diff --git a/.github/actions/validation/action.yml b/.github/actions/validation/action.yml index 288ad93..211b013 100644 --- a/.github/actions/validation/action.yml +++ b/.github/actions/validation/action.yml @@ -3,7 +3,6 @@ description: format, lint, test code runs: using: "composite" steps: - - name: ๐Ÿ“ฆ Install uv uses: astral-sh/setup-uv@v6 @@ -17,7 +16,7 @@ runs: - name: ๐Ÿฆพ ๐Ÿ’… ๐Ÿงช Install and validate extras run: | - extras=("sklearn" "huggingface") + extras=("sklearn" "huggingface" "polars" "pandas") for extra in "${extras[@]}"; do echo "๐Ÿฆพ Installing extra: $extra" diff --git a/Justfile b/Justfile index 1f64860..ef0312f 100644 --- a/Justfile +++ b/Justfile @@ -7,21 +7,34 @@ set quiet default: just --list --unsorted +# -------------------------------------------------- +# Developer Setup + # Synchronize the environment by installing all the dependencies dev-sync: uv sync --cache-dir .uv_cache --all-extras +# Synchronize the environment by installing the specified extra dependency +# Currently used within the CI to install extra dependencies and test them. dev-sync-extra extra: uv sync --cache-dir .uv_cache --extra {{extra}} # Synchronize the environment by installing all the dependencies except the dev ones prod-sync: - uv sync --cache-dir .uv_cache --all-extras --no-dev + uv sync --cache-dir .uv_cache --all-extras --no-default-groups + +# Synchronize the environment by installing the extra dependency +# specified. Doesn't install the dev dependencies. +prod-sync-extra extra: + uv sync --cache-dir .uv_cache --extra {{extra}} --no-default-groups # Install the pre-commit hooks install-hooks: uv run pre-commit install +# -------------------------------------------------- +# Validation + # Run ruff formatting format: uv run ruff format @@ -29,11 +42,28 @@ format: # Run ruff linting and mypy type checking lint: uv run ruff check --fix - uv run mypy --ignore-missing-imports --install-types --non-interactive --package ml3_drift + uv run mypy --ignore-missing-imports --install-types --non-interactive --package ml3_drift --python-version 3.10 + + +# Default value for testWorkers is auto (meaning all workers available) +# If you want to pass a custom value (such as 4): `just testWorkers=4 test` +# We also run ruff on tests files (it's so fast that it's worth it) + +# Little caveat: when running tests with only an extra installed, you'd like +# to avoid having docs dependencies installed (since, for instance, a mkdocs plugin +# requires Pandas, which is one of our extra dependencies). This happens by default +# since docs dependencies are not installed as default dependencies by uv (see pyproject.toml). +# They are only installed when building / serving the documentation. However, if you first +# build the documentation, then run the tests, you will have the docs dependencies installed. +# Should not be a practical problem (especially since in CI environments we don't install docs dependencies), +# but it's worth noting. # Run the tests with pytest +testWorkers := "auto" test: - uv run pytest --verbose --color=yes -n auto --exitfirst tests + uv run ruff format tests + uv run ruff check tests --fix + uv run pytest --verbose --color=yes -n {{testWorkers}} --exitfirst tests # Run linters, formatters and tests validate: format lint test @@ -43,11 +73,12 @@ validate: format lint test # Generate the documentation build-docs: - uv run mkdocs build + # Make sure mkdocs is installed + uv run --group docs mkdocs build # Serve the documentation locally serve-docs: - uv run mkdocs serve + uv run --group docs mkdocs serve # -------------------------------------------------- # Publishing diff --git a/examples/huggingface/text_embedding_monitoring.py b/examples/huggingface/text_embedding_monitoring.py index a54ceba..579175b 100644 --- a/examples/huggingface/text_embedding_monitoring.py +++ b/examples/huggingface/text_embedding_monitoring.py @@ -3,7 +3,10 @@ from ml3_drift.huggingface.drift_detection_pipeline import ( HuggingFaceDriftDetectionPipeline, ) -from ml3_drift.huggingface.univariate.ks import KSDriftDetector +from ml3_drift.monitoring.algorithms.batch.bonferroni import ( + BonferroniCorrectionAlgorithm, +) +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm from ml3_drift.callbacks.base import logger_callback @@ -37,7 +40,8 @@ # to monitor the drift in the embeddings. hf_pipe = HuggingFaceDriftDetectionPipeline( - drift_detector=KSDriftDetector( + drift_detector=BonferroniCorrectionAlgorithm( + algorithm=KSAlgorithm(p_value=0.05), callbacks=[ partial( logger_callback, diff --git a/pyproject.toml b/pyproject.toml index e92f5db..cdb70d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,19 +7,28 @@ dynamic = ["version"] license = { text = "Apache-2.0" } readme = "README.md" -dependencies = [] +dependencies = [ + "scipy>=1.15.3", +] # ------------------------------------------------- # Extra dependencies. This package is designed to be -# used within one extra at a time, hence we check each -# extra separately. Remember to update the list of extras -# in the validation action to ensure tests are run +# used with different libraries, which means that our code +# should work only when not all extras are installed. +# Remember to update the list of extras +# in the validation CICD to ensure tests are run # for your new extra [project.optional-dependencies] sklearn = ["scikit-learn>=1.6.1"] -huggingface = ["scipy>=1.15.2", "transformers[torch]>=4.52.3"] +huggingface = ["transformers[torch]>=4.52.3"] + +polars = ["polars>=1.31.0"] + +pandas = ["pandas>=2.2.3"] + +river = ["river>=0.22.0"] # ------------------------------------------------- @@ -28,12 +37,19 @@ huggingface = ["scipy>=1.15.2", "transformers[torch]>=4.52.3"] dev = [ "ipykernel>=6.29.5", "mypy>=1.15.0", - "pillow>=11.2.1", # for image support in tests "pre-commit>=4.1.0", "pytest>=8.3.4", "pytest-xdist>=3.6.1", "ruff>=0.9.5", - # for docs + # for image support in tests + "pillow>=11.2.1", +] + +docs = [ + "mkdocs-minify-plugin>=0.7.1", + "mkdocs-glightbox>=0.3.4", + "mkdocs-table-reader-plugin>=2.0.1", + "mkdocs-macros-plugin", "mkdocs>=1.5.0", "mkdocs-material>=9.5.0", "mkdocs-material-extensions>=1.1", @@ -41,18 +57,25 @@ dev = [ "pymdown-extensions>=9.9.1", "jinja2>=3.0", "markdown>=3.2", - "mkdocs-minify-plugin>=0.7.1", - "mkdocs-glightbox>=0.3.4", - "mkdocs-table-reader-plugin>=2.0.1", - "mkdocs-macros-plugin", "openpyxl", ] # ------------------------------------------------- +# Default groups for uv +[tool.uv] +default-groups = ["dev"] + +# ------------------------------------------------- + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" [tool.hatch.version] path = "src/ml3_drift/__init__.py" + + +# Set pytest folder +[tool.pytest.ini_options] +testpaths = ["tests"] diff --git a/ruff.toml b/ruff.toml index 0c95232..0162fc8 100644 --- a/ruff.toml +++ b/ruff.toml @@ -33,9 +33,6 @@ exclude = [ line-length = 88 indent-width = 4 -# Assume Python 3.9 -target-version = "py39" - [lint] # Enable Pyflakes (`F`) and a subset of the pycodestyle (`E`) codes by default. # Unlike Flake8, Ruff doesn't enable pycodestyle warnings (`W`) or diff --git a/src/ml3_drift/huggingface/univariate/__init__.py b/src/ml3_drift/analysis/__init__.py similarity index 100% rename from src/ml3_drift/huggingface/univariate/__init__.py rename to src/ml3_drift/analysis/__init__.py diff --git a/src/ml3_drift/sklearn/univariate/__init__.py b/src/ml3_drift/analysis/analyzer/__init__.py similarity index 100% rename from src/ml3_drift/sklearn/univariate/__init__.py rename to src/ml3_drift/analysis/analyzer/__init__.py diff --git a/src/ml3_drift/analysis/analyzer/base.py b/src/ml3_drift/analysis/analyzer/base.py new file mode 100644 index 0000000..70a16bf --- /dev/null +++ b/src/ml3_drift/analysis/analyzer/base.py @@ -0,0 +1,184 @@ +from abc import ABC, abstractmethod +import numpy as np +from typing import TYPE_CHECKING, Union +from typing_extensions import TypeIs + +from ml3_drift.analysis.report import Report +from ml3_drift.monitoring.algorithms.batch.bonferroni import ( + BonferroniCorrectionAlgorithm, +) +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm +from ml3_drift.monitoring.algorithms.batch.chi_square import ( + ChiSquareAlgorithm, +) +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + +if TYPE_CHECKING: + import pandas as pd + import polars as pl + +POLARS = True +try: + import polars as pl +except ModuleNotFoundError: + POLARS = False + + +PANDAS = True +try: + import pandas as pd +except ModuleNotFoundError: + PANDAS = False + + +class DataDriftAnalyzer(ABC): + """ + Analyze a dataset identifying the sequence of distributions due to data drifts. + + Parameters + ---------- + continuous_monitoring_algorithm: MonitoringAlgorithm | None + Algorithm used to monitor continuous data. If None, a default algorithm is used. + categorical_monitoring_algorithm: MonitoringAlgorithm | None + Algorithm used to monitor categorical data. If None, a default algorithm is used. + """ + + def __init__( + self, + continuous_monitoring_algorithm: MonitoringAlgorithm | None = None, + categorical_monitoring_algorithm: MonitoringAlgorithm | None = None, + ): + # We use default algorithms if None is provided. + if continuous_monitoring_algorithm is None: + continuous_monitoring_algorithm = BonferroniCorrectionAlgorithm( + algorithm=KSAlgorithm(), + ) + if categorical_monitoring_algorithm is None: + categorical_monitoring_algorithm = BonferroniCorrectionAlgorithm( + algorithm=ChiSquareAlgorithm(), + ) + + self.continuous_monitoring_algorithm = continuous_monitoring_algorithm + self.categorical_monitoring_algorithm = categorical_monitoring_algorithm + + def _is_list_str(self, columns: list[str] | list[int]) -> TypeIs[list[str]]: + """Verify if the input variable is a list of str in any element""" + + return all(isinstance(elem, str) for elem in columns) + + def _to_index( + self, + X: Union[np.ndarray, "pd.DataFrame", "pl.DataFrame"], + columns: list[str] | list[int] | None, + ) -> list[int]: + """Translate the list of columns in list of indices. + + If columns is None then all the indexes are returned. + If columns is list[int] then it is directly returned. + If columns is list[str] then the indexes are retrieved from column names, + in this case X must be a DataFrame.""" + + if columns is None: + return list(range(X.shape[0])) + + if self._is_list_str(columns): + if POLARS and isinstance(X, pl.DataFrame): + return [i for (i, c) in enumerate(X.columns) if c in columns] + elif PANDAS and isinstance(X, pd.DataFrame): + return [i for (i, c) in enumerate(X.columns) if c in columns] + else: + raise ValueError( + f"Type not valid, expecting polars DataFrame or pandas DataFrame when columns has string values. Got {type(X)}" + ) + return columns + + def _to_numpy( + self, X: Union[np.ndarray, "pd.DataFrame", "pl.DataFrame"] + ) -> np.ndarray: + """Transform input data into numpy array""" + + if POLARS and isinstance(X, pl.DataFrame): + return X.to_numpy() + elif PANDAS and isinstance(X, pd.DataFrame): + return X.to_numpy() + elif isinstance(X, np.ndarray): + return X + else: + raise ValueError( + f"Type not valid, expecting numpy array, polars DataFrame or pandas DataFrame. Got {type(X)}" + ) + + @abstractmethod + def _scan_data( + self, + X: np.ndarray, + y: np.ndarray | None, + continuous_columns_ids: list[int], + categorical_columns_ids: list[int], + y_categorical: bool, + ) -> Report: + """Scan the data to identify different data partitions according to monitoring algorithm.""" + + def analyze( + self, + X: Union[np.ndarray, "pd.DataFrame", "pl.DataFrame"], + y: Union[None, np.ndarray, "pd.DataFrame", "pl.DataFrame"], + continuous_columns: list[str] | list[int] | None, + categorical_columns: list[str] | list[int] | None, + y_categorical: bool, + ) -> Report: + """Analyze the data to split them into different distribution according to drift detectors. + + If target is provided then concept drift is used as split criterion, otherwise, it uses input drift. + + Parameters + ---------- + X: input data. Can be numpy array, pandas dataframe or polars dataframe + y: target data. It is optional and can be numpy array, pandas dataframe or polars dataframe + continuous_columns: if not None it is the indices or names of the columns that are continuous + categorical_columns: if not None it is the indices or names of the columns that are categorical + y_categorical: if True, then the target is categorical, otherwise it is considered as continuous + + Output + ------ + Report object containing information about identified data groups + """ + # Shape check + if y is not None and X.shape[0] != y.shape[0]: + raise ValueError( + f"When target y is not None it must have the same rows of input X. Got X: {X.shape} and y: {y.shape}" + ) + + # Continuous and categorical columns to canonical form + if continuous_columns is not None: + continuous_columns_ids = self._to_index(X, continuous_columns) + else: + continuous_columns_ids = [] + + if categorical_columns is not None: + categorical_columns_ids = self._to_index(X, categorical_columns) + else: + categorical_columns_ids = [] + + if not continuous_columns_ids and not categorical_columns_ids: + raise ValueError( + "At least one of continuous_columns or categorical_columns must be provided." + ) + # Input and target in canonical form + array_X = self._to_numpy(X) + + if y is not None: + array_y = self._to_numpy(y) + else: + array_y = None + + # Data analysis + report = self._scan_data( + array_X, + array_y, + continuous_columns_ids, + categorical_columns_ids, + y_categorical, + ) + + return report diff --git a/src/ml3_drift/analysis/analyzer/batch.py b/src/ml3_drift/analysis/analyzer/batch.py new file mode 100644 index 0000000..a85d88d --- /dev/null +++ b/src/ml3_drift/analysis/analyzer/batch.py @@ -0,0 +1,196 @@ +from collections import defaultdict +from copy import deepcopy +import numpy as np + +from ml3_drift.analysis.analyzer.base import DataDriftAnalyzer +from ml3_drift.analysis.report import Report +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + +from ml3_drift.models.monitoring import ( + MonitoringOutput, +) + + +class BatchDataDriftAnalyzer(DataDriftAnalyzer): + """Batch data drift analyzer splits data into mini batches of size `batch_size` + and through drift detection merges them into macro batch representing data that + belong to the same distribution. + + Data can belong to the same distribution even if they are not contiguous. + + Parameters + ---------- + continuous_monitoring_algorithm: MonitoringAlgorithm | None + Algorithm used to monitor continuous data. If None, a default algorithm is used. + categorical_monitoring_algorithm: MonitoringAlgorithm | None + Algorithm used to monitor categorical data. If None, a default algorithm is used. + batch_size: initial batch dimensions and also used as comparison_window_size + """ + + def __init__( + self, + continuous_monitoring_algorithm: MonitoringAlgorithm | None = None, + categorical_monitoring_algorithm: MonitoringAlgorithm | None = None, + batch_size: int = 100, + ): + super().__init__( + continuous_monitoring_algorithm=continuous_monitoring_algorithm, + categorical_monitoring_algorithm=categorical_monitoring_algorithm, + ) + + self.batch_size = batch_size + + def _prepare_data( + self, + X: np.ndarray, + y: np.ndarray | None, + continuous_columns_ids: list[int], + categorical_columns_ids: list[int], + y_categorical: bool, + batch_indexes: tuple[int, int], + ) -> tuple[np.ndarray, np.ndarray]: + continuous_data = X[batch_indexes[0] : batch_indexes[1], continuous_columns_ids] + categorical_data = X[ + batch_indexes[0] : batch_indexes[1], categorical_columns_ids + ] + if y is not None: + if y_categorical: + categorical_data = np.hstack( + [categorical_data, y[batch_indexes[0] : batch_indexes[1]]] + ) + else: + continuous_data = np.hstack( + [continuous_data, y[batch_indexes[0] : batch_indexes[1]]] + ) + + return continuous_data, categorical_data + + def _single_scan_data( + self, + X: np.ndarray, + y: np.ndarray | None, + continuous_columns_ids: list[int], + categorical_columns_ids: list[int], + y_categorical: bool, + first_batch_indexes: tuple[int, int], + second_batch_indexes: tuple[int, int], + ) -> tuple[MonitoringOutput | None, MonitoringOutput | None]: + """ + Inner helper method that performs a single scan of two batches + """ + + first_batch_cont, first_batch_cat = self._prepare_data( + X, + y, + continuous_columns_ids, + categorical_columns_ids, + y_categorical, + first_batch_indexes, + ) + second_batch_cont, second_batch_cat = self._prepare_data( + X, + y, + continuous_columns_ids, + categorical_columns_ids, + y_categorical, + second_batch_indexes, + ) + if len(continuous_columns_ids) > 0: + cont_algorithm = deepcopy(self.continuous_monitoring_algorithm).fit( + first_batch_cont + ) + cont_output = cont_algorithm.detect(second_batch_cont)[0] + else: + cont_output = None + if len(categorical_columns_ids) > 0: + cat_algorithm = deepcopy(self.categorical_monitoring_algorithm).fit( + first_batch_cat + ) + cat_output = cat_algorithm.detect(second_batch_cat)[0] + else: + cat_output = None + + return cont_output, cat_output + + def _scan_data( + self, + X: np.ndarray, + y: np.ndarray | None, + continuous_columns_ids: list[int], + categorical_columns_ids: list[int], + y_categorical: bool, + ) -> Report: + """Scan the data to identify different data partitions according to monitoring algorithm. + + - Step 0: split data into separate batches of size `batch_size` + - Step 1: For each adjacent batch perform drift detection + - Step 2: Merge batches that belong to the same distribution + - Step 3: For each non-adjacent merged group perform drift detection + - Step 4: Assign label merging groups that belong to the same distribution + + Step 3 and 4 are important because they identify groups in different time periods that belong + to the same distribution. + """ + # Step 0: compute batch indexes + batch_indexes = [ + (i, i + self.batch_size) for i in range(0, X.shape[0], self.batch_size) + ] + + # Step 1 and 2: drift detection for adjacent batches and merge into batches + merged_batches = [] + current_batch_start = 0 + for batch_id in range(len(batch_indexes) - 1): + current_batch_indexes = batch_indexes[batch_id] + next_batch_indexes = batch_indexes[batch_id + 1] + + cont_output, cat_output = self._single_scan_data( + X, + y, + continuous_columns_ids, + categorical_columns_ids, + y_categorical, + current_batch_indexes, + next_batch_indexes, + ) + + if (cont_output is not None and cont_output.drift_detected) | ( + cat_output is not None and cat_output.drift_detected + ): + # if a drift is detected then, we close the current batch and open a new one + merged_batches.append( + (current_batch_start, current_batch_indexes[1] - 1) + ) + current_batch_start = next_batch_indexes[0] + + # analysis is terminated, we add the last batch + merged_batches.append((current_batch_start, batch_indexes[-1][1] - 1)) + + # Step 3: non adjacent drift detection + non_adjacent_pairs = [ + (i, j) + for i in range(len(merged_batches)) + for j in range(i + 2, len(merged_batches)) + ] + same_distributions = defaultdict(list) + + # TODO: parallelize this loop by getting a N_JOBS parameter in the method / class constructor + for pair in non_adjacent_pairs: + cont_output, cat_output = self._single_scan_data( + X, + y, + continuous_columns_ids, + categorical_columns_ids, + y_categorical, + merged_batches[pair[0]], + merged_batches[pair[1]], + ) + + # if no drift is detected the two batches are considered to belong to the same distribution + # and are added to the same distribution list + if not ( + (cont_output is not None and cont_output.drift_detected) + | (cat_output is not None and cat_output.drift_detected) + ): + same_distributions[pair[0]].append(pair[1]) + + return Report(concepts=merged_batches, same_distributions=same_distributions) diff --git a/src/ml3_drift/analysis/analyzer/stream.py b/src/ml3_drift/analysis/analyzer/stream.py new file mode 100644 index 0000000..235f5b5 --- /dev/null +++ b/src/ml3_drift/analysis/analyzer/stream.py @@ -0,0 +1,128 @@ +import numpy as np + +from ml3_drift.analysis.analyzer.base import DataDriftAnalyzer +from ml3_drift.analysis.report import Report +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + + +class StreamDataDriftAnalyzer(DataDriftAnalyzer): + """Stream data drift analyzer runs the monitoring algorithm over the entire + dataset following a sliding window approach where `reference_size` are compared to + `comparison_window_size` data. + + When a drift is detected, a new data split is created. + + Parameters + ---------- + continuous_ma_builder: closure function that accepts int parameter as `comparison_window_size` + and returns an instance of a MonitoringAlgorithm + categorical_ma_builder: closure function that accepts int parameter as `comparison_window_size` + and returns an instance of a MonitoringAlgorithm + reference_size: number of samples used to initialize monitoring algorithm + comparison_window_size: number of samples used in the sliding window + """ + + def __init__( + self, + continuous_monitoring_algorithm: MonitoringAlgorithm | None = None, + categorical_monitoring_algorithm: MonitoringAlgorithm | None = None, + reference_size: int = 100, + comparison_window_size: int = 100, + ): + super().__init__( + continuous_monitoring_algorithm=continuous_monitoring_algorithm, + categorical_monitoring_algorithm=categorical_monitoring_algorithm, + ) + + self.reference_size = reference_size + self.comparison_window_size = comparison_window_size + + def _scan_data( + self, + X: np.ndarray, + y: np.ndarray | None, + continuous_columns_ids: list[int], + categorical_columns_ids: list[int], + y_categorical: bool, + ) -> Report: + """Scan the data to identify different data partitions according to monitoring algorithm. + + First, we build categorical and continuous data combining input and target. + Then, we initialize monitoring algorithms with the first data. + After that, we iterate over the remaining data samples updating the monitoring algorithm. + If a drift is detected and there are enough data to reset the algorithm, we reset them and + continue the analysis, otherwise we terminate it. + """ + concepts = [] + + # Continuous and categorical data creation + continuous_data = X[:, continuous_columns_ids] + categorical_data = X[:, categorical_columns_ids] + if y is not None: + if y_categorical: + categorical_data = np.hstack([categorical_data, y]) + else: + continuous_data = np.hstack([continuous_data, y]) + + if X.shape[0] < self.reference_size: + raise ValueError( + f"Data must have at least {self.reference_size}. Got {X.shape[0]}" + ) + + # Algorithm initialization + continuous_ma = self.continuous_monitoring_algorithm.fit( + continuous_data[: self.reference_size, :] + ) + + categorical_ma = self.categorical_monitoring_algorithm.fit( + categorical_data[: self.reference_size, :] + ) + + # actual data scan + concept_start = 0 + row_id = self.reference_size + available_data = (X.shape[0] - 1) > row_id + + while available_data: + continuous_output = continuous_ma.detect( + continuous_data[row_id : row_id + 1, :] + )[0] + categorical_output = categorical_ma.detect( + categorical_data[row_id : row_id + 1, :] + )[0] + + remaining_data = X.shape[0] - 1 - row_id + + if continuous_output.drift_detected | categorical_output.drift_detected: + concepts.append((concept_start, row_id)) + concept_start = row_id + 1 + # reset monitoring algorithm with past comparison_window_size data and newest one + + if remaining_data >= self.reference_size: + continuous_ma.fit( + continuous_data[ + row_id : row_id + self.reference_size, + :, + ] + ) + categorical_ma.fit( + categorical_data[ + row_id : row_id + self.reference_size, + :, + ] + ) + row_id = row_id + self.reference_size + else: + # concepts.append((row_id + 1, row_id + 1 + remaining_data)) + available_data = False + else: + row_id += 1 + available_data = remaining_data > 0 + + # If no drift is detected we have only one concept + if len(concepts) == 0: + concepts = [(concept_start, X.shape[0])] + else: + concepts.append((concepts[-1][1], X.shape[0])) + + return Report(concepts, {}) diff --git a/src/ml3_drift/analysis/report.py b/src/ml3_drift/analysis/report.py new file mode 100644 index 0000000..6c0995f --- /dev/null +++ b/src/ml3_drift/analysis/report.py @@ -0,0 +1,6 @@ +class Report: + def __init__( + self, concepts: list[tuple[int, int]], same_distributions: dict[int, list[int]] + ): + self.concepts = concepts + self.same_distributions = same_distributions diff --git a/src/ml3_drift/callbacks/base.py b/src/ml3_drift/callbacks/base.py index 0055193..9c587f4 100644 --- a/src/ml3_drift/callbacks/base.py +++ b/src/ml3_drift/callbacks/base.py @@ -18,6 +18,10 @@ def logger_callback( callback = partial(logger_callback, logger=logging.getLogger("drift_callback"), level=logging.INFO) """ + + if drift_info is None: + logger.log(level, "Drift Detected!") + return msg = f"Drift detected on feature at index {drift_info.feature_index} by drift detector {drift_info.drift_detector}." if drift_info.p_value is not None: diff --git a/src/ml3_drift/enums/__init__.py b/src/ml3_drift/enums/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml3_drift/enums/monitoring.py b/src/ml3_drift/enums/monitoring.py new file mode 100644 index 0000000..9a8c042 --- /dev/null +++ b/src/ml3_drift/enums/monitoring.py @@ -0,0 +1,18 @@ +from enum import Enum + + +class DataDimension(Enum): + UNIVARIATE = "univariate" + MULTIVARIATE = "multivariate" + + +class DataType(Enum): + CONTINUOUS = "continuous" + DISCRETE = "discrete" + MIX = "mix" + + +class MonitoringType(Enum): + OFFLINE = "offline" + ONLINE = "online" + ONLINE_ERROR_BASED = "online_error_based" diff --git a/src/ml3_drift/exceptions/__init__.py b/src/ml3_drift/exceptions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml3_drift/exceptions/monitoring.py b/src/ml3_drift/exceptions/monitoring.py new file mode 100644 index 0000000..4d94464 --- /dev/null +++ b/src/ml3_drift/exceptions/monitoring.py @@ -0,0 +1,3 @@ +class NotFittedError(Exception): + """Exception raised when detect(X) is called before fit(X) resulting + in using the monitoring algorithm before it is ready.""" diff --git a/src/ml3_drift/huggingface/base.py b/src/ml3_drift/huggingface/base.py deleted file mode 100644 index e603850..0000000 --- a/src/ml3_drift/huggingface/base.py +++ /dev/null @@ -1,64 +0,0 @@ -from abc import ABC, abstractmethod -from collections.abc import Callable - -import numpy as np - -from ml3_drift.callbacks.models import DriftInfo - - -class BaseDriftDetector(ABC): - """ - Base class for drift detectors. - """ - - def __init__(self, callbacks: list[Callable[[DriftInfo], None]] | None = None): - """ - Initialize the drift detector. - This method can be overridden in child classes to set up any necessary parameters. - """ - - self.is_fitted = False - if callbacks is not None: - self.callbacks: list[Callable[[DriftInfo], None]] = callbacks - else: - self.callbacks = [] - - def fit(self, X: np.ndarray) -> None: - """ - Fit the drift detector on the reference data. - - Args: - X (np.ndarray): Reference data to fit the drift detector on. - """ - self._fit(X) - self.is_fitted = True - - def detect(self, X: np.ndarray) -> None: - """ - Detect drift in the provided data. - - Args: - X (np.ndarray): Data to check for drift. - - Returns: - np.ndarray: The input data, unchanged. - """ - if not self.is_fitted: - raise RuntimeError("Drift detector must be fitted before detecting drift.") - - if drift_info_list := self._detect(X): - for drift_info in drift_info_list: - for callback in self.callbacks: - callback(drift_info) - - @abstractmethod - def _fit(self, X: np.ndarray) -> None: - """ - Core fit method that should be implemented in child classes - """ - - @abstractmethod - def _detect(self, X: np.ndarray) -> None: - """ - Core method for detecting drift that should be implemented in child classes. - """ diff --git a/src/ml3_drift/huggingface/drift_detection_pipeline.py b/src/ml3_drift/huggingface/drift_detection_pipeline.py index 68e8457..62538dc 100644 --- a/src/ml3_drift/huggingface/drift_detection_pipeline.py +++ b/src/ml3_drift/huggingface/drift_detection_pipeline.py @@ -2,11 +2,11 @@ import torch from transformers import Pipeline, pipeline -from ml3_drift.huggingface.base import BaseDriftDetector +from ml3_drift.monitoring.base.base import MonitoringAlgorithm class HuggingFaceDriftDetectionPipeline: - def __init__(self, drift_detector: BaseDriftDetector, **kwargs): + def __init__(self, drift_detector: MonitoringAlgorithm, **kwargs): # noqa: F821 """ Init """ @@ -111,12 +111,12 @@ def _to_numpy(self, data) -> np.ndarray: return data case 3: # Take mean over the second-to-last dimension - return data.mean(axis=1).reshape(1, -1) + return data.mean(axis=1).reshape(-1, 1) case 4: # Take mean over the second-to-last dimension and reshape - # so that each sample is a row and each feature is a column - return np.mean(data, axis=2).reshape(data.shape[0], -1) + # so that each sample is a column vector + return np.mean(data, axis=2).reshape(-1, data.shape[0]) case _: raise ValueError( "Shape mismatch detected: expected data to have 3 or 4 dimensions, " diff --git a/src/ml3_drift/huggingface/univariate/ks.py b/src/ml3_drift/huggingface/univariate/ks.py deleted file mode 100644 index d2e1771..0000000 --- a/src/ml3_drift/huggingface/univariate/ks.py +++ /dev/null @@ -1,60 +0,0 @@ -from collections.abc import Callable -from logging import getLogger -from scipy import stats -from ml3_drift.callbacks.models import DriftInfo -from ml3_drift.huggingface.base import BaseDriftDetector - - -logger = getLogger(__name__) - - -class KSDriftDetector(BaseDriftDetector): - """ - Drift detector based on the Kolmogorov-Smirnov test. - """ - - def __init__( - self, - p_value: float = 0.005, - callbacks: list[Callable[[DriftInfo], None]] | None = None, - ): - """ - Parameters - ---------- - p_value: float - p-value threshold for detecting drift. Default is 0.005. - """ - super().__init__(callbacks=callbacks) - self.p_value = p_value - - def _fit(self, X): - """ - Fit method. Saves reference data. - """ - self.X_ref_ = X - - def _detect(self, X): - """ - Core method for detecting drift - """ - - # list containing drift info events - drift_info_list = [] - - for i in range(X.shape[1]): - _, p_value = stats.ks_2samp( - self.X_ref_[:, i], - X[:, i], - ) - - if p_value < self.p_value: - drift_info_list.append( - DriftInfo( - feature_index=i, - drift_detector=self.__class__.__name__, - p_value=p_value, - threshold=self.p_value, - ) - ) - - return drift_info_list diff --git a/src/ml3_drift/models/__init__.py b/src/ml3_drift/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml3_drift/models/monitoring.py b/src/ml3_drift/models/monitoring.py new file mode 100644 index 0000000..bcdf373 --- /dev/null +++ b/src/ml3_drift/models/monitoring.py @@ -0,0 +1,38 @@ +from __future__ import annotations + +from dataclasses import dataclass + +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType + + +@dataclass(frozen=True, kw_only=True) +class MonitoringAlgorithmSpecs: + """Data class containing the specification of monitoring algorithms + that helps to define a taxonomy over them. + """ + + data_dimension: DataDimension + data_type: DataType + monitoring_type: MonitoringType + + +@dataclass(frozen=True, kw_only=True) +class MonitoringOutput: + """Data class containing the output of the monitoring algorithm + after one update step over a sample. + + If drift is detected then a drift info data model is present with + additional information about the drift""" + + drift_detected: bool + drift_info: DriftInfo | None + + +@dataclass(frozen=True, kw_only=True) +class DriftInfo: + """Data class containing information of the detected drift. + It is generated by the drift detector. + """ + + test_statistic: float | None = None + statistic_threshold: float | None = None diff --git a/src/ml3_drift/monitoring/__init__.py b/src/ml3_drift/monitoring/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/ml3_drift/monitoring/algorithms/batch/bonferroni.py b/src/ml3_drift/monitoring/algorithms/batch/bonferroni.py new file mode 100644 index 0000000..c79f658 --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/batch/bonferroni.py @@ -0,0 +1,101 @@ +from copy import deepcopy +from typing import Callable, TypeVar + +from numpy import ndarray +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_multivariate import MultivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.batch_monitoring_algorithm import ( + BatchMonitoringAlgorithm, +) + +T = TypeVar("T", bound=UnivariateMonitoringAlgorithm) + + +class BonferroniCorrectionAlgorithm( + BatchMonitoringAlgorithm, MultivariateMonitoringAlgorithm +): + """ + Extension of p-value based univariate algorithms with Bonferroni correction + to handle multivariate data + + Parameters + ---------- + algorithm: T (UnivariateMonitoringAlgorithm) + The univariate monitoring algorithm to be used for each dimension. + p_value: float, default=0.005 + The p-value threshold for detecting drift, will be adjusted using Bonferroni correction. + callbacks: list[Callable[[DriftInfo | None], None]] | None, optional + A list of callback functions that are called when a drift is detected. + Each callback receives a DriftInfo object containing information about the detected drift. + If not provided, no callbacks are used. The current type hint also includes + the case where drift_info is None (which happens for only some algorithms). This + will change in the future as it's not very useful to have a callback that + receives None as input. + """ + + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.MULTIVARIATE, + data_type=DataType.MIX, + monitoring_type=MonitoringType.OFFLINE, + ) + + def __init__( + self, + algorithm: T, + p_value: float = 0.005, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + super().__init__(comparison_size=None, callbacks=callbacks) + self.p_value = p_value + self.base_algorithm = algorithm + + # post fit attributes + self.dims = 0 + self.algorithms: list[T] = [] + + def _reset_internal_parameters(self): + self.algorithms = [] + self.dims = 0 + + def _fit(self, X: ndarray): + self.dims = X.shape[1] + for i in range(self.dims): + # Deepcopy the base algorithm to ensure each instance is independent + # and can be fitted to its own data slice. + algorithm = deepcopy(self.base_algorithm) + + # Check that the algorithm has a setter for p_value + # and set it accordingly + if ( + not hasattr(algorithm, "p_value") + or not self.base_algorithm.__class__.p_value.fset is not None # type: ignore + ): + raise ValueError( + f"Algorithm {algorithm.__class__.__name__} does not have a 'p_value' attribute." + ) + + algorithm.p_value = self.p_value / self.dims # Bonferroni correction + + algorithm.fit(X[:, i : i + 1]) + self.algorithms.append(algorithm) + + def _detect(self) -> MonitoringOutput: + drift_detected = False + for i, algorithm in enumerate(self.algorithms): + # Update the comparison data for the algorithm and call inner _detect method + algorithm.comparison_data = self.comparison_data[:, i : i + 1] + output = algorithm._detect() + if output.drift_detected: + drift_detected = True + + # Currently we output a drift_detected = True if any of the algorithms detected drift. + # However the drift information is not aggregated. This neds to be improved + return MonitoringOutput(drift_detected=drift_detected, drift_info=None) diff --git a/src/ml3_drift/monitoring/algorithms/batch/chi_square.py b/src/ml3_drift/monitoring/algorithms/batch/chi_square.py new file mode 100644 index 0000000..9b342c7 --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/batch/chi_square.py @@ -0,0 +1,108 @@ +import numpy as np +from typing import Callable + +from scipy import stats +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.batch_monitoring_algorithm import ( + BatchMonitoringAlgorithm, +) + + +class ChiSquareAlgorithm(BatchMonitoringAlgorithm, UnivariateMonitoringAlgorithm): + """Monitoring algorithm based on the Chi Square statistic test. + + Parameters + ---------- + p_value: float + p-value threshold for detecting drift. Default is 0.005. + callbacks: list[Callable[[DriftInfo | None], None]] | None, optional + A list of callback functions that are called when a drift is detected. + Each callback receives a DriftInfo object containing information about the detected drift. + If not provided, no callbacks are used. The current type hint also includes + the case where drift_info is None (which happens for only some algorithms). This + will change in the future as it's not very useful to have a callback that + receives None as input. + """ + + def __repr__(self): + return f"ChiSquareAlgorithm({self.p_value})" + + def __str__(self): + return f"ChiSquareAlgorithm({self.p_value})" + + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.UNIVARIATE, + data_type=DataType.DISCRETE, + monitoring_type=MonitoringType.OFFLINE, + ) + + def __init__( + self, + p_value: float = 0.005, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + super().__init__(comparison_size=None, callbacks=callbacks) + self._p_value = p_value + + # post fit attributes + self.reference_counts: dict[str | int, int] = {} + self.categories: list[str | int] = [] + + @property + def p_value(self) -> float: + """Get the p-value threshold for detecting drift.""" + return self._p_value + + @p_value.setter + def p_value(self, value: float): + """Set the p-value threshold for detecting drift""" + if value <= 0 or value >= 1: + raise ValueError("p_value must be in the range (0, 1).") + self._p_value = value + + def _reset_internal_parameters(self): + self.reference_counts = {} + self.categories = [] + + def _fit(self, X: np.ndarray): + """Saves input data without any additional computation""" + self.categories = list(np.unique(X[:, 0])) + self.reference_counts = self._compute_counts(X) + + def _compute_counts(self, X: np.ndarray) -> dict[str | int, int]: + """Compute the frequency for each category in the input data""" + counts = {} + for category in self.categories: + counts[category] = int(np.sum(X[:, 0] == category)) + return counts + + def _detect(self) -> MonitoringOutput: + """Compute the statistic and create the monitoring output object""" + comparison_counts = self._compute_counts(self.comparison_data) + + _, p_value, _, _ = stats.chi2_contingency( + np.column_stack( + ( + [self.reference_counts[category] for category in self.categories], + [comparison_counts[category] for category in self.categories], + ) + ) + ) + p_value = float(p_value) # type: ignore + + drift_detected = p_value < self.p_value + + return MonitoringOutput( + drift_detected=drift_detected, + drift_info=DriftInfo( + test_statistic=p_value, statistic_threshold=self.p_value + ), + ) diff --git a/src/ml3_drift/monitoring/algorithms/batch/ks.py b/src/ml3_drift/monitoring/algorithms/batch/ks.py new file mode 100644 index 0000000..c5abfbb --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/batch/ks.py @@ -0,0 +1,93 @@ +import numpy as np +from typing import Callable + +from scipy import stats +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.batch_monitoring_algorithm import ( + BatchMonitoringAlgorithm, +) + + +class KSAlgorithm(BatchMonitoringAlgorithm, UnivariateMonitoringAlgorithm): + """Monitoring algorithm based on the Kolmogorov-Smirnov statistic test. + + Parameters + ---------- + p_value: float + p-value threshold for detecting drift. Default is 0.005. + callbacks: list[Callable[[DriftInfo | None], None]] | None, optional + A list of callback functions that are called when a drift is detected. + Each callback receives a DriftInfo object containing information about the detected drift. + If not provided, no callbacks are used. The current type hint also includes + the case where drift_info is None (which happens for only some algorithms). This + will change in the future as it's not very useful to have a callback that + receives None as input. + """ + + def __repr__(self): + return f"KSAlgorithm({self.p_value})" + + def __str__(self): + return f"KSAlgorithm({self.p_value})" + + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.UNIVARIATE, + data_type=DataType.CONTINUOUS, + monitoring_type=MonitoringType.OFFLINE, + ) + + def __init__( + self, + p_value: float = 0.005, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + super().__init__( + comparison_size=None, + callbacks=callbacks, + ) + self._p_value = p_value + + # post fit attributes + self.X_ref_: np.ndarray = np.array([]) + + @property + def p_value(self) -> float: + """Get the p-value threshold for detecting drift.""" + return self._p_value + + @p_value.setter + def p_value(self, value: float): + """Set the p-value threshold for detecting drift""" + if value <= 0 or value >= 1: + raise ValueError("p_value must be in the range (0, 1).") + self._p_value = value + + def _reset_internal_parameters(self): + self.X_ref_ = np.array([]) + + def _fit(self, X: np.ndarray): + """Saves input data without any additional computation""" + self.X_ref_ = X + + def _detect(self) -> MonitoringOutput: + """Compute the statistic and create the monitoring output object""" + _, test_p_val = stats.ks_2samp(self.X_ref_, self.comparison_data) + + test_p_val_float = test_p_val.item() + + drift_detected = test_p_val_float < self.p_value + + return MonitoringOutput( + drift_detected=drift_detected, + drift_info=DriftInfo( + test_statistic=test_p_val_float, statistic_threshold=self.p_value + ), + ) diff --git a/src/ml3_drift/monitoring/algorithms/online/adwin.py b/src/ml3_drift/monitoring/algorithms/online/adwin.py new file mode 100644 index 0000000..2b17370 --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/online/adwin.py @@ -0,0 +1,77 @@ +from typing import Callable + +import numpy as np +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.online_monitorning_algorithm import ( + OnlineMonitorningAlgorithm, +) + +RIVER = True +try: + from river.drift.adwin import ADWIN as RiverADWIN +except ModuleNotFoundError: + RIVER = False + + +class ADWIN(OnlineMonitorningAlgorithm, UnivariateMonitoringAlgorithm): + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.MULTIVARIATE, + data_type=DataType.MIX, + monitoring_type=MonitoringType.ONLINE, + ) + + def __init__( + self, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + p_value: float = 0.002, + clock: float = 32, + max_buckets: int = 5, + min_window_length: int = 5, + grace_period: int = 10, + *args, + **kwargs, + ) -> None: + if not RIVER: + raise ModuleNotFoundError( + "River library is required for ADWIN algorithm. Please install it using pip install/ uv add ml3-drift[river]" + ) + self.p_value = p_value + self.clock = clock + self.max_buckets = max_buckets + self.min_window_length = min_window_length + self.grace_period = grace_period + self._args = args + self._kwargs = kwargs + super().__init__( + comparison_size=1, callbacks=callbacks + ) # since we add only one sample per step and river handles building the window internally we set comparison_size to 1 + + def _reset_internal_parameters(self): + self.drift_agent = RiverADWIN( + delta=self.p_value, + clock=self.clock, + max_buckets=self.max_buckets, + min_window_length=self.min_window_length, + grace_period=self.grace_period, + *self._args, + **self._kwargs, + ) + + def _fit(self, X: np.ndarray): + """Fit the KSWIN algorithm to the data.""" + self._validate(X) + self.reset_internal_parameters() + self.is_fitted = True + + def _detect(self): + self.drift_agent.update(self.comparison_data) + drift_detected = self.drift_agent.drift_detected + return MonitoringOutput(drift_detected=drift_detected, drift_info=None) diff --git a/src/ml3_drift/monitoring/algorithms/online/kswin.py b/src/ml3_drift/monitoring/algorithms/online/kswin.py new file mode 100644 index 0000000..4df084e --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/online/kswin.py @@ -0,0 +1,74 @@ +from typing import Callable + +import numpy as np +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.online_monitorning_algorithm import ( + OnlineMonitorningAlgorithm, +) + +RIVER = True +try: + from river.drift.kswin import KSWIN as RiverKSWIN +except ModuleNotFoundError: + RIVER = False + + +class KSWIN(OnlineMonitorningAlgorithm, UnivariateMonitoringAlgorithm): + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.MULTIVARIATE, + data_type=DataType.MIX, + monitoring_type=MonitoringType.ONLINE, + ) + + def __init__( + self, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + p_value: float = 0.00, + window_size: int = 100, + stat_size: int = 30, + seed: int | None = None, + *args, + **kwargs, + ) -> None: + if not RIVER: + raise ModuleNotFoundError( + "River library is required for KSWIN algorithm. Please install it using pip install/ uv add ml3-drift[river]" + ) + self.p_value = p_value + self.window_size = window_size + self.stat_size = stat_size + self.seed = seed + self._args = args + self._kwargs = kwargs + super().__init__( + comparison_size=1, callbacks=callbacks + ) # since we add only one sample per step and river handles building the window internally we set comparison_size to 1 + + def _reset_internal_parameters(self): + self.drift_agent = RiverKSWIN( + alpha=self.p_value, + window_size=self.window_size, + stat_size=self.stat_size, + seed=self.seed, + *self._args, + **self._kwargs, + ) + + def _fit(self, X: np.ndarray): + """Fit the KSWIN algorithm to the data.""" + self._validate(X) + self.reset_internal_parameters() + self.is_fitted = True + + def _detect(self): + self.drift_agent.update(self.comparison_data) + drift_detected = self.drift_agent.drift_detected + return MonitoringOutput(drift_detected=drift_detected, drift_info=None) diff --git a/src/ml3_drift/monitoring/algorithms/online/page_hinkley.py b/src/ml3_drift/monitoring/algorithms/online/page_hinkley.py new file mode 100644 index 0000000..2916f2f --- /dev/null +++ b/src/ml3_drift/monitoring/algorithms/online/page_hinkley.py @@ -0,0 +1,80 @@ +from typing import Callable + +import numpy as np + +from ml3_drift.enums.monitoring import DataDimension, DataType, MonitoringType +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) +from ml3_drift.monitoring.base.base_univariate import UnivariateMonitoringAlgorithm +from ml3_drift.monitoring.base.online_monitorning_algorithm import ( + OnlineMonitorningAlgorithm, +) + +RIVER = True +try: + from river.drift.page_hinkley import PageHinkley as RiverPageHinkley +except ModuleNotFoundError: + RIVER = False + + +class PageHinkley(OnlineMonitorningAlgorithm, UnivariateMonitoringAlgorithm): + @classmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + return MonitoringAlgorithmSpecs( + data_dimension=DataDimension.MULTIVARIATE, + data_type=DataType.MIX, + monitoring_type=MonitoringType.ONLINE, + ) + + def __init__( + self, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + min_instances: int = 30, + delta: float = 0.002, + alpha: float = 0.999, + threshold: int = 50, + mode: str = "both", + seed: int | None = None, + *args, + **kwargs, + ) -> None: + if not RIVER: + raise ModuleNotFoundError( + "River library is required for PageHinkley algorithm. Please install it using pip install/ uv add ml3-drift[river]" + ) + self.min_instances = min_instances + self.delta = delta + self.alpha = alpha + self.threshold = threshold + self.mode = mode + self.seed = seed + self._args = args + self._kwargs = kwargs + super().__init__( + callbacks=callbacks, comparison_size=1 + ) # since we add only one sample per step and river handles building the window internally we set comparison_size to 1 + + def _reset_internal_parameters(self): + self.drift_agent = RiverPageHinkley( + min_instances=self.min_instances, + delta=self.delta, + alpha=self.alpha, + threshold=self.threshold, + mode=self.mode, + *self._args, + **self._kwargs, + ) + + def _fit(self, X: np.ndarray): + """Fit the KSWIN algorithm to the data.""" + self._validate(X) + self.reset_internal_parameters() + self.is_fitted = True + + def _detect(self): + self.drift_agent.update(self.comparison_data) + drift_detected = self.drift_agent.drift_detected + return MonitoringOutput(drift_detected=drift_detected, drift_info=None) diff --git a/src/ml3_drift/monitoring/base/base.py b/src/ml3_drift/monitoring/base/base.py new file mode 100644 index 0000000..a33a89a --- /dev/null +++ b/src/ml3_drift/monitoring/base/base.py @@ -0,0 +1,139 @@ +from abc import ABC, abstractmethod +from typing_extensions import Self +from typing import Callable +import numpy as np + + +from ml3_drift.models.monitoring import ( + DriftInfo, + MonitoringAlgorithmSpecs, + MonitoringOutput, +) + + +class MonitoringAlgorithm(ABC): + """ + A Monitoring Algorithm is statistical method that analyzes sequential data + samples by comparing them against a reference dataset. + Based on the computed statistics, it can identify significant differences and signal a drift. + + This abstract class follows the standard scikit-learn interface, using the fit(X) method + to initialize the algorithm with reference data, and the detect(X) method to analyze new data samples + (serving a similar purpose to predict(X) in scikit-learn classifiers). + + According to the algorithm specifications, the monitoring can be performed in two modes: + - Online: where the algorithm analyzes data samples sequentially, using a sliding window approach. + - Offline: where the algorithm analyzes a batch of data samples at once, comparing them against + the reference dataset. + + The class also support a list of callbacks that are automatically called when a drift is detected. Each callback + receives a DriftInfo object containing information about the detected drift. + + Parameters + ---------- + comparison_size: int | None, optional + Only relevant in online monitoring algorithms. + It defines the size of the sliding window used for comparison. + callbacks: list[Callable[[DriftInfo | None], None]], optional + A list of callback functions that are called when a drift is detected. + Each callback receives a DriftInfo object containing information about the detected drift. + If not provided, no callbacks are used. + """ + + @classmethod + @abstractmethod + def specs(cls) -> MonitoringAlgorithmSpecs: + """ + Abstract property that returns the specifications of the monitoring algorithm. + """ + + def __init__( + self, + comparison_size: int | None = None, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + self.comparison_size = comparison_size + + self.callbacks = callbacks if callbacks is not None else [] + self.has_callbacks = len(self.callbacks) > 0 + + self.is_fitted = False + self.reset_internal_parameters() + + def _is_valid(self, X: np.ndarray) -> tuple[bool, str]: + """Additional validation performed by subclasses. + + If provided data is not valid then False is returned. + """ + return True, "" + + def _validate(self, X: np.ndarray): + """Input data validation calling subclasses _is_valid method. + + If provided data is not valid then ValueError is raised.""" + is_valid, message = self._is_valid(X) + if not is_valid: + raise ValueError(message) + + def reset_internal_parameters(self): + """ + Reset the internal parameters of the monitoring algorithm. + """ + + self.reference_size = 0 + self.data_shape = 0 + self.comparison_data = np.array([]) + + self._reset_internal_parameters() + + @abstractmethod + def _reset_internal_parameters(self): + """ + Abstract method to reset internal parameters of the monitoring algorithm. + This method should be implemented by subclasses to reset any internal state + or parameters that are specific to the algorithm. + """ + + @abstractmethod + def _fit(self, X: np.ndarray): + pass + + def fit(self, X: np.ndarray) -> Self: + """Initialize the monitoring algorithm using as reference the provided data. + + At the end of the fit procedure, it stores the number of reference samples and + the dimension of input data.""" + + self.reset_internal_parameters() + + n_dim = X.ndim + + if n_dim > 2: + raise ValueError(f"Data must be 1D or 2D array. Got {n_dim} dimensions.") + + if n_dim == 1: + X = X.reshape(-1, 1) + + self._validate(X) + + self._fit(X) + + self.is_fitted = True + + self.reference_size, self.data_shape = X.shape + + return self + + @abstractmethod + def _detect(self) -> MonitoringOutput: + pass + + @abstractmethod + def detect(self, X: np.ndarray) -> list[MonitoringOutput]: + """ + Analyze the provided data samples against the reference dataset + (which needs to be set by calling fit(X) first). + + If present, callbacks are called for each drifted sample. + """ + pass diff --git a/src/ml3_drift/monitoring/base/base_multivariate.py b/src/ml3_drift/monitoring/base/base_multivariate.py new file mode 100644 index 0000000..a261f17 --- /dev/null +++ b/src/ml3_drift/monitoring/base/base_multivariate.py @@ -0,0 +1,18 @@ +from abc import ABC +import numpy as np + +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + + +class MultivariateMonitoringAlgorithm(MonitoringAlgorithm, ABC): + """ + Base class for multivariate monitoring algorithm. + + It is currently used only to validate the input data dimension. + """ + + def _is_valid(self, X: np.ndarray) -> tuple[bool, str]: + if X.shape[1] >= 1: + return True, "" + else: + return False, f"X must be multi-dimensional vector. Got {X.shape}" diff --git a/src/ml3_drift/monitoring/base/base_univariate.py b/src/ml3_drift/monitoring/base/base_univariate.py new file mode 100644 index 0000000..3483e4c --- /dev/null +++ b/src/ml3_drift/monitoring/base/base_univariate.py @@ -0,0 +1,18 @@ +from abc import ABC +import numpy as np + +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + + +class UnivariateMonitoringAlgorithm(MonitoringAlgorithm, ABC): + """ + Base class for univariate monitoring algorithm. + + It is currently used only to validate the input data dimension. + """ + + def _is_valid(self, X: np.ndarray) -> tuple[bool, str]: + if X.shape[1] == 1: + return True, "" + else: + return False, f"X must be 1-dimensional vector. Got {X.shape}" diff --git a/src/ml3_drift/monitoring/base/batch_monitoring_algorithm.py b/src/ml3_drift/monitoring/base/batch_monitoring_algorithm.py new file mode 100644 index 0000000..3ee19f4 --- /dev/null +++ b/src/ml3_drift/monitoring/base/batch_monitoring_algorithm.py @@ -0,0 +1,63 @@ +from abc import ABC +from typing import Callable + +import numpy as np +from ml3_drift.exceptions.monitoring import NotFittedError +from ml3_drift.models.monitoring import DriftInfo, MonitoringOutput +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + + +class BatchMonitoringAlgorithm(MonitoringAlgorithm, ABC): + """ + Abstract base class for offline monitoring algorithms. + It implements the detect method for offline drift detection. + """ + + def __init__( + self, + comparison_size: int | None = None, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + super().__init__(comparison_size, callbacks) + + def _offline_detect(self, X: np.ndarray) -> list[MonitoringOutput]: + """ + In offline detection we compare reference data, coming from fit(X), with the + provided batch of data. + + Returns a single MonitoringOutput object containing the drift detection result. + """ + + # Comparison data are exactly the sample provided here. + self.comparison_data = X + return [self._detect()] + + def detect(self, X: np.ndarray) -> list[MonitoringOutput]: + """ + Analyze the provided data samples against the reference dataset + (which needs to be set by calling fit(X) first). + + If present, callbacks are called for each drifted sample. + """ + if not self.is_fitted: + raise NotFittedError("Algorithm must be fitted first.") + + if self.data_shape == 1 and len(X.shape) == 1: + X = X.reshape(-1, 1) + + elif X.shape[1] != self.data_shape: + raise ValueError( + f"Data must have the same shape as reference. Expected {self.data_shape}, got {X.shape[1]}" + ) + + self._validate(X) + + detection_output = self._offline_detect(X) + + if self.has_callbacks: + for sample_output in detection_output: + if sample_output.drift_detected: + for callback in self.callbacks: + callback(sample_output.drift_info) + + return detection_output diff --git a/src/ml3_drift/monitoring/base/online_monitorning_algorithm.py b/src/ml3_drift/monitoring/base/online_monitorning_algorithm.py new file mode 100644 index 0000000..11b6dc6 --- /dev/null +++ b/src/ml3_drift/monitoring/base/online_monitorning_algorithm.py @@ -0,0 +1,104 @@ +from abc import ABC +from typing import Callable + +import numpy as np +from ml3_drift.exceptions.monitoring import NotFittedError +from ml3_drift.models.monitoring import DriftInfo, MonitoringOutput +from ml3_drift.monitoring.base.base import MonitoringAlgorithm + + +class OnlineMonitorningAlgorithm(MonitoringAlgorithm, ABC): + """ + Base class for online monitoring algorithms. + """ + + def __init__( + self, + comparison_size: int | None = None, + callbacks: list[Callable[[DriftInfo | None], None]] | None = None, + ) -> None: + super().__init__(comparison_size, callbacks) + + def _online_detect(self, X: np.ndarray) -> list[MonitoringOutput]: + """In online detection, a sliding window is used over the samples to detect drift at each step. + + Test statistic is computed only when there is enough data for comparison. + Specifically the number of comparison data is defined by the attribute `comparison_size`. + + Therefore, the first `comparison_size` - 1 samples are not monitored and they produce a "no drift" output. + After that, any new sample is added to `comparison_data` by removing the oldest one, + the detect method is called and the output is returned. + """ + # Detection loop + + detection_output = [] + + # initialize comparison data with all the available data + samples_to_fill_comparison_data = min( + X.shape[0], self.comparison_size - self.comparison_data.shape[0] + ) + + if samples_to_fill_comparison_data > 0: + initial_comparison_data_size = self.comparison_data.shape[0] + data_to_add = X[:samples_to_fill_comparison_data] + if initial_comparison_data_size == 0: + self.comparison_data = data_to_add + else: + self.comparison_data = np.vstack([self.comparison_data, data_to_add]) + + # Explained in the docstring + detection_output = [ + MonitoringOutput(drift_detected=False, drift_info=None) + for _ in range(data_to_add.shape[0] - 1) + ] + + if self.comparison_data.shape[0] == self.comparison_size: + detection_output.append(self._detect()) + else: + detection_output.append( + MonitoringOutput(drift_detected=False, drift_info=None) + ) + + for i in range( + samples_to_fill_comparison_data, + X.shape[0] - samples_to_fill_comparison_data, + ): + self.comparison_data = np.vstack([self.comparison_data[1:], X[i : i + 1]]) + detection_output.append(self._detect()) + + return detection_output + + def detect(self, X: np.ndarray) -> list[MonitoringOutput]: + """ + Analyze the provided data samples against the reference dataset + (which needs to be set by calling fit(X) first). + + If present, callbacks are called for each drifted sample. + """ + if not self.is_fitted: + raise NotFittedError("Algorithm must be fitted first.") + + if self.data_shape == 1 and len(X.shape) == 1: + X = X.reshape(-1, 1) + + elif X.shape[1] != self.data_shape: + raise ValueError( + f"Data must have the same shape as reference. Expected {self.data_shape}, got {X.shape[1]}" + ) + + self._validate(X) + + if self.comparison_size is None: + raise ValueError( + "Comparison size must be defined for online monitoring algorithms." + ) + + detection_output = self._online_detect(X) + + if self.has_callbacks: + for sample_output in detection_output: + if sample_output.drift_detected: + for callback in self.callbacks: + callback(sample_output.drift_info) + + return detection_output diff --git a/src/ml3_drift/sklearn/base.py b/src/ml3_drift/sklearn/base.py index c5ed009..532bfe6 100644 --- a/src/ml3_drift/sklearn/base.py +++ b/src/ml3_drift/sklearn/base.py @@ -1,75 +1,94 @@ -from collections.abc import Callable +import numpy as np + from sklearn.base import BaseEstimator, TransformerMixin, check_is_fitted -from abc import ABC, abstractmethod from sklearn.utils.validation import validate_data +from ml3_drift.enums.monitoring import DataDimension, DataType +from ml3_drift.monitoring.base.base import MonitoringAlgorithm -from ml3_drift.callbacks.models import DriftInfo +from copy import deepcopy -class BaseDriftDetector(TransformerMixin, BaseEstimator, ABC): - """ - Base class for drift detector. - Base Drift Detectors are neither transformers nor predictors, they - just observe the data and detects drift, executing specified - actions when necessary. - For this reason, they implement both the transform and predict methods. +class SklearnDriftDetector(TransformerMixin, BaseEstimator): + """Adapter class for sklearn library. + + It is subclass of base sklearn classes and acts like a transformer but it do not + transform data it receives. A drift detector just observes data and detect drifts + executing provided callbacks. + + It implements both transform and predict methods. Parameters ---------- - callbacks: list[Callable[[DriftInfo], None] - list of callbacks function used to act when drift are detected + monitoring_algorithm: MonitoringAlgorithm + instance of the monitoring algorithm to use during drift detection. If it is + for univariate data, then one clone for each column is used. """ - def __init__(self, callbacks: list[Callable[[DriftInfo], None]] | None = None): - super().__init__() - self.callbacks = callbacks + def __repr__(self): + return f"SklearnDriftDetector({self.monitoring_algorithm})" - @abstractmethod - def _fit(self, X, y=None): - """ - Fit method that should be implemented in child classes - """ + def __str__(self): + return f"SklearnDriftDetector({self.monitoring_algorithm})" - @abstractmethod - def _detect(self, X) -> list[DriftInfo]: - """ - Core method for detecting drift that is implemented in child classes. - """ + def __init__(self, monitoring_algorithm: MonitoringAlgorithm): + super().__init__() + self.monitoring_algorithm = monitoring_algorithm + self._monitoring_algorithm_list: list[MonitoringAlgorithm] = [] def fit(self, X, y=None): + """Fit method. + + Validates data, and call fit method of monitoring algorithm. + If the monitoring algorithm is univariate then for each column a clone + is used. """ - Fit method. Calls _inner_fit and returns self - """ + self._monitoring_algorithm_list = [] + X = self._validate_data(X, reset=True) + + if ( + self.monitoring_algorithm.specs().data_dimension == DataDimension.UNIVARIATE + ) and (X.shape[1] > 1): + for i in range(X.shape[1]): + ma = deepcopy(self.monitoring_algorithm) + ma.fit(X[:, i]) + self._monitoring_algorithm_list.append(ma) + else: + self.monitoring_algorithm.fit(X) - X = self._validate_data(X, y, reset=True) - self._fit(X) self.is_fitted_ = True return self def transform(self, X): - """ - Transform method. Calls _detect method and return X. - This step does not change the data but only performs drift detection. + """Transform method. + + Calls detect to the internal monitoring algorithm and return X as it is. """ X = self._validate_data(X, reset=False) check_is_fitted(self) - if (drift_info_list := self._detect(X)) and (self.callbacks is not None): - for drift_info in drift_info_list: - for callback in self.callbacks: - callback(drift_info) + if ( + self.monitoring_algorithm.specs().data_dimension == DataDimension.UNIVARIATE + ) and (X.shape[1] > 1): + for i, ma in enumerate(self._monitoring_algorithm_list): + ma.detect(X[:, i]) + else: + self.monitoring_algorithm.detect(X) return X def predict(self, X): - """ - Predict method. Calls _detect method and return X. - This step does not change the data but only performs drift detection. + """Predict method. + + Calls detect to the internal monitoring algorithm and return X as it is. """ X = self._validate_data(X, reset=False) check_is_fitted(self) - if drift_info_list := self._detect(X) and self.callbacks is not None: - for drift_info in drift_info_list: - for callback in self._callbacks: - callback(drift_info) + if ( + self.monitoring_algorithm.specs().data_dimension == DataDimension.UNIVARIATE + ) and (X.shape[1] > 1): + for i, ma in enumerate(self._monitoring_algorithm_list): + ma.detect(X[:, i]) + else: + self.monitoring_algorithm.detect(X) + return X def _validate_data(self, X, y=None, reset=False): @@ -79,15 +98,83 @@ def _validate_data(self, X, y=None, reset=False): Child classes can override with their own validation methods if needed or just call the base class method with the custom parameters. """ - # Workaround since validate data doesn't return y if it is None + if self.monitoring_algorithm.specs().data_type in [ + DataType.DISCRETE, + DataType.MIX, + ]: + dtype = None + else: + dtype = "numeric" + if y is None: - X = validate_data(self, X, reset=reset, accept_sparse=False) + X = validate_data( + self, + X, + reset=reset, + accept_sparse=False, + ensure_all_finite=False, + dtype=dtype, + ) else: - X, _ = validate_data(self, X, y, reset=reset, accept_sparse=False) + X, _ = validate_data( + self, + X, + y, + reset=reset, + accept_sparse=False, + ensure_all_finite=False, + dtype=dtype, + ) + X = self._safe_nan_clean(X) + return X + + def _safe_nan_clean(self, X): + """ + Clean NaN values from the data. Different approach according to the type of data. + """ + # Extend with other types + if isinstance(X, np.ndarray): + if np.issubdtype(X.dtype, np.number): + if len(X.shape) > 1: + return X[~np.any(np.isnan(X), axis=1)] + else: + return X[~np.any(np.isnan(X))] + elif np.issubdtype(X.dtype, np.object_): + # Since the dtype is object we cannot apply the function np.isnan + # Therefore, we need to iterate over each row + # According to the number of columns we can have an array or a scalar + # if it is an array then we cast it to a list and check if it contains None + # if it is a scalar then we use np.isnan if it is a float or not None otherwise + return X[ + np.array( + [ + [ + not ( + (isinstance(x, np.ndarray) and None in x.tolist()) + or (isinstance(x, float) and np.isnan(x)) + or (x is None) + ) + ] + for x in X + ] + ).ravel() + ] + elif np.issubdtype(X.dtype, np.str_): + return X[~np.any(np.isin(X, [None, np.nan]))] + else: + raise ValueError("Unsupported data type") return X def __sklearn_tags__(self): tags = super().__sklearn_tags__() - # Currently empty, but can be used to add tags to the estimator + tags.input_tags.allow_nan = True + + if self.monitoring_algorithm.specs().data_type in [ + DataType.DISCRETE, + DataType.MIX, + ]: + tags.input_tags.categorical = True + tags.input_tags.string = True + return tags diff --git a/src/ml3_drift/sklearn/univariate/chi_square.py b/src/ml3_drift/sklearn/univariate/chi_square.py deleted file mode 100644 index 3f820a6..0000000 --- a/src/ml3_drift/sklearn/univariate/chi_square.py +++ /dev/null @@ -1,199 +0,0 @@ -from collections import defaultdict -from collections.abc import Callable -from logging import getLogger -import numpy as np -from scipy import stats -from ml3_drift.callbacks.models import DriftInfo -from ml3_drift.sklearn.base import BaseDriftDetector -from sklearn.utils.validation import validate_data - - -logger = getLogger(__name__) - - -class ChiSquareDriftDetector(BaseDriftDetector): - """ - Drift detector based on the Chi-Square test. - """ - - def __init__( - self, - p_value: float = 0.0005, - categories: dict | None = None, - callbacks: list[Callable[[DriftInfo], None]] | None = None, - ): - """ - Parameters - ---------- - p_value: float - p-value threshold for detecting drift. Default is 0.0005. - categories: dict or None - Dictionary with categories for each column. If None, the detector will try to infer categories - from the data used for fitting. - - """ - super().__init__(callbacks=callbacks) - self.p_value = p_value - self.categories = categories - - def _fit(self, X, y=None): - """ - Fit method. Saves reference data possible values - for each feature and their frequencies. - """ - - # TODO: check all values exist in categories if provided - - # If categories were not provided, infer them from reference data - if self.categories is None: - # We collect unique values for each column - # and convert them to a list - # We also remove NaN values - self.categories_ = { - i: np.unique(self._safe_nan_clean(X[:, i])).tolist() - for i in range(X.shape[1]) - } - else: - # Validate categories - self._validate_categories(self.categories) - self.categories_ = self.categories - - # Precompute reference frequencies - self.freqs_ref_ = self._get_frequencies(X) - - def _validate_data(self, X, y=None, reset=False): - """ - Validate data method. We override the base class method to - also allow NaN values in the data. - """ - - # Inspired from https://github.com/scikit-learn/scikit-learn/blob/98ed9dc73a86f5f11781a0e21f24c8f47979ec67/sklearn/preprocessing/_encoders.py#L47 - - # Workaround since validate data doesn't return y if it is None - if y is None: - X = validate_data( - self, - X, - reset=reset, - accept_sparse=False, - ensure_all_finite=False, - dtype=None, # input dtype is preserved - ) - else: - X, _ = validate_data( - self, - X, - y, - reset=reset, - accept_sparse=False, - ensure_all_finite=False, - dtype=None, # input dtype is preserved - ) - - return X - - def _safe_nan_clean(self, X): - """ - Clean NaN values from the data. Different approach according to the type of data. - """ - - # Extend with other types - if isinstance(X, np.ndarray): - if np.issubdtype(X.dtype, np.number): - return X[~np.isnan(X)] - elif np.issubdtype(X.dtype, np.object_): - return X[ - np.array( - [ - x is not None and not (isinstance(x, float) and np.isnan(x)) - for x in X - ] - ) - ] - elif np.issubdtype(X.dtype, np.str_): - return X[~np.isin(X, [None, np.nan])] - else: - raise ValueError("Unsupported data type") - raise ValueError("Unsupported data type") - - def _detect(self, X): - """ - Core method for detecting drift - """ - - # TODO: check all values exist in categories if provided - - # Compute frequencies for the new data - freqs_prod = self._get_frequencies(X) - # list containing drift info events - drift_info_list = [] - - for f in range(X.shape[1]): - _, p_value, _, _ = stats.chi2_contingency( - np.column_stack( - ( - list(self.freqs_ref_[f].values()), - list(freqs_prod[f].values()), - ) - ) - ) - - if p_value < self.p_value: - drift_info_list.append( - DriftInfo( - feature_index=f, - drift_detector=self.__class__.__name__, - p_value=p_value, - threshold=self.p_value, - ) - ) - - return drift_info_list - - def __sklearn_tags__(self): - tags = super().__sklearn_tags__() - # Input needs to be categorical - # We need to set also allow nan - # otherwise tests fail - tags.input_tags.categorical = True - tags.input_tags.allow_nan = True - tags.input_tags.string = True - return tags - - def _validate_categories(self, categories): - """ - Validate categories for each column. - """ - if not isinstance(categories, dict): - raise ValueError("Categories must be a dictionary") - - for i, cats in categories.items(): - if not isinstance(cats, list): - raise ValueError(f"Categories for column {i} must be a list") - if len(cats) == 0: - raise ValueError(f"Categories for column {i} cannot be empty") - # Check no NaN values in categories - if any(np.isnan(cat) or cat is None for cat in cats): - raise ValueError(f"Categories for column {i} cannot contain NaN values") - - def _get_frequencies(self, X) -> dict: - """ - Get frequencies for each category in a column. - - Returns a dict like: - { - feature_index: {possible_value_1: count, possible_value_2: count, ...}, - ... - } - """ - - # For each of the columns, get the frequencies of each category - freqs: dict = defaultdict(dict) - - for i in range(X.shape[1]): - # get counts for each category. Since self.categories_ - # doesn't contain NaN values, this is safe - for cat in self.categories_[i]: - freqs[i][cat] = int(np.sum(X[:, i] == cat)) - - return freqs diff --git a/src/ml3_drift/sklearn/univariate/ks.py b/src/ml3_drift/sklearn/univariate/ks.py deleted file mode 100644 index 485319a..0000000 --- a/src/ml3_drift/sklearn/univariate/ks.py +++ /dev/null @@ -1,64 +0,0 @@ -from collections.abc import Callable -from logging import getLogger -from scipy import stats -from ml3_drift.callbacks.models import DriftInfo -from ml3_drift.sklearn.base import BaseDriftDetector - - -logger = getLogger(__name__) - - -class KSDriftDetector(BaseDriftDetector): - """ - Drift detector based on the Kolmogorov-Smirnov test. - """ - - def __init__( - self, - callbacks: list[Callable[[DriftInfo], None]] | None = None, - p_value: float = 0.005, - ): - """ - Parameters - ---------- - p_value: float - p-value threshold for detecting drift. Default is 0.0005. - """ - super().__init__(callbacks=callbacks) - self.p_value = p_value - - def _fit(self, X, y=None): - """ - Fit method. Saves reference data. - """ - self.X_ref_ = X - - def _detect(self, X) -> list[DriftInfo]: - """ - Core method for detecting drift - """ - - # Output dict. Keys are columns, values are p_values - out = {} - # list containing drift info events - drift_info_list = [] - - for i in range(X.shape[1]): - _, p_value = stats.ks_2samp( - self.X_ref_[:, i], - X[:, i], - ) - out[i] = p_value - - if p_value < self.p_value: - drift_info_list.append( - DriftInfo( - feature_index=i, - drift_detector=self.__class__.__name__, - p_value=p_value, - threshold=self.p_value, - ) - ) - return drift_info_list - - # do something with out? diff --git a/tests/conftest.py b/tests/conftest.py index c2440de..2beef0d 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,4 @@ from importlib import import_module - import pytest import numpy as np @@ -43,3 +42,254 @@ def image_data(): """ return Image.fromarray(np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)) + + +@pytest.fixture +def abrupt_multivariate_drift_info(): + """ + A tabular data source fixture, simulating a multivariate abrupt + drift. + """ + np.random.seed(0) + drift_point1 = 1000 + drift_point2 = 2000 + first_stream_length = 1000 + second_stream_length = 1000 + third_stream_length = 1000 + num_concepts = 3 + + means = [ + np.array( + [ + int(np.random.uniform(size=1) * 20), + int(np.random.uniform(size=1) * 20), + ] + ).reshape(-1) + for _ in range(num_concepts) + ] + covariances = [np.eye(2) for _ in range(num_concepts)] + + first_stream = [ + np.around(i, 4) + for i in np.random.multivariate_normal( + means[0], covariances[0], first_stream_length + ) + ] + second_stream = [ + np.around(i, 4) + for i in np.random.multivariate_normal( + means[1], covariances[1], second_stream_length + ) + ] + third_stream = [ + np.around(i, 4) + for i in np.random.multivariate_normal( + means[2], covariances[2], third_stream_length + ) + ] + + final_error_stream = np.concatenate( + (first_stream, second_stream, third_stream), axis=0 + ) + + return final_error_stream, drift_point1, drift_point2 + + +@pytest.fixture +def abrupt_univariate_drift_info(): + """ + A tabular data source fixture, simulating a univariate abrupt drift. + """ + np.random.seed(0) + drift_point = 500 + first_stream_length = 500 + second_stream_length = 500 + + first_stream = [round(i, 4) for i in np.random.uniform(0, 4, first_stream_length)] + second_stream = [round(i, 4) for i in np.random.uniform(3, 7, second_stream_length)] + + final_error_stream = np.concatenate((first_stream, second_stream), axis=0) + + return final_error_stream, drift_point + + +@pytest.fixture +def abrupt_univariate_online_drift_info(): + """ + A tabular data source fixture, simulating a univariate abrupt drift. + """ + np.random.seed(0) + data_stream = np.concatenate( + [np.random.choice([0, 1], size=1000), np.random.choice(range(4, 8), size=1000)] + ) + # specific drift point from 500 + drift_point_1 = 1000 + data_stream = np.array(data_stream).reshape(-1, 1) + + return data_stream, drift_point_1 + + +@pytest.fixture +def abrupt_univariate_online_bidrift_info(): + """ + A tabular data source fixture, simulating a univariate abrupt drift. + """ + np.random.seed(0) + data_stream = np.concatenate( + [ + np.random.choice([0, 1], size=1000), + np.random.choice(range(4, 8), size=1000), + np.random.choice([0, 1], size=1000), + ] + ) + # specific drift point from 500 + drift_point_1 = 1000 + drift_point_2 = 2000 + data_stream = np.array(data_stream).reshape(-1, 1) + + return data_stream, drift_point_1, drift_point_2 + + +# ------------------------------------ +# Test factories + + +def build_data( + input_type: str, + y_type: str | None, + n_drifts: int, + n_samples: int, + n_cont: int, + n_cat: int, + data_format: str, + seed: int = 0, +): + """ + Helper function to generate data with specified characteristics. + + Args: + input_type (str): Type of input data ('cont', 'cat', 'mix'). + y_type (str | None): Type of target data ('cont', 'cat', or None). + n_drifts (int): Number of drifts to introduce in the data. + n_samples (int): Number of samples to generate for each concept - total + samples will be (n_drifts + 1) * n_samples. + n_cont (int): Number of continuous features. + n_cat (int): Number of categorical features. + data_format (str): Format of the data ('numpy', 'polars', 'pandas'). + seed (int): Random seed for reproducibility. + Returns: + tuple: Generated data (X, y, continuous_columns, categorical_columns, y_categorical). + """ + + rng = np.random.default_rng(seed) + + # Helper to generate data with drifts + def generate_with_drifts( + base_generator, drift_generator, n_drifts, n_samples, n_cols + ): + if n_drifts == 0: + return base_generator(size=(n_samples, n_cols)) + data = [] + for i in range(n_drifts + 1): + if i % 2 == 0: + d = base_generator(size=(n_samples, n_cols)) + else: + d = drift_generator(i, size=(n_samples, n_cols)) + data.append(d) + return np.vstack(data) + + # Continuous features + def cont_base_generator(size): + return rng.normal(loc=0, scale=0.1, size=size) + + def cont_drift_generator(i, size): + return rng.normal(loc=i + 10, scale=0.1, size=size) + + X_cont = generate_with_drifts( + cont_base_generator, cont_drift_generator, n_drifts, n_samples, n_cont + ) + + # Categorical features + def cat_base_generator(size): + return rng.binomial(1, 0.3, size=size) + + def cat_drift_generator(i, size): + return rng.binomial(1, 0.3 + (0.9 - 0.3) * (i / n_drifts), size=size) + + X_cat = generate_with_drifts( + cat_base_generator, cat_drift_generator, n_drifts, n_samples, n_cat + ) + + # Build input X + if input_type == "cont": + if data_format == "polars": + import polars as pl + + continuous_columns = list(map(str, range(n_cont))) + X = pl.from_numpy(X_cont, schema=continuous_columns) + elif data_format == "pandas": + import pandas as pd + + continuous_columns = list(map(str, range(n_cont))) + X = pd.DataFrame(X_cont) + X.columns = continuous_columns + else: + X = X_cont + continuous_columns = list(range(n_cont)) + categorical_columns = [] + elif input_type == "cat": + if data_format == "polars": + import polars as pl + + categorical_columns = list(map(str, range(n_cat))) + X = pl.from_numpy(X_cat, schema=categorical_columns) + elif data_format == "pandas": + import pandas as pd + + categorical_columns = list(map(str, range(n_cat))) + X = pd.DataFrame(X_cat) + X.columns = categorical_columns + else: + X = X_cat + categorical_columns = list(range(n_cat)) + continuous_columns = [] + elif input_type == "mix": + if data_format == "polars": + import polars as pl + + continuous_columns = list(map(str, range(n_cont))) + categorical_columns = list(map(str, range(n_cont, n_cat + n_cont))) + X = pl.from_numpy( + np.hstack([X_cont, X_cat]), + schema=continuous_columns + categorical_columns, + ) + elif data_format == "pandas": + import pandas as pd + + continuous_columns = list(map(str, range(n_cont))) + categorical_columns = list(map(str, range(n_cont, n_cat + n_cont))) + X = pd.DataFrame( + np.hstack([X_cont, X_cat]), + ) + X.columns = continuous_columns + categorical_columns + else: + X = np.hstack([X_cont, X_cat]) + continuous_columns = list(range(n_cont)) + categorical_columns = list(range(n_cont, n_cat + n_cont)) + else: + raise ValueError(f"Unknown input_type: {input_type}") + + # Generate target y if needed + y = None + y_categorical = False + if y_type == "cont": + y = generate_with_drifts( + cont_base_generator, cont_drift_generator, n_drifts, n_samples, 1 + ) + elif y_type == "cat": + y_categorical = True + y = generate_with_drifts( + cat_base_generator, cat_drift_generator, n_drifts, n_samples, 1 + ) + + return X, y, continuous_columns, categorical_columns, y_categorical diff --git a/tests/test_analyzer/test_analyzer.py b/tests/test_analyzer/test_analyzer.py new file mode 100644 index 0000000..aa09360 --- /dev/null +++ b/tests/test_analyzer/test_analyzer.py @@ -0,0 +1,134 @@ +import pytest +from ml3_drift.analysis.analyzer.batch import BatchDataDriftAnalyzer +from ml3_drift.monitoring.algorithms.batch.bonferroni import ( + BonferroniCorrectionAlgorithm, +) +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm +from ml3_drift.monitoring.algorithms.batch.chi_square import ChiSquareAlgorithm +from tests.conftest import is_module_available, build_data + +# Generate all possible combinations, then remove those that are not available +# in the current environment according to the installed extras. +input_types = ["cont", "cat", "mix"] +y_types = ["cont", "cat", None] +n_drifts = [0, 1, 2] +data_formats = ["numpy", "polars", "pandas"] +with_default_params = [True, False] + +input_definition_test_params: list[tuple[str, str | None, int, str, bool]] = [ + (input_type, y_type, n_drift, data_format, with_default) + for input_type in input_types + for y_type in y_types + for n_drift in n_drifts + for data_format in data_formats + for with_default in with_default_params +] + +if not is_module_available("polars"): + input_definition_test_params = [ + (input_type, y_type, n_drifts, data_format, with_default_params) + for input_type, y_type, n_drifts, data_format, with_default_params in input_definition_test_params + if data_format != "polars" + ] + +if not is_module_available("pandas"): + input_definition_test_params = [ + (input_type, y_type, n_drifts, data_format, with_default_params) + for input_type, y_type, n_drifts, data_format, with_default_params in input_definition_test_params + if data_format != "pandas" + ] + + +@pytest.mark.parametrize( + "input_type, y_type, n_drifts, data_format, with_default", + input_definition_test_params, +) +def test_batch_analyzer(input_type, y_type, n_drifts, data_format, with_default): + """ + Test the BatchDataDriftAnalyzer with various input types, y types, number of drifts, and data formats. + """ + n_samples = 300 + n_cont = 2 + n_cat = 2 + + X, y, continuous_columns, categorical_columns, y_categorical = build_data( + input_type=input_type, + y_type=y_type, + n_drifts=n_drifts, + n_samples=n_samples, + n_cont=n_cont, + n_cat=n_cat, + data_format=data_format, + seed=2, + ) + + if with_default: + continuous_algo = None + categorical_algo = None + + else: + continuous_algo = BonferroniCorrectionAlgorithm( + algorithm=KSAlgorithm(p_value=0.05) + ) + categorical_algo = BonferroniCorrectionAlgorithm( + algorithm=ChiSquareAlgorithm(p_value=0.05) + ) + + analyzer = BatchDataDriftAnalyzer( + continuous_monitoring_algorithm=continuous_algo, + categorical_monitoring_algorithm=categorical_algo, + batch_size=50, + ) + + # Run analyze + report = analyzer.analyze( + X, + y=y, + continuous_columns=continuous_columns if continuous_columns else None, + categorical_columns=categorical_columns if categorical_columns else None, + y_categorical=y_categorical, + ) + + assert hasattr(report, "concepts") + assert isinstance(report.concepts, list) + assert len(report.concepts) == n_drifts + 1 + + +def test_batch_analyzer_null_columns(): + """ + Test the BatchDataDriftAnalyzer with various input types, y types, number of drifts, and data formats. + """ + n_samples = 300 + n_cont = 0 + n_cat = 0 + + X, y, continuous_columns, categorical_columns, y_categorical = build_data( + input_type="cont", + y_type="cont", + n_drifts=1, + n_samples=n_samples, + n_cont=n_cont, + n_cat=n_cat, + data_format="numpy", + seed=2, + ) + + continuous_algo = BonferroniCorrectionAlgorithm(algorithm=KSAlgorithm(p_value=0.05)) + categorical_algo = BonferroniCorrectionAlgorithm( + algorithm=ChiSquareAlgorithm(p_value=0.05) + ) + + analyzer = BatchDataDriftAnalyzer( + continuous_monitoring_algorithm=continuous_algo, + categorical_monitoring_algorithm=categorical_algo, + batch_size=50, + ) + + with pytest.raises(ValueError): + analyzer.analyze( + X, + y=y, + continuous_columns=None, + categorical_columns=None, + y_categorical=y_categorical, + ) diff --git a/tests/test_huggingface/test_ks.py b/tests/test_huggingface/test_ks.py deleted file mode 100644 index eb21c53..0000000 --- a/tests/test_huggingface/test_ks.py +++ /dev/null @@ -1,34 +0,0 @@ -import numpy as np -from tests.conftest import is_module_available - -if is_module_available("transformers"): - from ml3_drift.huggingface.univariate.ks import KSDriftDetector -else: - import pytest - - pytest.skip( - "HuggingFace transformers module is not available.", - allow_module_level=True, - ) - - -class TestKSDriftDetector: - """ - Test suite for KSDriftDetector in the HuggingFace module - """ - - def test_fit(self): - """ - Test the fit method of KSDriftDetector. - """ - # Create a sample dataset - X = np.array([[1, 2], [2, 3], [3, 4]]) - - # Create an instance of the detector - detector = KSDriftDetector() - - # Fit the detector to the data - detector.fit(X) - - # Check that the detector is fitted - assert detector.is_fitted is True diff --git a/tests/test_huggingface/test_pipe.py b/tests/test_huggingface/test_pipe.py index 69b245a..71464ab 100644 --- a/tests/test_huggingface/test_pipe.py +++ b/tests/test_huggingface/test_pipe.py @@ -1,11 +1,12 @@ +from ml3_drift.monitoring.algorithms.batch.bonferroni import ( + BonferroniCorrectionAlgorithm, +) +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm from tests.conftest import is_module_available import pytest if is_module_available("transformers"): - from ml3_drift.huggingface.univariate.ks import ( - KSDriftDetector, - ) from ml3_drift.huggingface.drift_detection_pipeline import ( HuggingFaceDriftDetectionPipeline, ) @@ -27,13 +28,8 @@ def test_text(self, text_data, return_tensors): Test pipeline with text data for drift detection. """ - # Not optimal as we are loading a big model, - # but it didn't work with a simple model taken - # from here: - # https://github.com/huggingface/transformers/blob/6e3063422c4b1c014aa60c32b9254fd2902f0f28/tests/pipelines/test_pipelines_feature_extraction.py#L46 - # We should do something. pipe = HuggingFaceDriftDetectionPipeline( - drift_detector=KSDriftDetector(), + drift_detector=KSAlgorithm(p_value=0.05), task="feature-extraction", model="hf-internal-testing/tiny-random-distilbert", framework="pt", @@ -45,7 +41,7 @@ def test_text(self, text_data, return_tensors): ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (1, 32), ( + assert pipe._drift_detector.X_ref_.shape == (32, 1), ( "Reference data shape mismatch." ) @@ -60,7 +56,7 @@ def test_text(self, text_data, return_tensors): ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (1, 32), ( + assert pipe._drift_detector.X_ref_.shape == (32, 1), ( "Reference data shape mismatch." ) @@ -69,18 +65,24 @@ def test_text(self, text_data, return_tensors): return_tensors=return_tensors, ) + pipe = HuggingFaceDriftDetectionPipeline( + drift_detector=BonferroniCorrectionAlgorithm( + p_value=0.05, algorithm=KSAlgorithm() + ), + task="feature-extraction", + model="hf-internal-testing/tiny-random-distilbert", + framework="pt", + ) + pipe.fit_detector( [text_data, text_data], return_tensors=return_tensors, ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (2, 32), ( - "Reference data shape mismatch." - ) pipe( - text_data, + [text_data, text_data], return_tensors=return_tensors, ) @@ -90,10 +92,8 @@ def test_image(self, image_data, return_tensors): Test pipeline with image data for drift detection. """ - # Not optimal as we are loading a big model, - # We should do something. pipe = HuggingFaceDriftDetectionPipeline( - drift_detector=KSDriftDetector(), + drift_detector=KSAlgorithm(p_value=0.05), task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt", @@ -105,7 +105,7 @@ def test_image(self, image_data, return_tensors): ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (1, 32), ( + assert pipe._drift_detector.X_ref_.shape == (32, 1), ( "Reference data shape mismatch." ) @@ -120,7 +120,7 @@ def test_image(self, image_data, return_tensors): ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (1, 32), ( + assert pipe._drift_detector.X_ref_.shape == (32, 1), ( "Reference data shape mismatch." ) @@ -129,17 +129,23 @@ def test_image(self, image_data, return_tensors): return_tensors=return_tensors, ) + pipe = HuggingFaceDriftDetectionPipeline( + drift_detector=BonferroniCorrectionAlgorithm( + p_value=0.05, algorithm=KSAlgorithm() + ), + task="image-feature-extraction", + model="hf-internal-testing/tiny-random-vit", + framework="pt", + ) + pipe.fit_detector( [image_data, image_data], return_tensors=return_tensors, ) assert pipe._drift_detector.is_fitted - assert pipe._drift_detector.X_ref_.shape == (2, 32), ( - "Reference data shape mismatch." - ) pipe( - image_data, + [image_data, image_data], return_tensors=return_tensors, ) diff --git a/tests/test_monitoring/test_online/test_univariate.py b/tests/test_monitoring/test_online/test_univariate.py new file mode 100644 index 0000000..4068094 --- /dev/null +++ b/tests/test_monitoring/test_online/test_univariate.py @@ -0,0 +1,192 @@ +import pytest +from tests.conftest import is_module_available +import numpy as np +from ml3_drift.monitoring.algorithms.online.adwin import ADWIN +from ml3_drift.monitoring.algorithms.online.kswin import KSWIN +from ml3_drift.monitoring.algorithms.online.page_hinkley import PageHinkley + +if not is_module_available("river"): + for alg in [ADWIN, KSWIN, PageHinkley]: + # If River is not available, we expect the algorithms to raise a ModuleNotFoundError + with pytest.raises(ModuleNotFoundError): + alg() + # Prevent tests from running if sklearn is not available + pytest.skip(allow_module_level=True) + + +class TestContinuousUnivariateOnlineAlgorithms: + def test_kswin_univariate_one_drift(self, abrupt_univariate_online_drift_info): + data_stream, drift_point_1 = abrupt_univariate_online_drift_info + kswin_detector = KSWIN(p_value=0.0001) + kswin_detector.fit(data_stream) + output = kswin_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) > 0 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + assert first_drift_index >= drift_point_1, ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1}" + ) + + def test_kswin_univariate_two_drift(self, abrupt_univariate_online_bidrift_info): + data_stream, drift_point_1, drift_point_2 = ( + abrupt_univariate_online_bidrift_info + ) + data_stream = np.array(data_stream).reshape(-1, 1) + kswin_detector = KSWIN(p_value=0.0001) + kswin_detector.fit(data_stream) + output = kswin_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) == 2 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + second_drift_index = max(drift_detected_indices) + assert ( + first_drift_index >= drift_point_1 and first_drift_index < drift_point_2 + ), ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1} and before {drift_point_2}" + ) + assert second_drift_index >= drift_point_2, ( + f"Second drift detected at index {second_drift_index}, but expected at or after {drift_point_2}" + ) + + def test_adwin_univariate_one_drift(self, abrupt_univariate_online_drift_info): + data_stream, drift_point_1 = abrupt_univariate_online_drift_info + + adwin_detector = ADWIN() + adwin_detector.fit(data_stream) + output = adwin_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) > 0 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + assert first_drift_index >= drift_point_1, ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1}" + ) + + def test_adwin_univariate_two_drift(self, abrupt_univariate_online_bidrift_info): + data_stream, drift_point_1, drift_point_2 = ( + abrupt_univariate_online_bidrift_info + ) + data_stream = np.array(data_stream).reshape(-1, 1) + adwin_detector = ADWIN() + adwin_detector.fit(data_stream) + output = adwin_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) == 2 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + second_drift_index = max(drift_detected_indices) + assert ( + first_drift_index >= drift_point_1 and first_drift_index < drift_point_2 + ), ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1} and before {drift_point_2}" + ) + assert second_drift_index >= drift_point_2, ( + f"Second drift detected at index {second_drift_index}, but expected at or after {drift_point_2}" + ) + + def test_page_hinkley_univariate_one_drift( + self, abrupt_univariate_online_drift_info + ): + data_stream, drift_point_1 = abrupt_univariate_online_drift_info + hinkley_detector = PageHinkley() + hinkley_detector.fit(data_stream) + output = hinkley_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) > 0 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + assert first_drift_index >= drift_point_1, ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1}" + ) + + def test_page_hinkley_univariate_two_drift( + self, abrupt_univariate_online_bidrift_info + ): + data_stream, drift_point_1, drift_point_2 = ( + abrupt_univariate_online_bidrift_info + ) + data_stream = np.array(data_stream).reshape(-1, 1) + hinkley_detector = PageHinkley() + hinkley_detector.fit(data_stream) + output = hinkley_detector.detect(data_stream) + + # Get output elements where drift_detected is True + drift_detected_elements = [elem for elem in output if elem.drift_detected] + + # Get indices where drift was detected + drift_detected_indices = [ + i for i, elem in enumerate(output) if elem.drift_detected + ] + + assert any([elem.drift_detected for elem in output]) + # Verify we have detected drift elements + assert len(drift_detected_elements) == 2 + + # Verify that the first detected drift occurs at or after the drift point + first_drift_index = min(drift_detected_indices) + second_drift_index = max(drift_detected_indices) + assert ( + first_drift_index >= drift_point_1 and first_drift_index < drift_point_2 + ), ( + f"First drift detected at index {first_drift_index}, but expected at or after {drift_point_1} and before {drift_point_2}" + ) + assert second_drift_index >= drift_point_2, ( + f"Second drift detected at index {second_drift_index}, but expected at or after {drift_point_2}" + ) diff --git a/tests/test_monitoring/test_univariate/test_continuous/test_ks.py b/tests/test_monitoring/test_univariate/test_continuous/test_ks.py new file mode 100644 index 0000000..75f5299 --- /dev/null +++ b/tests/test_monitoring/test_univariate/test_continuous/test_ks.py @@ -0,0 +1,23 @@ +import numpy as np + +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm + + +def test_ks(): + """This test defines two univariate Gaussian distributions""" + rng = np.random.default_rng() + + mu_0, sigma_0 = 1.4, 0.4 + mu_1, sigma_1 = 2.4, 0.5 + + alg = KSAlgorithm() + + alg.fit(rng.normal(mu_0, sigma_0, size=(300, 1))) + + # Expecting no drift + output = alg.detect(rng.normal(mu_0, sigma_0, size=(300, 1))) + assert all([not elem.drift_detected for elem in output]) + + # Adding drifted data, expecting drift + output = alg.detect(rng.normal(mu_1, sigma_1, size=(300, 1))) + assert any([elem.drift_detected for elem in output]) diff --git a/tests/test_sklearn/test_chi_square.py b/tests/test_sklearn/test_chi_square.py deleted file mode 100644 index b3f6ced..0000000 --- a/tests/test_sklearn/test_chi_square.py +++ /dev/null @@ -1,96 +0,0 @@ -import numpy as np - -from tests.conftest import is_module_available - -if is_module_available("sklearn"): - from sklearn.linear_model import LinearRegression - from sklearn.pipeline import Pipeline - from sklearn.preprocessing import OneHotEncoder - from sklearn.utils.estimator_checks import parametrize_with_checks - - from ml3_drift.sklearn.univariate.chi_square import ChiSquareDriftDetector - -else: - import pytest - - pytest.skip( - allow_module_level=True, - ) - - -class TestChiSquareDriftDetector: - """ - Test suite for ChiSquareDriftDetector in the SKlearn module - """ - - @parametrize_with_checks([ChiSquareDriftDetector()]) - def test_sklearn_compatible_estimator(self, estimator, check): - """ - Sklearn utility to check estimator compliance. - See https://scikit-learn.org/stable/modules/generated/sklearn.utils.estimator_checks.parametrize_with_checks.html - """ - check(estimator) - - def test_fit(self): - """ - Test the fit method of ChiSquareDriftDetector. - """ - # Create a sample dataset - X = np.array([[1, 2], [2, 3], [3, 4]]) - y = np.array([0, 1, 0]) - - # Create an instance of the detector - detector = ChiSquareDriftDetector() - - # Fit the detector to the data - detector.fit(X, y) - - # Check that the detector is fitted - assert detector.is_fitted_ is True - - def test_fit_with_none(self): - """ - Test that the estimator can fit with None. - """ - detector = ChiSquareDriftDetector() - detector.fit(np.array([[2, 2, None, 3, np.nan]]).reshape(-1, 1)) - assert detector.is_fitted_ is True - assert list(detector.categories_.keys()) == [0] - assert list(detector.categories_.values()) == [[2, 3]] - assert detector.freqs_ref_[0] == {2: 2, 3: 1} - - detector.predict(np.array([[2, 2, None, 3, np.nan]]).reshape(-1, 1)) - - def test_in_pipeline(self): - """ - Test ChiSquareDriftDetector in a pipeline. - """ - # Create a sample dataset - - cat_pipe = Pipeline( - steps=[ - ("chi2", ChiSquareDriftDetector()), - ("onehot", OneHotEncoder()), - ( - "regr", - LinearRegression(), - ), # Not a proper model for categorical data, but just for demonstration - ] - ) - - train_cat_data = np.column_stack( - ( - np.random.choice(["A", "B", "C"], size=100), - np.random.choice(["X", "Y", "Z"], size=100), - ) - ) - - y = np.random.randn(100) - - # Fit the detector to the data - cat_pipe.fit(train_cat_data, y) - - # Check that the detector is fitted - assert cat_pipe.named_steps["chi2"].is_fitted_ is True - - cat_pipe.predict(train_cat_data) diff --git a/tests/test_sklearn/test_ks.py b/tests/test_sklearn/test_drift_detector.py similarity index 51% rename from tests/test_sklearn/test_ks.py rename to tests/test_sklearn/test_drift_detector.py index 59b86f5..4bb046b 100644 --- a/tests/test_sklearn/test_ks.py +++ b/tests/test_sklearn/test_drift_detector.py @@ -1,5 +1,7 @@ import pytest +from ml3_drift.monitoring.algorithms.batch.chi_square import ChiSquareAlgorithm +from ml3_drift.monitoring.algorithms.batch.ks import KSAlgorithm from tests.conftest import is_module_available if is_module_available("sklearn"): @@ -9,19 +11,24 @@ from sklearn.pipeline import Pipeline from sklearn.utils.estimator_checks import parametrize_with_checks - from ml3_drift.sklearn.univariate.ks import KSDriftDetector + from ml3_drift.sklearn.base import SklearnDriftDetector else: # Prevent tests from running if sklearn is not available pytest.skip(allow_module_level=True) -class TestKSDriftDetector: +class TestSklearnDriftDetector: """ Test suite for KSDriftDetector in the SKlearn module """ - @parametrize_with_checks([KSDriftDetector()]) + @parametrize_with_checks( + [ + SklearnDriftDetector(monitoring_algorithm=KSAlgorithm()), + SklearnDriftDetector(monitoring_algorithm=ChiSquareAlgorithm()), + ] + ) def test_sklearn_compatible_estimator(self, estimator, check): """ Sklearn utility to check estimator compliance. @@ -29,18 +36,28 @@ def test_sklearn_compatible_estimator(self, estimator, check): """ check(estimator) - def test_fails_with_none(self): + @pytest.mark.parametrize( + "detector", + [ + SklearnDriftDetector(monitoring_algorithm=KSAlgorithm()), + SklearnDriftDetector(monitoring_algorithm=ChiSquareAlgorithm()), + ], + ) + def test_supports_none(self, detector): """ Test that the estimator fails when fit with None. """ - detector = KSDriftDetector() - with pytest.raises(ValueError): - detector.fit(np.array([[2, 2, None, 3]]).reshape(-1, 1)) - - with pytest.raises(ValueError): - detector.fit(np.array([[2, 2, None, 3]]).reshape(-1, 1), None) - - def test_fit(self): + detector.fit(np.array([[2, 2, None, 3]]).reshape(-1, 1)) + detector.fit(np.array([[2, 2, None, 3]]).reshape(-1, 1), None) + + @pytest.mark.parametrize( + "detector", + [ + SklearnDriftDetector(monitoring_algorithm=KSAlgorithm()), + SklearnDriftDetector(monitoring_algorithm=ChiSquareAlgorithm()), + ], + ) + def test_fit(self, detector): """ Test the fit method of KSDriftDetector. """ @@ -48,16 +65,20 @@ def test_fit(self): X = np.array([[1, 2], [2, 3], [3, 4]]) y = np.array([0, 1, 0]) - # Create an instance of the detector - detector = KSDriftDetector() - # Fit the detector to the data detector.fit(X, y) # Check that the detector is fitted assert detector.is_fitted_ is True - def test_in_pipeline(self): + @pytest.mark.parametrize( + "detector", + [ + SklearnDriftDetector(monitoring_algorithm=KSAlgorithm()), + SklearnDriftDetector(monitoring_algorithm=ChiSquareAlgorithm()), + ], + ) + def test_in_pipeline(self, detector): """ Test KSDriftDetector in a pipeline. """ @@ -65,7 +86,7 @@ def test_in_pipeline(self): cat_pipe = Pipeline( steps=[ - ("ks", KSDriftDetector()), + ("detector", detector), ( "regr", LinearRegression(), @@ -75,17 +96,17 @@ def test_in_pipeline(self): train_cont_data = np.column_stack( ( - np.random.randn(100), - np.random.randn(100), + np.random.randint(5, size=(100,)), + np.random.randint(5, size=(100,)), ) ) - y = np.random.randn(100) + y = np.random.rand(100) # Fit the detector to the data cat_pipe.fit(train_cont_data, y) # Check that the detector is fitted - assert cat_pipe.named_steps["ks"].is_fitted_ is True + assert cat_pipe.named_steps["detector"].is_fitted_ is True cat_pipe.predict(train_cont_data) diff --git a/uv.lock b/uv.lock index ad01a6e..2c30558 100644 --- a/uv.lock +++ b/uv.lock @@ -1,5 +1,4 @@ version = 1 -revision = 2 requires-python = ">=3.10, <3.14" resolution-markers = [ "python_full_version >= '3.12'", @@ -9,69 +8,70 @@ resolution-markers = [ [[package]] name = "accelerate" -version = "1.7.0" +version 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