by significance. diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 7b0efd13..db017182 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -216,10 +216,12 @@ def _bayes_approach( mean_diff = np.zeros((num_perturbed, num_samples, num_continuous)) normalizer = 1 / task_config.num_refits - # Last appended dataloader is the baseline + # non-perturbed baseline dataset baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) for j in range(task_config.num_refits): + # We create as many models (refits) as indicated in the config file + # For each j (number of refits) we train a different model, but on the same data # Initialize model model: VAE = hydra.utils.instantiate( task_config.model, @@ -243,23 +245,32 @@ def _bayes_approach( model=model, train_dataloader=train_dataloader, ) + # Save the refits, to use them later if task_config.save_refits: - torch.save(model.state_dict(), model_path) + # pickle_protocol=4 is necessary for very big models + torch.save(model.state_dict(), model_path, pickle_protocol=4) model.eval() # Calculate baseline reconstruction + # For each model j, we get a different reconstruction for the baseline. + # We haven't perturbed anything yet, we are just + # getting the reconstruction for the baseline, to make sure that we get + # the same reconstruction for each refit, we cannot + # do it inside each process because the results might be different reconstruction_path = ( models_path / f"baseline_recon_{task_config.model.num_latent}_{j}.pt" ) if reconstruction_path.exists(): - logger.debug( - f"Loading baseline reconstruction from {reconstruction_path}, " - "in the worker function" - ) + logger.debug(f"Loading baseline reconstruction from {reconstruction_path}.") baseline_recon = torch.load(reconstruction_path) else: _, baseline_recon = model.reconstruct(baseline_dataloader) + # # Save the baseline reconstruction for each model + # logger.debug(f"Saving baseline reconstruction {j}") + # torch.save(baseline_recon, reconstruction_path, pickle_protocol=4) + # logger.debug(f"Saved baseline reconstruction {j}") + # Calculate perturb reconstruction => keep track of mean difference for i in range(num_perturbed): _, perturb_recon = model.reconstruct(dataloaders[i]) @@ -285,28 +296,53 @@ def _bayes_approach( bayes_mask = np.array(bayes_mask, dtype=bool) # Calculate Bayes probabilities - bayes_abs = np.abs(bayes_k) + bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C bayes_abs[bayes_mask] = np.min( bayes_abs ) # Bring feature_i feature_i associations to minimum + # Get only the significant associations: + # This will flatten the array, so we get all bayes_abs for all perturbed features + # vs all continuous features in one 1D array + # Then, we sort them, and get the indexes in the flattened array. So, we get an + # list of sorted indexes in the flatenned array sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C logger.debug(f"sort_ids are {sort_ids}") - + # bayes_p is the array from which elements will be taken. + # sort_ids contains the indices that determine the order in which elements should + # be taken from bayes_p. + # This operation essentially rearranges the elements of bayes_p based on the + # sorting order specified by sort_ids + # np.take considers the input array as if it were flattened when extracting + # elements using the provided indices. + # So, even though sort_ids is obtained from a flattened version of bayes_abs, + # np.take understands how to map these indices + # correctly to the original shape of bayes_p. prob = np.take(bayes_p, sort_ids) # 1D: N x C logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") - # Sort Bayes + # Sort bayes_k in descending order, aligning with the sorted bayes_abs. bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C # Calculate FDR fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) + # idx will contain the index of the element in fdr that is closest + # to task_config.sig_threshold. + # This line essentially finds the index where the False Discovery Rate (fdr) is + # closest to the significance threshold + # (task_config.sig_threshold). logger.debug(f"Index is {idx}") logger.debug(f"FDR range: [{fdr[0]:.3f} {fdr[-1]:.3f}]") + # Return elements only up to idx. They will be the significant findings + # sort_ids[:idx]: Indices of features sorted by significance. + # prob[:idx]: Probabilities of significant associations for selected features. + # fdr[:idx]: False Discovery Rate values for selected features. + # bayes_k[:idx]: Bayes Factors indicating the strength of evidence for selected + # associations. return sort_ids[:idx], prob[:idx], fdr[:idx], bayes_k[:idx] From aa2e5d9e62ed753c4b9a847551acd0d89cba1498 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 16:21:17 +0200 Subject: [PATCH 71/94] :art: just document in code that this cannot happen - :bug: also add missing imports --- src/move/tasks/bayes_parallel.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index a5f170ee..3403efbe 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -1,5 +1,5 @@ from pathlib import Path -from typing import Literal, cast +from typing import Literal, Union, cast import hydra import numpy as np @@ -10,7 +10,7 @@ from move.conf.schema import IdentifyAssociationsBayesConfig, MOVEConfig from move.core.logging import get_logger -from move.core.typing import BoolArray +from move.core.typing import BoolArray, FloatArray, IntArray from move.data.dataloaders import MOVEDataset from move.data.perturbations import ( ContinuousPerturbationType, @@ -90,6 +90,8 @@ def _bayes_approach_worker(args): if reconstruction_path.exists(): logger.debug(f"Loading baseline reconstruction from {reconstruction_path}.") baseline_recon = torch.load(reconstruction_path) + else: + raise FileNotFoundError("Baseline reconstruction not found.") logger.debug(f"Loading model {model_path}, using load function") model: VAE = hydra.utils.instantiate( @@ -245,6 +247,8 @@ def _bayes_approach_parallel( # getting the reconstruction for the baseline, to make sure that we get # the same reconstruction for each refit, we cannot # do it inside each process because the results might be different + # ! here the logic is a bit off. If the reconstruction path exist, the model + # ! does not to be loaded again. if reconstruction_path.exists(): logger.debug( From 4041fcb40e1f8abd2aa86d34e49164c305198682 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 16:42:57 +0200 Subject: [PATCH 72/94] :zap: improve CI speed, reduce stability (-> one refit only) --- .github/workflows/release.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index f20c104b..af5ec8d9 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -48,12 +48,12 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_ttest --cfg job - move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 + move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=1 - name: Identify associations - bayes factors run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=40 task.num_refits=25 + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=40 task.num_refits=1 run-tutorial-w-multiprocessing: name: Run - random_small - multiprocess runs-on: ubuntu-latest @@ -78,7 +78,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=40 task.num_refits=25 + move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=40 task.num_refits=1 run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest @@ -107,12 +107,12 @@ jobs: run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=4 + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=1 - name: Identify associations - KS run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_ks --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_ks task.training_loop.num_epochs=30 task.num_refits=4 + move-dl data=random_continuous task=random_continuous__id_assoc_ks task.training_loop.num_epochs=30 task.num_refits=1 publish: name: Publish package From 2d004e02bc3fa6b96ba4abc43cb4fd42a852f143 Mon Sep 17 00:00:00 2001 From: Henry Date: Mon, 8 Jul 2024 16:59:00 +0200 Subject: [PATCH 73/94] :bug: use default no. of epochs + t-test needs 4 refits --- .github/workflows/release.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index af5ec8d9..9bbc0595 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -48,12 +48,12 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_ttest --cfg job - move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=1 + move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=40 task.num_refits=1 + move-dl data=random_small task=random_small__id_assoc_bayes task.num_refits=1 run-tutorial-w-multiprocessing: name: Run - random_small - multiprocess runs-on: ubuntu-latest @@ -78,7 +78,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.training_loop.num_epochs=40 task.num_refits=1 + move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.num_refits=1 run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest @@ -102,17 +102,17 @@ jobs: # run: | # cd tutorial # move-dl data=random_continuous task=random_continuous__id_assoc_ttest --cfg job - # move-dl data=random_continuous task=random_continuous__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 + # move-dl data=random_continuous task=random_continuous__id_assoc_ttest task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.training_loop.num_epochs=30 task.num_refits=1 + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 - name: Identify associations - KS run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_ks --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_ks task.training_loop.num_epochs=30 task.num_refits=1 + move-dl data=random_continuous task=random_continuous__id_assoc_ks task.num_refits=1 publish: name: Publish package From 8d655286e46494cbb644586a51b82d3ef629a43c Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 10:18:47 +0200 Subject: [PATCH 74/94] :zap: do not run t-test check (for now) - see if results betw. single and multirun match. --- .github/workflows/release.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 9bbc0595..c10215a9 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -25,7 +25,7 @@ jobs: - name: Lint with flake8 run: flake8 src run-tutorial: - name: Run tutorial - random_small + name: Run - random_small - singleprocess runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -44,11 +44,11 @@ jobs: cd tutorial move-dl data=random_small task=random_small__latent --cfg job move-dl data=random_small task=random_small__latent - - name: Identify associations - t-test - run: | - cd tutorial - move-dl data=random_small task=random_small__id_assoc_ttest --cfg job - move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 + # - name: Identify associations - t-test + # run: | + # cd tutorial + # move-dl data=random_small task=random_small__id_assoc_ttest --cfg job + # move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial From 1dd67883b2bbbf45e7eaacf9b92087f99b7319c7 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 11:04:31 +0200 Subject: [PATCH 75/94] :zap: bump up bayes factor training --- .github/workflows/release.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index c10215a9..ef394129 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -53,7 +53,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.num_refits=1 + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 run-tutorial-w-multiprocessing: name: Run - random_small - multiprocess runs-on: ubuntu-latest @@ -78,7 +78,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true task.num_refits=1 + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 task.multiprocess=true run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest From dc9020eed0ea18f939e164e77233b28b65043e26 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 11:26:06 +0200 Subject: [PATCH 76/94] :art: train both refits with 100 epochs --- .github/workflows/release.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index ef394129..e579c197 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -43,7 +43,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__latent --cfg job - move-dl data=random_small task=random_small__latent + move-dl data=random_small task=random_small__latent task.training_loop.num_epochs=100 # - name: Identify associations - t-test # run: | # cd tutorial @@ -73,7 +73,7 @@ jobs: run: | cd tutorial move-dl data=random_small task=random_small__latent task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__latent task.multiprocess=true + move-dl data=random_small task=random_small__latent task.multiprocess=true task.training_loop.num_epochs=100 - name: Identify associations - bayes factors - multiprocess run: | cd tutorial From 9cd2a7bf644a0021a0cff95539fbda3164200ca5 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 13:04:51 +0200 Subject: [PATCH 77/94] :sparkles: add log2 option - decide wheather to log2 transform - default: do not in order to allow negative features which are then standard normalized --- src/move/conf/schema.py | 1 + src/move/data/preprocessing.py | 7 ++++--- src/move/tasks/encode_data.py | 2 +- tutorial/config/data/random_continuous.yaml | 9 +++++++-- tutorial/config/data/random_small.yaml | 8 ++++++-- 5 files changed, 19 insertions(+), 8 deletions(-) diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index 92d4dce5..a0ee14e2 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -32,6 +32,7 @@ class InputConfig: @dataclass class ContinuousInputConfig(InputConfig): scale: bool = True + log2: bool = False @dataclass diff --git a/src/move/data/preprocessing.py b/src/move/data/preprocessing.py index 5c648613..3ddbd036 100644 --- a/src/move/data/preprocessing.py +++ b/src/move/data/preprocessing.py @@ -64,19 +64,20 @@ def one_hot_encode_single(mapping: dict[str, int], value: Optional[str]) -> IntA return encoded_value -def scale(x: np.ndarray) -> tuple[FloatArray, BoolArray]: +def scale(x: np.ndarray, log2: bool = False) -> tuple[FloatArray, BoolArray]: """Center to mean and scale to unit variance. Convert NaN values to 0. Args: x: 2D array with samples in its rows and features in its columns + log2: whether to apply log2 transformation to the input Returns: Tuple containing (1) scaled output and (2) a 1D mask marking columns (i.e., features) without zero variance """ - # CHANGES: Allow for negative values, so only standardization - # logx = np.log2(x + 1) logx = x + if log2: + logx = np.log2(x + 1) mask_1d = ~np.isclose(np.nanstd(logx, axis=0), 0.0) scaled_x = standardize(logx[:, mask_1d], axis=0) scaled_x[np.isnan(scaled_x)] = 0 diff --git a/src/move/tasks/encode_data.py b/src/move/tasks/encode_data.py index f0a83633..8f6549a5 100644 --- a/src/move/tasks/encode_data.py +++ b/src/move/tasks/encode_data.py @@ -71,7 +71,7 @@ def encode_data(config: DataConfig): fig.savefig(fig_path) if scale: - values, mask_1d = preprocessing.scale(values) + values, mask_1d = preprocessing.scale(values, input_config.log2) names = names[mask_1d] logger.debug(f"Columns with zero variance: {np.sum(~mask_1d)}") io.dump_names(interim_data_path / f"{input_config.name}.txt", names) diff --git a/tutorial/config/data/random_continuous.yaml b/tutorial/config/data/random_continuous.yaml index 53150799..373eacec 100755 --- a/tutorial/config/data/random_continuous.yaml +++ b/tutorial/config/data/random_continuous.yaml @@ -16,5 +16,10 @@ sample_names: categorical_inputs: [] # no categorical inputs continuous_inputs: # a list of continuous datasets - - name: random.continuous.proteomics - - name: random.continuous.metagenomics + - name: random.continuous.proteomics # filename in raw_data_path + log2: true # log2 transform data + scale: true # scale data + - name: random.continuous.metagenomics # filename in raw_data_path + log2: true # log2 transform data + scale: true # scale data + diff --git a/tutorial/config/data/random_small.yaml b/tutorial/config/data/random_small.yaml index 5f1e8d18..ac99e264 100644 --- a/tutorial/config/data/random_small.yaml +++ b/tutorial/config/data/random_small.yaml @@ -17,5 +17,9 @@ categorical_inputs: # a list of categorical datasets - name: random.small.drugs continuous_inputs: # a list of continuous datasets - - name: random.small.proteomics - - name: random.small.metagenomics + - name: random.small.proteomics # filename in raw_data_path + scale: true # scale data + log2: true # log2 transform data + - name: random.small.metagenomics # filename in raw_data_path + scale: true # scale data + log2: true # log2 transform data From a4911d77ae10ae9dd5c42f7998c2bc4f632680b5 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 15:35:50 +0200 Subject: [PATCH 78/94] :art: document some more --- src/move/tasks/bayes_parallel.py | 20 ++++++++++---------- src/move/tasks/identify_associations.py | 9 +++++---- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 3403efbe..94633830 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -138,8 +138,11 @@ def _bayes_approach_worker(args): diff = np.ma.masked_array(mean_diff, mask=mask) diff_shape = diff.shape logger.debug(f"Calculated diff (masked) for feature {i}. Its shape is {diff_shape}") - prob = np.ma.compressed(np.mean(diff > 1e-8, axis=0)) - logger.debug(f"prob calculated for feature {i}. Starting to calculate bayes_k") + prob = np.ma.compressed(np.mean(diff > 1e-8, axis=0)) # 1D: C + logger.debug( + f"prob ({prob.shape}) calculated for feature {i}." + " Starting to calculate bayes_k" + ) # Calculate bayes factor bayes_k = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) @@ -200,10 +203,7 @@ def _bayes_approach_parallel( continuous_shapes=baseline_dataset.con_shapes, categorical_shapes=baseline_dataset.cat_shapes, ) - if j == 0: - # First, we see if the models are already created (if we trained them - # before). for each j, we check if model number j has already been created. - logger.debug(f"Model: {model}") + logger.debug(f"Model: {model} (j={j})") # Define paths for the baseline reconstruction and for the model reconstruction_path = ( @@ -315,7 +315,7 @@ def _bayes_approach_parallel( # Calculate Bayes probabilities bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) - bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C + bayes_p = np.exp(bayes_abs) / (1.0 + np.exp(bayes_abs)) # 2D: P x C bayes_abs[bayes_mask] = np.min( bayes_abs @@ -325,7 +325,7 @@ def _bayes_approach_parallel( # vs all continuous features in one 1D array # Then, we sort them, and get the indexes in the flattened array. So, we get an # list of sorted indexes in the flatenned array - sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C + sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: P*C logger.debug(f"sort_ids are {sort_ids}") # bayes_p is the array from which elements will be taken. # sort_ids contains the indices that determine the order in which elements should @@ -337,14 +337,14 @@ def _bayes_approach_parallel( # So, even though sort_ids is obtained from a flattened version of bayes_abs, # np.take understands how to map these indices # correctly to the original shape of bayes_p. - prob = np.take(bayes_p, sort_ids) # 1D: N x C + prob = np.take(bayes_p, sort_ids) # 1D: P*C logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") # Sort bayes_k in descending order, aligning with the sorted bayes_abs. bayes_k = np.take(bayes_k, sort_ids) # 1D: N x C # Calculate FDR - fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D + fdr = np.cumsum(1 - prob) / np.arange(1, prob.size + 1) # 1D: P*C idx = np.argmin(np.abs(fdr - task_config.sig_threshold)) # idx will contain the index of the element in fdr that is closest # to task_config.sig_threshold. diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index db017182..0dfb8dea 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -285,7 +285,8 @@ def _bayes_approach( mask = feature_mask[:, [i]] | nan_mask # 2D: N x C diff = np.ma.masked_array(mean_diff[i, :, :], mask=mask) # 2D: N x C prob = np.ma.compressed(np.mean(diff > 1e-8, axis=0)) # 1D: C - bayes_k[i, :] = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + computed_bayes_k = np.log(prob + 1e-8) - np.log(1 - prob + 1e-8) + bayes_k[i, :] = computed_bayes_k if task_config.target_value in CONTINUOUS_TARGET_VALUE: bayes_mask[i, :] = ( baseline_dataloader.dataset.con_all[0, :] @@ -298,7 +299,7 @@ def _bayes_approach( # Calculate Bayes probabilities bayes_abs = np.abs(bayes_k) # Dimensions are (num_perturbed, num_continuous) - bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: N x C + bayes_p = np.exp(bayes_abs) / (1 + np.exp(bayes_abs)) # 2D: P x C bayes_abs[bayes_mask] = np.min( bayes_abs @@ -308,7 +309,7 @@ def _bayes_approach( # vs all continuous features in one 1D array # Then, we sort them, and get the indexes in the flattened array. So, we get an # list of sorted indexes in the flatenned array - sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: N x C + sort_ids = np.argsort(bayes_abs, axis=None)[::-1] # 1D: P*C logger.debug(f"sort_ids are {sort_ids}") # bayes_p is the array from which elements will be taken. # sort_ids contains the indices that determine the order in which elements should @@ -320,7 +321,7 @@ def _bayes_approach( # So, even though sort_ids is obtained from a flattened version of bayes_abs, # np.take understands how to map these indices # correctly to the original shape of bayes_p. - prob = np.take(bayes_p, sort_ids) # 1D: N x C + prob = np.take(bayes_p, sort_ids) # 1D: P*C logger.debug(f"Bayes proba range: [{prob[-1]:.3f} {prob[0]:.3f}]") # Sort bayes_k in descending order, aligning with the sorted bayes_abs. From e0421bd18fd61c513935c8168fafc72f088ef166 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 15:36:13 +0200 Subject: [PATCH 79/94] :zap: test multiprocess on continuous tutorial --- .github/workflows/release.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index e579c197..4d01876f 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -55,7 +55,7 @@ jobs: move-dl data=random_small task=random_small__id_assoc_bayes --cfg job move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 run-tutorial-w-multiprocessing: - name: Run - random_small - multiprocess + name: Run - random_continuous - multiprocess runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -67,18 +67,18 @@ jobs: - name: Prepare tutorial data run: | cd tutorial - move-dl data=random_small task=encode_data --cfg job - move-dl data=random_small task=encode_data + move-dl data=random_continuous task=encode_data --cfg job + move-dl data=random_continuous task=encode_data - name: Train model and analyze latent space - multiprocess run: | cd tutorial - move-dl data=random_small task=random_small__latent task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__latent task.multiprocess=true task.training_loop.num_epochs=100 + move-dl data=random_continuous task=random_continuous__latent task.multiprocess=true --cfg job + move-dl data=random_continuous task=random_continuous__latent task.multiprocess=true task.num_refits=1 - name: Identify associations - bayes factors - multiprocess run: | cd tutorial - move-dl data=random_small task=random_small__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 task.multiprocess=true + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.multiprocess=true --cfg job + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 task.multiprocess=true run-tutorial-cont: name: Run tutorial - random_continuous runs-on: ubuntu-latest From c70d328f363357d98c83cfd623d2af7e6add57a2 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 15:45:26 +0200 Subject: [PATCH 80/94] :bug: remove non-exisitng key --- .github/workflows/release.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index 4d01876f..fd76752c 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -73,7 +73,7 @@ jobs: run: | cd tutorial move-dl data=random_continuous task=random_continuous__latent task.multiprocess=true --cfg job - move-dl data=random_continuous task=random_continuous__latent task.multiprocess=true task.num_refits=1 + move-dl data=random_continuous task=random_continuous__latent task.multiprocess=true - name: Identify associations - bayes factors - multiprocess run: | cd tutorial From 1c7231678326fb3bc283c2af1211d45cc946ff52 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 16:53:06 +0200 Subject: [PATCH 81/94] :sparkles: build dataloader fct - use everywhere. --- src/move/data/perturbations.py | 83 +++++++++++++++++----------------- 1 file changed, 42 insertions(+), 41 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 56d8f938..438ff814 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -22,21 +22,23 @@ logger = get_logger(__name__) -def _build_perturbed_dataloader(baseline_dataset, perturbed, batch_size): +def _build_dataloader( + cat_data, con_data, cat_shapes, con_shapes, batch_size, shuffle=False +): # currently for continuous data only - perturbed_dataset = MOVEDataset( - baseline_dataset.cat_all, - perturbed, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, + dataset = MOVEDataset( + cat_data, + con_data, + cat_shapes, + con_shapes, ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + dataloader = DataLoader( + dataset, + shuffle=shuffle, batch_size=batch_size, ) - return perturbed_dataloader + return dataloader def _pertub_cont_feat_col( @@ -113,15 +115,11 @@ def perturb_categorical_data( baseline_dataset.num_samples, *target_shape ) target_dataset[:, i, :] = torch.FloatTensor(target_value) - perturbed_dataset = MOVEDataset( - perturbed_cat, - baseline_dataset.con_all, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_dataloader( + cat_data=perturbed_cat, + con_data=baseline_dataset.con_all, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) @@ -161,9 +159,11 @@ def perturb_continuous_data( for i in range(num_features): perturbed_con = baseline_dataset.con_all.clone() perturbed_con[:, start_idx + i] = torch.FloatTensor([target_value]) - perturbed_dataloader = _build_perturbed_dataloader( - baseline_dataset=baseline_dataset, - perturbed=perturbed_con, + perturbed_dataloader = _build_dataloader( + cat_data=baseline_dataset.cat_all, + con_data=perturbed_con, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) @@ -209,18 +209,13 @@ def perturb_categorical_data_one( baseline_dataset.num_samples, *target_shape ) target_dataset[:, i, :] = torch.FloatTensor(target_value) - perturbed_dataset = MOVEDataset( - perturbed_cat, - baseline_dataset.con_all, - baseline_dataset.cat_shapes, - baseline_dataset.con_shapes, - ) - perturbed_dataloader = DataLoader( - perturbed_dataset, - shuffle=False, + perturbed_dataloader = _build_dataloader( + cat_data=perturbed_cat, + con_data=baseline_dataset.con_all, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) - return perturbed_dataloader @@ -254,9 +249,11 @@ def perturb_continuous_data_one( perturbed_con = baseline_dataset.con_all.clone() perturbed_con[:, start_idx + index_pert_feat] = torch.FloatTensor([target_value]) - perturbed_dataloader = _build_perturbed_dataloader( - baseline_dataset=baseline_dataset, - perturbed=perturbed_con, + perturbed_dataloader = _build_dataloader( + cat_data=baseline_dataset.cat_all, + con_data=perturbed_con, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) @@ -315,9 +312,11 @@ def perturb_continuous_data_extended( ) perturbations_list.append(perturbed_con[:, start_idx + i].numpy()) - perturbed_dataloader = _build_perturbed_dataloader( - baseline_dataset=baseline_dataset, - perturbed=perturbed_con, + perturbed_dataloader = _build_dataloader( + con_data=baseline_dataset.cat_all, + cat_data=perturbed_con, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) dataloaders.append(perturbed_dataloader) @@ -397,9 +396,11 @@ def perturb_continuous_data_extended_one( f"Creating perturbed dataset and dataloader for feature {index_pert_feat}" ) - perturbed_dataloader = _build_perturbed_dataloader( - baseline_dataset=baseline_dataset, - perturbed=perturbed_con, + perturbed_dataloader = _build_dataloader( + cat_data=baseline_dataset.cat_all, + con_data=perturbed_con, + cat_shapes=baseline_dataset.cat_shapes, + con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, ) From 58f08e434609c2f1bf0cae3e85d4ac99e0108cbb Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 17:38:18 +0200 Subject: [PATCH 82/94] :bug: fix minor bug (wrongly assigned feat) --- src/move/data/perturbations.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 438ff814..7ceab909 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -313,8 +313,8 @@ def perturb_continuous_data_extended( perturbations_list.append(perturbed_con[:, start_idx + i].numpy()) perturbed_dataloader = _build_dataloader( - con_data=baseline_dataset.cat_all, - cat_data=perturbed_con, + cat_data=baseline_dataset.cat_all, + con_data=perturbed_con, cat_shapes=baseline_dataset.cat_shapes, con_shapes=baseline_dataset.con_shapes, batch_size=baseline_dataloader.batch_size, From f895237cf32f50848c18a05f1b48818c9f0dd948 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 17:46:28 +0200 Subject: [PATCH 83/94] :zap: move masking code into main fct of module --- src/move/tasks/identify_associations.py | 34 ++++++++----------------- 1 file changed, 11 insertions(+), 23 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 0dfb8dea..91f26606 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -120,13 +120,6 @@ def prepare_for_categorical_perturbation( ) dataloaders.append(baseline_dataloader) - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - - assert baseline_dataset.con_all is not None - orig_con = baseline_dataset.con_all - nan_mask = (orig_con == 0).numpy() # NaN values encoded as 0s - logger.debug(f"# NaN values: {np.sum(nan_mask)}/{orig_con.numel()}") - target_dataset_idx = config.data.categorical_names.index(task_config.target_dataset) target_dataset = cat_list[target_dataset_idx] feature_mask = np.all(target_dataset == target_value, axis=2) # 2D: N x P @@ -134,7 +127,6 @@ def prepare_for_categorical_perturbation( return ( dataloaders, - nan_mask, feature_mask, ) @@ -183,15 +175,7 @@ def prepare_for_continuous_perturbation( logger.debug(f"Dataloaders length is {len(dataloaders)}") - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - - assert baseline_dataset.con_all is not None - orig_con = baseline_dataset.con_all - nan_mask = (orig_con == 0).numpy() # NaN values encoded as 0s - logger.debug(f"# NaN values: {np.sum(nan_mask)}/{orig_con.numel()}") - feature_mask = nan_mask - - return (dataloaders, nan_mask, feature_mask) + return dataloaders def _bayes_approach( @@ -839,6 +823,13 @@ def identify_associations(config: MOVEConfig) -> None: cat_list, con_list, shuffle=False, batch_size=task_config.batch_size ) + # nan_mask based on baseline dataset + baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) + assert baseline_dataset.con_all is not None + orig_con = baseline_dataset.con_all + nan_mask = (orig_con == 0).numpy() # NaN values encoded as 0s + logger.debug(f"# NaN values: {np.sum(nan_mask)}/{orig_con.numel()}") + # Indentify associations between continuous features: logger.info(f"Perturbing dataset: '{task_config.target_dataset}'") if task_config.target_value in CONTINUOUS_TARGET_VALUE: @@ -846,20 +837,17 @@ def identify_associations(config: MOVEConfig) -> None: logger.info(f"Perturbation type: {task_config.target_value}") output_subpath = Path(output_path) / "perturbation_visualization" output_subpath.mkdir(exist_ok=True, parents=True) - ( - dataloaders, - nan_mask, - feature_mask, - ) = prepare_for_continuous_perturbation( + + dataloaders = prepare_for_continuous_perturbation( config, output_subpath, baseline_dataloader ) + feature_mask = nan_mask # Identify associations between categorical and continuous features: else: logger.info("Beginning task: identify associations categorical") ( dataloaders, - nan_mask, feature_mask, ) = prepare_for_categorical_perturbation( config, interim_path, baseline_dataloader, cat_list From 5eb795451c148cdc92b7ac9dcc7f0811d1bc8a07 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 19:02:37 +0200 Subject: [PATCH 84/94] :art: move feat_mask creation out --- src/move/tasks/identify_associations.py | 25 ++++++++++++------------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 91f26606..0aeb1299 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -120,15 +120,7 @@ def prepare_for_categorical_perturbation( ) dataloaders.append(baseline_dataloader) - target_dataset_idx = config.data.categorical_names.index(task_config.target_dataset) - target_dataset = cat_list[target_dataset_idx] - feature_mask = np.all(target_dataset == target_value, axis=2) # 2D: N x P - feature_mask |= np.sum(target_dataset, axis=2) == 0 - - return ( - dataloaders, - feature_mask, - ) + return dataloaders def prepare_for_continuous_perturbation( @@ -846,10 +838,17 @@ def identify_associations(config: MOVEConfig) -> None: # Identify associations between categorical and continuous features: else: logger.info("Beginning task: identify associations categorical") - ( - dataloaders, - feature_mask, - ) = prepare_for_categorical_perturbation( + task_config = cast(IdentifyAssociationsConfig, config.task) + target_dataset_idx = config.data.categorical_names.index( + task_config.target_dataset + ) + target_dataset = cat_list[target_dataset_idx] + mappings = io.load_mappings(interim_path / "mappings.json") + target_mapping = mappings[task_config.target_dataset] + target_value = one_hot_encode_single(target_mapping, task_config.target_value) + feature_mask = np.all(target_dataset == target_value, axis=2) # 2D: N x P + feature_mask |= np.sum(target_dataset, axis=2) == 0 + dataloaders = prepare_for_categorical_perturbation( config, interim_path, baseline_dataloader, cat_list ) From 8c4e53b4f8f501d318f1c58df15a9ce5405b0837 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 19:53:03 +0200 Subject: [PATCH 85/94] :ambulance: temp. fix of CI - need to switch dataloader constructor for type of pertubation (cat or cont) --- src/move/tasks/bayes_parallel.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 94633830..7e9a6f40 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -70,9 +70,10 @@ def _bayes_approach_worker(args): # Create perturbed dataloader for the current feature (i) logger.debug(f"Creating perturbed dataloader for feature {i}") + # ! perturb_categorical_data_one for categorical data! perturbed_dataloader = perturb_continuous_data_extended_one( baseline_dataloader=baseline_dataloader, - con_dataset_names=config.data.categorical_names, # ! error: continuous_names + con_dataset_names=config.data.continuous_names, # ! error: continuous_names target_dataset_name=task_config.target_dataset, perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), index_pert_feat=i, From 49a93d03fb4063f20e7e72e97aa046bd2072ac70 Mon Sep 17 00:00:00 2001 From: Henry Date: Tue, 9 Jul 2024 19:55:52 +0200 Subject: [PATCH 86/94] :zap: do not build dataloaders for multiprocessing --- src/move/tasks/identify_associations.py | 34 +++++++++++++++++-------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 0aeb1299..7c5e225e 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -829,11 +829,17 @@ def identify_associations(config: MOVEConfig) -> None: logger.info(f"Perturbation type: {task_config.target_value}") output_subpath = Path(output_path) / "perturbation_visualization" output_subpath.mkdir(exist_ok=True, parents=True) - - dataloaders = prepare_for_continuous_perturbation( - config, output_subpath, baseline_dataloader - ) + if not task_config.multiprocess: + dataloaders = prepare_for_continuous_perturbation( + config, output_subpath, baseline_dataloader + ) feature_mask = nan_mask + con_dataset_names = config.data.continuous_names + target_idx = con_dataset_names.index( + task_config.target_dataset + ) # dataset index + logger.debug(f"Cont. shapes: {baseline_dataset.con_shapes} [take {target_idx}]") + num_perturbed = baseline_dataset.con_shapes[target_idx] # Identify associations between categorical and continuous features: else: @@ -848,13 +854,21 @@ def identify_associations(config: MOVEConfig) -> None: target_value = one_hot_encode_single(target_mapping, task_config.target_value) feature_mask = np.all(target_dataset == target_value, axis=2) # 2D: N x P feature_mask |= np.sum(target_dataset, axis=2) == 0 - dataloaders = prepare_for_categorical_perturbation( - config, interim_path, baseline_dataloader, cat_list + if not task_config.multiprocess: + dataloaders = prepare_for_categorical_perturbation( + config, interim_path, baseline_dataloader, cat_list + ) + num_perturbed = target_dataset.shape[-1] + logger.info( + f"Cat. shapes: {baseline_dataset.cat_shapes}" + f" [take {target_dataset_idx}]" ) + target_shape = baseline_dataset.cat_shapes[target_dataset_idx] + num_perturbed = target_shape[0] # APPROACH EVALUATION ########################## - num_perturbed = len(dataloaders) - 1 # P - logger.debug(f"# perturbed features: {num_perturbed}") + # num_perturbed = len(dataloaders) - 1 # P + # logger.debug(f"# perturbed features: {num_perturbed}") if task_type == "bayes": task_config = cast(IdentifyAssociationsBayesConfig, task_config) @@ -895,7 +909,7 @@ def identify_associations(config: MOVEConfig) -> None: elif task_type == "ttest": task_config = cast(IdentifyAssociationsTTestConfig, task_config) if task_config.multiprocess: - logger.warning("Multiprocessing is not supported for ttest approach.") + raise NotImplementedError("Multiprocessing is not supported for T-test.") sig_ids, *extra_cols = _ttest_approach( task_config, train_dataloader, @@ -915,7 +929,7 @@ def identify_associations(config: MOVEConfig) -> None: elif task_type == "ks": task_config = cast(IdentifyAssociationsKSConfig, task_config) if task_config.multiprocess: - logger.warning("Multiprocessing is not supported for KS approach.") + raise NotImplementedError("Multiprocessing is not supported for KS.") sig_ids, *extra_cols = _ks_approach( config, task_config, From 709c6748115518d455f6fc5b289583a4851b424a Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 10 Jul 2024 10:52:11 +0200 Subject: [PATCH 87/94] :construction: test t-test again, re-run pert. w/o model training --- .github/workflows/release.yaml | 56 +++++++++++++++++++++++++--------- 1 file changed, 41 insertions(+), 15 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index fd76752c..d633383a 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -24,7 +24,31 @@ jobs: run: pip install flake8 flake8-bugbear - name: Lint with flake8 run: flake8 src - run-tutorial: + # legacy testing of t-test + run-tutorial-ttest: + name: Run - random_small - t-test + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install dependencies + run: pip install . + - name: Prepare tutorial data + run: | + cd tutorial + move-dl data=random_small task=encode_data --cfg job + move-dl data=random_small task=encode_data + # - name: Identify associations - t-test + # at least 4 refits needed for t-test + - name: Identify associations - t-test + run: | + cd tutorial + move-dl data=random_small task=random_small__id_assoc_ttest --cfg job + move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 + # categorical dataset pertubation - single and multiprocessed + run-tutorial-cat-pert-single: name: Run - random_small - singleprocess runs-on: ubuntu-latest steps: @@ -44,17 +68,13 @@ jobs: cd tutorial move-dl data=random_small task=random_small__latent --cfg job move-dl data=random_small task=random_small__latent task.training_loop.num_epochs=100 - # - name: Identify associations - t-test - # run: | - # cd tutorial - # move-dl data=random_small task=random_small__id_assoc_ttest --cfg job - # move-dl data=random_small task=random_small__id_assoc_ttest task.training_loop.num_epochs=30 task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 - run-tutorial-w-multiprocessing: + # continous dataset perturbation - single and multiprocessed + run-tutorial-cont-pert-multi: name: Run - random_continuous - multiprocess runs-on: ubuntu-latest steps: @@ -78,9 +98,14 @@ jobs: run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 task.multiprocess=true - run-tutorial-cont: - name: Run tutorial - random_continuous + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 task.multiprocess=true + - name: Identify associations - bayes factors - singleprocess w/o training + run: | + cd tutorial + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.multiprocess=true --cfg job + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 + run-tutorial-cont-pert-single: + name: Run - random_continuous - singleprocess runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -98,16 +123,17 @@ jobs: cd tutorial move-dl data=random_continuous task=random_continuous__latent --cfg job move-dl data=random_continuous task=random_continuous__latent - # - name: Identify associations - t-test - # run: | - # cd tutorial - # move-dl data=random_continuous task=random_continuous__id_assoc_ttest --cfg job - # move-dl data=random_continuous task=random_continuous__id_assoc_ttest task.num_refits=4 - name: Identify associations - bayes factors run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes --cfg job move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 + - name: Identify associations - bayes factors - w/o training (repeat) + run: | + cd tutorial + move-dl data=random_continuous task=random_continuous__id_assoc_bayes --cfg job + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 + # this reuses the same model trained in analyze latent space - name: Identify associations - KS run: | cd tutorial From 6e65cc606b07e33f84194f6c1c6a8e1b1049102b Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 10 Jul 2024 11:33:11 +0200 Subject: [PATCH 88/94] :sparkles: add categorical pert. to multiprocessing --- .github/workflows/release.yaml | 39 +++++++++++++++++++++++-- src/move/tasks/bayes_parallel.py | 33 ++++++++++++++++----- src/move/tasks/identify_associations.py | 1 + 3 files changed, 63 insertions(+), 10 deletions(-) diff --git a/.github/workflows/release.yaml b/.github/workflows/release.yaml index d633383a..afe5e80c 100644 --- a/.github/workflows/release.yaml +++ b/.github/workflows/release.yaml @@ -73,6 +73,41 @@ jobs: cd tutorial move-dl data=random_small task=random_small__id_assoc_bayes --cfg job move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 + - name: Identify associations - bayes factors - w/o training + run: | + cd tutorial + move-dl data=random_small task=random_small__id_assoc_bayes --cfg job + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 + run-tutorial-cat-pert-multi: + name: Run - random_small - multiprocess + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + - name: Install dependencies + run: pip install . + - name: Prepare tutorial data + run: | + cd tutorial + move-dl data=random_small task=encode_data --cfg job + move-dl data=random_small task=encode_data + - name: Train model and analyze latent space - multiprocess + run: | + cd tutorial + move-dl data=random_small task=random_small__latent --cfg job + move-dl data=random_small task=random_small__latent task.training_loop.num_epochs=100 task.multiprocess=true + - name: Identify associations - bayes factors - multiprocess + run: | + cd tutorial + move-dl data=random_small task=random_small__id_assoc_bayes --cfg job + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 task.multiprocess=true + - name: Identify associations - bayes factors - multiprocess w/o training + run: | + cd tutorial + move-dl data=random_small task=random_small__id_assoc_bayes --cfg job + move-dl data=random_small task=random_small__id_assoc_bayes task.training_loop.num_epochs=100 task.num_refits=2 task.multiprocess=true # continous dataset perturbation - single and multiprocessed run-tutorial-cont-pert-multi: name: Run - random_continuous - multiprocess @@ -99,11 +134,11 @@ jobs: cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.multiprocess=true --cfg job move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 task.multiprocess=true - - name: Identify associations - bayes factors - singleprocess w/o training + - name: Identify associations - bayes factors - multiprocess w/o training run: | cd tutorial move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.multiprocess=true --cfg job - move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 + move-dl data=random_continuous task=random_continuous__id_assoc_bayes task.num_refits=1 task.multiprocess=true run-tutorial-cont-pert-single: name: Run - random_continuous - singleprocess runs-on: ubuntu-latest diff --git a/src/move/tasks/bayes_parallel.py b/src/move/tasks/bayes_parallel.py index 7e9a6f40..1db2b464 100644 --- a/src/move/tasks/bayes_parallel.py +++ b/src/move/tasks/bayes_parallel.py @@ -11,11 +11,14 @@ from move.conf.schema import IdentifyAssociationsBayesConfig, MOVEConfig from move.core.logging import get_logger from move.core.typing import BoolArray, FloatArray, IntArray +from move.data import io from move.data.dataloaders import MOVEDataset from move.data.perturbations import ( ContinuousPerturbationType, + perturb_categorical_data_one, perturb_continuous_data_extended_one, ) +from move.data.preprocessing import one_hot_encode_single from move.models.vae import VAE # We can do three types of statistical tests. Multiprocessing is only implemented @@ -70,14 +73,28 @@ def _bayes_approach_worker(args): # Create perturbed dataloader for the current feature (i) logger.debug(f"Creating perturbed dataloader for feature {i}") - # ! perturb_categorical_data_one for categorical data! - perturbed_dataloader = perturb_continuous_data_extended_one( - baseline_dataloader=baseline_dataloader, - con_dataset_names=config.data.continuous_names, # ! error: continuous_names - target_dataset_name=task_config.target_dataset, - perturbation_type=cast(ContinuousPerturbationType, task_config.target_value), - index_pert_feat=i, - ) + if task_config.target_value in CONTINUOUS_TARGET_VALUE: + perturbed_dataloader = perturb_continuous_data_extended_one( + baseline_dataloader=baseline_dataloader, + con_dataset_names=config.data.continuous_names, + target_dataset_name=task_config.target_dataset, + perturbation_type=cast( + ContinuousPerturbationType, task_config.target_value + ), + index_pert_feat=i, + ) + else: + interim_path = Path(config.data.interim_data_path) + mappings = io.load_mappings(interim_path / "mappings.json") + target_mapping = mappings[task_config.target_dataset] + target_value = one_hot_encode_single(target_mapping, task_config.target_value) + perturbed_dataloader = perturb_categorical_data_one( + baseline_dataloader=baseline_dataloader, + cat_dataset_names=config.data.categorical_names, + target_dataset_name=task_config.target_dataset, + target_value=target_value, + index_pert_feat=i, + ) logger.debug(f"created perturbed dataloader for feature {i}") # For each refit, reload baseline reconstruction (obtained in bayes_parallel diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index 7c5e225e..b7a58292 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -878,6 +878,7 @@ def identify_associations(config: MOVEConfig) -> None: task_config=task_config, train_dataloader=train_dataloader, baseline_dataloader=baseline_dataloader, + # perturbed dataloaders created in worker function models_path=models_path, num_perturbed=num_perturbed, num_samples=num_samples, From 4efbdd95cfc83ea7b28b248b55f5b4ccf598bb69 Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 10 Jul 2024 14:13:10 +0200 Subject: [PATCH 89/94] :fire: remove unused code --- src/move/data/perturbations.py | 46 ---------------------------------- 1 file changed, 46 deletions(-) diff --git a/src/move/data/perturbations.py b/src/move/data/perturbations.py index 7ceab909..cac02ef0 100644 --- a/src/move/data/perturbations.py +++ b/src/move/data/perturbations.py @@ -1,6 +1,5 @@ __all__ = [ "perturb_categorical_data", - "perturb_continuous_data", "perturb_continuous_data_extended_one", "perturb_continuous_data_extended", ] @@ -126,51 +125,6 @@ def perturb_categorical_data( return dataloaders -# not used anymore -def perturb_continuous_data( - baseline_dataloader: DataLoader, - con_dataset_names: list[str], - target_dataset_name: str, - target_value: float, -) -> list[DataLoader]: - """Add perturbations to continuous data. For each feature in the target - dataset, change its value to target. - - Args: - baseline_dataloader: Baseline dataloader - con_dataset_names: List of continuous dataset names - target_dataset_name: Target continuous dataset to perturb - target_value: Target value - - Returns: - List of dataloaders containing all perturbed datasets - """ - - baseline_dataset = cast(MOVEDataset, baseline_dataloader.dataset) - assert baseline_dataset.con_shapes is not None - assert baseline_dataset.con_all is not None - - target_idx = con_dataset_names.index(target_dataset_name) - splits = np.cumsum([0] + baseline_dataset.con_shapes) - start_idx = splits[target_idx] - num_features = baseline_dataset.con_shapes[target_idx] - - dataloaders = [] - for i in range(num_features): - perturbed_con = baseline_dataset.con_all.clone() - perturbed_con[:, start_idx + i] = torch.FloatTensor([target_value]) - perturbed_dataloader = _build_dataloader( - cat_data=baseline_dataset.cat_all, - con_data=perturbed_con, - cat_shapes=baseline_dataset.cat_shapes, - con_shapes=baseline_dataset.con_shapes, - batch_size=baseline_dataloader.batch_size, - ) - dataloaders.append(perturbed_dataloader) - - return dataloaders - - def perturb_categorical_data_one( baseline_dataloader: DataLoader, cat_dataset_names: list[str], From dab767acbc98592e322a0a0457cc929c6b9c4ead Mon Sep 17 00:00:00 2001 From: Henry Date: Wed, 10 Jul 2024 14:13:45 +0200 Subject: [PATCH 90/94] :art: remove unused argument --- src/move/tasks/identify_associations.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index b7a58292..b979cb50 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -79,7 +79,6 @@ def prepare_for_categorical_perturbation( config: MOVEConfig, interim_path: Path, baseline_dataloader: DataLoader, - cat_list: list[FloatArray], ) -> tuple[ list[DataLoader], BoolArray, @@ -93,7 +92,6 @@ def prepare_for_categorical_perturbation( config: main configuration file interim_path: path where the intermediate outputs are saved baseline_dataloader: reference dataloader that will be perturbed - cat_list: list of arrays with categorical data Returns: dataloaders: all dataloaders, including baseline appended last. @@ -856,7 +854,7 @@ def identify_associations(config: MOVEConfig) -> None: feature_mask |= np.sum(target_dataset, axis=2) == 0 if not task_config.multiprocess: dataloaders = prepare_for_categorical_perturbation( - config, interim_path, baseline_dataloader, cat_list + config, interim_path, baseline_dataloader ) num_perturbed = target_dataset.shape[-1] logger.info( From 980bbce7624dd5097d04655ea75717d1cf6a44ba Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 12 Jul 2024 14:01:35 +0200 Subject: [PATCH 91/94] :rewind: checkout developer version --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index d927ed34..cfc29395 100644 --- a/.gitignore +++ b/.gitignore @@ -54,11 +54,14 @@ docs/build/ docs/source/_templates/ # VS Code settings +.vscode + # macOS .DS_Store # Root folder /*.* +!/.gitignore !/.readthedocs.yaml !/LICENSE !/MANIFEST.in From c26b2dd11ef4e2b49622850e973fc6e841b8a88e Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 12 Jul 2024 14:02:13 +0200 Subject: [PATCH 92/94] :art: move shared key to base class --- src/move/conf/schema.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/src/move/conf/schema.py b/src/move/conf/schema.py index a0ee14e2..7f23e4fd 100644 --- a/src/move/conf/schema.py +++ b/src/move/conf/schema.py @@ -166,14 +166,13 @@ class IdentifyAssociationsConfig(TaskConfig): num_refits: int = MISSING sig_threshold: float = 0.05 save_refits: bool = False + multiprocess: bool = False @dataclass class IdentifyAssociationsBayesConfig(IdentifyAssociationsConfig): """Configure the probabilistic approach to identify associations.""" - multiprocess: bool = False # Default value is False - ... @@ -188,7 +187,6 @@ class IdentifyAssociationsTTestConfig(IdentifyAssociationsConfig): """ num_latent: list[int] = MISSING - multiprocess: bool = False # Multiprocessiong not implemented for t-test approach @dataclass @@ -210,7 +208,6 @@ class IdentifyAssociationsKSConfig(IdentifyAssociationsConfig): perturbed_feature_names: list[str] = field(default_factory=list) target_feature_names: list[str] = field(default_factory=list) - multiprocess: bool = False # Multiprocessiong not implemented for KS approach @dataclass From 05c173590ad4dc06b6cb68c5899c580918dd2eb5 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 12 Jul 2024 14:06:34 +0200 Subject: [PATCH 93/94] :fire: remove comments and code duplications --- src/move/tasks/analyze_latent.py | 5 ----- src/move/tasks/encode_data.py | 10 ++-------- 2 files changed, 2 insertions(+), 13 deletions(-) diff --git a/src/move/tasks/analyze_latent.py b/src/move/tasks/analyze_latent.py index 53481a42..baef1db2 100644 --- a/src/move/tasks/analyze_latent.py +++ b/src/move/tasks/analyze_latent.py @@ -406,16 +406,11 @@ def analyze_latent(config: MOVEConfig) -> None: for i, dataset_name in enumerate(config.data.continuous_names): logger.debug(f"Generating plot: feature importance '{dataset_name}'") - # NOT SURE IF IT WORKS THE SAME FOR CONTINUOUS FEATURES, CHECK THIS - # I did sth that did not work, I'll try again now. con_dataset_names = config.data.continuous_names target_idx = con_dataset_names.index(dataset_name) - # num_features = target_shape[0] num_features = test_dataset.con_shapes[target_idx] - # num_features = len(dataloaders) - # We will use this inside the loop that iterates over all features: # We create one diff per dataset, to not store all of them in memory z = model.project(test_dataloader) diff --git a/src/move/tasks/encode_data.py b/src/move/tasks/encode_data.py index 8f6549a5..3ce3cb5c 100644 --- a/src/move/tasks/encode_data.py +++ b/src/move/tasks/encode_data.py @@ -59,12 +59,6 @@ def encode_data(config: DataConfig): ) fig.savefig(fig_path) - values, mask_1d = preprocessing.scale(values) - names = names[mask_1d] - logger.debug(f"Columns with zero variance: {np.sum(~mask_1d)}") - io.dump_names(interim_data_path / f"{dataset_name}.txt", names) - np.save(interim_data_path / f"{dataset_name}.npy", values) - # Plotting the value distribution for all continuous datasets: fig = plot_value_distributions(values) fig_path = str(output_path / f"Value_distribution_{dataset_name}.png") @@ -74,5 +68,5 @@ def encode_data(config: DataConfig): values, mask_1d = preprocessing.scale(values, input_config.log2) names = names[mask_1d] logger.debug(f"Columns with zero variance: {np.sum(~mask_1d)}") - io.dump_names(interim_data_path / f"{input_config.name}.txt", names) - np.save(interim_data_path / f"{input_config.name}.npy", values) + io.dump_names(interim_data_path / f"{dataset_name}.txt", names) + np.save(interim_data_path / f"{dataset_name}.npy", values) From c5002cd1fc9ad9decfcb80570b665d4ce72b29f6 Mon Sep 17 00:00:00 2001 From: Henry Date: Fri, 12 Jul 2024 14:14:41 +0200 Subject: [PATCH 94/94] :art: update type hints, remove unused import --- src/move/tasks/identify_associations.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/src/move/tasks/identify_associations.py b/src/move/tasks/identify_associations.py index b979cb50..7f1b8532 100644 --- a/src/move/tasks/identify_associations.py +++ b/src/move/tasks/identify_associations.py @@ -9,7 +9,6 @@ import numpy as np import pandas as pd import torch -import torch.multiprocessing from omegaconf import OmegaConf from scipy.stats import ks_2samp, pearsonr # type: ignore from torch.utils.data import DataLoader @@ -79,11 +78,7 @@ def prepare_for_categorical_perturbation( config: MOVEConfig, interim_path: Path, baseline_dataloader: DataLoader, -) -> tuple[ - list[DataLoader], - BoolArray, - BoolArray, -]: +) -> list[DataLoader]: """ This function creates the required dataloaders and masks for further categorical association analysis. @@ -125,11 +120,7 @@ def prepare_for_continuous_perturbation( config: MOVEConfig, output_subpath: Path, baseline_dataloader: DataLoader, -) -> tuple[ - list[DataLoader], - BoolArray, - BoolArray, -]: +) -> list[DataLoader]: """ This function creates the required dataloaders and masks for further continuous association analysis.

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