diff --git a/.github/workflows/code_checks.yaml b/.github/workflows/code_checks.yaml new file mode 100644 index 0000000..470d6ef --- /dev/null +++ b/.github/workflows/code_checks.yaml @@ -0,0 +1,51 @@ +name: code checks +permissions: + contents: read + pull-requests: write + +on: + push: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + - '**.ipynb' + pull_request: + branches: + - main + paths: + - .pre-commit-config.yaml + - .github/workflows/code_checks.yml + - '**.py' + - uv.lock + - pyproject.toml + - '**.ipynb' + +jobs: + run-code-check: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + version: "latest" + enable-cache: true + + - name: "Set up Python" + uses: actions/setup-python@v5 + with: + python-version-file: ".python-version" + + - name: Install the project + run: uv sync --all-extras --dev + + - name: Install dependencies and check code + run: | + source .venv/bin/activate + pre-commit run --all-files diff --git a/.github/workflows/static_code_checks.yaml b/.github/workflows/static_code_checks.yaml deleted file mode 100644 index 9465c63..0000000 --- a/.github/workflows/static_code_checks.yaml +++ /dev/null @@ -1,26 +0,0 @@ -# only has to pass for python 3.8 -name: Static code checks - -on: - push: - branches: - main - pull_request: - branches: - main - -jobs: - run-code-check: - runs-on: ubuntu-latest - steps: - - - name: Checkout code - uses: actions/checkout@v3 - - - name: Set up Python 3.8 - uses: actions/setup-python@v3 - with: - python-version: 3.8 - - - name: precommit checker - uses: pre-commit/action@v2.0.3 diff --git a/.gitignore b/.gitignore index 90cfbb0..6e4cfac 100644 --- a/.gitignore +++ b/.gitignore @@ -81,9 +81,6 @@ target/ profile_default/ ipython_config.py -# pyenv -.python-version - # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 6821cff..e048c04 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 # Use the ref you want to point at + rev: v5.0.0 # Use the ref you want to point at hooks: - id: trailing-whitespace - id: check-ast @@ -11,29 +11,19 @@ repos: - id: end-of-file-fixer - id: mixed-line-ending args: [--fix=lf] - - id: requirements-txt-fixer - - id: trailing-whitespace + - id: detect-private-key + - id: check-byte-order-marker + - id: check-merge-conflict + - id: check-symlinks - id: check-yaml + args: [--unsafe] + - id: check-toml - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.0.280 + rev: v0.11.4 hooks: - id: ruff - - - repo: https://github.com/psf/black - rev: 23.7.0 - hooks: - - id: black - - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.4.1 - hooks: - - id: mypy - - - repo: https://github.com/nbQA-dev/nbQA - rev: 1.7.0 - hooks: - - id: nbqa-black - - id: nbqa-ruff - - id: nbqa-check-ast - - id: nbqa-mypy + args: [--fix, --exit-non-zero-on-fix] + types_or: [ python, pyi, jupyter ] + - id: ruff-format + types_or: [ python, pyi, jupyter ] diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..bd28b9c --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.9 diff --git a/README.md b/README.md index d9f74ae..34ed2f9 100644 --- a/README.md +++ b/README.md @@ -2,27 +2,56 @@ This repository contains reference implementations of three self-supervised learning techniques explored during the Vector Institute's Self-Supervised Learning (SSL) Bootcamp. -# Installing dependencies -``` -python3 -m venv /path/to/new/virtual/environment/ssl_env -source /path/to/new/virtual/environment/ssl_env/bin/activate -pip install --upgrade pip -pip install -r requirements.txt -``` +# Summary of Reference Implementations -If you are on the Vector Institute's Vaughan cluster, the environment is already set up and can be activated with +| Name | Description | Reference Implementation | +|------|-------------|-------| +Internal Contrastive Learning (ICL) + Latent Outlier Exposure (LOE)| ICL learns to maximize the mutual information between two complementary subsets based on the assumption that the relation between a subset of features and the rest of the features is class-dependent. LOE extends ICL to work with contaminated datasets. | [Anomaly Detection in Tabular Data with ICL](src/contrastive_learning/ICL/ICL.ipynb), [Latent Outlier Exposure for Anomaly Detection with Contaminated Data](src/contrastive_learning/LatentOE/LatentOE_Notebook.ipynb) +SimMTM | Reconstructs a time series signal from multiple randomly masked versions. Uses series-wise representation similarity to do a weighted aggregation of point-wise representations before reconstruction. | [Beijing PM2.5 Air Quality Forecasting](src/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb) +TabRet | TabRet is a pre-trainable Transformer-based model for tabular data and designed to work on a downstream task that contains columns not seen in pre-training. Unlike other methods, TabRet has an extra learning step before fine-tuning called retokenizing, which calibrates feature embeddings based on the masked autoencoding loss. | [Stroke Prediction with the BRFSS dataset](src/masked_modelling/tabret/TabRet.ipynb) +Data2Vec | Combines masked prediction with self-distillation to predict contextualized latent representations (produced by the teacher network) based on a partial/masked view of the input (given to the student network). | [Image Classification with STL-10 dataset](src/self_distillation/data2vec_vision.ipynb) + +# Setting up the environment +Prior to installing the dependencies for this project, it is recommended to install +[uv](https://github.com/astral-sh/uv?tab=readme-ov-file#installation) and create +a virtual environment. You may use whatever virtual environment management tool +that you like, including uv, conda, and virtualenv. + +With uv, you can create a virtual environment with the following command: + +```bash +uv venv -n --seed --python 3.9 /path/to/new/virtual/environment/ssl_env ``` -source /ssd003/projects/aieng/public/ssl_bootcamp_resources/venv/bin/activate -``` +This will create a new virtual environment in the specified path. + +**Note**: If you are using the Vector Institute's Vaughan cluster, a virtual +environment has already been created for you at `/ssd003/projects/aieng/public/ssl_bootcamp_resources/venv`. + +Once you have created a virtual environment, you can activate it with the command: -# Using pre-commit hooks -To check your code at commit time ``` -pre-commit install +source /path/to/new/virtual/environment/ssl_env/bin/activate ``` -You can also get pre-commit to fix your code +Then, you can install the dependencies for this project with the following command: + +```bash +git clone https://github.com/VectorInstitute/SSL-Bootcamp.git +cd SSL-Bootcamp +uv sync --no-cache --active --dev +``` +**Note**: The `--active` flag in the above command assumes that you have already +activated your virtual environment. If you prefer not to create a new virtual +environment yourself, you can omit the `--active` flag and uv will create a new virtual environment +for you in the `.venv` directory inside the project root. + +## Using pre-commit hooks +To ensure that your code adheres to the project's style and formatting guidelines, +you can use pre-commit hooks to check for common issues, such as code formatting, +linting, and security vulnerabilities. Run the following command before pushing +your code to the repository: + ``` pre-commit run --all-files ``` diff --git a/pyproject.toml b/pyproject.toml index 1d13778..781b376 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,33 +1,66 @@ -[build-system] -requires = ["setuptools", "wheel"] -build-backend = "setuptools.build_meta" - -[tool.black] -line-length = 88 - -[tool.mypy] -ignore_missing_imports = true -install_types = true -pretty = true -non_interactive = true -disallow_untyped_defs = true -no_implicit_optional = true -check_untyped_defs = true +[project] +name = "ssl-bootcamp" +version = "0.1.0" +description = "Reference implementations for the Vector Institute's self-supervised learning (SSL) bootcamp (2023)" +readme = "README.md" +authors = [{name = "Vector AI Engineering", email = "ai_engineering@vectorinstitute.ai"}] +license = "MIT" +repository = "https://github.com/VectorInstitute/SSL-Bootcamp" +requires-python = ">=3.9" +dependencies = [ + "copulas>=0.12.0", + "deepod>=0.4.1", + "ipykernel>=6.29.5", + "lightning==2.0.6", + "lightning-bolts>=0.7.0", + "matplotlib>=3.7.5", + "notebook>=7.3.3", + "numpy>=1.24.4", + "optuna>=4.2.1", + "pandas>=2.0.3", + "pyod>=2.0.4", + "pytorch-tabular==1.0.2", + "rtdl==0.0.13", + "scikit-learn>=1.3.2", + "scipy>=1.10.1", + "timm==0.9.2", + "toml>=0.10.2", + "torch==1.13.0", + "torchvision==0.14.0", + "transformers==4.31.0", + "transtab>=0.0.5", + "wandb>=0.19.9", + "xgboost>=2.1.4", +] + +[dependency-groups] +dev = [ + "pre-commit>=3.5.0", + "ruff>=0.11.4", +] [tool.ruff] -select = ["B", "C", "D", "E", "F", "I", "W"] -line-length = 88 +include = ["*.py", "pyproject.toml", "*.ipynb"] +line-length = 119 + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +docstring-code-format = true -[tool.ruff.isort] +[tool.ruff.lint] +select = ["A", "B", "C", "E", "F", "I", "W"] +fixable = ["A", "B", "COM", "C", "C4", "RET", "SIM", "ICN", "Q", "RSE", "E", "F", "I", "W", "PL"] +ignore = ["E501", "C901"] + +[tool.ruff.lint.isort] lines-after-imports = 2 -[tool.nbqa.addopts] -ruff = [ - "--ignore=D100,D203,D211,D212,D213,D401", - "--fix", - "--line-length=119", -] -black = ["--line-length=119"] +[tool.ruff.lint.pycodestyle] +max-doc-length = 119 + +[tool.ruff.lint.pydocstyle] +convention = "numpy" [tool.nbqa.md] blacken-docs = true diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 2c7c622..0000000 --- a/requirements.txt +++ /dev/null @@ -1,32 +0,0 @@ ---extra-index-url https://download.pytorch.org/whl/cu117 - -black -copulas -dataclasses -deepod -ipykernel -lightning==2.0.6 -lightning-bolts -matplotlib -mypy -nbqa[ruff,mypy,black,jupytext] -notebook -optuna -pandas -pre-commit -pyod -pytest -pytest-cov -pytorch_tabular==1.0.2 -rtdl==0.0.13 -scikit-learn -timm==0.9.2 -toml -torch==1.13.0+cu117 -torchvision==0.14.0+cu117 -transformers==4.31.0 -transtab -types-requests -types-setuptools -wandb -xgboost diff --git a/contrastive_learning/ICL/ICL.ipynb b/src/contrastive_learning/ICL/ICL.ipynb similarity index 100% rename from contrastive_learning/ICL/ICL.ipynb rename to src/contrastive_learning/ICL/ICL.ipynb diff --git a/contrastive_learning/ICL/data_generator.py b/src/contrastive_learning/ICL/data_generator.py similarity index 84% rename from contrastive_learning/ICL/data_generator.py rename to src/contrastive_learning/ICL/data_generator.py index e8415b9..df501d3 100644 --- a/contrastive_learning/ICL/data_generator.py +++ b/src/contrastive_learning/ICL/data_generator.py @@ -1,4 +1,5 @@ """Data Generator for creating the data.""" + import os import random from math import ceil @@ -57,9 +58,7 @@ def __init__( self.dataset_list_nlp: Optional[list] = None if os.path.exists("datasets/Classical"): self.dataset_list_classical = [ - os.path.splitext(_)[0] - for _ in os.listdir("datasets/Classical") - if os.path.splitext(_)[1] == ".npz" + os.path.splitext(_)[0] for _ in os.listdir("datasets/Classical") if os.path.splitext(_)[1] == ".npz" ] # classical AD datasets if os.path.exists("datasets/CV_by_ResNet18"): @@ -71,9 +70,7 @@ def __init__( if os.path.exists("datasets/NLP_by_BERT"): self.dataset_list_nlp = [ - os.path.splitext(_)[0] - for _ in os.listdir("datasets/NLP_by_BERT") - if os.path.splitext(_)[1] == ".npz" + os.path.splitext(_)[0] for _ in os.listdir("datasets/NLP_by_BERT") if os.path.splitext(_)[1] == ".npz" ] # NLP datasets # myutils function @@ -90,14 +87,14 @@ def generate_realistic_synthetic( # noqa: C901 """Generate the realistic synthetic outliers. Currently, four types of realistic synthetic outliers can be generated: - 1. local outliers: where normal data follows the GMM distribuion, and + 1. local outliers: where normal data follows the GMM distribution, and anomalies follow the GMM distribution with modified covariance - 2. global outliers: where normal data follows the GMM distribuion, and + 2. global outliers: where normal data follows the GMM distribution, and anomalies follow the uniform distribution 3. dependency outliers: where normal data follows the vine coupula distribution, and anomalies follow the independent distribution captured by GaussianKDE - 4. cluster outliers: where normal data follows the GMM distribuion, and + 4. cluster outliers: where normal data follows the GMM distribution, and anomalies follow the GMM distribution with modified mean. Parameters @@ -139,17 +136,13 @@ def generate_realistic_synthetic( # noqa: C901 n_components_list = list(np.arange(1, 10)) for n_components in n_components_list: - gm = GaussianMixture( - n_components=n_components, random_state=self.seed - ).fit(X) + gm = GaussianMixture(n_components=n_components, random_state=self.seed).fit(X) metric_list.append(gm.bic(X)) best_n_components = n_components_list[np.argmin(metric_list)] # refit based on the best n_components - gm = GaussianMixture( - n_components=best_n_components, random_state=self.seed - ).fit(X) + gm = GaussianMixture(n_components=best_n_components, random_state=self.seed).fit(X) # generate the synthetic normal data X_synthetic_normal = gm.sample(pts_n)[0] @@ -171,9 +164,7 @@ def generate_realistic_synthetic( # noqa: C901 low = np.min(X_synthetic_normal[:, i]) * (1 + percentage) high = np.max(X_synthetic_normal[:, i]) * (1 + percentage) - X_synthetic_anomalies.append( - np.random.uniform(low=low, high=high, size=pts_a) - ) + X_synthetic_anomalies.append(np.random.uniform(low=low, high=high, size=pts_a)) X_synthetic_anomalies = np.array(X_synthetic_anomalies).T @@ -193,7 +184,7 @@ def generate_realistic_synthetic( # noqa: C901 # generate the synthetic anomalies (dependency outliers) X_synthetic_anomalies = np.zeros((pts_a, X.shape[1])) - # using the GuassianKDE for generating independent feature + # using the GaussianKDE for generating independent feature for i in range(X.shape[1]): kde = GaussianKDE() kde.fit(X[:, i]) @@ -251,9 +242,7 @@ def add_irrelevant_features( X_min = np.min(X[:, idx]) X_max = np.max(X[:, idx]) - X_noise.append( - np.random.uniform(X_min, X_max, size=(X.shape[0], 1)) - ) + X_noise.append(np.random.uniform(X_min, X_max, size=(X.shape[0], 1))) # concat the irrelevant noise feature X_noise = np.hstack(X_noise) @@ -270,10 +259,8 @@ def add_label_contamination( """Add label contamination to the dataset.""" if noise_ratio != 0.0: # here we consider the label flips situation: a label is randomly - # filpped to another class with probability p (i.e., noise ratio) - idx_flips = np.random.choice( - np.arange(len(y)), int(len(y) * noise_ratio), replace=False - ) + # flipped to another class with probability p (i.e., noise ratio) + idx_flips = np.random.choice(np.arange(len(y)), int(len(y) * noise_ratio), replace=False) y[idx_flips] = 1 - y[idx_flips] # change 0 to 1 and 1 to 0 return X, y @@ -285,9 +272,7 @@ def generator( # noqa: C901 minmax: bool = True, la: Optional[Union[float, int]] = None, at_least_one_labeled: bool = False, - realistic_synthetic_mode: Optional[ - Literal["local", "cluster", "dependency", "global"] - ] = None, + realistic_synthetic_mode: Optional[Literal["local", "cluster", "dependency", "global"]] = None, alpha: int = 5, percentage: float = 0.1, noise_type: Optional[ @@ -332,36 +317,25 @@ def generator( # noqa: C901 The ratio of anomaly contamination. noise_ratio : float, default=0.05 The ratio of noise. - """ # noqa: E501 + """ # noqa: E501, W505 # set seed for reproducible results self.utils.set_seed(self.seed) # load dataset if self.dataset is None: - assert ( - X is not None and y is not None - ), "For customized dataset, you should provide the X and y!" + assert X is not None and y is not None, "For customized dataset, you should provide the X and y!" else: - if ( - self.dataset_list_classical is not None - and self.dataset in self.dataset_list_classical - ): + if self.dataset_list_classical is not None and self.dataset in self.dataset_list_classical: data = np.load( os.path.join("datasets", "Classical", self.dataset + ".npz"), allow_pickle=True, ) - elif ( - self.dataset_list_cv is not None - and self.dataset in self.dataset_list_cv - ): + elif self.dataset_list_cv is not None and self.dataset in self.dataset_list_cv: data = np.load( os.path.join("datasets", "CV_by_ResNet18", self.dataset + ".npz"), allow_pickle=True, ) - elif ( - self.dataset_list_nlp is not None - and self.dataset in self.dataset_list_nlp - ): + elif self.dataset_list_nlp is not None and self.dataset in self.dataset_list_nlp: data = np.load( os.path.join("datasets", "NLP_by_BERT", self.dataset + ".npz"), allow_pickle=True, @@ -383,13 +357,15 @@ def generator( # noqa: C901 else: raise TypeError("The type of `la` should be either float or int!") - # if the dataset is too small, generating duplicate smaples up to + # if the dataset is too small, generating duplicate samples up to # n_samples_threshold if len(y) < self.n_samples_threshold and self.generate_duplicates: # type: ignore # noqa: E501 print(f"generating duplicate samples for dataset {self.dataset}...") self.utils.set_seed(self.seed) idx_duplicate = np.random.choice( - np.arange(len(y)), self.n_samples_threshold, replace=True # type: ignore # noqa: E501 + np.arange(len(y)), + self.n_samples_threshold, + replace=True, # type: ignore # noqa: E501 ) X = X[idx_duplicate] y = y[idx_duplicate] @@ -435,9 +411,7 @@ def generator( # noqa: C901 alpha=alpha, percentage=percentage, ) - np.savez_compressed( - os.path.join("datasets", "synthetic", filepath), X=X, y=y - ) + np.savez_compressed(os.path.join("datasets", "synthetic", filepath), X=X, y=y) else: X, y = self.generate_realistic_synthetic( X, @@ -456,26 +430,18 @@ def generator( # noqa: C901 # show the statistic self.utils.data_description(X=X, y=y) - # spliting the current data to the training set and testing set - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=self.test_size, shuffle=True, stratify=y - ) + # splitting the current data to the training set and testing set + X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=self.test_size, shuffle=True, stratify=y) # we respectively generate the duplicated anomalies for the training and # testing set if noise_type == "duplicated_anomalies": - X_train, y_train = self.add_duplicated_anomalies( - X_train, y_train, duplicate_times=duplicate_times - ) - X_test, y_test = self.add_duplicated_anomalies( - X_test, y_test, duplicate_times=duplicate_times - ) + X_train, y_train = self.add_duplicated_anomalies(X_train, y_train, duplicate_times=duplicate_times) + X_test, y_test = self.add_duplicated_anomalies(X_test, y_test, duplicate_times=duplicate_times) # notice that label contamination can only be added in the training set if noise_type == "label_contamination": - X_train, y_train = self.add_label_contamination( - X_train, y_train, noise_ratio=noise_ratio - ) + X_train, y_train = self.add_label_contamination(X_train, y_train, noise_ratio=noise_ratio) # minmax scaling if minmax: @@ -489,21 +455,15 @@ def generator( # noqa: C901 if isinstance(la, float): if at_least_one_labeled: - idx_labeled_anomaly = np.random.choice( - idx_anomaly, ceil(la * len(idx_anomaly)), replace=False - ) + idx_labeled_anomaly = np.random.choice(idx_anomaly, ceil(la * len(idx_anomaly)), replace=False) else: - idx_labeled_anomaly = np.random.choice( - idx_anomaly, int(la * len(idx_anomaly)), replace=False - ) + idx_labeled_anomaly = np.random.choice(idx_anomaly, int(la * len(idx_anomaly)), replace=False) elif isinstance(la, int): if la > len(idx_anomaly): raise AssertionError( - "The number of labeled anomalies are greater than the total " - f"anomalies: {len(idx_anomaly)} !" + f"The number of labeled anomalies are greater than the total anomalies: {len(idx_anomaly)} !" ) - else: - idx_labeled_anomaly = np.random.choice(idx_anomaly, la, replace=False) + idx_labeled_anomaly = np.random.choice(idx_anomaly, la, replace=False) else: raise TypeError("The type of `la` should be either float or int!") diff --git a/contrastive_learning/ICL/datasets/Classical/10_cover.npz b/src/contrastive_learning/ICL/datasets/Classical/10_cover.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/10_cover.npz rename to src/contrastive_learning/ICL/datasets/Classical/10_cover.npz diff --git a/contrastive_learning/ICL/datasets/Classical/11_donors.npz b/src/contrastive_learning/ICL/datasets/Classical/11_donors.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/11_donors.npz rename to src/contrastive_learning/ICL/datasets/Classical/11_donors.npz diff --git a/contrastive_learning/ICL/datasets/Classical/12_fault.npz b/src/contrastive_learning/ICL/datasets/Classical/12_fault.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/12_fault.npz rename to src/contrastive_learning/ICL/datasets/Classical/12_fault.npz diff --git a/contrastive_learning/ICL/datasets/Classical/13_fraud.npz b/src/contrastive_learning/ICL/datasets/Classical/13_fraud.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/13_fraud.npz rename to src/contrastive_learning/ICL/datasets/Classical/13_fraud.npz diff --git a/contrastive_learning/ICL/datasets/Classical/14_glass.npz b/src/contrastive_learning/ICL/datasets/Classical/14_glass.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/14_glass.npz rename to src/contrastive_learning/ICL/datasets/Classical/14_glass.npz diff --git a/contrastive_learning/ICL/datasets/Classical/15_Hepatitis.npz b/src/contrastive_learning/ICL/datasets/Classical/15_Hepatitis.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/15_Hepatitis.npz rename to src/contrastive_learning/ICL/datasets/Classical/15_Hepatitis.npz diff --git a/contrastive_learning/ICL/datasets/Classical/16_http.npz b/src/contrastive_learning/ICL/datasets/Classical/16_http.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/16_http.npz rename to src/contrastive_learning/ICL/datasets/Classical/16_http.npz diff --git a/contrastive_learning/ICL/datasets/Classical/17_InternetAds.npz b/src/contrastive_learning/ICL/datasets/Classical/17_InternetAds.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/17_InternetAds.npz rename to src/contrastive_learning/ICL/datasets/Classical/17_InternetAds.npz diff --git a/contrastive_learning/ICL/datasets/Classical/18_Ionosphere.npz b/src/contrastive_learning/ICL/datasets/Classical/18_Ionosphere.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/18_Ionosphere.npz rename to src/contrastive_learning/ICL/datasets/Classical/18_Ionosphere.npz diff --git a/contrastive_learning/ICL/datasets/Classical/19_landsat.npz b/src/contrastive_learning/ICL/datasets/Classical/19_landsat.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/19_landsat.npz rename to src/contrastive_learning/ICL/datasets/Classical/19_landsat.npz diff --git a/contrastive_learning/ICL/datasets/Classical/1_ALOI.npz b/src/contrastive_learning/ICL/datasets/Classical/1_ALOI.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/1_ALOI.npz rename to src/contrastive_learning/ICL/datasets/Classical/1_ALOI.npz diff --git 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diff --git a/contrastive_learning/ICL/datasets/Classical/47_yeast.npz b/src/contrastive_learning/ICL/datasets/Classical/47_yeast.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/47_yeast.npz rename to src/contrastive_learning/ICL/datasets/Classical/47_yeast.npz diff --git a/contrastive_learning/ICL/datasets/Classical/4_breastw.npz b/src/contrastive_learning/ICL/datasets/Classical/4_breastw.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/4_breastw.npz rename to src/contrastive_learning/ICL/datasets/Classical/4_breastw.npz diff --git a/contrastive_learning/ICL/datasets/Classical/5_campaign.npz b/src/contrastive_learning/ICL/datasets/Classical/5_campaign.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/5_campaign.npz rename to src/contrastive_learning/ICL/datasets/Classical/5_campaign.npz diff --git a/contrastive_learning/ICL/datasets/Classical/6_cardio.npz b/src/contrastive_learning/ICL/datasets/Classical/6_cardio.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/6_cardio.npz rename to src/contrastive_learning/ICL/datasets/Classical/6_cardio.npz diff --git a/contrastive_learning/ICL/datasets/Classical/7_Cardiotocography.npz b/src/contrastive_learning/ICL/datasets/Classical/7_Cardiotocography.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/7_Cardiotocography.npz rename to src/contrastive_learning/ICL/datasets/Classical/7_Cardiotocography.npz diff --git a/contrastive_learning/ICL/datasets/Classical/8_celeba.npz b/src/contrastive_learning/ICL/datasets/Classical/8_celeba.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/8_celeba.npz rename to src/contrastive_learning/ICL/datasets/Classical/8_celeba.npz diff --git a/contrastive_learning/ICL/datasets/Classical/9_census.npz b/src/contrastive_learning/ICL/datasets/Classical/9_census.npz similarity index 100% rename from contrastive_learning/ICL/datasets/Classical/9_census.npz rename to src/contrastive_learning/ICL/datasets/Classical/9_census.npz diff --git a/contrastive_learning/ICL/myutils.py b/src/contrastive_learning/ICL/myutils.py similarity index 85% rename from contrastive_learning/ICL/myutils.py rename to src/contrastive_learning/ICL/myutils.py index a09d77a..77e54fe 100644 --- a/contrastive_learning/ICL/myutils.py +++ b/src/contrastive_learning/ICL/myutils.py @@ -1,4 +1,5 @@ """Utility functions for ICL.""" + import random from typing import Dict, Literal, Tuple, Union @@ -58,15 +59,11 @@ def metric( ) -> Dict[str, Union[float, np.ndarray]]: """Return aucroc and aucpr.""" aucroc = roc_auc_score(y_true=y_true, y_score=y_score) - aucpr = average_precision_score( - y_true=y_true, y_score=y_score, pos_label=pos_label - ) + aucpr = average_precision_score(y_true=y_true, y_score=y_score, pos_label=pos_label) return {"aucroc": aucroc, "aucpr": aucpr} - def sampler( - self, X_train: np.ndarray, y_train: np.ndarray, batch_size: int - ) -> Tuple[np.ndarray, np.ndarray]: + def sampler(self, X_train: np.ndarray, y_train: np.ndarray, batch_size: int) -> Tuple[np.ndarray, np.ndarray]: """Resample the data.""" index_u = np.where(y_train == 0)[0] index_a = np.where(y_train == 1)[0] @@ -87,10 +84,13 @@ def sampler( y_train_new = y_train[index_batch] else: X_train_new = np.append( - X_train_new, X_train[index_batch], axis=0 # type: ignore + X_train_new, + X_train[index_batch], + axis=0, # type: ignore ) y_train_new = np.append( - y_train_new, y_train[index_batch] # type: ignore + y_train_new, + y_train[index_batch], # type: ignore ) n += 1 @@ -117,10 +117,13 @@ def sampler_2( y_train_new = y_train[index_batch] else: X_train_new = np.append( - X_train_new, X_train[index_batch], axis=0 # type: ignore + X_train_new, + X_train[index_batch], + axis=0, # type: ignore ) y_train_new = np.append( - y_train_new, y_train[index_batch] # type: ignore + y_train_new, + y_train[index_batch], # type: ignore ) return X_train_new, y_train_new # type: ignore @@ -201,17 +204,13 @@ def sampler_pairs( X_train_tensor_right = X_train_tensor[index_right] # generate label - y_train_new = np.append( - np.repeat(s_a_a, batch_size // 4), np.repeat(s_a_u, batch_size // 4) - ) + y_train_new = np.append(np.repeat(s_a_a, batch_size // 4), np.repeat(s_a_u, batch_size // 4)) y_train_new = np.append(y_train_new, np.repeat(s_u_u, batch_size // 2)) y_train_new = torch.from_numpy(y_train_new).float() # shuffle index_shuffle = np.arange(len(y_train_new)) - index_shuffle = np.random.choice( - index_shuffle, len(index_shuffle), replace=False - ) + index_shuffle = np.random.choice(index_shuffle, len(index_shuffle), replace=False) X_train_tensor_left = X_train_tensor_left[index_shuffle] X_train_tensor_right = X_train_tensor_right[index_shuffle] @@ -250,9 +249,7 @@ def plot_grad_flow(self, named_parameters: dict) -> None: plt.title("Gradient flow") plt.grid(True) - def torch_cdf_loss( - self, tensor_a: torch.Tensor, tensor_b: torch.Tensor, p: int = 1 - ) -> torch.Tensor: + def torch_cdf_loss(self, tensor_a: torch.Tensor, tensor_b: torch.Tensor, p: int = 1) -> torch.Tensor: """Calculate the First Wasserstein Distance.""" # last-dimension is weight distribution # p is the norm of the distance, p=1 --> First Wasserstein Distance @@ -270,9 +267,7 @@ def torch_cdf_loss( if p == 1: cdf_distance = torch.sum(torch.abs((cdf_tensor_a - cdf_tensor_b)), dim=-1) elif p == 2: - cdf_distance = torch.sqrt( - torch.sum(torch.pow((cdf_tensor_a - cdf_tensor_b), 2), dim=-1) - ) + cdf_distance = torch.sqrt(torch.sum(torch.pow((cdf_tensor_a - cdf_tensor_b), 2), dim=-1)) else: cdf_distance = torch.pow( torch.sum(torch.pow(torch.abs(cdf_tensor_a - cdf_tensor_b), p), dim=-1), @@ -282,15 +277,11 @@ def torch_cdf_loss( cdf_loss = cdf_distance.mean() return cdf_loss - def torch_wasserstein_loss( - self, tensor_a: torch.Tensor, tensor_b: torch.Tensor - ) -> torch.Tensor: + def torch_wasserstein_loss(self, tensor_a: torch.Tensor, tensor_b: torch.Tensor) -> torch.Tensor: """Compute the first Wasserstein distance between two 1D distributions.""" return self.torch_cdf_loss(tensor_a, tensor_b, p=1) - def cal_loss( - self, y: torch.Tensor, y_pred: torch.Tensor, mode: Literal["devnet"] = "devnet" - ) -> torch.Tensor: + def cal_loss(self, y: torch.Tensor, y_pred: torch.Tensor, mode: Literal["devnet"] = "devnet") -> torch.Tensor: """Calculate the loss like devnet in PyTorch.""" if mode == "devnet": y_pred.squeeze_() @@ -307,18 +298,14 @@ def cal_loss( return loss - def result_process( - self, result_show: pd.DataFrame, name: str, std: bool = False - ) -> pd.DataFrame: + def result_process(self, result_show: pd.DataFrame, name: str, std: bool = False) -> pd.DataFrame: """Process the result.""" # average performance ave_metric = np.mean(result_show, axis=0).values std_metric = np.std(result_show, axis=0).values # statistical test - wilcoxon_df = pd.DataFrame( - data=None, index=result_show.columns, columns=result_show.columns - ) + wilcoxon_df = pd.DataFrame(data=None, index=result_show.columns, columns=result_show.columns) for i in range(wilcoxon_df.shape[0]): for j in range(wilcoxon_df.shape[1]): @@ -329,9 +316,7 @@ def result_process( )[1] # average rank - result_show.loc["Ave.rank"] = np.mean( - result_show.rank(ascending=False, method="dense", axis=1), axis=0 - ) + result_show.loc["Ave.rank"] = np.mean(result_show.rank(ascending=False, method="dense", axis=1), axis=0) # average metric if std: @@ -350,9 +335,7 @@ def result_process( for _ in result_show.index: if _ in ["Ave.rank", "p-value"]: - result_show.loc[_, :] = [ - format(round(_, 2), ".2f") for _ in result_show.loc[_, :].values - ] + result_show.loc[_, :] = [format(round(_, 2), ".2f") for _ in result_show.loc[_, :].values] # result_show = result_show.astype('float') # result_show = result_show.round(2) diff --git a/contrastive_learning/ICL/pyod_base.py b/src/contrastive_learning/ICL/pyod_base.py similarity index 79% rename from contrastive_learning/ICL/pyod_base.py rename to src/contrastive_learning/ICL/pyod_base.py index fe58c11..dd368a0 100644 --- a/contrastive_learning/ICL/pyod_base.py +++ b/src/contrastive_learning/ICL/pyod_base.py @@ -1,4 +1,5 @@ """PYOD wrapper.""" + from typing import Dict, Optional import numpy as np @@ -68,9 +69,7 @@ def grid_hp(self, model_name: str) -> Optional[list]: return param_grid_dict[model_name] - def grid_search( - self, X_train: np.ndarray, y_train: np.ndarray, ratio: float - ) -> Optional[dict]: + def grid_search(self, X_train: np.ndarray, y_train: np.ndarray, ratio: float) -> Optional[dict]: """Run a grid search for unsupervised models. Return the best hyper-parameters. The ratio refers to the ground truth @@ -85,14 +84,12 @@ def grid_search( # index of normal ana abnormal samples idx_a = np.where(y_train == 1)[0] idx_n = np.where(y_train == 0)[0] - idx_n = np.random.choice( - idx_n, int((len(idx_a) * (1 - ratio)) / ratio), replace=True - ) + idx_n = np.random.choice(idx_n, int((len(idx_a) * (1 - ratio)) / ratio), replace=True) idx = np.append(idx_n, idx_a) # combine np.random.shuffle(idx) # shuffle - # valiation set (and the same anomaly ratio as in the original dataset) + # validation set (and the same anomaly ratio as in the original dataset) X_val = X_train[idx] y_val = y_train[idx] @@ -114,9 +111,7 @@ def grid_search( try: # model performance on the validation set score_val = self.model.decision_function(X_val) - metric = self.utils.metric( - y_true=y_val, y_score=score_val, pos_label=1 - ) + metric = self.utils.metric(y_true=y_val, y_score=score_val, pos_label=1) metric_list.append(metric["aucpr"]) except Exception: @@ -163,54 +158,32 @@ def fit( # noqa: C901 # fit best on the best param if best_param is not None: if self.model_name in ["IForest", "FeatureBagging"]: - self.model = self.model_dict[self.model_name]( - n_estimators=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](n_estimators=best_param).fit(X_train) elif self.model_name == "OCSVM": - self.model = self.model_dict[self.model_name](kernel=best_param).fit( - X_train - ) + self.model = self.model_dict[self.model_name](kernel=best_param).fit(X_train) elif self.model_name in ["ABOD", "COF", "KNN", "LOF", "SOD"]: - self.model = self.model_dict[self.model_name]( - n_neighbors=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](n_neighbors=best_param).fit(X_train) elif self.model_name == "CBLOF": - self.model = self.model_dict[self.model_name]( - n_clusters=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](n_clusters=best_param).fit(X_train) elif self.model_name in ["HBOS", "LODA"]: - self.model = self.model_dict[self.model_name](n_bins=best_param).fit( - X_train - ) + self.model = self.model_dict[self.model_name](n_bins=best_param).fit(X_train) elif self.model_name == "LMDD": - self.model = self.model_dict[self.model_name]( - dis_measure=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](dis_measure=best_param).fit(X_train) elif self.model_name == "LOCI": - self.model = self.model_dict[self.model_name](alpha=best_param).fit( + self.model = self.model_dict[self.model_name](alpha=best_param).fit(X_train) + elif self.model_name == "LSCP": + self.model = self.model_dict[self.model_name](detector_list=[LOF(), LOF()], n_bins=best_param).fit( X_train ) - elif self.model_name == "LSCP": - self.model = self.model_dict[self.model_name]( - detector_list=[LOF(), LOF()], n_bins=best_param - ).fit(X_train) elif self.model_name == "PCA": - self.model = self.model_dict[self.model_name]( - n_components=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](n_components=best_param).fit(X_train) elif self.model_name == "SOS": - self.model = self.model_dict[self.model_name]( - perplexity=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](perplexity=best_param).fit(X_train) elif self.model_name in ["SOGAAL", "MOGAAL"]: - self.model = self.model_dict[self.model_name]( - stop_epochs=best_param - ).fit(X_train) + self.model = self.model_dict[self.model_name](stop_epochs=best_param).fit(X_train) elif self.model_name == "DeepSVDD": - self.model = self.model_dict[self.model_name](epochs=best_param).fit( - X_train - ) + self.model = self.model_dict[self.model_name](epochs=best_param).fit(X_train) elif self.model_name == "ICL": self.clf = ICL(device="cpu") self.model = self.clf(X_train) diff --git a/contrastive_learning/LatentOE/DATA/thyroid.mat b/src/contrastive_learning/LatentOE/DATA/thyroid.mat similarity index 100% rename from contrastive_learning/LatentOE/DATA/thyroid.mat rename to src/contrastive_learning/LatentOE/DATA/thyroid.mat diff --git a/contrastive_learning/LatentOE/LatentOE_Notebook.ipynb b/src/contrastive_learning/LatentOE/LatentOE_Notebook.ipynb similarity index 100% rename from contrastive_learning/LatentOE/LatentOE_Notebook.ipynb rename to src/contrastive_learning/LatentOE/LatentOE_Notebook.ipynb diff --git a/contrastive_learning/LatentOE/config/__init__.py b/src/contrastive_learning/LatentOE/config/__init__.py similarity index 100% rename from contrastive_learning/LatentOE/config/__init__.py rename to src/contrastive_learning/LatentOE/config/__init__.py diff --git a/contrastive_learning/LatentOE/config/base.py b/src/contrastive_learning/LatentOE/config/base.py similarity index 93% rename from contrastive_learning/LatentOE/config/base.py rename to src/contrastive_learning/LatentOE/config/base.py index 3b8a9d2..7ea961f 100644 --- a/contrastive_learning/LatentOE/config/base.py +++ b/src/contrastive_learning/LatentOE/config/base.py @@ -125,9 +125,7 @@ def config_dict(self) -> Dict[str, Any]: @staticmethod def parse_dataset(dataset_s: str) -> Any: """Return the dataset class.""" - assert ( - dataset_s in Config.datasets - ), f"Could not find {dataset_s} in dictionary!" + assert dataset_s in Config.datasets, f"Could not find {dataset_s} in dictionary!" return Config.datasets[dataset_s] @staticmethod @@ -139,9 +137,7 @@ def parse_model(model_s: str) -> Any: @staticmethod def parse_trainer(trainer_s: str) -> Any: """Return the trainer class.""" - assert ( - trainer_s in Config.trainers - ), f"Could not find {trainer_s} in dictionary!" + assert trainer_s in Config.trainers, f"Could not find {trainer_s} in dictionary!" return Config.trainers[trainer_s] @staticmethod @@ -171,9 +167,7 @@ def parse_scheduler(sched_dict: Dict[str, Any]) -> Any: sched_s = sched_dict["class"] args = sched_dict["args"] - assert ( - sched_s in Config.schedulers - ), f"Could not find {sched_s} in schedulers dictionary" + assert sched_s in Config.schedulers, f"Could not find {sched_s} in schedulers dictionary" return lambda opt: Config.schedulers[sched_s](opt, **args) @@ -186,9 +180,7 @@ def parse_early_stopper(stopper_dict: Dict[str, Any]) -> Any: stopper_s = stopper_dict["class"] args = stopper_dict["args"] - assert ( - stopper_s in Config.early_stoppers - ), f"Could not find {stopper_s} in early stoppers dictionary" + assert stopper_s in Config.early_stoppers, f"Could not find {stopper_s} in early stoppers dictionary" return lambda: Config.early_stoppers[stopper_s](**args) @@ -201,9 +193,7 @@ def from_dict(cls, dict_obj: Dict[Any, Any]) -> "Config": class Grid: """Specifies the configuration for multiple models.""" - def __init__( - self, path_or_dict: Union[Dict[str, Any], str], dataset_name: str - ) -> None: + def __init__(self, path_or_dict: Union[Dict[str, Any], str], dataset_name: str) -> None: """Initialize the grid.""" self.configs_dict = read_config_file(path_or_dict) self.configs_dict["dataset"] = [dataset_name] diff --git a/contrastive_learning/LatentOE/config/datasets.py b/src/contrastive_learning/LatentOE/config/datasets.py similarity index 100% rename from contrastive_learning/LatentOE/config/datasets.py rename to src/contrastive_learning/LatentOE/config/datasets.py diff --git a/contrastive_learning/LatentOE/config_files/config_arrhy.yml b/src/contrastive_learning/LatentOE/config_files/config_arrhy.yml similarity index 100% rename from contrastive_learning/LatentOE/config_files/config_arrhy.yml rename to src/contrastive_learning/LatentOE/config_files/config_arrhy.yml diff --git a/contrastive_learning/LatentOE/config_files/config_cifar10.yml b/src/contrastive_learning/LatentOE/config_files/config_cifar10.yml similarity index 100% rename from contrastive_learning/LatentOE/config_files/config_cifar10.yml rename to src/contrastive_learning/LatentOE/config_files/config_cifar10.yml diff --git a/contrastive_learning/LatentOE/config_files/config_fmnist.yml b/src/contrastive_learning/LatentOE/config_files/config_fmnist.yml similarity index 100% rename from contrastive_learning/LatentOE/config_files/config_fmnist.yml rename to src/contrastive_learning/LatentOE/config_files/config_fmnist.yml diff --git a/contrastive_learning/LatentOE/config_files/config_thyroid.yml b/src/contrastive_learning/LatentOE/config_files/config_thyroid.yml similarity index 100% rename from contrastive_learning/LatentOE/config_files/config_thyroid.yml rename to src/contrastive_learning/LatentOE/config_files/config_thyroid.yml diff --git a/contrastive_learning/LatentOE/evaluation/__init__.py b/src/contrastive_learning/LatentOE/evaluation/__init__.py similarity index 100% rename from contrastive_learning/LatentOE/evaluation/__init__.py rename to src/contrastive_learning/LatentOE/evaluation/__init__.py diff --git a/contrastive_learning/LatentOE/evaluation/experiments.py b/src/contrastive_learning/LatentOE/evaluation/experiments.py similarity index 89% rename from contrastive_learning/LatentOE/evaluation/experiments.py rename to src/contrastive_learning/LatentOE/evaluation/experiments.py index b4cd5e7..34d5fde 100644 --- a/contrastive_learning/LatentOE/evaluation/experiments.py +++ b/src/contrastive_learning/LatentOE/evaluation/experiments.py @@ -1,4 +1,5 @@ """Experiment runner for running experiments with a given model configuration.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -103,23 +104,17 @@ def run_test( except: # noqa batch_size = int(np.ceil(len(train_data) / 4)) - train_loader = DataLoader( - train_data, batch_size=batch_size, shuffle=True, drop_last=False - ) + train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, drop_last=False) if len(val_data) == 0: val_loader = None else: - val_loader = DataLoader( - val_data, batch_size=batch_size, shuffle=False, drop_last=False - ) + val_loader = DataLoader(val_data, batch_size=batch_size, shuffle=False, drop_last=False) if len(test_data) == 0: test_loader = None else: - test_loader = DataLoader( - test_data, batch_size=batch_size, shuffle=False, drop_last=False - ) + test_loader = DataLoader(test_data, batch_size=batch_size, shuffle=False, drop_last=False) model = TabNeutralAD(network(), x_dim, config=self.model_config) optimizer = optim_class( @@ -128,10 +123,7 @@ def run_test( weight_decay=self.model_config["l2"], ) - if sched_class is not None: - scheduler = sched_class(optimizer) - else: - scheduler = None + scheduler = sched_class(optimizer) if sched_class is not None else None trainer = NeutralADTrainer( model, diff --git a/contrastive_learning/LatentOE/evaluation/k_variants_eval.py b/src/contrastive_learning/LatentOE/evaluation/k_variants_eval.py similarity index 86% rename from contrastive_learning/LatentOE/evaluation/k_variants_eval.py rename to src/contrastive_learning/LatentOE/evaluation/k_variants_eval.py index 87b9441..5f9f592 100644 --- a/contrastive_learning/LatentOE/evaluation/k_variants_eval.py +++ b/src/contrastive_learning/LatentOE/evaluation/k_variants_eval.py @@ -1,4 +1,5 @@ """K-variants evaluation for risk assessment.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -105,9 +106,7 @@ def process_results(self) -> None: assessment_results["avg_TS_auc_all"] = avg_TS_auc.mean() assessment_results["std_TS_auc_all"] = avg_TS_auc.std() - with open( - os.path.join(self._NESTED_FOLDER, self._ASSESSMENT_FILENAME), "w" - ) as fp: + with open(os.path.join(self._NESTED_FOLDER, self._ASSESSMENT_FILENAME), "w") as fp: json.dump(assessment_results, fp, indent=0) def risk_assessment(self, experiment_class: Type[ExperimentRunner]) -> None: @@ -124,24 +123,17 @@ def risk_assessment(self, experiment_class: Type[ExperimentRunner]) -> None: if not os.path.exists(json_results): self._risk_assessment_helper(cls, experiment_class, folder) else: - print( - f"File {json_results} already present! Shutting down to " - "prevent loss of previous experiments" - ) + print(f"File {json_results} already present! Shutting down to prevent loss of previous experiments") continue self.process_results() - def _risk_assessment_helper( - self, cls: int, experiment_class: Type[ExperimentRunner], exp_path: str - ) -> None: + def _risk_assessment_helper(self, cls: int, experiment_class: Type[ExperimentRunner], exp_path: str) -> None: """Run the experiment and process the results.""" best_config = self.model_configs[0] experiment = experiment_class(best_config, exp_path) - logger = Logger( - str(os.path.join(experiment.exp_path, "experiment.log")), mode="a" - ) + logger = Logger(str(os.path.join(experiment.exp_path, "experiment.log")), mode="a") # logger = None val_auc_list, test_auc_list, test_ap_list, test_f1_list = [], [], [], [] @@ -158,13 +150,11 @@ def _risk_assessment_helper( torch.manual_seed(i + 40) torch.cuda.manual_seed(i + 40) torch.cuda.manual_seed_all(i + 40) - trainset, valset, testset = load_data( - self.data_name, cls, self.contamination - ) + trainset, valset, testset = load_data(self.data_name, cls, self.contamination) val_auc, test_auc, test_ap, test_f1, test_score = experiment.run_test( trainset, valset, testset, logger, self.contamination, self.query_num ) - print(f"Final training run {i + 1}: {val_auc}, {test_auc,test_ap, test_f1}") + print(f"Final training run {i + 1}: {val_auc}, {test_auc, test_ap, test_f1}") val_auc_list.append(val_auc) test_auc_list.append(test_auc) @@ -174,10 +164,9 @@ def _risk_assessment_helper( saved_results["scores_" + str(i)] = test_score.tolist() if best_config["save_scores"]: - save_path = os.path.join( - self._NESTED_FOLDER, str(cls) + self._FOLD_BASE, "scores_labels.json" - ) - json.dump(saved_results, open(save_path, "w")) + save_path = os.path.join(self._NESTED_FOLDER, str(cls) + self._FOLD_BASE, "scores_labels.json") + with open(save_path, "w") as fp: + json.dump(saved_results, fp, indent=0) # saved_results = json.load(open(save_path)) if logger is not None: @@ -191,12 +180,7 @@ def _risk_assessment_helper( + " TS auc: " + str(test_auc_list) ) - print( - "F1:" - + str(np.array(test_f1_list).mean()) - + "AUC:" - + str(np.array(test_auc_list).mean()) - ) + print("F1:" + str(np.array(test_f1_list).mean()) + "AUC:" + str(np.array(test_auc_list).mean())) with open(os.path.join(exp_path, self._RESULTS_FILENAME), "w") as fp: json.dump( { diff --git a/contrastive_learning/LatentOE/loader/__init__.py b/src/contrastive_learning/LatentOE/loader/__init__.py similarity index 100% rename from contrastive_learning/LatentOE/loader/__init__.py rename to src/contrastive_learning/LatentOE/loader/__init__.py diff --git a/contrastive_learning/LatentOE/loader/load_data.py b/src/contrastive_learning/LatentOE/loader/load_data.py similarity index 84% rename from contrastive_learning/LatentOE/loader/load_data.py rename to src/contrastive_learning/LatentOE/loader/load_data.py index 36d9d2e..3e2da55 100644 --- a/contrastive_learning/LatentOE/loader/load_data.py +++ b/src/contrastive_learning/LatentOE/loader/load_data.py @@ -1,4 +1,5 @@ """Dataset loading functions.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -41,9 +42,7 @@ def CIFAR10_feat( num_clean = train_clean.shape[0] num_contamination = int(contamination_rate / (1 - contamination_rate) * num_clean) - idx_contamination = np.random.choice( - np.arange(train_contamination.shape[0]), num_contamination, replace=False - ) + idx_contamination = np.random.choice(np.arange(train_contamination.shape[0]), num_contamination, replace=False) train_contamination = train_contamination[idx_contamination] train_data = torch.cat((train_clean, train_contamination), 0) @@ -71,9 +70,7 @@ def FMNIST_feat( num_clean = train_clean.shape[0] num_contamination = int(contamination_rate / (1 - contamination_rate) * num_clean) - idx_contamination = np.random.choice( - np.arange(train_contamination.shape[0]), num_contamination, replace=False - ) + idx_contamination = np.random.choice(np.arange(train_contamination.shape[0]), num_contamination, replace=False) train_contamination = train_contamination[idx_contamination] train_data = torch.cat((train_clean, train_contamination), 0) @@ -91,17 +88,13 @@ def synthetic_contamination( num_contamination = int(contamination_rate / (1 - contamination_rate) * num_clean) try: - idx_contamination = np.random.choice( - np.arange(anorm.shape[0]), num_contamination, replace=False - ) + idx_contamination = np.random.choice(np.arange(anorm.shape[0]), num_contamination, replace=False) except ValueError: - idx_contamination = np.random.choice( - np.arange(anorm.shape[0]), num_contamination, replace=True - ) + idx_contamination = np.random.choice(np.arange(anorm.shape[0]), num_contamination, replace=True) train_contamination = anorm[idx_contamination] - train_contamination = train_contamination + np.random.randn( - *train_contamination.shape - ) * np.std(anorm, 0, keepdims=True) + train_contamination = train_contamination + np.random.randn(*train_contamination.shape) * np.std( + anorm, 0, keepdims=True + ) train_data = np.concatenate([norm, train_contamination], 0) train_labels = np.zeros(train_data.shape[0]) train_labels[num_clean:] = 1 @@ -125,9 +118,7 @@ def Thyroid_data( test_label = np.zeros(test_data.shape[0]) test_label[num_split:] = 1 - train, train_label = synthetic_contamination( - train_norm, outliers, contamination_rate - ) + train, train_label = synthetic_contamination(train_norm, outliers, contamination_rate) return train, train_label, test_data, test_label @@ -148,9 +139,7 @@ def Arrhythmia_data( test_label = np.zeros(test_data.shape[0]) test_label[num_split:] = 1 - train, train_label = synthetic_contamination( - train_norm, outliers, contamination_rate - ) + train, train_label = synthetic_contamination(train_norm, outliers, contamination_rate) return train, train_label, test_data, test_label @@ -159,13 +148,9 @@ def load_data(data_name: str, cls: int, contamination_rate: float = 0.0) -> Tupl ## normal data with label 0, anomalies with label 1 if data_name == "cifar10_feat": - train, train_label, test, test_label = CIFAR10_feat( - cls, contamination_rate=contamination_rate - ) + train, train_label, test, test_label = CIFAR10_feat(cls, contamination_rate=contamination_rate) elif data_name == "fmnist_feat": - train, train_label, test, test_label = FMNIST_feat( - cls, contamination_rate=contamination_rate - ) + train, train_label, test, test_label = FMNIST_feat(cls, contamination_rate=contamination_rate) elif data_name == "thyroid": train, train_label, test, test_label = Thyroid_data(contamination_rate) elif data_name == "arrhythmia": diff --git a/contrastive_learning/LatentOE/loader/utils.py b/src/contrastive_learning/LatentOE/loader/utils.py similarity index 95% rename from contrastive_learning/LatentOE/loader/utils.py rename to src/contrastive_learning/LatentOE/loader/utils.py index aad8ebb..7dd3bcc 100644 --- a/contrastive_learning/LatentOE/loader/utils.py +++ b/src/contrastive_learning/LatentOE/loader/utils.py @@ -1,4 +1,5 @@ """Utility class for data loading.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -36,5 +37,4 @@ def __getitem__(self, idx: int) -> dict: """Return the data sample at the given index.""" label = self.labels[idx] sample = self.samples[idx] - data = {"sample": sample, "label": label} - return data + return {"sample": sample, "label": label} diff --git a/contrastive_learning/LatentOE/models/__init__.py b/src/contrastive_learning/LatentOE/models/__init__.py similarity index 100% rename from contrastive_learning/LatentOE/models/__init__.py rename to src/contrastive_learning/LatentOE/models/__init__.py diff --git a/contrastive_learning/LatentOE/models/feat_nets.py b/src/contrastive_learning/LatentOE/models/feat_nets.py similarity index 86% rename from contrastive_learning/LatentOE/models/feat_nets.py rename to src/contrastive_learning/LatentOE/models/feat_nets.py index 6a39ef9..08ab4f1 100644 --- a/contrastive_learning/LatentOE/models/feat_nets.py +++ b/src/contrastive_learning/LatentOE/models/feat_nets.py @@ -1,4 +1,5 @@ """Feature networks for latent OE models.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -17,8 +18,7 @@ import sys from typing import TYPE_CHECKING, Tuple -import torch.nn as nn -from torch import Tensor +from torch import Tensor, nn sys.path.append("..") @@ -53,9 +53,7 @@ def forward(self, x: Tensor) -> Tensor: class FeatEncoder(nn.Module): """Feature encoder.""" - def __init__( - self, x_dim: int, z_dim: int, bias: bool, num_layers: int, batch_norm: bool - ) -> None: + def __init__(self, x_dim: int, z_dim: int, bias: bool, num_layers: int, batch_norm: bool) -> None: """Init.""" super(FeatEncoder, self).__init__() @@ -81,9 +79,7 @@ def forward(self, x: Tensor) -> Tensor: class FeatNets: """Feature networks for latent OE models.""" - def _make_nets( - self, x_dim: int, config: "Config" - ) -> Tuple[nn.Module, nn.ModuleList]: + def _make_nets(self, x_dim: int, config: "Config") -> Tuple[nn.Module, nn.ModuleList]: """Return encoder and transformation networks.""" enc_nlayers = config["enc_nlayers"] z_dim = config["enc_zdim"] @@ -93,11 +89,6 @@ def _make_nets( batch_norm = config["batch_norm"] enc = FeatEncoder(x_dim, z_dim, False, enc_nlayers, batch_norm) - trans = nn.ModuleList( - [ - FeatTransformNet(x_dim, trans_hdim, False, trans_nlayers) - for _ in range(num_trans) - ] - ) + trans = nn.ModuleList([FeatTransformNet(x_dim, trans_hdim, False, trans_nlayers) for _ in range(num_trans)]) return enc, trans diff --git a/contrastive_learning/LatentOE/models/losses.py b/src/contrastive_learning/LatentOE/models/losses.py similarity index 90% rename from contrastive_learning/LatentOE/models/losses.py rename to src/contrastive_learning/LatentOE/models/losses.py index 04dcbcd..a62bc6d 100644 --- a/contrastive_learning/LatentOE/models/losses.py +++ b/src/contrastive_learning/LatentOE/models/losses.py @@ -1,4 +1,5 @@ """Loss functions for training the Latent Outlier Exposure (LOE) model.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -18,8 +19,8 @@ import numpy as np import torch -import torch.nn as nn import torch.nn.functional as F +from torch import nn class DCL(nn.Module): @@ -77,14 +78,10 @@ def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # Calculate the similarity matrix between embeddings using exponential # of the dot product with temperature. - sim_matrix = torch.exp( - torch.matmul(z, z.permute(0, 2, 1) / self.temp) - ) # n, k, k + sim_matrix = torch.exp(torch.matmul(z, z.permute(0, 2, 1) / self.temp)) # n, k, k # Create a mask to remove the similarity of embeddings with themselves. - mask = ( - torch.ones_like(sim_matrix).to(z) - torch.eye(num_trans).unsqueeze(0).to(z) - ).bool() + mask = (torch.ones_like(sim_matrix).to(z) - torch.eye(num_trans).unsqueeze(0).to(z)).bool() # Apply the mask to the similarity matrix to get similarity values between # different embeddings. @@ -96,9 +93,7 @@ def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # Calculate the positive similarity between original and transformed # embeddings to use in loss_a. - pos_sim = torch.exp( - torch.sum(z_trans * z_ori.unsqueeze(1), -1) / self.temp - ) # n, k-1 + pos_sim = torch.exp(torch.sum(z_trans * z_ori.unsqueeze(1), -1) / self.temp) # n, k-1 # Calculate the scale factor for loss tensor normalization. K = num_trans - 1 diff --git a/contrastive_learning/LatentOE/models/neutral_ad.py b/src/contrastive_learning/LatentOE/models/neutral_ad.py similarity index 97% rename from contrastive_learning/LatentOE/models/neutral_ad.py rename to src/contrastive_learning/LatentOE/models/neutral_ad.py index 0e1a9fc..b534c72 100644 --- a/contrastive_learning/LatentOE/models/neutral_ad.py +++ b/src/contrastive_learning/LatentOE/models/neutral_ad.py @@ -15,12 +15,11 @@ # You should have received a copy of the GNU Affero General Public License # along with this program. If not, see . - import sys from typing import TYPE_CHECKING, Union import torch -import torch.nn as nn +from torch import nn from .feat_nets import FeatNets # noqa: E402 from .tab_nets import TabNets # noqa: E402 @@ -64,9 +63,7 @@ class TabNeutralAD(nn.Module): """ - def __init__( - self, model: Union[FeatNets, TabNets], x_dim: int, config: "Config" - ) -> None: + def __init__(self, model: Union[FeatNets, TabNets], x_dim: int, config: "Config") -> None: """Initialize the Tabular Neutral Autoencoder.""" super(TabNeutralAD, self).__init__() diff --git a/contrastive_learning/LatentOE/models/neutral_ad_trainer.py b/src/contrastive_learning/LatentOE/models/neutral_ad_trainer.py similarity index 96% rename from contrastive_learning/LatentOE/models/neutral_ad_trainer.py rename to src/contrastive_learning/LatentOE/models/neutral_ad_trainer.py index 9825295..882ea94 100644 --- a/contrastive_learning/LatentOE/models/neutral_ad_trainer.py +++ b/src/contrastive_learning/LatentOE/models/neutral_ad_trainer.py @@ -1,4 +1,5 @@ """Trainer for the NeutralAD model.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -19,8 +20,8 @@ import numpy as np import torch -import torch.nn as nn from sklearn.metrics import average_precision_score, roc_auc_score +from torch import nn from torch.optim.lr_scheduler import _LRScheduler from torch.optim.optimizer import Optimizer from torch.utils.data import DataLoader @@ -67,9 +68,7 @@ class NeutralADTrainer: """ - def __init__( - self, model: nn.Module, loss_function: nn.Module, config: "Config" - ) -> None: + def __init__(self, model: nn.Module, loss_function: nn.Module, config: "Config") -> None: """Initialize the NeutralAD trainer.""" self.loss_fun = loss_function self.device = torch.device(config["device"]) @@ -78,9 +77,7 @@ def __init__( self.max_epochs = config["training_epochs"] self.warmup = 2 - def _train( - self, epoch: int, train_loader: DataLoader, optimizer: Optimizer - ) -> float: + def _train(self, epoch: int, train_loader: DataLoader, optimizer: Optimizer) -> float: """Perform a single training epoch. Parameters @@ -149,9 +146,7 @@ def _train( largest=True, sorted=False, ) - loss = torch.cat( - [loss_n[idx_n], 0.5 * loss_n[idx_a] + 0.5 * loss_a[idx_a]], 0 - ) + loss = torch.cat([loss_n[idx_n], 0.5 * loss_n[idx_a] + 0.5 * loss_a[idx_a]], 0) loss_mean = loss.mean() elif self.train_method == "refine": _, idx_n = torch.topk( @@ -323,7 +318,7 @@ def train( # noqa: C901 if epoch % log_every == 0 or epoch == 1: msg = ( f"Epoch: {epoch}, TR loss: {train_loss}, " - f"VAL loss: {valin_loss,valout_loss}, VL auc: {val_auc}, " + f"VAL loss: {valin_loss, valout_loss}, VL auc: {val_auc}, " f"VL ap: {val_ap}, VL f1: {val_f1} " ) diff --git a/contrastive_learning/LatentOE/models/tab_nets.py b/src/contrastive_learning/LatentOE/models/tab_nets.py similarity index 90% rename from contrastive_learning/LatentOE/models/tab_nets.py rename to src/contrastive_learning/LatentOE/models/tab_nets.py index 6c24c1e..8b7c5c4 100644 --- a/contrastive_learning/LatentOE/models/tab_nets.py +++ b/src/contrastive_learning/LatentOE/models/tab_nets.py @@ -1,4 +1,5 @@ """Tabular networks for latent OE.""" + # Latent Outlier Exposure for Anomaly Detection with Contaminated Data # Copyright (c) 2022 Robert Bosch GmbH # @@ -17,8 +18,7 @@ import sys from typing import TYPE_CHECKING, Tuple -import torch.nn as nn -from torch import Tensor +from torch import Tensor, nn sys.path.append("..") @@ -87,9 +87,7 @@ def forward(self, x: Tensor) -> Tensor: class TabNets: """Tabular networks for latent OE.""" - def _make_nets( - self, x_dim: int, config: "Config" - ) -> Tuple[nn.Module, nn.ModuleList]: + def _make_nets(self, x_dim: int, config: "Config") -> Tuple[nn.Module, nn.ModuleList]: enc_nlayers = config["enc_nlayers"] try: hdim = config["enc_hdim"] @@ -113,11 +111,6 @@ def _make_nets( batch_norm = config["batch_norm"] enc = TabEncoder(x_dim, hdim, zdim, False, enc_nlayers, batch_norm) - trans = nn.ModuleList( - [ - TabTransformNet(x_dim, trans_dim, False, trans_nlayers) - for _ in range(num_trans) - ] - ) + trans = nn.ModuleList([TabTransformNet(x_dim, trans_dim, False, trans_nlayers) for _ in range(num_trans)]) return enc, trans diff --git a/contrastive_learning/LatentOE/utils.py b/src/contrastive_learning/LatentOE/utils.py similarity index 83% rename from contrastive_learning/LatentOE/utils.py rename to src/contrastive_learning/LatentOE/utils.py index 62aae19..8513cf7 100644 --- a/contrastive_learning/LatentOE/utils.py +++ b/src/contrastive_learning/LatentOE/utils.py @@ -74,18 +74,17 @@ def __init__(self, filepath: str, mode: Literal["w", "a"], lock: Any = None): self.filepath = filepath if mode not in ["w", "a"]: raise AssertionError("Mode must be one of w, r or a") - else: - self.mode = mode + self.mode = mode self.lock = lock - def log(self, str: str) -> None: + def log(self, message: str) -> None: """Log a string to the file.""" if self.lock: self.lock.acquire() try: with open(self.filepath, self.mode) as f: - f.write(str + "\n") + f.write(message + "\n") except Exception as e: print(e) @@ -93,9 +92,7 @@ def log(self, str: str) -> None: self.lock.release() -def compute_f1_score( - target: npt.NDArray[Any], score: npt.NDArray[Any] -) -> Union[float, npt.NDArray[Any]]: +def compute_f1_score(target: npt.NDArray[Any], score: npt.NDArray[Any]) -> Union[float, npt.NDArray[Any]]: """Compute the F1 score given the target and predicted scores.""" normal_ratio = (target == 0).sum() / len(target) threshold = np.percentile(score, 100 * normal_ratio) @@ -202,30 +199,27 @@ def stop( test_score, # type: ignore[assignment] ) return False - else: - self.counter += 1 - return self.counter >= self.patience - else: - if val_loss <= self.local_val_optimum: # type: ignore - self.counter = 0 - self.local_val_optimum = val_loss - self.best_epoch = epoch - self.train_loss = train_loss # type: ignore[assignment] - self.val_loss, self.val_auc = val_loss, val_auc # type: ignore - ( - self.test_loss, - self.test_auc, - self.test_ap, - self.test_f1, - self.test_score, - ) = ( - test_loss, # type: ignore[assignment] - test_auc, # type: ignore[assignment] - test_ap, # type: ignore[assignment] - test_f1, # type: ignore[assignment] - test_score, # type: ignore[assignment] - ) - return False - else: - self.counter += 1 - return self.counter >= self.patience + self.counter += 1 + return self.counter >= self.patience + if val_loss <= self.local_val_optimum: # type: ignore + self.counter = 0 + self.local_val_optimum = val_loss + self.best_epoch = epoch + self.train_loss = train_loss # type: ignore[assignment] + self.val_loss, self.val_auc = val_loss, val_auc # type: ignore + ( + self.test_loss, + self.test_auc, + self.test_ap, + self.test_f1, + self.test_score, + ) = ( + test_loss, # type: ignore[assignment] + test_auc, # type: ignore[assignment] + test_ap, # type: ignore[assignment] + test_f1, # type: ignore[assignment] + test_score, # type: ignore[assignment] + ) + return False + self.counter += 1 + return self.counter >= self.patience diff --git a/contrastive_learning/README.md b/src/contrastive_learning/README.md similarity index 100% rename from contrastive_learning/README.md rename to src/contrastive_learning/README.md diff --git a/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb b/src/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb similarity index 96% rename from masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb rename to src/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb index 974c6a8..5adc95d 100644 --- a/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb +++ b/src/masked_modelling/simmtm/simmtm-BeijingPM25Quality-forecasting.ipynb @@ -16,7 +16,7 @@ "tags": [] }, "source": [ - "This notebook demonstrate an apllication of SimMTM, a simple self-supervised learning framework for time series modeling. Self-supervised learning is a learning paradigm that allows a model to learn a good representation from the input data itself. The learned representation will be beneficial to some downstream tasks such as forecasting, classification and outlier detection.\n", + "This notebook demonstrate an application of SimMTM, a simple self-supervised learning framework for time series modeling. Self-supervised learning is a learning paradigm that allows a model to learn a good representation from the input data itself. The learned representation will be beneficial to some downstream tasks such as forecasting, classification and outlier detection.\n", "\n", "Self-supervised learning has a lof of success and achieves state-of-the-art performance in some domains, especially in the image domain. In this demo, we will show a self-supervised learning method, SimMTM, in the time-series domain. SimMTM adopts both masked modeling and contrastive learning to learn a good representation of the input data. By using the learned representation and finetuning it, we achieve a significant improvement compared to the model without self-supervised learning." ] @@ -316,7 +316,7 @@ "source": [ "## 2 - Masked Modeling\n", "\n", - "SimMTM proposes to recover a time serie by the weighted sum of multiple masked points, which eases the reconstruction task by assembling ruined but complementary temporal variations." + "SimMTM proposes to recover a time series by the weighted sum of multiple masked points, which eases the reconstruction task by assembling ruined but complementary temporal variations." ] }, { @@ -723,7 +723,7 @@ "In this demo, we use a benchmask time series dataset called BeijingPM25Quality.\n", "This dataset is part of the Monash, UEA & UCR time series regression repository. http://tseregression.org/\n", "\n", - "The goal of this dataset is to predict PM2.5 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurments (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurments from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017." + "The goal of this dataset is to predict PM2.5 air quality in the city of Beijing. This dataset contains 17532 time series with 9 dimensions. This includes hourly air pollutants measurements (SO2, NO2, CO and O3), temperature, pressure, dew point, rainfall and windspeed measurements from 12 nationally controlled air quality monitoring sites. The air-quality data are from the Beijing Municipal Environmental Monitoring Center. The meteorological data in each air-quality site are matched with the nearest weather station from the China Meteorological Administration. The time period is from March 1st, 2013 to February 28th, 2017." ] }, { @@ -866,7 +866,7 @@ "\n", " if token_len != 2:\n", " raise TsFileParseException(\"timestamps tag requires an associated Boolean value\")\n", - " elif tokens[1] == \"true\":\n", + " if tokens[1] == \"true\":\n", " timestamps = True\n", " elif tokens[1] == \"false\":\n", " timestamps = False\n", @@ -884,9 +884,7 @@ " token_len = len(tokens)\n", " if token_len != 2:\n", " raise TsFileParseException(\"univariate tag requires an associated Boolean value\")\n", - " elif tokens[1] == \"true\":\n", - " pass\n", - " elif tokens[1] == \"false\":\n", + " if tokens[1] == \"true\" or tokens[1] == \"false\":\n", " pass\n", " else:\n", " raise TsFileParseException(\"invalid univariate value\")\n", @@ -948,9 +946,8 @@ "\n", " if data_started and not metadata_started:\n", " raise TsFileParseException(\"metadata must come before data\")\n", - " else:\n", - " has_data_tag = True\n", - " data_started = True\n", + " has_data_tag = True\n", + " data_started = True\n", " # If the 'data tag has been found then metadata has been parsed and data can be loaded\n", " elif data_started:\n", " # Check that a full set of metadata has been provided\n", @@ -1010,228 +1007,223 @@ " values_for_dimension = []\n", "\n", " char_num += 1\n", - " else:\n", - " # Check if we have reached a class label\n", - " if line[char_num] != \"(\" and target_labels:\n", - " class_val = line[char_num:].strip()\n", - "\n", - " # if class_val not in class_val_list:\n", - " # raise TsFileParseException(\n", - " # \"the class value '\" + class_val + \"' on line \" + str(\n", - " # line_num + 1) + \" is not valid\")\n", + " # Check if we have reached a class label\n", + " elif line[char_num] != \"(\" and target_labels:\n", + " class_val = line[char_num:].strip()\n", "\n", - " class_val_list.append(float(class_val))\n", - " char_num = line_len\n", + " # if class_val not in class_val_list:\n", + " # raise TsFileParseException(\n", + " # \"the class value '\" + class_val + \"' on line \" + str(\n", + " # line_num + 1) + \" is not valid\")\n", "\n", - " has_another_value = False\n", - " has_another_dimension = False\n", + " class_val_list.append(float(class_val))\n", + " char_num = line_len\n", "\n", - " timestamps_for_dimension = []\n", - " values_for_dimension = []\n", + " has_another_value = False\n", + " has_another_dimension = False\n", "\n", - " else:\n", - " # Read in the data contained within the next tuple\n", + " timestamps_for_dimension = []\n", + " values_for_dimension = []\n", "\n", - " if line[char_num] != \"(\" and not target_labels:\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" does not start with a '('\"\n", - " )\n", + " else:\n", + " # Read in the data contained within the next tuple\n", "\n", - " char_num += 1\n", - " tuple_data = \"\"\n", - "\n", - " while char_num < line_len and line[char_num] != \")\":\n", - " tuple_data += line[char_num]\n", - " char_num += 1\n", - "\n", - " if char_num >= line_len or line[char_num] != \")\":\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" does not end with a ')'\"\n", - " )\n", + " if line[char_num] != \"(\" and not target_labels:\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" does not start with a '('\"\n", + " )\n", "\n", - " # Read in any spaces immediately after the current tuple\n", + " char_num += 1\n", + " tuple_data = \"\"\n", "\n", + " while char_num < line_len and line[char_num] != \")\":\n", + " tuple_data += line[char_num]\n", " char_num += 1\n", "\n", - " while char_num < line_len and str.isspace(line[char_num]):\n", - " char_num += 1\n", + " if char_num >= line_len or line[char_num] != \")\":\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" does not end with a ')'\"\n", + " )\n", "\n", - " # Check if there is another value or dimension to process after this tuple\n", + " # Read in any spaces immediately after the current tuple\n", "\n", - " if char_num >= line_len:\n", - " has_another_value = False\n", - " has_another_dimension = False\n", - "\n", - " elif line[char_num] == \",\":\n", - " has_another_value = True\n", - " has_another_dimension = False\n", - "\n", - " elif line[char_num] == \":\":\n", - " has_another_value = False\n", - " has_another_dimension = True\n", + " char_num += 1\n", "\n", + " while char_num < line_len and str.isspace(line[char_num]):\n", " char_num += 1\n", "\n", - " # Get the numeric value for the tuple by\n", - " # reading from the end of the tuple data\n", - " # backwards to the last comma\n", + " # Check if there is another value or dimension to process after this tuple\n", "\n", - " last_comma_index = tuple_data.rfind(\",\")\n", - "\n", - " if last_comma_index == -1:\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains a tuple that has no comma inside of it\"\n", - " )\n", - "\n", - " try:\n", - " value = tuple_data[last_comma_index + 1 :]\n", - " value = float(value) # type: ignore\n", + " if char_num >= line_len:\n", + " has_another_value = False\n", + " has_another_dimension = False\n", "\n", - " except ValueError:\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains a tuple that does not have a valid numeric value\"\n", - " ) from None\n", + " elif line[char_num] == \",\":\n", + " has_another_value = True\n", + " has_another_dimension = False\n", "\n", - " # Check the type of timestamp that we have\n", + " elif line[char_num] == \":\":\n", + " has_another_value = False\n", + " has_another_dimension = True\n", "\n", - " timestamp = tuple_data[0:last_comma_index]\n", + " char_num += 1\n", "\n", + " # Get the numeric value for the tuple by\n", + " # reading from the end of the tuple data\n", + " # backwards to the last comma\n", + "\n", + " last_comma_index = tuple_data.rfind(\",\")\n", + "\n", + " if last_comma_index == -1:\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains a tuple that has no comma inside of it\"\n", + " )\n", + "\n", + " try:\n", + " value = tuple_data[last_comma_index + 1 :]\n", + " value = float(value) # type: ignore\n", + "\n", + " except ValueError:\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains a tuple that does not have a valid numeric value\"\n", + " ) from None\n", + "\n", + " # Check the type of timestamp that we have\n", + "\n", + " timestamp = tuple_data[0:last_comma_index]\n", + "\n", + " try:\n", + " timestamp = int(timestamp) # type: ignore\n", + " timestamp_is_int = True\n", + " timestamp_is_timestamp = False\n", + " except ValueError:\n", + " timestamp_is_int = False\n", + "\n", + " if not timestamp_is_int:\n", " try:\n", - " timestamp = int(timestamp) # type: ignore\n", - " timestamp_is_int = True\n", + " timestamp = float(timestamp) # type: ignore\n", + " timestamp_is_float = True\n", " timestamp_is_timestamp = False\n", " except ValueError:\n", - " timestamp_is_int = False\n", - "\n", - " if not timestamp_is_int:\n", - " try:\n", - " timestamp = float(timestamp) # type: ignore\n", - " timestamp_is_float = True\n", - " timestamp_is_timestamp = False\n", - " except ValueError:\n", - " timestamp_is_float = False\n", - "\n", - " if not timestamp_is_int and not timestamp_is_float:\n", - " try:\n", - " timestamp = timestamp.strip()\n", - " timestamp_is_timestamp = True\n", - " except ValueError:\n", - " timestamp_is_timestamp = False\n", - "\n", - " # Make sure that the timestamps in the file\n", - " # (not just this dimension or case) are consistent\n", - "\n", - " if (\n", - " not timestamp_is_timestamp\n", - " and not timestamp_is_int\n", - " and not timestamp_is_float\n", - " ):\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains a tuple that has an invalid timestamp '\"\n", - " + timestamp\n", - " + \"'\"\n", - " )\n", - "\n", - " if (\n", - " previous_timestamp_was_float is not None\n", - " and previous_timestamp_was_float\n", - " and not timestamp_is_float\n", - " ):\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains tuples where the timestamp format is inconsistent\"\n", - " )\n", - "\n", - " if (\n", - " previous_timestamp_was_int is not None\n", - " and previous_timestamp_was_int\n", - " and not timestamp_is_int\n", - " ):\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains tuples where the timestamp format is inconsistent\"\n", - " )\n", - "\n", - " if (\n", - " previous_timestamp_was_timestamp is not None\n", - " and previous_timestamp_was_timestamp\n", - " and not timestamp_is_timestamp\n", - " ):\n", - " raise TsFileParseException(\n", - " \"dimension \"\n", - " + str(this_line_num_dimensions + 1)\n", - " + \" on line \"\n", - " + str(line_num + 1)\n", - " + \" contains tuples where the timestamp format is inconsistent\"\n", - " )\n", - "\n", - " # Store the values\n", - "\n", - " timestamps_for_dimension += [timestamp]\n", - " values_for_dimension += [value]\n", + " timestamp_is_float = False\n", "\n", - " # If this was our first tuple then we store the type of timestamp we had\n", - "\n", - " if previous_timestamp_was_timestamp is None and timestamp_is_timestamp:\n", - " previous_timestamp_was_timestamp = True\n", - " previous_timestamp_was_int = False\n", - " previous_timestamp_was_float = False\n", - "\n", - " if previous_timestamp_was_int is None and timestamp_is_int:\n", - " previous_timestamp_was_timestamp = False\n", - " previous_timestamp_was_int = True\n", - " previous_timestamp_was_float = False\n", - "\n", - " if previous_timestamp_was_float is None and timestamp_is_float:\n", - " previous_timestamp_was_timestamp = False\n", - " previous_timestamp_was_int = False\n", - " previous_timestamp_was_float = True\n", - "\n", - " # See if we should add the data for this dimension\n", - "\n", - " if not has_another_value:\n", - " if len(instance_list) < (this_line_num_dimensions + 1):\n", - " instance_list.append([])\n", - "\n", - " if timestamp_is_timestamp:\n", - " timestamps_for_dimension = pd.DatetimeIndex(timestamps_for_dimension)\n", + " if not timestamp_is_int and not timestamp_is_float:\n", + " try:\n", + " timestamp = timestamp.strip()\n", + " timestamp_is_timestamp = True\n", + " except ValueError:\n", + " timestamp_is_timestamp = False\n", "\n", - " instance_list[this_line_num_dimensions].append(\n", - " pd.Series(\n", - " index=timestamps_for_dimension,\n", - " data=values_for_dimension,\n", - " )\n", + " # Make sure that the timestamps in the file\n", + " # (not just this dimension or case) are consistent\n", + "\n", + " if not timestamp_is_timestamp and not timestamp_is_int and not timestamp_is_float:\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains a tuple that has an invalid timestamp '\"\n", + " + timestamp\n", + " + \"'\"\n", + " )\n", + "\n", + " if (\n", + " previous_timestamp_was_float is not None\n", + " and previous_timestamp_was_float\n", + " and not timestamp_is_float\n", + " ):\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains tuples where the timestamp format is inconsistent\"\n", + " )\n", + "\n", + " if (\n", + " previous_timestamp_was_int is not None\n", + " and previous_timestamp_was_int\n", + " and not timestamp_is_int\n", + " ):\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains tuples where the timestamp format is inconsistent\"\n", + " )\n", + "\n", + " if (\n", + " previous_timestamp_was_timestamp is not None\n", + " and previous_timestamp_was_timestamp\n", + " and not timestamp_is_timestamp\n", + " ):\n", + " raise TsFileParseException(\n", + " \"dimension \"\n", + " + str(this_line_num_dimensions + 1)\n", + " + \" on line \"\n", + " + str(line_num + 1)\n", + " + \" contains tuples where the timestamp format is inconsistent\"\n", + " )\n", + "\n", + " # Store the values\n", + "\n", + " timestamps_for_dimension += [timestamp]\n", + " values_for_dimension += [value]\n", + "\n", + " # If this was our first tuple then we store the type of timestamp we had\n", + "\n", + " if previous_timestamp_was_timestamp is None and timestamp_is_timestamp:\n", + " previous_timestamp_was_timestamp = True\n", + " previous_timestamp_was_int = False\n", + " previous_timestamp_was_float = False\n", + "\n", + " if previous_timestamp_was_int is None and timestamp_is_int:\n", + " previous_timestamp_was_timestamp = False\n", + " previous_timestamp_was_int = True\n", + " previous_timestamp_was_float = False\n", + "\n", + " if previous_timestamp_was_float is None and timestamp_is_float:\n", + " previous_timestamp_was_timestamp = False\n", + " previous_timestamp_was_int = False\n", + " previous_timestamp_was_float = True\n", + "\n", + " # See if we should add the data for this dimension\n", + "\n", + " if not has_another_value:\n", + " if len(instance_list) < (this_line_num_dimensions + 1):\n", + " instance_list.append([])\n", + "\n", + " if timestamp_is_timestamp:\n", + " timestamps_for_dimension = pd.DatetimeIndex(timestamps_for_dimension)\n", + "\n", + " instance_list[this_line_num_dimensions].append(\n", + " pd.Series(\n", + " index=timestamps_for_dimension,\n", + " data=values_for_dimension,\n", " )\n", - " this_line_num_dimensions += 1\n", + " )\n", + " this_line_num_dimensions += 1\n", "\n", - " timestamps_for_dimension = []\n", - " values_for_dimension = []\n", + " timestamps_for_dimension = []\n", + " values_for_dimension = []\n", "\n", " elif has_another_value:\n", " raise TsFileParseException(\n", @@ -1283,7 +1275,7 @@ " + \" ends with a ',' that is not followed by another tuple\"\n", " )\n", "\n", - " elif has_another_dimension and target_labels:\n", + " if has_another_dimension and target_labels:\n", " raise TsFileParseException(\n", " \"dimension \"\n", " + str(this_line_num_dimensions + 1)\n", @@ -1292,7 +1284,7 @@ " + \" ends with a ':' while it should list a class value\"\n", " )\n", "\n", - " elif has_another_dimension and not target_labels:\n", + " if has_another_dimension and not target_labels:\n", " if len(instance_list) < (this_line_num_dimensions + 1):\n", " instance_list.append([])\n", "\n", @@ -1378,9 +1370,7 @@ "\n", " if metadata_started and not complete_regression_meta_data and not complete_classification_meta_data:\n", " raise TsFileParseException(\"metadata incomplete\")\n", - " elif metadata_started and not data_started:\n", - " raise TsFileParseException(\"file contained metadata but no data\")\n", - " elif metadata_started and data_started and len(instance_list) == 0:\n", + " if metadata_started and not data_started or metadata_started and data_started and len(instance_list) == 0:\n", " raise TsFileParseException(\"file contained metadata but no data\")\n", "\n", " # Create a DataFrame from the data parsed above\n", @@ -1394,13 +1384,10 @@ " if target_labels:\n", " if return_separate_X_and_y:\n", " return data, np.asarray(class_val_list)\n", - " else:\n", - " data[\"class_vals\"] = pd.Series(class_val_list)\n", - " return data\n", - " else:\n", + " data[\"class_vals\"] = pd.Series(class_val_list)\n", " return data\n", - " else:\n", - " raise TsFileParseException(\"empty file\")" + " return data\n", + " raise TsFileParseException(\"empty file\")" ] }, { diff --git a/masked_modelling/tabret/data_preprocessing.ipynb b/src/masked_modelling/tabret/data_preprocessing.ipynb similarity index 99% rename from masked_modelling/tabret/data_preprocessing.ipynb rename to src/masked_modelling/tabret/data_preprocessing.ipynb index ddbfd31..a50621b 100644 --- a/masked_modelling/tabret/data_preprocessing.ipynb +++ b/src/masked_modelling/tabret/data_preprocessing.ipynb @@ -4941,7 +4941,7 @@ " all_df = all_df.astype({col: \"int\"})\n", " else:\n", " continue\n", - "all_df = all_df.astype({col: \"float\" for col in [\"WEIGHT2\", \"HEIGHT3\", \"_BMI5\", \"WTKG3\", \"_STSTR\"]})" + "all_df = all_df.astype(dict.fromkeys([\"WEIGHT2\", \"HEIGHT3\", \"_BMI5\", \"WTKG3\", \"_STSTR\"], \"float\"))" ] }, { @@ -5850,7 +5850,7 @@ "low = np.arange(25, 76, 5)\n", "high = np.arange(29, 80, 5)\n", "stroke_df.loc[stroke_df[\"age\"] < 18, [\"_AGEG5YR\"]] = 14\n", - "stroke_df.loc[(18 <= stroke_df[\"age\"]) & (stroke_df[\"age\"] <= 24), [\"_AGEG5YR\"]] = 1\n", + "stroke_df.loc[(stroke_df[\"age\"] >= 18) & (stroke_df[\"age\"] <= 24), [\"_AGEG5YR\"]] = 1\n", "stroke_df.loc[stroke_df[\"age\"] >= 80, [\"_AGEG5YR\"]] = 13\n", "for k, i, j in zip(range(2, 13), low, high):\n", " stroke_df.loc[(i <= stroke_df[\"age\"]) & (stroke_df[\"age\"] <= j), [\"_AGEG5YR\"]] = k" diff --git a/masked_modelling/tabret/model/__init__.py b/src/masked_modelling/tabret/model/__init__.py similarity index 100% rename from masked_modelling/tabret/model/__init__.py rename to src/masked_modelling/tabret/model/__init__.py diff --git a/masked_modelling/tabret/model/ft_transformer.py b/src/masked_modelling/tabret/model/ft_transformer.py similarity index 88% rename from masked_modelling/tabret/model/ft_transformer.py rename to src/masked_modelling/tabret/model/ft_transformer.py index 770b2a6..36c6256 100644 --- a/masked_modelling/tabret/model/ft_transformer.py +++ b/src/masked_modelling/tabret/model/ft_transformer.py @@ -1,11 +1,11 @@ """Feature Tokenizer Transformer (FTTransformer) module.""" + import time import warnings from typing import Callable, Dict, List, Optional, Tuple, Union, cast import torch -import torch.nn as nn -from torch import Tensor +from torch import Tensor, nn from model.rtdl_modules import ( _INTERNAL_ERROR_MESSAGE, @@ -106,9 +106,7 @@ def add_parameters( continuous_columns: List[str], ) -> None: """Add parameters to the module.""" - weight_add, bias_add = self.get_dict_parameter( - continuous_columns, self.bias is not None - ) + weight_add, bias_add = self.get_dict_parameter(continuous_columns, self.bias is not None) self.weight.update(weight_add) if self.bias is not None: self.bias.update(bias_add) @@ -218,14 +216,11 @@ def __init__( self.__d_token = d_token self.embeddings = nn.ModuleDict( - { - key: nn.Embedding(cardinality, d_token) - for key, cardinality in cardinality_dict.items() - } + {key: nn.Embedding(cardinality, d_token) for key, cardinality in cardinality_dict.items()} ) if bias: bias = {} # type: ignore - for key in cardinality_dict.keys(): + for key in cardinality_dict: bias[key] = nn.Parameter(Tensor(1, d_token)) # type: ignore self.initialization_.apply(bias[key], d_token) # type: ignore self.bias = nn.ParameterDict(bias) @@ -246,10 +241,7 @@ def d_token(self) -> int: def add_parameters(self, cardinality_dict: Dict[str, int]) -> None: """Add parameters to the module.""" embeddings_add = nn.ModuleDict( - { - key: nn.Embedding(cardinality, self.__d_token) - for key, cardinality in cardinality_dict.items() - } + {key: nn.Embedding(cardinality, self.__d_token) for key, cardinality in cardinality_dict.items()} ) for parameter in embeddings_add.values(): self.initialization_.apply(parameter.weight, self.__d_token) @@ -257,7 +249,7 @@ def add_parameters(self, cardinality_dict: Dict[str, int]) -> None: if self.bias is not None: bias = {} - for key in cardinality_dict.keys(): + for key in cardinality_dict: bias[key] = nn.Parameter(Tensor(1, self.__d_token)) self.initialization_.apply(bias[key], self.__d_token) self.bias.update(nn.ParameterDict(bias)) @@ -340,10 +332,10 @@ def __init__( """ super().__init__() # assert len(continuous_columns) >= 0, "n_num_features must be non-negative" - assert ( - continuous_columns or cat_cardinality_dict - ), "at least one of n_num_features or cat_cardinalities must be \ + assert continuous_columns or cat_cardinality_dict, ( + "at least one of n_num_features or cat_cardinalities must be \ positive/non-empty" + ) self.initialization = "uniform" self.num_tokenizer = ( NumericalFeatureTokenizer( @@ -356,9 +348,7 @@ def __init__( else None ) self.cat_tokenizer = ( - CategoricalFeatureTokenizer( - cat_cardinality_dict, d_token, True, self.initialization - ) + CategoricalFeatureTokenizer(cat_cardinality_dict, d_token, True, self.initialization) if cat_cardinality_dict else None ) @@ -366,11 +356,7 @@ def __init__( @property def n_tokens(self) -> int: """The number of tokens.""" - return sum( - x.n_tokens - for x in [self.num_tokenizer, self.cat_tokenizer] - if x is not None - ) + return sum(x.n_tokens for x in [self.num_tokenizer, self.cat_tokenizer] if x is not None) @property def d_token(self) -> int: @@ -387,9 +373,9 @@ def add_attribute( cat_cardinality_dict: Optional[Dict[str, int]] = None, ) -> None: """Add attributes to the module.""" - assert ( - continuous_columns is not None or cat_cardinality_dict is not None - ), "At least one of n_num and cardinalities must be presented." + assert continuous_columns is not None or cat_cardinality_dict is not None, ( + "At least one of n_num and cardinalities must be presented." + ) if continuous_columns is not None: if self.num_tokenizer is not None: self.num_tokenizer.add_parameters(continuous_columns) @@ -411,9 +397,7 @@ def add_attribute( self.initialization, ) - def forward( - self, x_num: Optional[Dict[str, Tensor]], x_cat: Optional[Dict[str, Tensor]] - ) -> Tensor: + def forward(self, x_num: Optional[Dict[str, Tensor]], x_cat: Optional[Dict[str, Tensor]]) -> Tensor: """Perform the forward pass. Parameters @@ -437,9 +421,7 @@ def forward( AssertionError If the described requirements for the inputs are not met. """ - assert ( - x_num is not None or x_cat is not None - ), "At least one of x_num and x_cat must be presented" + assert x_num is not None or x_cat is not None, "At least one of x_num and x_cat must be presented" # assert _all_or_none( # [self.num_tokenizer, x_num] # ), "If self.num_tokenizer is (not) None, then x_num must (not) be None" @@ -526,10 +508,10 @@ def __init__( # noqa: C901 f"Do you mean last_layer_query_idx=[{last_layer_query_idx}] ?" ) if not prenormalization: - assert ( - not first_prenormalization - ), "If `prenormalization` is False, then `first_prenormalization` \ + assert not first_prenormalization, ( + "If `prenormalization` is False, then `first_prenormalization` \ must be False" + ) assert _all_or_none([n_tokens, kv_compression_ratio, kv_compression_sharing]), ( "If any of the following arguments is (not) None, \ then all of them must" @@ -546,15 +528,11 @@ def __init__( # noqa: C901 UserWarning, stacklevel=2, ) - assert ( - not first_prenormalization - ), "If prenormalization is False, then first_prenormalization is \ + assert not first_prenormalization, ( + "If prenormalization is False, then first_prenormalization is \ ignored and must be set to False" - if ( - prenormalization - and first_prenormalization - and self.WARNINGS["first_prenormalization"] - ): + ) + if prenormalization and first_prenormalization and self.WARNINGS["first_prenormalization"]: warnings.warn( "first_prenormalization is set to True. Are you sure about this? " "For example, the vanilla FTTransformer with " @@ -568,16 +546,12 @@ def __init__( # noqa: C901 def make_kv_compression() -> ModuleType: """Make the key and value compression layers.""" - assert ( - n_tokens and kv_compression_ratio - ), _INTERNAL_ERROR_MESSAGE # for mypy + assert n_tokens and kv_compression_ratio, _INTERNAL_ERROR_MESSAGE # for mypy # https://github.com/pytorch/fairseq/blob/1bba712622b8ae4efb3eb793a8a40da386fe11d0/examples/linformer/linformer_src/modules/multihead_linear_attention.py#L83 return nn.Linear(n_tokens, int(n_tokens * kv_compression_ratio), bias=False) self.shared_kv_compression = ( - make_kv_compression() - if kv_compression_ratio and kv_compression_sharing == "layerwise" - else None + make_kv_compression() if kv_compression_ratio and kv_compression_sharing == "layerwise" else None ) self.prenormalization = prenormalization @@ -608,23 +582,17 @@ def make_kv_compression() -> ModuleType: } ) if layer_idx or not prenormalization or first_prenormalization: - layer["attention_normalization"] = _make_nn_module( - attention_normalization, d_token - ) + layer["attention_normalization"] = _make_nn_module(attention_normalization, d_token) layer["ffn_normalization"] = _make_nn_module(ffn_normalization, d_token) if kv_compression_ratio and self.shared_kv_compression is None: layer["key_compression"] = make_kv_compression() if kv_compression_sharing == "headwise": layer["value_compression"] = make_kv_compression() else: - assert ( - kv_compression_sharing == "key-value" - ), _INTERNAL_ERROR_MESSAGE + assert kv_compression_sharing == "key-value", _INTERNAL_ERROR_MESSAGE self.blocks.append(layer) - def _get_kv_compressions( - self, layer: nn.ModuleDict - ) -> Tuple[Optional[nn.Module], Optional[nn.Module]]: + def _get_kv_compressions(self, layer: nn.ModuleDict) -> Tuple[Optional[nn.Module], Optional[nn.Module]]: return ( (self.shared_kv_compression, self.shared_kv_compression) if self.shared_kv_compression is not None @@ -649,9 +617,7 @@ def _start_residual( x_residual = layer[norm_key](x_residual) return x_residual - def _end_residual( - self, layer: nn.ModuleDict, stage: str, x: Tensor, x_residual: Tensor - ) -> Tensor: + def _end_residual(self, layer: nn.ModuleDict, stage: str, x: Tensor, x_residual: Tensor) -> Tensor: assert stage in ["attention", "ffn"], _INTERNAL_ERROR_MESSAGE x_residual = layer[f"{stage}_residual_dropout"](x_residual) x = x + x_residual @@ -661,15 +627,11 @@ def _end_residual( def forward(self, x: Tensor) -> Tensor: """Perform the forward pass.""" - assert ( - x.ndim == 3 - ), "The input must have 3 dimensions: (n_objects, n_tokens, d_token)" + assert x.ndim == 3, "The input must have 3 dimensions: (n_objects, n_tokens, d_token)" for layer_idx, layer in enumerate(self.blocks): layer = cast(nn.ModuleDict, layer) - query_idx = ( - self.last_layer_query_idx if layer_idx + 1 == len(self.blocks) else None - ) + query_idx = self.last_layer_query_idx if layer_idx + 1 == len(self.blocks) else None x_residual = self._start_residual(layer, "attention", x) x_residual, _ = layer["attention"]( x_residual if query_idx is None else x_residual[:, query_idx], @@ -690,9 +652,7 @@ def forward(self, x: Tensor) -> Tensor: @torch.no_grad() def get_attention(self, x: Tensor, layer_idx: Union[int, str] = 0) -> Tensor: """Get the attention matrix.""" - assert ( - x.ndim == 3 - ), "The input must have 3 dimensions: (n_objects, n_tokens, d_token)" + assert x.ndim == 3, "The input must have 3 dimensions: (n_objects, n_tokens, d_token)" N, L, _ = x.shape @@ -719,5 +679,4 @@ def layer_attention(index: int) -> Tensor: for idx in range(1, len(self.blocks)): attention = torch.matmul(layer_attention(idx), attention) return attention - else: - return layer_attention(layer_idx) + return layer_attention(layer_idx) diff --git a/masked_modelling/tabret/model/rtdl_modules.py b/src/masked_modelling/tabret/model/rtdl_modules.py similarity index 89% rename from masked_modelling/tabret/model/rtdl_modules.py rename to src/masked_modelling/tabret/model/rtdl_modules.py index 79a8010..f05a7b4 100644 --- a/masked_modelling/tabret/model/rtdl_modules.py +++ b/src/masked_modelling/tabret/model/rtdl_modules.py @@ -1,11 +1,11 @@ """RTDL modules.""" + import enum import math from typing import Any, Callable, Dict, List, Optional, Tuple, Union -import torch.nn as nn import torch.nn.functional as F -from torch import Tensor +from torch import Tensor, nn ModuleType = Union[str, Callable[..., nn.Module]] @@ -29,11 +29,7 @@ def geglu(x: Tensor) -> Tensor: def _is_glu_activation(activation: ModuleType) -> bool: - return ( - isinstance(activation, str) - and activation.endswith("GLU") - or activation in [ReGLU, GEGLU] - ) + return isinstance(activation, str) and activation.endswith("GLU") or activation in [ReGLU, GEGLU] def _all_or_none(values: List[nn.Module]) -> bool: @@ -83,19 +79,17 @@ def _make_nn_module(module_type: ModuleType, *args: Any) -> nn.Module: if isinstance(module_type, str): if module_type == "ReGLU": return ReGLU() - elif module_type == "GEGLU": + if module_type == "GEGLU": return GEGLU() - else: - try: - cls = getattr(nn, module_type) - except AttributeError as err: - raise ValueError( - f"Failed to construct the module {module_type} \ + try: + cls = getattr(nn, module_type) + except AttributeError as err: + raise ValueError( + f"Failed to construct the module {module_type} \ with the arguments {args}" - ) from err - return cls(*args) - else: - return module_type(*args) + ) from err + return cls(*args) + return module_type(*args) class MultiheadAttention(nn.Module): @@ -156,9 +150,7 @@ def __init__( # the "xavier" branch tries to follow torch.nn.MultiheadAttention; # the second condition checks if W_v plays the role of W_out; the latter one # is initialized with Kaiming in torch - if initialization == "xavier" and ( - m is not self.W_v or self.W_out is not None - ): + if initialization == "xavier" and (m is not self.W_v or self.W_out is not None): # gain is needed since W_qkv is represented with 3 separate layers (it # implies different fan_out) nn.init.xavier_uniform_(m.weight, gain=1 / math.sqrt(2)) @@ -202,9 +194,9 @@ def forward( The first element is the transformed tokens, and the second element \ is the attention statistics. """ - assert _all_or_none( - [key_compression, value_compression] - ), "If key_compression is (not) None, then value_compression must (not) be None" + assert _all_or_none([key_compression, value_compression]), ( + "If key_compression is (not) None, then value_compression must (not) be None" + ) q, k, v = self.W_q(x_q), self.W_k(x_kv), self.W_v(x_kv) for tensor in [q, k, v]: assert tensor.shape[-1] % self.n_heads == 0, _INTERNAL_ERROR_MESSAGE diff --git a/masked_modelling/tabret/model/tabret.py b/src/masked_modelling/tabret/model/tabret.py similarity index 80% rename from masked_modelling/tabret/model/tabret.py rename to src/masked_modelling/tabret/model/tabret.py index 550a405..3b6d1db 100644 --- a/masked_modelling/tabret/model/tabret.py +++ b/src/masked_modelling/tabret/model/tabret.py @@ -1,10 +1,11 @@ """TabRet model.""" + +import sys from typing import Any, Dict, List, Optional, Tuple, Union import torch -import torch.nn as nn import torch.nn.functional as F -from torch import Tensor +from torch import Tensor, nn from model.ft_transformer import FeatureTokenizer, Transformer from model.rtdl_modules import _TokenInitialization @@ -34,9 +35,7 @@ def __init__( self.feature_tokenizer = feature_tokenizer self.ft_norm = nn.LayerNorm(encoder_embed_dim) - self.alignment_layer = nn.Linear( - encoder_embed_dim, encoder_embed_dim, bias=True - ) + self.alignment_layer = nn.Linear(encoder_embed_dim, encoder_embed_dim, bias=True) self.encoder = encoder self.encoder_norm = nn.LayerNorm(encoder_embed_dim) @@ -45,17 +44,11 @@ def __init__( self.decoder_pos_embed = nn.ParameterDict() if continuous_columns is not None: self.decoder_pos_embed.update( - { - key: nn.Parameter(Tensor(1, 1, decoder_embed_dim)) - for key in continuous_columns - } + {key: nn.Parameter(Tensor(1, 1, decoder_embed_dim)) for key in continuous_columns} ) if cat_cardinality_dict is not None: self.decoder_pos_embed.update( - { - key: nn.Parameter(Tensor(1, 1, decoder_embed_dim)) - for key in cat_cardinality_dict.keys() - } + {key: nn.Parameter(Tensor(1, 1, decoder_embed_dim)) for key in cat_cardinality_dict} ) self.decoder = decoder @@ -63,16 +56,11 @@ def __init__( self.projectors = nn.ModuleDict() if continuous_columns is not None: - self.projectors.update( - {key: nn.Linear(decoder_embed_dim, 1) for key in continuous_columns} - ) + self.projectors.update({key: nn.Linear(decoder_embed_dim, 1) for key in continuous_columns}) if cat_cardinality_dict is not None: self.projectors.update( nn.ModuleDict( - { - key: nn.Linear(decoder_embed_dim, out_dim) - for key, out_dim in cat_cardinality_dict.items() - } + {key: nn.Linear(decoder_embed_dim, out_dim) for key, out_dim in cat_cardinality_dict.items()} ) ) @@ -116,28 +104,16 @@ def optimization_param_groups(self) -> List[Dict[str, Any]]: assert isinstance(getattr(self, no_wd_names[0], None), FeatureTokenizer) assert sum(1 for name, _ in self.named_modules() if no_wd_names[1] in name) == ( len(self.encoder.blocks) + len(self.decoder.blocks) - ) * 2 - int( - "attention_normalization" not in self.encoder.blocks[0] - ) - int( # type: ignore + ) * 2 - int("attention_normalization" not in self.encoder.blocks[0]) - int( # type: ignore "attention_normalization" not in self.decoder.blocks[0] ) - non_decay_parameters = get_parameter_names( - self, [nn.LayerNorm, FeatureTokenizer] - ) - decay_parameters = [ - name for name in non_decay_parameters if ".bias" not in name - ] + non_decay_parameters = get_parameter_names(self, [nn.LayerNorm, FeatureTokenizer]) + decay_parameters = [name for name in non_decay_parameters if ".bias" not in name] return [ + {"params": [p for n, p in self.named_parameters() if n in decay_parameters]}, { - "params": [ - p for n, p in self.named_parameters() if n in decay_parameters - ] - }, - { - "params": [ - p for n, p in self.named_parameters() if n not in decay_parameters - ], + "params": [p for n, p in self.named_parameters() if n not in decay_parameters], "weight_decay": 0.0, }, ] @@ -146,14 +122,12 @@ def freeze_parameters_wo_specific_columns(self, columns: List[str]) -> None: """Freeze all the parameters except the specific columns.""" for name, p in self.named_parameters(): name_split = name.split(".") - if len(name_split) > 1 and ( - name_split[-1] in columns or name_split[-2] in columns - ): + if len(name_split) > 1 and (name_split[-1] in columns or name_split[-2] in columns): p.requires_grad = True continue p.requires_grad = False - def freeze_transfomer(self) -> None: + def freeze_transformer(self) -> None: """Freeze the transformer.""" for p in self.alignment_layer.parameters(): p.requires_grad = False @@ -207,9 +181,9 @@ def add_attribute( cat_cardinality_dict: Optional[Dict[str, int]] = None, ) -> None: """Add new attributes to the model.""" - assert ( - continuous_columns is not None or cat_cardinality_dict is not None - ), "At least one of n_num and cardinalities must be presented" + assert continuous_columns is not None or cat_cardinality_dict is not None, ( + "At least one of n_num and cardinalities must be presented" + ) self.feature_tokenizer.add_attribute( continuous_columns=continuous_columns, cat_cardinality_dict=cat_cardinality_dict, @@ -218,7 +192,7 @@ def add_attribute( # add decoder pos embedding if continuous_columns is not None: for key in continuous_columns: - if key in self.decoder_pos_embed.keys(): + if key in self.decoder_pos_embed: continue pos_embed_add = nn.Parameter(Tensor(1, 1, self.decoder_embed_dim)) self.initialization_.apply(pos_embed_add, self.decoder_embed_dim) @@ -228,15 +202,13 @@ def add_attribute( if cat_cardinality_dict is not None: for key, cardinality in cat_cardinality_dict.items(): - if key in self.decoder_pos_embed.keys(): + if key in self.decoder_pos_embed: continue pos_embed_add = nn.Parameter(Tensor(1, 1, self.decoder_embed_dim)) self.initialization_.apply(pos_embed_add, self.decoder_embed_dim) self.decoder_pos_embed.update({key: pos_embed_add}) - self.projectors.update( - {key: nn.Linear(self.decoder_embed_dim, cardinality)} - ) + self.projectors.update({key: nn.Linear(self.decoder_embed_dim, cardinality)}) def save_attention_map( self, @@ -258,7 +230,7 @@ def save_attention_map( df = pd.DataFrame(attention.mean(0).cpu().numpy(), index=keys, columns=keys) df.to_csv(f"./attention/attention_{i}.csv") - exit() + sys.exit() def random_masking( self, @@ -269,10 +241,7 @@ def random_masking( """Mask the columns.""" N, L, D = x.shape - if mask_ratio >= 1: - len_keep = L - mask_ratio - else: - len_keep = int(L * (1 - mask_ratio)) + len_keep = L - mask_ratio if mask_ratio >= 1 else int(L * (1 - mask_ratio)) noise = torch.rand(N, L, device=x.device) # noise in [0, 1] if shuffle_idx_shift is not None: @@ -280,9 +249,7 @@ def random_masking( noise[:, shuffle_idx_shift[:, 0]] = 0 # sort noise for each sample - ids_shuffle = torch.argsort( - noise, dim=1 - ) # ascend: small is keep, large is remove + ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove ids_restore = torch.argsort(ids_shuffle, dim=1) # keep the first subset @@ -310,14 +277,10 @@ def custom_masking( x_masked = x[:, (1 - mask[0]).bool()] - ids_restore = torch.ones(N, L, device=x.device) * torch.arange( - L, device=x.device - ) + ids_restore = torch.ones(N, L, device=x.device) * torch.arange(L, device=x.device) return x_masked, mask, ids_restore.to(torch.int64) - def column_shuffle( - self, x: Tensor, column_shuffle_ratio: float, mask: Optional[List[int]] = None - ) -> Tensor: + def column_shuffle(self, x: Tensor, column_shuffle_ratio: float, mask: Optional[List[int]] = None) -> Tensor: """Shuffle the columns.""" N, L, _ = x.shape num_noise = int(L * column_shuffle_ratio) @@ -327,13 +290,11 @@ def column_shuffle( assert num_noise + len(mask) < L noise[mask] = 1 # sort noise for each sample - ids_shuffle = torch.argsort( - noise, dim=0 - ) # ascend: small is keep, large is remove + ids_shuffle = torch.argsort(noise, dim=0) # ascend: small is keep, large is remove - shuffle_idx_shift = torch.cat( - [ids_shuffle[:, None], torch.randint(N, (L, 1), device=x.device)], dim=1 - )[:num_noise] + shuffle_idx_shift = torch.cat([ids_shuffle[:, None], torch.randint(N, (L, 1), device=x.device)], dim=1)[ + :num_noise + ] for idx, shift in shuffle_idx_shift: x[:, idx] = x[:, idx].roll(shift.item(), 0) @@ -356,14 +317,16 @@ def forward_encoder( x, shuffle_idx_shift = self.column_shuffle( x, col_shuffle["ratio"], - mask=mask_ratio if type(mask_ratio) == list else None, + mask=mask_ratio if type(mask_ratio) is list else None, ) else: shuffle_idx_shift = None # masking: length -> length * mask_ratio - if type(mask_ratio) != list: + if type(mask_ratio) is not list: x, mask, ids_restore = self.random_masking( # type: ignore - x, mask_ratio, shuffle_idx_shift # type: ignore + x, + mask_ratio, + shuffle_idx_shift, # type: ignore ) else: x, mask, ids_restore = self.custom_masking(x, mask_ratio) @@ -384,13 +347,9 @@ def forward_decoder( """Forward pass for the decoder.""" # embed tokens x = self.decoder_embed(x) - mask_token = self.mask_token.repeat( - x.shape[0], ids_restore.shape[1] - x.shape[1], 1 - ) + mask_token = self.mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1) x_ = torch.cat([x, mask_token], dim=1) - x = torch.gather( - x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2]) - ) # unshuffle + x = torch.gather(x_, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle if shuffle_idx_shift is not None: _, _, d = x.shape @@ -459,10 +418,11 @@ def forward( if x_cat is not None: keys += list(x_cat.keys()) x, mask, ids_restore, shuffle_idx_shift = self.forward_encoder( - x_num, x_cat, mask_ratio, col_shuffle # type: ignore - ) - preds = self.forward_decoder( - x, ids_restore, keys, shuffle_idx_shift, col_shuffle + x_num, + x_cat, + mask_ratio, + col_shuffle, # type: ignore ) + preds = self.forward_decoder(x, ids_restore, keys, shuffle_idx_shift, col_shuffle) loss = self.forward_loss(x_num, x_cat, preds, mask) return loss, preds, mask diff --git a/masked_modelling/tabret/model/tabret_cls.py b/src/masked_modelling/tabret/model/tabret_cls.py similarity index 79% rename from masked_modelling/tabret/model/tabret_cls.py rename to src/masked_modelling/tabret/model/tabret_cls.py index f0cb4fe..95f0511 100644 --- a/masked_modelling/tabret/model/tabret_cls.py +++ b/src/masked_modelling/tabret/model/tabret_cls.py @@ -1,9 +1,9 @@ """TabRetClassifier class.""" + from typing import Dict, List, Optional import torch -import torch.nn as nn -from torch import Tensor +from torch import Tensor, nn from transtab.trainer_utils import get_parameter_names from model.ft_transformer import FeatureTokenizer @@ -30,12 +30,8 @@ def __init__(self, pretrained_tabret: TabRet, output_dim: int) -> None: self.decoder = pretrained_tabret.decoder self.decoder_norm = pretrained_tabret.decoder_norm - self.pos_embed_target = nn.Parameter( - Tensor(1, 1, pretrained_tabret.decoder_embed_dim) - ) - pretrained_tabret.initialization_.apply( - self.pos_embed_target, pretrained_tabret.decoder_embed_dim - ) + self.pos_embed_target = nn.Parameter(Tensor(1, 1, pretrained_tabret.decoder_embed_dim)) + pretrained_tabret.initialization_.apply(self.pos_embed_target, pretrained_tabret.decoder_embed_dim) self.classifier = nn.Linear(pretrained_tabret.decoder_embed_dim, output_dim) if output_dim > 1: @@ -45,22 +41,12 @@ def __init__(self, pretrained_tabret: TabRet, output_dim: int) -> None: def optimization_param_groups(self) -> List[Dict[str, Tensor]]: """Return the optimization parameter groups.""" - non_decay_parameters = get_parameter_names( - self, [nn.LayerNorm, FeatureTokenizer] - ) - decay_parameters = [ - name for name in non_decay_parameters if ".bias" not in name - ] + non_decay_parameters = get_parameter_names(self, [nn.LayerNorm, FeatureTokenizer]) + decay_parameters = [name for name in non_decay_parameters if ".bias" not in name] return [ + {"params": [p for n, p in self.named_parameters() if n in decay_parameters]}, { - "params": [ - p for n, p in self.named_parameters() if n in decay_parameters - ] - }, - { - "params": [ - p for n, p in self.named_parameters() if n not in decay_parameters - ], + "params": [p for n, p in self.named_parameters() if n not in decay_parameters], "weight_decay": 0.0, }, ] diff --git a/masked_modelling/tabret/model/utils.py b/src/masked_modelling/tabret/model/utils.py similarity index 83% rename from masked_modelling/tabret/model/utils.py rename to src/masked_modelling/tabret/model/utils.py index 87b0958..a3dc129 100644 --- a/masked_modelling/tabret/model/utils.py +++ b/src/masked_modelling/tabret/model/utils.py @@ -1,4 +1,5 @@ """Tabret model utils.""" + from typing import Dict, List, Tuple from torch import nn @@ -29,13 +30,9 @@ def get_diff_columns( pre_cat_cardinality_dict: Dict[str, int], ) -> Tuple[List[str], List[str], Dict[str, int]]: """Find the difference between the pretraining and finetuning columns.""" - continuous_columns = [ - col for col in continuous_columns if col not in pre_continuous_columns - ] + continuous_columns = [col for col in continuous_columns if col not in pre_continuous_columns] cat_cardinality_dict = { - col: item - for col, item in cat_cardinality_dict.items() - if col not in pre_cat_cardinality_dict + col: item for col, item in cat_cardinality_dict.items() if col not in pre_cat_cardinality_dict } diff_columns = continuous_columns + list(cat_cardinality_dict.keys()) diff --git a/masked_modelling/tabret/tabret.ipynb b/src/masked_modelling/tabret/tabret.ipynb similarity index 97% rename from masked_modelling/tabret/tabret.ipynb rename to src/masked_modelling/tabret/tabret.ipynb index 52a6b3a..4d28497 100644 --- a/masked_modelling/tabret/tabret.ipynb +++ b/src/masked_modelling/tabret/tabret.ipynb @@ -44,7 +44,12 @@ "from model.tabret_cls import TabRetClassifier\n", "from model.utils import get_diff_columns\n", "from pl_bolts.optimizers import LinearWarmupCosineAnnealingLR\n", - "from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor, ModelCheckpoint, RichProgressBar\n", + "from pytorch_lightning.callbacks import (\n", + " EarlyStopping,\n", + " LearningRateMonitor,\n", + " ModelCheckpoint,\n", + " RichProgressBar,\n", + ")\n", "from sklearn.metrics import accuracy_score, roc_auc_score\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, QuantileTransformer\n", @@ -100,7 +105,8 @@ " categorical_columns: Optional[Sequence[str]] = None,\n", " target: Optional[Union[str, Sequence[str]]] = None,\n", " ) -> None:\n", - " \"\"\"\n", + " \"\"\"Tabular dataset for tabular data.\n", + "\n", " Args:\n", " ----\n", " data (pandas.DataFrame): DataFrame.\n", @@ -136,7 +142,7 @@ " ----\n", " idx (int): The index of the sample in the dataset.\n", "\n", - " Returns:\n", + " Returns\n", " -------\n", " dict[str, Tensor]:\n", " The returned dict has the keys {\"target\", \"continuous\", \"categorical\"}\n", @@ -546,7 +552,11 @@ "metadata": {}, "outputs": [], "source": [ - "pre_trainer.fit(model=pre_model, train_dataloaders=pre_train_dataloader, val_dataloaders=pre_val_dataloader)" + "pre_trainer.fit(\n", + " model=pre_model,\n", + " train_dataloaders=pre_train_dataloader,\n", + " val_dataloaders=pre_val_dataloader,\n", + ")" ] }, { @@ -564,7 +574,7 @@ "\n", "Stroke prediction is used as a downstram task. \n", "\n", - "Regarding the data tranformation, for features that are common to both datasets, transformation models (like QuantileTransformer for continuous features and OrdinalEncoder for categorical ones) are first fitted using the pre-training dataset and then applied to the downstream dataset. On the other hand, for features unique to the downstream dataset, transformations are directly fitted and applied using that specific dataset. This way, the preprocessing aligns well with the pre-training scheme, allowing for a seamless transition to fine-tuning the model.\n", + "Regarding the data transformation, for features that are common to both datasets, transformation models (like QuantileTransformer for continuous features and OrdinalEncoder for categorical ones) are first fitted using the pre-training dataset and then applied to the downstream dataset. On the other hand, for features unique to the downstream dataset, transformations are directly fitted and applied using that specific dataset. This way, the preprocessing aligns well with the pre-training scheme, allowing for a seamless transition to fine-tuning the model.\n", "\n", "The dataset is divided into 80% training dataset and 20% test dataset. Of the training data, 100 samples are separated as a fine-tuning dataset." ] @@ -968,7 +978,13 @@ " dirpath=join(CHECKPOINT_DIR, \"retokenize\"),\n", " ),\n", " LearningRateMonitor(logging_interval=\"step\"),\n", - " EarlyStopping(monitor=\"val_loss\", min_delta=0.00, patience=ret_patience, verbose=True, mode=\"min\"),\n", + " EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " min_delta=0.00,\n", + " patience=ret_patience,\n", + " verbose=True,\n", + " mode=\"min\",\n", + " ),\n", " RichProgressBar(),\n", " # logger=WandbLogger(\n", " # project=\"tabret-retokenize\",\n", @@ -986,7 +1002,11 @@ "metadata": {}, "outputs": [], "source": [ - "ret_trainer.fit(model=ret_model, train_dataloaders=ret_train_dataloader, val_dataloaders=ret_val_dataloader)" + "ret_trainer.fit(\n", + " model=ret_model,\n", + " train_dataloaders=ret_train_dataloader,\n", + " val_dataloaders=ret_val_dataloader,\n", + ")" ] }, { @@ -1209,7 +1229,13 @@ " dirpath=join(CHECKPOINT_DIR, \"finetune\"),\n", " ),\n", " LearningRateMonitor(logging_interval=\"step\"),\n", - " EarlyStopping(monitor=\"val_loss\", min_delta=0.00, patience=fine_patience, verbose=False, mode=\"min\"),\n", + " EarlyStopping(\n", + " monitor=\"val_loss\",\n", + " min_delta=0.00,\n", + " patience=fine_patience,\n", + " verbose=False,\n", + " mode=\"min\",\n", + " ),\n", " RichProgressBar(),\n", " # logger=WandbLogger(\n", " # project=\"tabret-finetune\",\n", diff --git a/self_distillation/data2vec_vision.ipynb b/src/self_distillation/data2vec_vision.ipynb similarity index 99% rename from self_distillation/data2vec_vision.ipynb rename to src/self_distillation/data2vec_vision.ipynb index 93a04ab..9f67085 100644 --- a/self_distillation/data2vec_vision.ipynb +++ b/src/self_distillation/data2vec_vision.ipynb @@ -467,8 +467,7 @@ " delta = self._mask(mask, max_mask_patches)\n", " if delta == 0:\n", " break\n", - " else:\n", - " mask_count += delta\n", + " mask_count += delta\n", "\n", " return mask\n", "\n", @@ -682,9 +681,9 @@ " if self.max_update_epochs is None:\n", " self.max_update_epochs = trainer.max_epochs\n", "\n", - " assert (\n", - " self.max_update_epochs <= trainer.max_epochs\n", - " ), \"`max_update_epochs` must be less than or equal to max_epochs\"\n", + " assert self.max_update_epochs <= trainer.max_epochs, (\n", + " \"`max_update_epochs` must be less than or equal to max_epochs\"\n", + " )\n", " max_steps = len(trainer.train_dataloader) * self.max_update_epochs\n", " curr_step = pl_module.global_step\n", "\n", @@ -853,7 +852,9 @@ "\n", " if self.hparams.modality == \"vision\": # normalize the output of the last layer\n", " out[\"encoder_out\"] = F.layer_norm(\n", - " out[\"encoder_out\"].float(), out[\"encoder_out\"].shape[-1:], eps=1e-12 # type: ignore\n", + " out[\"encoder_out\"].float(), # type: ignore\n", + " out[\"encoder_out\"].shape[-1:], # type: ignore\n", + " eps=1e-12, # type: ignore\n", " )\n", " return out\n", "\n", diff --git a/uv.lock b/uv.lock new file mode 100644 index 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